Options and settings#

Pandas API on Spark has an options system that lets you customize some aspects of its behaviour, display-related options being those the user is most likely to adjust.

Options have a full “dotted-style”, case-insensitive name (e.g. display.max_rows). You can get/set options directly as attributes of the top-level options attribute:

>>> import pyspark.pandas as ps
>>> ps.options.display.max_rows
1000
>>> ps.options.display.max_rows = 10
>>> ps.options.display.max_rows
10

The API is composed of 3 relevant functions, available directly from the pandas_on_spark namespace:

Note: Developers can check out pyspark.pandas/config.py for more information.

>>> import pyspark.pandas as ps
>>> ps.get_option("display.max_rows")
1000
>>> ps.set_option("display.max_rows", 101)
>>> ps.get_option("display.max_rows")
101

Getting and setting options#

As described above, get_option() and set_option() are available from the pandas_on_spark namespace. To change an option, call set_option('option name', new_value).

>>> import pyspark.pandas as ps
>>> ps.get_option('compute.max_rows')
1000
>>> ps.set_option('compute.max_rows', 2000)
>>> ps.get_option('compute.max_rows')
2000

All options also have a default value, and you can use reset_option to do just that:

>>> import pyspark.pandas as ps
>>> ps.reset_option("display.max_rows")

>>> import pyspark.pandas as ps
>>> ps.get_option("display.max_rows")
1000
>>> ps.set_option("display.max_rows", 999)
>>> ps.get_option("display.max_rows")
999
>>> ps.reset_option("display.max_rows")
>>> ps.get_option("display.max_rows")
1000

option_context context manager has been exposed through the top-level API, allowing you to execute code with given option values. Option values are restored automatically when you exit the with block:

>>> with ps.option_context("display.max_rows", 10, "compute.max_rows", 5):
...    print(ps.get_option("display.max_rows"))
...    print(ps.get_option("compute.max_rows"))
10
5
>>> print(ps.get_option("display.max_rows"))
>>> print(ps.get_option("compute.max_rows"))
1000
1000

Operations on different DataFrames#

Pandas API on Spark disallows the operations on different DataFrames (or Series) by default to prevent expensive operations. It internally performs a join operation which can be expensive in general.

This can be enabled by setting compute.ops_on_diff_frames to True to allow such cases. See the examples below.

>>> import pyspark.pandas as ps
>>> ps.set_option('compute.ops_on_diff_frames', True)
>>> psdf1 = ps.range(5)
>>> psdf2 = ps.DataFrame({'id': [5, 4, 3]})
>>> (psdf1 - psdf2).sort_index()
    id
0 -5.0
1 -3.0
2 -1.0
3  NaN
4  NaN
>>> ps.reset_option('compute.ops_on_diff_frames')

>>> import pyspark.pandas as ps
>>> ps.set_option('compute.ops_on_diff_frames', True)
>>> psdf = ps.range(5)
>>> psser_a = ps.Series([1, 2, 3, 4])
>>> # 'psser_a' is not from 'psdf' DataFrame. So it is considered as a Series not from 'psdf'.
>>> psdf['new_col'] = psser_a
>>> psdf
   id  new_col
0   0      1.0
1   1      2.0
3   3      4.0
2   2      3.0
4   4      NaN
>>> ps.reset_option('compute.ops_on_diff_frames')

Default Index type#

In the pandas API on Spark, the default index is used in several cases, for instance, when Spark DataFrame is converted into pandas-on-Spark DataFrame. In this case, internally pandas API on Spark attaches a default index into pandas-on-Spark DataFrame.

There are several types of the default index that can be configured by compute.default_index_type as below:

sequence: It implements a sequence that increases one by one, by PySpark’s Window function without specifying a partition. Therefore, it can end up with a whole partition in a single node. This index type should be avoided when the data is large. See the example below:

>>> import pyspark.pandas as ps
>>> ps.set_option('compute.default_index_type', 'sequence')
>>> psdf = ps.range(3)
>>> ps.reset_option('compute.default_index_type')
>>> psdf.index
Index([0, 1, 2], dtype='int64')

This is conceptually equivalent to the PySpark example as below:

>>> from pyspark.sql import functions as sf, Window
>>> import pyspark.pandas as ps
>>> spark_df = ps.range(3).to_spark()
>>> sequential_index = sf.row_number().over(
...    Window.orderBy(sf.monotonically_increasing_id().asc())) - 1
>>> spark_df.select(sequential_index).rdd.map(lambda r: r[0]).collect()
[0, 1, 2]

distributed-sequence (default): It implements a sequence that increases one by one, by group-by and group-map approach in a distributed manner. It still generates the sequential index globally. If the default index must be the sequence in a large dataset, this index has to be used. See the example below:

>>> import pyspark.pandas as ps
>>> ps.set_option('compute.default_index_type', 'distributed-sequence')
>>> psdf = ps.range(3)
>>> ps.reset_option('compute.default_index_type')
>>> psdf.index
Index([0, 1, 2], dtype='int64')

This is conceptually equivalent to the PySpark example as below:

>>> import pyspark.pandas as ps
>>> spark_df = ps.range(3).to_spark()
>>> spark_df.rdd.zipWithIndex().map(lambda p: p[1]).collect()
[0, 1, 2]

distributed: It implements a monotonically increasing sequence simply by using PySpark’s monotonically_increasing_id function in a fully distributed manner. The values are indeterministic. If the index does not have to be a sequence that increases one by one, this index should be used. Performance-wise, this index almost does not have any penalty compared to other index types. See the example below:

>>> import pyspark.pandas as ps
>>> ps.set_option('compute.default_index_type', 'distributed')
>>> psdf = ps.range(3)
>>> ps.reset_option('compute.default_index_type')
>>> psdf.index
Index([25769803776, 60129542144, 94489280512], dtype='int64')

This is conceptually equivalent to the PySpark example as below:

>>> from pyspark.sql import functions as sf
>>> import pyspark.pandas as ps
>>> spark_df = ps.range(3).to_spark()
>>> spark_df.select(sf.monotonically_increasing_id()) \
...     .rdd.map(lambda r: r[0]).collect()
[25769803776, 60129542144, 94489280512]

Warning

It is very unlikely for this type of index to be used for computing two different dataframes because it is not guaranteed to have the same indexes in two dataframes. If you use this default index and turn on compute.ops_on_diff_frames, the result from the operations between two different DataFrames will likely be an unexpected output due to the indeterministic index values.

Available options#

Option

Default

Description

display.max_rows

1000

This sets the maximum number of rows pandas-on-Spark should output when printing out various output. For example, this value determines the number of rows to be shown at the repr() in a dataframe. Set None to unlimit the input length. Default is 1000.

compute.max_rows

1000

‘compute.max_rows’ sets the limit of the current pandas-on-Spark DataFrame. Set None to unlimit the input length. When the limit is set, it is executed by the shortcut by collecting the data into the driver, and then using the pandas API. If the limit is unset, the operation is executed by PySpark. Default is 1000.

compute.shortcut_limit

1000

‘compute.shortcut_limit’ sets the limit for a shortcut. It computes specified number of rows and use its schema. When the dataframe length is larger than this limit, pandas-on-Spark uses PySpark to compute.

compute.ops_on_diff_frames

False

This determines whether or not to operate between two different dataframes. For example, ‘combine_frames’ function internally performs a join operation which can be expensive in general. So, if compute.ops_on_diff_frames variable is not True, that method throws an exception.

compute.default_index_type

‘distributed-sequence’

This sets the default index type: sequence, distributed and distributed-sequence.

compute.default_index_cache

‘MEMORY_AND_DISK_SER’

This sets the default storage level for temporary RDDs cached in distributed-sequence indexing: ‘NONE’, ‘DISK_ONLY’, ‘DISK_ONLY_2’, ‘DISK_ONLY_3’, ‘MEMORY_ONLY’, ‘MEMORY_ONLY_2’, ‘MEMORY_ONLY_SER’, ‘MEMORY_ONLY_SER_2’, ‘MEMORY_AND_DISK’, ‘MEMORY_AND_DISK_2’, ‘MEMORY_AND_DISK_SER’, ‘MEMORY_AND_DISK_SER_2’, ‘OFF_HEAP’, ‘LOCAL_CHECKPOINT’.

compute.ordered_head

False

‘compute.ordered_head’ sets whether or not to operate head with natural ordering. pandas-on-Spark does not guarantee the row ordering so head could return some rows from distributed partitions. If ‘compute.ordered_head’ is set to True, pandas-on- Spark performs natural ordering beforehand, but it will cause a performance overhead.

compute.eager_check

True

‘compute.eager_check’ sets whether or not to launch some Spark jobs just for the sake of validation. If ‘compute.eager_check’ is set to True, pandas-on-Spark performs the validation beforehand, but it will cause a performance overhead. Otherwise, pandas-on-Spark skip the validation and will be slightly different from pandas. Affected APIs: Series.dot, Series.asof, Series.compare, FractionalExtensionOps.astype, IntegralExtensionOps.astype, FractionalOps.astype, DecimalOps.astype, skipna of statistical functions.

compute.isin_limit

80

‘compute.isin_limit’ sets the limit for filtering by ‘Column.isin(list)’. If the length of the ‘list’ is above the limit, broadcast join is used instead for better performance.

compute.pandas_fallback

False

‘compute.pandas_fallback’ sets whether or not to fallback automatically to Pandas’ implementation.

plotting.max_rows

1000

‘plotting.max_rows’ sets the visual limit on top-n- based plots such as plot.bar and plot.pie. If it is set to 1000, the first 1000 data points will be used for plotting. Default is 1000.

plotting.sample_ratio

None

‘plotting.sample_ratio’ sets the proportion of data that will be plotted for sample-based plots such as plot.line and plot.area. This option defaults to ‘plotting.max_rows’ option.

plotting.backend

‘plotly’

Backend to use for plotting. Default is plotly. Supports any package that has a top-level .plot method. Known options are: [matplotlib, plotly].