PyArrow Functionality - Pandas

.. _pyarrow:

PyArrow Functionality

pandas can utilize PyArrow <https://arrow.apache.org/docs/python/index.html>__ to extend functionality and improve the performance of various APIs. This includes:

More extensive data types <https://arrow.apache.org/docs/python/api/datatypes.html>__ compared to NumPy
Missing data support (NA) for all data types
Performant IO reader integration
Facilitate interoperability with other dataframe libraries based on the Apache Arrow specification (e.g. polars, cuDF)

To use this functionality, please ensure you have :ref:installed the minimum supported PyArrow version. <install.optional_dependencies>

Data Structure Integration

A :class:Series, :class:Index, or the columns of a :class:DataFrame can be directly backed by a :external+pyarrow:py:class:pyarrow.ChunkedArray which is similar to a NumPy array. To construct these from the main pandas data structures, you can pass in a string of the type followed by [pyarrow], e.g. "int64[pyarrow]" into the dtype parameter

.. ipython:: python

ser = pd.Series([-1.5, 0.2, None], dtype="float32[pyarrow]") ser

idx = pd.Index([True, None], dtype="bool[pyarrow]") idx

df = pd.DataFrame([[1, 2], [3, 4]], dtype="uint64[pyarrow]") df

.. note::

The string alias "string[pyarrow]" maps to pd.StringDtype("pyarrow") which is not equivalent to specifying dtype=pd.ArrowDtype(pa.string()). Generally, operations on the data will behave similarly except pd.StringDtype("pyarrow") can return NumPy-backed nullable types while pd.ArrowDtype(pa.string()) will return :class:ArrowDtype.

.. ipython:: python

  import pyarrow as pa
  data = list("abc")
  ser_sd = pd.Series(data, dtype="string[pyarrow]")
  ser_ad = pd.Series(data, dtype=pd.ArrowDtype(pa.string()))
  ser_ad.dtype == ser_sd.dtype
  ser_sd.str.contains("a")
  ser_ad.str.contains("a")

For PyArrow types that accept parameters, you can pass in a PyArrow type with those parameters into :class:ArrowDtype to use in the dtype parameter.

.. ipython:: python

import pyarrow as pa list_str_type = pa.list_(pa.string()) ser = pd.Series([["hello"], ["there"]], dtype=pd.ArrowDtype(list_str_type)) ser

.. ipython:: python

from datetime import time idx = pd.Index([time(12, 30), None], dtype=pd.ArrowDtype(pa.time64("us"))) idx

.. ipython:: python

from decimal import Decimal decimal_type = pd.ArrowDtype(pa.decimal128(3, scale=2)) data = [[Decimal("3.19"), None], [None, Decimal("-1.23")]] df = pd.DataFrame(data, dtype=decimal_type) df

If you already have an :external+pyarrow:py:class:pyarrow.Array or :external+pyarrow:py:class:pyarrow.ChunkedArray, you can pass it into :class:.arrays.ArrowExtensionArray to construct the associated :class:Series, :class:Index or :class:DataFrame object.

.. ipython:: python

pa_array = pa.array( [{"1": "2"}, {"10": "20"}, None], type=pa.map_(pa.string(), pa.string()), ) ser = pd.Series(pd.arrays.ArrowExtensionArray(pa_array)) ser

To retrieve a pyarrow :external+pyarrow:py:class:pyarrow.ChunkedArray from a :class:Series or :class:Index, you can call the pyarrow array constructor on the :class:Series or :class:Index.

.. ipython:: python

ser = pd.Series([1, 2, None], dtype="uint8[pyarrow]") pa.array(ser)

idx = pd.Index(ser) pa.array(idx)

To convert a :external+pyarrow:py:class:pyarrow.Table to a :class:DataFrame, you can call the :external+pyarrow:py:meth:pyarrow.Table.to_pandas method with types_mapper=pd.ArrowDtype.

.. ipython:: python

table = pa.table([pa.array([1, 2, 3], type=pa.int64())], names=["a"])

df = table.to_pandas(types_mapper=pd.ArrowDtype) df df.dtypes

Operations

PyArrow data structure integration is implemented through pandas' :class:~pandas.api.extensions.ExtensionArray :ref:interface <extending.extension-types>; therefore, supported functionality exists where this interface is integrated within the pandas API. Additionally, this functionality is accelerated with PyArrow compute functions <https://arrow.apache.org/docs/python/api/compute.html>__ where available. This includes:

Numeric aggregations
Numeric arithmetic
Numeric rounding
Logical and comparison functions
String functionality
Datetime functionality

The following are just some examples of operations that are accelerated by native PyArrow compute functions.

.. ipython:: python

import pyarrow as pa ser = pd.Series([-1.545, 0.211, None], dtype="float32[pyarrow]") ser.mean() ser + ser ser > (ser + 1)

ser.dropna() ser.isna() ser.fillna(0)

.. ipython:: python

ser_str = pd.Series(["a", "b", None], dtype=pd.ArrowDtype(pa.string())) ser_str.str.startswith("a")

.. ipython:: python

from datetime import datetime pa_type = pd.ArrowDtype(pa.timestamp("ns")) ser_dt = pd.Series([datetime(2022, 1, 1), None], dtype=pa_type) ser_dt.dt.strftime("%Y-%m")

I/O Reading

PyArrow also provides IO reading functionality that has been integrated into several pandas IO readers. The following functions provide an engine keyword that can dispatch to PyArrow to accelerate reading from an IO source.

:func:read_csv
:func:read_feather
:func:read_json
:func:read_orc
:func:read_parquet
:func:read_table (experimental)

.. ipython:: python

import io data = io.StringIO("""a,b,c 1,2.5,True 3,4.5,False """) df = pd.read_csv(data, engine="pyarrow") df

By default, these functions and all other IO reader functions return NumPy-backed data. These readers can return PyArrow-backed data by specifying the parameter dtype_backend="pyarrow". A reader does not need to set engine="pyarrow" to necessarily return PyArrow-backed data.

.. ipython:: python

import io
data = io.StringIO("""a,b,c,d,e,f,g,h,i
    1,2.5,True,a,,,,,
    3,4.5,False,b,6,7.5,True,a,
""")
df_pyarrow = pd.read_csv(data, dtype_backend="pyarrow")
df_pyarrow.dtypes

Several non-IO reader functions can also use the dtype_backend argument to return PyArrow-backed data including:

:func:to_numeric
:meth:DataFrame.convert_dtypes
:meth:Series.convert_dtypes