Abstract base class for custom 1-D array types.
pandas will recognize instances of this class as proper arrays
with a custom type and will not attempt to coerce them to objects. They
may be stored directly inside a DataFrame or Series.
The interface includes the following abstract methods that must be
implemented by subclasses:
A default repr displaying the type, (truncated) data, length,
and dtype is provided. It can be customized or replaced by
__repr__ : A default repr for the ExtensionArray.
_formatter : Print scalars inside a Series or DataFrame.
Some methods require casting the ExtensionArray to an ndarray of Python
objects with self.astype(object), which may be expensive. When
performance is a concern, we highly recommend overriding the following
factorize / _values_for_factorize
argsort / _values_for_argsort
The remaining methods implemented on this class should be performant,
as they only compose abstract methods. Still, a more efficient
implementation may be available, and these methods can be overridden.
One can implement methods to handle array reductions.
One can implement methods to handle parsing from strings that will be used
in methods such as pandas.io.parsers.read_csv.
This class does not inherit from ‘abc.ABCMeta’ for performance reasons.
Methods and properties required by the interface raise
pandas.errors.AbstractMethodError and no register method is
provided for registering virtual subclasses.
ExtensionArrays are limited to 1 dimension.
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 address 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. Pandas makes no
assumptions on how the data are stored, just that it can be converted
to a NumPy array.
The ExtensionArray interface does not impose any rules on how this data
is stored. However, currently, the backing data cannot be stored in
attributes called .values or ._values to ensure full compatibility
with pandas internals. But other names as .data, ._data,
._items, … can be freely used.
If implementing NumPy’s __array_ufunc__ interface, pandas expects
You defer by returning NotImplemented when any Series are present
in inputs. Pandas will extract the arrays and call the ufunc again.
You define a _HANDLED_TYPES tuple as an attribute on the class.
Pandas inspect this to determine whether the ufunc is valid for the
See NumPy universal functions for more.
By default, ExtensionArrays are not hashable. Immutable subclasses may
override this behavior.
An instance of ‘ExtensionDtype’.
The number of bytes needed to store this object in memory.
Extension Arrays are only allowed to be 1-dimensional.
Return a tuple of the array dimensions.
argsort([ascending, kind, na_position])
Return the indices that would sort this array.
Cast to a NumPy array with ‘dtype’.
Return a copy of the array.
Return ExtensionArray without NA values.
Encode the extension array as an enumerated type.
fillna([value, method, limit])
Fill NA/NaN values using the specified method.
Return if another array is equivalent to this array.
A 1-D array indicating if each value is missing.
Return a flattened view on this array.
Repeat elements of a ExtensionArray.
searchsorted(value[, side, sorter])
Find indices where elements should be inserted to maintain order.
Shift values by desired number.
take(indices, *[, allow_fill, fill_value])
Take elements from an array.
Compute the ExtensionArray of unique values.
Return a view on the array.
Concatenate multiple array of this dtype.
Formatting function for scalar values.
Reconstruct an ExtensionArray after factorization.
_from_sequence(scalars, *[, dtype, copy])
Construct a new ExtensionArray from a sequence of scalars.
_from_sequence_of_strings(strings, *[, …])
Construct a new ExtensionArray from a sequence of strings.
_reduce(name, *[, skipna])
Return a scalar result of performing the reduction operation.
Return values for sorting.
Return an array and missing value suitable for factorization.