Quickstart: Pandas API on Spark

This is a short introduction to pandas API on Spark, geared mainly for new users. This notebook shows you some key differences between pandas and pandas API on Spark. You can run this examples by yourself in ‘Live Notebook: pandas API on Spark’ at the quickstart page.

Customarily, we import pandas API on Spark as follows:

[1]:
import pandas as pd
import numpy as np
import pyspark.pandas as ps
from pyspark.sql import SparkSession

Object Creation

Creating a pandas-on-Spark Series by passing a list of values, letting pandas API on Spark create a default integer index:

[2]:
s = ps.Series([1, 3, 5, np.nan, 6, 8])
[3]:
s
[3]:
0    1.0
1    3.0
2    5.0
3    NaN
4    6.0
5    8.0
dtype: float64

Creating a pandas-on-Spark DataFrame by passing a dict of objects that can be converted to series-like.

[4]:
psdf = ps.DataFrame(
    {'a': [1, 2, 3, 4, 5, 6],
     'b': [100, 200, 300, 400, 500, 600],
     'c': ["one", "two", "three", "four", "five", "six"]},
    index=[10, 20, 30, 40, 50, 60])
[5]:
psdf
[5]:
a b c
10 1 100 one
20 2 200 two
30 3 300 three
40 4 400 four
50 5 500 five
60 6 600 six

Creating a pandas DataFrame by passing a numpy array, with a datetime index and labeled columns:

[6]:
dates = pd.date_range('20130101', periods=6)
[7]:
dates
[7]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
               '2013-01-05', '2013-01-06'],
              dtype='datetime64[ns]', freq='D')
[8]:
pdf = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
[9]:
pdf
[9]:
A B C D
2013-01-01 0.912558 -0.795645 -0.289115 0.187606
2013-01-02 -0.059703 -1.233897 0.316625 -1.226828
2013-01-03 0.332871 -1.262010 -0.434844 -0.579920
2013-01-04 0.924016 -1.022019 -0.405249 -1.036021
2013-01-05 -0.772209 -1.228099 0.068901 0.896679
2013-01-06 1.485582 -0.709306 -0.202637 -0.248766

Now, this pandas DataFrame can be converted to a pandas-on-Spark DataFrame

[10]:
psdf = ps.from_pandas(pdf)
[11]:
type(psdf)
[11]:
pyspark.pandas.frame.DataFrame

It looks and behaves the same as a pandas DataFrame.

[12]:
psdf
[12]:
A B C D
2013-01-01 0.912558 -0.795645 -0.289115 0.187606
2013-01-02 -0.059703 -1.233897 0.316625 -1.226828
2013-01-03 0.332871 -1.262010 -0.434844 -0.579920
2013-01-04 0.924016 -1.022019 -0.405249 -1.036021
2013-01-05 -0.772209 -1.228099 0.068901 0.896679
2013-01-06 1.485582 -0.709306 -0.202637 -0.248766

Also, it is possible to create a pandas-on-Spark DataFrame from Spark DataFrame easily.

Creating a Spark DataFrame from pandas DataFrame

[13]:
spark = SparkSession.builder.getOrCreate()
[14]:
sdf = spark.createDataFrame(pdf)
[15]:
sdf.show()
+--------------------+-------------------+--------------------+--------------------+
|                   A|                  B|                   C|                   D|
+--------------------+-------------------+--------------------+--------------------+
|    0.91255803205208|-0.7956452608556638|-0.28911463069772175| 0.18760566615081622|
|-0.05970271470242...| -1.233896949308984|  0.3166246451758431| -1.2268284000402265|
| 0.33287106947536615|-1.2620100816441786| -0.4348444277082644| -0.5799199651437185|
|  0.9240158461589916|-1.0220190956326003| -0.4052488880650239| -1.0360212104348547|
| -0.7722090016558953|-1.2280986385313222|  0.0689011451939635|  0.8966790729426755|
|  1.4855822995785612|-0.7093056426018517| -0.2026366848847041|-0.24876619876451092|
+--------------------+-------------------+--------------------+--------------------+

Creating pandas-on-Spark DataFrame from Spark DataFrame.

[16]:
psdf = sdf.to_pandas_on_spark()
[17]:
psdf
[17]:
A B C D
0 0.912558 -0.795645 -0.289115 0.187606
1 -0.059703 -1.233897 0.316625 -1.226828
2 0.332871 -1.262010 -0.434844 -0.579920
3 0.924016 -1.022019 -0.405249 -1.036021
4 -0.772209 -1.228099 0.068901 0.896679
5 1.485582 -0.709306 -0.202637 -0.248766

Having specific dtypes . Types that are common to both Spark and pandas are currently supported.

[18]:
psdf.dtypes
[18]:
A    float64
B    float64
C    float64
D    float64
dtype: object

Here is how to show top rows from the frame below.

Note that the data in a Spark dataframe does not preserve the natural order by default. The natural order can be preserved by setting compute.ordered_head option but it causes a performance overhead with sorting internally.

[19]:
psdf.head()
[19]:
A B C D
0 0.912558 -0.795645 -0.289115 0.187606
1 -0.059703 -1.233897 0.316625 -1.226828
2 0.332871 -1.262010 -0.434844 -0.579920
3 0.924016 -1.022019 -0.405249 -1.036021
4 -0.772209 -1.228099 0.068901 0.896679

Displaying the index, columns, and the underlying numpy data.

[20]:
psdf.index
[20]:
Int64Index([0, 1, 2, 3, 4, 5], dtype='int64')
[21]:
psdf.columns
[21]:
Index(['A', 'B', 'C', 'D'], dtype='object')
[22]:
psdf.to_numpy()
[22]:
array([[ 0.91255803, -0.79564526, -0.28911463,  0.18760567],
       [-0.05970271, -1.23389695,  0.31662465, -1.2268284 ],
       [ 0.33287107, -1.26201008, -0.43484443, -0.57991997],
       [ 0.92401585, -1.0220191 , -0.40524889, -1.03602121],
       [-0.772209  , -1.22809864,  0.06890115,  0.89667907],
       [ 1.4855823 , -0.70930564, -0.20263668, -0.2487662 ]])

Showing a quick statistic summary of your data

[23]:
psdf.describe()
[23]:
A B C D
count 6.000000 6.000000 6.000000 6.000000
mean 0.470519 -1.041829 -0.157720 -0.334542
std 0.809428 0.241511 0.294520 0.793014
min -0.772209 -1.262010 -0.434844 -1.226828
25% -0.059703 -1.233897 -0.405249 -1.036021
50% 0.332871 -1.228099 -0.289115 -0.579920
75% 0.924016 -0.795645 0.068901 0.187606
max 1.485582 -0.709306 0.316625 0.896679

Transposing your data

[24]:
psdf.T
[24]:
0 1 2 3 4 5
A 0.912558 -0.059703 0.332871 0.924016 -0.772209 1.485582
B -0.795645 -1.233897 -1.262010 -1.022019 -1.228099 -0.709306
C -0.289115 0.316625 -0.434844 -0.405249 0.068901 -0.202637
D 0.187606 -1.226828 -0.579920 -1.036021 0.896679 -0.248766

Sorting by its index

[25]:
psdf.sort_index(ascending=False)
[25]:
A B C D
5 1.485582 -0.709306 -0.202637 -0.248766
4 -0.772209 -1.228099 0.068901 0.896679
3 0.924016 -1.022019 -0.405249 -1.036021
2 0.332871 -1.262010 -0.434844 -0.579920
1 -0.059703 -1.233897 0.316625 -1.226828
0 0.912558 -0.795645 -0.289115 0.187606

Sorting by value

[26]:
psdf.sort_values(by='B')
[26]:
A B C D
2 0.332871 -1.262010 -0.434844 -0.579920
1 -0.059703 -1.233897 0.316625 -1.226828
4 -0.772209 -1.228099 0.068901 0.896679
3 0.924016 -1.022019 -0.405249 -1.036021
0 0.912558 -0.795645 -0.289115 0.187606
5 1.485582 -0.709306 -0.202637 -0.248766

Missing Data

Pandas API on Spark primarily uses the value np.nan to represent missing data. It is by default not included in computations.

[27]:
pdf1 = pdf.reindex(index=dates[0:4], columns=list(pdf.columns) + ['E'])
[28]:
pdf1.loc[dates[0]:dates[1], 'E'] = 1
[29]:
psdf1 = ps.from_pandas(pdf1)
[30]:
psdf1
[30]:
A B C D E
2013-01-01 0.912558 -0.795645 -0.289115 0.187606 1.0
2013-01-02 -0.059703 -1.233897 0.316625 -1.226828 1.0
2013-01-03 0.332871 -1.262010 -0.434844 -0.579920 NaN
2013-01-04 0.924016 -1.022019 -0.405249 -1.036021 NaN

To drop any rows that have missing data.

[31]:
psdf1.dropna(how='any')
[31]:
A B C D E
2013-01-01 0.912558 -0.795645 -0.289115 0.187606 1.0
2013-01-02 -0.059703 -1.233897 0.316625 -1.226828 1.0

Filling missing data.

[32]:
psdf1.fillna(value=5)
[32]:
A B C D E
2013-01-01 0.912558 -0.795645 -0.289115 0.187606 1.0
2013-01-02 -0.059703 -1.233897 0.316625 -1.226828 1.0
2013-01-03 0.332871 -1.262010 -0.434844 -0.579920 5.0
2013-01-04 0.924016 -1.022019 -0.405249 -1.036021 5.0

Operations

Stats

Performing a descriptive statistic:

[33]:
psdf.mean()
[33]:
A    0.470519
B   -1.041829
C   -0.157720
D   -0.334542
dtype: float64

Spark Configurations

Various configurations in PySpark could be applied internally in pandas API on Spark. For example, you can enable Arrow optimization to hugely speed up internal pandas conversion. See also PySpark Usage Guide for Pandas with Apache Arrow in PySpark documentation.

[34]:
prev = spark.conf.get("spark.sql.execution.arrow.enabled")  # Keep its default value.
ps.set_option("compute.default_index_type", "distributed")  # Use default index prevent overhead.
import warnings
warnings.filterwarnings("ignore")  # Ignore warnings coming from Arrow optimizations.
[35]:
spark.conf.set("spark.sql.execution.arrow.enabled", True)
%timeit ps.range(300000).to_pandas()
900 ms ± 186 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
[36]:
spark.conf.set("spark.sql.execution.arrow.enabled", False)
%timeit ps.range(300000).to_pandas()
3.08 s ± 227 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
[37]:
ps.reset_option("compute.default_index_type")
spark.conf.set("spark.sql.execution.arrow.enabled", prev)  # Set its default value back.

Grouping

By “group by” we are referring to a process involving one or more of the following steps:

  • Splitting the data into groups based on some criteria

  • Applying a function to each group independently

  • Combining the results into a data structure

[38]:
psdf = ps.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
                          'foo', 'bar', 'foo', 'foo'],
                    'B': ['one', 'one', 'two', 'three',
                          'two', 'two', 'one', 'three'],
                    'C': np.random.randn(8),
                    'D': np.random.randn(8)})
[39]:
psdf
[39]:
A B C D
0 foo one 1.039632 -0.571950
1 bar one 0.972089 1.085353
2 foo two -1.931621 -2.579164
3 bar three -0.654371 -0.340704
4 foo two -0.157080 0.893736
5 bar two 0.882795 0.024978
6 foo one -0.149384 0.201667
7 foo three -1.355136 0.693883

Grouping and then applying the sum() function to the resulting groups.

[40]:
psdf.groupby('A').sum()
[40]:
C D
A
bar 1.200513 0.769627
foo -2.553589 -1.361828

Grouping by multiple columns forms a hierarchical index, and again we can apply the sum function.

[41]:
psdf.groupby(['A', 'B']).sum()
[41]:
C D
A B
foo one 0.890248 -0.370283
two -2.088701 -1.685428
bar three -0.654371 -0.340704
foo three -1.355136 0.693883
bar two 0.882795 0.024978
one 0.972089 1.085353

Plotting

[42]:
pser = pd.Series(np.random.randn(1000),
                 index=pd.date_range('1/1/2000', periods=1000))
[43]:
psser = ps.Series(pser)
[44]:
psser = psser.cummax()
[45]:
psser.plot()