Aprende Converting Between Arrow and pandas

As a data scientist, you often work with both pandas DataFrames and Arrow tables. While pandas is the go-to tool for many data manipulation tasks, Arrow provides a high-performance, columnar memory format that is especially useful for analytics and interoperability between systems. PyArrow acts as the bridge between these two worlds, making it straightforward to convert data back and forth. Understanding how to move data efficiently between Arrow and pandas is essential for building fast, scalable data workflows.

To convert a pyarrow.Table to a pandas DataFrame, you use the to_pandas() method. This method takes the Arrow table and returns a pandas DataFrame, preserving the column names and data types as closely as possible. For example, suppose you have an Arrow table called table; you can obtain a DataFrame with df = table.to_pandas().

Converting in the other direction is just as simple. If you have a pandas DataFrame called df, you can create an Arrow table using pyarrow.Table.from_pandas(df). This function inspects the DataFrame's columns and types, and constructs an Arrow table with the appropriate schema. You can also specify additional options, such as how to handle nulls or preserve categorical data, but the basic conversion is as simple as calling from_pandas.

Here is a step-by-step demonstration of both conversions:


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import pandas as pd
import pyarrow as pa

# Create a pandas DataFrame
df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "score": [88.5, 92.0, 85.0]
})

# Convert pandas DataFrame to Arrow Table
table = pa.Table.from_pandas(df)

# Convert Arrow Table back to pandas DataFrame
df_converted = table.to_pandas()

print("Original DataFrame:")
print(df)
print("\nArrow Table schema:")
print(table.schema)
print("\nConverted DataFrame:")
print(df_converted)

Definition

Zero-copy conversion is a process where data is shared between two structures (such as an Arrow table and a pandas DataFrame) without duplicating the underlying memory. This allows for faster conversions and reduced memory usage, which is especially important with large datasets.

Although converting between Arrow tables and pandas DataFrames is convenient, there are some limitations and caveats to keep in mind. Not all pandas data types have a perfect equivalent in Arrow, and vice versa. For example, pandas supports objects and mixed types in columns, while Arrow expects well-defined, consistent column types. Some data, such as time zones or categorical data, may require additional handling to ensure they remain consistent after conversion. Performance can also vary depending on whether zero-copy conversion is possible; if the data types or memory layouts are incompatible, a full data copy may be required, which can impact speed and memory usage. Always verify that the converted data matches your expectations, especially when working with complex or custom types.

When to use Arrow-pandas conversion

When you need to process data in pandas after ingesting it from an Arrow-based system;
When you want to export processed DataFrames to Arrow for efficient storage or interoperability;
When you are integrating pandas with big data tools that use Arrow as a common format.

Technical details on memory sharing

Zero-copy conversion is possible when Arrow and pandas share compatible memory layouts;
If data types or column structures differ, PyArrow may need to allocate new memory and copy data;
Some conversions, such as those involving string or categorical columns, may not always be zero-copy.

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