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# PyArrow Functionality

pandas can utilize PyArrow to extend functionality and improve the performance of various APIs. This includes:

- More extensive data types 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 installed the minimum supported PyArrow version.

## Data Structure Integration

A `Series`, `Index`, or the columns of a `DataFrame` can be directly backed by a `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

    In [1]: ser = pd.Series([-1.5, 0.2, None], dtype="float32[pyarrow]")

    In [2]: ser
    Out[2]:
    0    -1.5
    1     0.2
    2    <NA>
    dtype: float[pyarrow]

    In [3]: idx = pd.Index([True, None], dtype="bool[pyarrow]")

    In [4]: idx
    Out[4]: Index([True, <NA>], dtype='bool[pyarrow]')

    In [5]: df = pd.DataFrame([[1, 2], [3, 4]], dtype="uint64[pyarrow]")

    In [6]: df
    Out[6]:
       0  1
    0  1  2
    1  3  4

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 `ArrowDtype`.

    In [7]: import pyarrow as pa

    In [8]: data = list("abc")

    In [9]: ser_sd = pd.Series(data, dtype="string[pyarrow]")

    In [10]: ser_ad = pd.Series(data, dtype=pd.ArrowDtype(pa.string()))

    In [11]: ser_ad.dtype == ser_sd.dtype
    Out[11]: False

    In [12]: ser_sd.str.contains("a")
    Out[12]:
    0     True
    1    False
    2    False
    dtype: boolean

    In [13]: ser_ad.str.contains("a")
    Out[13]:
    0     True
    1    False
    2    False
    dtype: bool[pyarrow]

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

    In [14]: import pyarrow as pa

    In [15]: list_str_type = pa.list_(pa.string())

    In [16]: ser = pd.Series([["hello"], ["there"]], dtype=pd.ArrowDtype(list_str_type))

    In [17]: ser
    Out[17]:
    0    ['hello']
    1    ['there']
    dtype: list<item: string>[pyarrow]

    In [18]: from datetime import time

    In [19]: idx = pd.Index([time(12, 30), None], dtype=pd.ArrowDtype(pa.time64("us")))

    In [20]: idx
    Out[20]: Index([12:30:00, <NA>], dtype='time64[us][pyarrow]')

    In [21]: from decimal import Decimal

    In [22]: decimal_type = pd.ArrowDtype(pa.decimal128(3, scale=2))

    In [23]: data = [[Decimal("3.19"), None], [None, Decimal("-1.23")]]

    In [24]: df = pd.DataFrame(data, dtype=decimal_type)

    In [25]: df
    Out[25]:
          0      1
    0  3.19   <NA>
    1  <NA>  -1.23

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

    In [26]: pa_array = pa.array(
       ....:     [{"1": "2"}, {"10": "20"}, None],
       ....:     type=pa.map_(pa.string(), pa.string()),
       ....: )
       ....:

    In [27]: ser = pd.Series(pd.arrays.ArrowExtensionArray(pa_array))

    In [28]: ser
    Out[28]:
    0      [('1', '2')]
    1    [('10', '20')]
    2              <NA>
    dtype: map<string, string>[pyarrow]

To retrieve a pyarrow `pyarrow.ChunkedArray` from a `Series` or `Index`, you can call the pyarrow array constructor on the `Series` or `Index`.

    In [29]: ser = pd.Series([1, 2, None], dtype="uint8[pyarrow]")

    In [30]: pa.array(ser)
    Out[30]:
    <pyarrow.lib.UInt8Array object at 0x7f1087e61780>
    [
      1,
      2,
      null
    ]

    In [31]: idx = pd.Index(ser)

    In [32]: pa.array(idx)
    Out[32]:
    <pyarrow.lib.UInt8Array object at 0x7f1087e3a380>
    [
      1,
      2,
      null
    ]

To convert a `pyarrow.Table` to a `DataFrame`, you can call the `pyarrow.Table.to_pandas()` method with `types_mapper=pd.ArrowDtype`.

    In [33]: table = pa.table([pa.array([1, 2, 3], type=pa.int64())], names=["a"])

    In [34]: df = table.to_pandas(types_mapper=pd.ArrowDtype)

    In [35]: df
    Out[35]:
       a
    0  1
    1  2
    2  3

    In [36]: df.dtypes
    Out[36]:
    a    int64[pyarrow]
    dtype: object

## Operations

PyArrow data structure integration is implemented through pandas’ `ExtensionArray` interface; therefore, supported functionality exists where this interface is integrated within the pandas API. Additionally, this functionality is accelerated with PyArrow compute functions 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.

    In [37]: import pyarrow as pa

    In [38]: ser = pd.Series([-1.545, 0.211, None], dtype="float32[pyarrow]")

    In [39]: ser.mean()
    Out[39]: -0.6669999808073044

    In [40]: ser + ser
    Out[40]:
    0    -3.09
    1    0.422
    2     <NA>
    dtype: float[pyarrow]

    In [41]: ser > (ser + 1)
    Out[41]:
    0    False
    1    False
    2     <NA>
    dtype: bool[pyarrow]

    In [42]: ser.dropna()
    Out[42]:
    0   -1.545
    1    0.211
    dtype: float[pyarrow]

    In [43]: ser.isna()
    Out[43]:
    0    False
    1    False
    2     True
    dtype: bool

    In [44]: ser.fillna(0)
    Out[44]:
    0   -1.545
    1    0.211
    2      0.0
    dtype: float[pyarrow]

    In [45]: ser_str = pd.Series(["a", "b", None], dtype=pd.ArrowDtype(pa.string()))

    In [46]: ser_str.str.startswith("a")
    Out[46]:
    0     True
    1    False
    2     <NA>
    dtype: bool[pyarrow]

    In [47]: from datetime import datetime

    In [48]: pa_type = pd.ArrowDtype(pa.timestamp("ns"))

    In [49]: ser_dt = pd.Series([datetime(2022, 1, 1), None], dtype=pa_type)

    In [50]: ser_dt.dt.strftime("%Y-%m")
    Out[50]:
    0    2022-01
    1       <NA>
    dtype: string[pyarrow]

## 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.

- `read_csv()`

- `read_json()`

- `read_orc()`

- `read_feather()`

    In [51]: import io

    In [52]: data = io.StringIO("""a,b,c
       ....:    1,2.5,True
       ....:    3,4.5,False
       ....: """)
       ....:

    In [53]: df = pd.read_csv(data, engine="pyarrow")

    In [54]: df
    Out[54]:
       a    b      c
    0  1  2.5   True
    1  3  4.5  False

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.

    In [55]: import io

    In [56]: 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,
       ....: """)
       ....:

    In [57]: df_pyarrow = pd.read_csv(data, dtype_backend="pyarrow")

    In [58]: df_pyarrow.dtypes
    Out[58]:
    a     int64[pyarrow]
    b    double[pyarrow]
    c      bool[pyarrow]
    d    string[pyarrow]
    e     int64[pyarrow]
    f    double[pyarrow]
    g      bool[pyarrow]
    h    string[pyarrow]
    i      null[pyarrow]
    dtype: object

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

- `to_numeric()`

- `DataFrame.convert_dtypes()`

- `Series.convert_dtypes()`