pandas.SparseArray¶
-
class
pandas.
SparseArray
(data, sparse_index=None, index=None, fill_value=None, kind='integer', dtype=None, copy=False)[source]¶ An ExtensionArray for storing sparse data.
Changed in version 0.24.0: Implements the ExtensionArray interface.
Parameters: - data : array-like
A dense array of values to store in the SparseArray. This may contain fill_value.
- sparse_index : SparseIndex, optional
- index : Index
- 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
- The fill_value argument
dtype.fill_value
if fill_value is None and dtype is aSparseDtype
data.dtype.fill_value
if fill_value is None and dtype is not aSparseDtype
and data is aSparseArray
.
- kind : {‘integer’, ‘block’}, default ‘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.
- ‘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
- 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 determinesself.sp_values
andself.fill_value
.- copy : bool, default False
Whether to explicitly copy the incoming data array.
Attributes
None Methods
None