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# Extending pandas

While pandas provides a rich set of methods, containers, and data types, your needs may not be fully satisfied. pandas offers a few options for extending pandas.

## Registering custom accessors

Libraries can use the decorators `pandas.api.extensions.register_dataframe_accessor()`, `pandas.api.extensions.register_series_accessor()`, and `pandas.api.extensions.register_index_accessor()`, to add additional “namespaces” to pandas objects. All of these follow a similar convention: you decorate a class, providing the name of attribute to add. The class’s `__init__` method gets the object being decorated. For example:

    @pd.api.extensions.register_dataframe_accessor("geo")
    class GeoAccessor:
        def __init__(self, pandas_obj):
            self._validate(pandas_obj)
            self._obj = pandas_obj

        @staticmethod
        def _validate(obj):
            # verify there is a column latitude and a column longitude
            if "latitude" not in obj.columns or "longitude" not in obj.columns:
                raise AttributeError("Must have 'latitude' and 'longitude'.")

        @property
        def center(self):
            # return the geographic center point of this DataFrame
            lat = self._obj.latitude
            lon = self._obj.longitude
            return (float(lon.mean()), float(lat.mean()))

        def plot(self):
            # plot this array's data on a map, e.g., using Cartopy
            pass

Now users can access your methods using the `geo` namespace:

    >>> ds = pd.DataFrame(
    ...     {"longitude": np.linspace(0, 10), "latitude": np.linspace(0, 20)}
    ... )
    >>> ds.geo.center
    (5.0, 10.0)
    >>> ds.geo.plot()
    # plots data on a map

This can be a convenient way to extend pandas objects without subclassing them. If you write a custom accessor, make a pull request adding it to our ecosystem page.

We highly recommend validating the data in your accessor’s `__init__`. In our `GeoAccessor`, we validate that the data contains the expected columns, raising an `AttributeError` when the validation fails. For a `Series` accessor, you should validate the `dtype` if the accessor applies only to certain dtypes.

## Extension types

Note

The `pandas.api.extensions.ExtensionDtype` and `pandas.api.extensions.ExtensionArray` APIs were experimental prior to pandas 1.5. Starting with version 1.5, future changes will follow the pandas deprecation policy.

pandas defines an interface for implementing data types and arrays that extend NumPy’s type system. pandas itself uses the extension system for some types that aren’t built into NumPy (categorical, period, interval, datetime with timezone).

Libraries can define a custom array and data type. When pandas encounters these objects, they will be handled properly (i.e. not converted to an ndarray of objects). Many methods like `pandas.isna()` will dispatch to the extension type’s implementation.

If you’re building a library that implements the interface, please publicize it on the ecosystem page.

The interface consists of two classes.

### `ExtensionDtype`

A `pandas.api.extensions.ExtensionDtype` is similar to a `numpy.dtype` object. It describes the data type. Implementers are responsible for a few unique items like the name.

One particularly important item is the `type` property. This should be the class that is the scalar type for your data. For example, if you were writing an extension array for IP Address data, this might be `ipaddress.IPv4Address`.

See the extension dtype source for interface definition.

`pandas.api.extensions.ExtensionDtype` can be registered to pandas to allow creation via a string dtype name. This allows one to instantiate `Series` and `.astype()` with a registered string name, for example `'category'` is a registered string accessor for the `CategoricalDtype`.

See the extension dtype dtypes for more on how to register dtypes.

### `ExtensionArray`

This class provides all the array-like functionality. ExtensionArrays are limited to 1 dimension. An ExtensionArray is linked to an ExtensionDtype via the `dtype` attribute.

pandas makes no restrictions on how an extension array is created via its `__new__` or `__init__`, and puts no restrictions on how you store your data. We do require that your array be convertible to a NumPy array, even if this is relatively expensive (as it is for `Categorical`).

They may be backed by none, one, or many NumPy arrays. For example, `pandas.Categorical` is an extension array backed by two arrays, one for codes and one for categories. An array of IPv6 addresses may be backed by a NumPy structured array with two fields, one for the lower 64 bits and one for the upper 64 bits. Or they may be backed by some other storage type, like Python lists.

See the extension array source for the interface definition. The docstrings and comments contain guidance for properly implementing the interface.

### `ExtensionArray` operator support

By default, there are no operators defined for the class `ExtensionArray`. There are two approaches for providing operator support for your ExtensionArray:

1. Define each of the operators on your `ExtensionArray` subclass.

2. Use an operator implementation from pandas that depends on operators that are already defined on the underlying elements (scalars) of the ExtensionArray.

Note

Regardless of the approach, you may want to set `__array_priority__` if you want your implementation to be called when involved in binary operations with NumPy arrays.

For the first approach, you define selected operators, e.g., `__add__`, `__le__`, etc. that you want your `ExtensionArray` subclass to support.

The second approach assumes that the underlying elements (i.e., scalar type) of the `ExtensionArray` have the individual operators already defined. In other words, if your `ExtensionArray` named `MyExtensionArray` is implemented so that each element is an instance of the class `MyExtensionElement`, then if the operators are defined for `MyExtensionElement`, the second approach will automatically define the operators for `MyExtensionArray`.

A mixin class, `ExtensionScalarOpsMixin` supports this second approach. If developing an `ExtensionArray` subclass, for example `MyExtensionArray`, can simply include `ExtensionScalarOpsMixin` as a parent class of `MyExtensionArray`, and then call the methods `_add_arithmetic_ops()` and/or `_add_comparison_ops()` to hook the operators into your `MyExtensionArray` class, as follows:

    from pandas.api.extensions import ExtensionArray, ExtensionScalarOpsMixin

    class MyExtensionArray(ExtensionArray, ExtensionScalarOpsMixin):
        pass

    MyExtensionArray._add_arithmetic_ops()
    MyExtensionArray._add_comparison_ops()

Note

Since `pandas` automatically calls the underlying operator on each element one-by-one, this might not be as performant as implementing your own version of the associated operators directly on the `ExtensionArray`.

For arithmetic operations, this implementation will try to reconstruct a new `ExtensionArray` with the result of the element-wise operation. Whether or not that succeeds depends on whether the operation returns a result that’s valid for the `ExtensionArray`. If an `ExtensionArray` cannot be reconstructed, an ndarray containing the scalars returned instead.

For ease of implementation and consistency with operations between pandas and NumPy ndarrays, we recommend not handling Series and Indexes in your binary ops. Instead, you should detect these cases and return `NotImplemented`. When pandas encounters an operation like `op(Series, ExtensionArray)`, pandas will

1. unbox the array from the `Series` (`Series.array`)

2. call `result = op(values, ExtensionArray)`

3. re-box the result in a `Series`

### NumPy universal functions

`Series` implements `__array_ufunc__`. As part of the implementation, pandas unboxes the `ExtensionArray` from the `Series`, applies the ufunc, and re-boxes it if necessary.

If applicable, we highly recommend that you implement `__array_ufunc__` in your extension array to avoid coercion to an ndarray. See the NumPy documentation for an example.

As part of your implementation, we require that you defer to pandas when a pandas container (`Series`, `DataFrame`, `Index`) is detected in `inputs`. If any of those is present, you should return `NotImplemented`. pandas will take care of unboxing the array from the container and re-calling the ufunc with the unwrapped input.

### Testing extension arrays

We provide a test suite for ensuring that your extension arrays satisfy the expected behavior. To use the test suite, you must provide several pytest fixtures and inherit from the base test class. The required fixtures are found in pandas-dev/pandas.

To use a test, subclass it:

    from pandas.tests.extension import base

    class TestConstructors(base.BaseConstructorsTests):
        pass

See pandas-dev/pandas for a list of all the tests available.

### Compatibility with Apache Arrow

An `ExtensionArray` can support conversion to / from `pyarrow` arrays (and thus support for example serialization to the Parquet file format) by implementing two methods: `ExtensionArray.__arrow_array__` and `ExtensionDtype.__from_arrow__`.

The `ExtensionArray.__arrow_array__` ensures that `pyarrow` knowns how to convert the specific extension array into a `pyarrow.Array` (also when included as a column in a pandas DataFrame):

    class MyExtensionArray(ExtensionArray):
        ...

        def __arrow_array__(self, type=None):
            # convert the underlying array values to a pyarrow Array
            import pyarrow

            return pyarrow.array(..., type=type)

The `ExtensionDtype.__from_arrow__` method then controls the conversion back from pyarrow to a pandas ExtensionArray. This method receives a pyarrow `Array` or `ChunkedArray` as only argument and is expected to return the appropriate pandas `ExtensionArray` for this dtype and the passed values:

    class ExtensionDtype:
        ...

        def __from_arrow__(self, array: pyarrow.Array/ChunkedArray) -> ExtensionArray:
            ...

See more in the Arrow documentation.

Those methods have been implemented for the nullable integer and string extension dtypes included in pandas, and ensure roundtrip to pyarrow and the Parquet file format.

## Subclassing pandas data structures

Warning

There are some easier alternatives before considering subclassing `pandas` data structures.

1. Extensible method chains with pipe

2. Use composition. See here.

3. Extending by registering an accessor

4. Extending by extension type

This section describes how to subclass `pandas` data structures to meet more specific needs. There are two points that need attention:

1. Override constructor properties.

2. Define original properties

Note

You can find a nice example in geopandas project.

### Override constructor properties

Each data structure has several constructor properties for returning a new data structure as the result of an operation. By overriding these properties, you can retain subclasses through `pandas` data manipulations.

There are 3 possible constructor properties to be defined on a subclass:

- `DataFrame/Series._constructor`: Used when a manipulation result has the same dimension as the original.

- `DataFrame._constructor_sliced`: Used when a `DataFrame` (sub-)class manipulation result should be a `Series` (sub-)class.

- `Series._constructor_expanddim`: Used when a `Series` (sub-)class manipulation result should be a `DataFrame` (sub-)class, e.g. `Series.to_frame()`.

Below example shows how to define `SubclassedSeries` and `SubclassedDataFrame` overriding constructor properties.

    class SubclassedSeries(pd.Series):
        @property
        def _constructor(self):
            return SubclassedSeries

        @property
        def _constructor_expanddim(self):
            return SubclassedDataFrame

    class SubclassedDataFrame(pd.DataFrame):
        @property
        def _constructor(self):
            return SubclassedDataFrame

        @property
        def _constructor_sliced(self):
            return SubclassedSeries

    >>> s = SubclassedSeries([1, 2, 3])
    >>> type(s)
    <class '__main__.SubclassedSeries'>

    >>> to_framed = s.to_frame()
    >>> type(to_framed)
    <class '__main__.SubclassedDataFrame'>

    >>> df = SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
    >>> df
       A  B  C
    0  1  4  7
    1  2  5  8
    2  3  6  9

    >>> type(df)
    <class '__main__.SubclassedDataFrame'>

    >>> sliced1 = df[["A", "B"]]
    >>> sliced1
       A  B
    0  1  4
    1  2  5
    2  3  6

    >>> type(sliced1)
    <class '__main__.SubclassedDataFrame'>

    >>> sliced2 = df["A"]
    >>> sliced2
    0    1
    1    2
    2    3
    Name: A, dtype: int64

    >>> type(sliced2)
    <class '__main__.SubclassedSeries'>

### Define original properties

To let original data structures have additional properties, you should let `pandas` know what properties are added. `pandas` maps unknown properties to data names overriding `__getattribute__`. Defining original properties can be done in one of 2 ways:

1. Define `_internal_names` and `_internal_names_set` for temporary properties which WILL NOT be passed to manipulation results.

2. Define `_metadata` for normal properties which will be passed to manipulation results.

Below is an example to define two original properties, “internal_cache” as a temporary property and “added_property” as a normal property

    class SubclassedDataFrame2(pd.DataFrame):

        # temporary properties
        _internal_names = pd.DataFrame._internal_names + ["internal_cache"]
        _internal_names_set = set(_internal_names)

        # normal properties
        _metadata = ["added_property"]

        @property
        def _constructor(self):
            return SubclassedDataFrame2

    >>> df = SubclassedDataFrame2({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
    >>> df
       A  B  C
    0  1  4  7
    1  2  5  8
    2  3  6  9

    >>> df.internal_cache = "cached"
    >>> df.added_property = "property"

    >>> df.internal_cache
    cached
    >>> df.added_property
    property

    # properties defined in _internal_names is reset after manipulation
    >>> df[["A", "B"]].internal_cache
    AttributeError: 'SubclassedDataFrame2' object has no attribute 'internal_cache'

    # properties defined in _metadata are retained
    >>> df[["A", "B"]].added_property
    property

## Plotting backends

pandas can be extended with third-party plotting backends. The main idea is letting users select a plotting backend different than the provided one based on Matplotlib. For example:

    >>> pd.set_option("plotting.backend", "backend.module")
    >>> pd.Series([1, 2, 3]).plot()

This would be more or less equivalent to:

    >>> import backend.module
    >>> backend.module.plot(pd.Series([1, 2, 3]))

The backend module can then use other visualization tools (Bokeh, Altair,…) to generate the plots.

Libraries implementing the plotting backend should use entry points to make their backend discoverable to pandas. The key is `"pandas_plotting_backends"`. For example, pandas registers the default “matplotlib” backend as follows.

    # in setup.py
    setup(  # noqa: F821
        ...,
        entry_points={
            "pandas_plotting_backends": [
                "matplotlib = pandas:plotting._matplotlib",
            ],
        },
    )

More information on how to implement a third-party plotting backend can be found at pandas-dev/pandas.

## Arithmetic with 3rd party types

In order to control how arithmetic works between a custom type and a pandas type, implement `__pandas_priority__`. Similar to numpy’s `__array_priority__` semantics, arithmetic methods on `DataFrame`, `Series`, and `Index` objects will delegate to `other`, if it has an attribute `__pandas_priority__` with a higher value.

By default, pandas objects try to operate with other objects, even if they are not types known to pandas:

    >>> pd.Series([1, 2]) + [10, 20]
    0    11
    1    22
    dtype: int64

In the example above, if `[10, 20]` was a custom type that can be understood as a list, pandas objects will still operate with it in the same way.

In some cases, it is useful to delegate to the other type the operation. For example, consider I implement a custom list object, and I want the result of adding my custom list with a pandas `Series` to be an instance of my list and not a `Series` as seen in the previous example. This is now possible by defining the `__pandas_priority__` attribute of my custom list, and setting it to a higher value, than the priority of the pandas objects I want to operate with.

The `__pandas_priority__` of `DataFrame`, `Series`, and `Index` are `4000`, `3000`, and `2000` respectively. The base `ExtensionArray.__pandas_priority__` is `1000`.

    class CustomList(list):
        __pandas_priority__ = 5000

        def __radd__(self, other):
            # return `self` and not the addition for simplicity
            return self

    custom = CustomList()
    series = pd.Series([1, 2, 3])

    # Series refuses to add custom, since it's an unknown type with higher priority
    assert series.__add__(custom) is NotImplemented

    # This will cause the custom class `__radd__` being used instead
    assert series + custom is custom