#
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# 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.
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"""
Wrappers around spark that correspond to common pandas functions.
"""
from typing import (
Any,
Callable,
Dict,
List,
Optional,
Set,
Sized,
Tuple,
Type,
Union,
cast,
no_type_check,
)
from collections.abc import Iterable
from datetime import tzinfo
from functools import reduce
from io import BytesIO
import json
import warnings
import numpy as np
import pandas as pd
from pandas.api.types import ( # type: ignore[attr-defined]
is_datetime64_dtype,
is_datetime64tz_dtype,
is_list_like,
)
from pandas.tseries.offsets import DateOffset
import pyarrow as pa
import pyarrow.parquet as pq
from pyspark.sql import functions as F, Column as PySparkColumn
from pyspark.sql.functions import pandas_udf
from pyspark.sql.types import (
ByteType,
ShortType,
IntegerType,
LongType,
FloatType,
DoubleType,
BooleanType,
TimestampType,
TimestampNTZType,
DecimalType,
StringType,
DateType,
StructType,
DataType,
)
from pyspark.sql.dataframe import DataFrame as PySparkDataFrame
from pyspark import pandas as ps
from pyspark.pandas._typing import Axis, Dtype, Label, Name
from pyspark.pandas.base import IndexOpsMixin
from pyspark.pandas.utils import (
align_diff_frames,
default_session,
is_name_like_tuple,
is_name_like_value,
name_like_string,
same_anchor,
scol_for,
validate_axis,
log_advice,
)
from pyspark.pandas.frame import DataFrame, _reduce_spark_multi
from pyspark.pandas.internal import (
InternalFrame,
DEFAULT_SERIES_NAME,
HIDDEN_COLUMNS,
SPARK_INDEX_NAME_FORMAT,
)
from pyspark.pandas.series import Series, first_series
from pyspark.pandas.spark.utils import as_nullable_spark_type, force_decimal_precision_scale
from pyspark.pandas.indexes import Index, DatetimeIndex, TimedeltaIndex
from pyspark.pandas.indexes.multi import MultiIndex
# For Supporting Spark Connect
from pyspark.sql.utils import get_column_class
__all__ = [
"from_pandas",
"range",
"read_csv",
"read_delta",
"read_table",
"read_spark_io",
"read_parquet",
"read_clipboard",
"read_excel",
"read_html",
"to_datetime",
"date_range",
"to_timedelta",
"timedelta_range",
"get_dummies",
"concat",
"melt",
"isna",
"isnull",
"notna",
"notnull",
"read_sql_table",
"read_sql_query",
"read_sql",
"read_json",
"merge",
"merge_asof",
"to_numeric",
"broadcast",
"read_orc",
]
def from_pandas(pobj: Union[pd.DataFrame, pd.Series, pd.Index]) -> Union[Series, DataFrame, Index]:
"""Create a pandas-on-Spark DataFrame, Series or Index from a pandas DataFrame, Series or Index.
This is similar to Spark's `SparkSession.createDataFrame()` with pandas DataFrame,
but this also works with pandas Series and picks the index.
Parameters
----------
pobj : pandas.DataFrame or pandas.Series
pandas DataFrame or Series to read.
Returns
-------
Series or DataFrame
If a pandas Series is passed in, this function returns a pandas-on-Spark Series.
If a pandas DataFrame is passed in, this function returns a pandas-on-Spark DataFrame.
"""
if isinstance(pobj, pd.Series):
return Series(pobj)
elif isinstance(pobj, pd.DataFrame):
return DataFrame(pobj)
elif isinstance(pobj, pd.Index):
return DataFrame(pd.DataFrame(index=pobj)).index
else:
raise TypeError("Unknown data type: {}".format(type(pobj).__name__))
_range = range # built-in range
[docs]def range(
start: int, end: Optional[int] = None, step: int = 1, num_partitions: Optional[int] = None
) -> DataFrame:
"""
Create a DataFrame with some range of numbers.
The resulting DataFrame has a single int64 column named `id`, containing elements in a range
from ``start`` to ``end`` (exclusive) with step value ``step``. If only the first parameter
(i.e. start) is specified, we treat it as the end value with the start value being 0.
This is like the range function in SparkSession and is used primarily for testing.
Parameters
----------
start : int
the start value (inclusive)
end : int, optional
the end value (exclusive)
step : int, optional, default 1
the incremental step
num_partitions : int, optional
the number of partitions of the DataFrame
Returns
-------
DataFrame
Examples
--------
When the first parameter is specified, we generate a range of values up till that number.
>>> ps.range(5)
id
0 0
1 1
2 2
3 3
4 4
When start, end, and step are specified:
>>> ps.range(start = 100, end = 200, step = 20)
id
0 100
1 120
2 140
3 160
4 180
"""
sdf = default_session().range(start=start, end=end, step=step, numPartitions=num_partitions)
return DataFrame(sdf)
[docs]def read_csv(
path: Union[str, List[str]],
sep: str = ",",
header: Union[str, int, None] = "infer",
names: Optional[Union[str, List[str]]] = None,
index_col: Optional[Union[str, List[str]]] = None,
usecols: Optional[Union[List[int], List[str], Callable[[str], bool]]] = None,
squeeze: bool = False,
mangle_dupe_cols: bool = True,
dtype: Optional[Union[str, Dtype, Dict[str, Union[str, Dtype]]]] = None,
nrows: Optional[int] = None,
parse_dates: bool = False,
quotechar: Optional[str] = None,
escapechar: Optional[str] = None,
comment: Optional[str] = None,
encoding: Optional[str] = None,
**options: Any,
) -> Union[DataFrame, Series]:
"""Read CSV (comma-separated) file into DataFrame or Series.
Parameters
----------
path : str or list
Path(s) of the CSV file(s) to be read.
sep : str, default ‘,’
Delimiter to use. Non empty string.
header : int, default ‘infer’
Whether to use the column names, and the start of the data.
Default behavior is to infer the column names: if no names are passed
the behavior is identical to `header=0` and column names are inferred from
the first line of the file, if column names are passed explicitly then
the behavior is identical to `header=None`. Explicitly pass `header=0` to be
able to replace existing names
names : str or array-like, optional
List of column names to use. If file contains no header row, then you should
explicitly pass `header=None`. Duplicates in this list will cause an error to be issued.
If a string is given, it should be a DDL-formatted string in Spark SQL, which is
preferred to avoid schema inference for better performance.
index_col: str or list of str, optional, default: None
Index column of table in Spark.
usecols : list-like or callable, optional
Return a subset of the columns. If list-like, all elements must either be
positional (i.e. integer indices into the document columns) or strings that
correspond to column names provided either by the user in names or inferred
from the document header row(s).
If callable, the callable function will be evaluated against the column names,
returning names where the callable function evaluates to `True`.
squeeze : bool, default False
If the parsed data only contains one column then return a Series.
.. deprecated:: 3.4.0
mangle_dupe_cols : bool, default True
Duplicate columns will be specified as 'X0', 'X1', ... 'XN', rather
than 'X' ... 'X'. Passing in False will cause data to be overwritten if
there are duplicate names in the columns.
Currently only `True` is allowed.
.. deprecated:: 3.4.0
dtype : Type name or dict of column -> type, default None
Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32} Use str or object
together with suitable na_values settings to preserve and not interpret dtype.
nrows : int, default None
Number of rows to read from the CSV file.
parse_dates : boolean or list of ints or names or list of lists or dict, default `False`.
Currently only `False` is allowed.
quotechar : str (length 1), optional
The character used to denote the start and end of a quoted item. Quoted items can include
the delimiter and it will be ignored.
escapechar : str (length 1), default None
One-character string used to escape other characters.
comment: str, optional
Indicates the line should not be parsed.
encoding: str, optional
Indicates the encoding to read file
options : dict
All other options passed directly into Spark's data source.
Returns
-------
DataFrame or Series
See Also
--------
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
Examples
--------
>>> ps.read_csv('data.csv') # doctest: +SKIP
Load multiple CSV files as a single DataFrame:
>>> ps.read_csv(['data-01.csv', 'data-02.csv']) # doctest: +SKIP
"""
# For latin-1 encoding is same as iso-8859-1, that's why its mapped to iso-8859-1.
encoding_mapping = {"latin-1": "iso-8859-1"}
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options")
if mangle_dupe_cols is not True:
raise ValueError("mangle_dupe_cols can only be `True`: %s" % mangle_dupe_cols)
if parse_dates is not False:
raise ValueError("parse_dates can only be `False`: %s" % parse_dates)
if usecols is not None and not callable(usecols):
usecols = list(usecols) # type: ignore[assignment]
if usecols is None or callable(usecols) or len(usecols) > 0:
reader = default_session().read
reader.option("inferSchema", True)
reader.option("sep", sep)
if header == "infer":
header = 0 if names is None else None
if header == 0:
reader.option("header", True)
elif header is None:
reader.option("header", False)
else:
raise ValueError("Unknown header argument {}".format(header))
if quotechar is not None:
reader.option("quote", quotechar)
if escapechar is not None:
reader.option("escape", escapechar)
if comment is not None:
if not isinstance(comment, str) or len(comment) != 1:
raise ValueError("Only length-1 comment characters supported")
reader.option("comment", comment)
reader.options(**options)
if encoding is not None:
reader.option("encoding", encoding_mapping.get(encoding, encoding))
column_labels: Dict[Any, str]
if isinstance(names, str):
sdf = reader.schema(names).csv(path)
column_labels = {col: col for col in sdf.columns}
else:
sdf = reader.csv(path)
if is_list_like(names):
names = list(names)
if len(set(names)) != len(names):
raise ValueError("Found non-unique column index")
if len(names) != len(sdf.columns):
raise ValueError(
"The number of names [%s] does not match the number "
"of columns [%d]. Try names by a Spark SQL DDL-formatted "
"string." % (len(sdf.schema), len(names))
)
column_labels = dict(zip(names, sdf.columns))
elif header is None:
column_labels = dict(enumerate(sdf.columns))
else:
column_labels = {col: col for col in sdf.columns}
if usecols is not None:
missing: List[Union[int, str]]
if callable(usecols):
column_labels = {
label: col for label, col in column_labels.items() if usecols(label)
}
missing = []
elif all(isinstance(col, int) for col in usecols):
usecols_ints = cast(List[int], usecols)
new_column_labels = {
label: col
for i, (label, col) in enumerate(column_labels.items())
if i in usecols_ints
}
missing = [
col
for col in usecols_ints
if (
col >= len(column_labels)
or list(column_labels)[col] not in new_column_labels
)
]
column_labels = new_column_labels
elif all(isinstance(col, str) for col in usecols):
new_column_labels = {
label: col for label, col in column_labels.items() if label in usecols
}
missing = [col for col in usecols if col not in new_column_labels]
column_labels = new_column_labels
else:
raise ValueError(
"'usecols' must either be list-like of all strings, "
"all unicode, all integers or a callable."
)
if len(missing) > 0:
raise ValueError(
"Usecols do not match columns, columns expected but not " "found: %s" % missing
)
if len(column_labels) > 0:
sdf = sdf.select([scol_for(sdf, col) for col in column_labels.values()])
else:
sdf = default_session().createDataFrame([], schema=StructType())
else:
sdf = default_session().createDataFrame([], schema=StructType())
column_labels = {}
if nrows is not None:
sdf = sdf.limit(nrows)
index_spark_column_names: List[str]
index_names: List[Label]
if index_col is not None:
if isinstance(index_col, (str, int)):
index_col = [index_col]
for col in index_col:
if col not in column_labels:
raise KeyError(col)
index_spark_column_names = [column_labels[col] for col in index_col]
index_names = [(col,) for col in index_col]
column_labels = {
label: col for label, col in column_labels.items() if label not in index_col
}
else:
log_advice(
"If `index_col` is not specified for `read_csv`, "
"the default index is attached which can cause additional overhead."
)
index_spark_column_names = []
index_names = []
psdf: DataFrame = DataFrame(
InternalFrame(
spark_frame=sdf,
index_spark_columns=[scol_for(sdf, col) for col in index_spark_column_names],
index_names=index_names,
column_labels=[
label if is_name_like_tuple(label) else (label,) for label in column_labels
],
data_spark_columns=[scol_for(sdf, col) for col in column_labels.values()],
)
)
if dtype is not None:
if isinstance(dtype, dict):
for col, tpe in dtype.items():
psdf[col] = psdf[col].astype(tpe)
else:
for col in psdf.columns:
psdf[col] = psdf[col].astype(dtype)
if squeeze and len(psdf.columns) == 1:
return first_series(psdf)
else:
return psdf
[docs]def read_json(
path: str, lines: bool = True, index_col: Optional[Union[str, List[str]]] = None, **options: Any
) -> DataFrame:
"""
Convert a JSON string to DataFrame.
Parameters
----------
path : string
File path
lines : bool, default True
Read the file as a JSON object per line. It should be always True for now.
index_col : str or list of str, optional, default: None
Index column of table in Spark.
options : dict
All other options passed directly into Spark's data source.
Examples
--------
>>> df = ps.DataFrame([['a', 'b'], ['c', 'd']],
... columns=['col 1', 'col 2'])
>>> df.to_json(path=r'%s/read_json/foo.json' % path, num_files=1)
>>> ps.read_json(
... path=r'%s/read_json/foo.json' % path
... ).sort_values(by="col 1")
col 1 col 2
0 a b
1 c d
>>> df.to_json(path=r'%s/read_json/foo.json' % path, num_files=1, lineSep='___')
>>> ps.read_json(
... path=r'%s/read_json/foo.json' % path, lineSep='___'
... ).sort_values(by="col 1")
col 1 col 2
0 a b
1 c d
You can preserve the index in the roundtrip as below.
>>> df.to_json(path=r'%s/read_json/bar.json' % path, num_files=1, index_col="index")
>>> ps.read_json(
... path=r'%s/read_json/bar.json' % path, index_col="index"
... ).sort_values(by="col 1") # doctest: +NORMALIZE_WHITESPACE
col 1 col 2
index
0 a b
1 c d
"""
if index_col is None:
log_advice(
"If `index_col` is not specified for `read_json`, "
"the default index is attached which can cause additional overhead."
)
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options")
if not lines:
raise NotImplementedError("lines=False is not implemented yet.")
return read_spark_io(path, format="json", index_col=index_col, **options)
[docs]def read_delta(
path: str,
version: Optional[str] = None,
timestamp: Optional[str] = None,
index_col: Optional[Union[str, List[str]]] = None,
**options: Any,
) -> DataFrame:
"""
Read a Delta Lake table on some file system and return a DataFrame.
If the Delta Lake table is already stored in the catalog (aka the metastore), use 'read_table'.
Parameters
----------
path : string
Path to the Delta Lake table.
version : string, optional
Specifies the table version (based on Delta's internal transaction version) to read from,
using Delta's time travel feature. This sets Delta's 'versionAsOf' option. Note that
this parameter and `timestamp` parameter cannot be used together, otherwise it will raise a
`ValueError`.
timestamp : string, optional
Specifies the table version (based on timestamp) to read from,
using Delta's time travel feature. This must be a valid date or timestamp string in Spark,
and sets Delta's 'timestampAsOf' option. Note that this parameter and `version` parameter
cannot be used together, otherwise it will raise a `ValueError`.
index_col : str or list of str, optional, default: None
Index column of table in Spark.
options
Additional options that can be passed onto Delta.
Returns
-------
DataFrame
See Also
--------
DataFrame.to_delta
read_table
read_spark_io
read_parquet
Examples
--------
>>> ps.range(1).to_delta('%s/read_delta/foo' % path) # doctest: +SKIP
>>> ps.read_delta('%s/read_delta/foo' % path) # doctest: +SKIP
id
0 0
>>> ps.range(10, 15, num_partitions=1).to_delta('%s/read_delta/foo' % path,
... mode='overwrite') # doctest: +SKIP
>>> ps.read_delta('%s/read_delta/foo' % path) # doctest: +SKIP
id
0 10
1 11
2 12
3 13
4 14
>>> ps.read_delta('%s/read_delta/foo' % path, version=0) # doctest: +SKIP
id
0 0
You can preserve the index in the roundtrip as below.
>>> ps.range(10, 15, num_partitions=1).to_delta(
... '%s/read_delta/bar' % path, index_col="index") # doctest: +SKIP
>>> ps.read_delta('%s/read_delta/bar' % path, index_col="index") # doctest: +SKIP
id
index
0 10
1 11
2 12
3 13
4 14
"""
if index_col is None:
log_advice(
"If `index_col` is not specified for `read_delta`, "
"the default index is attached which can cause additional overhead."
)
if version is not None and timestamp is not None:
raise ValueError("version and timestamp cannot be used together.")
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options")
if version is not None:
options["versionAsOf"] = version
if timestamp is not None:
options["timestampAsOf"] = timestamp
return read_spark_io(path, format="delta", index_col=index_col, **options)
[docs]def read_table(name: str, index_col: Optional[Union[str, List[str]]] = None) -> DataFrame:
"""
Read a Spark table and return a DataFrame.
Parameters
----------
name : string
Table name in Spark.
index_col : str or list of str, optional, default: None
Index column of table in Spark.
Returns
-------
DataFrame
See Also
--------
DataFrame.to_table
read_delta
read_parquet
read_spark_io
Examples
--------
>>> ps.range(1).to_table('%s.my_table' % db)
>>> ps.read_table('%s.my_table' % db)
id
0 0
>>> ps.range(1).to_table('%s.my_table' % db, index_col="index")
>>> ps.read_table('%s.my_table' % db, index_col="index") # doctest: +NORMALIZE_WHITESPACE
id
index
0 0
"""
if index_col is None:
log_advice(
"If `index_col` is not specified for `read_table`, "
"the default index is attached which can cause additional overhead."
)
sdf = default_session().read.table(name)
index_spark_columns, index_names = _get_index_map(sdf, index_col)
return DataFrame(
InternalFrame(
spark_frame=sdf, index_spark_columns=index_spark_columns, index_names=index_names
)
)
[docs]def read_spark_io(
path: Optional[str] = None,
format: Optional[str] = None,
schema: Union[str, "StructType"] = None,
index_col: Optional[Union[str, List[str]]] = None,
**options: Any,
) -> DataFrame:
"""Load a DataFrame from a Spark data source.
Parameters
----------
path : string, optional
Path to the data source.
format : string, optional
Specifies the output data source format. Some common ones are:
- 'delta'
- 'parquet'
- 'orc'
- 'json'
- 'csv'
schema : string or StructType, optional
Input schema. If none, Spark tries to infer the schema automatically.
The schema can either be a Spark StructType, or a DDL-formatted string like
`col0 INT, col1 DOUBLE`.
index_col : str or list of str, optional, default: None
Index column of table in Spark.
options : dict
All other options passed directly into Spark's data source.
See Also
--------
DataFrame.to_spark_io
DataFrame.read_table
DataFrame.read_delta
DataFrame.read_parquet
Examples
--------
>>> ps.range(1).to_spark_io('%s/read_spark_io/data.parquet' % path)
>>> ps.read_spark_io(
... '%s/read_spark_io/data.parquet' % path, format='parquet', schema='id long')
id
0 0
>>> ps.range(10, 15, num_partitions=1).to_spark_io('%s/read_spark_io/data.json' % path,
... format='json', lineSep='__')
>>> ps.read_spark_io(
... '%s/read_spark_io/data.json' % path, format='json', schema='id long', lineSep='__')
id
0 10
1 11
2 12
3 13
4 14
You can preserve the index in the roundtrip as below.
>>> ps.range(10, 15, num_partitions=1).to_spark_io('%s/read_spark_io/data.orc' % path,
... format='orc', index_col="index")
>>> ps.read_spark_io(
... path=r'%s/read_spark_io/data.orc' % path, format="orc", index_col="index")
... # doctest: +NORMALIZE_WHITESPACE
id
index
0 10
1 11
2 12
3 13
4 14
"""
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options")
sdf = default_session().read.load(path=path, format=format, schema=schema, **options)
index_spark_columns, index_names = _get_index_map(sdf, index_col)
return DataFrame(
InternalFrame(
spark_frame=sdf, index_spark_columns=index_spark_columns, index_names=index_names
)
)
[docs]def read_parquet(
path: str,
columns: Optional[List[str]] = None,
index_col: Optional[List[str]] = None,
pandas_metadata: bool = False,
**options: Any,
) -> DataFrame:
"""Load a parquet object from the file path, returning a DataFrame.
Parameters
----------
path : string
File path
columns : list, default=None
If not None, only these columns will be read from the file.
index_col : str or list of str, optional, default: None
Index column of table in Spark.
pandas_metadata : bool, default: False
If True, try to respect the metadata if the Parquet file is written from pandas.
options : dict
All other options passed directly into Spark's data source.
Returns
-------
DataFrame
See Also
--------
DataFrame.to_parquet
DataFrame.read_table
DataFrame.read_delta
DataFrame.read_spark_io
Examples
--------
>>> ps.range(1).to_parquet('%s/read_spark_io/data.parquet' % path)
>>> ps.read_parquet('%s/read_spark_io/data.parquet' % path, columns=['id'])
id
0 0
You can preserve the index in the roundtrip as below.
>>> ps.range(1).to_parquet('%s/read_spark_io/data.parquet' % path, index_col="index")
>>> ps.read_parquet('%s/read_spark_io/data.parquet' % path, columns=['id'], index_col="index")
... # doctest: +NORMALIZE_WHITESPACE
id
index
0 0
"""
if index_col is None:
log_advice(
"If `index_col` is not specified for `read_parquet`, "
"the default index is attached which can cause additional overhead."
)
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options")
if columns is not None:
columns = list(columns)
index_names = None
if index_col is None and pandas_metadata:
# Try to read pandas metadata
@pandas_udf( # type: ignore[call-overload]
"index_col array<string>, index_names array<string>"
)
def read_index_metadata(pser: pd.Series) -> pd.DataFrame:
binary = pser.iloc[0]
metadata = pq.ParquetFile(pa.BufferReader(binary)).metadata.metadata
if b"pandas" in metadata:
pandas_metadata = json.loads(metadata[b"pandas"].decode("utf8"))
if all(isinstance(col, str) for col in pandas_metadata["index_columns"]):
index_col = []
index_names = []
for col in pandas_metadata["index_columns"]:
index_col.append(col)
for column in pandas_metadata["columns"]:
if column["field_name"] == col:
index_names.append(column["name"])
break
else:
index_names.append(None)
return pd.DataFrame({"index_col": [index_col], "index_names": [index_names]})
return pd.DataFrame({"index_col": [None], "index_names": [None]})
index_col, index_names = (
default_session()
.read.format("binaryFile")
.load(path)
.limit(1)
.select(read_index_metadata("content").alias("index_metadata"))
.select("index_metadata.*")
.head()
)
psdf = read_spark_io(path=path, format="parquet", options=options, index_col=index_col)
if columns is not None:
new_columns = [c for c in columns if c in psdf.columns]
if len(new_columns) > 0:
psdf = psdf[new_columns]
else:
sdf = default_session().createDataFrame([], schema=StructType())
index_spark_columns, index_names = _get_index_map(sdf, index_col)
psdf = DataFrame(
InternalFrame(
spark_frame=sdf,
index_spark_columns=index_spark_columns,
index_names=index_names,
)
)
if index_names is not None:
psdf.index.names = index_names
return psdf
[docs]def read_clipboard(sep: str = r"\s+", **kwargs: Any) -> DataFrame:
r"""
Read text from clipboard and pass to read_csv. See read_csv for the
full argument list
Parameters
----------
sep : str, default '\s+'
A string or regex delimiter. The default of '\s+' denotes
one or more whitespace characters.
See Also
--------
DataFrame.to_clipboard : Write text out to clipboard.
Returns
-------
parsed : DataFrame
"""
return cast(DataFrame, from_pandas(pd.read_clipboard(sep, **kwargs)))
[docs]def read_excel(
io: Union[str, Any],
sheet_name: Union[str, int, List[Union[str, int]], None] = 0,
header: Union[int, List[int]] = 0,
names: Optional[List] = None,
index_col: Optional[List[int]] = None,
usecols: Optional[Union[int, str, List[Union[int, str]], Callable[[str], bool]]] = None,
squeeze: bool = False,
dtype: Optional[Dict[str, Union[str, Dtype]]] = None,
engine: Optional[str] = None,
converters: Optional[Dict] = None,
true_values: Optional[Any] = None,
false_values: Optional[Any] = None,
skiprows: Optional[Union[int, List[int]]] = None,
nrows: Optional[int] = None,
na_values: Optional[Any] = None,
keep_default_na: bool = True,
verbose: bool = False,
parse_dates: Union[bool, List, Dict] = False,
date_parser: Optional[Callable] = None,
thousands: Optional[str] = None,
comment: Optional[str] = None,
skipfooter: int = 0,
convert_float: bool = True,
mangle_dupe_cols: bool = True,
**kwds: Any,
) -> Union[DataFrame, Series, Dict[str, Union[DataFrame, Series]]]:
"""
Read an Excel file into a pandas-on-Spark DataFrame or Series.
Support both `xls` and `xlsx` file extensions from a local filesystem or URL.
Support an option to read a single sheet or a list of sheets.
Parameters
----------
io : str, file descriptor, pathlib.Path, ExcelFile or xlrd.Book
The string could be a URL. The value URL must be available in Spark's DataFrameReader.
.. note::
If the underlying Spark is below 3.0, the parameter as a string is not supported.
You can use `ps.from_pandas(pd.read_excel(...))` as a workaround.
sheet_name : str, int, list, or None, default 0
Strings are used for sheet names. Integers are used in zero-indexed
sheet positions. Lists of strings/integers are used to request
multiple sheets. Specify None to get all sheets.
Available cases:
* Defaults to ``0``: 1st sheet as a `DataFrame`
* ``1``: 2nd sheet as a `DataFrame`
* ``"Sheet1"``: Load sheet with name "Sheet1"
* ``[0, 1, "Sheet5"]``: Load first, second and sheet named "Sheet5"
as a dict of `DataFrame`
* None: All sheets.
header : int, list of int, default 0
Row (0-indexed) to use for the column labels of the parsed
DataFrame. If a list of integers is passed those row positions will
be combined into a ``MultiIndex``. Use None if there is no header.
names : array-like, default None
List of column names to use. If file contains no header row,
then you should explicitly pass header=None.
index_col : int, list of int, default None
Column (0-indexed) to use as the row labels of the DataFrame.
Pass None if there is no such column. If a list is passed,
those columns will be combined into a ``MultiIndex``. If a
subset of data is selected with ``usecols``, index_col
is based on the subset.
usecols : int, str, list-like, or callable default None
Return a subset of the columns.
* If None, then parse all columns.
* If str, then indicates comma separated list of Excel column letters
and column ranges (e.g. "A:E" or "A,C,E:F"). Ranges are inclusive of
both sides.
* If list of int, then indicates list of column numbers to be parsed.
* If list of string, then indicates list of column names to be parsed.
* If callable, then evaluate each column name against it and parse the
column if the callable returns ``True``.
squeeze : bool, default False
If the parsed data only contains one column then return a Series.
.. deprecated:: 3.4.0
dtype : Type name or dict of column -> type, default None
Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
Use `object` to preserve data as stored in Excel and not interpret dtype.
If converters are specified, they will be applied INSTEAD
of dtype conversion.
engine : str, default None
If io is not a buffer or path, this must be set to identify io.
Acceptable values are None or xlrd.
converters : dict, default None
Dict of functions for converting values in certain columns. Keys can
either be integers or column labels, values are functions that take one
input argument, the Excel cell content, and return the transformed
content.
true_values : list, default None
Values to consider as True.
false_values : list, default None
Values to consider as False.
skiprows : list-like
Rows to skip at the beginning (0-indexed).
nrows : int, default None
Number of rows to parse.
na_values : scalar, str, list-like, or dict, default None
Additional strings to recognize as NA/NaN. If dict passed, specific
per-column NA values. By default the following values are interpreted
as NaN.
keep_default_na : bool, default True
If na_values are specified and keep_default_na is False the default NaN
values are overridden, otherwise they're appended to.
verbose : bool, default False
Indicate number of NA values placed in non-numeric columns.
parse_dates : bool, list-like, or dict, default False
The behavior is as follows:
* bool. If True -> try parsing the index.
* list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
each as a separate date column.
* list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as
a single date column.
* dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call
result 'foo'
If a column or index contains an unparseable date, the entire column or
index will be returned unaltered as an object data type. For non-standard
datetime parsing, use ``pd.to_datetime`` after ``pd.read_csv``
Note: A fast-path exists for iso8601-formatted dates.
date_parser : function, optional
Function to use for converting a sequence of string columns to an array of
datetime instances. The default uses ``dateutil.parser.parser`` to do the
conversion. pandas-on-Spark will try to call `date_parser` in three different ways,
advancing to the next if an exception occurs: 1) Pass one or more arrays
(as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
string values from the columns defined by `parse_dates` into a single array
and pass that; and 3) call `date_parser` once for each row using one or
more strings (corresponding to the columns defined by `parse_dates`) as
arguments.
thousands : str, default None
Thousands separator for parsing string columns to numeric. Note that
this parameter is only necessary for columns stored as TEXT in Excel,
any numeric columns will automatically be parsed, regardless of display
format.
comment : str, default None
Comments out remainder of line. Pass a character or characters to this
argument to indicate comments in the input file. Any data between the
comment string and the end of the current line is ignored.
skipfooter : int, default 0
Rows at the end to skip (0-indexed).
convert_float : bool, default True
Convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric
data will be read in as floats: Excel stores all numbers as floats
internally.
.. deprecated:: 3.4.0
mangle_dupe_cols : bool, default True
Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
'X'...'X'. Passing in False will cause data to be overwritten if there
are duplicate names in the columns.
.. deprecated:: 3.4.0
**kwds : optional
Optional keyword arguments can be passed to ``TextFileReader``.
Returns
-------
DataFrame or dict of DataFrames
DataFrame from the passed in Excel file. See notes in sheet_name
argument for more information on when a dict of DataFrames is returned.
See Also
--------
DataFrame.to_excel : Write DataFrame to an Excel file.
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
read_csv : Read a comma-separated values (csv) file into DataFrame.
Examples
--------
The file can be read using the file name as string or an open file object:
>>> ps.read_excel('tmp.xlsx', index_col=0) # doctest: +SKIP
Name Value
0 string1 1
1 string2 2
2 #Comment 3
>>> ps.read_excel(open('tmp.xlsx', 'rb'),
... sheet_name='Sheet3') # doctest: +SKIP
Unnamed: 0 Name Value
0 0 string1 1
1 1 string2 2
2 2 #Comment 3
Index and header can be specified via the `index_col` and `header` arguments
>>> ps.read_excel('tmp.xlsx', index_col=None, header=None) # doctest: +SKIP
0 1 2
0 NaN Name Value
1 0.0 string1 1
2 1.0 string2 2
3 2.0 #Comment 3
Column types are inferred but can be explicitly specified
>>> ps.read_excel('tmp.xlsx', index_col=0,
... dtype={'Name': str, 'Value': float}) # doctest: +SKIP
Name Value
0 string1 1.0
1 string2 2.0
2 #Comment 3.0
True, False, and NA values, and thousands separators have defaults,
but can be explicitly specified, too. Supply the values you would like
as strings or lists of strings!
>>> ps.read_excel('tmp.xlsx', index_col=0,
... na_values=['string1', 'string2']) # doctest: +SKIP
Name Value
0 None 1
1 None 2
2 #Comment 3
Comment lines in the excel input file can be skipped using the `comment` kwarg
>>> ps.read_excel('tmp.xlsx', index_col=0, comment='#') # doctest: +SKIP
Name Value
0 string1 1.0
1 string2 2.0
2 None NaN
"""
def pd_read_excel(
io_or_bin: Any, sn: Union[str, int, List[Union[str, int]], None], sq: bool
) -> pd.DataFrame:
return pd.read_excel(
io=BytesIO(io_or_bin) if isinstance(io_or_bin, (bytes, bytearray)) else io_or_bin,
sheet_name=sn,
header=header,
names=names,
index_col=index_col,
usecols=usecols,
squeeze=sq,
dtype=dtype,
engine=engine,
converters=converters,
true_values=true_values,
false_values=false_values,
skiprows=skiprows,
nrows=nrows,
na_values=na_values,
keep_default_na=keep_default_na,
verbose=verbose,
parse_dates=parse_dates, # type: ignore[arg-type]
date_parser=date_parser,
thousands=thousands,
comment=comment,
skipfooter=skipfooter,
convert_float=convert_float,
mangle_dupe_cols=mangle_dupe_cols,
**kwds,
)
if isinstance(io, str):
# 'binaryFile' format is available since Spark 3.0.0.
binaries = default_session().read.format("binaryFile").load(io).select("content").head(2)
io_or_bin = binaries[0][0]
single_file = len(binaries) == 1
else:
io_or_bin = io
single_file = True
pdf_or_psers = pd_read_excel(io_or_bin, sn=sheet_name, sq=squeeze)
if single_file:
if isinstance(pdf_or_psers, dict):
return {
sn: cast(Union[DataFrame, Series], from_pandas(pdf_or_pser))
for sn, pdf_or_pser in pdf_or_psers.items()
}
else:
return cast(Union[DataFrame, Series], from_pandas(pdf_or_psers))
else:
def read_excel_on_spark(
pdf_or_pser: Union[pd.DataFrame, pd.Series],
sn: Union[str, int, List[Union[str, int]], None],
) -> Union[DataFrame, Series]:
if isinstance(pdf_or_pser, pd.Series):
pdf = pdf_or_pser.to_frame()
else:
pdf = pdf_or_pser
psdf = cast(DataFrame, from_pandas(pdf))
return_schema = force_decimal_precision_scale(
as_nullable_spark_type(psdf._internal.spark_frame.drop(*HIDDEN_COLUMNS).schema)
)
def output_func(pdf: pd.DataFrame) -> pd.DataFrame:
pdf = pd.concat(
[pd_read_excel(bin, sn=sn, sq=False) for bin in pdf[pdf.columns[0]]]
)
reset_index = pdf.reset_index()
for name, col in reset_index.items():
dt = col.dtype
if is_datetime64_dtype(dt) or is_datetime64tz_dtype(dt):
continue
reset_index[name] = col.replace({np.nan: None})
pdf = reset_index
# Just positionally map the column names to given schema's.
return pdf.rename(columns=dict(zip(pdf.columns, return_schema.names)))
sdf = (
default_session()
.read.format("binaryFile")
.load(io)
.select("content")
.mapInPandas(lambda iterator: map(output_func, iterator), schema=return_schema)
)
psdf = DataFrame(psdf._internal.with_new_sdf(sdf))
if squeeze and len(psdf.columns) == 1:
return first_series(psdf)
else:
return psdf
if isinstance(pdf_or_psers, dict):
return {
sn: read_excel_on_spark(pdf_or_pser, sn) for sn, pdf_or_pser in pdf_or_psers.items()
}
else:
return read_excel_on_spark(pdf_or_psers, sheet_name)
[docs]def read_html(
io: Union[str, Any],
match: str = ".+",
flavor: Optional[str] = None,
header: Optional[Union[int, List[int]]] = None,
index_col: Optional[Union[int, List[int]]] = None,
skiprows: Optional[Union[int, List[int], slice]] = None,
attrs: Optional[Dict[str, str]] = None,
parse_dates: bool = False,
thousands: str = ",",
encoding: Optional[str] = None,
decimal: str = ".",
converters: Optional[Dict] = None,
na_values: Optional[Any] = None,
keep_default_na: bool = True,
displayed_only: bool = True,
) -> List[DataFrame]:
r"""Read HTML tables into a ``list`` of ``DataFrame`` objects.
Parameters
----------
io : str or file-like
A URL, a file-like object, or a raw string containing HTML. Note that
lxml only accepts the http, FTP and file URL protocols. If you have a
URL that starts with ``'https'`` you might try removing the ``'s'``.
match : str or compiled regular expression, optional
The set of tables containing text matching this regex or string will be
returned. Unless the HTML is extremely simple you will probably need to
pass a non-empty string here. Defaults to '.+' (match any non-empty
string). The default value will return all tables contained on a page.
This value is converted to a regular expression so that there is
consistent behavior between Beautiful Soup and lxml.
flavor : str or None, container of strings
The parsing engine to use. 'bs4' and 'html5lib' are synonymous with
each other, they are both there for backwards compatibility. The
default of ``None`` tries to use ``lxml`` to parse and if that fails it
falls back on ``bs4`` + ``html5lib``.
header : int or list-like or None, optional
The row (or list of rows for a :class:`~ps.MultiIndex`) to use to
make the columns headers.
index_col : int or list-like or None, optional
The column (or list of columns) to use to create the index.
skiprows : int or list-like or slice or None, optional
0-based. Number of rows to skip after parsing the column integer. If a
sequence of integers or a slice is given, will skip the rows indexed by
that sequence. Note that a single element sequence means 'skip the nth
row' whereas an integer means 'skip n rows'.
attrs : dict or None, optional
This is a dictionary of attributes that you can pass to use to identify
the table in the HTML. These are not checked for validity before being
passed to lxml or Beautiful Soup. However, these attributes must be
valid HTML table attributes to work correctly. For example, ::
attrs = {'id': 'table'}
is a valid attribute dictionary because the 'id' HTML tag attribute is
a valid HTML attribute for *any* HTML tag as per `this document
<http://www.w3.org/TR/html-markup/global-attributes.html>`__. ::
attrs = {'asdf': 'table'}
is *not* a valid attribute dictionary because 'asdf' is not a valid
HTML attribute even if it is a valid XML attribute. Valid HTML 4.01
table attributes can be found `here
<http://www.w3.org/TR/REC-html40/struct/tables.html#h-11.2>`__. A
working draft of the HTML 5 spec can be found `here
<http://www.w3.org/TR/html-markup/table.html>`__. It contains the
latest information on table attributes for the modern web.
parse_dates : bool, optional
See :func:`~ps.read_csv` for more details.
thousands : str, optional
Separator to use to parse thousands. Defaults to ``','``.
encoding : str or None, optional
The encoding used to decode the web page. Defaults to ``None``.``None``
preserves the previous encoding behavior, which depends on the
underlying parser library (e.g., the parser library will try to use
the encoding provided by the document).
decimal : str, default '.'
Character to recognize as decimal point (example: use ',' for European
data).
converters : dict, default None
Dict of functions for converting values in certain columns. Keys can
either be integers or column labels, values are functions that take one
input argument, the cell (not column) content, and return the
transformed content.
na_values : iterable, default None
Custom NA values
keep_default_na : bool, default True
If na_values are specified and keep_default_na is False the default NaN
values are overridden, otherwise they're appended to
displayed_only : bool, default True
Whether elements with "display: none" should be parsed
Returns
-------
dfs : list of DataFrames
See Also
--------
read_csv
DataFrame.to_html
"""
pdfs = pd.read_html(
io=io,
match=match,
flavor=flavor,
header=header,
index_col=index_col,
skiprows=skiprows,
attrs=attrs,
parse_dates=parse_dates,
thousands=thousands,
encoding=encoding,
decimal=decimal,
converters=converters,
na_values=na_values,
keep_default_na=keep_default_na,
displayed_only=displayed_only,
)
return cast(List[DataFrame], [from_pandas(pdf) for pdf in pdfs])
# TODO: add `coerce_float` and 'parse_dates' parameters
[docs]def read_sql_table(
table_name: str,
con: str,
schema: Optional[str] = None,
index_col: Optional[Union[str, List[str]]] = None,
columns: Optional[Union[str, List[str]]] = None,
**options: Any,
) -> DataFrame:
"""
Read SQL database table into a DataFrame.
Given a table name and a JDBC URI, returns a DataFrame.
Parameters
----------
table_name : str
Name of SQL table in database.
con : str
A JDBC URI could be provided as str.
.. note:: The URI must be JDBC URI instead of Python's database URI.
schema : str, default None
Name of SQL schema in database to query (if database flavor
supports this). Uses default schema if None (default).
index_col : str or list of str, optional, default: None
Column(s) to set as index(MultiIndex).
columns : list, default None
List of column names to select from SQL table.
options : dict
All other options passed directly into Spark's JDBC data source.
Returns
-------
DataFrame
A SQL table is returned as two-dimensional data structure with labeled
axes.
See Also
--------
read_sql_query : Read SQL query into a DataFrame.
read_sql : Read SQL query or database table into a DataFrame.
Examples
--------
>>> ps.read_sql_table('table_name', 'jdbc:postgresql:db_name') # doctest: +SKIP
"""
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options")
reader = default_session().read
reader.option("dbtable", table_name)
reader.option("url", con)
if schema is not None:
reader.schema(schema)
reader.options(**options)
sdf = reader.format("jdbc").load()
index_spark_columns, index_names = _get_index_map(sdf, index_col)
psdf: DataFrame = DataFrame(
InternalFrame(
spark_frame=sdf, index_spark_columns=index_spark_columns, index_names=index_names
)
)
if columns is not None:
if isinstance(columns, str):
columns = [columns]
psdf = psdf[columns]
return psdf
# TODO: add `coerce_float`, `params`, and 'parse_dates' parameters
[docs]def read_sql_query(
sql: str, con: str, index_col: Optional[Union[str, List[str]]] = None, **options: Any
) -> DataFrame:
"""Read SQL query into a DataFrame.
Returns a DataFrame corresponding to the result set of the query
string. Optionally provide an `index_col` parameter to use one of the
columns as the index, otherwise default index will be used.
.. note:: Some database might hit the issue of Spark: SPARK-27596
Parameters
----------
sql : string SQL query
SQL query to be executed.
con : str
A JDBC URI could be provided as str.
.. note:: The URI must be JDBC URI instead of Python's database URI.
index_col : string or list of strings, optional, default: None
Column(s) to set as index(MultiIndex).
options : dict
All other options passed directly into Spark's JDBC data source.
Returns
-------
DataFrame
See Also
--------
read_sql_table : Read SQL database table into a DataFrame.
read_sql
Examples
--------
>>> ps.read_sql_query('SELECT * FROM table_name', 'jdbc:postgresql:db_name') # doctest: +SKIP
"""
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options")
reader = default_session().read
reader.option("query", sql)
reader.option("url", con)
reader.options(**options)
sdf = reader.format("jdbc").load()
index_spark_columns, index_names = _get_index_map(sdf, index_col)
return DataFrame(
InternalFrame(
spark_frame=sdf, index_spark_columns=index_spark_columns, index_names=index_names
)
)
# TODO: add `coerce_float`, `params`, and 'parse_dates' parameters
[docs]def read_sql(
sql: str,
con: str,
index_col: Optional[Union[str, List[str]]] = None,
columns: Optional[Union[str, List[str]]] = None,
**options: Any,
) -> DataFrame:
"""
Read SQL query or database table into a DataFrame.
This function is a convenience wrapper around ``read_sql_table`` and
``read_sql_query`` (for backward compatibility). It will delegate
to the specific function depending on the provided input. A SQL query
will be routed to ``read_sql_query``, while a database table name will
be routed to ``read_sql_table``. Note that the delegated function might
have more specific notes about their functionality not listed here.
.. note:: Some database might hit the issue of Spark: SPARK-27596
Parameters
----------
sql : string
SQL query to be executed or a table name.
con : str
A JDBC URI could be provided as str.
.. note:: The URI must be JDBC URI instead of Python's database URI.
index_col : string or list of strings, optional, default: None
Column(s) to set as index(MultiIndex).
columns : list, default: None
List of column names to select from SQL table (only used when reading
a table).
options : dict
All other options passed directly into Spark's JDBC data source.
Returns
-------
DataFrame
See Also
--------
read_sql_table : Read SQL database table into a DataFrame.
read_sql_query : Read SQL query into a DataFrame.
Examples
--------
>>> ps.read_sql('table_name', 'jdbc:postgresql:db_name') # doctest: +SKIP
>>> ps.read_sql('SELECT * FROM table_name', 'jdbc:postgresql:db_name') # doctest: +SKIP
"""
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options")
striped = sql.strip()
if " " not in striped: # TODO: identify the table name or not more precisely.
return read_sql_table(sql, con, index_col=index_col, columns=columns, **options)
else:
return read_sql_query(sql, con, index_col=index_col, **options)
[docs]@no_type_check
def to_datetime(
arg,
errors: str = "raise",
format: Optional[str] = None,
unit: Optional[str] = None,
infer_datetime_format: bool = False,
origin: str = "unix",
):
"""
Convert argument to datetime.
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
or DataFrame/dict-like
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as NaT
- If 'ignore', then invalid parsing will return the input
format : string, default None
strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
all the way up to nanoseconds.
unit : string, default None
unit of the arg (D,s,ms,us,ns) denote the unit, which is an
integer or float number. This will be based off the origin.
Example, with unit='ms' and origin='unix' (the default), this
would calculate the number of milliseconds to the unix epoch start.
infer_datetime_format : boolean, default False
If True and no `format` is given, attempt to infer the format of the
datetime strings, and if it can be inferred, switch to a faster
method of parsing them. In some cases this can increase the parsing
speed by ~5-10x.
origin : scalar, default 'unix'
Define the reference date. The numeric values would be parsed as number
of units (defined by `unit`) since this reference date.
- If 'unix' (or POSIX) time; origin is set to 1970-01-01.
- If 'julian', unit must be 'D', and origin is set to beginning of
Julian Calendar. Julian day number 0 is assigned to the day starting
at noon on January 1, 4713 BC.
- If Timestamp convertible, origin is set to Timestamp identified by
origin.
Returns
-------
ret : datetime if parsing succeeded.
Return type depends on input:
- list-like: DatetimeIndex
- Series: Series of datetime64 dtype
- scalar: Timestamp
In case when it is not possible to return designated types (e.g. when
any element of input is before Timestamp.min or after Timestamp.max)
return will have datetime.datetime type (or corresponding
array/Series).
Examples
--------
Assembling a datetime from multiple columns of a DataFrame. The keys can be
common abbreviations like ['year', 'month', 'day', 'minute', 'second',
'ms', 'us', 'ns']) or plurals of the same
>>> df = ps.DataFrame({'year': [2015, 2016],
... 'month': [2, 3],
... 'day': [4, 5]})
>>> ps.to_datetime(df)
0 2015-02-04
1 2016-03-05
dtype: datetime64[ns]
If a date does not meet the `timestamp limitations
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html
#timeseries-timestamp-limits>`_, passing errors='ignore'
will return the original input instead of raising any exception.
Passing errors='coerce' will force an out-of-bounds date to NaT,
in addition to forcing non-dates (or non-parseable dates) to NaT.
>>> ps.to_datetime('13000101', format='%Y%m%d', errors='ignore') # doctest: +SKIP
datetime.datetime(1300, 1, 1, 0, 0)
>>> ps.to_datetime('13000101', format='%Y%m%d', errors='coerce')
NaT
Passing infer_datetime_format=True can often-times speedup a parsing
if its not an ISO8601 format exactly, but in a regular format.
>>> s = ps.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 1000)
>>> s.head()
0 3/11/2000
1 3/12/2000
2 3/13/2000
3 3/11/2000
4 3/12/2000
dtype: object
>>> import timeit
>>> timeit.timeit(
... lambda: repr(ps.to_datetime(s, infer_datetime_format=True)),
... number = 1) # doctest: +SKIP
0.35832712500000063
>>> timeit.timeit(
... lambda: repr(ps.to_datetime(s, infer_datetime_format=False)),
... number = 1) # doctest: +SKIP
0.8895321660000004
Using a unix epoch time
>>> ps.to_datetime(1490195805, unit='s')
Timestamp('2017-03-22 15:16:45')
>>> ps.to_datetime(1490195805433502912, unit='ns')
Timestamp('2017-03-22 15:16:45.433502912')
Using a non-unix epoch origin
>>> ps.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))
DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], dtype='datetime64[ns]', freq=None)
"""
# mappings for assembling units
# From pandas: pandas.core.tools.datetimes
_unit_map = {
"year": "year",
"years": "year",
"month": "month",
"months": "month",
"day": "day",
"days": "day",
"hour": "h",
"hours": "h",
"minute": "m",
"minutes": "m",
"second": "s",
"seconds": "s",
"ms": "ms",
"millisecond": "ms",
"milliseconds": "ms",
"us": "us",
"microsecond": "us",
"microseconds": "us",
}
def pandas_to_datetime(
pser_or_pdf: Union[pd.DataFrame, pd.Series], cols: Optional[List[str]] = None
) -> Series[np.datetime64]:
if isinstance(pser_or_pdf, pd.DataFrame):
pser_or_pdf = pser_or_pdf[cols]
return pd.to_datetime(
pser_or_pdf,
errors=errors,
format=format,
unit=unit,
infer_datetime_format=infer_datetime_format,
origin=origin,
)
if isinstance(arg, Series):
return arg.pandas_on_spark.transform_batch(pandas_to_datetime)
if isinstance(arg, DataFrame):
unit = {k: _unit_map[k.lower()] for k in arg.keys() if k.lower() in _unit_map}
unit_rev = {v: k for k, v in unit.items()}
list_cols = [unit_rev["year"], unit_rev["month"], unit_rev["day"]]
for u in ["h", "m", "s", "ms", "us"]:
value = unit_rev.get(u)
if value is not None and value in arg:
list_cols.append(value)
psdf = arg[list_cols]
return psdf.pandas_on_spark.transform_batch(pandas_to_datetime, list_cols)
return pd.to_datetime(
arg,
errors=errors,
format=format,
unit=unit,
infer_datetime_format=infer_datetime_format,
origin=origin,
)
# TODO(SPARK-42621): Add `inclusive` parameter and replace `closed`.
# See https://github.com/pandas-dev/pandas/issues/40245
[docs]def date_range(
start: Union[str, Any] = None,
end: Union[str, Any] = None,
periods: Optional[int] = None,
freq: Optional[Union[str, DateOffset]] = None,
tz: Optional[Union[str, tzinfo]] = None,
normalize: bool = False,
name: Optional[str] = None,
closed: Optional[str] = None,
**kwargs: Any,
) -> DatetimeIndex:
"""
Return a fixed frequency DatetimeIndex.
Parameters
----------
start : str or datetime-like, optional
Left bound for generating dates.
end : str or datetime-like, optional
Right bound for generating dates.
periods : int, optional
Number of periods to generate.
freq : str or DateOffset, default 'D'
Frequency strings can have multiples, e.g. '5H'.
tz : str or tzinfo, optional
Time zone name for returning localized DatetimeIndex, for example
'Asia/Hong_Kong'. By default, the resulting DatetimeIndex is
time zone naive.
normalize : bool, default False
Normalize start/end dates to midnight before generating date range.
name : str, default None
Name of the resulting DatetimeIndex.
closed : {None, 'left', 'right'}, optional
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None, the default).
.. deprecated:: 3.4.0
**kwargs
For compatibility. Has no effect on the result.
Returns
-------
rng : DatetimeIndex
See Also
--------
DatetimeIndex : An immutable container for datetimes.
Notes
-----
Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
exactly three must be specified. If ``freq`` is omitted, the resulting
``DatetimeIndex`` will have ``periods`` linearly spaced elements between
``start`` and ``end`` (closed on both sides).
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
**Specifying the values**
The next four examples generate the same `DatetimeIndex`, but vary
the combination of `start`, `end` and `periods`.
Specify `start` and `end`, with the default daily frequency.
>>> ps.date_range(start='1/1/2018', end='1/08/2018') # doctest: +SKIP
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
'2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
dtype='datetime64[ns]', freq=None)
Specify `start` and `periods`, the number of periods (days).
>>> ps.date_range(start='1/1/2018', periods=8) # doctest: +SKIP
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
'2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
dtype='datetime64[ns]', freq=None)
Specify `end` and `periods`, the number of periods (days).
>>> ps.date_range(end='1/1/2018', periods=8) # doctest: +SKIP
DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28',
'2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'],
dtype='datetime64[ns]', freq=None)
Specify `start`, `end`, and `periods`; the frequency is generated
automatically (linearly spaced).
>>> ps.date_range(
... start='2018-04-24', end='2018-04-27', periods=3
... ) # doctest: +SKIP
DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00',
'2018-04-27 00:00:00'],
dtype='datetime64[ns]', freq=None)
**Other Parameters**
Changed the `freq` (frequency) to ``'M'`` (month end frequency).
>>> ps.date_range(start='1/1/2018', periods=5, freq='M') # doctest: +SKIP
DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
'2018-05-31'],
dtype='datetime64[ns]', freq=None)
Multiples are allowed
>>> ps.date_range(start='1/1/2018', periods=5, freq='3M') # doctest: +SKIP
DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
'2019-01-31'],
dtype='datetime64[ns]', freq=None)
`freq` can also be specified as an Offset object.
>>> ps.date_range(
... start='1/1/2018', periods=5, freq=pd.offsets.MonthEnd(3)
... ) # doctest: +SKIP
DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
'2019-01-31'],
dtype='datetime64[ns]', freq=None)
`closed` controls whether to include `start` and `end` that are on the
boundary. The default includes boundary points on either end.
>>> ps.date_range(
... start='2017-01-01', end='2017-01-04', closed=None
... ) # doctest: +SKIP
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'],
dtype='datetime64[ns]', freq=None)
Use ``closed='left'`` to exclude `end` if it falls on the boundary.
>>> ps.date_range(
... start='2017-01-01', end='2017-01-04', closed='left'
... ) # doctest: +SKIP
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'], dtype='datetime64[ns]', freq=None)
Use ``closed='right'`` to exclude `start` if it falls on the boundary.
>>> ps.date_range(
... start='2017-01-01', end='2017-01-04', closed='right'
... ) # doctest: +SKIP
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq=None)
"""
assert freq not in ["N", "ns"], "nanoseconds is not supported"
assert tz is None, "Localized DatetimeIndex is not supported"
if closed is not None:
warnings.warn(
"Argument `closed` is deprecated in 3.4.0 and will be removed in 4.0.0.",
FutureWarning,
)
return cast(
DatetimeIndex,
ps.from_pandas(
pd.date_range(
start=start,
end=end,
periods=periods,
freq=freq,
tz=tz,
normalize=normalize,
name=name,
closed=closed,
**kwargs,
)
),
)
[docs]@no_type_check
def to_timedelta(
arg,
unit: Optional[str] = None,
errors: str = "raise",
):
"""
Convert argument to timedelta.
Parameters
----------
arg : str, timedelta, list-like or Series
The data to be converted to timedelta.
unit : str, optional
Denotes the unit of the arg for numeric `arg`. Defaults to ``"ns"``.
Possible values:
* 'W'
* 'D' / 'days' / 'day'
* 'hours' / 'hour' / 'hr' / 'h'
* 'm' / 'minute' / 'min' / 'minutes' / 'T'
* 'S' / 'seconds' / 'sec' / 'second'
* 'ms' / 'milliseconds' / 'millisecond' / 'milli' / 'millis' / 'L'
* 'us' / 'microseconds' / 'microsecond' / 'micro' / 'micros' / 'U'
* 'ns' / 'nanoseconds' / 'nano' / 'nanos' / 'nanosecond' / 'N'
Must not be specified when `arg` context strings and ``errors="raise"``.
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception.
- If 'coerce', then invalid parsing will be set as NaT.
- If 'ignore', then invalid parsing will return the input.
Returns
-------
ret : timedelta64, TimedeltaIndex or Series of timedelta64 if parsing succeeded.
See Also
--------
DataFrame.astype : Cast argument to a specified dtype.
to_datetime : Convert argument to datetime.
Notes
-----
If the precision is higher than nanoseconds, the precision of the duration is
truncated to nanoseconds for string inputs.
Examples
--------
Parsing a single string to a Timedelta:
>>> ps.to_timedelta('1 days 06:05:01.00003')
Timedelta('1 days 06:05:01.000030')
>>> ps.to_timedelta('15.5us') # doctest: +SKIP
Timedelta('0 days 00:00:00.000015500')
Parsing a list or array of strings:
>>> ps.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan']) # doctest: +SKIP
TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015500', NaT],
dtype='timedelta64[ns]', freq=None)
Converting numbers by specifying the `unit` keyword argument:
>>> ps.to_timedelta(np.arange(5), unit='s') # doctest: +SKIP
TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01', '0 days 00:00:02',
'0 days 00:00:03', '0 days 00:00:04'],
dtype='timedelta64[ns]', freq=None)
>>> ps.to_timedelta(np.arange(5), unit='d') # doctest: +NORMALIZE_WHITESPACE
TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
dtype='timedelta64[ns]', freq=None)
"""
def pandas_to_timedelta(pser: pd.Series) -> np.timedelta64:
return pd.to_timedelta(
arg=pser,
unit=unit,
errors=errors,
)
if isinstance(arg, Series):
return arg.transform(pandas_to_timedelta)
else:
return pd.to_timedelta(
arg=arg,
unit=unit,
errors=errors,
)
[docs]def timedelta_range(
start: Union[str, Any] = None,
end: Union[str, Any] = None,
periods: Optional[int] = None,
freq: Optional[Union[str, DateOffset]] = None,
name: Optional[str] = None,
closed: Optional[str] = None,
) -> TimedeltaIndex:
"""
Return a fixed frequency TimedeltaIndex, with day as the default frequency.
Parameters
----------
start : str or timedelta-like, optional
Left bound for generating timedeltas.
end : str or timedelta-like, optional
Right bound for generating timedeltas.
periods : int, optional
Number of periods to generate.
freq : str or DateOffset, default 'D'
Frequency strings can have multiples, e.g. '5H'.
name : str, default None
Name of the resulting TimedeltaIndex.
closed : {None, 'left', 'right'}, optional
Make the interval closed with respect to the given frequency to
the 'left', 'right', or both sides (None, the default).
Returns
-------
TimedeltaIndex
Notes
-----
Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
exactly three must be specified. If ``freq`` is omitted, the resulting
``TimedeltaIndex`` will have ``periods`` linearly spaced elements between
``start`` and ``end`` (closed on both sides).
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
>>> ps.timedelta_range(start='1 day', periods=4) # doctest: +NORMALIZE_WHITESPACE
TimedeltaIndex(['1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None)
The closed parameter specifies which endpoint is included.
The default behavior is to include both endpoints.
>>> ps.timedelta_range(start='1 day', periods=4, closed='right')
... # doctest: +NORMALIZE_WHITESPACE
TimedeltaIndex(['2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None)
The freq parameter specifies the frequency of the TimedeltaIndex.
Only fixed frequencies can be passed, non-fixed frequencies such as ‘M’ (month end) will raise.
>>> ps.timedelta_range(start='1 day', end='2 days', freq='6H')
... # doctest: +NORMALIZE_WHITESPACE
TimedeltaIndex(['1 days 00:00:00', '1 days 06:00:00', '1 days 12:00:00',
'1 days 18:00:00', '2 days 00:00:00'],
dtype='timedelta64[ns]', freq=None)
Specify start, end, and periods; the frequency is generated automatically (linearly spaced).
>>> ps.timedelta_range(start='1 day', end='5 days', periods=4)
... # doctest: +NORMALIZE_WHITESPACE
TimedeltaIndex(['1 days 00:00:00', '2 days 08:00:00', '3 days 16:00:00',
'5 days 00:00:00'],
dtype='timedelta64[ns]', freq=None)
"""
assert freq not in ["N", "ns"], "nanoseconds is not supported"
return cast(
TimedeltaIndex,
ps.from_pandas(
pd.timedelta_range(
start=start,
end=end,
periods=periods,
freq=freq,
name=name,
closed=closed,
)
),
)
[docs]def get_dummies(
data: Union[DataFrame, Series],
prefix: Optional[Union[str, List[str], Dict[str, str]]] = None,
prefix_sep: str = "_",
dummy_na: bool = False,
columns: Optional[Union[Name, List[Name]]] = None,
sparse: bool = False,
drop_first: bool = False,
dtype: Optional[Union[str, Dtype]] = None,
) -> DataFrame:
"""
Convert categorical variable into dummy/indicator variables, also
known as one hot encoding.
Parameters
----------
data : array-like, Series, or DataFrame
prefix : string, list of strings, or dict of strings, default None
String to append DataFrame column names.
Pass a list with length equal to the number of columns
when calling get_dummies on a DataFrame. Alternatively, `prefix`
can be a dictionary mapping column names to prefixes.
prefix_sep : string, default '_'
If appending prefix, separator/delimiter to use. Or pass a
list or dictionary as with `prefix.`
dummy_na : bool, default False
Add a column to indicate NaNs, if False NaNs are ignored.
columns : list-like, default None
Column names in the DataFrame to be encoded.
If `columns` is None then all the columns with
`object` or `category` dtype will be converted.
sparse : bool, default False
Whether the dummy-encoded columns should be be backed by
a :class:`SparseArray` (True) or a regular NumPy array (False).
In pandas-on-Spark, this value must be "False".
drop_first : bool, default False
Whether to get k-1 dummies out of k categorical levels by removing the
first level.
dtype : dtype, default np.uint8
Data type for new columns. Only a single dtype is allowed.
Returns
-------
dummies : DataFrame
See Also
--------
Series.str.get_dummies
Examples
--------
>>> s = ps.Series(list('abca'))
>>> ps.get_dummies(s)
a b c
0 1 0 0
1 0 1 0
2 0 0 1
3 1 0 0
>>> df = ps.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
... 'C': [1, 2, 3]},
... columns=['A', 'B', 'C'])
>>> ps.get_dummies(df, prefix=['col1', 'col2'])
C col1_a col1_b col2_a col2_b col2_c
0 1 1 0 0 1 0
1 2 0 1 1 0 0
2 3 1 0 0 0 1
>>> ps.get_dummies(ps.Series(list('abcaa')))
a b c
0 1 0 0
1 0 1 0
2 0 0 1
3 1 0 0
4 1 0 0
>>> ps.get_dummies(ps.Series(list('abcaa')), drop_first=True)
b c
0 0 0
1 1 0
2 0 1
3 0 0
4 0 0
>>> ps.get_dummies(ps.Series(list('abc')), dtype=float)
a b c
0 1.0 0.0 0.0
1 0.0 1.0 0.0
2 0.0 0.0 1.0
"""
if sparse is not False:
raise NotImplementedError("get_dummies currently does not support sparse")
if columns is not None and not is_list_like(columns):
raise TypeError("Input must be a list-like for parameter `columns`")
if dtype is None:
dtype = "byte"
if isinstance(data, Series):
if prefix is not None:
prefix = [str(prefix)]
psdf = data.to_frame()
column_labels = psdf._internal.column_labels
remaining_columns = []
else:
if isinstance(prefix, str):
raise NotImplementedError(
"get_dummies currently does not support prefix as string types"
)
psdf = data.copy()
if columns is None:
column_labels = [
label
for label in psdf._internal.column_labels
if isinstance(
psdf._internal.spark_type_for(label), _get_dummies_default_accept_types
)
]
else:
if is_name_like_tuple(columns):
column_labels = [
label
for label in psdf._internal.column_labels
if label[: len(columns)] == columns
]
if len(column_labels) == 0:
raise KeyError(name_like_string(columns))
if prefix is None:
prefix = [
str(label[len(columns) :])
if len(label) > len(columns) + 1
else label[len(columns)]
if len(label) == len(columns) + 1
else ""
for label in column_labels
]
elif any(isinstance(col, tuple) for col in columns) and any(
not is_name_like_tuple(col) for col in columns
):
raise ValueError(
"Expected tuple, got {}".format(
type(set(col for col in columns if not is_name_like_tuple(col)).pop())
)
)
else:
column_labels = [
label
for key in columns
for label in psdf._internal.column_labels
if label == key or label[0] == key
]
if len(column_labels) == 0:
if columns is None:
return psdf
raise KeyError("{} not in index".format(columns))
if prefix is None:
prefix = [str(label) if len(label) > 1 else label[0] for label in column_labels]
column_labels_set = set(column_labels)
remaining_columns = [
(
psdf[label]
if psdf._internal.column_labels_level == 1
else psdf[label].rename(name_like_string(label))
)
for label in psdf._internal.column_labels
if label not in column_labels_set
]
if any(
not isinstance(psdf._internal.spark_type_for(label), _get_dummies_acceptable_types)
for label in column_labels
):
raise NotImplementedError(
"get_dummies currently only accept {} values".format(
", ".join(
[cast(Type[DataType], t).typeName() for t in _get_dummies_acceptable_types]
)
)
)
if prefix is not None and len(column_labels) != len(prefix):
raise ValueError(
"Length of 'prefix' ({}) did not match the length of "
"the columns being encoded ({}).".format(len(prefix), len(column_labels))
)
elif isinstance(prefix, dict):
prefix = [prefix[column_label[0]] for column_label in column_labels]
all_values = _reduce_spark_multi(
psdf._internal.spark_frame,
[F.collect_set(psdf._internal.spark_column_for(label)) for label in column_labels],
)
for i, label in enumerate(column_labels):
values = all_values[i]
if isinstance(values, np.ndarray):
values = values.tolist()
values = sorted(values)
if drop_first:
values = values[1:]
def column_name(v: Any) -> Name:
if prefix is None or cast(List[str], prefix)[i] == "":
return v
else:
return "{}{}{}".format(cast(List[str], prefix)[i], prefix_sep, v)
for value in values:
remaining_columns.append(
(psdf[label].notnull() & (psdf[label] == value))
.astype(dtype)
.rename(column_name(value))
)
if dummy_na:
remaining_columns.append(psdf[label].isnull().astype(dtype).rename(column_name(np.nan)))
return psdf[remaining_columns]
# TODO: there are many parameters to implement and support. See pandas's pd.concat.
[docs]def concat(
objs: List[Union[DataFrame, Series]],
axis: Axis = 0,
join: str = "outer",
ignore_index: bool = False,
sort: bool = False,
) -> Union[Series, DataFrame]:
"""
Concatenate pandas-on-Spark objects along a particular axis with optional set logic
along the other axes.
Parameters
----------
objs : a sequence of Series or DataFrame
Any None objects will be dropped silently unless
they are all None in which case a ValueError will be raised
axis : {0/'index', 1/'columns'}, default 0
The axis to concatenate along.
join : {'inner', 'outer'}, default 'outer'
How to handle indexes on other axis (or axes).
ignore_index : bool, default False
If True, do not use the index values along the concatenation axis. The
resulting axis will be labeled 0, ..., n - 1. This is useful if you are
concatenating objects where the concatenation axis does not have
meaningful indexing information. Note the index values on the other
axes are still respected in the join.
sort : bool, default False
Sort non-concatenation axis if it is not already aligned.
Returns
-------
object, type of objs
When concatenating all ``Series`` along the index (axis=0), a
``Series`` is returned. When ``objs`` contains at least one
``DataFrame``, a ``DataFrame`` is returned. When concatenating along
the columns (axis=1), a ``DataFrame`` is returned.
See Also
--------
Series.append : Concatenate Series.
DataFrame.join : Join DataFrames using indexes.
DataFrame.merge : Merge DataFrames by indexes or columns.
Examples
--------
>>> from pyspark.pandas.config import set_option, reset_option
>>> set_option("compute.ops_on_diff_frames", True)
Combine two ``Series``.
>>> s1 = ps.Series(['a', 'b'])
>>> s2 = ps.Series(['c', 'd'])
>>> ps.concat([s1, s2])
0 a
1 b
0 c
1 d
dtype: object
Clear the existing index and reset it in the result
by setting the ``ignore_index`` option to ``True``.
>>> ps.concat([s1, s2], ignore_index=True)
0 a
1 b
2 c
3 d
dtype: object
Combine two ``DataFrame`` objects with identical columns.
>>> df1 = ps.DataFrame([['a', 1], ['b', 2]],
... columns=['letter', 'number'])
>>> df1
letter number
0 a 1
1 b 2
>>> df2 = ps.DataFrame([['c', 3], ['d', 4]],
... columns=['letter', 'number'])
>>> df2
letter number
0 c 3
1 d 4
>>> ps.concat([df1, df2])
letter number
0 a 1
1 b 2
0 c 3
1 d 4
Combine ``DataFrame`` and ``Series`` objects with different columns.
>>> ps.concat([df2, s1])
letter number 0
0 c 3.0 None
1 d 4.0 None
0 None NaN a
1 None NaN b
Combine ``DataFrame`` objects with overlapping columns
and return everything. Columns outside the intersection will
be filled with ``None`` values.
>>> df3 = ps.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
... columns=['letter', 'number', 'animal'])
>>> df3
letter number animal
0 c 3 cat
1 d 4 dog
>>> ps.concat([df1, df3])
letter number animal
0 a 1 None
1 b 2 None
0 c 3 cat
1 d 4 dog
Sort the columns.
>>> ps.concat([df1, df3], sort=True)
animal letter number
0 None a 1
1 None b 2
0 cat c 3
1 dog d 4
Combine ``DataFrame`` objects with overlapping columns
and return only those that are shared by passing ``inner`` to
the ``join`` keyword argument.
>>> ps.concat([df1, df3], join="inner")
letter number
0 a 1
1 b 2
0 c 3
1 d 4
>>> df4 = ps.DataFrame([['bird', 'polly'], ['monkey', 'george']],
... columns=['animal', 'name'])
Combine with column axis.
>>> ps.concat([df1, df4], axis=1)
letter number animal name
0 a 1 bird polly
1 b 2 monkey george
>>> reset_option("compute.ops_on_diff_frames")
"""
if isinstance(objs, (DataFrame, IndexOpsMixin)) or not isinstance(
objs, Iterable
): # TODO: support dict
raise TypeError(
"first argument must be an iterable of pandas-on-Spark "
"objects, you passed an object of type "
'"{name}"'.format(name=type(objs).__name__)
)
if len(cast(Sized, objs)) == 0:
raise ValueError("No objects to concatenate")
objs = list(filter(lambda obj: obj is not None, objs))
if len(objs) == 0:
raise ValueError("All objects passed were None")
for obj in objs:
if not isinstance(obj, (Series, DataFrame)):
raise TypeError(
"cannot concatenate object of type "
"'{name}"
"; only ps.Series "
"and ps.DataFrame are valid".format(name=type(objs).__name__)
)
if join not in ["inner", "outer"]:
raise ValueError("Only can inner (intersect) or outer (union) join the other axis.")
axis = validate_axis(axis)
psdf: DataFrame
if axis == 1:
psdfs: List[DataFrame] = [
obj.to_frame() if isinstance(obj, Series) else obj for obj in objs
]
level: int = min(psdf._internal.column_labels_level for psdf in psdfs)
psdfs = [
DataFrame._index_normalized_frame(level, psdf)
if psdf._internal.column_labels_level > level
else psdf
for psdf in psdfs
]
concat_psdf = psdfs[0]
column_labels: List[Label] = concat_psdf._internal.column_labels.copy()
psdfs_not_same_anchor = []
for psdf in psdfs[1:]:
duplicated = [label for label in psdf._internal.column_labels if label in column_labels]
if len(duplicated) > 0:
pretty_names = [name_like_string(label) for label in duplicated]
raise ValueError(
"Labels have to be unique; however, got duplicated labels %s." % pretty_names
)
column_labels.extend(psdf._internal.column_labels)
if same_anchor(concat_psdf, psdf):
concat_psdf = DataFrame(
concat_psdf._internal.with_new_columns(
[
concat_psdf._psser_for(label)
for label in concat_psdf._internal.column_labels
]
+ [psdf._psser_for(label) for label in psdf._internal.column_labels]
)
)
else:
psdfs_not_same_anchor.append(psdf)
if len(psdfs_not_same_anchor) > 0:
@no_type_check
def resolve_func(psdf, this_column_labels, that_column_labels):
raise AssertionError("This should not happen.")
for psdf in psdfs_not_same_anchor:
if join == "inner":
concat_psdf = align_diff_frames(
resolve_func,
concat_psdf,
psdf,
fillna=False,
how="inner",
)
elif join == "outer":
concat_psdf = align_diff_frames(
resolve_func,
concat_psdf,
psdf,
fillna=False,
how="full",
)
concat_psdf = concat_psdf[column_labels]
if ignore_index:
concat_psdf.columns = list( # type: ignore[assignment]
map(str, _range(len(concat_psdf.columns)))
)
if sort:
concat_psdf = concat_psdf.sort_index()
return concat_psdf
# Series, Series ...
# We should return Series if objects are all Series.
should_return_series = all(map(lambda obj: isinstance(obj, Series), objs))
# DataFrame, Series ... & Series, Series ...
# In this case, we should return DataFrame.
new_objs: List[DataFrame] = []
num_series = 0
series_names = set()
for obj in objs:
if isinstance(obj, Series):
num_series += 1
series_names.add(obj.name)
new_objs.append(obj.to_frame(DEFAULT_SERIES_NAME))
else:
assert isinstance(obj, DataFrame)
new_objs.append(obj)
column_labels_levels: Set[int] = set(obj._internal.column_labels_level for obj in new_objs)
if len(column_labels_levels) != 1:
raise ValueError("MultiIndex columns should have the same levels")
# DataFrame, DataFrame, ...
# All Series are converted into DataFrame and then compute concat.
if not ignore_index:
indices_of_psdfs = [psdf.index for psdf in new_objs]
index_of_first_psdf = indices_of_psdfs[0]
for index_of_psdf in indices_of_psdfs:
if index_of_first_psdf.names != index_of_psdf.names:
raise ValueError(
"Index type and names should be same in the objects to concatenate. "
"You passed different indices "
"{index_of_first_psdf} and {index_of_psdf}".format(
index_of_first_psdf=index_of_first_psdf.names,
index_of_psdf=index_of_psdf.names,
)
)
column_labels_of_psdfs = [psdf._internal.column_labels for psdf in new_objs]
index_names_of_psdfs: List[List[Optional[Label]]]
if ignore_index:
index_names_of_psdfs = [[] for _ in new_objs]
else:
index_names_of_psdfs = [psdf._internal.index_names for psdf in new_objs]
if all(name == index_names_of_psdfs[0] for name in index_names_of_psdfs) and all(
idx == column_labels_of_psdfs[0] for idx in column_labels_of_psdfs
):
# If all columns are in the same order and values, use it.
psdfs = new_objs
else:
if join == "inner":
interested_columns = set.intersection(*map(lambda x: set(x), column_labels_of_psdfs))
# Keep the column order with its firsts DataFrame.
merged_columns = [
label for label in column_labels_of_psdfs[0] if label in interested_columns
]
# If sort is True, sort to follow pandas 1.4+ behavior.
if sort:
# FIXME: better ordering
merged_columns = sorted(merged_columns, key=name_like_string)
psdfs = [psdf[merged_columns] for psdf in new_objs]
elif join == "outer":
merged_columns = []
for labels in column_labels_of_psdfs:
merged_columns.extend(label for label in labels if label not in merged_columns)
assert len(merged_columns) > 0
# If sort is True, always sort
if sort:
# FIXME: better ordering
merged_columns = sorted(merged_columns, key=name_like_string)
psdfs = []
for psdf in new_objs:
columns_to_add = list(set(merged_columns) - set(psdf._internal.column_labels))
# TODO: NaN and None difference for missing values. pandas seems to be filling NaN.
sdf = psdf._internal.resolved_copy.spark_frame
for label in columns_to_add:
sdf = sdf.withColumn(name_like_string(label), F.lit(None))
data_columns = psdf._internal.data_spark_column_names + [
name_like_string(label) for label in columns_to_add
]
psdf = DataFrame(
psdf._internal.copy(
spark_frame=sdf,
index_spark_columns=[
scol_for(sdf, col) for col in psdf._internal.index_spark_column_names
],
column_labels=(psdf._internal.column_labels + columns_to_add),
data_spark_columns=[scol_for(sdf, col) for col in data_columns],
data_fields=(psdf._internal.data_fields + ([None] * len(columns_to_add))),
)
)
psdfs.append(psdf[merged_columns])
if ignore_index:
sdfs = [
psdf._internal.spark_frame.select(psdf._internal.data_spark_columns) for psdf in psdfs
]
else:
sdfs = [
psdf._internal.spark_frame.select(
psdf._internal.index_spark_columns + psdf._internal.data_spark_columns
)
for psdf in psdfs
]
concatenated = reduce(lambda x, y: x.union(y), sdfs)
if ignore_index:
index_spark_column_names = []
index_names = []
index_fields = []
else:
index_spark_column_names = psdfs[0]._internal.index_spark_column_names
index_names = psdfs[0]._internal.index_names
index_fields = psdfs[0]._internal.index_fields
result_psdf: DataFrame = DataFrame(
psdfs[0]._internal.copy(
spark_frame=concatenated,
index_spark_columns=[scol_for(concatenated, col) for col in index_spark_column_names],
index_names=index_names,
index_fields=index_fields,
data_spark_columns=[
scol_for(concatenated, col) for col in psdfs[0]._internal.data_spark_column_names
],
data_fields=None, # TODO: dtypes?
)
)
if should_return_series:
# If all input were Series, we should return Series.
if len(series_names) == 1:
name = series_names.pop()
else:
name = None
return first_series(result_psdf).rename(name)
else:
return result_psdf
[docs]def melt(
frame: DataFrame,
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:
return DataFrame.melt(frame, id_vars, value_vars, var_name, value_name)
melt.__doc__ = DataFrame.melt.__doc__
[docs]@no_type_check
def isna(obj):
"""
Detect missing values for an array-like object.
This function takes a scalar or array-like object and indicates
whether values are missing (``NaN`` in numeric arrays, ``None`` or ``NaN``
in object arrays).
Parameters
----------
obj : scalar or array-like
Object to check for null or missing values.
Returns
-------
bool or array-like of bool
For scalar input, returns a scalar boolean.
For array input, returns an array of boolean indicating whether each
corresponding element is missing.
See Also
--------
Series.isna : Detect missing values in a Series.
Series.isnull : Detect missing values in a Series.
DataFrame.isna : Detect missing values in a DataFrame.
DataFrame.isnull : Detect missing values in a DataFrame.
Index.isna : Detect missing values in an Index.
Index.isnull : Detect missing values in an Index.
Examples
--------
Scalar arguments (including strings) result in a scalar boolean.
>>> ps.isna('dog')
False
>>> ps.isna(np.nan)
True
ndarrays result in an ndarray of booleans.
>>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]])
>>> array
array([[ 1., nan, 3.],
[ 4., 5., nan]])
>>> ps.isna(array)
array([[False, True, False],
[False, False, True]])
For Series and DataFrame, the same type is returned, containing booleans.
>>> df = ps.DataFrame({'a': ['ant', 'bee', 'cat'], 'b': ['dog', None, 'fly']})
>>> df
a b
0 ant dog
1 bee None
2 cat fly
>>> ps.isna(df)
a b
0 False False
1 False True
2 False False
>>> ps.isnull(df.b)
0 False
1 True
2 False
Name: b, dtype: bool
"""
# TODO: Add back:
# notnull : Boolean inverse of pandas.isnull.
# into the See Also in the docstring. It does not find the method in the latest numpydoc.
if isinstance(obj, (DataFrame, Series)):
return obj.isnull()
else:
return pd.isnull(obj)
isnull = isna
[docs]@no_type_check
def notna(obj):
"""
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA.
Non-missing values get mapped to True. NA values, such as None or
:attr:`numpy.NaN`, get mapped to False values.
Returns
-------
bool or array-like of bool
Mask of bool values for each element that
indicates whether an element is not an NA value.
See Also
--------
isna : Detect missing values for an array-like object.
Series.notna : Boolean inverse of Series.isna.
DataFrame.notnull : Boolean inverse of DataFrame.isnull.
Index.notna : Boolean inverse of Index.isna.
Index.notnull : Boolean inverse of Index.isnull.
Examples
--------
Show which entries in a DataFrame are not NA.
>>> df = ps.DataFrame({'age': [5, 6, np.NaN],
... 'born': [pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... 'name': ['Alfred', 'Batman', ''],
... 'toy': [None, 'Batmobile', 'Joker']})
>>> df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 Joker
>>> df.notnull()
age born name toy
0 True False True False
1 True True True True
2 False True True True
Show which entries in a Series are not NA.
>>> ser = ps.Series([5, 6, np.NaN])
>>> ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>> ps.notna(ser)
0 True
1 True
2 False
dtype: bool
>>> ps.notna(ser.index)
True
"""
# TODO: Add back:
# Series.notnull :Boolean inverse of Series.isnull.
# DataFrame.notna :Boolean inverse of DataFrame.isna.
# into the See Also in the docstring. It does not find the method in the latest numpydoc.
if isinstance(obj, (DataFrame, Series)):
return obj.notna()
else:
return pd.notna(obj)
notnull = notna
[docs]def merge(
obj: DataFrame,
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; preserve key
order.
right: use only keys from right frame, like a SQL right outer join; preserve key
order.
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;
preserve the order of the left keys.
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.
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 = ps.merge(df1, 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])
>>> ps.merge(left_psdf, right_psdf, left_index=True, right_index=True).sort_index()
A B
1 2 x
>>> ps.merge(left_psdf, right_psdf, left_index=True, right_index=True, how='left').sort_index()
A B
0 1 None
1 2 x
>>> ps.merge(left_psdf, right_psdf, left_index=True, right_index=True, how='right').sort_index()
A B
1 2.0 x
2 NaN y
>>> ps.merge(left_psdf, 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.
"""
return obj.merge(
right,
how=how,
on=on,
left_on=left_on,
right_on=right_on,
left_index=left_index,
right_index=right_index,
suffixes=suffixes,
)
[docs]def merge_asof(
left: Union[DataFrame, Series],
right: Union[DataFrame, Series],
on: Optional[Name] = None,
left_on: Optional[Name] = None,
right_on: Optional[Name] = None,
left_index: bool = False,
right_index: bool = False,
by: Optional[Union[Name, List[Name]]] = None,
left_by: Optional[Union[Name, List[Name]]] = None,
right_by: Optional[Union[Name, List[Name]]] = None,
suffixes: Tuple[str, str] = ("_x", "_y"),
tolerance: Optional[Any] = None,
allow_exact_matches: bool = True,
direction: str = "backward",
) -> DataFrame:
"""
Perform an asof merge.
This is like a left-join except that we match on nearest
key rather than equal keys.
For each row in the left DataFrame:
- A "backward" search selects the last row in the right DataFrame whose
'on' key is less than or equal to the left's key.
- A "forward" search selects the first row in the right DataFrame whose
'on' key is greater than or equal to the left's key.
- A "nearest" search selects the row in the right DataFrame who's 'on'
key is closest in absolute distance to the left's key.
Optionally match on equivalent keys with 'by' before searching with 'on'.
.. versionadded:: 3.3.0
Parameters
----------
left : DataFrame or named Series
right : DataFrame or named Series
on : label
Field name to join on. Must be found in both DataFrames.
The data MUST be ordered. This must be a numeric column,
such as datetimelike, integer, or float. On or left_on/right_on
must be given.
left_on : label
Field name to join on in left DataFrame.
right_on : label
Field name to join on in right DataFrame.
left_index : bool
Use the index of the left DataFrame as the join key.
right_index : bool
Use the index of the right DataFrame as the join key.
by : column name or list of column names
Match on these columns before performing merge operation.
left_by : column name
Field names to match on in the left DataFrame.
right_by : column name
Field names to match on in the right DataFrame.
suffixes : 2-length sequence (tuple, list, ...)
Suffix to apply to overlapping column names in the left and right
side, respectively.
tolerance : int or Timedelta, optional, default None
Select asof tolerance within this range; must be compatible
with the merge index.
allow_exact_matches : bool, default True
- If True, allow matching with the same 'on' value
(i.e. less-than-or-equal-to / greater-than-or-equal-to)
- If False, don't match the same 'on' value
(i.e., strictly less-than / strictly greater-than).
direction : 'backward' (default), 'forward', or 'nearest'
Whether to search for prior, subsequent, or closest matches.
Returns
-------
merged : DataFrame
See Also
--------
merge : Merge with a database-style join.
merge_ordered : Merge with optional filling/interpolation.
Examples
--------
>>> left = ps.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
>>> left
a left_val
0 1 a
1 5 b
2 10 c
>>> right = ps.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
>>> right
a right_val
0 1 1
1 2 2
2 3 3
3 6 6
4 7 7
>>> ps.merge_asof(left, right, on="a").sort_values("a").reset_index(drop=True)
a left_val right_val
0 1 a 1
1 5 b 3
2 10 c 7
>>> ps.merge_asof(
... left,
... right,
... on="a",
... allow_exact_matches=False
... ).sort_values("a").reset_index(drop=True)
a left_val right_val
0 1 a NaN
1 5 b 3.0
2 10 c 7.0
>>> ps.merge_asof(
... left,
... right,
... on="a",
... direction="forward"
... ).sort_values("a").reset_index(drop=True)
a left_val right_val
0 1 a 1.0
1 5 b 6.0
2 10 c NaN
>>> ps.merge_asof(
... left,
... right,
... on="a",
... direction="nearest"
... ).sort_values("a").reset_index(drop=True)
a left_val right_val
0 1 a 1
1 5 b 6
2 10 c 7
We can use indexed DataFrames as well.
>>> left = ps.DataFrame({"left_val": ["a", "b", "c"]}, index=[1, 5, 10])
>>> left
left_val
1 a
5 b
10 c
>>> right = ps.DataFrame({"right_val": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])
>>> right
right_val
1 1
2 2
3 3
6 6
7 7
>>> ps.merge_asof(left, right, left_index=True, right_index=True).sort_index()
left_val right_val
1 a 1
5 b 3
10 c 7
Here is a real-world times-series example
>>> quotes = ps.DataFrame(
... {
... "time": [
... pd.Timestamp("2016-05-25 13:30:00.023"),
... pd.Timestamp("2016-05-25 13:30:00.023"),
... pd.Timestamp("2016-05-25 13:30:00.030"),
... pd.Timestamp("2016-05-25 13:30:00.041"),
... pd.Timestamp("2016-05-25 13:30:00.048"),
... pd.Timestamp("2016-05-25 13:30:00.049"),
... pd.Timestamp("2016-05-25 13:30:00.072"),
... pd.Timestamp("2016-05-25 13:30:00.075")
... ],
... "ticker": [
... "GOOG",
... "MSFT",
... "MSFT",
... "MSFT",
... "GOOG",
... "AAPL",
... "GOOG",
... "MSFT"
... ],
... "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
... "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03]
... }
... )
>>> quotes
time ticker bid ask
0 2016-05-25 13:30:00.023 GOOG 720.50 720.93
1 2016-05-25 13:30:00.023 MSFT 51.95 51.96
2 2016-05-25 13:30:00.030 MSFT 51.97 51.98
3 2016-05-25 13:30:00.041 MSFT 51.99 52.00
4 2016-05-25 13:30:00.048 GOOG 720.50 720.93
5 2016-05-25 13:30:00.049 AAPL 97.99 98.01
6 2016-05-25 13:30:00.072 GOOG 720.50 720.88
7 2016-05-25 13:30:00.075 MSFT 52.01 52.03
>>> trades = ps.DataFrame(
... {
... "time": [
... pd.Timestamp("2016-05-25 13:30:00.023"),
... pd.Timestamp("2016-05-25 13:30:00.038"),
... pd.Timestamp("2016-05-25 13:30:00.048"),
... pd.Timestamp("2016-05-25 13:30:00.048"),
... pd.Timestamp("2016-05-25 13:30:00.048")
... ],
... "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
... "price": [51.95, 51.95, 720.77, 720.92, 98.0],
... "quantity": [75, 155, 100, 100, 100]
... }
... )
>>> trades
time ticker price quantity
0 2016-05-25 13:30:00.023 MSFT 51.95 75
1 2016-05-25 13:30:00.038 MSFT 51.95 155
2 2016-05-25 13:30:00.048 GOOG 720.77 100
3 2016-05-25 13:30:00.048 GOOG 720.92 100
4 2016-05-25 13:30:00.048 AAPL 98.00 100
By default we are taking the asof of the quotes
>>> ps.merge_asof(
... trades, quotes, on="time", by="ticker"
... ).sort_values(["time", "ticker", "price"]).reset_index(drop=True)
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96
1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98
2 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN
3 2016-05-25 13:30:00.048 GOOG 720.77 100 720.50 720.93
4 2016-05-25 13:30:00.048 GOOG 720.92 100 720.50 720.93
We only asof within 2ms between the quote time and the trade time
>>> ps.merge_asof(
... trades,
... quotes,
... on="time",
... by="ticker",
... tolerance=sf.expr("INTERVAL 2 MILLISECONDS") # pd.Timedelta("2ms")
... ).sort_values(["time", "ticker", "price"]).reset_index(drop=True)
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96
1 2016-05-25 13:30:00.038 MSFT 51.95 155 NaN NaN
2 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN
3 2016-05-25 13:30:00.048 GOOG 720.77 100 720.50 720.93
4 2016-05-25 13:30:00.048 GOOG 720.92 100 720.50 720.93
We only asof within 10ms between the quote time and the trade time
and we exclude exact matches on time. However *prior* data will
propagate forward
>>> ps.merge_asof(
... trades,
... quotes,
... on="time",
... by="ticker",
... tolerance=sf.expr("INTERVAL 10 MILLISECONDS"), # pd.Timedelta("10ms")
... allow_exact_matches=False
... ).sort_values(["time", "ticker", "price"]).reset_index(drop=True)
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 NaN NaN
1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98
2 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN
3 2016-05-25 13:30:00.048 GOOG 720.77 100 NaN NaN
4 2016-05-25 13:30:00.048 GOOG 720.92 100 NaN 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(left, Series):
left = left.to_frame()
if isinstance(right, 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_as_of_names = list(map(left._internal.spark_column_name_for, to_list(on)))
right_as_of_names = list(map(right._internal.spark_column_name_for, to_list(on)))
else:
if left_index:
if isinstance(left.index, MultiIndex):
raise ValueError("left can only have one index")
left_as_of_names = left._internal.index_spark_column_names
else:
left_as_of_names = list(map(left._internal.spark_column_name_for, to_list(left_on)))
if right_index:
if isinstance(right.index, MultiIndex):
raise ValueError("right can only have one index")
right_as_of_names = right._internal.index_spark_column_names
else:
right_as_of_names = list(map(right._internal.spark_column_name_for, to_list(right_on)))
if left_as_of_names and not right_as_of_names:
raise ValueError("Must pass right_on or right_index=True")
if right_as_of_names and not left_as_of_names:
raise ValueError("Must pass left_on or left_index=True")
if not left_as_of_names and not right_as_of_names:
common = list(left.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_as_of_names = list(map(left._internal.spark_column_name_for, to_list(common)))
right_as_of_names = list(map(right._internal.spark_column_name_for, to_list(common)))
if len(left_as_of_names) != 1:
raise ValueError("can only asof on a key for left")
if len(right_as_of_names) != 1:
raise ValueError("can only asof on a key for right")
if by:
if left_by or right_by:
raise ValueError('Can only pass argument "by" OR "left_by" and "right_by".')
left_join_on_names = list(map(left._internal.spark_column_name_for, to_list(by)))
right_join_on_names = list(map(right._internal.spark_column_name_for, to_list(by)))
else:
left_join_on_names = list(map(left._internal.spark_column_name_for, to_list(left_by)))
right_join_on_names = list(map(right._internal.spark_column_name_for, to_list(right_by)))
if left_join_on_names and not right_join_on_names:
raise ValueError("missing right_by")
if right_join_on_names and not left_join_on_names:
raise ValueError("missing left_by")
if len(left_join_on_names) != len(right_join_on_names):
raise ValueError("left_by and right_by must be same length")
# We should distinguish the name to avoid ambiguous column name after merging.
right_prefix = "__right_"
right_as_of_names = [right_prefix + right_as_of_name for right_as_of_name in right_as_of_names]
right_join_on_names = [
right_prefix + right_join_on_name for right_join_on_name in right_join_on_names
]
left_as_of_name = left_as_of_names[0]
right_as_of_name = right_as_of_names[0]
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 = left._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_as_of_column = scol_for(left_table, left_as_of_name)
right_as_of_column = scol_for(right_table, right_as_of_name)
if left_join_on_names:
left_join_on_columns = [scol_for(left_table, label) for label in left_join_on_names]
right_join_on_columns = [scol_for(right_table, label) for label in right_join_on_names]
on = reduce(
lambda lft, rgt: lft & rgt,
[lft == rgt for lft, rgt in zip(left_join_on_columns, right_join_on_columns)],
)
else:
on = None
Column = get_column_class()
if tolerance is not None and not isinstance(tolerance, Column):
tolerance = F.lit(tolerance)
as_of_joined_table = left_table._joinAsOf(
right_table,
leftAsOfColumn=left_as_of_column,
rightAsOfColumn=right_as_of_column,
on=on,
how="left",
tolerance=tolerance,
allowExactMatches=allow_exact_matches,
direction=direction,
)
# 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) -> Column: # type: ignore[valid-type]
return scol_for(as_of_joined_table, left_internal.spark_column_name_for(label))
def right_scol_for(label: Label) -> Column: # type: ignore[valid-type]
return scol_for(as_of_joined_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_as_of_names + left_join_on_names) and (
(right_prefix + spark_column_name) in (right_as_of_names + right_join_on_names)
):
pass
else:
col = col + left_suffix
scol = scol.alias(col) # type: ignore[attr-defined]
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) # type: ignore[attr-defined]
if label in duplicate_columns:
spark_column_name = left_internal.spark_column_name_for(label)
if spark_column_name in left_as_of_names + left_join_on_names and (
(right_prefix + spark_column_name) in right_as_of_names + right_join_on_names
):
continue
else:
col = col + right_suffix
scol = scol.alias(col) # type: ignore[attr-defined]
label = tuple([str(label[0]) + right_suffix] + list(label[1:]))
exprs.append(scol)
data_columns.append(col)
column_labels.append(label)
# Retain indices if they are used for joining
if left_index or right_index:
index_spark_column_names = [
SPARK_INDEX_NAME_FORMAT(i) for i in range(len(left_internal.index_spark_column_names))
]
left_index_scols = [
scol.alias(name)
for scol, name in zip(left_internal.index_spark_columns, index_spark_column_names)
]
exprs.extend(left_index_scols)
index_names = left_internal.index_names
else:
index_spark_column_names = []
index_names = []
selected_columns = as_of_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]@no_type_check
def to_numeric(arg, errors="raise"):
"""
Convert argument to a numeric type.
Parameters
----------
arg : scalar, list, tuple, 1-d array, or Series
Argument to be converted.
errors : {'raise', 'coerce'}, default 'raise'
* If 'coerce', then invalid parsing will be set as NaN.
* If 'raise', then invalid parsing will raise an exception.
* If 'ignore', then invalid parsing will return the input.
.. note:: 'ignore' doesn't work yet when `arg` is pandas-on-Spark Series.
Returns
-------
ret : numeric if parsing succeeded.
See Also
--------
DataFrame.astype : Cast argument to a specified dtype.
to_datetime : Convert argument to datetime.
to_timedelta : Convert argument to timedelta.
numpy.ndarray.astype : Cast a numpy array to a specified type.
Examples
--------
>>> psser = ps.Series(['1.0', '2', '-3'])
>>> psser
0 1.0
1 2
2 -3
dtype: object
>>> ps.to_numeric(psser)
0 1.0
1 2.0
2 -3.0
dtype: float32
If given Series contains invalid value to cast float, just cast it to `np.nan`
when `errors` is set to "coerce".
>>> psser = ps.Series(['apple', '1.0', '2', '-3'])
>>> psser
0 apple
1 1.0
2 2
3 -3
dtype: object
>>> ps.to_numeric(psser, errors="coerce")
0 NaN
1 1.0
2 2.0
3 -3.0
dtype: float32
Also support for list, tuple, np.array, or a scalar
>>> ps.to_numeric(['1.0', '2', '-3'])
array([ 1., 2., -3.])
>>> ps.to_numeric(('1.0', '2', '-3'))
array([ 1., 2., -3.])
>>> ps.to_numeric(np.array(['1.0', '2', '-3']))
array([ 1., 2., -3.])
>>> ps.to_numeric('1.0')
1.0
"""
if isinstance(arg, Series):
if errors == "coerce":
return arg._with_new_scol(arg.spark.column.cast("float"))
elif errors == "raise":
scol = arg.spark.column
scol_casted = scol.cast("float")
cond = F.when(
F.assert_true(scol.isNull() | scol_casted.isNotNull()).isNull(), scol_casted
)
return arg._with_new_scol(cond)
elif errors == "ignore":
raise NotImplementedError("'ignore' is not implemented yet, when the `arg` is Series.")
else:
raise ValueError("invalid error value specified")
else:
return pd.to_numeric(arg, errors=errors)
[docs]def broadcast(obj: DataFrame) -> DataFrame:
"""
Marks a DataFrame as small enough for use in broadcast joins.
.. deprecated:: 3.2.0
Use :func:`DataFrame.spark.hint` instead.
Parameters
----------
obj : DataFrame
Returns
-------
ret : DataFrame with broadcast hint.
See Also
--------
DataFrame.merge : Merge DataFrame objects with a database-style join.
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.
Examples
--------
>>> df1 = ps.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
... 'value': [1, 2, 3, 5]},
... columns=['lkey', 'value']).set_index('lkey')
>>> df2 = ps.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
... 'value': [5, 6, 7, 8]},
... columns=['rkey', 'value']).set_index('rkey')
>>> merged = df1.merge(ps.broadcast(df2), left_index=True, right_index=True)
>>> merged.spark.explain() # doctest: +ELLIPSIS
== Physical Plan ==
...
...BroadcastHashJoin...
...
"""
warnings.warn(
"`broadcast` has been deprecated and might be removed in a future version. "
"Use `DataFrame.spark.hint` with 'broadcast' for `name` parameter instead.",
FutureWarning,
)
if not isinstance(obj, DataFrame):
raise TypeError("Invalid type : expected DataFrame got {}".format(type(obj).__name__))
return DataFrame(
obj._internal.with_new_sdf(F.broadcast(obj._internal.resolved_copy.spark_frame))
)
[docs]def read_orc(
path: str,
columns: Optional[List[str]] = None,
index_col: Optional[Union[str, List[str]]] = None,
**options: Any,
) -> "DataFrame":
"""
Load an ORC object from the file path, returning a DataFrame.
Parameters
----------
path : str
The path string storing the ORC file to be read.
columns : list, default None
If not None, only these columns will be read from the file.
index_col : str or list of str, optional, default: None
Index column of table in Spark.
options : dict
All other options passed directly into Spark's data source.
Returns
-------
DataFrame
Examples
--------
>>> ps.range(1).to_orc('%s/read_spark_io/data.orc' % path)
>>> ps.read_orc('%s/read_spark_io/data.orc' % path, columns=['id'])
id
0 0
You can preserve the index in the roundtrip as below.
>>> ps.range(1).to_orc('%s/read_spark_io/data.orc' % path, index_col="index")
>>> ps.read_orc('%s/read_spark_io/data.orc' % path, columns=['id'], index_col="index")
... # doctest: +NORMALIZE_WHITESPACE
id
index
0 0
"""
if "options" in options and isinstance(options.get("options"), dict) and len(options) == 1:
options = options.get("options")
psdf = read_spark_io(path, format="orc", index_col=index_col, **options)
if columns is not None:
psdf_columns = psdf.columns
new_columns = list()
for column in list(columns):
if column in psdf_columns:
new_columns.append(column)
else:
raise ValueError("Unknown column name '{}'".format(column))
psdf = psdf[new_columns]
return psdf
def _get_index_map(
sdf: PySparkDataFrame, index_col: Optional[Union[str, List[str]]] = None
) -> Tuple[Optional[List[PySparkColumn]], Optional[List[Label]]]:
index_spark_columns: Optional[List[PySparkColumn]]
index_names: Optional[List[Label]]
if index_col is not None:
if isinstance(index_col, str):
index_col = [index_col]
sdf_columns = set(sdf.columns)
for col in index_col:
if col not in sdf_columns:
raise KeyError(col)
index_spark_columns = [scol_for(sdf, col) for col in index_col]
index_names = [(col,) for col in index_col]
else:
index_spark_columns = None
index_names = None
return index_spark_columns, index_names
_get_dummies_default_accept_types = (DecimalType, StringType, DateType)
_get_dummies_acceptable_types = _get_dummies_default_accept_types + (
ByteType,
ShortType,
IntegerType,
LongType,
FloatType,
DoubleType,
BooleanType,
TimestampType,
TimestampNTZType,
)
def _test() -> None:
import os
import doctest
import shutil
import sys
import tempfile
import uuid
from pyspark.sql import SparkSession
import pyspark.pandas.namespace
os.chdir(os.environ["SPARK_HOME"])
globs = pyspark.pandas.namespace.__dict__.copy()
globs["ps"] = pyspark.pandas
globs["sf"] = F
spark = (
SparkSession.builder.master("local[4]")
.appName("pyspark.pandas.namespace tests")
.getOrCreate()
)
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.namespace,
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()