#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
A wrapper class for Spark DataFrame to behave like pandas DataFrame.
"""
from collections import defaultdict, namedtuple
from collections.abc import Mapping
import re
import warnings
import inspect
import json
import types
from functools import partial, reduce
import sys
from itertools import zip_longest, chain
from types import TracebackType
from typing import (
Any,
Callable,
Dict,
Generic,
IO,
Iterable,
Iterator,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
no_type_check,
TYPE_CHECKING,
)
import datetime
import numpy as np
import pandas as pd
from pandas.api.types import ( # type: ignore[attr-defined]
is_bool_dtype,
is_list_like,
is_dict_like,
is_scalar,
)
from pandas.tseries.frequencies import DateOffset, to_offset
if TYPE_CHECKING:
from pandas.io.formats.style import Styler
from pandas.core.dtypes.common import infer_dtype_from_object
from pandas.core.accessor import CachedAccessor
from pandas.core.dtypes.inference import is_sequence
from pyspark import StorageLevel
from pyspark.sql import Column as PySparkColumn, DataFrame as PySparkDataFrame, functions as F
from pyspark.sql.functions import pandas_udf
from pyspark.sql.types import (
ArrayType,
BooleanType,
DataType,
DoubleType,
NumericType,
Row,
StringType,
StructField,
StructType,
DecimalType,
TimestampType,
TimestampNTZType,
)
from pyspark.sql.window import Window
from pyspark import pandas as ps # For running doctests and reference resolution in PyCharm.
from pyspark.pandas._typing import (
Axis,
DataFrameOrSeries,
Dtype,
Label,
Name,
Scalar,
T,
)
from pyspark.pandas.accessors import PandasOnSparkFrameMethods
from pyspark.pandas.config import option_context, get_option
from pyspark.pandas.correlation import (
compute,
CORRELATION_VALUE_1_COLUMN,
CORRELATION_VALUE_2_COLUMN,
CORRELATION_CORR_OUTPUT_COLUMN,
CORRELATION_COUNT_OUTPUT_COLUMN,
)
from pyspark.pandas.spark import functions as SF
from pyspark.pandas.spark.accessors import SparkFrameMethods, CachedSparkFrameMethods
from pyspark.pandas.utils import (
align_diff_frames,
column_labels_level,
combine_frames,
default_session,
is_name_like_tuple,
is_name_like_value,
is_testing,
name_like_string,
same_anchor,
scol_for,
validate_arguments_and_invoke_function,
validate_axis,
validate_bool_kwarg,
validate_how,
validate_mode,
verify_temp_column_name,
log_advice,
)
from pyspark.pandas.generic import Frame
from pyspark.pandas.internal import (
InternalField,
InternalFrame,
HIDDEN_COLUMNS,
NATURAL_ORDER_COLUMN_NAME,
SPARK_INDEX_NAME_FORMAT,
SPARK_DEFAULT_INDEX_NAME,
SPARK_DEFAULT_SERIES_NAME,
SPARK_INDEX_NAME_PATTERN,
)
from pyspark.pandas.missing.frame import MissingPandasLikeDataFrame
from pyspark.pandas.typedef.typehints import (
as_spark_type,
infer_return_type,
pandas_on_spark_type,
spark_type_to_pandas_dtype,
DataFrameType,
SeriesType,
ScalarType,
create_tuple_for_frame_type,
)
from pyspark.pandas.plot import PandasOnSparkPlotAccessor
# For supporting Spark Connect
from pyspark.sql.utils import get_column_class, get_dataframe_class
if TYPE_CHECKING:
from pyspark.sql._typing import OptionalPrimitiveType
from pyspark.pandas.groupby import DataFrameGroupBy
from pyspark.pandas.resample import DataFrameResampler
from pyspark.pandas.indexes import Index
from pyspark.pandas.series import Series
# These regular expression patterns are compiled and defined here to avoid compiling the same
# pattern every time it is used in _repr_ and _repr_html_ in DataFrame.
# Two patterns basically seek the footer string from Pandas'
REPR_PATTERN = re.compile(r"\n\n\[(?P<rows>[0-9]+) rows x (?P<columns>[0-9]+) columns\]$")
REPR_HTML_PATTERN = re.compile(
r"\n\<p\>(?P<rows>[0-9]+) rows × (?P<columns>[0-9]+) columns\<\/p\>\n\<\/div\>$"
)
_flex_doc_FRAME = """
Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`).
Equivalent to ``{equiv}``. With the reverse version, `{reverse}`.
Among flexible wrappers (`add`, `sub`, `mul`, `div`) to
arithmetic operators: `+`, `-`, `*`, `/`, `//`.
Parameters
----------
other : scalar
Any single data
Returns
-------
DataFrame
Result of the arithmetic operation.
Examples
--------
>>> df = ps.DataFrame({{'angles': [0, 3, 4],
... 'degrees': [360, 180, 360]}},
... index=['circle', 'triangle', 'rectangle'],
... columns=['angles', 'degrees'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which returns the same
results. Also, the reverse version.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(df)
angles degrees
circle 0 720
triangle 6 360
rectangle 8 720
>>> df + df + df
angles degrees
circle 0 1080
triangle 9 540
rectangle 12 1080
>>> df.radd(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide and true divide by constant with reverse version.
>>> df / 10
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
>>> df.truediv(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rtruediv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract by constant with reverse version.
>>> df - 1
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
>>> df.sub(1)
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
>>> df.rsub(1)
angles degrees
circle 1 -359
triangle -2 -179
rectangle -3 -359
Multiply by constant with the reverse version.
>>> df * 1
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
>>> df.mul(1)
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
>>> df.rmul(1)
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Floor Divide by constant with reverse version.
>>> df // 10
angles degrees
circle 0.0 36.0
triangle 0.0 18.0
rectangle 0.0 36.0
>>> df.floordiv(10)
angles degrees
circle 0.0 36.0
triangle 0.0 18.0
rectangle 0.0 36.0
>>> df.rfloordiv(10) # doctest: +SKIP
angles degrees
circle inf 0.0
triangle 3.0 0.0
rectangle 2.0 0.0
Mod by constant with reverse version.
>>> df % 2
angles degrees
circle 0 0
triangle 1 0
rectangle 0 0
>>> df.mod(2)
angles degrees
circle 0 0
triangle 1 0
rectangle 0 0
>>> df.rmod(2)
angles degrees
circle NaN 2
triangle 2.0 2
rectangle 2.0 2
Power by constant with reverse version.
>>> df ** 2
angles degrees
circle 0.0 129600.0
triangle 9.0 32400.0
rectangle 16.0 129600.0
>>> df.pow(2)
angles degrees
circle 0.0 129600.0
triangle 9.0 32400.0
rectangle 16.0 129600.0
>>> df.rpow(2)
angles degrees
circle 1.0 2.348543e+108
triangle 8.0 1.532496e+54
rectangle 16.0 2.348543e+108
"""
[docs]class DataFrame(Frame, Generic[T]):
"""
pandas-on-Spark DataFrame that corresponds to pandas DataFrame logically. This holds Spark
DataFrame internally.
:ivar _internal: an internal immutable Frame to manage metadata.
:type _internal: InternalFrame
Parameters
----------
data : numpy ndarray (structured or homogeneous), dict, pandas DataFrame,
Spark DataFrame, pandas-on-Spark DataFrame or pandas-on-Spark Series.
Dict can contain Series, arrays, constants, or list-like objects
index : Index or array-like
Index to use for the resulting frame. Will default to RangeIndex if
no indexing information part of input data and no index provided
columns : Index or array-like
Column labels to use for the resulting frame. Will default to
RangeIndex (0, 1, 2, ..., n) if no column labels are provided
dtype : dtype, default None
Data type to force. Only a single dtype is allowed. If None, infer
copy : boolean, default False
Copy data from inputs. Only affects DataFrame / 2d ndarray input
.. versionchanged:: 3.4.0
Since 3.4.0, it deals with `data` and `index` in this approach:
1, when `data` is a distributed dataset (Internal DataFrame/Spark DataFrame/
pandas-on-Spark DataFrame/pandas-on-Spark Series), it will first parallelize
the `index` if necessary, and then try to combine the `data` and `index`;
Note that if `data` and `index` doesn't have the same anchor, then
`compute.ops_on_diff_frames` should be turned on;
2, when `data` is a local dataset (Pandas DataFrame/numpy ndarray/list/etc),
it will first collect the `index` to driver if necessary, and then apply
the `pandas.DataFrame(...)` creation internally;
Examples
--------
Constructing DataFrame from a dictionary.
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = ps.DataFrame(data=d, columns=['col1', 'col2'])
>>> df
col1 col2
0 1 3
1 2 4
Constructing DataFrame from pandas DataFrame
>>> df = ps.DataFrame(pd.DataFrame(data=d, columns=['col1', 'col2']))
>>> df
col1 col2
0 1 3
1 2 4
Notice that the inferred dtype is int64.
>>> df.dtypes
col1 int64
col2 int64
dtype: object
To enforce a single dtype:
>>> df = ps.DataFrame(data=d, dtype=np.int8)
>>> df.dtypes
col1 int8
col2 int8
dtype: object
Constructing DataFrame from numpy ndarray:
>>> import numpy as np
>>> ps.DataFrame(data=np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 0]]),
... columns=['a', 'b', 'c', 'd', 'e'])
a b c d e
0 1 2 3 4 5
1 6 7 8 9 0
Constructing DataFrame from numpy ndarray with Pandas index:
>>> import numpy as np
>>> import pandas as pd
>>> ps.DataFrame(data=np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 0]]),
... index=pd.Index([1, 4]), columns=['a', 'b', 'c', 'd', 'e'])
a b c d e
1 1 2 3 4 5
4 6 7 8 9 0
Constructing DataFrame from numpy ndarray with pandas-on-Spark index:
>>> import numpy as np
>>> import pandas as pd
>>> ps.DataFrame(data=np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 0]]),
... index=ps.Index([1, 4]), columns=['a', 'b', 'c', 'd', 'e'])
a b c d e
1 1 2 3 4 5
4 6 7 8 9 0
Constructing DataFrame from Pandas DataFrame with Pandas index:
>>> import numpy as np
>>> import pandas as pd
>>> pdf = pd.DataFrame(data=np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 0]]),
... columns=['a', 'b', 'c', 'd', 'e'])
>>> ps.DataFrame(data=pdf, index=pd.Index([1, 4]))
a b c d e
1 6.0 7.0 8.0 9.0 0.0
4 NaN NaN NaN NaN NaN
Constructing DataFrame from Pandas DataFrame with pandas-on-Spark index:
>>> import numpy as np
>>> import pandas as pd
>>> pdf = pd.DataFrame(data=np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 0]]),
... columns=['a', 'b', 'c', 'd', 'e'])
>>> ps.DataFrame(data=pdf, index=ps.Index([1, 4]))
a b c d e
1 6.0 7.0 8.0 9.0 0.0
4 NaN NaN NaN NaN NaN
Constructing DataFrame from Spark DataFrame with Pandas index:
>>> import pandas as pd
>>> sdf = spark.createDataFrame([("Data", 1), ("Bricks", 2)], ["x", "y"])
>>> ps.DataFrame(data=sdf, index=pd.Index([0, 1, 2]))
Traceback (most recent call last):
...
ValueError: Cannot combine the series or dataframe...'compute.ops_on_diff_frames' option.
Enable 'compute.ops_on_diff_frames' to combine SparkDataFrame and Pandas index
>>> with ps.option_context("compute.ops_on_diff_frames", True):
... ps.DataFrame(data=sdf, index=pd.Index([0, 1, 2]))
x y
0 Data 1.0
1 Bricks 2.0
2 None NaN
Constructing DataFrame from Spark DataFrame with pandas-on-Spark index:
>>> import pandas as pd
>>> sdf = spark.createDataFrame([("Data", 1), ("Bricks", 2)], ["x", "y"])
>>> ps.DataFrame(data=sdf, index=ps.Index([0, 1, 2]))
Traceback (most recent call last):
...
ValueError: Cannot combine the series or dataframe...'compute.ops_on_diff_frames' option.
Enable 'compute.ops_on_diff_frames' to combine Spark DataFrame and pandas-on-Spark index
>>> with ps.option_context("compute.ops_on_diff_frames", True):
... ps.DataFrame(data=sdf, index=ps.Index([0, 1, 2]))
x y
0 Data 1.0
1 Bricks 2.0
2 None NaN
"""
def __init__( # type: ignore[no-untyped-def]
self, data=None, index=None, columns=None, dtype=None, copy=False
):
SparkDataFrame = get_dataframe_class()
index_assigned = False
if isinstance(data, InternalFrame):
assert columns is None
assert dtype is None
assert not copy
if index is None:
internal = data
elif isinstance(data, SparkDataFrame):
assert columns is None
assert dtype is None
assert not copy
if index is None:
internal = InternalFrame(spark_frame=data, index_spark_columns=None)
elif isinstance(data, ps.DataFrame):
assert columns is None
assert dtype is None
assert not copy
if index is None:
internal = data._internal
elif isinstance(data, ps.Series):
assert dtype is None
assert not copy
# For pandas compatibility when `columns` contains only one valid column.
if columns is not None:
assert isinstance(columns, (dict, list, tuple))
assert len(columns) == 1
columns = list(columns.keys()) if isinstance(columns, dict) else columns
assert columns[0] == data._internal.data_spark_column_names[0]
if index is None:
internal = data.to_frame()._internal
else:
from pyspark.pandas.indexes.base import Index
if index is not None and isinstance(index, Index):
# with local data, collect ps.Index to driver
# to avoid mismatched results between
# ps.DataFrame([1, 2], index=ps.Index([1, 2]))
# and
# pd.DataFrame([1, 2], index=pd.Index([1, 2]))
index = index._to_pandas()
pdf = pd.DataFrame(data=data, index=index, columns=columns, dtype=dtype, copy=copy)
internal = InternalFrame.from_pandas(pdf)
index_assigned = True
if index is not None and not index_assigned:
# TODO(SPARK-40226): Support MultiIndex
if isinstance(index, (ps.MultiIndex, pd.MultiIndex)):
raise ValueError("Cannot combine a Distributed Dataset with a MultiIndex")
data_df = ps.DataFrame(data=data, index=None, columns=columns, dtype=dtype, copy=copy)
index_ps = ps.Index(index)
index_df = index_ps.to_frame()
if same_anchor(data_df, index_df):
data_labels = data_df._internal.column_labels
data_pssers = [data_df._psser_for(label) for label in data_labels]
index_labels = index_df._internal.column_labels
index_pssers = [index_df._psser_for(label) for label in index_labels]
internal = data_df._internal.with_new_columns(data_pssers + index_pssers)
combined = ps.DataFrame(internal).set_index(index_labels)
combined.index.name = index_ps.name
else:
# drop un-matched rows in `data`
# note that `combine_frames` cannot work with a MultiIndex for now
combined = combine_frames(data_df, index_df, how="right")
combined_labels = combined._internal.column_labels
index_labels = [label for label in combined_labels if label[0] == "that"]
combined = combined.set_index(index_labels)
combined._internal._column_labels = data_df._internal.column_labels
combined._internal._column_label_names = data_df._internal._column_label_names
combined._internal._index_names = index_df._internal.column_labels
combined.index.name = index_ps.name
internal = combined._internal
object.__setattr__(self, "_internal_frame", internal)
@property
def _pssers(self) -> Dict[Label, "Series"]:
"""Return a dict of column label -> Series which anchors `self`."""
from pyspark.pandas.series import Series
if not hasattr(self, "_psseries"):
object.__setattr__(
self,
"_psseries",
{label: Series(data=self, index=label) for label in self._internal.column_labels},
)
else:
psseries = cast(Dict[Label, Series], self._psseries) # type: ignore[has-type]
assert len(self._internal.column_labels) == len(psseries), (
len(self._internal.column_labels),
len(psseries),
)
if any(self is not psser._psdf for psser in psseries.values()):
# Refresh the dict to contain only Series anchoring `self`.
self._psseries = {
label: (
psseries[label]
if self is psseries[label]._psdf
else Series(data=self, index=label)
)
for label in self._internal.column_labels
}
return self._psseries
@property
def _internal(self) -> InternalFrame:
return cast(InternalFrame, self._internal_frame) # type: ignore[has-type]
def _update_internal_frame(
self,
internal: InternalFrame,
check_same_anchor: bool = True,
anchor_force_disconnect: bool = False,
) -> None:
"""
Update InternalFrame with the given one.
If the column_label is changed or the new InternalFrame is not the same `anchor` or the
`anchor_force_disconnect` flag is set to True, disconnect the original anchor and create
a new one.
If `check_same_anchor` is `False`, checking whether the same anchor is ignored
and force to update the InternalFrame, e.g., replacing the internal with the resolved_copy,
updating the underlying Spark DataFrame which need to combine a different Spark DataFrame.
Parameters
----------
internal : InternalFrame
The new InternalFrame
check_same_anchor : bool
Whether checking the same anchor
anchor_force_disconnect : bool
Force to disconnect the original anchor and create a new one
"""
from pyspark.pandas.series import Series
if hasattr(self, "_psseries"):
psseries = {}
for old_label, new_label in zip_longest(
self._internal.column_labels, internal.column_labels
):
if old_label is not None:
psser = self._pssers[old_label]
renamed = old_label != new_label
not_same_anchor = check_same_anchor and not same_anchor(internal, psser)
if renamed or not_same_anchor or anchor_force_disconnect:
psdf: DataFrame = DataFrame(self._internal.select_column(old_label))
psser._update_anchor(psdf)
psser = None
else:
psser = None
if new_label is not None:
if psser is None:
psser = Series(data=self, index=new_label)
psseries[new_label] = psser
self._psseries = psseries
self._internal_frame = internal
if hasattr(self, "_repr_pandas_cache"):
del self._repr_pandas_cache
@property
def ndim(self) -> int:
"""
Return an int representing the number of array dimensions.
return 2 for DataFrame.
Examples
--------
>>> df = ps.DataFrame([[1, 2], [4, 5], [7, 8]],
... index=['cobra', 'viper', None],
... columns=['max_speed', 'shield'])
>>> df # doctest: +SKIP
max_speed shield
cobra 1 2
viper 4 5
None 7 8
>>> df.ndim
2
"""
return 2
@property
def axes(self) -> List:
"""
Return a list representing the axes of the DataFrame.
It has the row axis labels and column axis labels as the only members.
They are returned in that order.
Examples
--------
>>> df = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.axes # doctest: +SKIP
[Int64Index([0, 1], dtype='int64'), Index(['col1', 'col2'], dtype='object')]
"""
return [self.index, self.columns]
def _reduce_for_stat_function(
self,
sfun: Callable[["Series"], PySparkColumn],
name: str,
axis: Optional[Axis] = None,
numeric_only: bool = True,
skipna: bool = True,
**kwargs: Any,
) -> "Series":
"""
Applies sfun to each column and returns a pd.Series where the number of rows equals the
number of columns.
Parameters
----------
sfun : either an 1-arg function that takes a Column and returns a Column, or
a 2-arg function that takes a Column and its DataType and returns a Column.
axis: used only for sanity check because the series only supports index axis.
name : original pandas API name.
axis : axis to apply. 0 or 1, or 'index' or 'columns.
numeric_only : bool, default True
Include only float, int, boolean columns. False is not supported. This parameter
is mainly for pandas compatibility. Only 'DataFrame.count' uses this parameter
currently.
skipna : bool, default True
Exclude NA/null values when computing the result.
"""
from pyspark.pandas.series import Series, first_series
axis = validate_axis(axis)
if axis == 0:
min_count = kwargs.get("min_count", 0)
exprs = [F.lit(None).cast(StringType()).alias(SPARK_DEFAULT_INDEX_NAME)]
new_column_labels = []
for label in self._internal.column_labels:
psser = self._psser_for(label)
is_numeric_or_boolean = isinstance(
psser.spark.data_type, (NumericType, BooleanType)
)
keep_column = not numeric_only or is_numeric_or_boolean
if keep_column:
if not skipna and get_option("compute.eager_check") and psser.hasnans:
scol = F.first(F.lit(np.nan))
else:
scol = sfun(psser)
if min_count > 0:
scol = F.when(Frame._count_expr(psser) >= min_count, scol)
exprs.append(scol.alias(name_like_string(label)))
new_column_labels.append(label)
if len(exprs) == 1:
return Series([])
sdf = self._internal.spark_frame.select(*exprs)
# The data is expected to be small so it's fine to transpose/use the default index.
with ps.option_context("compute.max_rows", 1):
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, SPARK_DEFAULT_INDEX_NAME)],
column_labels=new_column_labels,
column_label_names=self._internal.column_label_names,
)
return first_series(DataFrame(internal).transpose())
else:
# Here we execute with the first 1000 to get the return type.
# If the records were less than 1000, it uses pandas API directly for a shortcut.
limit = get_option("compute.shortcut_limit")
pdf = self.head(limit + 1)._to_internal_pandas()
pser = getattr(pdf, name)(axis=axis, numeric_only=numeric_only, **kwargs)
if len(pdf) <= limit:
return Series(pser)
@pandas_udf(returnType=as_spark_type(pser.dtype.type)) # type: ignore[call-overload]
def calculate_columns_axis(*cols: pd.Series) -> pd.Series:
return getattr(pd.concat(cols, axis=1), name)(
axis=axis, numeric_only=numeric_only, **kwargs
)
column_name = verify_temp_column_name(
self._internal.spark_frame.select(self._internal.index_spark_columns),
"__calculate_columns_axis__",
)
sdf = self._internal.spark_frame.select(
self._internal.index_spark_columns
+ [calculate_columns_axis(*self._internal.data_spark_columns).alias(column_name)]
)
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col) for col in self._internal.index_spark_column_names
],
index_names=self._internal.index_names,
index_fields=self._internal.index_fields,
)
return first_series(DataFrame(internal)).rename(pser.name)
def _psser_for(self, label: Label) -> "Series":
"""
Create Series with a proper column label.
The given label must be verified to exist in `InternalFrame.column_labels`.
For example, in some method, self is like:
>>> self = ps.range(3)
`self._psser_for(label)` can be used with `InternalFrame.column_labels`:
>>> self._psser_for(self._internal.column_labels[0])
0 0
1 1
2 2
Name: id, dtype: int64
`self._psser_for(label)` must not be used directly with user inputs.
In that case, `self[label]` should be used instead, which checks the label exists or not:
>>> self['id']
0 0
1 1
2 2
Name: id, dtype: int64
"""
return self._pssers[label]
def _apply_series_op(
self,
op: Callable[["Series"], Union["Series", PySparkColumn]],
should_resolve: bool = False,
) -> "DataFrame":
applied = []
for label in self._internal.column_labels:
applied.append(op(self._psser_for(label)))
internal = self._internal.with_new_columns(applied)
if should_resolve:
internal = internal.resolved_copy
return DataFrame(internal)
# Arithmetic Operators
def _map_series_op(self, op: str, other: Any) -> "DataFrame":
from pyspark.pandas.base import IndexOpsMixin
if not isinstance(other, DataFrame) and (
isinstance(other, IndexOpsMixin) or is_sequence(other)
):
raise TypeError(
"%s with a sequence is currently not supported; "
"however, got %s." % (op, type(other).__name__)
)
if isinstance(other, DataFrame):
if self._internal.column_labels_level != other._internal.column_labels_level:
raise ValueError("cannot join with no overlapping index names")
if not same_anchor(self, other):
# Different DataFrames
def apply_op(
psdf: DataFrame,
this_column_labels: List[Label],
that_column_labels: List[Label],
) -> Iterator[Tuple["Series", Label]]:
for this_label, that_label in zip(this_column_labels, that_column_labels):
yield (
getattr(psdf._psser_for(this_label), op)(
psdf._psser_for(that_label)
).rename(this_label),
this_label,
)
return align_diff_frames(apply_op, self, other, fillna=True, how="full")
else:
applied = []
column_labels = []
for label in self._internal.column_labels:
if label in other._internal.column_labels:
applied.append(getattr(self._psser_for(label), op)(other._psser_for(label)))
else:
applied.append(
F.lit(None)
.cast(self._internal.spark_type_for(label))
.alias(name_like_string(label))
)
column_labels.append(label)
for label in other._internal.column_labels:
if label not in column_labels:
applied.append(
F.lit(None)
.cast(other._internal.spark_type_for(label))
.alias(name_like_string(label))
)
column_labels.append(label)
internal = self._internal.with_new_columns(applied, column_labels=column_labels)
return DataFrame(internal)
else:
return self._apply_series_op(lambda psser: getattr(psser, op)(other))
def __add__(self, other: Any) -> "DataFrame":
return self._map_series_op("add", other)
def __radd__(self, other: Any) -> "DataFrame":
return self._map_series_op("radd", other)
def __truediv__(self, other: Any) -> "DataFrame":
return self._map_series_op("truediv", other)
def __rtruediv__(self, other: Any) -> "DataFrame":
return self._map_series_op("rtruediv", other)
def __mul__(self, other: Any) -> "DataFrame":
return self._map_series_op("mul", other)
def __rmul__(self, other: Any) -> "DataFrame":
return self._map_series_op("rmul", other)
def __sub__(self, other: Any) -> "DataFrame":
return self._map_series_op("sub", other)
def __rsub__(self, other: Any) -> "DataFrame":
return self._map_series_op("rsub", other)
def __pow__(self, other: Any) -> "DataFrame":
return self._map_series_op("pow", other)
def __rpow__(self, other: Any) -> "DataFrame":
return self._map_series_op("rpow", other)
def __mod__(self, other: Any) -> "DataFrame":
return self._map_series_op("mod", other)
def __rmod__(self, other: Any) -> "DataFrame":
return self._map_series_op("rmod", other)
def __floordiv__(self, other: Any) -> "DataFrame":
return self._map_series_op("floordiv", other)
def __rfloordiv__(self, other: Any) -> "DataFrame":
return self._map_series_op("rfloordiv", other)
def __abs__(self) -> "DataFrame":
return self._apply_series_op(lambda psser: abs(psser))
def __neg__(self) -> "DataFrame":
return self._apply_series_op(lambda psser: -psser)
[docs] def add(self, other: Any) -> "DataFrame":
return self + other
# create accessor for plot
plot = CachedAccessor("plot", PandasOnSparkPlotAccessor)
# create accessor for Spark related methods.
spark = CachedAccessor("spark", SparkFrameMethods)
# create accessor for pandas-on-Spark specific methods.
pandas_on_spark = CachedAccessor("pandas_on_spark", PandasOnSparkFrameMethods)
# keep the name "koalas" for backward compatibility.
koalas = CachedAccessor("koalas", PandasOnSparkFrameMethods)
[docs] @no_type_check
def hist(self, bins=10, **kwds):
return self.plot.hist(bins, **kwds)
hist.__doc__ = PandasOnSparkPlotAccessor.hist.__doc__
[docs] @no_type_check
def boxplot(self, **kwds):
return self.plot.box(**kwds)
boxplot.__doc__ = PandasOnSparkPlotAccessor.box.__doc__
[docs] @no_type_check
def kde(self, bw_method=None, ind=None, **kwds):
return self.plot.kde(bw_method, ind, **kwds)
kde.__doc__ = PandasOnSparkPlotAccessor.kde.__doc__
add.__doc__ = _flex_doc_FRAME.format(
desc="Addition", op_name="+", equiv="dataframe + other", reverse="radd"
)
[docs] def radd(self, other: Any) -> "DataFrame":
return other + self
radd.__doc__ = _flex_doc_FRAME.format(
desc="Addition", op_name="+", equiv="other + dataframe", reverse="add"
)
[docs] def div(self, other: Any) -> "DataFrame":
return self / other
div.__doc__ = _flex_doc_FRAME.format(
desc="Floating division", op_name="/", equiv="dataframe / other", reverse="rdiv"
)
divide = div
[docs] def rdiv(self, other: Any) -> "DataFrame":
return other / self
rdiv.__doc__ = _flex_doc_FRAME.format(
desc="Floating division", op_name="/", equiv="other / dataframe", reverse="div"
)
[docs] def truediv(self, other: Any) -> "DataFrame":
return self / other
truediv.__doc__ = _flex_doc_FRAME.format(
desc="Floating division", op_name="/", equiv="dataframe / other", reverse="rtruediv"
)
[docs] def rtruediv(self, other: Any) -> "DataFrame":
return other / self
rtruediv.__doc__ = _flex_doc_FRAME.format(
desc="Floating division", op_name="/", equiv="other / dataframe", reverse="truediv"
)
[docs] def mul(self, other: Any) -> "DataFrame":
return self * other
mul.__doc__ = _flex_doc_FRAME.format(
desc="Multiplication", op_name="*", equiv="dataframe * other", reverse="rmul"
)
multiply = mul
[docs] def rmul(self, other: Any) -> "DataFrame":
return other * self
rmul.__doc__ = _flex_doc_FRAME.format(
desc="Multiplication", op_name="*", equiv="other * dataframe", reverse="mul"
)
[docs] def sub(self, other: Any) -> "DataFrame":
return self - other
sub.__doc__ = _flex_doc_FRAME.format(
desc="Subtraction", op_name="-", equiv="dataframe - other", reverse="rsub"
)
subtract = sub
[docs] def rsub(self, other: Any) -> "DataFrame":
return other - self
rsub.__doc__ = _flex_doc_FRAME.format(
desc="Subtraction", op_name="-", equiv="other - dataframe", reverse="sub"
)
[docs] def mod(self, other: Any) -> "DataFrame":
return self % other
mod.__doc__ = _flex_doc_FRAME.format(
desc="Modulo", op_name="%", equiv="dataframe % other", reverse="rmod"
)
[docs] def rmod(self, other: Any) -> "DataFrame":
return other % self
rmod.__doc__ = _flex_doc_FRAME.format(
desc="Modulo", op_name="%", equiv="other % dataframe", reverse="mod"
)
[docs] def pow(self, other: Any) -> "DataFrame":
return self**other
pow.__doc__ = _flex_doc_FRAME.format(
desc="Exponential power of series", op_name="**", equiv="dataframe ** other", reverse="rpow"
)
[docs] def rpow(self, other: Any) -> "DataFrame":
return other**self
rpow.__doc__ = _flex_doc_FRAME.format(
desc="Exponential power", op_name="**", equiv="other ** dataframe", reverse="pow"
)
[docs] def floordiv(self, other: Any) -> "DataFrame":
return self // other
floordiv.__doc__ = _flex_doc_FRAME.format(
desc="Integer division", op_name="//", equiv="dataframe // other", reverse="rfloordiv"
)
[docs] def rfloordiv(self, other: Any) -> "DataFrame":
return other // self
rfloordiv.__doc__ = _flex_doc_FRAME.format(
desc="Integer division", op_name="//", equiv="other // dataframe", reverse="floordiv"
)
# Comparison Operators
def __eq__(self, other: Any) -> "DataFrame": # type: ignore[override]
return self._map_series_op("eq", other)
def __ne__(self, other: Any) -> "DataFrame": # type: ignore[override]
return self._map_series_op("ne", other)
def __lt__(self, other: Any) -> "DataFrame":
return self._map_series_op("lt", other)
def __le__(self, other: Any) -> "DataFrame":
return self._map_series_op("le", other)
def __ge__(self, other: Any) -> "DataFrame":
return self._map_series_op("ge", other)
def __gt__(self, other: Any) -> "DataFrame":
return self._map_series_op("gt", other)
[docs] def eq(self, other: Any) -> "DataFrame":
"""
Compare if the current value is equal to the other.
>>> df = ps.DataFrame({'a': [1, 2, 3, 4],
... 'b': [1, np.nan, 1, np.nan]},
... index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df.eq(1)
a b
a True True
b False False
c False True
d False False
"""
return self == other
equals = eq
[docs] def gt(self, other: Any) -> "DataFrame":
"""
Compare if the current value is greater than the other.
>>> df = ps.DataFrame({'a': [1, 2, 3, 4],
... 'b': [1, np.nan, 1, np.nan]},
... index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df.gt(2)
a b
a False False
b False False
c True False
d True False
"""
return self > other
[docs] def ge(self, other: Any) -> "DataFrame":
"""
Compare if the current value is greater than or equal to the other.
>>> df = ps.DataFrame({'a': [1, 2, 3, 4],
... 'b': [1, np.nan, 1, np.nan]},
... index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df.ge(1)
a b
a True True
b True False
c True True
d True False
"""
return self >= other
[docs] def lt(self, other: Any) -> "DataFrame":
"""
Compare if the current value is less than the other.
>>> df = ps.DataFrame({'a': [1, 2, 3, 4],
... 'b': [1, np.nan, 1, np.nan]},
... index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df.lt(1)
a b
a False False
b False False
c False False
d False False
"""
return self < other
[docs] def le(self, other: Any) -> "DataFrame":
"""
Compare if the current value is less than or equal to the other.
>>> df = ps.DataFrame({'a': [1, 2, 3, 4],
... 'b': [1, np.nan, 1, np.nan]},
... index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df.le(2)
a b
a True True
b True False
c False True
d False False
"""
return self <= other
[docs] def ne(self, other: Any) -> "DataFrame":
"""
Compare if the current value is not equal to the other.
>>> df = ps.DataFrame({'a': [1, 2, 3, 4],
... 'b': [1, np.nan, 1, np.nan]},
... index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df.ne(1)
a b
a False False
b True True
c True False
d True True
"""
return self != other
[docs] def applymap(self, func: Callable[[Any], Any]) -> "DataFrame":
"""
Apply a function to a Dataframe elementwise.
This method applies a function that accepts and returns a scalar
to every element of a DataFrame.
.. note:: this API executes the function once to infer the type which is
potentially expensive, for instance, when the dataset is created after
aggregations or sorting.
To avoid this, specify return type in ``func``, for instance, as below:
>>> def square(x) -> np.int32:
... return x ** 2
pandas-on-Spark uses return type hints and does not try to infer the type.
Parameters
----------
func : callable
Python function returns a single value from a single value.
Returns
-------
DataFrame
Transformed DataFrame.
Examples
--------
>>> df = ps.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
0 1
0 1.000 2.120
1 3.356 4.567
>>> def str_len(x) -> int:
... return len(str(x))
>>> df.applymap(str_len)
0 1
0 3 4
1 5 5
>>> def power(x) -> float:
... return x ** 2
>>> df.applymap(power)
0 1
0 1.000000 4.494400
1 11.262736 20.857489
You can omit type hints and let pandas-on-Spark infer its type.
>>> df.applymap(lambda x: x ** 2)
0 1
0 1.000000 4.494400
1 11.262736 20.857489
"""
# TODO: We can implement shortcut theoretically since it creates new DataFrame
# anyway and we don't have to worry about operations on different DataFrames.
return self._apply_series_op(lambda psser: psser.apply(func))
# TODO: not all arguments are implemented comparing to pandas' for now.
[docs] def aggregate(self, func: Union[List[str], Dict[Name, List[str]]]) -> "DataFrame":
"""Aggregate using one or more operations over the specified axis.
Parameters
----------
func : dict or a list
a dict mapping from column name (string) to
aggregate functions (list of strings).
If a list is given, the aggregation is performed against
all columns.
Returns
-------
DataFrame
Notes
-----
`agg` is an alias for `aggregate`. Use the alias.
See Also
--------
DataFrame.apply : Invoke function on DataFrame.
DataFrame.transform : Only perform transforming type operations.
DataFrame.groupby : Perform operations over groups.
Series.aggregate : The equivalent function for Series.
Examples
--------
>>> df = ps.DataFrame([[1, 2, 3],
... [4, 5, 6],
... [7, 8, 9],
... [np.nan, np.nan, np.nan]],
... columns=['A', 'B', 'C'])
>>> df
A B C
0 1.0 2.0 3.0
1 4.0 5.0 6.0
2 7.0 8.0 9.0
3 NaN NaN NaN
Aggregate these functions over the rows.
>>> df.agg(['sum', 'min'])[['A', 'B', 'C']].sort_index()
A B C
min 1.0 2.0 3.0
sum 12.0 15.0 18.0
Different aggregations per column.
>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})[['A', 'B']].sort_index()
A B
max NaN 8.0
min 1.0 2.0
sum 12.0 NaN
For multi-index columns:
>>> df.columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C")])
>>> df.agg(['sum', 'min'])[[("X", "A"), ("X", "B"), ("Y", "C")]].sort_index()
X Y
A B C
min 1.0 2.0 3.0
sum 12.0 15.0 18.0
>>> aggregated = df.agg({("X", "A") : ['sum', 'min'], ("X", "B") : ['min', 'max']})
>>> aggregated[[("X", "A"), ("X", "B")]].sort_index() # doctest: +NORMALIZE_WHITESPACE
X
A B
max NaN 8.0
min 1.0 2.0
sum 12.0 NaN
"""
from pyspark.pandas.groupby import GroupBy
if isinstance(func, list):
if all((isinstance(f, str) for f in func)):
func = dict([(column, func) for column in self.columns])
else:
raise ValueError(
"If the given function is a list, it "
"should only contains function names as strings."
)
if not isinstance(func, dict) or not all(
is_name_like_value(key)
and (
isinstance(value, str)
or (isinstance(value, list) and all(isinstance(v, str) for v in value))
)
for key, value in func.items()
):
raise ValueError(
"aggs must be a dict mapping from column name to aggregate "
"functions (string or list of strings)."
)
with option_context("compute.default_index_type", "distributed"):
psdf: DataFrame = DataFrame(GroupBy._spark_groupby(self, func))
# The codes below basically convert:
#
# A B
# sum min min max
# 0 12.0 1.0 2.0 8.0
#
# to:
# A B
# max NaN 8.0
# min 1.0 2.0
# sum 12.0 NaN
#
# Aggregated output is usually pretty much small.
return psdf.stack().droplevel(0)[list(func.keys())]
agg = aggregate
[docs] def corr(self, method: str = "pearson", min_periods: Optional[int] = None) -> "DataFrame":
"""
Compute pairwise correlation of columns, excluding NA/null values.
.. versionadded:: 3.3.0
Parameters
----------
method : {'pearson', 'spearman', 'kendall'}
* pearson : standard correlation coefficient
* spearman : Spearman rank correlation
* kendall : Kendall Tau correlation coefficient
.. versionchanged:: 3.4.0
support 'kendall' for method parameter
min_periods : int, optional
Minimum number of observations required per pair of columns
to have a valid result.
.. versionadded:: 3.4.0
Returns
-------
DataFrame
See Also
--------
DataFrame.corrwith
Series.corr
Notes
-----
1. Pearson, Kendall and Spearman correlation are currently computed using pairwise
complete observations.
2. The complexity of Kendall correlation is O(#row * #row), if the dataset is too
large, sampling ahead of correlation computation is recommended.
Examples
--------
>>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
... columns=['dogs', 'cats'])
>>> df.corr('pearson')
dogs cats
dogs 1.000000 -0.851064
cats -0.851064 1.000000
>>> df.corr('spearman')
dogs cats
dogs 1.000000 -0.948683
cats -0.948683 1.000000
>>> df.corr('kendall')
dogs cats
dogs 1.000000 -0.912871
cats -0.912871 1.000000
"""
if method not in ["pearson", "spearman", "kendall"]:
raise ValueError(f"Invalid method {method}")
if min_periods is not None and not isinstance(min_periods, int):
raise TypeError(f"Invalid min_periods type {type(min_periods).__name__}")
min_periods = 1 if min_periods is None else min_periods
internal = self._internal.resolved_copy
numeric_labels = [
label
for label in internal.column_labels
if isinstance(internal.spark_type_for(label), (NumericType, BooleanType))
]
numeric_scols = [
internal.spark_column_for(label).cast("double") for label in numeric_labels
]
numeric_col_names: List[str] = [name_like_string(label) for label in numeric_labels]
num_scols = len(numeric_scols)
sdf = internal.spark_frame
index_1_col_name = verify_temp_column_name(sdf, "__corr_index_1_temp_column__")
index_2_col_name = verify_temp_column_name(sdf, "__corr_index_2_temp_column__")
# simple dataset
# +---+---+----+
# | A| B| C|
# +---+---+----+
# | 1| 2| 3.0|
# | 4| 1|NULL|
# +---+---+----+
pair_scols = []
for i in range(0, num_scols):
for j in range(i, num_scols):
pair_scols.append(
F.struct(
F.lit(i).alias(index_1_col_name),
F.lit(j).alias(index_2_col_name),
numeric_scols[i].alias(CORRELATION_VALUE_1_COLUMN),
numeric_scols[j].alias(CORRELATION_VALUE_2_COLUMN),
)
)
# +-------------------+-------------------+-------------------+-------------------+
# |__tmp_index_1_col__|__tmp_index_2_col__|__tmp_value_1_col__|__tmp_value_2_col__|
# +-------------------+-------------------+-------------------+-------------------+
# | 0| 0| 1.0| 1.0|
# | 0| 1| 1.0| 2.0|
# | 0| 2| 1.0| 3.0|
# | 1| 1| 2.0| 2.0|
# | 1| 2| 2.0| 3.0|
# | 2| 2| 3.0| 3.0|
# | 0| 0| 4.0| 4.0|
# | 0| 1| 4.0| 1.0|
# | 0| 2| NULL| NULL|
# | 1| 1| 1.0| 1.0|
# | 1| 2| NULL| NULL|
# | 2| 2| NULL| NULL|
# +-------------------+-------------------+-------------------+-------------------+
sdf = sdf.select(F.inline(F.array(*pair_scols)))
sdf = compute(sdf=sdf, groupKeys=[index_1_col_name, index_2_col_name], method=method)
if method == "kendall":
sdf = sdf.withColumn(
CORRELATION_CORR_OUTPUT_COLUMN,
F.when(F.col(index_1_col_name) == F.col(index_2_col_name), F.lit(1.0)).otherwise(
F.col(CORRELATION_CORR_OUTPUT_COLUMN)
),
)
sdf = sdf.withColumn(
CORRELATION_CORR_OUTPUT_COLUMN,
F.when(F.col(CORRELATION_COUNT_OUTPUT_COLUMN) < min_periods, F.lit(None)).otherwise(
F.col(CORRELATION_CORR_OUTPUT_COLUMN)
),
)
# +-------------------+-------------------+----------------+
# |__tmp_index_1_col__|__tmp_index_2_col__|__tmp_corr_col__|
# +-------------------+-------------------+----------------+
# | 2| 2| NULL|
# | 1| 2| NULL|
# | 2| 1| NULL|
# | 1| 1| 1.0|
# | 0| 0| 1.0|
# | 0| 1| -1.0|
# | 1| 0| -1.0|
# | 0| 2| NULL|
# | 2| 0| NULL|
# +-------------------+-------------------+----------------+
auxiliary_col_name = verify_temp_column_name(sdf, "__corr_auxiliary_temp_column__")
sdf = sdf.withColumn(
auxiliary_col_name,
F.explode(
F.when(
F.col(index_1_col_name) == F.col(index_2_col_name),
F.lit([0]),
).otherwise(F.lit([0, 1]))
),
).select(
F.when(F.col(auxiliary_col_name) == 0, F.col(index_1_col_name))
.otherwise(F.col(index_2_col_name))
.alias(index_1_col_name),
F.when(F.col(auxiliary_col_name) == 0, F.col(index_2_col_name))
.otherwise(F.col(index_1_col_name))
.alias(index_2_col_name),
F.col(CORRELATION_CORR_OUTPUT_COLUMN),
)
# +-------------------+--------------------+
# |__tmp_index_1_col__| __tmp_array_col__|
# +-------------------+--------------------+
# | 0|[{0, 1.0}, {1, -1...|
# | 1|[{0, -1.0}, {1, 1...|
# | 2|[{0, null}, {1, n...|
# +-------------------+--------------------+
array_col_name = verify_temp_column_name(sdf, "__corr_array_temp_column__")
sdf = (
sdf.groupby(index_1_col_name)
.agg(
F.array_sort(
F.collect_list(
F.struct(F.col(index_2_col_name), F.col(CORRELATION_CORR_OUTPUT_COLUMN))
)
).alias(array_col_name)
)
.orderBy(index_1_col_name)
)
for i in range(0, num_scols):
sdf = sdf.withColumn(auxiliary_col_name, F.get(F.col(array_col_name), i)).withColumn(
numeric_col_names[i],
F.col(f"{auxiliary_col_name}.{CORRELATION_CORR_OUTPUT_COLUMN}"),
)
index_col_names: List[str] = []
if internal.column_labels_level > 1:
for level in range(0, internal.column_labels_level):
index_col_name = SPARK_INDEX_NAME_FORMAT(level)
indices = [label[level] for label in numeric_labels]
sdf = sdf.withColumn(index_col_name, F.get(F.lit(indices), F.col(index_1_col_name)))
index_col_names.append(index_col_name)
else:
sdf = sdf.withColumn(
SPARK_DEFAULT_INDEX_NAME,
F.get(F.lit(numeric_col_names), F.col(index_1_col_name)),
)
index_col_names = [SPARK_DEFAULT_INDEX_NAME]
sdf = sdf.select(*index_col_names, *numeric_col_names)
return DataFrame(
InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, index_col_name) for index_col_name in index_col_names
],
column_labels=numeric_labels,
column_label_names=internal.column_label_names,
)
)
[docs] def corrwith(
self, other: DataFrameOrSeries, axis: Axis = 0, drop: bool = False, method: str = "pearson"
) -> "Series":
"""
Compute pairwise correlation.
Pairwise correlation is computed between rows or columns of
DataFrame with rows or columns of Series or DataFrame. DataFrames
are first aligned along both axes before computing the
correlations.
.. versionadded:: 3.4.0
Parameters
----------
other : DataFrame, Series
Object with which to compute correlations.
axis : int, default 0 or 'index'
Can only be set to 0 now.
drop : bool, default False
Drop missing indices from result.
method : {'pearson', 'spearman', 'kendall'}
* pearson : standard correlation coefficient
* spearman : Spearman rank correlation
* kendall : Kendall Tau correlation coefficient
Returns
-------
Series
Pairwise correlations.
See Also
--------
DataFrame.corr : Compute pairwise correlation of columns.
Examples
--------
>>> df1 = ps.DataFrame({
... "A":[1, 5, 7, 8],
... "X":[5, 8, 4, 3],
... "C":[10, 4, 9, 3]})
>>> df1.corrwith(df1[["X", "C"]]).sort_index()
A NaN
C 1.0
X 1.0
dtype: float64
>>> df2 = ps.DataFrame({
... "A":[5, 3, 6, 4],
... "B":[11, 2, 4, 3],
... "C":[4, 3, 8, 5]})
>>> with ps.option_context("compute.ops_on_diff_frames", True):
... df1.corrwith(df2).sort_index()
A -0.041703
B NaN
C 0.395437
X NaN
dtype: float64
>>> with ps.option_context("compute.ops_on_diff_frames", True):
... df1.corrwith(df2, method="kendall").sort_index()
A 0.0
B NaN
C 0.0
X NaN
dtype: float64
>>> with ps.option_context("compute.ops_on_diff_frames", True):
... df1.corrwith(df2.B, method="spearman").sort_index()
A -0.4
C 0.8
X -0.2
dtype: float64
>>> with ps.option_context("compute.ops_on_diff_frames", True):
... df2.corrwith(df1.X).sort_index()
A -0.597614
B -0.151186
C -0.642857
dtype: float64
"""
from pyspark.pandas.series import Series, first_series
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError("corrwith currently only works for axis=0")
if method not in ["pearson", "spearman", "kendall"]:
raise ValueError(f"Invalid method {method}")
if not isinstance(other, (DataFrame, Series)):
raise TypeError("unsupported type: {}".format(type(other).__name__))
right_is_series = isinstance(other, Series)
if same_anchor(self, other):
combined = self
this = self
that = other
else:
combined = combine_frames(self, other, how="inner")
this = combined["this"]
that = combined["that"]
sdf = combined._internal.spark_frame
index_col_name = verify_temp_column_name(sdf, "__corrwith_index_temp_column__")
this_numeric_column_labels: List[Label] = []
for column_label in this._internal.column_labels:
if isinstance(this._internal.spark_type_for(column_label), (NumericType, BooleanType)):
this_numeric_column_labels.append(column_label)
that_numeric_column_labels: List[Label] = []
for column_label in that._internal.column_labels:
if isinstance(that._internal.spark_type_for(column_label), (NumericType, BooleanType)):
that_numeric_column_labels.append(column_label)
intersect_numeric_column_labels: List[Label] = []
diff_numeric_column_labels: List[Label] = []
pair_scols = []
if right_is_series:
intersect_numeric_column_labels = this_numeric_column_labels
that_scol = that._internal.spark_column_for(that_numeric_column_labels[0]).cast(
"double"
)
for numeric_column_label in intersect_numeric_column_labels:
this_scol = this._internal.spark_column_for(numeric_column_label).cast("double")
pair_scols.append(
F.struct(
F.lit(name_like_string(numeric_column_label)).alias(index_col_name),
this_scol.alias(CORRELATION_VALUE_1_COLUMN),
that_scol.alias(CORRELATION_VALUE_2_COLUMN),
)
)
else:
for numeric_column_label in this_numeric_column_labels:
if numeric_column_label in that_numeric_column_labels:
intersect_numeric_column_labels.append(numeric_column_label)
else:
diff_numeric_column_labels.append(numeric_column_label)
for numeric_column_label in that_numeric_column_labels:
if numeric_column_label not in this_numeric_column_labels:
diff_numeric_column_labels.append(numeric_column_label)
for numeric_column_label in intersect_numeric_column_labels:
this_scol = this._internal.spark_column_for(numeric_column_label).cast("double")
that_scol = that._internal.spark_column_for(numeric_column_label).cast("double")
pair_scols.append(
F.struct(
F.lit(name_like_string(numeric_column_label)).alias(index_col_name),
this_scol.alias(CORRELATION_VALUE_1_COLUMN),
that_scol.alias(CORRELATION_VALUE_2_COLUMN),
)
)
if len(pair_scols) > 0:
sdf = sdf.select(F.inline(F.array(*pair_scols)))
sdf = compute(sdf=sdf, groupKeys=[index_col_name], method=method).select(
index_col_name, CORRELATION_CORR_OUTPUT_COLUMN
)
else:
sdf = self._internal.spark_frame.select(
F.lit(None).cast("string").alias(index_col_name),
F.lit(None).cast("double").alias(CORRELATION_CORR_OUTPUT_COLUMN),
).limit(0)
if not drop and len(diff_numeric_column_labels) > 0:
sdf2 = (
self._internal.spark_frame.select(
F.lit([name_like_string(label) for label in diff_numeric_column_labels]).alias(
index_col_name
)
)
.limit(1)
.select(F.explode(index_col_name).alias(index_col_name))
)
sdf = sdf.unionByName(sdf2, allowMissingColumns=True)
sdf = sdf.withColumn(
NATURAL_ORDER_COLUMN_NAME,
F.monotonically_increasing_id(),
)
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, index_col_name)],
column_labels=[(CORRELATION_CORR_OUTPUT_COLUMN,)],
column_label_names=self._internal.column_label_names,
)
sser = first_series(DataFrame(internal))
sser.name = None
return sser
[docs] def items(self) -> Iterator[Tuple[Name, "Series"]]:
"""
Iterator over (column name, Series) pairs.
Iterates over the DataFrame columns, returning a tuple with
the column name and the content as a Series.
Returns
-------
label : object
The column names for the DataFrame being iterated over.
content : Series
The column entries belonging to each label, as a Series.
Examples
--------
>>> df = ps.DataFrame({'species': ['bear', 'bear', 'marsupial'],
... 'population': [1864, 22000, 80000]},
... index=['panda', 'polar', 'koala'],
... columns=['species', 'population'])
>>> df
species population
panda bear 1864
polar bear 22000
koala marsupial 80000
>>> for label, content in df.iteritems():
... print('label:', label)
... print('content:', content.to_string())
...
... # doctest: +SKIP
label: species
content: panda bear
polar bear
koala marsupial
label: population
content: panda 1864
polar 22000
koala 80000
"""
return (
(label if len(label) > 1 else label[0], self._psser_for(label))
for label in self._internal.column_labels
)
[docs] def iterrows(self) -> Iterator[Tuple[Name, pd.Series]]:
"""
Iterate over DataFrame rows as (index, Series) pairs.
Yields
------
index : label or tuple of label
The index of the row. A tuple for a `MultiIndex`.
data : pandas.Series
The data of the row as a Series.
it : generator
A generator that iterates over the rows of the frame.
Notes
-----
1. Because ``iterrows`` returns a Series for each row,
it does **not** preserve dtypes across the rows (dtypes are
preserved across columns for DataFrames). For example,
>>> df = ps.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int 1.0
float 1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64
To preserve dtypes while iterating over the rows, it is better
to use :meth:`itertuples` which returns namedtuples of the values
and which is generally faster than ``iterrows``.
2. You should **never modify** something you are iterating over.
This is not guaranteed to work in all cases. Depending on the
data types, the iterator returns a copy and not a view, and writing
to it will have no effect.
"""
columns = self.columns
internal_index_columns = self._internal.index_spark_column_names
internal_data_columns = self._internal.data_spark_column_names
def extract_kv_from_spark_row(row: Row) -> Tuple[Name, Any]:
k = (
row[internal_index_columns[0]]
if len(internal_index_columns) == 1
else tuple(row[c] for c in internal_index_columns)
)
v = [row[c] for c in internal_data_columns]
return k, v
for k, v in map(
extract_kv_from_spark_row, self._internal.resolved_copy.spark_frame.toLocalIterator()
):
s = pd.Series(v, index=columns, name=k)
yield k, s
[docs] def itertuples(
self, index: bool = True, name: Optional[str] = "PandasOnSpark"
) -> Iterator[Tuple]:
"""
Iterate over DataFrame rows as namedtuples.
Parameters
----------
index : bool, default True
If True, return the index as the first element of the tuple.
name : str or None, default "PandasOnSpark"
The name of the returned namedtuples or None to return regular
tuples.
Returns
-------
iterator
An object to iterate over namedtuples for each row in the
DataFrame with the first field possibly being the index and
following fields being the column values.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)
pairs.
DataFrame.items : Iterate over (column name, Series) pairs.
Notes
-----
The column names will be renamed to positional names if they are
invalid Python identifiers, repeated, or start with an underscore.
Examples
--------
>>> df = ps.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
... index=['dog', 'hawk'])
>>> df
num_legs num_wings
dog 4 0
hawk 2 2
>>> for row in df.itertuples():
... print(row)
...
PandasOnSpark(Index='dog', num_legs=4, num_wings=0)
PandasOnSpark(Index='hawk', num_legs=2, num_wings=2)
By setting the `index` parameter to False we can remove the index
as the first element of the tuple:
>>> for row in df.itertuples(index=False):
... print(row)
...
PandasOnSpark(num_legs=4, num_wings=0)
PandasOnSpark(num_legs=2, num_wings=2)
With the `name` parameter set we set a custom name for the yielded
namedtuples:
>>> for row in df.itertuples(name='Animal'):
... print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)
"""
fields = list(self.columns)
if index:
fields.insert(0, "Index")
index_spark_column_names = self._internal.index_spark_column_names
data_spark_column_names = self._internal.data_spark_column_names
def extract_kv_from_spark_row(row: Row) -> Tuple[Name, Any]:
k = (
row[index_spark_column_names[0]]
if len(index_spark_column_names) == 1
else tuple(row[c] for c in index_spark_column_names)
)
v = [row[c] for c in data_spark_column_names]
return k, v
can_return_named_tuples = sys.version_info >= (3, 7) or len(self.columns) + index < 255
if name is not None and can_return_named_tuples:
itertuple = namedtuple(name, fields, rename=True) # type: ignore[misc]
for k, v in map(
extract_kv_from_spark_row,
self._internal.resolved_copy.spark_frame.toLocalIterator(),
):
yield itertuple._make(([k] if index else []) + list(v))
else:
for k, v in map(
extract_kv_from_spark_row,
self._internal.resolved_copy.spark_frame.toLocalIterator(),
):
yield tuple(([k] if index else []) + list(v))
[docs] def iteritems(self) -> Iterator[Tuple[Name, "Series"]]:
"""
This is an alias of ``items``.
.. deprecated:: 3.4.0
iteritems is deprecated and will be removed in a future version.
Use .items instead.
"""
warnings.warn(
"Deprecated in 3.4.0, and will be removed in 4.0.0. Use DataFrame.items instead.",
FutureWarning,
)
return self.items()
[docs] def to_clipboard(self, excel: bool = True, sep: Optional[str] = None, **kwargs: Any) -> None:
"""
Copy object to the system clipboard.
Write a text representation of object to the system clipboard.
This can be pasted into Excel, for example.
.. note:: This method should only be used if the resulting DataFrame is expected
to be small, as all the data is loaded into the driver's memory.
Parameters
----------
excel : bool, default True
- True, use the provided separator, writing in a csv format for
allowing easy pasting into excel.
- False, write a string representation of the object to the
clipboard.
sep : str, default ``'\\t'``
Field delimiter.
**kwargs
These parameters will be passed to DataFrame.to_csv.
Notes
-----
Requirements for your platform.
- Linux : `xclip`, or `xsel` (with `gtk` or `PyQt4` modules)
- Windows : none
- OS X : none
See Also
--------
read_clipboard : Read text from clipboard.
Examples
--------
Copy the contents of a DataFrame to the clipboard.
>>> df = ps.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C']) # doctest: +SKIP
>>> df.to_clipboard(sep=',') # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6
We can omit the index by passing the keyword `index` and setting
it to false.
>>> df.to_clipboard(sep=',', index=False) # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6
This function also works for Series:
>>> df = ps.Series([1, 2, 3, 4, 5, 6, 7], name='x') # doctest: +SKIP
>>> df.to_clipboard(sep=',') # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # 0, 1
... # 1, 2
... # 2, 3
... # 3, 4
... # 4, 5
... # 5, 6
... # 6, 7
"""
args = locals()
psdf = self
return validate_arguments_and_invoke_function(
psdf._to_internal_pandas(), self.to_clipboard, pd.DataFrame.to_clipboard, args
)
[docs] def to_html(
self,
buf: Optional[IO[str]] = None,
columns: Optional[Sequence[Name]] = None,
col_space: Optional[Union[str, int, Dict[Name, Union[str, int]]]] = None,
header: bool = True,
index: bool = True,
na_rep: str = "NaN",
formatters: Optional[
Union[List[Callable[[Any], str]], Dict[Name, Callable[[Any], str]]]
] = None,
float_format: Optional[Callable[[float], str]] = None,
sparsify: Optional[bool] = None,
index_names: bool = True,
justify: Optional[str] = None,
max_rows: Optional[int] = None,
max_cols: Optional[int] = None,
show_dimensions: bool = False,
decimal: str = ".",
bold_rows: bool = True,
classes: Optional[Union[str, list, tuple]] = None,
escape: bool = True,
notebook: bool = False,
border: Optional[int] = None,
table_id: Optional[str] = None,
render_links: bool = False,
) -> Optional[str]:
"""
Render a DataFrame as an HTML table.
.. note:: This method should only be used if the resulting pandas object is expected
to be small, as all the data is loaded into the driver's memory. If the input
is large, set max_rows parameter.
Parameters
----------
buf : StringIO-like, optional
Buffer to write to.
columns : sequence, optional, default None
The subset of columns to write. Writes all columns by default.
col_space : int, optional
The minimum width of each column.
header : bool, optional
Write out the column names. If a list of strings is given, it
is assumed to be aliases for the column names
index : bool, optional, default True
Whether to print index (row) labels.
na_rep : str, optional, default 'NaN'
String representation of NAN to use.
formatters : list or dict of one-param. functions, optional
Formatter functions to apply to columns' elements by position or
name.
The result of each function must be a Unicode string.
List must be of length equal to the number of columns.
float_format : one-parameter function, optional, default None
Formatter function to apply to columns' elements if they are
floats. The result of this function must be a Unicode string.
sparsify : bool, optional, default True
Set to False for a DataFrame with a hierarchical index to print
every multiindex key at each row.
index_names : bool, optional, default True
Prints the names of the indexes.
justify : str, default None
How to justify the column labels. If None uses the option from
the print configuration (controlled by set_option), 'right' out
of the box. Valid values are
* left
* right
* center
* justify
* justify-all
* start
* end
* inherit
* match-parent
* initial
* unset.
max_rows : int, optional
Maximum number of rows to display in the console.
max_cols : int, optional
Maximum number of columns to display in the console.
show_dimensions : bool, default False
Display DataFrame dimensions (number of rows by number of columns).
decimal : str, default '.'
Character recognized as decimal separator, e.g. ',' in Europe.
bold_rows : bool, default True
Make the row labels bold in the output.
classes : str or list or tuple, default None
CSS class(es) to apply to the resulting html table.
escape : bool, default True
Convert the characters <, >, and & to HTML-safe sequences.
notebook : {True, False}, default False
Whether the generated HTML is for IPython Notebook.
border : int
A ``border=border`` attribute is included in the opening
`<table>` tag. By default ``pd.options.html.border``.
table_id : str, optional
A css id is included in the opening `<table>` tag if specified.
render_links : bool, default False
Convert URLs to HTML links (only works with pandas 0.24+).
Returns
-------
str (or Unicode, depending on data and options)
String representation of the dataframe.
See Also
--------
to_string : Convert DataFrame to a string.
"""
# Make sure locals() call is at the top of the function so we don't capture local variables.
args = locals()
if max_rows is not None:
psdf = self.head(max_rows)
else:
psdf = self
return validate_arguments_and_invoke_function(
psdf._to_internal_pandas(), self.to_html, pd.DataFrame.to_html, args
)
[docs] def to_string(
self,
buf: Optional[IO[str]] = None,
columns: Optional[Sequence[Name]] = None,
col_space: Optional[Union[str, int, Dict[Name, Union[str, int]]]] = None,
header: bool = True,
index: bool = True,
na_rep: str = "NaN",
formatters: Optional[
Union[List[Callable[[Any], str]], Dict[Name, Callable[[Any], str]]]
] = None,
float_format: Optional[Callable[[float], str]] = None,
sparsify: Optional[bool] = None,
index_names: bool = True,
justify: Optional[str] = None,
max_rows: Optional[int] = None,
max_cols: Optional[int] = None,
show_dimensions: bool = False,
decimal: str = ".",
line_width: Optional[int] = None,
) -> Optional[str]:
"""
Render a DataFrame to a console-friendly tabular output.
.. note:: This method should only be used if the resulting pandas object is expected
to be small, as all the data is loaded into the driver's memory. If the input
is large, set max_rows parameter.
Parameters
----------
buf : StringIO-like, optional
Buffer to write to.
columns : sequence, optional, default None
The subset of columns to write. Writes all columns by default.
col_space : int, optional
The minimum width of each column.
header : bool, optional
Write out the column names. If a list of strings is given, it
is assumed to be aliases for the column names
index : bool, optional, default True
Whether to print index (row) labels.
na_rep : str, optional, default 'NaN'
String representation of NAN to use.
formatters : list or dict of one-param. functions, optional
Formatter functions to apply to columns' elements by position or
name.
The result of each function must be a Unicode string.
List must be of length equal to the number of columns.
float_format : one-parameter function, optional, default None
Formatter function to apply to columns' elements if they are
floats. The result of this function must be a Unicode string.
sparsify : bool, optional, default True
Set to False for a DataFrame with a hierarchical index to print
every multiindex key at each row.
index_names : bool, optional, default True
Prints the names of the indexes.
justify : str, default None
How to justify the column labels. If None uses the option from
the print configuration (controlled by set_option), 'right' out
of the box. Valid values are
* left
* right
* center
* justify
* justify-all
* start
* end
* inherit
* match-parent
* initial
* unset.
max_rows : int, optional
Maximum number of rows to display in the console.
max_cols : int, optional
Maximum number of columns to display in the console.
show_dimensions : bool, default False
Display DataFrame dimensions (number of rows by number of columns).
decimal : str, default '.'
Character recognized as decimal separator, e.g. ',' in Europe.
line_width : int, optional
Width to wrap a line in characters.
Returns
-------
str (or Unicode, depending on data and options)
String representation of the dataframe.
See Also
--------
to_html : Convert DataFrame to HTML.
Examples
--------
>>> df = ps.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}, columns=['col1', 'col2'])
>>> print(df.to_string())
col1 col2
0 1 4
1 2 5
2 3 6
>>> print(df.to_string(max_rows=2))
col1 col2
0 1 4
1 2 5
"""
# Make sure locals() call is at the top of the function so we don't capture local variables.
args = locals()
if max_rows is not None:
psdf = self.head(max_rows)
else:
psdf = self
return validate_arguments_and_invoke_function(
psdf._to_internal_pandas(), self.to_string, pd.DataFrame.to_string, args
)
[docs] def to_dict(self, orient: str = "dict", into: Type = dict) -> Union[List, Mapping]:
"""
Convert the DataFrame to a dictionary.
The type of the key-value pairs can be customized with the parameters
(see below).
.. note:: This method should only be used if the resulting pandas DataFrame is expected
to be small, as all the data is loaded into the driver's memory.
Parameters
----------
orient : str {'dict', 'list', 'series', 'split', 'records', 'index'}
Determines the type of the values of the dictionary.
- 'dict' (default) : dict like {column -> {index -> value}}
- 'list' : dict like {column -> [values]}
- 'series' : dict like {column -> Series(values)}
- 'split' : dict like
{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
- 'records' : list like
[{column -> value}, ... , {column -> value}]
- 'index' : dict like {index -> {column -> value}}
Abbreviations are allowed. `s` indicates `series` and `sp`
indicates `split`.
into : class, default dict
The collections.abc.Mapping subclass used for all Mappings
in the return value. Can be the actual class or an empty
instance of the mapping type you want. If you want a
collections.defaultdict, you must pass it initialized.
Returns
-------
dict, list or collections.abc.Mapping
Return a collections.abc.Mapping object representing the DataFrame.
The resulting transformation depends on the `orient` parameter.
Examples
--------
>>> df = ps.DataFrame({'col1': [1, 2],
... 'col2': [0.5, 0.75]},
... index=['row1', 'row2'],
... columns=['col1', 'col2'])
>>> df
col1 col2
row1 1 0.50
row2 2 0.75
>>> df_dict = df.to_dict()
>>> sorted([(key, sorted(values.items())) for key, values in df_dict.items()])
[('col1', [('row1', 1), ('row2', 2)]), ('col2', [('row1', 0.5), ('row2', 0.75)])]
You can specify the return orientation.
>>> df_dict = df.to_dict('series')
>>> sorted(df_dict.items())
[('col1', row1 1
row2 2
Name: col1, dtype: int64), ('col2', row1 0.50
row2 0.75
Name: col2, dtype: float64)]
>>> df_dict = df.to_dict('split')
>>> sorted(df_dict.items()) # doctest: +ELLIPSIS
[('columns', ['col1', 'col2']), ('data', [[1..., 0.75]]), ('index', ['row1', 'row2'])]
>>> df_dict = df.to_dict('records')
>>> [sorted(values.items()) for values in df_dict] # doctest: +ELLIPSIS
[[('col1', 1...), ('col2', 0.5)], [('col1', 2...), ('col2', 0.75)]]
>>> df_dict = df.to_dict('index')
>>> sorted([(key, sorted(values.items())) for key, values in df_dict.items()])
[('row1', [('col1', 1), ('col2', 0.5)]), ('row2', [('col1', 2), ('col2', 0.75)])]
You can also specify the mapping type.
>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])), \
('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
If you want a `defaultdict`, you need to initialize it:
>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd) # doctest: +ELLIPSIS
[defaultdict(<class 'list'>, {'col..., 'col...}), \
defaultdict(<class 'list'>, {'col..., 'col...})]
"""
# Make sure locals() call is at the top of the function so we don't capture local variables.
args = locals()
psdf = self
return validate_arguments_and_invoke_function(
psdf._to_internal_pandas(), self.to_dict, pd.DataFrame.to_dict, args
)
[docs] def to_latex(
self,
buf: Optional[IO[str]] = None,
columns: Optional[List[Name]] = None,
col_space: Optional[int] = None,
header: bool = True,
index: bool = True,
na_rep: str = "NaN",
formatters: Optional[
Union[List[Callable[[Any], str]], Dict[Name, Callable[[Any], str]]]
] = None,
float_format: Optional[Callable[[float], str]] = None,
sparsify: Optional[bool] = None,
index_names: bool = True,
bold_rows: bool = False,
column_format: Optional[str] = None,
longtable: Optional[bool] = None,
escape: Optional[bool] = None,
encoding: Optional[str] = None,
decimal: str = ".",
multicolumn: Optional[bool] = None,
multicolumn_format: Optional[str] = None,
multirow: Optional[bool] = None,
) -> Optional[str]:
r"""
Render an object to a LaTeX tabular environment table.
Render an object to a tabular environment table. You can splice this into a LaTeX
document. Requires usepackage{booktabs}.
.. note:: This method should only be used if the resulting pandas object is expected
to be small, as all the data is loaded into the driver's memory. If the input
is large, consider alternative formats.
Parameters
----------
buf : file descriptor or None
Buffer to write to. If None, the output is returned as a string.
columns : list of label, optional
The subset of columns to write. Writes all columns by default.
col_space : int, optional
The minimum width of each column.
.. deprecated:: 3.4.0
header : bool or list of str, default True
Write out the column names. If a list of strings is given, it is assumed to be aliases
for the column names.
index : bool, default True
Write row names (index).
na_rep : str, default ‘NaN’
Missing data representation.
formatters : list of functions or dict of {str: function}, optional
Formatter functions to apply to columns’ elements by position or name. The result of
each function must be a Unicode string. List must be of length equal to the number of
columns.
float_format : str, optional
Format string for floating point numbers.
sparsify : bool, optional
Set to False for a DataFrame with a hierarchical index to print every multiindex key at
each row. By default the value will be read from the config module.
index_names : bool, default True
Prints the names of the indexes.
bold_rows : bool, default False
Make the row labels bold in the output.
column_format : str, optional
The columns format as specified in LaTeX table format e.g. ‘rcl’ for 3 columns. By
default, ‘l’ will be used for all columns except columns of numbers, which default
to ‘r’.
longtable : bool, optional
By default the value will be read from the pandas config module. Use a longtable
environment instead of tabular. Requires adding a usepackage{longtable} to your LaTeX
preamble.
escape : bool, optional
By default the value will be read from the pandas config module. When set to False
prevents from escaping latex special characters in column names.
encoding : str, optional
A string representing the encoding to use in the output file, defaults to ‘ascii’ on
Python 2 and ‘utf-8’ on Python 3.
decimal : str, default ‘.’
Character recognized as decimal separator, e.g. ‘,’ in Europe.
multicolumn : bool, default True
Use multicolumn to enhance MultiIndex columns. The default will be read from the config
module.
multicolumn_format : str, default ‘l’
The alignment for multicolumns, similar to column_format The default will be read from
the config module.
multirow : bool, default False
Use multirow to enhance MultiIndex rows. Requires adding a usepackage{multirow} to your
LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained
rows, separating groups via clines. The default will be read from the pandas config
module.
Returns
-------
str or None
If buf is None, returns the resulting LateX format as a string. Otherwise returns None.
See Also
--------
DataFrame.to_string : Render a DataFrame to a console-friendly
tabular output.
DataFrame.to_html : Render a DataFrame as an HTML table.
Examples
--------
>>> df = ps.DataFrame({'name': ['Raphael', 'Donatello'],
... 'mask': ['red', 'purple'],
... 'weapon': ['sai', 'bo staff']},
... columns=['name', 'mask', 'weapon'])
>>> print(df.to_latex(index=False)) # doctest: +NORMALIZE_WHITESPACE
\begin{tabular}{lll}
\toprule
name & mask & weapon \\
\midrule
Raphael & red & sai \\
Donatello & purple & bo staff \\
\bottomrule
\end{tabular}
"""
warnings.warn(
"Argument `col_space` will be removed in 4.0.0.",
FutureWarning,
)
args = locals()
psdf = self
return validate_arguments_and_invoke_function(
psdf._to_internal_pandas(), self.to_latex, pd.DataFrame.to_latex, args
)
# TODO: enable doctests once we drop Spark 2.3.x (due to type coercion logic
# when creating arrays)
[docs] def transpose(self) -> "DataFrame":
"""
Transpose index and columns.
Reflect the DataFrame over its main diagonal by writing rows as columns
and vice-versa. The property :attr:`.T` is an accessor to the method
:meth:`transpose`.
.. note:: This method is based on an expensive operation due to the nature
of big data. Internally it needs to generate each row for each value, and
then group twice - it is a huge operation. To prevent misuse, this method
has the 'compute.max_rows' default limit of input length and raises a ValueError.
>>> from pyspark.pandas.config import option_context
>>> with option_context('compute.max_rows', 1000): # doctest: +NORMALIZE_WHITESPACE
... ps.DataFrame({'a': range(1001)}).transpose()
Traceback (most recent call last):
...
ValueError: Current DataFrame's length exceeds the given limit of 1000 rows.
Please set 'compute.max_rows' by using 'pyspark.pandas.config.set_option'
to retrieve more than 1000 rows. Note that, before changing the
'compute.max_rows', this operation is considerably expensive.
Returns
-------
DataFrame
The transposed DataFrame.
Notes
-----
Transposing a DataFrame with mixed dtypes will result in a homogeneous
DataFrame with the coerced dtype. For instance, if int and float have
to be placed in same column, it becomes float. If type coercion is not
possible, it fails.
Also, note that the values in index should be unique because they become
unique column names.
In addition, if Spark 2.3 is used, the types should always be exactly same.
Examples
--------
**Square DataFrame with homogeneous dtype**
>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = ps.DataFrame(data=d1, columns=['col1', 'col2'])
>>> df1
col1 col2
0 1 3
1 2 4
>>> df1_transposed = df1.T.sort_index() # doctest: +SKIP
>>> df1_transposed # doctest: +SKIP
0 1
col1 1 2
col2 3 4
When the dtype is homogeneous in the original DataFrame, we get a
transposed DataFrame with the same dtype:
>>> df1.dtypes
col1 int64
col2 int64
dtype: object
>>> df1_transposed.dtypes # doctest: +SKIP
0 int64
1 int64
dtype: object
**Non-square DataFrame with mixed dtypes**
>>> d2 = {'score': [9.5, 8],
... 'kids': [0, 0],
... 'age': [12, 22]}
>>> df2 = ps.DataFrame(data=d2, columns=['score', 'kids', 'age'])
>>> df2
score kids age
0 9.5 0 12
1 8.0 0 22
>>> df2_transposed = df2.T.sort_index() # doctest: +SKIP
>>> df2_transposed # doctest: +SKIP
0 1
age 12.0 22.0
kids 0.0 0.0
score 9.5 8.0
When the DataFrame has mixed dtypes, we get a transposed DataFrame with
the coerced dtype:
>>> df2.dtypes
score float64
kids int64
age int64
dtype: object
>>> df2_transposed.dtypes # doctest: +SKIP
0 float64
1 float64
dtype: object
"""
max_compute_count = get_option("compute.max_rows")
if max_compute_count is not None:
pdf = self.head(max_compute_count + 1)._to_internal_pandas()
if len(pdf) > max_compute_count:
raise ValueError(
"Current DataFrame's length exceeds the given limit of {0} rows. "
"Please set 'compute.max_rows' by using 'pyspark.pandas.config.set_option' "
"to retrieve more than {0} rows. Note that, before changing the "
"'compute.max_rows', this operation is considerably expensive.".format(
max_compute_count
)
)
return DataFrame(pdf.transpose())
# Explode the data to be pairs.
#
# For instance, if the current input DataFrame is as below:
#
# +------+------+------+------+------+
# |index1|index2|(a,x1)|(a,x2)|(b,x3)|
# +------+------+------+------+------+
# | y1| z1| 1| 0| 0|
# | y2| z2| 0| 50| 0|
# | y3| z3| 3| 2| 1|
# +------+------+------+------+------+
#
# Output of `exploded_df` becomes as below:
#
# +-----------------+-----------------+-----------------+-----+
# | index|__index_level_0__|__index_level_1__|value|
# +-----------------+-----------------+-----------------+-----+
# |{"a":["y1","z1"]}| a| x1| 1|
# |{"a":["y1","z1"]}| a| x2| 0|
# |{"a":["y1","z1"]}| b| x3| 0|
# |{"a":["y2","z2"]}| a| x1| 0|
# |{"a":["y2","z2"]}| a| x2| 50|
# |{"a":["y2","z2"]}| b| x3| 0|
# |{"a":["y3","z3"]}| a| x1| 3|
# |{"a":["y3","z3"]}| a| x2| 2|
# |{"a":["y3","z3"]}| b| x3| 1|
# +-----------------+-----------------+-----------------+-----+
pairs = F.explode(
F.array(
*[
F.struct(
*[
F.lit(col).alias(SPARK_INDEX_NAME_FORMAT(i))
for i, col in enumerate(label)
],
*[self._internal.spark_column_for(label).alias("value")],
)
for label in self._internal.column_labels
]
)
)
exploded_df = self._internal.spark_frame.withColumn("pairs", pairs).select(
[
F.to_json(
F.struct(
F.array(*[scol for scol in self._internal.index_spark_columns]).alias("a")
)
).alias("index"),
F.col("pairs.*"),
]
)
# After that, executes pivot with key and its index column.
# Note that index column should contain unique values since column names
# should be unique.
internal_index_columns = [
SPARK_INDEX_NAME_FORMAT(i) for i in range(self._internal.column_labels_level)
]
pivoted_df = exploded_df.groupBy(internal_index_columns).pivot("index")
transposed_df = pivoted_df.agg(F.first(F.col("value")))
new_data_columns = list(
filter(lambda x: x not in internal_index_columns, transposed_df.columns)
)
column_labels = [
None if len(label) == 1 and label[0] is None else label
for label in (tuple(json.loads(col)["a"]) for col in new_data_columns)
]
internal = InternalFrame(
spark_frame=transposed_df,
index_spark_columns=[scol_for(transposed_df, col) for col in internal_index_columns],
index_names=self._internal.column_label_names,
column_labels=column_labels,
data_spark_columns=[scol_for(transposed_df, col) for col in new_data_columns],
column_label_names=self._internal.index_names,
)
return DataFrame(internal)
T = property(transpose)
[docs] def apply(
self, func: Callable, axis: Axis = 0, args: Sequence[Any] = (), **kwds: Any
) -> Union["Series", "DataFrame", "Index"]:
"""
Apply a function along an axis of the DataFrame.
Objects passed to the function are Series objects whose index is
either the DataFrame's index (``axis=0``) or the DataFrame's columns
(``axis=1``).
See also `Transform and apply a function
<https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/transform_apply.html>`_.
.. note:: when `axis` is 0 or 'index', the `func` is unable to access
to the whole input series. pandas-on-Spark internally splits the input series into
multiple batches and calls `func` with each batch multiple times. Therefore, operations
such as global aggregations are impossible. See the example below.
>>> # This case does not return the length of whole series but of the batch internally
... # used.
... def length(s) -> int:
... return len(s)
...
>>> df = ps.DataFrame({'A': range(1000)})
>>> df.apply(length, axis=0) # doctest: +SKIP
0 83
1 83
2 83
...
10 83
11 83
dtype: int32
.. note:: this API executes the function once to infer the type which is
potentially expensive, for instance, when the dataset is created after
aggregations or sorting.
To avoid this, specify the return type as `Series` or scalar value in ``func``,
for instance, as below:
>>> def square(s) -> ps.Series[np.int32]:
... return s ** 2
pandas-on-Spark uses return type hints and does not try to infer the type.
In case when axis is 1, it requires to specify `DataFrame` or scalar value
with type hints as below:
>>> def plus_one(x) -> ps.DataFrame[int, [float, float]]:
... return x + 1
If the return type is specified as `DataFrame`, the output column names become
`c0, c1, c2 ... cn`. These names are positionally mapped to the returned
DataFrame in ``func``.
To specify the column names, you can assign them in a pandas style as below:
>>> def plus_one(x) -> ps.DataFrame[("index", int), [("a", float), ("b", float)]]:
... return x + 1
>>> pdf = pd.DataFrame({'a': [1, 2, 3], 'b': [3, 4, 5]})
>>> def plus_one(x) -> ps.DataFrame[
... (pdf.index.name, pdf.index.dtype), zip(pdf.dtypes, pdf.columns)]:
... return x + 1
Parameters
----------
func : function
Function to apply to each column or row.
axis : {0 or 'index', 1 or 'columns'}, default 0
Axis along which the function is applied:
* 0 or 'index': apply function to each column.
* 1 or 'columns': apply function to each row.
args : tuple
Positional arguments to pass to `func` in addition to the
array/series.
**kwds
Additional keyword arguments to pass as keywords arguments to
`func`.
Returns
-------
Series or DataFrame
Result of applying ``func`` along the given axis of the
DataFrame.
See Also
--------
DataFrame.applymap : For elementwise operations.
DataFrame.aggregate : Only perform aggregating type operations.
DataFrame.transform : Only perform transforming type operations.
Series.apply : The equivalent function for Series.
Examples
--------
>>> df = ps.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
>>> df
A B
0 4 9
1 4 9
2 4 9
Using a numpy universal function (in this case the same as
``np.sqrt(df)``):
>>> def sqrt(x) -> ps.Series[float]:
... return np.sqrt(x)
...
>>> df.apply(sqrt, axis=0)
A B
0 2.0 3.0
1 2.0 3.0
2 2.0 3.0
You can omit type hints and let pandas-on-Spark infer its type.
>>> df.apply(np.sqrt, axis=0)
A B
0 2.0 3.0
1 2.0 3.0
2 2.0 3.0
When `axis` is 1 or 'columns', it applies the function for each row.
>>> def summation(x) -> np.int64:
... return np.sum(x)
...
>>> df.apply(summation, axis=1)
0 13
1 13
2 13
dtype: int64
You can omit type hints and let pandas-on-Spark infer its type.
>>> df.apply(np.sum, axis=1)
0 13
1 13
2 13
dtype: int64
>>> df.apply(max, axis=1)
0 9
1 9
2 9
dtype: int64
Returning a list-like will result in a Series
>>> df.apply(lambda x: [1, 2], axis=1)
0 [1, 2]
1 [1, 2]
2 [1, 2]
dtype: object
To specify the types when `axis` is '1', it should use DataFrame[...]
annotation. In this case, the column names are automatically generated.
>>> def identify(x) -> ps.DataFrame[('index', int), [('A', np.int64), ('B', np.int64)]]:
... return x
...
>>> df.apply(identify, axis=1) # doctest: +NORMALIZE_WHITESPACE
A B
index
0 4 9
1 4 9
2 4 9
You can also specify extra arguments.
>>> def plus_two(a, b, c) -> ps.DataFrame[np.int64, [np.int64, np.int64]]:
... return a + b + c
...
>>> df.apply(plus_two, axis=1, args=(1,), c=3)
c0 c1
0 8 13
1 8 13
2 8 13
"""
from pyspark.pandas.groupby import GroupBy
from pyspark.pandas.series import first_series
if not isinstance(func, types.FunctionType):
assert callable(func), "the first argument should be a callable function."
f = func
# Note that the return type hints specified here affects actual return
# type in Spark (e.g., infer_return_type). And MyPy does not allow
# redefinition of a function.
func = lambda *args, **kwargs: f(*args, **kwargs) # noqa: E731
axis = validate_axis(axis)
should_return_series = False
spec = inspect.getfullargspec(func)
return_sig = spec.annotations.get("return", None)
should_infer_schema = return_sig is None
should_retain_index = should_infer_schema
def apply_func(pdf: pd.DataFrame) -> pd.DataFrame:
pdf_or_pser = pdf.apply(func, axis=axis, args=args, **kwds) # type: ignore[arg-type]
if isinstance(pdf_or_pser, pd.Series):
return pdf_or_pser.to_frame()
else:
return pdf_or_pser
self_applied: DataFrame = DataFrame(self._internal.resolved_copy)
column_labels: Optional[List[Label]] = None
if should_infer_schema:
# Here we execute with the first 1000 to get the return type.
# If the records were less than 1000, it uses pandas API directly for a shortcut.
log_advice(
"If the type hints is not specified for `apply`, "
"it is expensive to infer the data type internally."
)
limit = get_option("compute.shortcut_limit")
pdf = self_applied.head(limit + 1)._to_internal_pandas()
applied = pdf.apply(func, axis=axis, args=args, **kwds) # type: ignore[arg-type]
psser_or_psdf = ps.from_pandas(applied)
if len(pdf) <= limit:
return psser_or_psdf
psdf = psser_or_psdf
if isinstance(psser_or_psdf, ps.Series):
should_return_series = True
psdf = psser_or_psdf._psdf
index_fields = [field.normalize_spark_type() for field in psdf._internal.index_fields]
data_fields = [field.normalize_spark_type() for field in psdf._internal.data_fields]
return_schema = StructType([field.struct_field for field in index_fields + data_fields])
output_func = GroupBy._make_pandas_df_builder_func(
self_applied, apply_func, return_schema, retain_index=should_retain_index
)
sdf = self_applied._internal.to_internal_spark_frame.mapInPandas(
lambda iterator: map(output_func, iterator), schema=return_schema
)
# If schema is inferred, we can restore indexes too.
internal = psdf._internal.with_new_sdf(
spark_frame=sdf, index_fields=index_fields, data_fields=data_fields
)
else:
return_type = infer_return_type(func)
require_index_axis = isinstance(return_type, SeriesType)
require_column_axis = isinstance(return_type, DataFrameType)
index_fields = None
if require_index_axis:
if axis != 0:
raise TypeError(
"The given function should specify a scalar or a series as its type "
"hints when axis is 0 or 'index'; however, the return type "
"was %s" % return_sig
)
dtype = cast(SeriesType, return_type).dtype
spark_type = cast(SeriesType, return_type).spark_type
data_fields = [
InternalField(
dtype=dtype, struct_field=StructField(name=name, dataType=spark_type)
)
for name in self_applied.columns
]
return_schema = StructType([field.struct_field for field in data_fields])
elif require_column_axis:
if axis != 1:
raise TypeError(
"The given function should specify a scalar or a frame as its type "
"hints when axis is 1 or 'column'; however, the return type "
"was %s" % return_sig
)
index_fields = cast(DataFrameType, return_type).index_fields
should_retain_index = len(index_fields) > 0
data_fields = cast(DataFrameType, return_type).data_fields
return_schema = cast(DataFrameType, return_type).spark_type
else:
# any axis is fine.
should_return_series = True
spark_type = cast(ScalarType, return_type).spark_type
dtype = cast(ScalarType, return_type).dtype
data_fields = [
InternalField(
dtype=dtype,
struct_field=StructField(
name=SPARK_DEFAULT_SERIES_NAME, dataType=spark_type
),
)
]
return_schema = StructType([field.struct_field for field in data_fields])
column_labels = [None]
output_func = GroupBy._make_pandas_df_builder_func(
self_applied, apply_func, return_schema, retain_index=should_retain_index
)
sdf = self_applied._internal.to_internal_spark_frame.mapInPandas(
lambda iterator: map(output_func, iterator), schema=return_schema
)
index_spark_columns = None
index_names: Optional[List[Optional[Tuple[Any, ...]]]] = None
if should_retain_index:
index_spark_columns = [
scol_for(sdf, index_field.struct_field.name) for index_field in index_fields
]
if not any(
[
SPARK_INDEX_NAME_PATTERN.match(index_field.struct_field.name)
for index_field in index_fields
]
):
index_names = [(index_field.struct_field.name,) for index_field in index_fields]
internal = InternalFrame(
spark_frame=sdf,
index_names=index_names,
index_spark_columns=index_spark_columns,
index_fields=index_fields,
data_fields=data_fields,
column_labels=column_labels,
)
result: DataFrame = DataFrame(internal)
if should_return_series:
return first_series(result)
else:
return result
[docs] def pop(self, item: Name) -> "DataFrame":
"""
Return item and drop from frame. Raise KeyError if not found.
Parameters
----------
item : str
Label of column to be popped.
Returns
-------
Series
Examples
--------
>>> df = ps.DataFrame([('falcon', 'bird', 389.0),
... ('parrot', 'bird', 24.0),
... ('lion', 'mammal', 80.5),
... ('monkey','mammal', np.nan)],
... columns=('name', 'class', 'max_speed'))
>>> df
name class max_speed
0 falcon bird 389.0
1 parrot bird 24.0
2 lion mammal 80.5
3 monkey mammal NaN
>>> df.pop('class')
0 bird
1 bird
2 mammal
3 mammal
Name: class, dtype: object
>>> df
name max_speed
0 falcon 389.0
1 parrot 24.0
2 lion 80.5
3 monkey NaN
Also support for MultiIndex
>>> df = ps.DataFrame([('falcon', 'bird', 389.0),
... ('parrot', 'bird', 24.0),
... ('lion', 'mammal', 80.5),
... ('monkey','mammal', np.nan)],
... columns=('name', 'class', 'max_speed'))
>>> columns = [('a', 'name'), ('a', 'class'), ('b', 'max_speed')]
>>> df.columns = pd.MultiIndex.from_tuples(columns)
>>> df
a b
name class max_speed
0 falcon bird 389.0
1 parrot bird 24.0
2 lion mammal 80.5
3 monkey mammal NaN
>>> df.pop('a')
name class
0 falcon bird
1 parrot bird
2 lion mammal
3 monkey mammal
>>> df
b
max_speed
0 389.0
1 24.0
2 80.5
3 NaN
"""
result = self[item]
self._update_internal_frame(self.drop(columns=item)._internal)
return result
# TODO: add axis parameter can work when '1' or 'columns'
[docs] def xs(self, key: Name, axis: Axis = 0, level: Optional[int] = None) -> DataFrameOrSeries:
"""
Return cross-section from the DataFrame.
This method takes a `key` argument to select data at a particular
level of a MultiIndex.
Parameters
----------
key : label or tuple of label
Label contained in the index, or partially in a MultiIndex.
axis : 0 or 'index', default 0
Axis to retrieve cross-section on.
currently only support 0 or 'index'
level : object, defaults to first n levels (n=1 or len(key))
In case of a key partially contained in a MultiIndex, indicate
which levels are used. Levels can be referred by label or position.
Returns
-------
DataFrame or Series
Cross-section from the original DataFrame
corresponding to the selected index levels.
See Also
--------
DataFrame.loc : Access a group of rows and columns
by label(s) or a boolean array.
DataFrame.iloc : Purely integer-location based indexing
for selection by position.
Examples
--------
>>> d = {'num_legs': [4, 4, 2, 2],
... 'num_wings': [0, 0, 2, 2],
... 'class': ['mammal', 'mammal', 'mammal', 'bird'],
... 'animal': ['cat', 'dog', 'bat', 'penguin'],
... 'locomotion': ['walks', 'walks', 'flies', 'walks']}
>>> df = ps.DataFrame(data=d)
>>> df = df.set_index(['class', 'animal', 'locomotion'])
>>> df # doctest: +NORMALIZE_WHITESPACE
num_legs num_wings
class animal locomotion
mammal cat walks 4 0
dog walks 4 0
bat flies 2 2
bird penguin walks 2 2
Get values at specified index
>>> df.xs('mammal') # doctest: +NORMALIZE_WHITESPACE
num_legs num_wings
animal locomotion
cat walks 4 0
dog walks 4 0
bat flies 2 2
Get values at several indexes
>>> df.xs(('mammal', 'dog')) # doctest: +NORMALIZE_WHITESPACE
num_legs num_wings
locomotion
walks 4 0
>>> df.xs(('mammal', 'dog', 'walks')) # doctest: +NORMALIZE_WHITESPACE
num_legs 4
num_wings 0
Name: (mammal, dog, walks), dtype: int64
Get values at specified index and level
>>> df.xs('cat', level=1) # doctest: +NORMALIZE_WHITESPACE
num_legs num_wings
class locomotion
mammal walks 4 0
"""
from pyspark.pandas.series import first_series
if not is_name_like_value(key):
raise TypeError("'key' should be a scalar value or tuple that contains scalar values")
if level is not None and is_name_like_tuple(key):
raise KeyError(key)
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError('axis should be either 0 or "index" currently.')
if not is_name_like_tuple(key):
key = (key,)
if len(key) > self._internal.index_level:
raise KeyError(
"Key length ({}) exceeds index depth ({})".format(
len(key), self._internal.index_level
)
)
if level is None:
level = 0
rows = [
self._internal.index_spark_columns[lvl] == index for lvl, index in enumerate(key, level)
]
internal = self._internal.with_filter(reduce(lambda x, y: x & y, rows))
if len(key) == self._internal.index_level:
psdf: DataFrame = DataFrame(internal)
pdf = psdf.head(2)._to_internal_pandas()
if len(pdf) == 0:
raise KeyError(key)
elif len(pdf) > 1:
return psdf
else:
return first_series(DataFrame(pdf.transpose()))
else:
index_spark_columns = (
internal.index_spark_columns[:level]
+ internal.index_spark_columns[level + len(key) :]
)
index_names = internal.index_names[:level] + internal.index_names[level + len(key) :]
index_fields = internal.index_fields[:level] + internal.index_fields[level + len(key) :]
internal = internal.copy(
index_spark_columns=index_spark_columns,
index_names=index_names,
index_fields=index_fields,
).resolved_copy
return DataFrame(internal)
# TODO(SPARK-42620): Add `inclusive` parameter and replace `include_start` & `include_end`.
# See https://github.com/pandas-dev/pandas/issues/43248
[docs] def between_time(
self,
start_time: Union[datetime.time, str],
end_time: Union[datetime.time, str],
include_start: bool = True,
include_end: bool = True,
axis: Axis = 0,
) -> "DataFrame":
"""
Select values between particular times of the day (example: 9:00-9:30 AM).
By setting ``start_time`` to be later than ``end_time``,
you can get the times that are *not* between the two times.
Parameters
----------
start_time : datetime.time or str
Initial time as a time filter limit.
end_time : datetime.time or str
End time as a time filter limit.
include_start : bool, default True
Whether the start time needs to be included in the result.
.. deprecated:: 3.4.0
include_end : bool, default True
Whether the end time needs to be included in the result.
.. deprecated:: 3.4.0
axis : {0 or 'index', 1 or 'columns'}, default 0
Determine range time on index or columns value.
Returns
-------
DataFrame
Data from the original object filtered to the specified dates range.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
at_time : Select values at a particular time of the day.
first : Select initial periods of time series based on a date offset.
last : Select final periods of time series based on a date offset.
DatetimeIndex.indexer_between_time : Get just the index locations for
values between particular times of the day.
Examples
--------
>>> idx = pd.date_range('2018-04-09', periods=4, freq='1D20min')
>>> psdf = ps.DataFrame({'A': [1, 2, 3, 4]}, index=idx)
>>> psdf
A
2018-04-09 00:00:00 1
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
2018-04-12 01:00:00 4
>>> psdf.between_time('0:15', '0:45') # doctest: +SKIP
A
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
You get the times that are *not* between two times by setting
``start_time`` later than ``end_time``:
>>> psdf.between_time('0:45', '0:15') # doctest: +SKIP
A
2018-04-09 00:00:00 1
2018-04-12 01:00:00 4
"""
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError("between_time currently only works for axis=0")
if not isinstance(self.index, ps.DatetimeIndex):
raise TypeError("Index must be DatetimeIndex")
psdf = self.copy()
psdf.index.name = verify_temp_column_name(psdf, "__index_name__")
return_types = [psdf.index.dtype] + list(psdf.dtypes)
def pandas_between_time( # type: ignore[no-untyped-def]
pdf,
) -> ps.DataFrame[return_types]: # type: ignore[valid-type]
return pdf.between_time(start_time, end_time, include_start, include_end).reset_index()
# apply_batch will remove the index of the pandas-on-Spark DataFrame and attach a
# default index, which will never be used. Use "distributed" index as a dummy to
# avoid overhead.
with option_context("compute.default_index_type", "distributed"):
psdf = psdf.pandas_on_spark.apply_batch(pandas_between_time)
return DataFrame(
self._internal.copy(
spark_frame=psdf._internal.spark_frame,
index_spark_columns=psdf._internal.data_spark_columns[:1],
index_fields=psdf._internal.data_fields[:1],
data_spark_columns=psdf._internal.data_spark_columns[1:],
data_fields=psdf._internal.data_fields[1:],
)
)
# TODO: implement axis=1
[docs] def at_time(
self, time: Union[datetime.time, str], asof: bool = False, axis: Axis = 0
) -> "DataFrame":
"""
Select values at particular time of day (example: 9:30AM).
Parameters
----------
time : datetime.time or str
axis : {0 or 'index', 1 or 'columns'}, default 0
Returns
-------
DataFrame
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
between_time : Select values between particular times of the day.
DatetimeIndex.indexer_at_time : Get just the index locations for
values at particular time of the day.
Examples
--------
>>> idx = pd.date_range('2018-04-09', periods=4, freq='12H')
>>> psdf = ps.DataFrame({'A': [1, 2, 3, 4]}, index=idx)
>>> psdf
A
2018-04-09 00:00:00 1
2018-04-09 12:00:00 2
2018-04-10 00:00:00 3
2018-04-10 12:00:00 4
>>> psdf.at_time('12:00')
A
2018-04-09 12:00:00 2
2018-04-10 12:00:00 4
"""
if asof:
raise NotImplementedError("'asof' argument is not supported")
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError("at_time currently only works for axis=0")
if not isinstance(self.index, ps.DatetimeIndex):
raise TypeError("Index must be DatetimeIndex")
psdf = self.copy()
psdf.index.name = verify_temp_column_name(psdf, "__index_name__")
return_types = [psdf.index.dtype] + list(psdf.dtypes)
def pandas_at_time( # type: ignore[no-untyped-def]
pdf,
) -> ps.DataFrame[return_types]: # type: ignore[valid-type]
return pdf.at_time(time, asof, axis).reset_index()
# apply_batch will remove the index of the pandas-on-Spark DataFrame and attach
# a default index, which will never be used. Use "distributed" index as a dummy
# to avoid overhead.
with option_context("compute.default_index_type", "distributed"):
psdf = psdf.pandas_on_spark.apply_batch(pandas_at_time)
return DataFrame(
self._internal.copy(
spark_frame=psdf._internal.spark_frame,
index_spark_columns=psdf._internal.data_spark_columns[:1],
index_fields=psdf._internal.data_fields[:1],
data_spark_columns=psdf._internal.data_spark_columns[1:],
data_fields=psdf._internal.data_fields[1:],
)
)
[docs] def where(
self,
cond: DataFrameOrSeries,
other: Union[DataFrameOrSeries, Any] = np.nan,
axis: Axis = None,
) -> "DataFrame":
"""
Replace values where the condition is False.
Parameters
----------
cond : boolean DataFrame
Where cond is True, keep the original value. Where False,
replace with corresponding value from other.
other : scalar, DataFrame
Entries where cond is False are replaced with corresponding value from other.
axis : int, default None
Can only be set to 0 now for compatibility with pandas.
Returns
-------
DataFrame
Examples
--------
>>> from pyspark.pandas.config import set_option, reset_option
>>> set_option("compute.ops_on_diff_frames", True)
>>> df1 = ps.DataFrame({'A': [0, 1, 2, 3, 4], 'B':[100, 200, 300, 400, 500]})
>>> df2 = ps.DataFrame({'A': [0, -1, -2, -3, -4], 'B':[-100, -200, -300, -400, -500]})
>>> df1
A B
0 0 100
1 1 200
2 2 300
3 3 400
4 4 500
>>> df2
A B
0 0 -100
1 -1 -200
2 -2 -300
3 -3 -400
4 -4 -500
>>> df1.where(df1 > 0).sort_index()
A B
0 NaN 100.0
1 1.0 200.0
2 2.0 300.0
3 3.0 400.0
4 4.0 500.0
>>> df1.where(df1 > 1, 10).sort_index()
A B
0 10 100
1 10 200
2 2 300
3 3 400
4 4 500
>>> df1.where(df1 > 1, df1 + 100).sort_index()
A B
0 100 100
1 101 200
2 2 300
3 3 400
4 4 500
>>> df1.where(df1 > 1, df2).sort_index()
A B
0 0 100
1 -1 200
2 2 300
3 3 400
4 4 500
When the column name of cond is different from self, it treats all values are False
>>> cond = ps.DataFrame({'C': [0, -1, -2, -3, -4], 'D':[4, 3, 2, 1, 0]}) % 3 == 0
>>> cond
C D
0 True False
1 False True
2 False False
3 True False
4 False True
>>> df1.where(cond).sort_index()
A B
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
When the type of cond is Series, it just check boolean regardless of column name
>>> cond = ps.Series([1, 2]) > 1
>>> cond
0 False
1 True
dtype: bool
>>> df1.where(cond).sort_index()
A B
0 NaN NaN
1 1.0 200.0
2 NaN NaN
3 NaN NaN
4 NaN NaN
>>> reset_option("compute.ops_on_diff_frames")
"""
from pyspark.pandas.series import Series
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError('axis should be either 0 or "index" currently.')
tmp_cond_col_name = "__tmp_cond_col_{}__".format
tmp_other_col_name = "__tmp_other_col_{}__".format
psdf = self.copy()
tmp_cond_col_names = [
tmp_cond_col_name(name_like_string(label)) for label in self._internal.column_labels
]
if isinstance(cond, DataFrame):
cond = cond[
[
(
cond._internal.spark_column_for(label)
if label in cond._internal.column_labels
else F.lit(False)
).alias(name)
for label, name in zip(self._internal.column_labels, tmp_cond_col_names)
]
]
psdf[tmp_cond_col_names] = cond
elif isinstance(cond, Series):
cond = cond.to_frame()
cond = cond[
[cond._internal.data_spark_columns[0].alias(name) for name in tmp_cond_col_names]
]
psdf[tmp_cond_col_names] = cond
else:
raise TypeError("type of cond must be a DataFrame or Series")
tmp_other_col_names = [
tmp_other_col_name(name_like_string(label)) for label in self._internal.column_labels
]
if isinstance(other, DataFrame):
other = other[
[
(
other._internal.spark_column_for(label)
if label in other._internal.column_labels
else F.lit(np.nan)
).alias(name)
for label, name in zip(self._internal.column_labels, tmp_other_col_names)
]
]
psdf[tmp_other_col_names] = other
elif isinstance(other, Series):
other = other.to_frame()
other = other[
[other._internal.data_spark_columns[0].alias(name) for name in tmp_other_col_names]
]
psdf[tmp_other_col_names] = other
else:
for label in self._internal.column_labels:
psdf[tmp_other_col_name(name_like_string(label))] = other
# above logic make spark dataframe looks like below:
# +-----------------+---+---+------------------+-------------------+------------------+--...
# |__index_level_0__| A| B|__tmp_cond_col_A__|__tmp_other_col_A__|__tmp_cond_col_B__|__...
# +-----------------+---+---+------------------+-------------------+------------------+--...
# | 0| 0|100| true| 0| false| ...
# | 1| 1|200| false| -1| false| ...
# | 3| 3|400| true| -3| false| ...
# | 2| 2|300| false| -2| true| ...
# | 4| 4|500| false| -4| false| ...
# +-----------------+---+---+------------------+-------------------+------------------+--...
data_spark_columns = []
for label in self._internal.column_labels:
data_spark_columns.append(
F.when(
psdf[tmp_cond_col_name(name_like_string(label))].spark.column,
psdf._internal.spark_column_for(label),
)
.otherwise(psdf[tmp_other_col_name(name_like_string(label))].spark.column)
.alias(psdf._internal.spark_column_name_for(label))
)
return DataFrame(
psdf._internal.with_new_columns(
data_spark_columns, column_labels=self._internal.column_labels # TODO: dtypes?
)
)
[docs] def mask(
self, cond: DataFrameOrSeries, other: Union[DataFrameOrSeries, Any] = np.nan
) -> "DataFrame":
"""
Replace values where the condition is True.
Parameters
----------
cond : boolean DataFrame
Where cond is False, keep the original value. Where True,
replace with corresponding value from other.
other : scalar, DataFrame
Entries where cond is True are replaced with corresponding value from other.
Returns
-------
DataFrame
Examples
--------
>>> from pyspark.pandas.config import set_option, reset_option
>>> set_option("compute.ops_on_diff_frames", True)
>>> df1 = ps.DataFrame({'A': [0, 1, 2, 3, 4], 'B':[100, 200, 300, 400, 500]})
>>> df2 = ps.DataFrame({'A': [0, -1, -2, -3, -4], 'B':[-100, -200, -300, -400, -500]})
>>> df1
A B
0 0 100
1 1 200
2 2 300
3 3 400
4 4 500
>>> df2
A B
0 0 -100
1 -1 -200
2 -2 -300
3 -3 -400
4 -4 -500
>>> df1.mask(df1 > 0).sort_index()
A B
0 0.0 NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
>>> df1.mask(df1 > 1, 10).sort_index()
A B
0 0 10
1 1 10
2 10 10
3 10 10
4 10 10
>>> df1.mask(df1 > 1, df1 + 100).sort_index()
A B
0 0 200
1 1 300
2 102 400
3 103 500
4 104 600
>>> df1.mask(df1 > 1, df2).sort_index()
A B
0 0 -100
1 1 -200
2 -2 -300
3 -3 -400
4 -4 -500
>>> reset_option("compute.ops_on_diff_frames")
"""
from pyspark.pandas.series import Series
if not isinstance(cond, (DataFrame, Series)):
raise TypeError("type of cond must be a DataFrame or Series")
cond_inversed = cond._apply_series_op(lambda psser: ~psser)
return self.where(cond_inversed, other)
@property
def index(self) -> "Index":
"""The index (row labels) Column of the DataFrame.
Currently not supported when the DataFrame has no index.
See Also
--------
Index
"""
from pyspark.pandas.indexes.base import Index
return Index._new_instance(self)
@property
def empty(self) -> bool:
"""
Returns true if the current DataFrame is empty. Otherwise, returns false.
Examples
--------
>>> ps.range(10).empty
False
>>> ps.range(0).empty
True
>>> ps.DataFrame({}, index=list('abc')).empty
True
"""
return (
len(self._internal.column_labels) == 0
or self._internal.resolved_copy.spark_frame.isEmpty()
)
@property
def style(self) -> "Styler":
"""
Property returning a Styler object containing methods for
building a styled HTML representation for the DataFrame.
Examples
--------
>>> ps.range(1001).style # doctest: +SKIP
<pandas.io.formats.style.Styler object at ...>
"""
max_results = get_option("compute.max_rows")
if max_results is not None:
pdf = self.head(max_results + 1)._to_internal_pandas()
if len(pdf) > max_results:
warnings.warn(
"'style' property will only use top %s rows." % max_results, UserWarning
)
return pdf.head(max_results).style
else:
return self._to_internal_pandas().style
[docs] def set_index(
self,
keys: Union[Name, List[Name]],
drop: bool = True,
append: bool = False,
inplace: bool = False,
) -> Optional["DataFrame"]:
"""Set the DataFrame index (row labels) using one or more existing columns.
Set the DataFrame index (row labels) using one or more existing
columns or arrays (of the correct length). The index can replace the
existing index or expand on it.
Parameters
----------
keys : label or array-like or list of labels/arrays
This parameter can be either a single column key, a single array of
the same length as the calling DataFrame, or a list containing an
arbitrary combination of column keys and arrays. Here, "array"
encompasses :class:`Series`, :class:`Index` and ``np.ndarray``.
drop : bool, default True
Delete columns to be used as the new index.
append : bool, default False
Whether to append columns to existing index.
inplace : bool, default False
Modify the DataFrame in place (do not create a new object).
Returns
-------
DataFrame
Changed row labels.
See Also
--------
DataFrame.reset_index : Opposite of set_index.
Examples
--------
>>> df = ps.DataFrame({'month': [1, 4, 7, 10],
... 'year': [2012, 2014, 2013, 2014],
... 'sale': [55, 40, 84, 31]},
... columns=['month', 'year', 'sale'])
>>> df
month year sale
0 1 2012 55
1 4 2014 40
2 7 2013 84
3 10 2014 31
Set the index to become the 'month' column:
>>> df.set_index('month') # doctest: +NORMALIZE_WHITESPACE
year sale
month
1 2012 55
4 2014 40
7 2013 84
10 2014 31
Create a MultiIndex using columns 'year' and 'month':
>>> df.set_index(['year', 'month']) # doctest: +NORMALIZE_WHITESPACE
sale
year month
2012 1 55
2014 4 40
2013 7 84
2014 10 31
"""
inplace = validate_bool_kwarg(inplace, "inplace")
key_list: List[Label]
if is_name_like_tuple(keys):
key_list = [cast(Label, keys)]
elif is_name_like_value(keys):
key_list = [(keys,)]
else:
key_list = [key if is_name_like_tuple(key) else (key,) for key in keys]
columns = set(self._internal.column_labels)
for key in key_list:
if key not in columns:
raise KeyError(name_like_string(key))
if drop:
column_labels = [
label for label in self._internal.column_labels if label not in key_list
]
else:
column_labels = self._internal.column_labels
if append:
index_spark_columns = self._internal.index_spark_columns + [
self._internal.spark_column_for(label) for label in key_list
]
index_names = self._internal.index_names + key_list
index_fields = self._internal.index_fields + [
self._internal.field_for(label) for label in key_list
]
else:
index_spark_columns = [self._internal.spark_column_for(label) for label in key_list]
index_names = key_list
index_fields = [self._internal.field_for(label) for label in key_list]
internal = self._internal.copy(
index_spark_columns=index_spark_columns,
index_names=index_names,
index_fields=index_fields,
column_labels=column_labels,
data_spark_columns=[self._internal.spark_column_for(label) for label in column_labels],
data_fields=[self._internal.field_for(label) for label in column_labels],
)
if inplace:
self._update_internal_frame(internal)
return None
else:
return DataFrame(internal)
[docs] def reset_index(
self,
level: Optional[Union[int, Name, Sequence[Union[int, Name]]]] = None,
drop: bool = False,
inplace: bool = False,
col_level: int = 0,
col_fill: str = "",
) -> Optional["DataFrame"]:
"""Reset the index, or a level of it.
For DataFrame with multi-level index, return new DataFrame with labeling information in
the columns under the index names, defaulting to 'level_0', 'level_1', etc. if any are None.
For a standard index, the index name will be used (if set), otherwise a default 'index' or
'level_0' (if 'index' is already taken) will be used.
Parameters
----------
level : int, str, tuple, or list, default None
Only remove the given levels from the index. Removes all levels by
default.
drop : bool, default False
Do not try to insert index into dataframe columns. This reset
the index to the default integer index.
inplace : bool, default False
Modify the DataFrame in place (do not create a new object).
col_level : int or str, default 0
If the columns have multiple levels, determines which level the
labels are inserted into. By default it is inserted into the first
level.
col_fill : object, default ''
If the columns have multiple levels, determines how the other
levels are named. If None then the index name is repeated.
Returns
-------
DataFrame
DataFrame with the new index.
See Also
--------
DataFrame.set_index : Opposite of reset_index.
Examples
--------
>>> df = ps.DataFrame([('bird', 389.0),
... ('bird', 24.0),
... ('mammal', 80.5),
... ('mammal', np.nan)],
... index=['falcon', 'parrot', 'lion', 'monkey'],
... columns=('class', 'max_speed'))
>>> df
class max_speed
falcon bird 389.0
parrot bird 24.0
lion mammal 80.5
monkey mammal NaN
When we reset the index, the old index is added as a column. Unlike pandas, pandas-on-Spark
does not automatically add a sequential index. The following 0, 1, 2, 3 are only
there when we display the DataFrame.
>>> df.reset_index()
index class max_speed
0 falcon bird 389.0
1 parrot bird 24.0
2 lion mammal 80.5
3 monkey mammal NaN
We can use the `drop` parameter to avoid the old index being added as
a column:
>>> df.reset_index(drop=True)
class max_speed
0 bird 389.0
1 bird 24.0
2 mammal 80.5
3 mammal NaN
You can also use `reset_index` with `MultiIndex`.
>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
... ('bird', 'parrot'),
... ('mammal', 'lion'),
... ('mammal', 'monkey')],
... names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
... ('species', 'type')])
>>> df = ps.DataFrame([(389.0, 'fly'),
... ( 24.0, 'fly'),
... ( 80.5, 'run'),
... (np.nan, 'jump')],
... index=index,
... columns=columns)
>>> df # doctest: +NORMALIZE_WHITESPACE
speed species
max type
class name
bird falcon 389.0 fly
parrot 24.0 fly
mammal lion 80.5 run
monkey NaN jump
If the index has multiple levels, we can reset a subset of them:
>>> df.reset_index(level='class') # doctest: +NORMALIZE_WHITESPACE
class speed species
max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
If we are not dropping the index, by default, it is placed in the top
level. We can place it in another level:
>>> df.reset_index(level='class', col_level=1) # doctest: +NORMALIZE_WHITESPACE
speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
When the index is inserted under another level, we can specify under
which one with the parameter `col_fill`:
>>> df.reset_index(level='class', col_level=1,
... col_fill='species') # doctest: +NORMALIZE_WHITESPACE
species speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
If we specify a nonexistent level for `col_fill`, it is created:
>>> df.reset_index(level='class', col_level=1,
... col_fill='genus') # doctest: +NORMALIZE_WHITESPACE
genus speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
"""
inplace = validate_bool_kwarg(inplace, "inplace")
multi_index = self._internal.index_level > 1
def rename(index: int) -> Label:
if multi_index:
return ("level_{}".format(index),)
else:
if ("index",) not in self._internal.column_labels:
return ("index",)
else:
return ("level_{}".format(index),)
if level is None:
new_column_labels = [
name if name is not None else rename(i)
for i, name in enumerate(self._internal.index_names)
]
new_data_spark_columns = [
scol.alias(name_like_string(label))
for scol, label in zip(self._internal.index_spark_columns, new_column_labels)
]
new_data_fields = self._internal.index_fields
index_spark_columns = []
index_names = []
index_fields = []
else:
if is_list_like(level):
level = list(cast(Sequence[Union[int, Name]], level))
if isinstance(level, int) or is_name_like_tuple(level):
level_list = [cast(Union[int, Label], level)]
elif is_name_like_value(level):
level_list = [(level,)]
else:
level_list = [
lvl if isinstance(lvl, int) or is_name_like_tuple(lvl) else (lvl,)
for lvl in level
]
if all(isinstance(lvl, int) for lvl in level_list):
int_level_list = cast(List[int], level_list)
for lev in int_level_list:
if lev >= self._internal.index_level:
raise IndexError(
"Too many levels: Index has only {} level, not {}".format(
self._internal.index_level, lev + 1
)
)
idx = int_level_list
elif all(is_name_like_tuple(lev) for lev in level_list):
idx = []
for label in cast(List[Label], level_list):
try:
i = self._internal.index_names.index(label)
idx.append(i)
except ValueError:
if multi_index:
raise KeyError("Level unknown not found")
else:
raise KeyError(
"Level unknown must be same as name ({})".format(
name_like_string(self._internal.index_names[0])
)
)
else:
raise ValueError("Level should be all int or all string.")
idx.sort()
new_column_labels = []
new_data_spark_columns = []
new_data_fields = []
index_spark_columns = self._internal.index_spark_columns.copy()
index_names = self._internal.index_names.copy()
index_fields = self._internal.index_fields.copy()
for i in idx[::-1]:
name = index_names.pop(i)
new_column_labels.insert(0, name if name is not None else rename(i))
scol = index_spark_columns.pop(i)
new_data_spark_columns.insert(0, scol.alias(name_like_string(name)))
new_data_fields.insert(0, index_fields.pop(i).copy(name=name_like_string(name)))
if drop:
new_data_spark_columns = []
new_column_labels = []
new_data_fields = []
for label in new_column_labels:
if label in self._internal.column_labels:
raise ValueError("cannot insert {}, already exists".format(name_like_string(label)))
if self._internal.column_labels_level > 1:
column_depth = len(self._internal.column_labels[0])
if col_level >= column_depth:
raise IndexError(
"Too many levels: Index has only {} levels, not {}".format(
column_depth, col_level + 1
)
)
if any(col_level + len(label) > column_depth for label in new_column_labels):
raise ValueError("Item must have length equal to number of levels.")
new_column_labels = [
tuple(
([col_fill] * col_level)
+ list(label)
+ ([col_fill] * (column_depth - (len(label) + col_level)))
)
for label in new_column_labels
]
internal = self._internal.copy(
index_spark_columns=index_spark_columns,
index_names=index_names,
index_fields=index_fields,
column_labels=new_column_labels + self._internal.column_labels,
data_spark_columns=new_data_spark_columns + self._internal.data_spark_columns,
data_fields=new_data_fields + self._internal.data_fields,
)
if inplace:
self._update_internal_frame(internal)
return None
else:
return DataFrame(internal)
[docs] def isnull(self) -> "DataFrame":
"""
Detects missing values for items in the current Dataframe.
Return a boolean same-sized Dataframe indicating if the values are NA.
NA values, such as None or numpy.NaN, gets mapped to True values.
Everything else gets mapped to False values.
See Also
--------
DataFrame.notnull
Examples
--------
>>> df = ps.DataFrame([(.2, .3), (.0, None), (.6, None), (.2, .1)])
>>> df.isnull()
0 1
0 False False
1 False True
2 False True
3 False False
>>> df = ps.DataFrame([[None, 'bee', None], ['dog', None, 'fly']])
>>> df.isnull()
0 1 2
0 True False True
1 False True False
"""
return self._apply_series_op(lambda psser: psser.isnull())
isna = isnull
[docs] def notnull(self) -> "DataFrame":
"""
Detects non-missing values for items in the current Dataframe.
This function takes a dataframe and indicates whether it's
values are valid (not missing, which is ``NaN`` in numeric
datatypes, ``None`` or ``NaN`` in objects and ``NaT`` in datetimelike).
See Also
--------
DataFrame.isnull
Examples
--------
>>> df = ps.DataFrame([(.2, .3), (.0, None), (.6, None), (.2, .1)])
>>> df.notnull()
0 1
0 True True
1 True False
2 True False
3 True True
>>> df = ps.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']])
>>> df.notnull()
0 1 2
0 True True True
1 True False True
"""
return self._apply_series_op(lambda psser: psser.notnull())
notna = notnull
[docs] def insert(
self,
loc: int,
column: Name,
value: Union[Scalar, "Series", Iterable],
allow_duplicates: bool = False,
) -> None:
"""
Insert column into DataFrame at specified location.
Raises a ValueError if `column` is already contained in the DataFrame,
unless `allow_duplicates` is set to True.
Parameters
----------
loc : int
Insertion index. Must verify 0 <= loc <= len(columns).
column : str, number, or hashable object
Label of the inserted column.
value : int, Series, or array-like
allow_duplicates : bool, optional
Examples
--------
>>> psdf = ps.DataFrame([1, 2, 3])
>>> psdf.sort_index()
0
0 1
1 2
2 3
>>> psdf.insert(0, 'x', 4)
>>> psdf.sort_index()
x 0
0 4 1
1 4 2
2 4 3
>>> from pyspark.pandas.config import set_option, reset_option
>>> set_option("compute.ops_on_diff_frames", True)
>>> psdf.insert(1, 'y', [5, 6, 7])
>>> psdf.sort_index()
x y 0
0 4 5 1
1 4 6 2
2 4 7 3
>>> psdf.insert(2, 'z', ps.Series([8, 9, 10]))
>>> psdf.sort_index()
x y z 0
0 4 5 8 1
1 4 6 9 2
2 4 7 10 3
>>> reset_option("compute.ops_on_diff_frames")
"""
if not isinstance(loc, int):
raise TypeError("loc must be int")
assert 0 <= loc <= len(self.columns)
assert allow_duplicates is False
if not is_name_like_value(column):
raise TypeError(
'"column" should be a scalar value or tuple that contains scalar values'
)
# TODO(SPARK-37723): Support tuple for non-MultiIndex column name.
if is_name_like_tuple(column):
if self._internal.column_labels_level > 1:
if len(column) != len(self.columns.levels): # type: ignore[attr-defined]
# To be consistent with pandas
raise ValueError('"column" must have length equal to number of column levels.')
else:
raise NotImplementedError(
"Assigning column name as tuple is only supported for MultiIndex columns "
"for now."
)
if column in self.columns:
raise ValueError("cannot insert %s, already exists" % str(column))
psdf = self.copy()
psdf[column] = value
columns = psdf.columns[:-1].insert(loc, psdf.columns[-1])
psdf = psdf[columns]
self._update_internal_frame(psdf._internal)
# TODO: add frep and axis parameter
[docs] def shift(self, periods: int = 1, fill_value: Optional[Any] = None) -> "DataFrame":
"""
Shift DataFrame by desired number of periods.
.. note:: the current implementation of shift uses Spark's Window without
specifying partition specification. This leads to moving all data into
a single partition in a single machine and could cause serious
performance degradation. Avoid this method with very large datasets.
Parameters
----------
periods : int
Number of periods to shift. Can be positive or negative.
fill_value : object, optional
The scalar value to use for newly introduced missing values.
The default depends on the dtype of self. For numeric data, np.nan is used.
Returns
-------
Copy of input DataFrame, shifted.
Examples
--------
>>> df = ps.DataFrame({'Col1': [10, 20, 15, 30, 45],
... 'Col2': [13, 23, 18, 33, 48],
... 'Col3': [17, 27, 22, 37, 52]},
... columns=['Col1', 'Col2', 'Col3'])
>>> df.shift(periods=3)
Col1 Col2 Col3
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 10.0 13.0 17.0
4 20.0 23.0 27.0
>>> df.shift(periods=3, fill_value=0)
Col1 Col2 Col3
0 0 0 0
1 0 0 0
2 0 0 0
3 10 13 17
4 20 23 27
"""
return self._apply_series_op(
lambda psser: psser._shift(periods, fill_value), should_resolve=True
)
# TODO: axis should support 1 or 'columns' either at this moment
[docs] def diff(self, periods: int = 1, axis: Axis = 0) -> "DataFrame":
"""
First discrete difference of element.
Calculates the difference of a DataFrame element compared with another element in the
DataFrame (default is the element in the same column of the previous row).
.. note:: the current implementation of diff uses Spark's Window without
specifying partition specification. This leads to moving all data into
a single partition in a single machine and could cause serious
performance degradation. Avoid this method with very large datasets.
Parameters
----------
periods : int, default 1
Periods to shift for calculating difference, accepts negative values.
axis : int, default 0 or 'index'
Can only be set to 0 now.
Returns
-------
diffed : DataFrame
Examples
--------
>>> df = ps.DataFrame({'a': [1, 2, 3, 4, 5, 6],
... 'b': [1, 1, 2, 3, 5, 8],
... 'c': [1, 4, 9, 16, 25, 36]}, columns=['a', 'b', 'c'])
>>> df
a b c
0 1 1 1
1 2 1 4
2 3 2 9
3 4 3 16
4 5 5 25
5 6 8 36
>>> df.diff()
a b c
0 NaN NaN NaN
1 1.0 0.0 3.0
2 1.0 1.0 5.0
3 1.0 1.0 7.0
4 1.0 2.0 9.0
5 1.0 3.0 11.0
Difference with previous column
>>> df.diff(periods=3)
a b c
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 3.0 2.0 15.0
4 3.0 4.0 21.0
5 3.0 6.0 27.0
Difference with following row
>>> df.diff(periods=-1)
a b c
0 -1.0 0.0 -3.0
1 -1.0 -1.0 -5.0
2 -1.0 -1.0 -7.0
3 -1.0 -2.0 -9.0
4 -1.0 -3.0 -11.0
5 NaN NaN NaN
"""
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError('axis should be either 0 or "index" currently.')
return self._apply_series_op(lambda psser: psser._diff(periods), should_resolve=True)
# TODO: axis should support 1 or 'columns' either at this moment
[docs] def nunique(
self,
axis: Axis = 0,
dropna: bool = True,
approx: bool = False,
rsd: float = 0.05,
) -> "Series":
"""
Return number of unique elements in the object.
Excludes NA values by default.
Parameters
----------
axis : int, default 0 or 'index'
Can only be set to 0 now.
dropna : bool, default True
Don’t include NaN in the count.
approx: bool, default False
If False, will use the exact algorithm and return the exact number of unique.
If True, it uses the HyperLogLog approximate algorithm, which is significantly faster
for large amounts of data.
Note: This parameter is specific to pandas-on-Spark and is not found in pandas.
rsd: float, default 0.05
Maximum estimation error allowed in the HyperLogLog algorithm.
Note: Just like ``approx`` this parameter is specific to pandas-on-Spark.
Returns
-------
The number of unique values per column as a pandas-on-Spark Series.
Examples
--------
>>> df = ps.DataFrame({'A': [1, 2, 3], 'B': [np.nan, 3, np.nan]})
>>> df.nunique()
A 3
B 1
dtype: int64
>>> df.nunique(dropna=False)
A 3
B 2
dtype: int64
On big data, we recommend using the approximate algorithm to speed up this function.
The result will be very close to the exact unique count.
>>> df.nunique(approx=True)
A 3
B 1
dtype: int64
"""
from pyspark.pandas.series import first_series
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError('axis should be either 0 or "index" currently.')
sdf = self._internal.spark_frame.select(
[F.lit(None).cast(StringType()).alias(SPARK_DEFAULT_INDEX_NAME)]
+ [
self._psser_for(label)._nunique(dropna, approx, rsd)
for label in self._internal.column_labels
]
)
# The data is expected to be small so it's fine to transpose/use the default index.
with ps.option_context("compute.max_rows", 1):
internal = self._internal.copy(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, SPARK_DEFAULT_INDEX_NAME)],
index_names=[None],
index_fields=[None],
data_spark_columns=[
scol_for(sdf, col) for col in self._internal.data_spark_column_names
],
data_fields=None,
)
return first_series(DataFrame(internal).transpose())
[docs] def round(self, decimals: Union[int, Dict[Name, int], "Series"] = 0) -> "DataFrame":
"""
Round a DataFrame to a variable number of decimal places.
Parameters
----------
decimals : int, dict, Series
Number of decimal places to round each column to. If an int is
given, round each column to the same number of places.
Otherwise dict and Series round to variable numbers of places.
Column names should be in the keys if `decimals` is a
dict-like, or in the index if `decimals` is a Series. Any
columns not included in `decimals` will be left as is. Elements
of `decimals` which are not columns of the input will be
ignored.
.. note:: If `decimals` is a Series, it is expected to be small,
as all the data is loaded into the driver's memory.
Returns
-------
DataFrame
See Also
--------
Series.round
Examples
--------
>>> df = ps.DataFrame({'A':[0.028208, 0.038683, 0.877076],
... 'B':[0.992815, 0.645646, 0.149370],
... 'C':[0.173891, 0.577595, 0.491027]},
... columns=['A', 'B', 'C'],
... index=['first', 'second', 'third'])
>>> df
A B C
first 0.028208 0.992815 0.173891
second 0.038683 0.645646 0.577595
third 0.877076 0.149370 0.491027
>>> df.round(2)
A B C
first 0.03 0.99 0.17
second 0.04 0.65 0.58
third 0.88 0.15 0.49
>>> df.round({'A': 1, 'C': 2})
A B C
first 0.0 0.992815 0.17
second 0.0 0.645646 0.58
third 0.9 0.149370 0.49
>>> decimals = ps.Series([1, 0, 2], index=['A', 'B', 'C'])
>>> df.round(decimals)
A B C
first 0.0 1.0 0.17
second 0.0 1.0 0.58
third 0.9 0.0 0.49
"""
if isinstance(decimals, ps.Series):
decimals_dict = {
k if isinstance(k, tuple) else (k,): v
for k, v in decimals._to_internal_pandas().items()
}
elif isinstance(decimals, dict):
decimals_dict = {k if is_name_like_tuple(k) else (k,): v for k, v in decimals.items()}
elif isinstance(decimals, int):
decimals_dict = {k: decimals for k in self._internal.column_labels}
else:
raise TypeError("decimals must be an integer, a dict-like or a Series")
def op(psser: ps.Series) -> Union[ps.Series, PySparkColumn]:
label = psser._column_label
if label in decimals_dict:
return F.round(psser.spark.column, decimals_dict[label])
else:
return psser
return self._apply_series_op(op)
def _mark_duplicates(
self,
subset: Optional[Union[Name, List[Name]]] = None,
keep: Union[bool, str] = "first",
) -> Tuple[PySparkDataFrame, str]:
if subset is None:
subset_list = self._internal.column_labels
else:
if is_name_like_tuple(subset):
subset_list = [cast(Label, subset)]
elif is_name_like_value(subset):
subset_list = [(subset,)]
else:
subset_list = [sub if is_name_like_tuple(sub) else (sub,) for sub in subset]
diff = set(subset_list).difference(set(self._internal.column_labels))
if len(diff) > 0:
raise KeyError(", ".join([name_like_string(d) for d in diff]))
group_cols = [self._internal.spark_column_name_for(label) for label in subset_list]
sdf = self._internal.resolved_copy.spark_frame
column = verify_temp_column_name(sdf, "__duplicated__")
if keep == "first" or keep == "last":
if keep == "first":
ord_func = F.asc
else:
ord_func = F.desc
window = (
Window.partitionBy(*group_cols)
.orderBy(ord_func(NATURAL_ORDER_COLUMN_NAME))
.rowsBetween(Window.unboundedPreceding, Window.currentRow)
)
sdf = sdf.withColumn(column, F.row_number().over(window) > 1)
elif not keep:
window = Window.partitionBy(*group_cols).rowsBetween(
Window.unboundedPreceding, Window.unboundedFollowing
)
sdf = sdf.withColumn(column, F.count("*").over(window) > 1)
else:
raise ValueError("'keep' only supports 'first', 'last' and False")
return sdf, column
[docs] def duplicated(
self,
subset: Optional[Union[Name, List[Name]]] = None,
keep: Union[bool, str] = "first",
) -> "Series":
"""
Return boolean Series denoting duplicate rows, optionally only considering certain columns.
Parameters
----------
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicates,
default use all of the columns
keep : {'first', 'last', False}, default 'first'
- ``first`` : Mark duplicates as ``True`` except for the first occurrence.
- ``last`` : Mark duplicates as ``True`` except for the last occurrence.
- False : Mark all duplicates as ``True``.
Returns
-------
duplicated : Series
Examples
--------
>>> df = ps.DataFrame({'a': [1, 1, 1, 3], 'b': [1, 1, 1, 4], 'c': [1, 1, 1, 5]},
... columns = ['a', 'b', 'c'])
>>> df
a b c
0 1 1 1
1 1 1 1
2 1 1 1
3 3 4 5
>>> df.duplicated().sort_index()
0 False
1 True
2 True
3 False
dtype: bool
Mark duplicates as ``True`` except for the last occurrence.
>>> df.duplicated(keep='last').sort_index()
0 True
1 True
2 False
3 False
dtype: bool
Mark all duplicates as ``True``.
>>> df.duplicated(keep=False).sort_index()
0 True
1 True
2 True
3 False
dtype: bool
"""
from pyspark.pandas.series import first_series
sdf, column = self._mark_duplicates(subset, keep)
sdf = sdf.select(
self._internal.index_spark_columns
+ [scol_for(sdf, column).alias(SPARK_DEFAULT_SERIES_NAME)]
)
return first_series(
DataFrame(
InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col) for col in self._internal.index_spark_column_names
],
index_names=self._internal.index_names,
index_fields=self._internal.index_fields,
column_labels=[None],
data_spark_columns=[scol_for(sdf, SPARK_DEFAULT_SERIES_NAME)],
)
)
)
# TODO: support other as DataFrame or array-like
[docs] def dot(self, other: "Series") -> "Series":
"""
Compute the matrix multiplication between the DataFrame and others.
This method computes the matrix product between the DataFrame and the
values of an other Series
It can also be called using ``self @ other`` in Python >= 3.5.
.. note:: This method is based on an expensive operation due to the nature
of big data. Internally it needs to generate each row for each value, and
then group twice - it is a huge operation. To prevent misuse, this method
has the 'compute.max_rows' default limit of input length and raises a ValueError.
>>> from pyspark.pandas.config import option_context
>>> with option_context(
... 'compute.max_rows', 1000, "compute.ops_on_diff_frames", True
... ): # doctest: +NORMALIZE_WHITESPACE
... psdf = ps.DataFrame({'a': range(1001)})
... psser = ps.Series([2], index=['a'])
... psdf.dot(psser)
Traceback (most recent call last):
...
ValueError: Current DataFrame's length exceeds the given limit of 1000 rows.
Please set 'compute.max_rows' by using 'pyspark.pandas.config.set_option'
to retrieve more than 1000 rows. Note that, before changing the
'compute.max_rows', this operation is considerably expensive.
Parameters
----------
other : Series
The other object to compute the matrix product with.
Returns
-------
Series
Return the matrix product between self and other as a Series.
See Also
--------
Series.dot: Similar method for Series.
Notes
-----
The dimensions of DataFrame and other must be compatible to
compute the matrix multiplication. In addition, the column names of
DataFrame and the index of other must contain the same values, as they
will be aligned prior to the multiplication.
The dot method for Series computes the inner product, instead of the
matrix product here.
Examples
--------
>>> from pyspark.pandas.config import set_option, reset_option
>>> set_option("compute.ops_on_diff_frames", True)
>>> psdf = ps.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> psser = ps.Series([1, 1, 2, 1])
>>> psdf.dot(psser)
0 -4
1 5
dtype: int64
Note how shuffling of the objects does not change the result.
>>> psser2 = psser.reindex([1, 0, 2, 3])
>>> psdf.dot(psser2)
0 -4
1 5
dtype: int64
>>> psdf @ psser2
0 -4
1 5
dtype: int64
>>> reset_option("compute.ops_on_diff_frames")
"""
if not isinstance(other, ps.Series):
raise TypeError("Unsupported type {}".format(type(other).__name__))
else:
return cast(ps.Series, other.dot(self.transpose())).rename(None)
def __matmul__(self, other: "Series") -> "Series":
"""
Matrix multiplication using binary `@` operator in Python>=3.5.
"""
return self.dot(other)
[docs] def to_table(
self,
name: str,
format: Optional[str] = None,
mode: str = "w",
partition_cols: Optional[Union[str, List[str]]] = None,
index_col: Optional[Union[str, List[str]]] = None,
**options: Any,
) -> None:
if index_col is None:
log_advice(
"If `index_col` is not specified for `to_table`, "
"the existing index is lost when converting to table."
)
mode = validate_mode(mode)
return self.spark.to_table(name, format, mode, partition_cols, index_col, **options)
to_table.__doc__ = SparkFrameMethods.to_table.__doc__
[docs] def to_delta(
self,
path: str,
mode: str = "w",
partition_cols: Optional[Union[str, List[str]]] = None,
index_col: Optional[Union[str, List[str]]] = None,
**options: "OptionalPrimitiveType",
) -> None:
"""
Write the DataFrame out as a Delta Lake table.
Parameters
----------
path : str, required
Path to write to.
mode : str
Python write mode, default 'w'.
.. note:: mode can accept the strings for Spark writing mode.
Such as 'append', 'overwrite', 'ignore', 'error', 'errorifexists'.
- 'append' (equivalent to 'a'): Append the new data to existing data.
- 'overwrite' (equivalent to 'w'): Overwrite existing data.
- 'ignore': Silently ignore this operation if data already exists.
- 'error' or 'errorifexists': Throw an exception if data already exists.
partition_cols : str or list of str, optional, default None
Names of partitioning columns
index_col: str or list of str, optional, default: None
Column names to be used in Spark to represent pandas-on-Spark's index. The index name
in pandas-on-Spark is ignored. By default the index is always lost.
options : dict
All other options passed directly into Delta Lake.
See Also
--------
read_delta
DataFrame.to_parquet
DataFrame.to_table
DataFrame.to_spark_io
Examples
--------
>>> df = ps.DataFrame(dict(
... date=list(pd.date_range('2012-1-1 12:00:00', periods=3, freq='M')),
... country=['KR', 'US', 'JP'],
... code=[1, 2 ,3]), columns=['date', 'country', 'code'])
>>> df
date country code
0 2012-01-31 12:00:00 KR 1
1 2012-02-29 12:00:00 US 2
2 2012-03-31 12:00:00 JP 3
Create a new Delta Lake table, partitioned by one column:
>>> df.to_delta('%s/to_delta/foo' % path, partition_cols='date') # doctest: +SKIP
Partitioned by two columns:
>>> df.to_delta('%s/to_delta/bar' % path,
... partition_cols=['date', 'country']) # doctest: +SKIP
Overwrite an existing table's partitions, using the 'replaceWhere' capability in Delta:
>>> df.to_delta('%s/to_delta/bar' % path,
... mode='overwrite', replaceWhere='date >= "2012-01-01"') # doctest: +SKIP
"""
if index_col is None:
log_advice(
"If `index_col` is not specified for `to_delta`, "
"the existing index is lost when converting to Delta."
)
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options") # type: ignore[assignment]
mode = validate_mode(mode)
self.spark.to_spark_io(
path=path,
mode=mode,
format="delta",
partition_cols=partition_cols,
index_col=index_col,
**options,
)
[docs] def to_parquet(
self,
path: str,
mode: str = "w",
partition_cols: Optional[Union[str, List[str]]] = None,
compression: Optional[str] = None,
index_col: Optional[Union[str, List[str]]] = None,
**options: Any,
) -> None:
"""
Write the DataFrame out as a Parquet file or directory.
Parameters
----------
path : str, required
Path to write to.
mode : str
Python write mode, default 'w'.
.. note:: mode can accept the strings for Spark writing mode.
Such as 'append', 'overwrite', 'ignore', 'error', 'errorifexists'.
- 'append' (equivalent to 'a'): Append the new data to existing data.
- 'overwrite' (equivalent to 'w'): Overwrite existing data.
- 'ignore': Silently ignore this operation if data already exists.
- 'error' or 'errorifexists': Throw an exception if data already exists.
partition_cols : str or list of str, optional, default None
Names of partitioning columns
compression : str {'none', 'uncompressed', 'snappy', 'gzip', 'lzo', 'brotli', 'lz4', 'zstd'}
Compression codec to use when saving to file. If None is set, it uses the
value specified in `spark.sql.parquet.compression.codec`.
index_col: str or list of str, optional, default: None
Column names to be used in Spark to represent pandas-on-Spark's index. The index name
in pandas-on-Spark is ignored. By default the index is always lost.
options : dict
All other options passed directly into Spark's data source.
See Also
--------
read_parquet
DataFrame.to_delta
DataFrame.to_table
DataFrame.to_spark_io
Examples
--------
>>> df = ps.DataFrame(dict(
... date=list(pd.date_range('2012-1-1 12:00:00', periods=3, freq='M')),
... country=['KR', 'US', 'JP'],
... code=[1, 2 ,3]), columns=['date', 'country', 'code'])
>>> df
date country code
0 2012-01-31 12:00:00 KR 1
1 2012-02-29 12:00:00 US 2
2 2012-03-31 12:00:00 JP 3
>>> df.to_parquet('%s/to_parquet/foo.parquet' % path, partition_cols='date')
>>> df.to_parquet(
... '%s/to_parquet/foo.parquet' % path,
... mode = 'overwrite',
... partition_cols=['date', 'country'])
Notes
-----
pandas API on Spark writes Parquet files into the directory, `path`, and writes
multiple part files in the directory unlike pandas.
pandas API on Spark respects HDFS's property such as 'fs.default.name'.
"""
if index_col is None:
log_advice(
"If `index_col` is not specified for `to_parquet`, "
"the existing index is lost when converting to Parquet."
)
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options")
mode = validate_mode(mode)
builder = self.to_spark(index_col=index_col).write.mode(mode)
if partition_cols is not None:
builder.partitionBy(partition_cols)
if compression is not None:
builder.option("compression", compression)
builder.options(**options).format("parquet").save(path)
[docs] def to_orc(
self,
path: str,
mode: str = "w",
partition_cols: Optional[Union[str, List[str]]] = None,
index_col: Optional[Union[str, List[str]]] = None,
**options: "OptionalPrimitiveType",
) -> None:
"""
Write a DataFrame to the ORC format.
Parameters
----------
path : str
Path to write to.
mode : str
Python write mode, default 'w'.
.. note:: mode can accept the strings for Spark writing mode.
Such as 'append', 'overwrite', 'ignore', 'error', 'errorifexists'.
- 'append' (equivalent to 'a'): Append the new data to existing data.
- 'overwrite' (equivalent to 'w'): Overwrite existing data.
- 'ignore': Silently ignore this operation if data already exists.
- 'error' or 'errorifexists': Throw an exception if data already exists.
partition_cols : str or list of str, optional, default None
Names of partitioning columns
index_col: str or list of str, optional, default: None
Column names to be used in Spark to represent pandas-on-Spark's index. The index name
in pandas-on-Spark is ignored. By default the index is always lost.
options : dict
All other options passed directly into Spark's data source.
See Also
--------
read_orc
DataFrame.to_delta
DataFrame.to_parquet
DataFrame.to_table
DataFrame.to_spark_io
Examples
--------
>>> df = ps.DataFrame(dict(
... date=list(pd.date_range('2012-1-1 12:00:00', periods=3, freq='M')),
... country=['KR', 'US', 'JP'],
... code=[1, 2 ,3]), columns=['date', 'country', 'code'])
>>> df
date country code
0 2012-01-31 12:00:00 KR 1
1 2012-02-29 12:00:00 US 2
2 2012-03-31 12:00:00 JP 3
>>> df.to_orc('%s/to_orc/foo.orc' % path, partition_cols='date')
>>> df.to_orc(
... '%s/to_orc/foo.orc' % path,
... mode = 'overwrite',
... partition_cols=['date', 'country'])
Notes
-----
pandas API on Spark writes ORC files into the directory, `path`, and writes
multiple part files in the directory unlike pandas.
pandas API on Spark respects HDFS's property such as 'fs.default.name'.
"""
if index_col is None:
log_advice(
"If `index_col` is not specified for `to_orc`, "
"the existing index is lost when converting to ORC."
)
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options") # type: ignore[assignment]
mode = validate_mode(mode)
self.spark.to_spark_io(
path=path,
mode=mode,
format="orc",
partition_cols=partition_cols,
index_col=index_col,
**options,
)
[docs] def to_spark_io(
self,
path: Optional[str] = None,
format: Optional[str] = None,
mode: str = "overwrite",
partition_cols: Optional[Union[str, List[str]]] = None,
index_col: Optional[Union[str, List[str]]] = None,
**options: "OptionalPrimitiveType",
) -> None:
"""An alias for :func:`DataFrame.spark.to_spark_io`.
See :meth:`pyspark.pandas.spark.accessors.SparkFrameMethods.to_spark_io`.
.. deprecated:: 3.2.0
Use :func:`DataFrame.spark.to_spark_io` instead.
"""
warnings.warn("Deprecated in 3.2, Use DataFrame.spark.to_spark_io instead.", FutureWarning)
return self.spark.to_spark_io(path, format, mode, partition_cols, index_col, **options)
to_spark_io.__doc__ = SparkFrameMethods.to_spark_io.__doc__
[docs] def to_spark(self, index_col: Optional[Union[str, List[str]]] = None) -> PySparkDataFrame:
if index_col is None:
log_advice(
"If `index_col` is not specified for `to_spark`, "
"the existing index is lost when converting to Spark DataFrame."
)
return self._to_spark(index_col)
to_spark.__doc__ = SparkFrameMethods.__doc__
def _to_spark(self, index_col: Optional[Union[str, List[str]]] = None) -> PySparkDataFrame:
"""
Same as `to_spark()`, without issuing the advice log when `index_col` is not specified
for internal usage.
"""
return self.spark.frame(index_col)
[docs] def to_pandas(self) -> pd.DataFrame:
"""
Return a pandas DataFrame.
.. note:: This method should only be used if the resulting pandas DataFrame is expected
to be small, as all the data is loaded into the driver's memory.
Examples
--------
>>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
... columns=['dogs', 'cats'])
>>> df.to_pandas()
dogs cats
0 0.2 0.3
1 0.0 0.6
2 0.6 0.0
3 0.2 0.1
"""
log_advice(
"`to_pandas` loads all data into the driver's memory. "
"It should only be used if the resulting pandas DataFrame is expected to be small."
)
return self._to_pandas()
def _to_pandas(self) -> pd.DataFrame:
"""
Same as `to_pandas()`, without issuing the advice log for internal usage.
"""
return self._internal.to_pandas_frame.copy()
[docs] def assign(self, **kwargs: Any) -> "DataFrame":
"""
Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones.
Existing columns that are re-assigned will be overwritten.
Parameters
----------
**kwargs : dict of {str: callable, Series or Index}
The column names are keywords. If the values are
callable, they are computed on the DataFrame and
assigned to the new columns. The callable must not
change input DataFrame (though pandas-on-Spark doesn't check it).
If the values are not callable, (e.g. a Series or a literal),
they are simply assigned.
Returns
-------
DataFrame
A new DataFrame with the new columns in addition to
all the existing columns.
Examples
--------
>>> df = ps.DataFrame({'temp_c': [17.0, 25.0]},
... index=['Portland', 'Berkeley'])
>>> df
temp_c
Portland 17.0
Berkeley 25.0
Where the value is a callable, evaluated on `df`:
>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
Alternatively, the same behavior can be achieved by directly
referencing an existing Series or sequence and you can also
create multiple columns within the same assign.
>>> assigned = df.assign(temp_f=df['temp_c'] * 9 / 5 + 32,
... temp_k=df['temp_c'] + 273.15,
... temp_idx=df.index)
>>> assigned[['temp_c', 'temp_f', 'temp_k', 'temp_idx']]
temp_c temp_f temp_k temp_idx
Portland 17.0 62.6 290.15 Portland
Berkeley 25.0 77.0 298.15 Berkeley
Notes
-----
Assigning multiple columns within the same ``assign`` is possible
but you cannot refer to newly created or modified columns. This
feature is supported in pandas for Python 3.6 and later but not in
pandas-on-Spark. In pandas-on-Spark, all items are computed first,
and then assigned.
"""
return self._assign(kwargs)
def _assign(self, kwargs: Any) -> "DataFrame":
assert isinstance(kwargs, dict)
from pyspark.pandas.indexes import MultiIndex
from pyspark.pandas.series import IndexOpsMixin
Column = get_column_class()
for k, v in kwargs.items():
is_invalid_assignee = (
not (isinstance(v, (IndexOpsMixin, Column)) or callable(v) or is_scalar(v))
) or isinstance(v, MultiIndex)
if is_invalid_assignee:
raise TypeError(
"Column assignment doesn't support type " "{0}".format(type(v).__name__)
)
if callable(v):
kwargs[k] = v(self)
pairs = {
(k if is_name_like_tuple(k) else (k,)): (
(v.spark.column, v._internal.data_fields[0])
if isinstance(v, IndexOpsMixin) and not isinstance(v, MultiIndex)
else (v, None)
if isinstance(v, Column)
else (F.lit(v), None)
)
for k, v in kwargs.items()
}
scols = []
data_fields = []
for label in self._internal.column_labels:
for i in range(len(label)):
if label[: len(label) - i] in pairs:
scol, field = pairs[label[: len(label) - i]]
name = self._internal.spark_column_name_for(label)
scol = scol.alias(name)
if field is not None:
field = field.copy(name=name)
break
else:
scol = self._internal.spark_column_for(label)
field = self._internal.field_for(label)
scols.append(scol)
data_fields.append(field)
column_labels = self._internal.column_labels.copy()
for label, (scol, field) in pairs.items():
if label not in set(i[: len(label)] for i in self._internal.column_labels):
name = name_like_string(label)
scols.append(scol.alias(name))
if field is not None:
field = field.copy(name=name)
data_fields.append(field)
column_labels.append(label)
level = self._internal.column_labels_level
column_labels = [
tuple(list(label) + ([""] * (level - len(label)))) for label in column_labels
]
internal = self._internal.with_new_columns(
cast(Sequence[Union[PySparkColumn, "Series"]], scols),
column_labels=column_labels,
data_fields=data_fields,
)
return DataFrame(internal)
[docs] @staticmethod
def from_records(
data: Union[np.ndarray, List[tuple], dict, pd.DataFrame],
index: Union[str, list, np.ndarray] = None,
exclude: list = None,
columns: list = None,
coerce_float: bool = False,
nrows: int = None,
) -> "DataFrame":
"""
Convert structured or recorded ndarray to DataFrame.
Parameters
----------
data : ndarray (structured dtype), list of tuples, dict, or DataFrame
index : string, list of fields, array-like
Field of array to use as the index, alternately a specific set of input labels to use
exclude : sequence, default None
Columns or fields to exclude
columns : sequence, default None
Column names to use. If the passed data do not have names associated with them, this
argument provides names for the columns. Otherwise this argument indicates the order of
the columns in the result (any names not found in the data will become all-NA columns)
coerce_float : boolean, default False
Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to
floating point, useful for SQL result sets
nrows : int, default None
Number of rows to read if data is an iterator
Returns
-------
df : DataFrame
Examples
--------
Use dict as input
>>> ps.DataFrame.from_records({'A': [1, 2, 3]})
A
0 1
1 2
2 3
Use list of tuples as input
>>> ps.DataFrame.from_records([(1, 2), (3, 4)])
0 1
0 1 2
1 3 4
Use NumPy array as input
>>> ps.DataFrame.from_records(np.eye(3))
0 1 2
0 1.0 0.0 0.0
1 0.0 1.0 0.0
2 0.0 0.0 1.0
"""
return DataFrame(
pd.DataFrame.from_records(data, index, exclude, columns, coerce_float, nrows)
)
[docs] def to_records(
self,
index: bool = True,
column_dtypes: Optional[Union[str, Dtype, Dict[Name, Union[str, Dtype]]]] = None,
index_dtypes: Optional[Union[str, Dtype, Dict[Name, Union[str, Dtype]]]] = None,
) -> np.recarray:
"""
Convert DataFrame to a NumPy record array.
Index will be included as the first field of the record array if
requested.
.. note:: This method should only be used if the resulting NumPy ndarray is
expected to be small, as all the data is loaded into the driver's memory.
Parameters
----------
index : bool, default True
Include index in resulting record array, stored in 'index'
field or using the index label, if set.
column_dtypes : str, type, dict, default None
If a string or type, the data type to store all columns. If
a dictionary, a mapping of column names and indices (zero-indexed)
to specific data types.
index_dtypes : str, type, dict, default None
If a string or type, the data type to store all index levels. If
a dictionary, a mapping of index level names and indices
(zero-indexed) to specific data types.
This mapping is applied only if `index=True`.
Returns
-------
numpy.recarray
NumPy ndarray with the DataFrame labels as fields and each row
of the DataFrame as entries.
See Also
--------
DataFrame.from_records: Convert structured or record ndarray
to DataFrame.
numpy.recarray: An ndarray that allows field access using
attributes, analogous to typed columns in a
spreadsheet.
Examples
--------
>>> df = ps.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
... index=['a', 'b'])
>>> df
A B
a 1 0.50
b 2 0.75
>>> df.to_records() # doctest: +SKIP
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])
The index can be excluded from the record array:
>>> df.to_records(index=False) # doctest: +SKIP
rec.array([(1, 0.5 ), (2, 0.75)],
dtype=[('A', '<i8'), ('B', '<f8')])
Specification of dtype for columns is new in pandas 0.24.0.
Data types can be specified for the columns:
>>> df.to_records(column_dtypes={"A": "int32"}) # doctest: +SKIP
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('index', 'O'), ('A', '<i4'), ('B', '<f8')])
Specification of dtype for index is new in pandas 0.24.0.
Data types can also be specified for the index:
>>> df.to_records(index_dtypes="<S2") # doctest: +SKIP
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('index', 'S2'), ('A', '<i8'), ('B', '<f8')])
"""
args = locals()
psdf = self
return validate_arguments_and_invoke_function(
psdf._to_internal_pandas(), self.to_records, pd.DataFrame.to_records, args
)
[docs] def copy(self, deep: bool = True) -> "DataFrame":
"""
Make a copy of this object's indices and data.
Parameters
----------
deep : bool, default True
this parameter is not supported but just dummy parameter to match pandas.
Returns
-------
copy : DataFrame
Examples
--------
>>> df = ps.DataFrame({'x': [1, 2], 'y': [3, 4], 'z': [5, 6], 'w': [7, 8]},
... columns=['x', 'y', 'z', 'w'])
>>> df
x y z w
0 1 3 5 7
1 2 4 6 8
>>> df_copy = df.copy()
>>> df_copy
x y z w
0 1 3 5 7
1 2 4 6 8
"""
return DataFrame(self._internal)
[docs] def dropna(
self,
axis: Axis = 0,
how: str = "any",
thresh: Optional[int] = None,
subset: Optional[Union[Name, List[Name]]] = None,
inplace: bool = False,
) -> Optional["DataFrame"]:
"""
Remove missing values.
Parameters
----------
axis : {0 or 'index'}, default 0
Determine if rows or columns which contain missing values are
removed.
* 0, or 'index' : Drop rows which contain missing values.
how : {'any', 'all'}, default 'any'
Determine if row or column is removed from DataFrame, when we have
at least one NA or all NA.
* 'any' : If any NA values are present, drop that row or column.
* 'all' : If all values are NA, drop that row or column.
thresh : int, optional
Require that many non-NA values.
subset : array-like, optional
Labels along other axis to consider, e.g. if you are dropping rows
these would be a list of columns to include.
inplace : bool, default False
If True, do operation inplace and return None.
Returns
-------
DataFrame
DataFrame with NA entries dropped from it.
See Also
--------
DataFrame.drop : Drop specified labels from columns.
DataFrame.isnull: Indicate missing values.
DataFrame.notnull : Indicate existing (non-missing) values.
Examples
--------
>>> df = ps.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
... "toy": [None, 'Batmobile', 'Bullwhip'],
... "born": [None, "1940-04-25", None]},
... columns=['name', 'toy', 'born'])
>>> df
name toy born
0 Alfred None None
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip None
Drop the rows where at least one element is missing.
>>> df.dropna()
name toy born
1 Batman Batmobile 1940-04-25
Drop the columns where at least one element is missing.
>>> df.dropna(axis='columns')
name
0 Alfred
1 Batman
2 Catwoman
Drop the rows where all elements are missing.
>>> df.dropna(how='all')
name toy born
0 Alfred None None
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip None
Keep only the rows with at least 2 non-NA values.
>>> df.dropna(thresh=2)
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip None
Define in which columns to look for missing values.
>>> df.dropna(subset=['name', 'born'])
name toy born
1 Batman Batmobile 1940-04-25
Keep the DataFrame with valid entries in the same variable.
>>> df.dropna(inplace=True)
>>> df
name toy born
1 Batman Batmobile 1940-04-25
"""
axis = validate_axis(axis)
inplace = validate_bool_kwarg(inplace, "inplace")
if thresh is None:
if how is None:
raise TypeError("must specify how or thresh")
elif how not in ("any", "all"):
raise ValueError("invalid how option: {h}".format(h=how))
labels: Optional[List[Label]]
if subset is not None:
if isinstance(subset, str):
labels = [(subset,)]
elif isinstance(subset, tuple):
labels = [subset]
else:
labels = [sub if isinstance(sub, tuple) else (sub,) for sub in subset]
else:
labels = None
if axis == 0:
if labels is not None:
invalids = [label for label in labels if label not in self._internal.column_labels]
if len(invalids) > 0:
raise KeyError(invalids)
else:
labels = self._internal.column_labels
cnt = reduce(
lambda x, y: x + y,
[
F.when(self._psser_for(label).notna().spark.column, 1).otherwise(0)
for label in labels
],
F.lit(0),
)
if thresh is not None:
pred = cnt >= F.lit(int(thresh))
elif how == "any":
pred = cnt == F.lit(len(labels))
elif how == "all":
pred = cnt > F.lit(0)
internal = self._internal.with_filter(pred)
if inplace:
self._update_internal_frame(internal)
return None
else:
return DataFrame(internal)
else:
assert axis == 1
internal = self._internal.resolved_copy
if labels is not None:
if any(len(lbl) != internal.index_level for lbl in labels):
raise ValueError(
"The length of each subset must be the same as the index size."
)
cond = reduce(
lambda x, y: x | y,
[
reduce(
lambda x, y: x & y,
[
scol == F.lit(part)
for part, scol in zip(lbl, internal.index_spark_columns)
],
)
for lbl in labels
],
)
internal = internal.with_filter(cond)
psdf: DataFrame = DataFrame(internal)
null_counts = []
for label in internal.column_labels:
psser = psdf._psser_for(label)
cond = psser.isnull().spark.column
null_counts.append(
F.sum(F.when(~cond, 1).otherwise(0)).alias(name_like_string(label))
)
counts = internal.spark_frame.select(null_counts + [F.count("*")]).head()
if thresh is not None:
column_labels = [
label
for label, cnt in zip(internal.column_labels, counts)
if (cnt or 0) >= int(thresh)
]
elif how == "any":
column_labels = [
label
for label, cnt in zip(internal.column_labels, counts)
if (cnt or 0) == counts[-1]
]
elif how == "all":
column_labels = [
label for label, cnt in zip(internal.column_labels, counts) if (cnt or 0) > 0
]
psdf = self[column_labels]
if inplace:
self._update_internal_frame(psdf._internal)
return None
else:
return psdf
# TODO: add 'limit' when value parameter exists
[docs] def fillna(
self,
value: Optional[Union[Any, Dict[Name, Any]]] = None,
method: Optional[str] = None,
axis: Optional[Axis] = None,
inplace: bool = False,
limit: Optional[int] = None,
) -> Optional["DataFrame"]:
"""Fill NA/NaN values.
.. note:: the current implementation of 'method' parameter in fillna uses Spark's Window
without specifying partition specification. This leads to moving all data into
a single partition in a single machine and could cause serious
performance degradation. Avoid this method with very large datasets.
Parameters
----------
value : scalar, dict, Series
Value to use to fill holes. alternately a dict/Series of values
specifying which value to use for each column.
DataFrame is not supported.
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
Method to use for filling holes in reindexed Series pad / ffill: propagate last valid
observation forward to next valid backfill / bfill:
use NEXT valid observation to fill gap
axis : {0 or `index`}
1 and `columns` are not supported.
inplace : boolean, default False
Fill in place (do not create a new object)
limit : int, default None
If method is specified, this is the maximum number of consecutive NaN values to
forward/backward fill. In other words, if there is a gap with more than this number of
consecutive NaNs, it will only be partially filled. If method is not specified,
this is the maximum number of entries along the entire axis where NaNs will be filled.
Must be greater than 0 if not None
Returns
-------
DataFrame
DataFrame with NA entries filled.
Examples
--------
>>> df = ps.DataFrame({
... 'A': [None, 3, None, None],
... 'B': [2, 4, None, 3],
... 'C': [None, None, None, 1],
... 'D': [0, 1, 5, 4]
... },
... columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 1.0 4
Replace all NaN elements with 0s.
>>> df.fillna(0)
A B C D
0 0.0 2.0 0.0 0
1 3.0 4.0 0.0 1
2 0.0 0.0 0.0 5
3 0.0 3.0 1.0 4
We can also propagate non-null values forward or backward.
>>> df.fillna(method='ffill')
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 3.0 4.0 NaN 5
3 3.0 3.0 1.0 4
Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
2, and 3 respectively.
>>> values = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
>>> df.fillna(value=values)
A B C D
0 0.0 2.0 2.0 0
1 3.0 4.0 2.0 1
2 0.0 1.0 2.0 5
3 0.0 3.0 1.0 4
"""
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError("fillna currently only works for axis=0 or axis='index'")
if value is not None:
if not isinstance(value, (float, int, str, bool, dict, pd.Series)):
raise TypeError("Unsupported type %s" % type(value).__name__)
if limit is not None:
raise ValueError("limit parameter for value is not support now")
if isinstance(value, pd.Series):
value = value.to_dict()
if isinstance(value, dict):
for v in value.values():
if not isinstance(v, (float, int, str, bool)):
raise TypeError("Unsupported type %s" % type(v).__name__)
value = {k if is_name_like_tuple(k) else (k,): v for k, v in value.items()}
def op(psser: ps.Series) -> ps.Series:
label = psser._column_label
for k, v in value.items():
if k == label[: len(k)]:
return psser._fillna(
value=value[k], method=method, axis=axis, limit=limit
)
else:
return psser
else:
def op(psser: ps.Series) -> ps.Series:
return psser._fillna(value=value, method=method, axis=axis, limit=limit)
elif method is not None:
def op(psser: ps.Series) -> ps.Series:
return psser._fillna(value=value, method=method, axis=axis, limit=limit)
else:
raise ValueError("Must specify a fillna 'value' or 'method' parameter.")
psdf = self._apply_series_op(op, should_resolve=(method is not None))
inplace = validate_bool_kwarg(inplace, "inplace")
if inplace:
self._update_internal_frame(psdf._internal, check_same_anchor=False)
return None
else:
return psdf
[docs] def interpolate(
self,
method: str = "linear",
limit: Optional[int] = None,
limit_direction: Optional[str] = None,
limit_area: Optional[str] = None,
) -> "DataFrame":
if method not in ["linear"]:
raise NotImplementedError("interpolate currently works only for method='linear'")
if (limit is not None) and (not limit > 0):
raise ValueError("limit must be > 0.")
if (limit_direction is not None) and (
limit_direction not in ["forward", "backward", "both"]
):
raise ValueError("invalid limit_direction: '{}'".format(limit_direction))
if (limit_area is not None) and (limit_area not in ["inside", "outside"]):
raise ValueError("invalid limit_area: '{}'".format(limit_area))
numeric_col_names = []
for label in self._internal.column_labels:
psser = self._psser_for(label)
if isinstance(psser.spark.data_type, (NumericType, BooleanType)):
numeric_col_names.append(psser.name)
psdf = self[numeric_col_names]
return psdf._apply_series_op(
lambda psser: psser._interpolate(
method=method, limit=limit, limit_direction=limit_direction, limit_area=limit_area
),
should_resolve=True,
)
[docs] def replace(
self,
to_replace: Optional[Union[Any, List, Tuple, Dict]] = None,
value: Optional[Any] = None,
inplace: bool = False,
limit: Optional[int] = None,
regex: bool = False,
method: str = "pad",
) -> Optional["DataFrame"]:
"""
Returns a new DataFrame replacing a value with another value.
Parameters
----------
to_replace : int, float, string, list, tuple or dict
Value to be replaced.
value : int, float, string, list or tuple
Value to use to replace holes. The replacement value must be an int, float,
or string.
If value is a list or tuple, value should be of the same length with to_replace.
inplace : boolean, default False
Fill in place (do not create a new object)
Returns
-------
DataFrame
Object after replacement.
Examples
--------
>>> df = ps.DataFrame({"name": ['Ironman', 'Captain America', 'Thor', 'Hulk'],
... "weapon": ['Mark-45', 'Shield', 'Mjolnir', 'Smash']},
... columns=['name', 'weapon'])
>>> df
name weapon
0 Ironman Mark-45
1 Captain America Shield
2 Thor Mjolnir
3 Hulk Smash
Scalar `to_replace` and `value`
>>> df.replace('Ironman', 'War-Machine')
name weapon
0 War-Machine Mark-45
1 Captain America Shield
2 Thor Mjolnir
3 Hulk Smash
List like `to_replace` and `value`
>>> df.replace(['Ironman', 'Captain America'], ['Rescue', 'Hawkeye'], inplace=True)
>>> df
name weapon
0 Rescue Mark-45
1 Hawkeye Shield
2 Thor Mjolnir
3 Hulk Smash
Dicts can be used to specify different replacement values for different existing values
To use a dict in this way the value parameter should be None
>>> df.replace({'Mjolnir': 'Stormbuster'})
name weapon
0 Rescue Mark-45
1 Hawkeye Shield
2 Thor Stormbuster
3 Hulk Smash
Dict can specify that different values should be replaced in different columns
The value parameter should not be None in this case
>>> df.replace({'weapon': 'Mjolnir'}, 'Stormbuster')
name weapon
0 Rescue Mark-45
1 Hawkeye Shield
2 Thor Stormbuster
3 Hulk Smash
Nested dictionaries
The value parameter should be None to use a nested dict in this way
>>> df.replace({'weapon': {'Mjolnir': 'Stormbuster'}})
name weapon
0 Rescue Mark-45
1 Hawkeye Shield
2 Thor Stormbuster
3 Hulk Smash
"""
if method != "pad":
raise NotImplementedError("replace currently works only for method='pad")
if limit is not None:
raise NotImplementedError("replace currently works only when limit=None")
if regex is not False:
raise NotImplementedError("replace currently doesn't supports regex")
inplace = validate_bool_kwarg(inplace, "inplace")
if value is not None and not isinstance(value, (int, float, str, list, tuple, dict)):
raise TypeError("Unsupported type {}".format(type(value).__name__))
if to_replace is not None and not isinstance(
to_replace, (int, float, str, list, tuple, dict)
):
raise TypeError("Unsupported type {}".format(type(to_replace).__name__))
if isinstance(value, (list, tuple)) and isinstance(to_replace, (list, tuple)):
if len(value) != len(to_replace):
raise ValueError("Length of to_replace and value must be same")
if isinstance(to_replace, dict) and (
value is not None or all(isinstance(i, dict) for i in to_replace.values())
):
to_replace_dict = to_replace
def op(psser: ps.Series) -> ps.Series:
if psser.name in to_replace_dict:
return psser.replace(
to_replace=to_replace_dict[psser.name], value=value, regex=regex
)
else:
return psser
else:
def op(psser: ps.Series) -> ps.Series:
return psser.replace(to_replace=to_replace, value=value, regex=regex)
psdf = self._apply_series_op(op)
if inplace:
self._update_internal_frame(psdf._internal)
return None
else:
return psdf
[docs] def clip(self, lower: Union[float, int] = None, upper: Union[float, int] = None) -> "DataFrame":
"""
Trim values at input threshold(s).
Assigns values outside boundary-to-boundary values.
Parameters
----------
lower : float or int, default None
Minimum threshold value. All values below this threshold will be set to it.
upper : float or int, default None
Maximum threshold value. All values above this threshold will be set to it.
Returns
-------
DataFrame
DataFrame with the values outside the clip boundaries replaced.
Examples
--------
>>> ps.DataFrame({'A': [0, 2, 4]}).clip(1, 3)
A
0 1
1 2
2 3
Notes
-----
One difference between this implementation and pandas is that running
pd.DataFrame({'A': ['a', 'b']}).clip(0, 1) will crash with "TypeError: '<=' not supported
between instances of 'str' and 'int'" while ps.DataFrame({'A': ['a', 'b']}).clip(0, 1)
will output the original DataFrame, simply ignoring the incompatible types.
"""
if is_list_like(lower) or is_list_like(upper):
raise TypeError(
"List-like value are not supported for 'lower' and 'upper' at the " + "moment"
)
if lower is None and upper is None:
return self
return self._apply_series_op(lambda psser: psser.clip(lower=lower, upper=upper))
[docs] def head(self, n: int = 5) -> "DataFrame":
"""
Return the first `n` rows.
This function returns the first `n` rows for the object based
on position. It is useful for quickly testing if your object
has the right type of data in it.
Parameters
----------
n : int, default 5
Number of rows to select.
Returns
-------
obj_head : same type as caller
The first `n` rows of the caller object.
Examples
--------
>>> df = ps.DataFrame({'animal':['alligator', 'bee', 'falcon', 'lion',
... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the first 5 lines
>>> df.head()
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
Viewing the first `n` lines (three in this case)
>>> df.head(3)
animal
0 alligator
1 bee
2 falcon
"""
if n < 0:
n = len(self) + n
if n <= 0:
return DataFrame(self._internal.with_filter(F.lit(False)))
else:
sdf = self._internal.resolved_copy.spark_frame
if get_option("compute.ordered_head"):
sdf = sdf.orderBy(NATURAL_ORDER_COLUMN_NAME)
return DataFrame(self._internal.with_new_sdf(sdf.limit(n)))
[docs] def last(self, offset: Union[str, DateOffset]) -> "DataFrame":
"""
Select final periods of time series data based on a date offset.
When having a DataFrame with dates as index, this function can
select the last few rows based on a date offset.
Parameters
----------
offset : str or DateOffset
The offset length of the data that will be selected. For instance,
'3D' will display all the rows having their index within the last 3 days.
Returns
-------
DataFrame
A subset of the caller.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
Examples
--------
>>> index = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> psdf = ps.DataFrame({'A': [1, 2, 3, 4]}, index=index)
>>> psdf
A
2018-04-09 1
2018-04-11 2
2018-04-13 3
2018-04-15 4
Get the rows for the last 3 days:
>>> psdf.last('3D')
A
2018-04-13 3
2018-04-15 4
Notice the data for 3 last calendar days were returned, not the last
3 observed days in the dataset, and therefore data for 2018-04-11 was
not returned.
"""
# Check index type should be format DateTime
if not isinstance(self.index, ps.DatetimeIndex):
raise TypeError("'last' only supports a DatetimeIndex")
from_date = cast(
int,
cast(datetime.datetime, self.index.max()) - cast(datetime.timedelta, to_offset(offset)),
)
return cast(DataFrame, self.loc[from_date:])
[docs] def first(self, offset: Union[str, DateOffset]) -> "DataFrame":
"""
Select first periods of time series data based on a date offset.
When having a DataFrame with dates as index, this function can
select the first few rows based on a date offset.
Parameters
----------
offset : str or DateOffset
The offset length of the data that will be selected. For instance,
'3D' will display all the rows having their index within the first 3 days.
Returns
-------
DataFrame
A subset of the caller.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
Examples
--------
>>> index = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> psdf = ps.DataFrame({'A': [1, 2, 3, 4]}, index=index)
>>> psdf
A
2018-04-09 1
2018-04-11 2
2018-04-13 3
2018-04-15 4
Get the rows for the last 3 days:
>>> psdf.first('3D')
A
2018-04-09 1
2018-04-11 2
Notice the data for 3 first calendar days were returned, not the first
3 observed days in the dataset, and therefore data for 2018-04-13 was
not returned.
"""
# Check index type should be format DatetimeIndex
if not isinstance(self.index, ps.DatetimeIndex):
raise TypeError("'first' only supports a DatetimeIndex")
to_date = cast(
int,
cast(datetime.datetime, self.index.min()) + cast(datetime.timedelta, to_offset(offset)),
)
return cast(DataFrame, self.loc[:to_date])
[docs] def pivot_table(
self,
values: Optional[Union[Name, List[Name]]] = None,
index: Optional[List[Name]] = None,
columns: Optional[Name] = None,
aggfunc: Union[str, Dict[Name, str]] = "mean",
fill_value: Optional[Any] = None,
) -> "DataFrame":
"""
Create a spreadsheet-style pivot table as a DataFrame. The levels in
the pivot table will be stored in MultiIndex objects (hierarchical
indexes) on the index and columns of the result DataFrame.
Parameters
----------
values : column to aggregate.
They should be either a list less than three or a string.
index : column (string) or list of columns
If an array is passed, it must be the same length as the data.
The list should contain string.
columns : column
Columns used in the pivot operation. Only one column is supported and
it should be a string.
aggfunc : function (string), dict, default mean
If dict is passed, the key is column to aggregate and value
is function or list of functions.
fill_value : scalar, default None
Value to replace missing values with.
Returns
-------
table : DataFrame
Examples
--------
>>> df = ps.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
... "bar", "bar", "bar", "bar"],
... "B": ["one", "one", "one", "two", "two",
... "one", "one", "two", "two"],
... "C": ["small", "large", "large", "small",
... "small", "large", "small", "small",
... "large"],
... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
... "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]},
... columns=['A', 'B', 'C', 'D', 'E'])
>>> df
A B C D E
0 foo one small 1 2
1 foo one large 2 4
2 foo one large 2 5
3 foo two small 3 5
4 foo two small 3 6
5 bar one large 4 6
6 bar one small 5 8
7 bar two small 6 9
8 bar two large 7 9
This first example aggregates values by taking the sum.
>>> table = df.pivot_table(values='D', index=['A', 'B'],
... columns='C', aggfunc='sum')
>>> table.sort_index() # doctest: +NORMALIZE_WHITESPACE
C large small
A B
bar one 4.0 5
two 7.0 6
foo one 4.0 1
two NaN 6
We can also fill missing values using the `fill_value` parameter.
>>> table = df.pivot_table(values='D', index=['A', 'B'],
... columns='C', aggfunc='sum', fill_value=0)
>>> table.sort_index() # doctest: +NORMALIZE_WHITESPACE
C large small
A B
bar one 4 5
two 7 6
foo one 4 1
two 0 6
We can also calculate multiple types of aggregations for any given
value column.
>>> table = df.pivot_table(values=['D'], index =['C'],
... columns="A", aggfunc={'D': 'mean'})
>>> table.sort_index() # doctest: +NORMALIZE_WHITESPACE
D
A bar foo
C
large 5.5 2.000000
small 5.5 2.333333
The next example aggregates on multiple values.
>>> table = df.pivot_table(index=['C'], columns="A", values=['D', 'E'],
... aggfunc={'D': 'mean', 'E': 'sum'})
>>> table.sort_index() # doctest: +NORMALIZE_WHITESPACE
D E
A bar foo bar foo
C
large 5.5 2.000000 15 9
small 5.5 2.333333 17 13
"""
if not is_name_like_value(columns):
raise TypeError("columns should be one column name.")
if not is_name_like_value(values) and not (
isinstance(values, list) and all(is_name_like_value(v) for v in values)
):
raise TypeError("values should be one column or list of columns.")
if not isinstance(aggfunc, str) and (
not isinstance(aggfunc, dict)
or not all(
is_name_like_value(key) and isinstance(value, str) for key, value in aggfunc.items()
)
):
raise TypeError(
"aggfunc must be a dict mapping from column name "
"to aggregate functions (string)."
)
if isinstance(aggfunc, dict) and index is None:
raise NotImplementedError(
"pivot_table doesn't support aggfunc" " as dict and without index."
)
if isinstance(values, list) and index is None:
raise NotImplementedError("values can't be a list without index.")
if columns not in self.columns:
raise ValueError("Wrong columns {}.".format(name_like_string(columns)))
if not is_name_like_tuple(columns):
columns = (columns,)
if isinstance(values, list):
values = [col if is_name_like_tuple(col) else (col,) for col in values]
if not all(
isinstance(self._internal.spark_type_for(col), NumericType) for col in values
):
raise TypeError("values should be a numeric type.")
else:
values = values if is_name_like_tuple(values) else (values,)
if not isinstance(self._internal.spark_type_for(values), NumericType):
raise TypeError("values should be a numeric type.")
if isinstance(aggfunc, str):
if isinstance(values, list):
agg_cols = [
F.expr(
"{1}(`{0}`) as `{0}`".format(
self._internal.spark_column_name_for(value), aggfunc
)
)
for value in values
]
else:
agg_cols = [
F.expr(
"{1}(`{0}`) as `{0}`".format(
self._internal.spark_column_name_for(values), aggfunc
)
)
]
elif isinstance(aggfunc, dict):
aggfunc = {
key if is_name_like_tuple(key) else (key,): value for key, value in aggfunc.items()
}
agg_cols = [
F.expr(
"{1}(`{0}`) as `{0}`".format(self._internal.spark_column_name_for(key), value)
)
for key, value in aggfunc.items()
]
agg_columns = [key for key, _ in aggfunc.items()]
if set(agg_columns) != set(values):
raise ValueError("Columns in aggfunc must be the same as values.")
sdf = self._internal.resolved_copy.spark_frame
if index is None:
sdf = (
sdf.groupBy()
.pivot(pivot_col=self._internal.spark_column_name_for(columns))
.agg(*agg_cols)
)
elif isinstance(index, list):
index = [label if is_name_like_tuple(label) else (label,) for label in index]
sdf = (
sdf.groupBy([self._internal.spark_column_name_for(label) for label in index])
.pivot(pivot_col=self._internal.spark_column_name_for(columns))
.agg(*agg_cols)
)
else:
raise TypeError("index should be a None or a list of columns.")
if fill_value is not None and isinstance(fill_value, (int, float)):
sdf = sdf.fillna(fill_value)
psdf: DataFrame
if index is not None:
index_columns = [self._internal.spark_column_name_for(label) for label in index]
index_fields = [self._internal.field_for(label) for label in index]
if isinstance(values, list):
data_columns = [column for column in sdf.columns if column not in index_columns]
if len(values) > 1:
# If we have two values, Spark will return column's name
# in this format: column_values, where column contains
# their values in the DataFrame and values is
# the column list passed to the pivot_table().
# E.g. if column is b and values is ['b','e'],
# then ['2_b', '2_e', '3_b', '3_e'].
# We sort the columns of Spark DataFrame by values.
data_columns.sort(key=lambda x: x.split("_", 1)[1])
sdf = sdf.select(index_columns + data_columns)
column_name_to_index = dict(
zip(self._internal.data_spark_column_names, self._internal.column_labels)
)
column_labels = [
tuple(list(column_name_to_index[name.split("_")[1]]) + [name.split("_")[0]])
for name in data_columns
]
column_label_names = (
[cast(Optional[Name], None)] * column_labels_level(values)
) + [columns]
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in index_columns],
index_names=index,
index_fields=index_fields,
column_labels=column_labels,
data_spark_columns=[scol_for(sdf, col) for col in data_columns],
column_label_names=column_label_names,
)
psdf = DataFrame(internal)
else:
column_labels = [tuple(list(values[0]) + [column]) for column in data_columns]
column_label_names = ([cast(Optional[Name], None)] * len(values[0])) + [columns]
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in index_columns],
index_names=index,
index_fields=index_fields,
column_labels=column_labels,
data_spark_columns=[scol_for(sdf, col) for col in data_columns],
column_label_names=column_label_names,
)
psdf = DataFrame(internal)
else:
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in index_columns],
index_names=index,
index_fields=index_fields,
column_label_names=[columns],
)
psdf = DataFrame(internal)
else:
index_values = values
index_map: Dict[str, Optional[Label]] = {}
for i, index_value in enumerate(index_values):
colname = SPARK_INDEX_NAME_FORMAT(i)
sdf = sdf.withColumn(colname, F.lit(index_value))
index_map[colname] = None
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in index_map.keys()],
index_names=list(index_map.values()),
column_label_names=[columns],
)
psdf = DataFrame(internal)
psdf_columns = psdf.columns
if isinstance(psdf_columns, pd.MultiIndex):
psdf.columns = psdf_columns.set_levels(
psdf_columns.levels[-1].astype( # type: ignore[index]
spark_type_to_pandas_dtype(self._psser_for(columns).spark.data_type)
),
level=-1,
)
else:
psdf.columns = psdf_columns.astype(
spark_type_to_pandas_dtype(self._psser_for(columns).spark.data_type)
)
return psdf
[docs] def pivot(
self,
index: Optional[Name] = None,
columns: Optional[Name] = None,
values: Optional[Name] = None,
) -> "DataFrame":
"""
Return reshaped DataFrame organized by given index / column values.
Reshape data (produce a "pivot" table) based on column values. Uses
unique values from specified `index` / `columns` to form axes of the
resulting DataFrame. This function does not support data
aggregation.
Parameters
----------
index : string, optional
Column to use to make new frame's index. If None, uses
existing index.
columns : string
Column to use to make new frame's columns.
values : string, object or a list of the previous
Column(s) to use for populating new frame's values.
Returns
-------
DataFrame
Returns reshaped DataFrame.
See Also
--------
DataFrame.pivot_table : Generalization of pivot that can handle
duplicate values for one index/column pair.
Examples
--------
>>> df = ps.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
... 'two'],
... 'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
... 'baz': [1, 2, 3, 4, 5, 6],
... 'zoo': ['x', 'y', 'z', 'q', 'w', 't']},
... columns=['foo', 'bar', 'baz', 'zoo'])
>>> df
foo bar baz zoo
0 one A 1 x
1 one B 2 y
2 one C 3 z
3 two A 4 q
4 two B 5 w
5 two C 6 t
>>> df.pivot(index='foo', columns='bar', values='baz').sort_index()
... # doctest: +NORMALIZE_WHITESPACE
bar A B C
foo
one 1 2 3
two 4 5 6
>>> df.pivot(columns='bar', values='baz').sort_index() # doctest: +NORMALIZE_WHITESPACE
bar A B C
0 1.0 NaN NaN
1 NaN 2.0 NaN
2 NaN NaN 3.0
3 4.0 NaN NaN
4 NaN 5.0 NaN
5 NaN NaN 6.0
Notice that, unlike pandas raises an ValueError when duplicated values are found.
Pandas-on-Spark's pivot still works with its first value it meets during operation because
pivot is an expensive operation, and it is preferred to permissively execute over failing
fast when processing large data.
>>> df = ps.DataFrame({"foo": ['one', 'one', 'two', 'two'],
... "bar": ['A', 'A', 'B', 'C'],
... "baz": [1, 2, 3, 4]}, columns=['foo', 'bar', 'baz'])
>>> df
foo bar baz
0 one A 1
1 one A 2
2 two B 3
3 two C 4
>>> df.pivot(index='foo', columns='bar', values='baz').sort_index()
... # doctest: +NORMALIZE_WHITESPACE
bar A B C
foo
one 1.0 NaN NaN
two NaN 3.0 4.0
It also supports multi-index and multi-index column.
>>> df.columns = pd.MultiIndex.from_tuples([('a', 'foo'), ('a', 'bar'), ('b', 'baz')])
>>> df = df.set_index(('a', 'bar'), append=True)
>>> df # doctest: +NORMALIZE_WHITESPACE
a b
foo baz
(a, bar)
0 A one 1
1 A one 2
2 B two 3
3 C two 4
>>> df.pivot(columns=('a', 'foo'), values=('b', 'baz')).sort_index()
... # doctest: +NORMALIZE_WHITESPACE
('a', 'foo') one two
(a, bar)
0 A 1.0 NaN
1 A 2.0 NaN
2 B NaN 3.0
3 C NaN 4.0
"""
if columns is None:
raise ValueError("columns should be set.")
if values is None:
raise ValueError("values should be set.")
should_use_existing_index = index is not None
if should_use_existing_index:
df = self
index_labels = [index]
else:
# The index after `reset_index()` will never be used, so use "distributed" index
# as a dummy to avoid overhead.
with option_context("compute.default_index_type", "distributed"):
df = self.reset_index()
index_labels = df._internal.column_labels[: self._internal.index_level]
df = df.pivot_table(index=index_labels, columns=columns, values=values, aggfunc="first")
if should_use_existing_index:
return df
else:
internal = df._internal.copy(index_names=self._internal.index_names)
return DataFrame(internal)
@property
def columns(self) -> pd.Index:
"""The column labels of the DataFrame."""
names = [
name if name is None or len(name) > 1 else name[0]
for name in self._internal.column_label_names
]
if self._internal.column_labels_level > 1:
columns = pd.MultiIndex.from_tuples(self._internal.column_labels, names=names)
else:
columns = pd.Index([label[0] for label in self._internal.column_labels], name=names[0])
return columns
@columns.setter
def columns(self, columns: Union[pd.Index, List[Name]]) -> None:
if isinstance(columns, pd.MultiIndex):
column_labels = columns.tolist()
else:
column_labels = [
col if is_name_like_tuple(col, allow_none=False) else (col,) for col in columns
]
if len(self._internal.column_labels) != len(column_labels):
raise ValueError(
"Length mismatch: Expected axis has {} elements, "
"new values have {} elements".format(
len(self._internal.column_labels), len(column_labels)
)
)
column_label_names: Optional[List]
if isinstance(columns, pd.Index):
column_label_names = [
name if is_name_like_tuple(name) else (name,) for name in columns.names
]
else:
column_label_names = None
pssers = [
self._psser_for(label).rename(name)
for label, name in zip(self._internal.column_labels, column_labels)
]
self._update_internal_frame(
self._internal.with_new_columns(pssers, column_label_names=column_label_names)
)
@property
def dtypes(self) -> pd.Series:
"""Return the dtypes in the DataFrame.
This returns a Series with the data type of each column. The result's index is the original
DataFrame's columns. Columns with mixed types are stored with the object dtype.
Returns
-------
pd.Series
The data type of each column.
Examples
--------
>>> df = ps.DataFrame({'a': list('abc'),
... 'b': list(range(1, 4)),
... 'c': np.arange(3, 6).astype('i1'),
... 'd': np.arange(4.0, 7.0, dtype='float64'),
... 'e': [True, False, True],
... 'f': pd.date_range('20130101', periods=3)},
... columns=['a', 'b', 'c', 'd', 'e', 'f'])
>>> df.dtypes
a object
b int64
c int8
d float64
e bool
f datetime64[ns]
dtype: object
"""
return pd.Series(
[self._psser_for(label).dtype for label in self._internal.column_labels],
index=pd.Index(
[label if len(label) > 1 else label[0] for label in self._internal.column_labels]
),
)
[docs] def select_dtypes(
self,
include: Optional[Union[str, List[str]]] = None,
exclude: Optional[Union[str, List[str]]] = None,
) -> "DataFrame":
"""
Return a subset of the DataFrame's columns based on the column dtypes.
Parameters
----------
include, exclude : scalar or list-like
A selection of dtypes or strings to be included/excluded. At least
one of these parameters must be supplied. It also takes Spark SQL
DDL type strings, for instance, 'string' and 'date'.
Returns
-------
DataFrame
The subset of the frame including the dtypes in ``include`` and
excluding the dtypes in ``exclude``.
Raises
------
ValueError
* If both of ``include`` and ``exclude`` are empty
>>> df = ps.DataFrame({'a': [1, 2] * 3,
... 'b': [True, False] * 3,
... 'c': [1.0, 2.0] * 3})
>>> df.select_dtypes()
Traceback (most recent call last):
...
ValueError: at least one of include or exclude must be nonempty
* If ``include`` and ``exclude`` have overlapping elements
>>> df = ps.DataFrame({'a': [1, 2] * 3,
... 'b': [True, False] * 3,
... 'c': [1.0, 2.0] * 3})
>>> df.select_dtypes(include='a', exclude='a')
Traceback (most recent call last):
...
ValueError: include and exclude overlap on {'a'}
Notes
-----
* To select datetimes, use ``np.datetime64``, ``'datetime'`` or
``'datetime64'``
Examples
--------
>>> df = ps.DataFrame({'a': [1, 2] * 3,
... 'b': [True, False] * 3,
... 'c': [1.0, 2.0] * 3,
... 'd': ['a', 'b'] * 3}, columns=['a', 'b', 'c', 'd'])
>>> df
a b c d
0 1 True 1.0 a
1 2 False 2.0 b
2 1 True 1.0 a
3 2 False 2.0 b
4 1 True 1.0 a
5 2 False 2.0 b
>>> df.select_dtypes(include='bool')
b
0 True
1 False
2 True
3 False
4 True
5 False
>>> df.select_dtypes(include=['float64'], exclude=['int'])
c
0 1.0
1 2.0
2 1.0
3 2.0
4 1.0
5 2.0
>>> df.select_dtypes(include=['int'], exclude=['float64'])
a
0 1
1 2
2 1
3 2
4 1
5 2
>>> df.select_dtypes(exclude=['int'])
b c d
0 True 1.0 a
1 False 2.0 b
2 True 1.0 a
3 False 2.0 b
4 True 1.0 a
5 False 2.0 b
Spark SQL DDL type strings can be used as well.
>>> df.select_dtypes(exclude=['string'])
a b c
0 1 True 1.0
1 2 False 2.0
2 1 True 1.0
3 2 False 2.0
4 1 True 1.0
5 2 False 2.0
"""
from pyspark.sql.types import _parse_datatype_string
include_list: List[str]
if not is_list_like(include):
include_list = [cast(str, include)] if include is not None else []
else:
include_list = list(include)
exclude_list: List[str]
if not is_list_like(exclude):
exclude_list = [cast(str, exclude)] if exclude is not None else []
else:
exclude_list = list(exclude)
if not any((include_list, exclude_list)):
raise ValueError("at least one of include or exclude must be " "nonempty")
# can't both include AND exclude!
if set(include_list).intersection(set(exclude_list)):
raise ValueError(
"include and exclude overlap on {inc_ex}".format(
inc_ex=set(include_list).intersection(set(exclude_list))
)
)
# Handle Spark types
include_spark_type = []
for inc in include_list:
try:
include_spark_type.append(_parse_datatype_string(inc))
except BaseException:
pass
exclude_spark_type = []
for exc in exclude_list:
try:
exclude_spark_type.append(_parse_datatype_string(exc))
except BaseException:
pass
# Handle pandas types
include_numpy_type = []
for inc in include_list:
try:
include_numpy_type.append(infer_dtype_from_object(inc))
except BaseException:
pass
exclude_numpy_type = []
for exc in exclude_list:
try:
exclude_numpy_type.append(infer_dtype_from_object(exc))
except BaseException:
pass
column_labels = []
for label in self._internal.column_labels:
if len(include_list) > 0:
should_include = (
infer_dtype_from_object(self._psser_for(label).dtype.name) in include_numpy_type
or self._internal.spark_type_for(label) in include_spark_type
)
else:
should_include = not (
infer_dtype_from_object(self._psser_for(label).dtype.name) in exclude_numpy_type
or self._internal.spark_type_for(label) in exclude_spark_type
)
if should_include:
column_labels.append(label)
return DataFrame(
self._internal.with_new_columns([self._psser_for(label) for label in column_labels])
)
[docs] def droplevel(
self, level: Union[int, Name, List[Union[int, Name]]], axis: Axis = 0
) -> "DataFrame":
"""
Return DataFrame with requested index / column level(s) removed.
Parameters
----------
level: int, str, or list-like
If a string is given, must be the name of a level If list-like, elements must
be names or positional indexes of levels.
axis: {0 or ‘index’, 1 or ‘columns’}, default 0
Returns
-------
DataFrame with requested index / column level(s) removed.
Examples
--------
>>> df = ps.DataFrame(
... [[3, 4], [7, 8], [11, 12]],
... index=pd.MultiIndex.from_tuples([(1, 2), (5, 6), (9, 10)], names=["a", "b"]),
... )
>>> df.columns = pd.MultiIndex.from_tuples([
... ('c', 'e'), ('d', 'f')
... ], names=['level_1', 'level_2'])
>>> df # doctest: +NORMALIZE_WHITESPACE
level_1 c d
level_2 e f
a b
1 2 3 4
5 6 7 8
9 10 11 12
>>> df.droplevel('a') # doctest: +NORMALIZE_WHITESPACE
level_1 c d
level_2 e f
b
2 3 4
6 7 8
10 11 12
>>> df.droplevel('level_2', axis=1) # doctest: +NORMALIZE_WHITESPACE
level_1 c d
a b
1 2 3 4
5 6 7 8
9 10 11 12
"""
axis = validate_axis(axis)
if axis == 0:
if not isinstance(level, (tuple, list)): # huh?
level = [level]
names = self.index.names
nlevels = self._internal.index_level
int_level = set()
for n in level:
if isinstance(n, int):
if n < 0:
n = n + nlevels
if n < 0:
raise IndexError(
"Too many levels: Index has only {} levels, "
"{} is not a valid level number".format(nlevels, (n - nlevels))
)
if n >= nlevels:
raise IndexError(
"Too many levels: Index has only {} levels, not {}".format(
nlevels, (n + 1)
)
)
else:
if n not in names:
raise KeyError("Level {} not found".format(n))
n = names.index(n)
int_level.add(n)
if len(level) >= nlevels:
raise ValueError(
"Cannot remove {} levels from an index with {} levels: "
"at least one level must be left.".format(len(level), nlevels)
)
index_spark_columns, index_names, index_fields = zip(
*[
item
for i, item in enumerate(
zip(
self._internal.index_spark_columns,
self._internal.index_names,
self._internal.index_fields,
)
)
if i not in int_level
]
)
internal = self._internal.copy(
index_spark_columns=list(index_spark_columns),
index_names=list(index_names),
index_fields=list(index_fields),
)
return DataFrame(internal)
else:
psdf = self.copy()
psdf.columns = psdf.columns.droplevel(level) # type: ignore[arg-type]
return psdf
[docs] def drop(
self,
labels: Optional[Union[Name, List[Name]]] = None,
axis: Optional[Axis] = 0,
index: Union[Name, List[Name]] = None,
columns: Union[Name, List[Name]] = None,
) -> "DataFrame":
"""
Drop specified labels from columns.
Remove rows and/or columns by specifying label names and corresponding axis,
or by specifying directly index and/or column names.
Drop rows of a MultiIndex DataFrame is not supported yet.
Parameters
----------
labels : single label or list-like
Column labels to drop.
axis : {0 or 'index', 1 or 'columns'}, default 0
.. versionchanged:: 3.3
Set dropping by index is default.
index : single label or list-like
Alternative to specifying axis (``labels, axis=0``
is equivalent to ``index=columns``).
.. versionchanged:: 3.3
Added dropping rows by 'index'.
columns : single label or list-like
Alternative to specifying axis (``labels, axis=1``
is equivalent to ``columns=labels``).
Returns
-------
dropped : DataFrame
See Also
--------
Series.dropna
Examples
--------
>>> df = ps.DataFrame(np.arange(12).reshape(3, 4), columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
Drop columns
>>> df.drop(['B', 'C'], axis=1)
A D
0 0 3
1 4 7
2 8 11
>>> df.drop(columns=['B', 'C'])
A D
0 0 3
1 4 7
2 8 11
Drop a row by index
>>> df.drop([0, 1])
A B C D
2 8 9 10 11
>>> df.drop(index=[0, 1], columns='A')
B C D
2 9 10 11
Also support dropping columns for MultiIndex
>>> df = ps.DataFrame({'x': [1, 2], 'y': [3, 4], 'z': [5, 6], 'w': [7, 8]},
... columns=['x', 'y', 'z', 'w'])
>>> columns = [('a', 'x'), ('a', 'y'), ('b', 'z'), ('b', 'w')]
>>> df.columns = pd.MultiIndex.from_tuples(columns)
>>> df # doctest: +NORMALIZE_WHITESPACE
a b
x y z w
0 1 3 5 7
1 2 4 6 8
>>> df.drop(labels='a', axis=1) # doctest: +NORMALIZE_WHITESPACE
b
z w
0 5 7
1 6 8
Notes
-----
Currently, dropping rows of a MultiIndex DataFrame is not supported yet.
"""
if labels is not None:
if index is not None or columns is not None:
raise ValueError("Cannot specify both 'labels' and 'index'/'columns'")
axis = validate_axis(axis)
if axis == 1:
return self.drop(index=index, columns=labels)
else:
return self.drop(index=labels, columns=columns)
else:
if index is None and columns is None:
raise ValueError("Need to specify at least one of 'labels' or 'columns' or 'index'")
internal = self._internal
if index is not None:
if is_name_like_tuple(index) or is_name_like_value(index):
index = [index]
if len(index) > 0:
if internal.index_level == 1:
internal = internal.resolved_copy
if len(index) <= ps.get_option("compute.isin_limit"):
self_index_type = self.index.spark.data_type
cond = ~internal.index_spark_columns[0].isin(
[F.lit(label).cast(self_index_type) for label in index]
)
internal = internal.with_filter(cond)
else:
index_sdf_col = "__index"
index_sdf = default_session().createDataFrame(
pd.DataFrame({index_sdf_col: index})
)
joined_sdf = internal.spark_frame.join(
other=F.broadcast(index_sdf),
on=(
internal.index_spark_columns[0]
== scol_for(index_sdf, index_sdf_col)
),
how="anti",
)
internal = internal.with_new_sdf(joined_sdf)
else:
raise NotImplementedError(
"Drop rows of MultiIndex DataFrame is not supported yet"
)
if columns is not None:
if is_name_like_tuple(columns):
columns = [columns]
elif is_name_like_value(columns):
columns = [(columns,)]
else:
columns = [col if is_name_like_tuple(col) else (col,) for col in columns]
if len(columns) > 0:
drop_column_labels = set(
label
for label in internal.column_labels
for col in columns
if label[: len(col)] == col
)
if len(drop_column_labels) == 0:
raise KeyError(columns)
keep_columns_and_labels = [
(column, label)
for column, label in zip(
self._internal.data_spark_column_names, self._internal.column_labels
)
if label not in drop_column_labels
]
cols, labels = (
zip(*keep_columns_and_labels)
if len(keep_columns_and_labels) > 0
else ([], [])
)
internal = internal.with_new_columns(
[self._psser_for(label) for label in labels]
)
return DataFrame(internal)
def _prepare_sort_by_scols(self, by: Union[Name, List[Name]]) -> List[PySparkColumn]:
if is_name_like_value(by):
by = [by]
else:
assert is_list_like(by), type(by)
new_by = []
for colname in by:
ser = self[colname]
if not isinstance(ser, ps.Series):
raise ValueError(
"The column %s is not unique. For a multi-index, the label must be a tuple "
"with elements corresponding to each level." % name_like_string(colname)
)
new_by.append(ser.spark.column)
return new_by
def _sort(
self,
by: Sequence[PySparkColumn],
ascending: Union[bool, List[bool]],
na_position: str,
keep: str = "first",
) -> "DataFrame":
if isinstance(ascending, bool):
ascending = [ascending] * len(by)
if len(ascending) != len(by):
raise ValueError(
"Length of ascending ({}) != length of by ({})".format(len(ascending), len(by))
)
if na_position not in ("first", "last"):
raise ValueError("invalid na_position: '{}'".format(na_position))
Column = get_column_class()
# Mapper: Get a spark colum
# n function for (ascending, na_position) combination
mapper = {
(True, "first"): Column.asc_nulls_first,
(True, "last"): Column.asc_nulls_last,
(False, "first"): Column.desc_nulls_first,
(False, "last"): Column.desc_nulls_last,
}
by = [mapper[(asc, na_position)](scol) for scol, asc in zip(by, ascending)]
natural_order_scol = F.col(NATURAL_ORDER_COLUMN_NAME)
if keep == "last":
natural_order_scol = Column.desc(natural_order_scol)
elif keep == "all":
raise NotImplementedError("`keep`=all is not implemented yet.")
elif keep != "first":
raise ValueError('keep must be either "first", "last" or "all".')
sdf = self._internal.resolved_copy.spark_frame.sort(*by, natural_order_scol)
return DataFrame(self._internal.with_new_sdf(sdf))
[docs] def sort_values(
self,
by: Union[Name, List[Name]],
ascending: Union[bool, List[bool]] = True,
inplace: bool = False,
na_position: str = "last",
ignore_index: bool = False,
) -> Optional["DataFrame"]:
"""
Sort by the values along either axis.
Parameters
----------
by : str or list of str
ascending : bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
inplace : bool, default False
if True, perform operation in-place
na_position : {'first', 'last'}, default 'last'
`first` puts NaNs at the beginning, `last` puts NaNs at the end
ignore_index : bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
Returns
-------
sorted_obj : DataFrame
Examples
--------
>>> df = ps.DataFrame({
... 'col1': ['A', 'B', None, 'D', 'C'],
... 'col2': [2, 9, 8, 7, 4],
... 'col3': [0, 9, 4, 2, 3],
... },
... columns=['col1', 'col2', 'col3'],
... index=['a', 'b', 'c', 'd', 'e'])
>>> df
col1 col2 col3
a A 2 0
b B 9 9
c None 8 4
d D 7 2
e C 4 3
Sort by col1
>>> df.sort_values(by=['col1'])
col1 col2 col3
a A 2 0
b B 9 9
e C 4 3
d D 7 2
c None 8 4
Ignore index for the resulting axis
>>> df.sort_values(by=['col1'], ignore_index=True)
col1 col2 col3
0 A 2 0
1 B 9 9
2 C 4 3
3 D 7 2
4 None 8 4
Sort Descending
>>> df.sort_values(by='col1', ascending=False)
col1 col2 col3
d D 7 2
e C 4 3
b B 9 9
a A 2 0
c None 8 4
Sort by multiple columns
>>> df = ps.DataFrame({
... 'col1': ['A', 'A', 'B', None, 'D', 'C'],
... 'col2': [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... },
... columns=['col1', 'col2', 'col3'])
>>> df.sort_values(by=['col1', 'col2'])
col1 col2 col3
1 A 1 1
0 A 2 0
2 B 9 9
5 C 4 3
4 D 7 2
3 None 8 4
"""
inplace = validate_bool_kwarg(inplace, "inplace")
new_by = self._prepare_sort_by_scols(by)
psdf = self._sort(by=new_by, ascending=ascending, na_position=na_position)
if inplace:
if ignore_index:
psdf.reset_index(drop=True, inplace=inplace)
self._update_internal_frame(psdf._internal)
return None
else:
return psdf.reset_index(drop=True) if ignore_index else psdf
[docs] def sort_index(
self,
axis: Axis = 0,
level: Optional[Union[int, List[int]]] = None,
ascending: bool = True,
inplace: bool = False,
kind: str = None,
na_position: str = "last",
ignore_index: bool = False,
) -> Optional["DataFrame"]:
"""
Sort object by labels (along an axis)
Parameters
----------
axis : index, columns to direct sorting. Currently, only axis = 0 is supported.
level : int or level name or list of ints or list of level names
if not None, sort on values in specified index level(s)
ascending : boolean, default True
Sort ascending vs. descending
inplace : bool, default False
if True, perform operation in-place
kind : str, default None
pandas-on-Spark does not allow specifying the sorting algorithm now,
default None
na_position : {‘first’, ‘last’}, default ‘last’
first puts NaNs at the beginning, last puts NaNs at the end. Not implemented for
MultiIndex.
ignore_index : bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
.. versionadded:: 3.4.0
Returns
-------
sorted_obj : DataFrame
Examples
--------
>>> df = ps.DataFrame({'A': [2, 1, np.nan]}, index=['b', 'a', np.nan])
>>> df.sort_index() # doctest: +SKIP
A
a 1.0
b 2.0
None NaN
>>> df.sort_index(ascending=False) # doctest: +SKIP
A
b 2.0
a 1.0
None NaN
>>> df.sort_index(na_position='first') # doctest: +SKIP
A
None NaN
a 1.0
b 2.0
>>> df.sort_index(ignore_index=True)
A
0 1.0
1 2.0
2 NaN
>>> df.sort_index(inplace=True)
>>> df # doctest: +SKIP
A
a 1.0
b 2.0
None NaN
>>> df = ps.DataFrame({'A': range(4), 'B': range(4)[::-1]},
... index=[['b', 'b', 'a', 'a'], [1, 0, 1, 0]],
... columns=['A', 'B'])
>>> df.sort_index()
A B
a 0 3 0
1 2 1
b 0 1 2
1 0 3
>>> df.sort_index(level=1)
A B
b 0 1 2
a 0 3 0
b 1 0 3
a 1 2 1
>>> df.sort_index(level=[1, 0])
A B
a 0 3 0
b 0 1 2
a 1 2 1
b 1 0 3
>>> df.sort_index(ignore_index=True)
A B
0 3 0
1 2 1
2 1 2
3 0 3
"""
inplace = validate_bool_kwarg(inplace, "inplace")
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError("No other axis than 0 are supported now")
if kind is not None:
raise NotImplementedError("Specifying the sorting algorithm is not supported now.")
if level is None or (is_list_like(level) and len(level) == 0): # type: ignore[arg-type]
by = self._internal.index_spark_columns
elif is_list_like(level):
by = [
self._internal.index_spark_columns[lvl] for lvl in level # type: ignore[union-attr]
]
else:
by = [self._internal.index_spark_columns[level]] # type: ignore[index]
psdf = self._sort(by=by, ascending=ascending, na_position=na_position)
if inplace:
if ignore_index:
psdf.reset_index(drop=True, inplace=inplace)
self._update_internal_frame(psdf._internal)
return None
else:
return psdf.reset_index(drop=True) if ignore_index else psdf
[docs] def swaplevel(
self, i: Union[int, Name] = -2, j: Union[int, Name] = -1, axis: Axis = 0
) -> "DataFrame":
"""
Swap levels i and j in a MultiIndex on a particular axis.
Parameters
----------
i, j : int or str
Levels of the indices to be swapped. Can pass level name as string.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to swap levels on. 0 or 'index' for row-wise, 1 or
'columns' for column-wise.
Returns
-------
DataFrame
DataFrame with levels swapped in MultiIndex.
Examples
--------
>>> midx = pd.MultiIndex.from_arrays(
... [['red', 'blue'], [1, 2], ['s', 'm']], names = ['color', 'number', 'size'])
>>> midx # doctest: +SKIP
MultiIndex([( 'red', 1, 's'),
('blue', 2, 'm')],
names=['color', 'number', 'size'])
Swap levels in a MultiIndex on index.
>>> psdf = ps.DataFrame({'x': [5, 6], 'y':[5, 6]}, index=midx)
>>> psdf # doctest: +NORMALIZE_WHITESPACE
x y
color number size
red 1 s 5 5
blue 2 m 6 6
>>> psdf.swaplevel() # doctest: +NORMALIZE_WHITESPACE
x y
color size number
red s 1 5 5
blue m 2 6 6
>>> psdf.swaplevel(0, 1) # doctest: +NORMALIZE_WHITESPACE
x y
number color size
1 red s 5 5
2 blue m 6 6
>>> psdf.swaplevel('number', 'size') # doctest: +NORMALIZE_WHITESPACE
x y
color size number
red s 1 5 5
blue m 2 6 6
Swap levels in a MultiIndex on columns.
>>> psdf = ps.DataFrame({'x': [5, 6], 'y':[5, 6]})
>>> psdf.columns = midx
>>> psdf
color red blue
number 1 2
size s m
0 5 5
1 6 6
>>> psdf.swaplevel(axis=1)
color red blue
size s m
number 1 2
0 5 5
1 6 6
>>> psdf.swaplevel(axis=1)
color red blue
size s m
number 1 2
0 5 5
1 6 6
>>> psdf.swaplevel(0, 1, axis=1)
number 1 2
color red blue
size s m
0 5 5
1 6 6
>>> psdf.swaplevel('number', 'color', axis=1)
number 1 2
color red blue
size s m
0 5 5
1 6 6
"""
axis = validate_axis(axis)
if axis == 0:
internal = self._swaplevel_index(i, j)
else:
assert axis == 1
internal = self._swaplevel_columns(i, j)
return DataFrame(internal)
[docs] def swapaxes(self, i: Axis, j: Axis, copy: bool = True) -> "DataFrame":
"""
Interchange axes and swap values axes appropriately.
.. note:: This method is based on an expensive operation due to the nature
of big data. Internally it needs to generate each row for each value, and
then group twice - it is a huge operation. To prevent misuse, this method
has the 'compute.max_rows' default limit of input length and raises a ValueError.
>>> from pyspark.pandas.config import option_context
>>> with option_context('compute.max_rows', 1000): # doctest: +NORMALIZE_WHITESPACE
... ps.DataFrame({'a': range(1001)}).swapaxes(i=0, j=1)
Traceback (most recent call last):
...
ValueError: Current DataFrame's length exceeds the given limit of 1000 rows.
Please set 'compute.max_rows' by using 'pyspark.pandas.config.set_option'
to retrieve more than 1000 rows. Note that, before changing the
'compute.max_rows', this operation is considerably expensive.
Parameters
----------
i: {0 or 'index', 1 or 'columns'}. The axis to swap.
j: {0 or 'index', 1 or 'columns'}. The axis to swap.
copy : bool, default True.
Returns
-------
DataFrame
Examples
--------
>>> psdf = ps.DataFrame(
... [[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=['x', 'y', 'z'], columns=['a', 'b', 'c']
... )
>>> psdf
a b c
x 1 2 3
y 4 5 6
z 7 8 9
>>> psdf.swapaxes(i=1, j=0)
x y z
a 1 4 7
b 2 5 8
c 3 6 9
>>> psdf.swapaxes(i=1, j=1)
a b c
x 1 2 3
y 4 5 6
z 7 8 9
"""
assert copy is True
i = validate_axis(i)
j = validate_axis(j)
return self.copy() if i == j else self.transpose()
def _swaplevel_columns(self, i: Union[int, Name], j: Union[int, Name]) -> InternalFrame:
assert isinstance(self.columns, pd.MultiIndex)
for index in (i, j):
if not isinstance(index, int) and index not in self.columns.names:
raise KeyError("Level %s not found" % index)
i = i if isinstance(i, int) else self.columns.names.index(i)
j = j if isinstance(j, int) else self.columns.names.index(j)
for index in (i, j):
if index >= len(self.columns) or index < -len(self.columns):
raise IndexError(
"Too many levels: Columns have only %s levels, "
"%s is not a valid level number" % (self._internal.index_level, index)
)
column_label_names = self._internal.column_label_names.copy()
column_label_names[i], column_label_names[j], = (
column_label_names[j],
column_label_names[i],
)
column_labels = self._internal._column_labels
column_label_list = [list(label) for label in column_labels]
for label_list in column_label_list:
label_list[i], label_list[j] = label_list[j], label_list[i]
column_labels = [tuple(x) for x in column_label_list]
internal = self._internal.copy(
column_label_names=list(column_label_names), column_labels=list(column_labels)
)
return internal
def _swaplevel_index(self, i: Union[int, Name], j: Union[int, Name]) -> InternalFrame:
assert isinstance(self.index, ps.MultiIndex)
for index in (i, j):
if not isinstance(index, int) and index not in self.index.names:
raise KeyError("Level %s not found" % index)
i = i if isinstance(i, int) else self.index.names.index(i)
j = j if isinstance(j, int) else self.index.names.index(j)
for index in (i, j):
if index >= self._internal.index_level or index < -self._internal.index_level:
raise IndexError(
"Too many levels: Index has only %s levels, "
"%s is not a valid level number" % (self._internal.index_level, index)
)
index_map = list(
zip(
self._internal.index_spark_columns,
self._internal.index_names,
self._internal.index_fields,
)
)
index_map[i], index_map[j] = index_map[j], index_map[i]
index_spark_columns, index_names, index_fields = zip(*index_map)
internal = self._internal.copy(
index_spark_columns=list(index_spark_columns),
index_names=list(index_names),
index_fields=list(index_fields),
)
return internal
[docs] def nlargest(
self, n: int, columns: Union[Name, List[Name]], keep: str = "first"
) -> "DataFrame":
"""
Return the first `n` rows ordered by `columns` in descending order.
Return the first `n` rows with the largest values in `columns`, in
descending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to
``df.sort_values(columns, ascending=False).head(n)``, but more
performant in pandas.
In pandas-on-Spark, thanks to Spark's lazy execution and query optimizer,
the two would have same performance.
Parameters
----------
n : int
Number of rows to return.
columns : label or list of labels
Column label(s) to order by.
keep : {'first', 'last'}, default 'first'. 'all' is not implemented yet.
Determines which duplicates (if any) to keep.
- ``first`` : Keep the first occurrence.
- ``last`` : Keep the last occurrence.
Returns
-------
DataFrame
The first `n` rows ordered by the given columns in descending
order.
See Also
--------
DataFrame.nsmallest : Return the first `n` rows ordered by `columns` in
ascending order.
DataFrame.sort_values : Sort DataFrame by the values.
DataFrame.head : Return the first `n` rows without re-ordering.
Notes
-----
This function cannot be used with all column types. For example, when
specifying columns with `object` or `category` dtypes, ``TypeError`` is
raised.
Examples
--------
>>> df = ps.DataFrame({'X': [1, 2, 3, 5, 6, 7, np.nan],
... 'Y': [6, 7, 8, 9, 10, 11, 12]})
>>> df
X Y
0 1.0 6
1 2.0 7
2 3.0 8
3 5.0 9
4 6.0 10
5 7.0 11
6 NaN 12
In the following example, we will use ``nlargest`` to select the three
rows having the largest values in column "X".
>>> df.nlargest(n=3, columns='X')
X Y
5 7.0 11
4 6.0 10
3 5.0 9
To order by the largest values in column "Y" and then "X", we can
specify multiple columns like in the next example.
>>> df.nlargest(n=3, columns=['Y', 'X'])
X Y
6 NaN 12
5 7.0 11
4 6.0 10
The examples below show how ties are resolved, which is decided by `keep`.
>>> tied_df = ps.DataFrame({'X': [1, 2, 2, 3, 3]}, index=['a', 'b', 'c', 'd', 'e'])
>>> tied_df
X
a 1
b 2
c 2
d 3
e 3
When using keep='first' (default), ties are resolved in order:
>>> tied_df.nlargest(3, 'X')
X
d 3
e 3
b 2
>>> tied_df.nlargest(3, 'X', keep='first')
X
d 3
e 3
b 2
When using keep='last', ties are resolved in reverse order:
>>> tied_df.nlargest(3, 'X', keep='last')
X
e 3
d 3
c 2
"""
by_scols = self._prepare_sort_by_scols(columns)
return self._sort(by=by_scols, ascending=False, na_position="last", keep=keep).head(n=n)
[docs] def nsmallest(
self, n: int, columns: Union[Name, List[Name]], keep: str = "first"
) -> "DataFrame":
"""
Return the first `n` rows ordered by `columns` in ascending order.
Return the first `n` rows with the smallest values in `columns`, in
ascending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to ``df.sort_values(columns, ascending=True).head(n)``,
but more performant. In pandas-on-Spark, thanks to Spark's lazy execution and query
optimizer, the two would have same performance.
Parameters
----------
n : int
Number of items to retrieve.
columns : list or str
Column name or names to order by.
keep : {'first', 'last'}, default 'first'. 'all' is not implemented yet.
Determines which duplicates (if any) to keep.
- ``first`` : Keep the first occurrence.
- ``last`` : Keep the last occurrence.
Returns
-------
DataFrame
See Also
--------
DataFrame.nlargest : Return the first `n` rows ordered by `columns` in
descending order.
DataFrame.sort_values : Sort DataFrame by the values.
DataFrame.head : Return the first `n` rows without re-ordering.
Examples
--------
>>> df = ps.DataFrame({'X': [1, 2, 3, 5, 6, 7, np.nan],
... 'Y': [6, 7, 8, 9, 10, 11, 12]})
>>> df
X Y
0 1.0 6
1 2.0 7
2 3.0 8
3 5.0 9
4 6.0 10
5 7.0 11
6 NaN 12
In the following example, we will use ``nsmallest`` to select the
three rows having the smallest values in column "X".
>>> df.nsmallest(n=3, columns='X') # doctest: +NORMALIZE_WHITESPACE
X Y
0 1.0 6
1 2.0 7
2 3.0 8
To order by the smallest values in column "Y" and then "X", we can
specify multiple columns like in the next example.
>>> df.nsmallest(n=3, columns=['Y', 'X']) # doctest: +NORMALIZE_WHITESPACE
X Y
0 1.0 6
1 2.0 7
2 3.0 8
The examples below show how ties are resolved, which is decided by `keep`.
>>> tied_df = ps.DataFrame({'X': [1, 1, 2, 2, 3]}, index=['a', 'b', 'c', 'd', 'e'])
>>> tied_df
X
a 1
b 1
c 2
d 2
e 3
When using keep='first' (default), ties are resolved in order:
>>> tied_df.nsmallest(3, 'X')
X
a 1
b 1
c 2
>>> tied_df.nsmallest(3, 'X', keep='first')
X
a 1
b 1
c 2
When using keep='last', ties are resolved in reverse order:
>>> tied_df.nsmallest(3, 'X', keep='last')
X
b 1
a 1
d 2
"""
by_scols = self._prepare_sort_by_scols(columns)
return self._sort(by=by_scols, ascending=True, na_position="last", keep=keep).head(n=n)
[docs] def isin(self, values: Union[List, Dict]) -> "DataFrame":
"""
Whether each element in the DataFrame is contained in values.
Parameters
----------
values : iterable or dict
The sequence of values to test. If values are a dict,
the keys must be the column names, which must match.
Series and DataFrame are not supported.
Returns
-------
DataFrame
DataFrame of booleans showing whether each element in the DataFrame
is contained in values.
Examples
--------
>>> df = ps.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
... index=['falcon', 'dog'],
... columns=['num_legs', 'num_wings'])
>>> df
num_legs num_wings
falcon 2 2
dog 4 0
When ``values`` is a list check whether every value in the DataFrame
is present in the list (which animals have 0 or 2 legs or wings)
>>> df.isin([0, 2])
num_legs num_wings
falcon True True
dog False True
When ``values`` is a dict, we can pass values to check for each
column separately:
>>> df.isin({'num_wings': [0, 3]})
num_legs num_wings
falcon False False
dog False True
"""
if isinstance(values, (pd.DataFrame, pd.Series)):
raise NotImplementedError("DataFrame and Series are not supported")
if isinstance(values, dict) and not set(values.keys()).issubset(self.columns):
raise AttributeError(
"'DataFrame' object has no attribute %s"
% (set(values.keys()).difference(self.columns))
)
data_spark_columns = []
if isinstance(values, dict):
for i, col in enumerate(self.columns):
if col in values:
item = values[col]
item = item.tolist() if isinstance(item, np.ndarray) else list(item)
scol = self._internal.spark_column_for(self._internal.column_labels[i]).isin(
[F.lit(v) for v in item]
)
scol = F.coalesce(scol, F.lit(False))
else:
scol = F.lit(False)
data_spark_columns.append(scol.alias(self._internal.data_spark_column_names[i]))
elif is_list_like(values):
values = (
cast(np.ndarray, values).tolist()
if isinstance(values, np.ndarray)
else list(values)
)
for label in self._internal.column_labels:
scol = self._internal.spark_column_for(label).isin([F.lit(v) for v in values])
scol = F.coalesce(scol, F.lit(False))
data_spark_columns.append(scol.alias(self._internal.spark_column_name_for(label)))
else:
raise TypeError("Values should be iterable, Series, DataFrame or dict.")
return DataFrame(
self._internal.with_new_columns(
data_spark_columns,
data_fields=[
field.copy(dtype=np.dtype("bool"), spark_type=BooleanType(), nullable=False)
for field in self._internal.data_fields
],
)
)
@property
def shape(self) -> Tuple[int, int]:
"""
Return a tuple representing the dimensionality of the DataFrame.
Examples
--------
>>> df = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.shape
(2, 2)
>>> df = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4],
... 'col3': [5, 6]})
>>> df.shape
(2, 3)
"""
return len(self), len(self.columns)
[docs] def merge(
self,
right: "DataFrame",
how: str = "inner",
on: Optional[Union[Name, List[Name]]] = None,
left_on: Optional[Union[Name, List[Name]]] = None,
right_on: Optional[Union[Name, List[Name]]] = None,
left_index: bool = False,
right_index: bool = False,
suffixes: Tuple[str, str] = ("_x", "_y"),
) -> "DataFrame":
"""
Merge DataFrame objects with a database-style join.
The index of the resulting DataFrame will be one of the following:
- 0...n if no index is used for merging
- Index of the left DataFrame if merged only on the index of the right DataFrame
- Index of the right DataFrame if merged only on the index of the left DataFrame
- All involved indices if merged using the indices of both DataFrames
e.g. if `left` with indices (a, x) and `right` with indices (b, x), the result will
be an index (x, a, b)
Parameters
----------
right: Object to merge with.
how: Type of merge to be performed.
{'left', 'right', 'outer', 'inner'}, default 'inner'
left: use only keys from left frame, like a SQL left outer join; not preserve
key order unlike pandas.
right: use only keys from right frame, like a SQL right outer join; not preserve
key order unlike pandas.
outer: use union of keys from both frames, like a SQL full outer join; sort keys
lexicographically.
inner: use intersection of keys from both frames, like a SQL inner join;
not preserve the order of the left keys unlike pandas.
on: Column or index level names to join on. These must be found in both DataFrames. If on
is None and not merging on indexes then this defaults to the intersection of the
columns in both DataFrames.
left_on: Column or index level names to join on in the left DataFrame. Can also
be an array or list of arrays of the length of the left DataFrame.
These arrays are treated as if they are columns.
right_on: Column or index level names to join on in the right DataFrame. Can also
be an array or list of arrays of the length of the right DataFrame.
These arrays are treated as if they are columns.
left_index: Use the index from the left DataFrame as the join key(s). If it is a
MultiIndex, the number of keys in the other DataFrame (either the index or a number of
columns) must match the number of levels.
right_index: Use the index from the right DataFrame as the join key. Same caveats as
left_index.
suffixes: Suffix to apply to overlapping column names in the left and right side,
respectively.
Returns
-------
DataFrame
A DataFrame of the two merged objects.
See Also
--------
DataFrame.join : Join columns of another DataFrame.
DataFrame.update : Modify in place using non-NA values from another DataFrame.
DataFrame.hint : Specifies some hint on the current DataFrame.
broadcast : Marks a DataFrame as small enough for use in broadcast joins.
Examples
--------
>>> df1 = ps.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
... 'value': [1, 2, 3, 5]},
... columns=['lkey', 'value'])
>>> df2 = ps.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
... 'value': [5, 6, 7, 8]},
... columns=['rkey', 'value'])
>>> df1
lkey value
0 foo 1
1 bar 2
2 baz 3
3 foo 5
>>> df2
rkey value
0 foo 5
1 bar 6
2 baz 7
3 foo 8
Merge df1 and df2 on the lkey and rkey columns. The value columns have
the default suffixes, _x and _y, appended.
>>> merged = df1.merge(df2, left_on='lkey', right_on='rkey')
>>> merged.sort_values(by=['lkey', 'value_x', 'rkey', 'value_y']) # doctest: +ELLIPSIS
lkey value_x rkey value_y
...bar 2 bar 6
...baz 3 baz 7
...foo 1 foo 5
...foo 1 foo 8
...foo 5 foo 5
...foo 5 foo 8
>>> left_psdf = ps.DataFrame({'A': [1, 2]})
>>> right_psdf = ps.DataFrame({'B': ['x', 'y']}, index=[1, 2])
>>> left_psdf.merge(right_psdf, left_index=True, right_index=True).sort_index()
A B
1 2 x
>>> left_psdf.merge(right_psdf, left_index=True, right_index=True, how='left').sort_index()
A B
0 1 None
1 2 x
>>> left_psdf.merge(right_psdf, left_index=True, right_index=True, how='right').sort_index()
A B
1 2.0 x
2 NaN y
>>> left_psdf.merge(right_psdf, left_index=True, right_index=True, how='outer').sort_index()
A B
0 1.0 None
1 2.0 x
2 NaN y
Notes
-----
As described in #263, joining string columns currently returns None for missing values
instead of NaN.
"""
def to_list(os: Optional[Union[Name, List[Name]]]) -> List[Label]:
if os is None:
return []
elif is_name_like_tuple(os):
return [cast(Label, os)]
elif is_name_like_value(os):
return [(os,)]
else:
return [o if is_name_like_tuple(o) else (o,) for o in os]
if isinstance(right, ps.Series):
right = right.to_frame()
if on:
if left_on or right_on:
raise ValueError(
'Can only pass argument "on" OR "left_on" and "right_on", '
"not a combination of both."
)
left_key_names = list(map(self._internal.spark_column_name_for, to_list(on)))
right_key_names = list(map(right._internal.spark_column_name_for, to_list(on)))
else:
# TODO: need special handling for multi-index.
if left_index:
left_key_names = self._internal.index_spark_column_names
else:
left_key_names = list(map(self._internal.spark_column_name_for, to_list(left_on)))
if right_index:
right_key_names = right._internal.index_spark_column_names
else:
right_key_names = list(
map(right._internal.spark_column_name_for, to_list(right_on))
)
if left_key_names and not right_key_names:
raise ValueError("Must pass right_on or right_index=True")
if right_key_names and not left_key_names:
raise ValueError("Must pass left_on or left_index=True")
if not left_key_names and not right_key_names:
common = list(self.columns.intersection(right.columns))
if len(common) == 0:
raise ValueError(
"No common columns to perform merge on. Merge options: "
"left_on=None, right_on=None, left_index=False, right_index=False"
)
left_key_names = list(map(self._internal.spark_column_name_for, to_list(common)))
right_key_names = list(map(right._internal.spark_column_name_for, to_list(common)))
if len(left_key_names) != len(right_key_names):
raise ValueError("len(left_keys) must equal len(right_keys)")
# We should distinguish the name to avoid ambiguous column name after merging.
right_prefix = "__right_"
right_key_names = [right_prefix + right_key_name for right_key_name in right_key_names]
how = validate_how(how)
def resolve(internal: InternalFrame, side: str) -> InternalFrame:
def rename(col: str) -> str:
return "__{}_{}".format(side, col)
internal = internal.resolved_copy
sdf = internal.spark_frame
sdf = sdf.select(
*[
scol_for(sdf, col).alias(rename(col))
for col in sdf.columns
if col not in HIDDEN_COLUMNS
],
*HIDDEN_COLUMNS,
)
return internal.copy(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, rename(col)) for col in internal.index_spark_column_names
],
index_fields=[
field.copy(name=rename(field.name)) for field in internal.index_fields
],
data_spark_columns=[
scol_for(sdf, rename(col)) for col in internal.data_spark_column_names
],
data_fields=[field.copy(name=rename(field.name)) for field in internal.data_fields],
)
left_internal = self._internal.resolved_copy
right_internal = resolve(right._internal, "right")
left_table = left_internal.spark_frame.alias("left_table")
right_table = right_internal.spark_frame.alias("right_table")
left_key_columns = [scol_for(left_table, label) for label in left_key_names]
right_key_columns = [scol_for(right_table, label) for label in right_key_names]
join_condition = reduce(
lambda x, y: x & y,
[lkey == rkey for lkey, rkey in zip(left_key_columns, right_key_columns)],
)
joined_table = left_table.join(right_table, join_condition, how=how)
# Unpack suffixes tuple for convenience
left_suffix = suffixes[0]
right_suffix = suffixes[1]
# Append suffixes to columns with the same name to avoid conflicts later
duplicate_columns = set(left_internal.column_labels) & set(right_internal.column_labels)
exprs = []
data_columns = []
column_labels = []
def left_scol_for(label: Label) -> PySparkColumn:
return scol_for(left_table, left_internal.spark_column_name_for(label))
def right_scol_for(label: Label) -> PySparkColumn:
return scol_for(right_table, right_internal.spark_column_name_for(label))
for label in left_internal.column_labels:
col = left_internal.spark_column_name_for(label)
scol = left_scol_for(label)
if label in duplicate_columns:
spark_column_name = left_internal.spark_column_name_for(label)
if (
spark_column_name in left_key_names
and (right_prefix + spark_column_name) in right_key_names
):
right_scol = right_scol_for(label)
if how == "right":
scol = right_scol.alias(col)
elif how == "full":
scol = F.when(scol.isNotNull(), scol).otherwise(right_scol).alias(col)
else:
pass
else:
col = col + left_suffix
scol = scol.alias(col)
label = tuple([str(label[0]) + left_suffix] + list(label[1:]))
exprs.append(scol)
data_columns.append(col)
column_labels.append(label)
for label in right_internal.column_labels:
# recover `right_prefix` here.
col = right_internal.spark_column_name_for(label)[len(right_prefix) :]
scol = right_scol_for(label).alias(col)
if label in duplicate_columns:
spark_column_name = left_internal.spark_column_name_for(label)
if (
spark_column_name in left_key_names
and (right_prefix + spark_column_name) in right_key_names
):
continue
else:
col = col + right_suffix
scol = scol.alias(col)
label = tuple([str(label[0]) + right_suffix] + list(label[1:]))
exprs.append(scol)
data_columns.append(col)
column_labels.append(label)
left_index_scols = left_internal.index_spark_columns
right_index_scols = right_internal.index_spark_columns
# Retain indices if they are used for joining
if left_index:
if right_index:
if how in ("inner", "left"):
exprs.extend(left_index_scols)
index_spark_column_names = left_internal.index_spark_column_names
index_names = left_internal.index_names
elif how == "right":
exprs.extend(right_index_scols)
index_spark_column_names = right_internal.index_spark_column_names
index_names = right_internal.index_names
else:
index_spark_column_names = left_internal.index_spark_column_names
index_names = left_internal.index_names
for col, left_scol, right_scol in zip(
index_spark_column_names, left_index_scols, right_index_scols
):
scol = F.when(left_scol.isNotNull(), left_scol).otherwise(right_scol)
exprs.append(scol.alias(col))
else:
exprs.extend(right_index_scols)
index_spark_column_names = right_internal.index_spark_column_names
index_names = right_internal.index_names
elif right_index:
exprs.extend(left_index_scols)
index_spark_column_names = left_internal.index_spark_column_names
index_names = left_internal.index_names
else:
index_spark_column_names = []
index_names = []
selected_columns = joined_table.select(*exprs)
internal = InternalFrame(
spark_frame=selected_columns,
index_spark_columns=[
scol_for(selected_columns, col) for col in index_spark_column_names
],
index_names=index_names,
column_labels=column_labels,
data_spark_columns=[scol_for(selected_columns, col) for col in data_columns],
)
return DataFrame(internal)
[docs] def join(
self,
right: "DataFrame",
on: Optional[Union[Name, List[Name]]] = None,
how: str = "left",
lsuffix: str = "",
rsuffix: str = "",
) -> "DataFrame":
"""
Join columns of another DataFrame.
Join columns with `right` DataFrame either on index or on a key column. Efficiently join
multiple DataFrame objects by index at once by passing a list.
Parameters
----------
right: DataFrame, Series
on: str, list of str, or array-like, optional
Column or index level name(s) in the caller to join on the index in `right`, otherwise
joins index-on-index. If multiple values given, the `right` DataFrame must have a
MultiIndex. Can pass an array as the join key if it is not already contained in the
calling DataFrame. Like an Excel VLOOKUP operation.
how: {'left', 'right', 'outer', 'inner'}, default 'left'
How to handle the operation of the two objects.
* left: use `left` frame’s index (or column if on is specified).
* right: use `right`’s index.
* outer: form union of `left` frame’s index (or column if on is specified) with
right’s index, and sort it. lexicographically.
* inner: form intersection of `left` frame’s index (or column if on is specified)
with `right`’s index, preserving the order of the `left`’s one.
lsuffix : str, default ''
Suffix to use from left frame's overlapping columns.
rsuffix : str, default ''
Suffix to use from `right` frame's overlapping columns.
Returns
-------
DataFrame
A dataframe containing columns from both the `left` and `right`.
See Also
--------
DataFrame.merge: For column(s)-on-columns(s) operations.
DataFrame.update : Modify in place using non-NA values from another DataFrame.
DataFrame.hint : Specifies some hint on the current DataFrame.
broadcast : Marks a DataFrame as small enough for use in broadcast joins.
Notes
-----
Parameters on, lsuffix, and rsuffix are not supported when passing a list of DataFrame
objects.
Examples
--------
>>> psdf1 = ps.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
... 'A': ['A0', 'A1', 'A2', 'A3']},
... columns=['key', 'A'])
>>> psdf2 = ps.DataFrame({'key': ['K0', 'K1', 'K2'],
... 'B': ['B0', 'B1', 'B2']},
... columns=['key', 'B'])
>>> psdf1
key A
0 K0 A0
1 K1 A1
2 K2 A2
3 K3 A3
>>> psdf2
key B
0 K0 B0
1 K1 B1
2 K2 B2
Join DataFrames using their indexes.
>>> join_psdf = psdf1.join(psdf2, lsuffix='_left', rsuffix='_right')
>>> join_psdf.sort_values(by=join_psdf.columns)
key_left A key_right B
0 K0 A0 K0 B0
1 K1 A1 K1 B1
2 K2 A2 K2 B2
3 K3 A3 None None
If we want to join using the key columns, we need to set key to be the index in both df and
right. The joined DataFrame will have key as its index.
>>> join_psdf = psdf1.set_index('key').join(psdf2.set_index('key'))
>>> join_psdf.sort_values(by=join_psdf.columns) # doctest: +NORMALIZE_WHITESPACE
A B
key
K0 A0 B0
K1 A1 B1
K2 A2 B2
K3 A3 None
Another option to join using the key columns is to use the on parameter. DataFrame.join
always uses right’s index but we can use any column in df. This method does not preserve
the original DataFrame’s index in the result unlike pandas.
>>> join_psdf = psdf1.join(psdf2.set_index('key'), on='key')
>>> join_psdf.index # doctest: +SKIP
Int64Index([0, 1, 2, 3], dtype='int64')
"""
if isinstance(right, ps.Series):
common = list(self.columns.intersection([right.name]))
else:
common = list(self.columns.intersection(right.columns))
if len(common) > 0 and not lsuffix and not rsuffix:
raise ValueError(
"columns overlap but no suffix specified: " "{rename}".format(rename=common)
)
need_set_index = False
if on:
if not is_list_like(on):
on = [on]
if len(on) != right._internal.index_level:
raise ValueError(
'len(left_on) must equal the number of levels in the index of "right"'
)
need_set_index = len(set(on) & set(self.index.names)) == 0
if need_set_index:
self = self.set_index(on)
join_psdf = self.merge(
right, left_index=True, right_index=True, how=how, suffixes=(lsuffix, rsuffix)
)
return join_psdf.reset_index() if need_set_index else join_psdf
[docs] def combine_first(self, other: "DataFrame") -> "DataFrame":
"""
Update null elements with value in the same location in `other`.
Combine two DataFrame objects by filling null values in one DataFrame
with non-null values from other DataFrame. The row and column indexes
of the resulting DataFrame will be the union of the two.
.. versionadded:: 3.3.0
Parameters
----------
other : DataFrame
Provided DataFrame to use to fill null values.
Returns
-------
DataFrame
Examples
--------
>>> ps.set_option("compute.ops_on_diff_frames", True)
>>> df1 = ps.DataFrame({'A': [None, 0], 'B': [None, 4]})
>>> df2 = ps.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2).sort_index()
A B
0 1.0 3.0
1 0.0 4.0
Null values persist if the location of that null value does not exist in other
>>> df1 = ps.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df2 = ps.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2).sort_index()
A B C
0 NaN 4.0 NaN
1 0.0 3.0 1.0
2 NaN 3.0 1.0
>>> ps.reset_option("compute.ops_on_diff_frames")
"""
if not isinstance(other, DataFrame):
raise TypeError("`combine_first` only allows `DataFrame` for parameter `other`")
if same_anchor(self, other):
combined = self
this = self
that = other
else:
combined = combine_frames(self, other)
this = combined["this"]
that = combined["that"]
intersect_column_labels = set(self._internal.column_labels).intersection(
set(other._internal.column_labels)
)
column_labels, data_spark_columns = [], []
for column_label in this._internal.column_labels:
this_scol = this._internal.spark_column_for(column_label)
if column_label in intersect_column_labels:
that_scol = that._internal.spark_column_for(column_label)
this_scol_name = this._internal.spark_column_name_for(column_label)
combined_scol = (
F.when(this_scol.isNull(), that_scol).otherwise(this_scol).alias(this_scol_name)
)
data_spark_columns.append(combined_scol)
else:
data_spark_columns.append(this_scol)
column_labels.append(column_label)
for column_label in that._internal.column_labels:
if column_label not in intersect_column_labels:
that_scol = that._internal.spark_column_for(column_label)
data_spark_columns.append(that_scol)
column_labels.append(column_label)
internal = combined._internal.copy(
column_labels=column_labels,
data_spark_columns=data_spark_columns,
data_fields=None, # TODO: dtype?
column_label_names=self._internal.column_label_names,
)
return DataFrame(internal)
[docs] def append(
self,
other: "DataFrame",
ignore_index: bool = False,
verify_integrity: bool = False,
sort: bool = False,
) -> "DataFrame":
"""
Append rows of other to the end of caller, returning a new object.
Columns in other that are not in the caller are added as new columns.
.. deprecated:: 3.4.0
Parameters
----------
other : DataFrame or Series/dict-like object, or list of these
The data to append.
ignore_index : boolean, default False
If True, do not use the index labels.
verify_integrity : boolean, default False
If True, raise ValueError on creating index with duplicates.
sort : boolean, default False
Currently not supported.
Returns
-------
appended : DataFrame
Examples
--------
>>> df = ps.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
>>> df.append(df)
A B
0 1 2
1 3 4
0 1 2
1 3 4
>>> df.append(df, ignore_index=True)
A B
0 1 2
1 3 4
2 1 2
3 3 4
"""
warnings.warn(
"The DataFrame.append method is deprecated "
"and will be removed in 4.0.0. "
"Use pyspark.pandas.concat instead.",
FutureWarning,
)
if isinstance(other, ps.Series):
raise TypeError("DataFrames.append() does not support appending Series to DataFrames")
if sort:
raise NotImplementedError("The 'sort' parameter is currently not supported")
if not ignore_index:
index_scols = self._internal.index_spark_columns
if len(index_scols) != other._internal.index_level:
raise ValueError("Both DataFrames have to have the same number of index levels")
if (
verify_integrity
and len(index_scols) > 0
and (
self._internal.spark_frame.select(index_scols)
.intersect(
other._internal.spark_frame.select(other._internal.index_spark_columns)
)
.count()
)
> 0
):
raise ValueError("Indices have overlapping values")
# Lazy import to avoid circular dependency issues
from pyspark.pandas.namespace import concat
return cast(DataFrame, concat([self, other], ignore_index=ignore_index))
# TODO: add 'filter_func' and 'errors' parameter
[docs] def update(self, other: "DataFrame", join: str = "left", overwrite: bool = True) -> None:
"""
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
Parameters
----------
other : DataFrame, or Series
join : 'left', default 'left'
Only left join is implemented, keeping the index and columns of the original object.
overwrite : bool, default True
How to handle non-NA values for overlapping keys:
* True: overwrite original DataFrame's values with values from `other`.
* False: only update values that are NA in the original DataFrame.
Returns
-------
None : method directly changes calling object
See Also
--------
DataFrame.merge : For column(s)-on-columns(s) operations.
DataFrame.join : Join columns of another DataFrame.
DataFrame.hint : Specifies some hint on the current DataFrame.
broadcast : Marks a DataFrame as small enough for use in broadcast joins.
Examples
--------
>>> df = ps.DataFrame({'A': [1, 2, 3], 'B': [400, 500, 600]}, columns=['A', 'B'])
>>> new_df = ps.DataFrame({'B': [4, 5, 6], 'C': [7, 8, 9]}, columns=['B', 'C'])
>>> df.update(new_df)
>>> df.sort_index()
A B
0 1 4
1 2 5
2 3 6
The DataFrame's length does not increase because of the update,
only values at matching index/column labels are updated.
>>> df = ps.DataFrame({'A': ['a', 'b', 'c'], 'B': ['x', 'y', 'z']}, columns=['A', 'B'])
>>> new_df = ps.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']}, columns=['B'])
>>> df.update(new_df)
>>> df.sort_index()
A B
0 a d
1 b e
2 c f
For Series, its name attribute must be set.
>>> df = ps.DataFrame({'A': ['a', 'b', 'c'], 'B': ['x', 'y', 'z']}, columns=['A', 'B'])
>>> new_column = ps.Series(['d', 'e'], name='B', index=[0, 2])
>>> df.update(new_column)
>>> df.sort_index()
A B
0 a d
1 b y
2 c e
If `other` contains None the corresponding values are not updated in the original dataframe.
>>> df = ps.DataFrame({'A': [1, 2, 3], 'B': [400, 500, 600]}, columns=['A', 'B'])
>>> new_df = ps.DataFrame({'B': [4, None, 6]}, columns=['B'])
>>> df.update(new_df)
>>> df.sort_index()
A B
0 1 4.0
1 2 500.0
2 3 6.0
"""
if join != "left":
raise NotImplementedError("Only left join is supported")
if isinstance(other, ps.Series):
other = other.to_frame()
update_columns = list(
set(self._internal.column_labels).intersection(set(other._internal.column_labels))
)
update_sdf = self.join(
other[update_columns], rsuffix="_new"
)._internal.resolved_copy.spark_frame
data_fields = self._internal.data_fields.copy()
for column_labels in update_columns:
column_name = self._internal.spark_column_name_for(column_labels)
old_col = scol_for(update_sdf, column_name)
new_col = scol_for(
update_sdf, other._internal.spark_column_name_for(column_labels) + "_new"
)
if overwrite:
update_sdf = update_sdf.withColumn(
column_name, F.when(new_col.isNull(), old_col).otherwise(new_col)
)
else:
update_sdf = update_sdf.withColumn(
column_name, F.when(old_col.isNull(), new_col).otherwise(old_col)
)
data_fields[self._internal.column_labels.index(column_labels)] = None
sdf = update_sdf.select(
*[scol_for(update_sdf, col) for col in self._internal.spark_column_names],
*HIDDEN_COLUMNS,
)
internal = self._internal.with_new_sdf(sdf, data_fields=data_fields)
self._update_internal_frame(internal, check_same_anchor=False)
[docs] def cov(self, min_periods: Optional[int] = None, ddof: int = 1) -> "DataFrame":
"""
Compute pairwise covariance of columns, excluding NA/null values.
Compute the pairwise covariance among the series of a DataFrame.
The returned data frame is the `covariance matrix
<https://en.wikipedia.org/wiki/Covariance_matrix>`__ of the columns
of the DataFrame.
Both NA and null values are automatically excluded from the
calculation. (See the note below about bias from missing values.)
A threshold can be set for the minimum number of
observations for each value created. Comparisons with observations
below this threshold will be returned as ``NaN``.
This method is generally used for the analysis of time series data to
understand the relationship between different measures across time.
.. versionadded:: 3.3.0
Parameters
----------
min_periods : int, optional
Minimum number of observations required per pair of columns
to have a valid result.
ddof : int, default 1
Delta degrees of freedom. The divisor used in calculations
is ``N - ddof``, where ``N`` represents the number of elements.
.. versionadded:: 3.4.0
Returns
-------
DataFrame
The covariance matrix of the series of the DataFrame.
See Also
--------
Series.cov : Compute covariance with another Series.
Examples
--------
>>> df = ps.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
... columns=['dogs', 'cats'])
>>> df.cov()
dogs cats
dogs 0.666667 -1.000000
cats -1.000000 1.666667
>>> np.random.seed(42)
>>> df = ps.DataFrame(np.random.randn(1000, 5),
... columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()
a b c d e
a 0.998438 -0.020161 0.059277 -0.008943 0.014144
b -0.020161 1.059352 -0.008543 -0.024738 0.009826
c 0.059277 -0.008543 1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486 0.921297 -0.013692
e 0.014144 0.009826 -0.000271 -0.013692 0.977795
>>> df.cov(ddof=2)
a b c d e
a 0.999439 -0.020181 0.059336 -0.008952 0.014159
b -0.020181 1.060413 -0.008551 -0.024762 0.009836
c 0.059336 -0.008551 1.011683 -0.001487 -0.000271
d -0.008952 -0.024762 -0.001487 0.922220 -0.013705
e 0.014159 0.009836 -0.000271 -0.013705 0.978775
>>> df.cov(ddof=-1)
a b c d e
a 0.996444 -0.020121 0.059158 -0.008926 0.014116
b -0.020121 1.057235 -0.008526 -0.024688 0.009807
c 0.059158 -0.008526 1.008650 -0.001483 -0.000270
d -0.008926 -0.024688 -0.001483 0.919456 -0.013664
e 0.014116 0.009807 -0.000270 -0.013664 0.975842
**Minimum number of periods**
This method also supports an optional ``min_periods`` keyword
that specifies the required minimum number of non-NA observations for
each column pair to have a valid result:
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3),
... columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan
>>> df.loc[df.index[5:10], 'b'] = np.nan
>>> sdf = ps.from_pandas(df)
>>> sdf.cov(min_periods=12)
a b c
a 0.316741 NaN -0.150812
b NaN 1.248003 0.191417
c -0.150812 0.191417 0.895202
"""
if not isinstance(ddof, int):
raise TypeError("ddof must be integer")
min_periods = 1 if min_periods is None else min_periods
# Only compute covariance for Boolean and Numeric except Decimal
psdf = self[
[
col
for col in self.columns
if isinstance(self[col].spark.data_type, BooleanType)
or (
isinstance(self[col].spark.data_type, NumericType)
and not isinstance(self[col].spark.data_type, DecimalType)
)
]
]
num_cols = len(psdf.columns)
cov = np.zeros([num_cols, num_cols])
if num_cols == 0:
return DataFrame()
if len(psdf) < min_periods:
cov.fill(np.nan)
return DataFrame(cov, columns=psdf.columns, index=psdf.columns)
data_cols = psdf._internal.data_spark_column_names
cov_scols = []
count_not_null_scols = []
# Count number of null row between two columns
# Example:
# a b c
# 0 1 1 1
# 1 NaN 2 2
# 2 3 NaN 3
# 3 4 4 4
#
# a b c
# a count(a, a) count(a, b) count(a, c)
# b count(b, b) count(b, c)
# c count(c, c)
#
# count_not_null_scols =
# [F.count(a, a), F.count(a, b), F.count(a, c), F.count(b, b), F.count(b, c), F.count(c, c)]
for r in range(0, num_cols):
for c in range(r, num_cols):
count_not_null_scols.append(
F.count(
F.when(F.col(data_cols[r]).isNotNull() & F.col(data_cols[c]).isNotNull(), 1)
)
)
count_not_null = (
psdf._internal.spark_frame.replace(float("nan"), None)
.select(*count_not_null_scols)
.head(1)[0]
)
# Calculate covariance between two columns
# Example:
# with min_periods = 3
# a b c
# 0 1 1 1
# 1 NaN 2 2
# 2 3 NaN 3
# 3 4 4 4
#
# a b c
# a cov(a, a) None cov(a, c)
# b cov(b, b) cov(b, c)
# c cov(c, c)
#
# cov_scols = [F.cov(a, a), None, F.cov(a, c), F.cov(b, b), F.cov(b, c), F.cov(c, c)]
step = 0
for r in range(0, num_cols):
step += r
for c in range(r, num_cols):
cov_scols.append(
SF.covar(
F.col(data_cols[r]).cast("double"), F.col(data_cols[c]).cast("double"), ddof
)
if count_not_null[r * num_cols + c - step] >= min_periods
else F.lit(None)
)
pair_cov = psdf._internal.spark_frame.select(*cov_scols).head(1)[0]
# Convert from row to 2D array
# Example:
# pair_cov = [cov(a, a), None, cov(a, c), cov(b, b), cov(b, c), cov(c, c)]
#
# cov =
#
# a b c
# a cov(a, a) None cov(a, c)
# b cov(b, b) cov(b, c)
# c cov(c, c)
step = 0
for r in range(0, num_cols):
step += r
for c in range(r, num_cols):
cov[r][c] = pair_cov[r * num_cols + c - step]
# Copy values
# Example:
# cov =
# a b c
# a cov(a, a) None cov(a, c)
# b None cov(b, b) cov(b, c)
# c cov(a, c) cov(b, c) cov(c, c)
cov = cov + cov.T - np.diag(np.diag(cov))
return DataFrame(cov, columns=psdf.columns, index=psdf.columns)
[docs] def sample(
self,
n: Optional[int] = None,
frac: Optional[float] = None,
replace: bool = False,
random_state: Optional[int] = None,
ignore_index: bool = False,
) -> "DataFrame":
"""
Return a random sample of items from an axis of object.
Please call this function using named argument by specifying the ``frac`` argument.
You can use `random_state` for reproducibility. However, note that different from pandas,
specifying a seed in pandas-on-Spark/Spark does not guarantee the sampled rows will
be fixed. The result set depends on not only the seed, but also how the data is distributed
across machines and to some extent network randomness when shuffle operations are involved.
Even in the simplest case, the result set will depend on the system's CPU core count.
Parameters
----------
n : int, optional
Number of items to return. This is currently NOT supported. Use frac instead.
frac : float, optional
Fraction of axis items to return.
replace : bool, default False
Sample with or without replacement.
random_state : int, optional
Seed for the random number generator (if int).
ignore_index : bool, default False
If True, the resulting index will be labeled 0, 1, …, n - 1.
.. versionadded:: 3.4.0
Returns
-------
Series or DataFrame
A new object of same type as caller containing the sampled items.
Examples
--------
>>> df = ps.DataFrame({'num_legs': [2, 4, 8, 0],
... 'num_wings': [2, 0, 0, 0],
... 'num_specimen_seen': [10, 2, 1, 8]},
... index=['falcon', 'dog', 'spider', 'fish'],
... columns=['num_legs', 'num_wings', 'num_specimen_seen'])
>>> df # doctest: +SKIP
num_legs num_wings num_specimen_seen
falcon 2 2 10
dog 4 0 2
spider 8 0 1
fish 0 0 8
A random 25% sample of the ``DataFrame``.
Note that we use `random_state` to ensure the reproducibility of
the examples.
>>> df.sample(frac=0.25, random_state=1) # doctest: +SKIP
num_legs num_wings num_specimen_seen
falcon 2 2 10
fish 0 0 8
A random 50% sample of the ``DataFrame``, while ignoring the index.
>>> df.sample(frac=0.5, random_state=1, ignore_index=True) # doctest: +SKIP
num_legs num_wings num_specimen_seen
0 4 0 2
1 8 0 1
2 0 0 8
Extract 25% random elements from the ``Series`` ``df['num_legs']`` with replacement
so, the same items could appear more than once.
>>> df['num_legs'].sample(frac=0.4, replace=True, random_state=1) # doctest: +SKIP
falcon 2
spider 8
spider 8
Name: num_legs, dtype: int64
Specifying the exact number of items to return is not supported now.
>>> df.sample(n=5) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
NotImplementedError: Function sample currently does not support specifying ...
"""
# Note: we don't run any of the doctests because the result can change depending on the
# system's core count.
if n is not None:
raise NotImplementedError(
"Function sample currently does not support specifying "
"exact number of items to return. Use frac instead."
)
if frac is None:
raise ValueError("frac must be specified.")
sdf = self._internal.resolved_copy.spark_frame.sample(
withReplacement=replace, fraction=frac, seed=random_state
)
if ignore_index:
return DataFrame(sdf.drop(*self._internal.index_spark_column_names))
else:
return DataFrame(self._internal.with_new_sdf(sdf))
[docs] def astype(self, dtype: Union[str, Dtype, Dict[Name, Union[str, Dtype]]]) -> "DataFrame":
"""
Cast a pandas-on-Spark object to a specified dtype ``dtype``.
Parameters
----------
dtype : data type, or dict of column name -> data type
Use a numpy.dtype or Python type to cast entire pandas-on-Spark object to
the same type. Alternatively, use {col: dtype, ...}, where col is a
column label and dtype is a numpy.dtype or Python type to cast one
or more of the DataFrame's columns to column-specific types.
Returns
-------
casted : same type as caller
See Also
--------
to_datetime : Convert argument to datetime.
Examples
--------
>>> df = ps.DataFrame({'a': [1, 2, 3], 'b': [1, 2, 3]}, dtype='int64')
>>> df
a b
0 1 1
1 2 2
2 3 3
Convert to float type:
>>> df.astype('float')
a b
0 1.0 1.0
1 2.0 2.0
2 3.0 3.0
Convert to int64 type back:
>>> df.astype('int64')
a b
0 1 1
1 2 2
2 3 3
Convert column a to float type:
>>> df.astype({'a': float})
a b
0 1.0 1
1 2.0 2
2 3.0 3
"""
applied = []
if is_dict_like(dtype):
dtype_dict = cast(Dict[Name, Union[str, Dtype]], dtype)
for col_name in dtype_dict.keys():
if col_name not in self.columns:
raise KeyError(
"Only a column name can be used for the "
"key in a dtype mappings argument."
)
for col_name, col in self.items():
if col_name in dtype_dict:
applied.append(col.astype(dtype=dtype_dict[col_name]))
else:
applied.append(col)
else:
for col_name, col in self.items():
applied.append(col.astype(dtype=cast(Union[str, Dtype], dtype)))
return DataFrame(self._internal.with_new_columns(applied))
[docs] def add_prefix(self, prefix: str) -> "DataFrame":
"""
Prefix labels with string `prefix`.
For Series, the row labels are prefixed.
For DataFrame, the column labels are prefixed.
Parameters
----------
prefix : str
The string to add before each label.
Returns
-------
DataFrame
New DataFrame with updated labels.
See Also
--------
Series.add_prefix: Prefix row labels with string `prefix`.
Series.add_suffix: Suffix row labels with string `suffix`.
DataFrame.add_suffix: Suffix column labels with string `suffix`.
Examples
--------
>>> df = ps.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]}, columns=['A', 'B'])
>>> df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>> df.add_prefix('col_')
col_A col_B
0 1 3
1 2 4
2 3 5
3 4 6
"""
assert isinstance(prefix, str)
return self._apply_series_op(
lambda psser: psser.rename(tuple([prefix + i for i in psser._column_label]))
)
[docs] def add_suffix(self, suffix: str) -> "DataFrame":
"""
Suffix labels with string `suffix`.
For Series, the row labels are suffixed.
For DataFrame, the column labels are suffixed.
Parameters
----------
suffix : str
The string to add before each label.
Returns
-------
DataFrame
New DataFrame with updated labels.
See Also
--------
Series.add_prefix: Prefix row labels with string `prefix`.
Series.add_suffix: Suffix row labels with string `suffix`.
DataFrame.add_prefix: Prefix column labels with string `prefix`.
Examples
--------
>>> df = ps.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]}, columns=['A', 'B'])
>>> df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>> df.add_suffix('_col')
A_col B_col
0 1 3
1 2 4
2 3 5
3 4 6
"""
assert isinstance(suffix, str)
return self._apply_series_op(
lambda psser: psser.rename(tuple([i + suffix for i in psser._column_label]))
)
# TODO: include and exclude should be implemented.
[docs] def describe(self, percentiles: Optional[List[float]] = None) -> "DataFrame":
"""
Generate descriptive statistics that summarize the central tendency,
dispersion and shape of a dataset's distribution, excluding
``NaN`` values.
Analyzes both numeric and object series, as well
as ``DataFrame`` column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.
Parameters
----------
percentiles : list of ``float`` in range [0.0, 1.0], default [0.25, 0.5, 0.75]
A list of percentiles to be computed.
Returns
-------
DataFrame
Summary statistics of the Dataframe provided.
See Also
--------
DataFrame.count: Count number of non-NA/null observations.
DataFrame.max: Maximum of the values in the object.
DataFrame.min: Minimum of the values in the object.
DataFrame.mean: Mean of the values.
DataFrame.std: Standard deviation of the observations.
Notes
-----
For numeric data, the result's index will include ``count``,
``mean``, ``std``, ``min``, ``25%``, ``50%``, ``75%``, ``max``.
For object data (e.g. strings or timestamps), the result’s index will include
``count``, ``unique``, ``top``, and ``freq``.
The ``top`` is the most common value. The ``freq`` is the most common value’s frequency.
Timestamps also include the ``first`` and ``last`` items.
Examples
--------
Describing a numeric ``Series``.
>>> s = ps.Series([1, 2, 3])
>>> s.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.0
50% 2.0
75% 3.0
max 3.0
dtype: float64
Describing a ``DataFrame``. Only numeric fields are returned.
>>> df = ps.DataFrame({'numeric1': [1, 2, 3],
... 'numeric2': [4.0, 5.0, 6.0],
... 'object': ['a', 'b', 'c']
... },
... columns=['numeric1', 'numeric2', 'object'])
>>> df.describe()
numeric1 numeric2
count 3.0 3.0
mean 2.0 5.0
std 1.0 1.0
min 1.0 4.0
25% 1.0 4.0
50% 2.0 5.0
75% 3.0 6.0
max 3.0 6.0
For multi-index columns:
>>> df.columns = [('num', 'a'), ('num', 'b'), ('obj', 'c')]
>>> df.describe() # doctest: +NORMALIZE_WHITESPACE
num
a b
count 3.0 3.0
mean 2.0 5.0
std 1.0 1.0
min 1.0 4.0
25% 1.0 4.0
50% 2.0 5.0
75% 3.0 6.0
max 3.0 6.0
>>> df[('num', 'b')].describe()
count 3.0
mean 5.0
std 1.0
min 4.0
25% 4.0
50% 5.0
75% 6.0
max 6.0
Name: (num, b), dtype: float64
Describing a ``DataFrame`` and selecting custom percentiles.
>>> df = ps.DataFrame({'numeric1': [1, 2, 3],
... 'numeric2': [4.0, 5.0, 6.0]
... },
... columns=['numeric1', 'numeric2'])
>>> df.describe(percentiles = [0.85, 0.15])
numeric1 numeric2
count 3.0 3.0
mean 2.0 5.0
std 1.0 1.0
min 1.0 4.0
15% 1.0 4.0
50% 2.0 5.0
85% 3.0 6.0
max 3.0 6.0
Describing a column from a ``DataFrame`` by accessing it as
an attribute.
>>> df.numeric1.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.0
50% 2.0
75% 3.0
max 3.0
Name: numeric1, dtype: float64
Describing a column from a ``DataFrame`` by accessing it as
an attribute and selecting custom percentiles.
>>> df.numeric1.describe(percentiles = [0.85, 0.15])
count 3.0
mean 2.0
std 1.0
min 1.0
15% 1.0
50% 2.0
85% 3.0
max 3.0
Name: numeric1, dtype: float64
"""
psser_numeric: List[Series] = []
psser_string: List[Series] = []
psser_timestamp: List[Series] = []
spark_data_types: List[DataType] = []
column_labels: Optional[List[Label]] = []
column_names: List[str] = []
for label in self._internal.column_labels:
psser = self._psser_for(label)
spark_data_type = psser.spark.data_type
if isinstance(spark_data_type, NumericType):
psser_numeric.append(psser)
column_labels.append(label)
spark_data_types.append(spark_data_type)
elif isinstance(spark_data_type, (TimestampType, TimestampNTZType)):
psser_timestamp.append(psser)
column_labels.append(label)
spark_data_types.append(spark_data_type)
else:
psser_string.append(psser)
column_names.append(self._internal.spark_column_name_for(label))
if percentiles is not None:
if any((p < 0.0) or (p > 1.0) for p in percentiles):
raise ValueError("Percentiles should all be in the interval [0, 1]")
# appending 50% if not in percentiles already
percentiles = (percentiles + [0.5]) if 0.5 not in percentiles else percentiles
else:
percentiles = [0.25, 0.5, 0.75]
# Identify the cases
is_all_string_type = (
len(psser_numeric) == 0 and len(psser_timestamp) == 0 and len(psser_string) > 0
)
is_all_numeric_type = len(psser_numeric) > 0 and len(psser_timestamp) == 0
has_timestamp_type = len(psser_timestamp) > 0
has_numeric_type = len(psser_numeric) > 0
if is_all_string_type:
# Handling string type columns
# We will retrive the `count`, `unique`, `top` and `freq`.
internal = self._internal.resolved_copy
exprs_string = [
internal.spark_column_for(psser._column_label) for psser in psser_string
]
sdf = internal.spark_frame.select(*exprs_string)
# Get `count` & `unique` for each columns
counts, uniques = map(lambda x: x[1:], sdf.summary("count", "count_distinct").take(2))
# Handling Empty DataFrame
if len(counts) == 0 or counts[0] == "0":
data = dict()
for psser in psser_string:
data[psser.name] = [0, 0, np.nan, np.nan]
return DataFrame(data, index=["count", "unique", "top", "freq"])
# Get `top` & `freq` for each columns
tops = []
freqs = []
# TODO(SPARK-37711): We should do it in single pass since invoking Spark job
# for every columns is too expensive.
for column in exprs_string:
top, freq = sdf.groupby(column).count().sort("count", ascending=False).first()
tops.append(str(top))
freqs.append(str(freq))
stats = [counts, uniques, tops, freqs]
stats_names = ["count", "unique", "top", "freq"]
result: DataFrame = DataFrame(
data=stats,
index=stats_names,
columns=column_names,
)
elif is_all_numeric_type:
# Handling numeric columns
exprs_numeric = [
psser._dtype_op.nan_to_null(psser).spark.column for psser in psser_numeric
]
formatted_perc = ["{:.0%}".format(p) for p in sorted(percentiles)]
stats = ["count", "mean", "stddev", "min", *formatted_perc, "max"]
# In this case, we can simply use `summary` to calculate the stats.
sdf = self._internal.spark_frame.select(*exprs_numeric).summary(*stats)
sdf = sdf.replace("stddev", "std", subset=["summary"])
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, "summary")],
column_labels=column_labels,
data_spark_columns=[
scol_for(sdf, self._internal.spark_column_name_for(label))
for label in column_labels
],
)
result = DataFrame(internal).astype("float64")
elif has_timestamp_type:
internal = self._internal.resolved_copy
column_names = [
internal.spark_column_name_for(column_label) for column_label in column_labels
]
column_length = len(column_labels)
# Apply stat functions for each column.
count_exprs = map(F.count, column_names)
min_exprs = map(F.min, column_names)
# Here we try to flat the multiple maps into single list that contains each calculated
# percentile using `chain`.
# e.g. flat the `[<map object at 0x7fc1907dc280>, <map object at 0x7fc1907dcc70>]`
# to `[Column<'percentile_approx(A, 0.2, 10000)'>,
# Column<'percentile_approx(B, 0.2, 10000)'>,
# Column<'percentile_approx(A, 0.5, 10000)'>,
# Column<'percentile_approx(B, 0.5, 10000)'>]`
perc_exprs = chain(
*[
map(F.percentile_approx, column_names, [percentile] * column_length)
for percentile in percentiles
]
)
max_exprs = map(F.max, column_names)
mean_exprs = []
for column_name, spark_data_type in zip(column_names, spark_data_types):
mean_exprs.append(F.mean(column_name).astype(spark_data_type))
exprs = [*count_exprs, *mean_exprs, *min_exprs, *perc_exprs, *max_exprs]
formatted_perc = ["{:.0%}".format(p) for p in sorted(percentiles)]
stats_names = ["count", "mean", "min", *formatted_perc, "max"]
# If not all columns are timestamp type,
# we also need to calculate the `std` for numeric columns
if has_numeric_type:
std_exprs = []
for label, spark_data_type in zip(column_labels, spark_data_types):
column_name = label[0]
if isinstance(spark_data_type, (TimestampType, TimestampNTZType)):
std_exprs.append(F.lit(None).alias("stddev_samp({})".format(column_name)))
else:
std_exprs.append(F.stddev(column_name))
exprs.extend(std_exprs)
stats_names.append("std")
# Select stats for all columns at once.
sdf = internal.spark_frame.select(exprs)
stat_values = sdf.first()
num_stats = int(len(exprs) / column_length)
# `column_name_stats_kv` is key-value store that has column name as key, and
# the stats as values e.g. {"A": [{count_value}, {min_value}, ...],
# "B": [{count_value}, {min_value} ...]}
column_name_stats_kv: Dict[str, List[str]] = defaultdict(list)
for i, column_name in enumerate(column_names):
for first_stat_idx in range(num_stats):
column_name_stats_kv[column_name].append(
stat_values[(first_stat_idx * column_length) + i]
)
# For timestamp type columns, we should cast the column type to string.
for key, spark_data_type in zip(column_name_stats_kv, spark_data_types):
if isinstance(spark_data_type, (TimestampType, TimestampNTZType)):
column_name_stats_kv[key] = [str(value) for value in column_name_stats_kv[key]]
result: DataFrame = DataFrame( # type: ignore[no-redef]
data=column_name_stats_kv,
index=stats_names,
columns=column_names,
)
else:
# Empty DataFrame without column
raise ValueError("Cannot describe a DataFrame without columns")
return result
[docs] def drop_duplicates(
self,
subset: Optional[Union[Name, List[Name]]] = None,
keep: Union[bool, str] = "first",
inplace: bool = False,
ignore_index: bool = False,
) -> Optional["DataFrame"]:
"""
Return DataFrame with duplicate rows removed, optionally only
considering certain columns.
Parameters
----------
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by
default use all the columns.
keep : {'first', 'last', False}, default 'first'
Determines which duplicates (if any) to keep.
- ``first`` : Drop duplicates except for the first occurrence.
- ``last`` : Drop duplicates except for the last occurrence.
- False : Drop all duplicates.
inplace : boolean, default False
Whether to drop duplicates in place or to return a copy.
ignore_index : boolean, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
Returns
-------
DataFrame
DataFrame with duplicates removed or None if ``inplace=True``.
>>> df = ps.DataFrame(
... {'a': [1, 2, 2, 2, 3], 'b': ['a', 'a', 'a', 'c', 'd']}, columns = ['a', 'b'])
>>> df
a b
0 1 a
1 2 a
2 2 a
3 2 c
4 3 d
>>> df.drop_duplicates().sort_index()
a b
0 1 a
1 2 a
3 2 c
4 3 d
>>> df.drop_duplicates(ignore_index=True).sort_index()
a b
0 1 a
1 2 a
2 2 c
3 3 d
>>> df.drop_duplicates('a').sort_index()
a b
0 1 a
1 2 a
4 3 d
>>> df.drop_duplicates(['a', 'b']).sort_index()
a b
0 1 a
1 2 a
3 2 c
4 3 d
>>> df.drop_duplicates(keep='last').sort_index()
a b
0 1 a
2 2 a
3 2 c
4 3 d
>>> df.drop_duplicates(keep=False).sort_index()
a b
0 1 a
3 2 c
4 3 d
"""
inplace = validate_bool_kwarg(inplace, "inplace")
sdf, column = self._mark_duplicates(subset, keep)
sdf = sdf.where(~scol_for(sdf, column)).drop(column)
internal = self._internal.with_new_sdf(sdf)
psdf: DataFrame = DataFrame(internal)
if inplace:
if ignore_index:
psdf.reset_index(drop=True, inplace=inplace)
self._update_internal_frame(psdf._internal)
return None
else:
return psdf.reset_index(drop=True) if ignore_index else psdf
[docs] def reindex(
self,
labels: Optional[Sequence[Any]] = None,
index: Optional[Union["Index", Sequence[Any]]] = None,
columns: Optional[Union[pd.Index, Sequence[Any]]] = None,
axis: Optional[Axis] = None,
copy: Optional[bool] = True,
fill_value: Optional[Any] = None,
) -> "DataFrame":
"""
Conform DataFrame to new index with optional filling logic, placing
NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
``copy=False``.
Parameters
----------
labels: array-like, optional
New labels / index to conform the axis specified by ‘axis’ to.
index, columns: array-like, optional
New labels / index to conform to, should be specified using keywords.
Preferably an Index object to avoid duplicating data
axis: int or str, optional
Axis to target. Can be either the axis name (‘index’, ‘columns’) or
number (0, 1).
copy : bool, default True
Return a new object, even if the passed indexes are the same.
fill_value : scalar, default np.NaN
Value to use for missing values. Defaults to NaN, but can be any
"compatible" value.
Returns
-------
DataFrame with changed index.
See Also
--------
DataFrame.set_index : Set row labels.
DataFrame.reset_index : Remove row labels or move them to new columns.
Examples
--------
``DataFrame.reindex`` supports two calling conventions
* ``(index=index_labels, columns=column_labels, ...)``
* ``(labels, axis={'index', 'columns'}, ...)``
We *highly* recommend using keyword arguments to clarify your
intent.
Create a dataframe with some fictional data.
>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> df = ps.DataFrame({
... 'http_status': [200, 200, 404, 404, 301],
... 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
... index=index,
... columns=['http_status', 'response_time'])
>>> df
http_status response_time
Firefox 200 0.04
Chrome 200 0.02
Safari 404 0.07
IE10 404 0.08
Konqueror 301 1.00
Create a new index and reindex the dataframe. By default
values in the new index that do not have corresponding
records in the dataframe are assigned ``NaN``.
>>> new_index= ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
... 'Chrome']
>>> df.reindex(new_index).sort_index()
http_status response_time
Chrome 200.0 0.02
Comodo Dragon NaN NaN
IE10 404.0 0.08
Iceweasel NaN NaN
Safari 404.0 0.07
We can fill in the missing values by passing a value to
the keyword ``fill_value``.
>>> df.reindex(new_index, fill_value=0, copy=False).sort_index()
http_status response_time
Chrome 200 0.02
Comodo Dragon 0 0.00
IE10 404 0.08
Iceweasel 0 0.00
Safari 404 0.07
We can also reindex the columns.
>>> df.reindex(columns=['http_status', 'user_agent']).sort_index()
http_status user_agent
Chrome 200 NaN
Firefox 200 NaN
IE10 404 NaN
Konqueror 301 NaN
Safari 404 NaN
Or we can use "axis-style" keyword arguments
>>> df.reindex(['http_status', 'user_agent'], axis="columns").sort_index()
http_status user_agent
Chrome 200 NaN
Firefox 200 NaN
IE10 404 NaN
Konqueror 301 NaN
Safari 404 NaN
To further illustrate the filling functionality in
``reindex``, we will create a dataframe with a
monotonically increasing index (for example, a sequence
of dates).
>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> df2 = ps.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
... index=date_index)
>>> df2.sort_index()
prices
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
Suppose we decide to expand the dataframe to cover a wider
date range.
>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2).sort_index()
prices
2009-12-29 NaN
2009-12-30 NaN
2009-12-31 NaN
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
2010-01-07 NaN
"""
if axis is not None and (index is not None or columns is not None):
raise TypeError("Cannot specify both 'axis' and any of 'index' or 'columns'.")
if labels is not None:
axis = validate_axis(axis)
if axis == 0:
index = labels
elif axis == 1:
columns = labels
if index is not None and not is_list_like(index):
raise TypeError(
"Index must be called with a collection of some kind, "
"%s was passed" % type(index)
)
if columns is not None and not is_list_like(columns):
raise TypeError(
"Columns must be called with a collection of some kind, "
"%s was passed" % type(columns)
)
df = self
if index is not None:
df = df._reindex_index(index, fill_value)
if columns is not None:
df = df._reindex_columns(columns, fill_value)
# Copy
if copy and df is self:
return df.copy()
else:
return df
def _reindex_index(
self, index: Optional[Union["Index", Sequence[Any]]], fill_value: Optional[Any]
) -> "DataFrame":
# When axis is index, we can mimic pandas by a right outer join.
nlevels = self._internal.index_level
assert nlevels <= 1 or (
isinstance(index, ps.MultiIndex) and nlevels == index.nlevels
), "MultiIndex DataFrame can only be reindexed with a similar pandas-on-Spark MultiIndex."
index_columns = self._internal.index_spark_column_names
frame = self._internal.resolved_copy.spark_frame.drop(NATURAL_ORDER_COLUMN_NAME)
if isinstance(index, ps.Index):
if nlevels != index.nlevels:
return DataFrame(index._internal.with_new_columns([])).reindex(
columns=self.columns, fill_value=fill_value
)
index_names = index._internal.index_names
scols = index._internal.index_spark_columns
labels = index._internal.spark_frame.select(
[scol.alias(index_column) for scol, index_column in zip(scols, index_columns)]
)
else:
index = ps.Index(list(index))
labels = index._internal.spark_frame.select(index.spark.column.alias(index_columns[0]))
index_names = self._internal.index_names
if fill_value is not None:
frame_index_columns = [
verify_temp_column_name(frame, "__frame_index_column_{}__".format(i))
for i in range(nlevels)
]
index_scols = [
scol_for(frame, index_col).alias(frame_index_col)
for index_col, frame_index_col in zip(index_columns, frame_index_columns)
]
scols = self._internal.resolved_copy.data_spark_columns
frame = frame.select(index_scols + scols)
temp_fill_value = verify_temp_column_name(frame, "__fill_value__")
labels = labels.withColumn(temp_fill_value, F.lit(fill_value))
frame_index_scols = [scol_for(frame, col) for col in frame_index_columns]
labels_index_scols = [scol_for(labels, col) for col in index_columns]
joined_df = frame.join(
labels,
on=[fcol == lcol for fcol, lcol in zip(frame_index_scols, labels_index_scols)],
how="right",
)
joined_df = joined_df.select(
*labels_index_scols,
*[
F.when(
reduce(
lambda c1, c2: c1 & c2,
[
fcol.isNull() & lcol.isNotNull()
for fcol, lcol in zip(frame_index_scols, labels_index_scols)
],
),
scol_for(joined_df, temp_fill_value),
)
.otherwise(scol_for(joined_df, col))
.alias(col)
for col in self._internal.data_spark_column_names
],
)
data_fields = None
else:
joined_df = frame.join(labels, on=index_columns, how="right")
data_fields = [field.copy(nullable=True) for field in self._internal.data_fields]
sdf = joined_df.drop(NATURAL_ORDER_COLUMN_NAME)
internal = self._internal.copy(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col) for col in self._internal.index_spark_column_names
],
index_names=index_names,
index_fields=[
field.copy(name=name)
for field, name in zip(
index._internal.index_fields, self._internal.index_spark_column_names
)
],
data_spark_columns=[
scol_for(sdf, col) for col in self._internal.data_spark_column_names
],
data_fields=data_fields,
)
return DataFrame(internal)
def _reindex_columns(
self, columns: Optional[Union[pd.Index, Sequence[Any]]], fill_value: Optional[Any]
) -> "DataFrame":
level = self._internal.column_labels_level
if level > 1:
label_columns = list(columns)
for col in label_columns:
if not isinstance(col, tuple):
raise TypeError("Expected tuple, got {}".format(type(col).__name__))
else:
label_columns = [(col,) for col in columns]
for col in label_columns:
if len(col) != level:
raise ValueError(
"shape (1,{}) doesn't match the shape (1,{})".format(len(col), level)
)
fill_value = np.nan if fill_value is None else fill_value
scols_or_pssers: List[Union[PySparkColumn, "Series"]] = []
labels = []
for label in label_columns:
if label in self._internal.column_labels:
scols_or_pssers.append(self._psser_for(label))
else:
scols_or_pssers.append(F.lit(fill_value).alias(name_like_string(label)))
labels.append(label)
if isinstance(columns, pd.Index):
column_label_names = [
name if is_name_like_tuple(name) else (name,) for name in columns.names
]
internal = self._internal.with_new_columns(
scols_or_pssers, column_labels=labels, column_label_names=column_label_names
)
else:
internal = self._internal.with_new_columns(scols_or_pssers, column_labels=labels)
return DataFrame(internal)
[docs] def reindex_like(self, other: "DataFrame", copy: bool = True) -> "DataFrame":
"""
Return a DataFrame with matching indices as other object.
Conform the object to the same index on all axes. Places NA/NaN in locations
having no value in the previous index. A new object is produced unless the
new index is equivalent to the current one and copy=False.
Parameters
----------
other : DataFrame
Its row and column indices are used to define the new indices
of this object.
copy : bool, default True
Return a new object, even if the passed indexes are the same.
Returns
-------
DataFrame
DataFrame with changed indices on each axis.
See Also
--------
DataFrame.set_index : Set row labels.
DataFrame.reset_index : Remove row labels or move them to new columns.
DataFrame.reindex : Change to new indices or expand indices.
Notes
-----
Same as calling
``.reindex(index=other.index, columns=other.columns,...)``.
Examples
--------
>>> df1 = ps.DataFrame([[24.3, 75.7, 'high'],
... [31, 87.8, 'high'],
... [22, 71.6, 'medium'],
... [35, 95, 'medium']],
... columns=['temp_celsius', 'temp_fahrenheit',
... 'windspeed'],
... index=pd.date_range(start='2014-02-12',
... end='2014-02-15', freq='D'))
>>> df1
temp_celsius temp_fahrenheit windspeed
2014-02-12 24.3 75.7 high
2014-02-13 31.0 87.8 high
2014-02-14 22.0 71.6 medium
2014-02-15 35.0 95.0 medium
>>> df2 = ps.DataFrame([[28, 'low'],
... [30, 'low'],
... [35.1, 'medium']],
... columns=['temp_celsius', 'windspeed'],
... index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
... '2014-02-15']))
>>> df2
temp_celsius windspeed
2014-02-12 28.0 low
2014-02-13 30.0 low
2014-02-15 35.1 medium
>>> df2.reindex_like(df1).sort_index() # doctest: +NORMALIZE_WHITESPACE
temp_celsius temp_fahrenheit windspeed
2014-02-12 28.0 NaN low
2014-02-13 30.0 NaN low
2014-02-14 NaN NaN None
2014-02-15 35.1 NaN medium
"""
if isinstance(other, DataFrame):
return self.reindex(index=other.index, columns=other.columns, copy=copy)
else:
raise TypeError("other must be a pandas-on-Spark DataFrame")
[docs] def melt(
self,
id_vars: Optional[Union[Name, List[Name]]] = None,
value_vars: Optional[Union[Name, List[Name]]] = None,
var_name: Optional[Union[str, List[str]]] = None,
value_name: str = "value",
) -> "DataFrame":
"""
Unpivot a DataFrame from wide format to long format, optionally
leaving identifier variables set.
This function is useful to massage a DataFrame into a format where one
or more columns are identifier variables (`id_vars`), while all other
columns, considered measured variables (`value_vars`), are "unpivoted" to
the row axis, leaving just two non-identifier columns, 'variable' and
'value'.
Parameters
----------
frame : DataFrame
id_vars : tuple, list, or ndarray, optional
Column(s) to use as identifier variables.
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that
are not set as `id_vars`.
var_name : scalar, default 'variable'
Name to use for the 'variable' column. If None it uses `frame.columns.name` or
‘variable’.
value_name : scalar, default 'value'
Name to use for the 'value' column.
Returns
-------
DataFrame
Unpivoted DataFrame.
Examples
--------
>>> df = ps.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
... 'B': {0: 1, 1: 3, 2: 5},
... 'C': {0: 2, 1: 4, 2: 6}},
... columns=['A', 'B', 'C'])
>>> df
A B C
0 a 1 2
1 b 3 4
2 c 5 6
>>> ps.melt(df)
variable value
0 A a
1 B 1
2 C 2
3 A b
4 B 3
5 C 4
6 A c
7 B 5
8 C 6
>>> df.melt(id_vars='A')
A variable value
0 a B 1
1 a C 2
2 b B 3
3 b C 4
4 c B 5
5 c C 6
>>> df.melt(value_vars='A')
variable value
0 A a
1 A b
2 A c
>>> ps.melt(df, id_vars=['A', 'B'])
A B variable value
0 a 1 C 2
1 b 3 C 4
2 c 5 C 6
>>> df.melt(id_vars=['A'], value_vars=['C'])
A variable value
0 a C 2
1 b C 4
2 c C 6
The names of 'variable' and 'value' columns can be customized:
>>> ps.melt(df, id_vars=['A'], value_vars=['B'],
... var_name='myVarname', value_name='myValname')
A myVarname myValname
0 a B 1
1 b B 3
2 c B 5
"""
column_labels = self._internal.column_labels
if id_vars is None:
id_vars = []
else:
if isinstance(id_vars, tuple):
if self._internal.column_labels_level == 1:
id_vars = [idv if is_name_like_tuple(idv) else (idv,) for idv in id_vars]
else:
raise ValueError(
"id_vars must be a list of tuples" " when columns are a MultiIndex"
)
elif is_name_like_value(id_vars):
id_vars = [(id_vars,)]
else:
id_vars = [idv if is_name_like_tuple(idv) else (idv,) for idv in id_vars]
non_existence_col = [idv for idv in id_vars if idv not in column_labels]
if len(non_existence_col) != 0:
raveled_column_labels: np.ndarray[Any, np.dtype[Any]] = np.ravel(column_labels)
missing = [
nec for nec in np.ravel(non_existence_col) if nec not in raveled_column_labels
]
if len(missing) != 0:
raise KeyError(
"The following 'id_vars' are not present"
" in the DataFrame: {}".format(missing)
)
else:
raise KeyError(
"None of {} are in the {}".format(non_existence_col, column_labels)
)
if value_vars is None:
value_vars = []
else:
if isinstance(value_vars, tuple):
if self._internal.column_labels_level == 1:
value_vars = [
valv if is_name_like_tuple(valv) else (valv,) for valv in value_vars
]
else:
raise ValueError(
"value_vars must be a list of tuples" " when columns are a MultiIndex"
)
elif is_name_like_value(value_vars):
value_vars = [(value_vars,)]
else:
value_vars = [valv if is_name_like_tuple(valv) else (valv,) for valv in value_vars]
non_existence_col = [valv for valv in value_vars if valv not in column_labels]
if len(non_existence_col) != 0:
raveled_column_labels = np.ravel(column_labels)
missing = [
nec for nec in np.ravel(non_existence_col) if nec not in raveled_column_labels
]
if len(missing) != 0:
raise KeyError(
"The following 'value_vars' are not present"
" in the DataFrame: {}".format(missing)
)
else:
raise KeyError(
"None of {} are in the {}".format(non_existence_col, column_labels)
)
if len(value_vars) == 0:
value_vars = column_labels
column_labels = [label for label in column_labels if label not in id_vars]
sdf = self._internal.spark_frame
if var_name is None:
if (
self._internal.column_labels_level == 1
and self._internal.column_label_names[0] is None
):
var_name = ["variable"]
else:
var_name = [
name_like_string(name) if name is not None else "variable_{}".format(i)
for i, name in enumerate(self._internal.column_label_names)
]
elif isinstance(var_name, str):
var_name = [var_name]
pairs = F.explode(
F.array(
*[
F.struct(
*[F.lit(c).alias(name) for c, name in zip(label, var_name)],
*[self._internal.spark_column_for(label).alias(value_name)],
)
for label in column_labels
if label in value_vars
]
)
)
columns = (
[
self._internal.spark_column_for(label).alias(name_like_string(label))
for label in id_vars
]
+ [F.col("pairs.`%s`" % name) for name in var_name]
+ [F.col("pairs.`%s`" % value_name)]
)
exploded_df = sdf.withColumn("pairs", pairs).select(columns)
return DataFrame(
InternalFrame(
spark_frame=exploded_df,
index_spark_columns=None,
column_labels=(
[label if len(label) == 1 else (name_like_string(label),) for label in id_vars]
+ [(name,) for name in var_name]
+ [(value_name,)]
),
)
)
[docs] def stack(self) -> DataFrameOrSeries:
"""
Stack the prescribed level(s) from columns to index.
Return a reshaped DataFrame or Series having a multi-level
index with one or more new inner-most levels compared to the current
DataFrame. The new inner-most levels are created by pivoting the
columns of the current dataframe:
- if the columns have a single level, the output is a Series
- if the columns have multiple levels, the new index
level(s) is (are) taken from the prescribed level(s) and
the output is a DataFrame.
The new index levels are sorted.
Returns
-------
DataFrame or Series
Stacked dataframe or series.
See Also
--------
DataFrame.unstack : Unstack prescribed level(s) from index axis
onto column axis.
DataFrame.pivot : Reshape dataframe from long format to wide
format.
DataFrame.pivot_table : Create a spreadsheet-style pivot table
as a DataFrame.
Notes
-----
The function is named by analogy with a collection of books
being reorganized from being side by side on a horizontal
position (the columns of the dataframe) to being stacked
vertically on top of each other (in the index of the
dataframe).
Examples
--------
**Single level columns**
>>> df_single_level_cols = ps.DataFrame([[0, 1], [2, 3]],
... index=['cat', 'dog'],
... columns=['weight', 'height'])
Stacking a dataframe with a single level column axis returns a Series:
>>> df_single_level_cols
weight height
cat 0 1
dog 2 3
>>> df_single_level_cols.stack().sort_index()
cat height 1
weight 0
dog height 3
weight 2
dtype: int64
**Multi level columns: simple case**
>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
... ('weight', 'pounds')])
>>> df_multi_level_cols1 = ps.DataFrame([[1, 2], [2, 4]],
... index=['cat', 'dog'],
... columns=multicol1)
Stacking a dataframe with a multi-level column axis:
>>> df_multi_level_cols1 # doctest: +NORMALIZE_WHITESPACE
weight
kg pounds
cat 1 2
dog 2 4
>>> df_multi_level_cols1.stack().sort_index()
weight
cat kg 1
pounds 2
dog kg 2
pounds 4
**Missing values**
>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
... ('height', 'm')])
>>> df_multi_level_cols2 = ps.DataFrame([[1.0, 2.0], [3.0, 4.0]],
... index=['cat', 'dog'],
... columns=multicol2)
It is common to have missing values when stacking a dataframe
with multi-level columns, as the stacked dataframe typically
has more values than the original dataframe. Missing values
are filled with NaNs:
>>> df_multi_level_cols2
weight height
kg m
cat 1.0 2.0
dog 3.0 4.0
>>> df_multi_level_cols2.stack().sort_index() # doctest: +SKIP
height weight
cat kg NaN 1.0
m 2.0 NaN
dog kg NaN 3.0
m 4.0 NaN
"""
from pyspark.pandas.series import first_series
if len(self._internal.column_labels) == 0:
return DataFrame(
self._internal.copy(
column_label_names=self._internal.column_label_names[:-1]
).with_filter(F.lit(False))
)
column_labels: Dict[Label, Dict[Any, PySparkColumn]] = defaultdict(dict)
index_values = set()
should_returns_series = False
for label in self._internal.column_labels:
new_label = label[:-1]
if len(new_label) == 0:
new_label = None
should_returns_series = True
value = label[-1]
scol = self._internal.spark_column_for(label)
column_labels[new_label][value] = scol
index_values.add(value)
column_labels = dict(sorted(column_labels.items(), key=lambda x: x[0]))
index_name = self._internal.column_label_names[-1]
column_label_names = self._internal.column_label_names[:-1]
if len(column_label_names) == 0:
column_label_names = [None]
index_column = SPARK_INDEX_NAME_FORMAT(self._internal.index_level)
data_columns = [name_like_string(label) for label in column_labels]
structs = [
F.struct(
*[F.lit(value).alias(index_column)],
*[
(
column_labels[label][value]
if value in column_labels[label]
else F.lit(None)
).alias(name)
for label, name in zip(column_labels, data_columns)
],
).alias(value)
for value in index_values
]
pairs = F.explode(F.array(*structs))
sdf = self._internal.spark_frame.withColumn("pairs", pairs)
sdf = sdf.select(
self._internal.index_spark_columns
+ [sdf["pairs"][index_column].alias(index_column)]
+ [sdf["pairs"][name].alias(name) for name in data_columns]
)
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col)
for col in (self._internal.index_spark_column_names + [index_column])
],
index_names=self._internal.index_names + [index_name],
index_fields=self._internal.index_fields + [None],
column_labels=list(column_labels),
data_spark_columns=[scol_for(sdf, col) for col in data_columns],
column_label_names=column_label_names,
)
psdf: DataFrame = DataFrame(internal)
if should_returns_series:
return first_series(psdf)
else:
return psdf
[docs] def unstack(self) -> DataFrameOrSeries:
"""
Pivot the (necessarily hierarchical) index labels.
Returns a DataFrame having a new level of column labels whose inner-most level
consists of the pivoted index labels.
If the index is not a MultiIndex, the output will be a Series.
.. note:: If the index is a MultiIndex, the output DataFrame could be very wide, and
it could cause a serious performance degradation since Spark partitions its row based.
Returns
-------
Series or DataFrame
See Also
--------
DataFrame.pivot : Pivot a table based on column values.
DataFrame.stack : Pivot a level of the column labels (inverse operation from unstack).
Examples
--------
>>> df = ps.DataFrame({"A": {"0": "a", "1": "b", "2": "c"},
... "B": {"0": "1", "1": "3", "2": "5"},
... "C": {"0": "2", "1": "4", "2": "6"}},
... columns=["A", "B", "C"])
>>> df
A B C
0 a 1 2
1 b 3 4
2 c 5 6
>>> df.unstack().sort_index()
A 0 a
1 b
2 c
B 0 1
1 3
2 5
C 0 2
1 4
2 6
dtype: object
>>> df.columns = pd.MultiIndex.from_tuples([('X', 'A'), ('X', 'B'), ('Y', 'C')])
>>> df.unstack().sort_index()
X A 0 a
1 b
2 c
B 0 1
1 3
2 5
Y C 0 2
1 4
2 6
dtype: object
For MultiIndex case:
>>> df = ps.DataFrame({"A": ["a", "b", "c"],
... "B": [1, 3, 5],
... "C": [2, 4, 6]},
... columns=["A", "B", "C"])
>>> df = df.set_index('A', append=True)
>>> df # doctest: +NORMALIZE_WHITESPACE
B C
A
0 a 1 2
1 b 3 4
2 c 5 6
>>> df.unstack().sort_index() # doctest: +NORMALIZE_WHITESPACE
B C
A a b c a b c
0 1.0 NaN NaN 2.0 NaN NaN
1 NaN 3.0 NaN NaN 4.0 NaN
2 NaN NaN 5.0 NaN NaN 6.0
"""
from pyspark.pandas.series import first_series
if self._internal.index_level > 1:
# The index after `reset_index()` will never be used, so use "distributed" index
# as a dummy to avoid overhead.
with option_context("compute.default_index_type", "distributed"):
df = self.reset_index()
index = df._internal.column_labels[: self._internal.index_level - 1]
columns = df.columns[self._internal.index_level - 1]
df = df.pivot_table(
index=index, columns=columns, values=self._internal.column_labels, aggfunc="first"
)
internal = df._internal.copy(
index_names=self._internal.index_names[:-1],
index_fields=df._internal.index_fields[: self._internal.index_level - 1],
column_label_names=(
df._internal.column_label_names[:-1]
+ [
None
if self._internal.index_names[-1] is None
else df._internal.column_label_names[-1]
]
),
)
return DataFrame(internal)
# TODO: Codes here are similar with melt. Should we deduplicate?
column_labels = self._internal.column_labels
ser_name = SPARK_DEFAULT_SERIES_NAME
sdf = self._internal.spark_frame
new_index_columns = [
SPARK_INDEX_NAME_FORMAT(i) for i in range(self._internal.column_labels_level)
]
new_index_map = list(zip_longest(new_index_columns, self._internal.column_label_names, []))
pairs = F.explode(
F.array(
*[
F.struct(
*[F.lit(c).alias(name) for c, name in zip(idx, new_index_columns)],
*[self._internal.spark_column_for(idx).alias(ser_name)],
)
for idx in column_labels
]
)
)
columns = [
F.col("pairs.%s" % name)
for name in new_index_columns[: self._internal.column_labels_level]
] + [F.col("pairs.%s" % ser_name)]
new_index_len = len(new_index_columns)
existing_index_columns = []
for i, (index_name, index_field) in enumerate(
zip(self._internal.index_names, self._internal.index_fields)
):
name = SPARK_INDEX_NAME_FORMAT(i + new_index_len)
new_index_map.append((name, index_name, index_field.copy(name=name)))
existing_index_columns.append(self._internal.index_spark_columns[i].alias(name))
exploded_df = sdf.withColumn("pairs", pairs).select(existing_index_columns + columns)
index_spark_column_names, index_names, index_fields = zip(*new_index_map)
return first_series(
DataFrame(
InternalFrame(
exploded_df,
index_spark_columns=[
scol_for(exploded_df, col) for col in index_spark_column_names
],
index_names=list(index_names),
index_fields=list(index_fields),
column_labels=[None],
)
)
)
# TODO: axis, level and **kwargs should be implemented.
[docs] def all(
self, axis: Axis = 0, bool_only: Optional[bool] = None, skipna: bool = True
) -> "Series":
"""
Return whether all elements are True.
Returns True unless there is at least one element within a series that is
False or equivalent (e.g. zero or empty)
Parameters
----------
axis : {0 or 'index'}, default 0
Indicate which axis or axes should be reduced.
* 0 / 'index' : reduce the index, return a Series whose index is the
original column labels.
bool_only : bool, default None
Include only boolean columns. If None, will attempt to use everything,
then use only boolean data.
skipna : boolean, default True
Exclude NA values, such as None or numpy.NaN.
If an entire row/column is NA values and `skipna` is True,
then the result will be True, as for an empty row/column.
If `skipna` is False, numpy.NaNs are treated as True because these are
not equal to zero, Nones are treated as False.
Returns
-------
Series
Examples
--------
Create a dataframe from a dictionary.
>>> df = ps.DataFrame({
... 'col1': [True, True, True],
... 'col2': [True, False, False],
... 'col3': [0, 0, 0],
... 'col4': [1, 2, 3],
... 'col5': [True, True, None],
... 'col6': [True, False, None]},
... columns=['col1', 'col2', 'col3', 'col4', 'col5', 'col6'])
Default behavior checks if column-wise values all return True.
>>> df.all()
col1 True
col2 False
col3 False
col4 True
col5 True
col6 False
dtype: bool
Include NA values when set `skipna=False`.
>>> df[['col5', 'col6']].all(skipna=False)
col5 False
col6 False
dtype: bool
Include only boolean columns when set `bool_only=True`.
>>> df.all(bool_only=True)
col1 True
col2 False
dtype: bool
"""
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError('axis should be either 0 or "index" currently.')
column_labels = self._internal.column_labels
if bool_only:
column_labels = self._bool_column_labels(column_labels)
if len(column_labels) == 0:
return ps.Series([], dtype=bool)
applied: List[PySparkColumn] = []
for label in column_labels:
scol = self._internal.spark_column_for(label)
if isinstance(self._internal.spark_type_for(label), NumericType) or skipna:
# np.nan takes no effect to the result; None takes no effect if `skipna`
all_col = F.min(F.coalesce(scol.cast("boolean"), F.lit(True)))
else:
# Take None as False when not `skipna`
all_col = F.min(F.when(scol.isNull(), F.lit(False)).otherwise(scol.cast("boolean")))
applied.append(F.when(all_col.isNull(), True).otherwise(all_col))
return self._result_aggregated(column_labels, applied)
# TODO: axis, skipna, level and **kwargs should be implemented.
[docs] def any(self, axis: Axis = 0, bool_only: Optional[bool] = None) -> "Series":
"""
Return whether any element is True.
Returns False unless there is at least one element within a series that is
True or equivalent (e.g. non-zero or non-empty).
Parameters
----------
axis : {0 or 'index'}, default 0
Indicate which axis or axes should be reduced.
* 0 / 'index' : reduce the index, return a Series whose index is the
original column labels.
bool_only : bool, default None
Include only boolean columns. If None, will attempt to use everything,
then use only boolean data.
Returns
-------
Series
Examples
--------
Create a dataframe from a dictionary.
>>> df = ps.DataFrame({
... 'col1': [False, False, False],
... 'col2': [True, False, False],
... 'col3': [0, 0, 1],
... 'col4': [0, 1, 2],
... 'col5': [False, False, None],
... 'col6': [True, False, None]},
... columns=['col1', 'col2', 'col3', 'col4', 'col5', 'col6'])
Default behavior checks if column-wise values all return True.
>>> df.any()
col1 False
col2 True
col3 True
col4 True
col5 False
col6 True
dtype: bool
Include only boolean columns when set `bool_only=True`.
>>> df.any(bool_only=True)
col1 False
col2 True
dtype: bool
Returns empty Series when the DataFrame is empty.
>>> df[[]].any()
Series([], dtype: bool)
"""
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError('axis should be either 0 or "index" currently.')
column_labels = self._internal.column_labels
if bool_only:
column_labels = self._bool_column_labels(column_labels)
if len(column_labels) == 0:
return ps.Series([], dtype=bool)
applied: List[PySparkColumn] = []
for label in column_labels:
scol = self._internal.spark_column_for(label)
any_col = F.max(F.coalesce(scol.cast("boolean"), F.lit(False)))
applied.append(F.when(any_col.isNull(), False).otherwise(any_col))
return self._result_aggregated(column_labels, applied)
def _bool_column_labels(self, column_labels: List[Label]) -> List[Label]:
"""
Filter column labels of boolean columns (without None).
"""
bool_column_labels = []
for label in column_labels:
psser = self._psser_for(label)
if is_bool_dtype(psser):
# Rely on dtype rather than spark type because
# columns that consist of bools and Nones should be excluded
# if bool_only is True
bool_column_labels.append(label)
return bool_column_labels
def _result_aggregated(
self, column_labels: List[Label], scols: Sequence[PySparkColumn]
) -> "Series":
"""
Given aggregated Spark columns and respective column labels from the original
pandas-on-Spark DataFrame, construct the result Series.
"""
from pyspark.pandas.series import first_series
cols = []
result_scol_name = "value"
for label, applied_col in zip(column_labels, scols):
cols.append(
F.struct(
*[F.lit(col).alias(SPARK_INDEX_NAME_FORMAT(i)) for i, col in enumerate(label)],
*[applied_col.alias(result_scol_name)],
)
)
# Statements under this comment implement spark frame transformations as below:
# From:
# +-------------------------------------------------------------------------------------+
# |arrays |
# +-------------------------------------------------------------------------------------+
# |[{col1, true}, {col2, true}, {col3, false}, {col4, true}]|
# +-------------------------------------------------------------------------------------+
# To:
# +-------------+
# |col |
# +-------------+
# |{col1, true} |
# |{col2, true} |
# |{col3, false}|
# |{col4, true} |
# +-------------+
# To:
# +-----------------+-----+
# |__index_level_0__|value|
# +-----------------+-----+
# |col1 |true |
# |col2 |true |
# |col3 |false|
# |col4 |true |
# +-----------------+-----+
sdf = self._internal.spark_frame.select(F.array(*cols).alias("arrays")).select(
F.explode(F.col("arrays"))
)
sdf = sdf.selectExpr("col.*")
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, SPARK_INDEX_NAME_FORMAT(i))
for i in range(self._internal.column_labels_level)
],
index_names=self._internal.column_label_names,
column_labels=[None],
data_spark_columns=[scol_for(sdf, result_scol_name)],
)
# (cont.) The result Series should look as below:
# col1 False
# col2 True
# col3 True
# col4 True
# dtype: bool
return first_series(DataFrame(internal))
# TODO: add axis, pct, na_option parameter
[docs] def rank(
self, method: str = "average", ascending: bool = True, numeric_only: Optional[bool] = None
) -> "DataFrame":
"""
Compute numerical data ranks (1 through n) along axis. Equal values are
assigned a rank that is the average of the ranks of those values.
.. note:: the current implementation of rank uses Spark's Window without
specifying partition specification. This leads to moving all data into
a single partition in a single machine and could cause serious
performance degradation. Avoid this method with very large datasets.
Parameters
----------
method : {'average', 'min', 'max', 'first', 'dense'}
* average: average rank of group
* min: lowest rank in group
* max: highest rank in group
* first: ranks assigned in order they appear in the array
* dense: like 'min', but rank always increases by 1 between groups
ascending : boolean, default True
False for ranks by high (1) to low (N)
numeric_only : bool, optional
For DataFrame objects, rank only numeric columns if set to True.
Returns
-------
ranks : same type as caller
Examples
--------
>>> df = ps.DataFrame({'A': [1, 2, 2, 3], 'B': [4, 3, 2, 1]}, columns=['A', 'B'])
>>> df
A B
0 1 4
1 2 3
2 2 2
3 3 1
>>> df.rank().sort_index()
A B
0 1.0 4.0
1 2.5 3.0
2 2.5 2.0
3 4.0 1.0
If method is set to 'min', it uses lowest rank in group.
>>> df.rank(method='min').sort_index()
A B
0 1.0 4.0
1 2.0 3.0
2 2.0 2.0
3 4.0 1.0
If method is set to 'max', it uses highest rank in group.
>>> df.rank(method='max').sort_index()
A B
0 1.0 4.0
1 3.0 3.0
2 3.0 2.0
3 4.0 1.0
If method is set to 'dense', it leaves no gaps in group.
>>> df.rank(method='dense').sort_index()
A B
0 1.0 4.0
1 2.0 3.0
2 2.0 2.0
3 3.0 1.0
If numeric_only is set to 'True', rank only numeric columns.
>>> df = ps.DataFrame({'A': [1, 2, 2, 3], 'B': ['a', 'b', 'd', 'c']}, columns= ['A', 'B'])
>>> df
A B
0 1 a
1 2 b
2 2 d
3 3 c
>>> df.rank(numeric_only=True)
A
0 1.0
1 2.5
2 2.5
3 4.0
"""
warnings.warn(
"Default value of `numeric_only` will be changed to `False` "
"instead of `None` in 4.0.0.",
FutureWarning,
)
if numeric_only:
numeric_col_names = []
for label in self._internal.column_labels:
psser = self._psser_for(label)
if isinstance(psser.spark.data_type, (NumericType, BooleanType)):
numeric_col_names.append(psser.name)
psdf = self[numeric_col_names] if numeric_only else self
return psdf._apply_series_op(
lambda psser: psser._rank(method=method, ascending=ascending), should_resolve=True
)
[docs] def filter(
self,
items: Optional[Sequence[Any]] = None,
like: Optional[str] = None,
regex: Optional[str] = None,
axis: Optional[Axis] = None,
) -> "DataFrame":
"""
Subset rows or columns of dataframe according to labels in
the specified index.
Note that this routine does not filter a dataframe on its
contents. The filter is applied to the labels of the index.
Parameters
----------
items : list-like
Keep labels from axis which are in items.
like : string
Keep labels from axis for which "like in label == True".
regex : string (regular expression)
Keep labels from axis for which re.search(regex, label) == True.
axis : int or string axis name
The axis to filter on. By default this is the info axis,
'index' for Series, 'columns' for DataFrame.
Returns
-------
same type as input object
See Also
--------
DataFrame.loc
Notes
-----
The ``items``, ``like``, and ``regex`` parameters are
enforced to be mutually exclusive.
``axis`` defaults to the info axis that is used when indexing
with ``[]``.
Examples
--------
>>> df = ps.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
... index=['mouse', 'rabbit'],
... columns=['one', 'two', 'three'])
>>> # select columns by name
>>> df.filter(items=['one', 'three'])
one three
mouse 1 3
rabbit 4 6
>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
one three
mouse 1 3
rabbit 4 6
>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
one two three
rabbit 4 5 6
For a Series,
>>> # select rows by name
>>> df.one.filter(items=['rabbit'])
rabbit 4
Name: one, dtype: int64
>>> # select rows by regular expression
>>> df.one.filter(regex='e$')
mouse 1
Name: one, dtype: int64
>>> # select rows containing 'bbi'
>>> df.one.filter(like='bbi')
rabbit 4
Name: one, dtype: int64
"""
if sum(x is not None for x in (items, like, regex)) > 1:
raise TypeError(
"Keyword arguments `items`, `like`, or `regex` " "are mutually exclusive"
)
axis = validate_axis(axis, none_axis=1)
index_scols = self._internal.index_spark_columns
if items is not None:
if is_list_like(items):
items = list(items)
else:
raise ValueError("items should be a list-like object.")
if axis == 0:
if len(index_scols) == 1:
if len(items) <= ps.get_option("compute.isin_limit"):
col = index_scols[0].isin([F.lit(item) for item in items])
return DataFrame(self._internal.with_filter(col))
else:
item_sdf_col = verify_temp_column_name(
self._internal.spark_frame, "__item__"
)
item_sdf = default_session().createDataFrame(
pd.DataFrame({item_sdf_col: items})
)
joined_sdf = self._internal.spark_frame.join(
other=F.broadcast(item_sdf),
on=(index_scols[0] == scol_for(item_sdf, item_sdf_col)),
how="semi",
)
return DataFrame(self._internal.with_new_sdf(joined_sdf))
else:
# for multi-index
col = None
for item in items:
if not isinstance(item, tuple):
raise TypeError("Unsupported type {}".format(type(item).__name__))
if not item:
raise ValueError("The item should not be empty.")
midx_col = None
for i, element in enumerate(item):
if midx_col is None:
midx_col = index_scols[i] == F.lit(element)
else:
midx_col = midx_col & (index_scols[i] == F.lit(element))
if col is None:
col = midx_col
else:
col = col | midx_col
return DataFrame(self._internal.with_filter(col))
else:
return self[items]
elif like is not None:
if axis == 0:
col = None
for index_scol in index_scols:
if col is None:
col = index_scol.contains(like)
else:
col = col | index_scol.contains(like)
return DataFrame(self._internal.with_filter(col))
else:
column_labels = self._internal.column_labels
output_labels = [label for label in column_labels if any(like in i for i in label)]
return self[output_labels]
elif regex is not None:
if axis == 0:
col = None
for index_scol in index_scols:
if col is None:
col = index_scol.rlike(regex)
else:
col = col | index_scol.rlike(regex)
return DataFrame(self._internal.with_filter(col))
else:
column_labels = self._internal.column_labels
matcher = re.compile(regex)
output_labels = [
label
for label in column_labels
if any(matcher.search(i) is not None for i in label)
]
return self[output_labels]
else:
raise TypeError("Must pass either `items`, `like`, or `regex`")
[docs] def rename(
self,
mapper: Optional[Union[Dict, Callable[[Any], Any]]] = None,
index: Optional[Union[Dict, Callable[[Any], Any]]] = None,
columns: Optional[Union[Dict, Callable[[Any], Any]]] = None,
axis: Axis = "index",
inplace: bool = False,
level: Optional[int] = None,
errors: str = "ignore",
) -> Optional["DataFrame"]:
"""
Alter axes labels.
Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series
will be left as-is. Extra labels listed don’t throw an error.
Parameters
----------
mapper : dict-like or function
Dict-like or functions transformations to apply to that axis’ values.
Use either `mapper` and `axis` to specify the axis to target with `mapper`, or `index`
and `columns`.
index : dict-like or function
Alternative to specifying axis ("mapper, axis=0" is equivalent to "index=mapper").
columns : dict-like or function
Alternative to specifying axis ("mapper, axis=1" is equivalent to "columns=mapper").
axis : int or str, default 'index'
Axis to target with mapper. Can be either the axis name ('index', 'columns') or
number (0, 1).
inplace : bool, default False
Whether to return a new DataFrame.
level : int or level name, default None
In case of a MultiIndex, only rename labels in the specified level.
errors : {'ignore', 'raise'}, default 'ignore'
If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`, or `columns`
contains labels that are not present in the Index being transformed. If 'ignore',
existing keys will be renamed, and extra keys will be ignored.
Returns
-------
DataFrame with the renamed axis labels.
Raises
------
`KeyError`
If any of the labels is not found in the selected axis and "errors='raise'".
Examples
--------
>>> psdf1 = ps.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> psdf1.rename(columns={"A": "a", "B": "c"}) # doctest: +NORMALIZE_WHITESPACE
a c
0 1 4
1 2 5
2 3 6
>>> psdf1.rename(index={1: 10, 2: 20}) # doctest: +NORMALIZE_WHITESPACE
A B
0 1 4
10 2 5
20 3 6
>>> psdf1.rename(columns={"A": "a", "C": "c"}, errors="raise")
Traceback (most recent call last):
...
KeyError: 'Index include value which is not in the `mapper`'
>>> def str_lower(s) -> str:
... return str.lower(s)
>>> psdf1.rename(str_lower, axis='columns') # doctest: +NORMALIZE_WHITESPACE
a b
0 1 4
1 2 5
2 3 6
>>> def mul10(x) -> int:
... return x * 10
>>> psdf1.rename(mul10, axis='index') # doctest: +NORMALIZE_WHITESPACE
A B
0 1 4
10 2 5
20 3 6
>>> idx = pd.MultiIndex.from_tuples([('X', 'A'), ('X', 'B'), ('Y', 'C'), ('Y', 'D')])
>>> psdf2 = ps.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], columns=idx)
>>> psdf2.rename(columns=str_lower, level=0) # doctest: +NORMALIZE_WHITESPACE
x y
A B C D
0 1 2 3 4
1 5 6 7 8
>>> psdf3 = ps.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8]], index=idx, columns=list('ab'))
>>> psdf3.rename(index=str_lower) # doctest: +NORMALIZE_WHITESPACE
a b
x a 1 2
b 3 4
y c 5 6
d 7 8
"""
def gen_mapper_fn(
mapper: Union[Dict, Callable[[Any], Any]], skip_return_type: bool = False
) -> Tuple[Callable[[Any], Any], Dtype, DataType]:
if isinstance(mapper, dict):
mapper_dict = mapper
type_set = set(map(lambda x: type(x), mapper_dict.values()))
if len(type_set) > 1:
raise ValueError("Mapper dict should have the same value type.")
dtype, spark_return_type = pandas_on_spark_type(list(type_set)[0])
def mapper_fn(x: Any) -> Any:
if x in mapper_dict:
return mapper_dict[x]
else:
if errors == "raise":
raise KeyError("Index include value which is not in the `mapper`")
return x
return mapper_fn, dtype, spark_return_type
elif callable(mapper):
mapper_callable = cast(Callable, mapper)
def mapper_fn(x: Any) -> Any:
return mapper_callable(x)
if skip_return_type:
return mapper_fn, None, None
else:
return_type = cast(ScalarType, infer_return_type(mapper))
dtype = return_type.dtype
spark_return_type = return_type.spark_type
return mapper_fn, dtype, spark_return_type
else:
raise ValueError(
"`mapper` or `index` or `columns` should be "
"either dict-like or function type."
)
index_mapper_fn = None
index_mapper_ret_stype = None
columns_mapper_fn = None
inplace = validate_bool_kwarg(inplace, "inplace")
if mapper:
axis = validate_axis(axis)
if axis == 0:
index_mapper_fn, index_mapper_ret_dtype, index_mapper_ret_stype = gen_mapper_fn(
mapper
)
elif axis == 1:
columns_mapper_fn, _, _ = gen_mapper_fn(mapper)
else:
if index:
index_mapper_fn, index_mapper_ret_dtype, index_mapper_ret_stype = gen_mapper_fn(
index
)
if columns:
columns_mapper_fn, _, _ = gen_mapper_fn(columns, skip_return_type=True)
if not index and not columns:
raise ValueError("Either `index` or `columns` should be provided.")
psdf = self.copy()
if index_mapper_fn:
# rename index labels, if `level` is None, rename all index columns, otherwise only
# rename the corresponding level index.
# implement this by transform the underlying spark dataframe,
# Example:
# suppose the psdf index column in underlying spark dataframe is "index_0", "index_1",
# if rename level 0 index labels, will do:
# ``psdf._sdf.withColumn("index_0", mapper_fn_udf(col("index_0"))``
# if rename all index labels (`level` is None), then will do:
# ```
# psdf._sdf.withColumn("index_0", mapper_fn_udf(col("index_0"))
# .withColumn("index_1", mapper_fn_udf(col("index_1"))
# ```
index_columns = psdf._internal.index_spark_column_names
num_indices = len(index_columns)
if level is not None and (level < 0 or level >= num_indices):
raise ValueError("level should be an integer between [0, %s)" % num_indices)
@pandas_udf(returnType=index_mapper_ret_stype) # type: ignore[call-overload]
def index_mapper_udf(s: pd.Series) -> pd.Series:
return s.map(index_mapper_fn)
index_spark_columns = psdf._internal.index_spark_columns.copy()
index_fields = psdf._internal.index_fields.copy()
if level is None:
for i in range(num_indices):
index_spark_columns[i] = index_mapper_udf(index_spark_columns[i]).alias(
index_columns[i]
)
index_fields[i] = index_fields[i].copy(
dtype=index_mapper_ret_dtype,
spark_type=index_mapper_ret_stype,
nullable=True,
)
else:
index_spark_columns[level] = index_mapper_udf(index_spark_columns[level]).alias(
index_columns[level]
)
index_fields[level] = index_fields[level].copy(
dtype=index_mapper_ret_dtype,
spark_type=index_mapper_ret_stype,
nullable=True,
)
psdf = DataFrame(
psdf._internal.copy(
index_spark_columns=index_spark_columns, index_fields=index_fields
)
)
if columns_mapper_fn:
# rename column name.
# Will modify the `_internal._column_labels` and transform underlying spark dataframe
# to the same column name with `_internal._column_labels`.
if level:
if level < 0 or level >= psdf._internal.column_labels_level:
raise ValueError("level should be an integer between [0, column_labels_level)")
def gen_new_column_labels_entry(column_labels_entry: Label) -> Label:
if level is None:
# rename all level columns
return tuple(map(columns_mapper_fn, column_labels_entry))
else:
# only rename specified level column
entry_list = list(column_labels_entry)
entry_list[level] = columns_mapper_fn(entry_list[level])
return tuple(entry_list)
new_column_labels = list(map(gen_new_column_labels_entry, psdf._internal.column_labels))
new_data_pssers = [
psdf._psser_for(old_label).rename(new_label)
for old_label, new_label in zip(psdf._internal.column_labels, new_column_labels)
]
psdf = DataFrame(psdf._internal.with_new_columns(new_data_pssers))
if inplace:
self._update_internal_frame(psdf._internal)
return None
else:
return psdf
[docs] def rename_axis(
self,
mapper: Union[Any, Sequence[Any], Dict[Name, Any], Callable[[Name], Any]] = None,
index: Union[Any, Sequence[Any], Dict[Name, Any], Callable[[Name], Any]] = None,
columns: Union[Any, Sequence[Any], Dict[Name, Any], Callable[[Name], Any]] = None,
axis: Optional[Axis] = 0,
inplace: Optional[bool] = False,
) -> Optional["DataFrame"]:
"""
Set the name of the axis for the index or columns.
Parameters
----------
mapper : scalar, list-like, optional
A scalar, list-like, dict-like or functions transformations to
apply to the axis name attribute.
index, columns : scalar, list-like, dict-like or function, optional
A scalar, list-like, dict-like or functions transformations to
apply to that axis' values.
Use either ``mapper`` and ``axis`` to
specify the axis to target with ``mapper``, or ``index``
and/or ``columns``.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to rename.
inplace : bool, default False
Modifies the object directly, instead of creating a new DataFrame.
Returns
-------
DataFrame, or None if `inplace` is True.
See Also
--------
Series.rename : Alter Series index labels or name.
DataFrame.rename : Alter DataFrame index labels or name.
Index.rename : Set new names on index.
Notes
-----
``DataFrame.rename_axis`` supports two calling conventions
* ``(index=index_mapper, columns=columns_mapper, ...)``
* ``(mapper, axis={'index', 'columns'}, ...)``
The first calling convention will only modify the names of
the index and/or the names of the Index object that is the columns.
The second calling convention will modify the names of the
corresponding index specified by axis.
We *highly* recommend using keyword arguments to clarify your
intent.
Examples
--------
>>> df = ps.DataFrame({"num_legs": [4, 4, 2],
... "num_arms": [0, 0, 2]},
... index=["dog", "cat", "monkey"],
... columns=["num_legs", "num_arms"])
>>> df
num_legs num_arms
dog 4 0
cat 4 0
monkey 2 2
>>> df = df.rename_axis("animal").sort_index()
>>> df # doctest: +NORMALIZE_WHITESPACE
num_legs num_arms
animal
cat 4 0
dog 4 0
monkey 2 2
>>> df = df.rename_axis("limbs", axis="columns").sort_index()
>>> df # doctest: +NORMALIZE_WHITESPACE
limbs num_legs num_arms
animal
cat 4 0
dog 4 0
monkey 2 2
**MultiIndex**
>>> index = pd.MultiIndex.from_product([['mammal'],
... ['dog', 'cat', 'monkey']],
... names=['type', 'name'])
>>> df = ps.DataFrame({"num_legs": [4, 4, 2],
... "num_arms": [0, 0, 2]},
... index=index,
... columns=["num_legs", "num_arms"])
>>> df # doctest: +NORMALIZE_WHITESPACE
num_legs num_arms
type name
mammal dog 4 0
cat 4 0
monkey 2 2
>>> df.rename_axis(index={'type': 'class'}).sort_index() # doctest: +NORMALIZE_WHITESPACE
num_legs num_arms
class name
mammal cat 4 0
dog 4 0
monkey 2 2
>>> df.rename_axis(index=str.upper).sort_index() # doctest: +NORMALIZE_WHITESPACE
num_legs num_arms
TYPE NAME
mammal cat 4 0
dog 4 0
monkey 2 2
"""
def gen_names(
v: Union[Any, Sequence[Any], Dict[Name, Any], Callable[[Name], Any]],
curnames: List[Name],
) -> List[Label]:
newnames: List[Name]
if is_scalar(v):
newnames = [cast(Name, v)]
elif is_list_like(v) and not is_dict_like(v):
newnames = list(cast(Sequence[Name], v))
elif is_dict_like(v):
v_dict = cast(Dict[Name, Name], v)
newnames = [v_dict[name] if name in v_dict else name for name in curnames]
elif callable(v):
v_callable = cast(Callable[[Name], Name], v)
newnames = [v_callable(name) for name in curnames]
else:
raise ValueError(
"`mapper` or `index` or `columns` should be "
"either dict-like or function type."
)
if len(newnames) != len(curnames):
raise ValueError(
"Length of new names must be {}, got {}".format(len(curnames), len(newnames))
)
return [name if is_name_like_tuple(name) else (name,) for name in newnames]
if mapper is not None and (index is not None or columns is not None):
raise TypeError("Cannot specify both 'mapper' and any of 'index' or 'columns'.")
if mapper is not None:
axis = validate_axis(axis)
if axis == 0:
index = mapper
elif axis == 1:
columns = mapper
column_label_names = (
gen_names(columns, self.columns.names)
if columns is not None
else self._internal.column_label_names
)
index_names = (
gen_names(index, self.index.names) if index is not None else self._internal.index_names
)
internal = self._internal.copy(
index_names=index_names, column_label_names=column_label_names
)
if inplace:
self._update_internal_frame(internal)
return None
else:
return DataFrame(internal)
[docs] def keys(self) -> pd.Index:
"""
Return alias for columns.
Returns
-------
Index
Columns of the DataFrame.
Examples
--------
>>> df = ps.DataFrame([[1, 2], [4, 5], [7, 8]],
... index=['cobra', 'viper', 'sidewinder'],
... columns=['max_speed', 'shield'])
>>> df
max_speed shield
cobra 1 2
viper 4 5
sidewinder 7 8
>>> df.keys()
Index(['max_speed', 'shield'], dtype='object')
"""
return self.columns
[docs] def pct_change(self, periods: int = 1) -> "DataFrame":
"""
Percentage change between the current and a prior element.
.. note:: the current implementation of this API uses Spark's Window without
specifying partition specification. This leads to moving all data into
a single partition in a single machine and could cause serious
performance degradation. Avoid this method with very large datasets.
Parameters
----------
periods : int, default 1
Periods to shift for forming percent change.
Returns
-------
DataFrame
Examples
--------
Percentage change in French franc, Deutsche Mark, and Italian lira
from 1980-01-01 to 1980-03-01.
>>> df = ps.DataFrame({
... 'FR': [4.0405, 4.0963, 4.3149],
... 'GR': [1.7246, 1.7482, 1.8519],
... 'IT': [804.74, 810.01, 860.13]},
... index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
FR GR IT
1980-01-01 4.0405 1.7246 804.74
1980-02-01 4.0963 1.7482 810.01
1980-03-01 4.3149 1.8519 860.13
>>> df.pct_change()
FR GR IT
1980-01-01 NaN NaN NaN
1980-02-01 0.013810 0.013684 0.006549
1980-03-01 0.053365 0.059318 0.061876
You can set periods to shift for forming percent change
>>> df.pct_change(2)
FR GR IT
1980-01-01 NaN NaN NaN
1980-02-01 NaN NaN NaN
1980-03-01 0.067912 0.073814 0.06883
"""
window = Window.orderBy(NATURAL_ORDER_COLUMN_NAME).rowsBetween(-periods, -periods)
def op(psser: ps.Series) -> PySparkColumn:
prev_row = F.lag(psser.spark.column, periods).over(window)
return ((psser.spark.column - prev_row) / prev_row).alias(
psser._internal.data_spark_column_names[0]
)
return self._apply_series_op(op, should_resolve=True)
# TODO: axis = 1
[docs] def idxmax(self, axis: Axis = 0) -> "Series":
"""
Return index of first occurrence of maximum over requested axis.
NA/null values are excluded.
.. note:: This API collect all rows with maximum value using `to_pandas()`
because we suppose the number of rows with max values are usually small in general.
Parameters
----------
axis : 0 or 'index'
Can only be set to 0 now.
Returns
-------
Series
See Also
--------
Series.idxmax
Examples
--------
>>> psdf = ps.DataFrame({'a': [1, 2, 3, 2],
... 'b': [4.0, 2.0, 3.0, 1.0],
... 'c': [300, 200, 400, 200]})
>>> psdf
a b c
0 1 4.0 300
1 2 2.0 200
2 3 3.0 400
3 2 1.0 200
>>> psdf.idxmax()
a 2
b 0
c 2
dtype: int64
For Multi-column Index
>>> psdf = ps.DataFrame({'a': [1, 2, 3, 2],
... 'b': [4.0, 2.0, 3.0, 1.0],
... 'c': [300, 200, 400, 200]})
>>> psdf.columns = pd.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])
>>> psdf
a b c
x y z
0 1 4.0 300
1 2 2.0 200
2 3 3.0 400
3 2 1.0 200
>>> psdf.idxmax()
a x 2
b y 0
c z 2
dtype: int64
"""
max_cols = map(lambda scol: F.max(scol), self._internal.data_spark_columns)
sdf_max = self._internal.spark_frame.select(*max_cols).head()
# `sdf_max` looks like below
# +------+------+------+
# |(a, x)|(b, y)|(c, z)|
# +------+------+------+
# | 3| 4.0| 400|
# +------+------+------+
conds = (
scol == max_val for scol, max_val in zip(self._internal.data_spark_columns, sdf_max)
)
cond = reduce(lambda x, y: x | y, conds)
psdf: DataFrame = DataFrame(self._internal.with_filter(cond))
return cast(ps.Series, ps.from_pandas(psdf._to_internal_pandas().idxmax()))
# TODO: axis = 1
[docs] def idxmin(self, axis: Axis = 0) -> "Series":
"""
Return index of first occurrence of minimum over requested axis.
NA/null values are excluded.
.. note:: This API collect all rows with minimum value using `to_pandas()`
because we suppose the number of rows with min values are usually small in general.
Parameters
----------
axis : 0 or 'index'
Can only be set to 0 now.
Returns
-------
Series
See Also
--------
Series.idxmin
Examples
--------
>>> psdf = ps.DataFrame({'a': [1, 2, 3, 2],
... 'b': [4.0, 2.0, 3.0, 1.0],
... 'c': [300, 200, 400, 200]})
>>> psdf
a b c
0 1 4.0 300
1 2 2.0 200
2 3 3.0 400
3 2 1.0 200
>>> psdf.idxmin()
a 0
b 3
c 1
dtype: int64
For Multi-column Index
>>> psdf = ps.DataFrame({'a': [1, 2, 3, 2],
... 'b': [4.0, 2.0, 3.0, 1.0],
... 'c': [300, 200, 400, 200]})
>>> psdf.columns = pd.MultiIndex.from_tuples([('a', 'x'), ('b', 'y'), ('c', 'z')])
>>> psdf
a b c
x y z
0 1 4.0 300
1 2 2.0 200
2 3 3.0 400
3 2 1.0 200
>>> psdf.idxmin()
a x 0
b y 3
c z 1
dtype: int64
"""
min_cols = map(lambda scol: F.min(scol), self._internal.data_spark_columns)
sdf_min = self._internal.spark_frame.select(*min_cols).head()
conds = (
scol == min_val for scol, min_val in zip(self._internal.data_spark_columns, sdf_min)
)
cond = reduce(lambda x, y: x | y, conds)
psdf: DataFrame = DataFrame(self._internal.with_filter(cond))
return cast(ps.Series, ps.from_pandas(psdf._to_internal_pandas().idxmin()))
# TODO(SPARK-41619): Add `show_counts` parameter and replace with `null_counts`.
[docs] def info(
self,
verbose: Optional[bool] = None,
buf: Optional[IO[str]] = None,
max_cols: Optional[int] = None,
) -> None:
"""
Print a concise summary of a DataFrame.
This method prints information about a DataFrame including
the index dtype and column dtypes, non-null values and memory usage.
Parameters
----------
verbose : bool, optional
Whether to print the full summary.
buf : writable buffer, defaults to sys.stdout
Where to send the output. By default the output is printed to
sys.stdout. Pass a writable buffer if you need to further process
the output.
max_cols : int, optional
When to switch from the verbose to the truncated output. If the
DataFrame has more than `max_cols` columns, the truncated output
is used.
null_counts : bool, optional
Whether to show the non-null counts.
.. deprecated:: 3.4.0
Returns
-------
None
This method prints a summary of a DataFrame and returns None.
See Also
--------
DataFrame.describe: Generate descriptive statistics of DataFrame
columns.
Examples
--------
>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = ps.DataFrame(
... {"int_col": int_values, "text_col": text_values, "float_col": float_values},
... columns=['int_col', 'text_col', 'float_col'])
>>> df
int_col text_col float_col
0 1 alpha 0.00
1 2 beta 0.25
2 3 gamma 0.50
3 4 delta 0.75
4 5 epsilon 1.00
Prints information of all columns:
>>> df.info(verbose=True) # doctest: +SKIP
<class 'pyspark.pandas.frame.DataFrame'>
Index: 5 entries, 0 to 4
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 int_col 5 non-null int64
1 text_col 5 non-null object
2 float_col 5 non-null float64
dtypes: float64(1), int64(1), object(1)
Prints a summary of columns count and its dtypes but not per column
information:
>>> df.info(verbose=False) # doctest: +SKIP
<class 'pyspark.pandas.frame.DataFrame'>
Index: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
Pipe output of DataFrame.info to buffer instead of sys.stdout, get
buffer content and writes to a text file:
>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open('%s/info.txt' % path, "w",
... encoding="utf-8") as f:
... _ = f.write(s)
>>> with open('%s/info.txt' % path) as f:
... f.readlines() # doctest: +SKIP
["<class 'pyspark.pandas.frame.DataFrame'>\\n",
'Index: 5 entries, 0 to 4\\n',
'Data columns (total 3 columns):\\n',
' # Column Non-Null Count Dtype \\n',
'--- ------ -------------- ----- \\n',
' 0 int_col 5 non-null int64 \\n',
' 1 text_col 5 non-null object \\n',
' 2 float_col 5 non-null float64\\n',
'dtypes: float64(1), int64(1), object(1)']
"""
# To avoid pandas' existing config affects pandas-on-Spark.
# TODO: should we have corresponding pandas-on-Spark configs?
with pd.option_context(
"display.max_info_columns", sys.maxsize, "display.max_info_rows", sys.maxsize
):
try:
# hack to use pandas' info as is.
object.__setattr__(self, "_data", self)
count_func = self.count
self.count = ( # type: ignore[assignment]
lambda: count_func()._to_pandas() # type: ignore[assignment, misc, union-attr]
)
return pd.DataFrame.info(
self, # type: ignore[arg-type]
verbose=verbose,
buf=buf,
max_cols=max_cols,
memory_usage=False,
)
finally:
del self._data
self.count = count_func # type: ignore[assignment]
# TODO: fix parameter 'axis' and 'numeric_only' to work same as pandas'
[docs] def quantile(
self,
q: Union[float, Iterable[float]] = 0.5,
axis: Axis = 0,
numeric_only: bool = True,
accuracy: int = 10000,
) -> DataFrameOrSeries:
"""
Return value at the given quantile.
.. note:: Unlike pandas', the quantile in pandas-on-Spark is an approximated quantile
based upon approximate percentile computation because computing quantile across a
large dataset is extremely expensive.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
0 <= q <= 1, the quantile(s) to compute.
axis : int or str, default 0 or 'index'
Can only be set to 0 now.
numeric_only : bool, default True
If False, the quantile of datetime and time delta data will be computed as well.
Can only be set to True now.
accuracy : int, optional
Default accuracy of approximation. Larger value means better accuracy.
The relative error can be deduced by 1.0 / accuracy.
Returns
-------
Series or DataFrame
If q is an array, a DataFrame will be returned where the
index is q, the columns are the columns of self, and the values are the quantiles.
If q is a float, a Series will be returned where the
index is the columns of self and the values are the quantiles.
Examples
--------
>>> psdf = ps.DataFrame({'a': [1, 2, 3, 4, 5], 'b': [6, 7, 8, 9, 0]})
>>> psdf
a b
0 1 6
1 2 7
2 3 8
3 4 9
4 5 0
>>> psdf.quantile(.5)
a 3.0
b 7.0
Name: 0.5, dtype: float64
>>> psdf.quantile([.25, .5, .75])
a b
0.25 2.0 6.0
0.50 3.0 7.0
0.75 4.0 8.0
"""
warnings.warn(
"Default value of `numeric_only` will be changed to `False` "
"instead of `True` in 4.0.0.",
FutureWarning,
)
axis = validate_axis(axis)
if axis != 0:
raise NotImplementedError('axis should be either 0 or "index" currently.')
if not isinstance(accuracy, int):
raise TypeError(
"accuracy must be an integer; however, got [%s]" % type(accuracy).__name__
)
qq: Union[float, List[float]] = list(q) if isinstance(q, Iterable) else q
for v in qq if isinstance(qq, list) else [qq]:
if not isinstance(v, float):
raise TypeError(
"q must be a float or an array of floats; however, [%s] found." % type(v)
)
if v < 0.0 or v > 1.0:
raise ValueError("percentiles should all be in the interval [0, 1].")
def quantile(psser: "Series") -> PySparkColumn:
spark_type = psser.spark.data_type
spark_column = psser.spark.column
if isinstance(spark_type, (BooleanType, NumericType)):
return F.percentile_approx(spark_column.cast(DoubleType()), qq, accuracy)
else:
raise TypeError(
"Could not convert {} ({}) to numeric".format(
spark_type_to_pandas_dtype(spark_type), spark_type.simpleString()
)
)
if isinstance(qq, list):
# First calculate the percentiles from all columns and map it to each `quantiles`
# by creating each entry as a struct. So, it becomes an array of structs as below:
#
# +-----------------------------------------+
# | arrays|
# +-----------------------------------------+
# |[[0.25, 2, 6], [0.5, 3, 7], [0.75, 4, 8]]|
# +-----------------------------------------+
percentile_cols: List[PySparkColumn] = []
percentile_col_names: List[str] = []
column_labels: List[Label] = []
for label, column in zip(
self._internal.column_labels, self._internal.data_spark_column_names
):
psser = self._psser_for(label)
is_numeric_or_boolean = isinstance(
psser.spark.data_type, (NumericType, BooleanType)
)
keep_column = not numeric_only or is_numeric_or_boolean
if keep_column:
percentile_col = quantile(psser)
percentile_cols.append(percentile_col.alias(column))
percentile_col_names.append(column)
column_labels.append(label)
if len(percentile_cols) == 0:
return DataFrame(index=qq)
sdf = self._internal.spark_frame.select(percentile_cols)
# Here, after select percentile cols, a spark_frame looks like below:
# +---------+---------+
# | a| b|
# +---------+---------+
# |[2, 3, 4]|[6, 7, 8]|
# +---------+---------+
cols_dict: Dict[str, List[PySparkColumn]] = {}
for column in percentile_col_names:
cols_dict[column] = list()
for i in range(len(qq)):
cols_dict[column].append(scol_for(sdf, column)[i].alias(column))
internal_index_column = SPARK_DEFAULT_INDEX_NAME
cols = []
for i, col in enumerate(zip(*cols_dict.values())):
cols.append(F.struct(F.lit(qq[i]).alias(internal_index_column), *col))
sdf = sdf.select(F.array(*cols).alias("arrays"))
# And then, explode it and manually set the index.
# +-----------------+---+---+
# |__index_level_0__| a| b|
# +-----------------+---+---+
# | 0.25| 2| 6|
# | 0.5| 3| 7|
# | 0.75| 4| 8|
# +-----------------+---+---+
sdf = sdf.select(F.explode(F.col("arrays"))).selectExpr("col.*")
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, internal_index_column)],
column_labels=column_labels,
data_spark_columns=[scol_for(sdf, col) for col in percentile_col_names],
)
return DataFrame(internal)
else:
return self._reduce_for_stat_function(
quantile, name="quantile", numeric_only=numeric_only
).rename(qq)
[docs] def query(self, expr: str, inplace: bool = False) -> Optional["DataFrame"]:
"""
Query the columns of a DataFrame with a boolean expression.
.. note:: Internal columns that starting with a '__' prefix are able to access, however,
they are not supposed to be accessed.
.. note:: This API delegates to Spark SQL so the syntax follows Spark SQL. Therefore, the
pandas specific syntax such as `@` is not supported. If you want the pandas syntax,
you can work around with :meth:`DataFrame.pandas_on_spark.apply_batch`, but you should
be aware that `query_func` will be executed at different nodes in a distributed manner.
So, for example to use `@` syntax, make sure the variable is serialized by
putting it within the closure as below.
>>> df = ps.DataFrame({'A': range(2000), 'B': range(2000)})
>>> def query_func(pdf):
... num = 1995
... return pdf.query('A > @num')
>>> df.pandas_on_spark.apply_batch(query_func)
A B
1996 1996 1996
1997 1997 1997
1998 1998 1998
1999 1999 1999
Parameters
----------
expr : str
The query string to evaluate.
You can refer to column names that contain spaces by surrounding
them in backticks.
For example, if one of your columns is called ``a a`` and you want
to sum it with ``b``, your query should be ```a a` + b``.
inplace : bool
Whether the query should modify the data in place or return
a modified copy.
Returns
-------
DataFrame
DataFrame resulting from the provided query expression.
Examples
--------
>>> df = ps.DataFrame({'A': range(1, 6),
... 'B': range(10, 0, -2),
... 'C C': range(10, 5, -1)})
>>> df
A B C C
0 1 10 10
1 2 8 9
2 3 6 8
3 4 4 7
4 5 2 6
>>> df.query('A > B')
A B C C
4 5 2 6
The previous expression is equivalent to
>>> df[df.A > df.B]
A B C C
4 5 2 6
For columns with spaces in their name, you can use backtick quoting.
>>> df.query('B == `C C`')
A B C C
0 1 10 10
The previous expression is equivalent to
>>> df[df.B == df['C C']]
A B C C
0 1 10 10
"""
if isinstance(self.columns, pd.MultiIndex):
raise TypeError("Doesn't support for MultiIndex columns")
if not isinstance(expr, str):
raise TypeError(
"expr must be a string to be evaluated, {} given".format(type(expr).__name__)
)
inplace = validate_bool_kwarg(inplace, "inplace")
data_columns = [label[0] for label in self._internal.column_labels]
sdf = self._internal.spark_frame.select(
self._internal.index_spark_columns
+ [
scol.alias(col)
for scol, col in zip(self._internal.data_spark_columns, data_columns)
]
).filter(expr)
internal = self._internal.with_new_sdf(sdf, data_columns=data_columns)
if inplace:
self._update_internal_frame(internal)
return None
else:
return DataFrame(internal)
[docs] def take(self, indices: List[int], axis: Axis = 0, **kwargs: Any) -> "DataFrame":
"""
Return the elements in the given *positional* indices along an axis.
This means that we are not indexing according to actual values in
the index attribute of the object. We are indexing according to the
actual position of the element in the object.
Parameters
----------
indices : array-like
An array of ints indicating which positions to take.
axis : {0 or 'index', 1 or 'columns', None}, default 0
The axis on which to select elements. ``0`` means that we are
selecting rows, ``1`` means that we are selecting columns.
**kwargs
For compatibility with :meth:`numpy.take`. Has no effect on the
output.
Returns
-------
taken : same type as caller
An array-like containing the elements taken from the object.
See Also
--------
DataFrame.loc : Select a subset of a DataFrame by labels.
DataFrame.iloc : Select a subset of a DataFrame by positions.
numpy.take : Take elements from an array along an axis.
Examples
--------
>>> df = ps.DataFrame([('falcon', 'bird', 389.0),
... ('parrot', 'bird', 24.0),
... ('lion', 'mammal', 80.5),
... ('monkey', 'mammal', np.nan)],
... columns=['name', 'class', 'max_speed'],
... index=[0, 2, 3, 1])
>>> df
name class max_speed
0 falcon bird 389.0
2 parrot bird 24.0
3 lion mammal 80.5
1 monkey mammal NaN
Take elements at positions 0 and 3 along the axis 0 (default).
Note how the actual indices selected (0 and 1) do not correspond to
our selected indices 0 and 3. That's because we are selecting the 0th
and 3rd rows, not rows whose indices equal 0 and 3.
>>> df.take([0, 3]).sort_index()
name class max_speed
0 falcon bird 389.0
1 monkey mammal NaN
Take elements at indices 1 and 2 along the axis 1 (column selection).
>>> df.take([1, 2], axis=1)
class max_speed
0 bird 389.0
2 bird 24.0
3 mammal 80.5
1 mammal NaN
We may take elements using negative integers for positive indices,
starting from the end of the object, just like with Python lists.
>>> df.take([-1, -2]).sort_index()
name class max_speed
1 monkey mammal NaN
3 lion mammal 80.5
"""
axis = validate_axis(axis)
if not is_list_like(indices) or isinstance(indices, (dict, set)):
raise TypeError("`indices` must be a list-like except dict or set")
if axis == 0:
return cast(DataFrame, self.iloc[indices, :])
else:
return cast(DataFrame, self.iloc[:, indices])
[docs] def eval(self, expr: str, inplace: bool = False) -> Optional[DataFrameOrSeries]:
"""
Evaluate a string describing operations on DataFrame columns.
Operates on columns only, not specific rows or elements. This allows
`eval` to run arbitrary code, which can make you vulnerable to code
injection if you pass user input to this function.
Parameters
----------
expr : str
The expression string to evaluate.
inplace : bool, default False
If the expression contains an assignment, whether to perform the
operation inplace and mutate the existing DataFrame. Otherwise,
a new DataFrame is returned.
Returns
-------
The result of the evaluation.
See Also
--------
DataFrame.query : Evaluates a boolean expression to query the columns
of a frame.
DataFrame.assign : Can evaluate an expression or function to create new
values for a column.
eval : Evaluate a Python expression as a string using various
backends.
Examples
--------
>>> df = ps.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
>>> df.eval('A + B')
0 11
1 10
2 9
3 8
4 7
dtype: int64
Assignment is allowed though by default the original DataFrame is not
modified.
>>> df.eval('C = A + B')
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
Use ``inplace=True`` to modify the original DataFrame.
>>> df.eval('C = A + B', inplace=True)
>>> df
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
"""
from pyspark.pandas.series import first_series
if isinstance(self.columns, pd.MultiIndex):
raise TypeError("`eval` is not supported for multi-index columns")
inplace = validate_bool_kwarg(inplace, "inplace")
should_return_series = False
series_name = None
should_return_scalar = False
# Since `eval_func` doesn't have a type hint, inferring the schema is always preformed
# in the `apply_batch`. Hence, the variables `should_return_series`, `series_name`,
# and `should_return_scalar` can be updated.
def eval_func(pdf): # type: ignore[no-untyped-def]
nonlocal should_return_series
nonlocal series_name
nonlocal should_return_scalar
result_inner = pdf.eval(expr, inplace=inplace)
if inplace:
result_inner = pdf
if isinstance(result_inner, pd.Series):
should_return_series = True
series_name = result_inner.name
result_inner = result_inner.to_frame()
elif is_scalar(result_inner):
should_return_scalar = True
result_inner = pd.Series(result_inner).to_frame()
return result_inner
result = self.pandas_on_spark.apply_batch(eval_func)
if inplace:
# Here, the result is always a frame because the error is thrown during schema inference
# from pandas.
self._update_internal_frame(result._internal, check_same_anchor=False)
return None
elif should_return_series:
return first_series(result).rename(series_name)
elif should_return_scalar:
return first_series(result)[0]
else:
# Returns a frame
return result
[docs] def explode(self, column: Name, ignore_index: bool = False) -> "DataFrame":
"""
Transform each element of a list-like to a row, replicating index values.
Parameters
----------
column : str or tuple
Column to explode.
ignore_index : bool, default False
If True, the resulting index will be labeled 0, 1, …, n - 1.
Returns
-------
DataFrame
Exploded lists to rows of the subset columns;
index will be duplicated for these rows.
See Also
--------
DataFrame.unstack : Pivot a level of the (necessarily hierarchical)
index labels.
DataFrame.melt : Unpivot a DataFrame from wide format to long format.
Examples
--------
>>> df = ps.DataFrame({'A': [[1, 2, 3], [], [3, 4]], 'B': 1})
>>> df
A B
0 [1, 2, 3] 1
1 [] 1
2 [3, 4] 1
>>> df.explode('A')
A B
0 1.0 1
0 2.0 1
0 3.0 1
1 NaN 1
2 3.0 1
2 4.0 1
>>> df.explode('A', ignore_index=True)
A B
0 1.0 1
1 2.0 1
2 3.0 1
3 NaN 1
4 3.0 1
5 4.0 1
"""
from pyspark.pandas.series import Series
if not is_name_like_value(column):
raise TypeError("column must be a scalar")
psdf: DataFrame = DataFrame(self._internal.resolved_copy)
psser = psdf[column]
if not isinstance(psser, Series):
raise ValueError(
"The column %s is not unique. For a multi-index, the label must be a tuple "
"with elements corresponding to each level." % name_like_string(column)
)
if not isinstance(psser.spark.data_type, ArrayType):
return self.copy()
sdf = psdf._internal.spark_frame.withColumn(
psser._internal.data_spark_column_names[0], F.explode_outer(psser.spark.column)
)
data_fields = psdf._internal.data_fields.copy()
idx = psdf._internal.column_labels.index(psser._column_label)
field = data_fields[idx]
spark_type = cast(ArrayType, field.spark_type).elementType
dtype = spark_type_to_pandas_dtype(spark_type)
data_fields[idx] = field.copy(dtype=dtype, spark_type=spark_type, nullable=True)
internal = psdf._internal.with_new_sdf(sdf, data_fields=data_fields)
result_df: DataFrame = DataFrame(internal)
return result_df.reset_index(drop=True) if ignore_index else result_df
[docs] def mad(self, axis: Axis = 0) -> "Series":
"""
Return the mean absolute deviation of values.
.. deprecated:: 3.4.0
Parameters
----------
axis : {index (0), columns (1)}
Axis for the function to be applied on.
Examples
--------
>>> df = ps.DataFrame({'a': [1, 2, 3, np.nan], 'b': [0.1, 0.2, 0.3, np.nan]},
... columns=['a', 'b'])
>>> df.mad()
a 0.666667
b 0.066667
dtype: float64
>>> df.mad(axis=1) # doctest: +SKIP
0 0.45
1 0.90
2 1.35
3 NaN
dtype: float64
"""
warnings.warn(
"The 'mad' method is deprecated and will be removed in 4.0.0. "
"To compute the same result, you may do `(df - df.mean()).abs().mean()`.",
FutureWarning,
)
from pyspark.pandas.series import first_series
axis = validate_axis(axis)
if axis == 0:
def get_spark_column(psdf: DataFrame, label: Label) -> PySparkColumn:
scol = psdf._internal.spark_column_for(label)
col_type = psdf._internal.spark_type_for(label)
if isinstance(col_type, BooleanType):
scol = scol.cast("integer")
return scol
new_column_labels: List[Label] = []
for label in self._internal.column_labels:
# Filtering out only columns of numeric and boolean type column.
dtype = self._psser_for(label).spark.data_type
if isinstance(dtype, (NumericType, BooleanType)):
new_column_labels.append(label)
new_columns = [
F.avg(get_spark_column(self, label)).alias(name_like_string(label))
for label in new_column_labels
]
mean_data = self._internal.spark_frame.select(*new_columns).first()
new_columns = [
F.avg(
F.abs(get_spark_column(self, label) - mean_data[name_like_string(label)])
).alias(name_like_string(label))
for label in new_column_labels
]
sdf = self._internal.spark_frame.select(
*[F.lit(None).cast(StringType()).alias(SPARK_DEFAULT_INDEX_NAME)], *new_columns
)
# The data is expected to be small so it's fine to transpose/use the default index.
with ps.option_context("compute.max_rows", 1):
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, SPARK_DEFAULT_INDEX_NAME)],
column_labels=new_column_labels,
column_label_names=self._internal.column_label_names,
)
return first_series(DataFrame(internal).transpose())
else:
@pandas_udf(returnType=DoubleType()) # type: ignore[call-overload]
def calculate_columns_axis(*cols: pd.Series) -> pd.Series:
return pd.concat(cols, axis=1).mad(axis=1)
internal = self._internal.copy(
column_labels=[None],
data_spark_columns=[
calculate_columns_axis(*self._internal.data_spark_columns).alias(
SPARK_DEFAULT_SERIES_NAME
)
],
data_fields=[None],
column_label_names=None,
)
return first_series(DataFrame(internal))
[docs] def mode(self, axis: Axis = 0, numeric_only: bool = False, dropna: bool = True) -> "DataFrame":
"""
Get the mode(s) of each element along the selected axis.
The mode of a set of values is the value that appears most often.
It can be multiple values.
.. versionadded:: 3.4.0
Parameters
----------
axis : {0 or 'index'}, default 0
Axis for the function to be applied on.
numeric_only : bool, default False
If True, only apply to numeric columns.
dropna : bool, default True
Don't consider counts of NaN/NaT.
Returns
-------
DataFrame
The modes of each column or row.
See Also
--------
Series.mode : Return the highest frequency value in a Series.
Series.value_counts : Return the counts of values in a Series.
Examples
--------
>>> df = ps.DataFrame([('bird', 2, 2),
... ('mammal', 4, np.nan),
... ('arthropod', 8, 0),
... ('bird', 2, np.nan)],
... index=('falcon', 'horse', 'spider', 'ostrich'),
... columns=('species', 'legs', 'wings'))
>>> df
species legs wings
falcon bird 2 2.0
horse mammal 4 NaN
spider arthropod 8 0.0
ostrich bird 2 NaN
By default missing values are not considered, and the mode of wings
are both 0 and 2. Because the resulting DataFrame has two rows,
the second row of ``species`` and ``legs`` contains ``NaN``.
>>> df.mode()
species legs wings
0 bird 2.0 0.0
1 None NaN 2.0
Setting ``dropna=False`` ``NaN`` values are considered and they can be
the mode (like for wings).
>>> df.mode(dropna=False)
species legs wings
0 bird 2 NaN
Setting ``numeric_only=True``, only the mode of numeric columns is
computed, and columns of other types are ignored.
>>> df.mode(numeric_only=True)
legs wings
0 2.0 0.0
1 NaN 2.0
"""
axis = validate_axis(axis, none_axis=0)
if axis != 0:
raise ValueError('axis should be either 0 or "index" currently.')
if numeric_only is None and axis == 0:
numeric_only = True
warnings.warn(
"Default value of `numeric_only` will be changed to `False` "
"instead of `True` in 4.0.0.",
FutureWarning,
)
mode_scols: List[PySparkColumn] = []
mode_col_names: List[str] = []
mode_labels: List[Label] = []
for label, col_name in zip(
self._internal.column_labels, self._internal.data_spark_column_names
):
psser = self._psser_for(label)
is_numeric = isinstance(psser.spark.data_type, (NumericType, BooleanType))
if not numeric_only or is_numeric:
scol = psser.spark.column
mode_scol = SF.mode(scol, dropna).alias(col_name)
mode_scols.append(mode_scol)
mode_col_names.append(col_name)
mode_labels.append(label)
# Here, after aggregation, a spark_frame looks like below:
# +-------+----+----------+
# |species|legs| wings|
# +-------+----+----------+
# | [bird]| [2]|[0.0, 2.0]|
# +-------+----+----------+
sdf = self._internal.spark_frame.select(mode_scols)
sdf = sdf.select(*[F.array_sort(F.col(name)).alias(name) for name in mode_col_names])
zip_col_name = verify_temp_column_name(sdf, "__mode_zip_tmp_col__")
explode_col_name = verify_temp_column_name(sdf, "__mode_explode_tmp_col__")
# After this transformation, sdf turns out to be:
# +-------+----+-----+
# |species|legs|wings|
# +-------+----+-----+
# | bird| 2| 0.0|
# | NULL|NULL| 2.0|
# +-------+----+-----+
sdf = (
sdf.select(F.arrays_zip(*[F.col(name) for name in mode_col_names]).alias(zip_col_name))
.select(F.explode(F.col(zip_col_name)).alias(explode_col_name))
.select(
*[
F.col("{0}.{1}".format(explode_col_name, name)).alias(name)
for name in mode_col_names
]
)
)
sdf = sdf.withColumn(SPARK_DEFAULT_INDEX_NAME, F.monotonically_increasing_id())
internal = InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, SPARK_DEFAULT_INDEX_NAME)],
column_labels=mode_labels,
data_spark_columns=[scol_for(sdf, col) for col in mode_col_names],
)
return DataFrame(internal)
[docs] def tail(self, n: int = 5) -> "DataFrame":
"""
Return the last `n` rows.
This function returns last `n` rows from the object based on
position. It is useful for quickly verifying data, for example,
after sorting or appending rows.
For negative values of `n`, this function returns all rows except
the first `n` rows, equivalent to ``df[n:]``.
Parameters
----------
n : int, default 5
Number of rows to select.
Returns
-------
type of caller
The last `n` rows of the caller object.
See Also
--------
DataFrame.head : The first `n` rows of the caller object.
Examples
--------
>>> df = ps.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last 5 lines
>>> df.tail() # doctest: +SKIP
animal
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last `n` lines (three in this case)
>>> df.tail(3) # doctest: +SKIP
animal
6 shark
7 whale
8 zebra
For negative values of `n`
>>> df.tail(-3) # doctest: +SKIP
animal
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
"""
if not isinstance(n, int):
raise TypeError("bad operand type for unary -: '{}'".format(type(n).__name__))
if n < 0:
n = len(self) + n
if n <= 0:
return ps.DataFrame(self._internal.with_filter(F.lit(False)))
# Should use `resolved_copy` here for the case like `(psdf + 1).tail()`
sdf = self._internal.resolved_copy.spark_frame
rows = sdf.tail(n)
new_sdf = default_session().createDataFrame(rows, sdf.schema)
return DataFrame(self._internal.with_new_sdf(new_sdf))
[docs] def align(
self,
other: DataFrameOrSeries,
join: str = "outer",
axis: Optional[Axis] = None,
copy: bool = True,
) -> Tuple["DataFrame", DataFrameOrSeries]:
"""
Align two objects on their axes with the specified join method.
Join method is specified for each axis Index.
Parameters
----------
other : DataFrame or Series
join : {{'outer', 'inner', 'left', 'right'}}, default 'outer'
axis : allowed axis of the other object, default None
Align on index (0), columns (1), or both (None).
copy : bool, default True
Always returns new objects. If copy=False and no reindexing is
required then original objects are returned.
Returns
-------
(left, right) : (DataFrame, type of other)
Aligned objects.
Examples
--------
>>> ps.set_option("compute.ops_on_diff_frames", True)
>>> df1 = ps.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}, index=[10, 20, 30])
>>> df2 = ps.DataFrame({"a": [4, 5, 6], "c": ["d", "e", "f"]}, index=[10, 11, 12])
Align both axis:
>>> aligned_l, aligned_r = df1.align(df2)
>>> aligned_l.sort_index()
a b c
10 1.0 a NaN
11 NaN None NaN
12 NaN None NaN
20 2.0 b NaN
30 3.0 c NaN
>>> aligned_r.sort_index()
a b c
10 4.0 NaN d
11 5.0 NaN e
12 6.0 NaN f
20 NaN NaN None
30 NaN NaN None
Align only axis=0 (index):
>>> aligned_l, aligned_r = df1.align(df2, axis=0)
>>> aligned_l.sort_index()
a b
10 1.0 a
11 NaN None
12 NaN None
20 2.0 b
30 3.0 c
>>> aligned_r.sort_index()
a c
10 4.0 d
11 5.0 e
12 6.0 f
20 NaN None
30 NaN None
Align only axis=1 (column):
>>> aligned_l, aligned_r = df1.align(df2, axis=1)
>>> aligned_l.sort_index()
a b c
10 1 a NaN
20 2 b NaN
30 3 c NaN
>>> aligned_r.sort_index()
a b c
10 4 NaN d
11 5 NaN e
12 6 NaN f
Align with the join type "inner":
>>> aligned_l, aligned_r = df1.align(df2, join="inner")
>>> aligned_l.sort_index()
a
10 1
>>> aligned_r.sort_index()
a
10 4
Align with a Series:
>>> s = ps.Series([7, 8, 9], index=[10, 11, 12])
>>> aligned_l, aligned_r = df1.align(s, axis=0)
>>> aligned_l.sort_index()
a b
10 1.0 a
11 NaN None
12 NaN None
20 2.0 b
30 3.0 c
>>> aligned_r.sort_index()
10 7.0
11 8.0
12 9.0
20 NaN
30 NaN
dtype: float64
>>> ps.reset_option("compute.ops_on_diff_frames")
"""
from pyspark.pandas.series import Series, first_series
if not isinstance(other, (DataFrame, Series)):
raise TypeError("unsupported type: {}".format(type(other).__name__))
how = validate_how(join)
axis = validate_axis(axis, None)
right_is_series = isinstance(other, Series)
if right_is_series:
if axis is None:
raise ValueError("Must specify axis=0 or 1")
elif axis != 0:
raise NotImplementedError(
"align currently only works for axis=0 when right is Series"
)
left = self
right = other
if (axis is None or axis == 0) and not same_anchor(left, right):
combined = combine_frames(left, right, how=how)
left = combined["this"]
right = combined["that"]
if right_is_series:
right = first_series(cast(DataFrame[Any], right)).rename(other.name)
if (
axis is None or axis == 1
) and left._internal.column_labels != right._internal.column_labels:
if left._internal.column_labels_level != right._internal.column_labels_level:
raise ValueError("cannot join with no overlapping index names")
left = left.copy()
right = right.copy()
if how == "full":
column_labels = sorted(
list(set(left._internal.column_labels) | set(right._internal.column_labels))
)
elif how == "inner":
column_labels = sorted(
list(set(left._internal.column_labels) & set(right._internal.column_labels))
)
elif how == "left":
column_labels = left._internal.column_labels
else:
column_labels = right._internal.column_labels
for label in column_labels:
if label not in left._internal.column_labels:
left[label] = F.lit(None).cast(DoubleType())
left = left[column_labels]
for label in column_labels:
if label not in right._internal.column_labels:
right[label] = F.lit(None).cast(DoubleType())
right = right[column_labels]
return (left.copy(), right.copy()) if copy else (left, right)
[docs] @staticmethod
def from_dict(
data: Dict[Name, Sequence[Any]],
orient: str = "columns",
dtype: Union[str, Dtype] = None,
columns: Optional[List[Name]] = None,
) -> "DataFrame":
"""
Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index
allowing dtype specification.
Parameters
----------
data : dict
Of the form {field : array-like} or {field : dict}.
orient : {'columns', 'index'}, default 'columns'
The "orientation" of the data. If the keys of the passed dict
should be the columns of the resulting DataFrame, pass 'columns'
(default). Otherwise, if the keys should be rows, pass 'index'.
dtype : dtype, default None
Data type to force, otherwise infer.
columns : list, default None
Column labels to use when ``orient='index'``. Raises a ValueError
if used with ``orient='columns'``.
Returns
-------
DataFrame
See Also
--------
DataFrame.from_records : DataFrame from structured ndarray, sequence
of tuples or dicts, or DataFrame.
DataFrame : DataFrame object creation using constructor.
Examples
--------
By default the keys of the dict become the DataFrame columns:
>>> data = {'col_1': [3, 2, 1, 0], 'col_2': [10, 20, 30, 40]}
>>> ps.DataFrame.from_dict(data)
col_1 col_2
0 3 10
1 2 20
2 1 30
3 0 40
Specify ``orient='index'`` to create the DataFrame using dictionary
keys as rows:
>>> data = {'row_1': [3, 2, 1, 0], 'row_2': [10, 20, 30, 40]}
>>> ps.DataFrame.from_dict(data, orient='index').sort_index()
0 1 2 3
row_1 3 2 1 0
row_2 10 20 30 40
When using the 'index' orientation, the column names can be
specified manually:
>>> ps.DataFrame.from_dict(data, orient='index',
... columns=['A', 'B', 'C', 'D']).sort_index()
A B C D
row_1 3 2 1 0
row_2 10 20 30 40
"""
return DataFrame(
pd.DataFrame.from_dict(
data, orient=orient, dtype=dtype, columns=columns # type: ignore[arg-type]
)
)
# Override the `groupby` to specify the actual return type annotation.
[docs] def groupby(
self,
by: Union[Name, "Series", List[Union[Name, "Series"]]],
axis: Axis = 0,
as_index: bool = True,
dropna: bool = True,
) -> "DataFrameGroupBy":
return cast(
"DataFrameGroupBy", super().groupby(by=by, axis=axis, as_index=as_index, dropna=dropna)
)
groupby.__doc__ = Frame.groupby.__doc__
def _build_groupby(
self, by: List[Union["Series", Label]], as_index: bool, dropna: bool
) -> "DataFrameGroupBy":
from pyspark.pandas.groupby import DataFrameGroupBy
return DataFrameGroupBy._build(self, by, as_index=as_index, dropna=dropna)
[docs] def resample(
self,
rule: str,
closed: Optional[str] = None,
label: Optional[str] = None,
on: Optional["Series"] = None,
) -> "DataFrameResampler":
"""
Resample time-series data.
Convenience method for frequency conversion and resampling of time series.
The object must have a datetime-like index (only support `DatetimeIndex` for now),
or the caller must pass the label of a datetime-like
series/index to the ``on`` keyword parameter.
.. versionadded:: 3.4.0
Parameters
----------
rule : str
The offset string or object representing target conversion.
Currently, supported units are {'Y', 'A', 'M', 'D', 'H',
'T', 'MIN', 'S'}.
closed : {{'right', 'left'}}, default None
Which side of bin interval is closed. The default is 'left'
for all frequency offsets except for 'A', 'Y' and 'M' which all
have a default of 'right'.
label : {{'right', 'left'}}, default None
Which bin edge label to label bucket with. The default is 'left'
for all frequency offsets except for 'A', 'Y' and 'M' which all
have a default of 'right'.
on : Series, optional
For a DataFrame, column to use instead of index for resampling.
Column must be datetime-like.
Returns
-------
DataFrameResampler
See Also
--------
Series.resample : Resample a Series.
groupby : Group by mapping, function, label, or list of labels.
"""
from pyspark.pandas.indexes import DatetimeIndex
from pyspark.pandas.resample import DataFrameResampler
if on is None and not isinstance(self.index, DatetimeIndex):
raise NotImplementedError("resample currently works only for DatetimeIndex")
if on is not None and not isinstance(
as_spark_type(on.dtype), (TimestampType, TimestampNTZType)
):
raise NotImplementedError("`on` currently works only for TimestampType")
agg_columns: List[ps.Series] = []
for column_label in self._internal.column_labels:
if isinstance(self._internal.spark_type_for(column_label), (NumericType, BooleanType)):
agg_columns.append(self._psser_for(column_label))
if len(agg_columns) == 0:
raise ValueError("No available aggregation columns!")
return DataFrameResampler(
psdf=self,
resamplekey=on,
rule=rule,
closed=closed,
label=label,
agg_columns=agg_columns,
)
def _to_internal_pandas(self) -> pd.DataFrame:
"""
Return a pandas DataFrame directly from _internal to avoid overhead of copy.
This method is for internal use only.
"""
return self._internal.to_pandas_frame
def _get_or_create_repr_pandas_cache(self, n: int) -> Union[pd.DataFrame, pd.Series]:
if not hasattr(self, "_repr_pandas_cache") or n not in self._repr_pandas_cache:
object.__setattr__(
self, "_repr_pandas_cache", {n: self.head(n + 1)._to_internal_pandas()}
)
return self._repr_pandas_cache[n]
def __repr__(self) -> str:
max_display_count = get_option("display.max_rows")
if max_display_count is None:
return self._to_internal_pandas().to_string()
pdf = cast("DataFrame", self._get_or_create_repr_pandas_cache(max_display_count))
pdf_length = len(pdf)
pdf = cast("DataFrame", pdf.iloc[:max_display_count])
if pdf_length > max_display_count:
repr_string = pdf.to_string(show_dimensions=True)
match = REPR_PATTERN.search(repr_string)
if match is not None:
nrows = match.group("rows")
ncols = match.group("columns")
footer = "\n\n[Showing only the first {nrows} rows x {ncols} columns]".format(
nrows=nrows, ncols=ncols
)
return REPR_PATTERN.sub(footer, repr_string)
return pdf.to_string()
def _repr_html_(self) -> str:
max_display_count = get_option("display.max_rows")
if max_display_count is None:
return self._to_internal_pandas().to_html(notebook=True)
pdf = self._get_or_create_repr_pandas_cache(max_display_count)
pdf_length = len(pdf)
pdf = pdf.iloc[:max_display_count]
if pdf_length > max_display_count:
repr_html = pdf.to_html(show_dimensions=True, notebook=True)
match = REPR_HTML_PATTERN.search(repr_html)
if match is not None:
nrows = match.group("rows")
ncols = match.group("columns")
by = chr(215)
footer = (
"\n<p>Showing only the first {rows} rows "
"{by} {cols} columns</p>\n</div>".format(rows=nrows, by=by, cols=ncols)
)
return REPR_HTML_PATTERN.sub(footer, repr_html)
return pdf.to_html(notebook=True)
def __getitem__(self, key: Any) -> Any:
from pyspark.pandas.series import Series
if key is None:
raise KeyError("none key")
elif isinstance(key, Series):
return self.loc[key.astype(bool)]
elif isinstance(key, slice):
if any(type(n) == int or None for n in [key.start, key.stop]):
# Seems like pandas Frame always uses int as positional search when slicing
# with ints.
return self.iloc[key]
return self.loc[key]
elif is_name_like_value(key):
return self.loc[:, key]
elif is_list_like(key):
return self.loc[:, list(key)]
def __setitem__(self, key: Any, value: Any) -> None:
from pyspark.pandas.series import Series
if isinstance(value, (DataFrame, Series)) and not same_anchor(value, self):
# Different Series or DataFrames
level = self._internal.column_labels_level
key = DataFrame._index_normalized_label(level, key)
value = DataFrame._index_normalized_frame(level, value)
def assign_columns(
psdf: DataFrame, this_column_labels: List[Label], that_column_labels: List[Label]
) -> Iterator[Tuple["Series", Label]]:
assert len(key) == len(that_column_labels)
# Note that here intentionally uses `zip_longest` that combine
# that_columns.
for k, this_label, that_label in zip_longest(
key, this_column_labels, that_column_labels
):
yield (psdf._psser_for(that_label), tuple(["that", *k]))
if this_label is not None and this_label[1:] != k:
yield (psdf._psser_for(this_label), this_label)
psdf = align_diff_frames(assign_columns, self, value, fillna=False, how="left")
elif isinstance(value, list):
if len(self) != len(value):
raise ValueError("Length of values does not match length of index")
# TODO: avoid using default index?
with option_context(
"compute.default_index_type",
"distributed-sequence",
"compute.ops_on_diff_frames",
True,
):
psdf = self.reset_index()
psdf[key] = ps.DataFrame(value)
psdf = psdf.set_index(psdf.columns[: self._internal.index_level])
psdf.index.names = self.index.names
elif isinstance(key, list):
assert isinstance(value, DataFrame)
# Same DataFrames.
field_names = value.columns
psdf = self._assign({k: value[c] for k, c in zip(key, field_names)})
else:
# Same Series.
psdf = self._assign({key: value})
# Since Spark 3.4, df.__setitem__ generates a new dataframe instead of operating
# in-place to follow pandas v1.4 behavior, see also SPARK-38946.
self._update_internal_frame(psdf._internal, anchor_force_disconnect=True)
@staticmethod
def _index_normalized_label(level: int, labels: Union[Name, Sequence[Name]]) -> List[Label]:
"""
Returns a label that is normalized against the current column index level.
For example, the key "abc" can be ("abc", "", "") if the current Frame has
a multi-index for its column
"""
if is_name_like_tuple(labels):
labels = [labels]
elif is_name_like_value(labels):
labels = [(labels,)]
else:
labels = [k if is_name_like_tuple(k) else (k,) for k in labels]
if any(len(label) > level for label in labels):
raise KeyError(
"Key length ({}) exceeds index depth ({})".format(
max(len(label) for label in labels), level
)
)
return [tuple(list(label) + ([""] * (level - len(label)))) for label in labels]
@staticmethod
def _index_normalized_frame(level: int, psser_or_psdf: DataFrameOrSeries) -> "DataFrame":
"""
Returns a frame that is normalized against the current column index level.
For example, the name in `pd.Series([...], name="abc")` can be can be
("abc", "", "") if the current DataFrame has a multi-index for its column
"""
from pyspark.pandas.series import Series
if isinstance(psser_or_psdf, Series):
psdf = psser_or_psdf.to_frame()
else:
assert isinstance(psser_or_psdf, DataFrame), type(psser_or_psdf)
psdf = psser_or_psdf.copy()
psdf.columns = pd.MultiIndex.from_tuples(
[
tuple([name_like_string(label)] + ([""] * (level - 1)))
for label in psdf._internal.column_labels
],
)
return psdf
def __getattr__(self, key: str) -> Any:
if key.startswith("__"):
raise AttributeError(key)
if hasattr(MissingPandasLikeDataFrame, key):
property_or_func = getattr(MissingPandasLikeDataFrame, key)
if isinstance(property_or_func, property):
return property_or_func.fget(self)
else:
return partial(property_or_func, self)
try:
return self.loc[:, key]
except KeyError:
raise AttributeError(
"'%s' object has no attribute '%s'" % (self.__class__.__name__, key)
)
def __setattr__(self, key: str, value: Any) -> None:
try:
object.__getattribute__(self, key)
return object.__setattr__(self, key, value)
except AttributeError:
pass
if (key,) in self._internal.column_labels:
self[key] = value
else:
msg = "pandas-on-Spark doesn't allow columns to be created via a new attribute name"
if is_testing():
raise AssertionError(msg)
else:
warnings.warn(msg, UserWarning)
def __len__(self) -> int:
return self._internal.resolved_copy.spark_frame.count()
def __dir__(self) -> Iterable[str]:
fields = [
f for f in self._internal.resolved_copy.spark_frame.schema.fieldNames() if " " not in f
]
return list(super().__dir__()) + fields
def __iter__(self) -> Iterator[Name]:
return iter(self.columns)
# NDArray Compat
def __array_ufunc__(
self, ufunc: Callable, method: str, *inputs: Any, **kwargs: Any
) -> "DataFrame":
# TODO: is it possible to deduplicate it with '_map_series_op'?
if all(isinstance(inp, DataFrame) for inp in inputs) and any(
not same_anchor(inp, inputs[0]) for inp in inputs
):
# binary only
assert len(inputs) == 2
this = inputs[0]
that = inputs[1]
if this._internal.column_labels_level != that._internal.column_labels_level:
raise ValueError("cannot join with no overlapping index names")
# Different DataFrames
def apply_op(
psdf: DataFrame, this_column_labels: List[Label], that_column_labels: List[Label]
) -> Iterator[Tuple["Series", Label]]:
for this_label, that_label in zip(this_column_labels, that_column_labels):
yield (
ufunc(
psdf._psser_for(this_label), psdf._psser_for(that_label), **kwargs
).rename(this_label),
this_label,
)
return align_diff_frames(apply_op, this, that, fillna=True, how="full")
else:
# DataFrame and Series
applied = []
this = inputs[0]
assert all(inp is this for inp in inputs if isinstance(inp, DataFrame))
for label in this._internal.column_labels:
arguments = []
for inp in inputs:
arguments.append(inp[label] if isinstance(inp, DataFrame) else inp)
# both binary and unary.
applied.append(ufunc(*arguments, **kwargs).rename(label))
internal = this._internal.with_new_columns(applied)
return DataFrame(internal)
def __class_getitem__(cls, params: Any) -> object:
# See https://github.com/python/typing/issues/193
# we always wraps the given type hints by a tuple to mimic the variadic generic.
return create_tuple_for_frame_type(params)
def _reduce_spark_multi(sdf: PySparkDataFrame, aggs: List[PySparkColumn]) -> Any:
"""
Performs a reduction on a spark DataFrame, the functions being known SQL aggregate functions.
"""
SparkDataFrame = get_dataframe_class()
assert isinstance(sdf, SparkDataFrame)
sdf0 = sdf.agg(*aggs)
lst = sdf0.limit(2).toPandas()
assert len(lst) == 1, (sdf, lst)
row = lst.iloc[0]
lst2 = list(row)
assert len(lst2) == len(aggs), (row, lst2)
return lst2
class CachedDataFrame(DataFrame):
"""
Cached pandas-on-Spark DataFrame, which corresponds to pandas DataFrame logically, but
internally it caches the corresponding Spark DataFrame.
"""
def __init__(self, internal: InternalFrame, storage_level: Optional[StorageLevel] = None):
if storage_level is None:
object.__setattr__(self, "_cached", internal.spark_frame.cache())
elif isinstance(storage_level, StorageLevel):
object.__setattr__(self, "_cached", internal.spark_frame.persist(storage_level))
else:
raise TypeError(
"Only a valid pyspark.StorageLevel type is acceptable for the `storage_level`"
)
super().__init__(internal)
def __enter__(self) -> "CachedDataFrame":
return self
def __exit__(
self,
exception_type: Optional[Type[BaseException]],
exception_value: Optional[BaseException],
traceback: Optional[TracebackType],
) -> Optional[bool]:
self.spark.unpersist()
return None
# create accessor for Spark related methods.
spark = CachedAccessor("spark", CachedSparkFrameMethods)
def _test() -> None:
import os
import doctest
import shutil
import sys
import tempfile
import uuid
from pyspark.sql import SparkSession
import pyspark.pandas.frame
os.chdir(os.environ["SPARK_HOME"])
globs = pyspark.pandas.frame.__dict__.copy()
globs["ps"] = pyspark.pandas
spark = (
SparkSession.builder.master("local[4]").appName("pyspark.pandas.frame tests").getOrCreate()
)
globs["spark"] = spark
db_name = "db%s" % str(uuid.uuid4()).replace("-", "")
spark.sql("CREATE DATABASE %s" % db_name)
globs["db"] = db_name
path = tempfile.mkdtemp()
globs["path"] = path
(failure_count, test_count) = doctest.testmod(
pyspark.pandas.frame,
globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
)
shutil.rmtree(path, ignore_errors=True)
spark.sql("DROP DATABASE IF EXISTS %s CASCADE" % db_name)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()