From/to pandas and PySpark DataFrames¶
Users from pandas and/or PySpark face API compatibility issue sometimes when they work with pandas API on Spark. Since pandas API on Spark does not target 100% compatibility of both pandas and PySpark, users need to do some workaround to port their pandas and/or PySpark codes or get familiar with pandas API on Spark in this case. This page aims to describe it.
pandas¶
pandas users can access the full pandas API by calling DataFrame.to_pandas().
pandas-on-Spark DataFrame and pandas DataFrame are similar. However, the former is distributed
and the latter is in a single machine. When converting to each other, the data is
transferred between multiple machines and the single client machine.
For example, if you need to call pandas_df.values of pandas DataFrame, you can do
as below:
>>> import pyspark.pandas as ps
>>>
>>> psdf = ps.range(10)
>>> pdf = psdf.to_pandas()
>>> pdf.values
array([[0],
[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8],
[9]])
pandas DataFrame can be a pandas-on-Spark DataFrame easily as below:
>>> ps.from_pandas(pdf)
id
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
Note that converting pandas-on-Spark DataFrame to pandas requires to collect all the data into the client machine; therefore, if possible, it is recommended to use pandas API on Spark or PySpark APIs instead.
PySpark¶
PySpark users can access the full PySpark APIs by calling DataFrame.to_spark().
pandas-on-Spark DataFrame and Spark DataFrame are virtually interchangeable.
For example, if you need to call spark_df.filter(...) of Spark DataFrame, you can do
as below:
>>> import pyspark.pandas as ps
>>>
>>> psdf = ps.range(10)
>>> sdf = psdf.to_spark().filter("id > 5")
>>> sdf.show()
+---+
| id|
+---+
| 6|
| 7|
| 8|
| 9|
+---+
Spark DataFrame can be a pandas-on-Spark DataFrame easily as below:
>>> sdf.pandas_api()
id
0 6
1 7
2 8
3 9
However, note that a new default index is created when pandas-on-Spark DataFrame is created from Spark DataFrame. See Default Index Type. In order to avoid this overhead, specify the column to use as an index when possible.
>>> # Create a pandas-on-Spark DataFrame with an explicit index.
... psdf = ps.DataFrame({'id': range(10)}, index=range(10))
>>> # Keep the explicit index.
... sdf = psdf.to_spark(index_col='index')
>>> # Call Spark APIs
... sdf = sdf.filter("id > 5")
>>> # Uses the explicit index to avoid to create default index.
... sdf.pandas_api(index_col='index')
id
index
6 6
7 7
8 8
9 9