Optimize conversion between PySpark and pandas DataFrames

Apache Arrow is an in-memory columnar data format used in Apache Spark to efficiently transfer data between JVM and Python processes. This is beneficial to Python developers that work with pandas and NumPy data. However, its usage is not automatic and requires some minor changes to configuration or code to take full advantage and ensure compatibility.

PyArrow versions

PyArrow is installed in Databricks Runtime. For information on the version of PyArrow available in each Databricks Runtime version, see the Databricks runtime release notes.

Supported SQL types

All Spark SQL data types are supported by Arrow-based conversion except MapType, ArrayType of TimestampType, and nested StructType. StructType is represented as a pandas.DataFrame instead of pandas.Series. BinaryType is supported only when PyArrow is equal to or higher than 0.10.0.

Convert PySpark DataFrames to and from pandas DataFrames

Arrow is available as an optimization when converting a PySpark DataFrame to a pandas DataFrame with toPandas() and when creating a PySpark DataFrame from a pandas DataFrame with createDataFrame(pandas_df). To use Arrow for these methods, set the Spark configuration spark.sql.execution.arrow.enabled to true. This configuration is disabled by default.

In addition, optimizations enabled by spark.sql.execution.arrow.enabled could fall back to a non-Arrow implementation if an error occurs before the computation within Spark. You can control this behavior using the Spark configuration spark.sql.execution.arrow.fallback.enabled.


import numpy as np
import pandas as pd

# Enable Arrow-based columnar data transfers
spark.conf.set("spark.sql.execution.arrow.enabled", "true")

# Generate a pandas DataFrame
pdf = pd.DataFrame(np.random.rand(100, 3))

# Create a Spark DataFrame from a pandas DataFrame using Arrow
df = spark.createDataFrame(pdf)

# Convert the Spark DataFrame back to a pandas DataFrame using Arrow
result_pdf = df.select("*").toPandas()

Using the Arrow optimizations produces the same results as when Arrow is not enabled. Even with Arrow, toPandas() results in the collection of all records in the DataFrame to the driver program and should be done on a small subset of the data.

In addition, not all Spark data types are supported and an error can be raised if a column has an unsupported type. If an error occurs during createDataFrame(), Spark falls back to create the DataFrame without Arrow.