pandas.arrays.SparseArray#

class pandas.arrays.SparseArray(data, sparse_index=None, index=None, fill_value=None, kind='integer', dtype=None, copy=False)[source]#

An ExtensionArray for storing sparse data.

Parameters

dataarray-like or scalar

A dense array of values to store in the SparseArray. This may contain fill_value.

sparse_indexSparseIndex, optional

indexIndex

Deprecated since version 1.4.0: Use a function like np.full to construct an array with the desired repeats of the scalar value instead.

fill_valuescalar, optional

Elements in data that are fill_value are not stored in the SparseArray. For memory savings, this should be the most common value in data. By default, fill_value depends on the dtype of data:

data.dtype	na_value
float	`np.nan`
int	`0`
bool	False
datetime64	`pd.NaT`
timedelta64	`pd.NaT`

The fill value is potentially specified in three ways. In order of precedence, these are

The fill_value argument
dtype.fill_value if fill_value is None and dtype is a SparseDtype
data.dtype.fill_value if fill_value is None and dtype is not a SparseDtype and data is a SparseArray.

kindstr

Can be ‘integer’ or ‘block’, default is ‘integer’. The type of storage for sparse locations.

‘block’: Stores a block and block_length for each contiguous span of sparse values. This is best when sparse data tends to be clumped together, with large regions of fill-value values between sparse values.
‘integer’: uses an integer to store the location of each sparse value.

dtypenp.dtype or SparseDtype, optional

The dtype to use for the SparseArray. For numpy dtypes, this determines the dtype of self.sp_values. For SparseDtype, this determines self.sp_values and self.fill_value.

copybool, default False

Whether to explicitly copy the incoming data array.

Examples

>>> from pandas.arrays import SparseArray
>>> arr = SparseArray([0, 0, 1, 2])
>>> arr
[0, 0, 1, 2]
Fill: 0
IntIndex
Indices: array([2, 3], dtype=int32)

Attributes

None

Methods

None

pandas.Categorical.__array__

pandas.SparseDtype