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 0x7f328372f340>
[
  1,
  2,
  null
]

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

In [32]: pa.array(idx)
Out[32]: 
<pyarrow.lib.UInt8Array object at 0x7f3299e1eb00>
[
  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.

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: