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

data :array-like or scalar

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

sparse_index :SparseIndex, optional

index :Index

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_value :scalar, 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

1. The fill_value argument

2. dtype.fill_value if fill_value is None and dtype is a SparseDtype

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

kind :str

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.

dtype :np.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.

copy :bool, 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