pandas.SparseDtype#

class pandas.SparseDtype(dtype=<class 'numpy.float64'>, fill_value=None)[source]#

Dtype for data stored in SparseArray.

SparseDtype is used as the data type for SparseArray, enabling more efficient storage of data that contains a significant number of repetitive values typically represented by a fill value. It supports any scalar dtype as the underlying data type of the non-fill values.

Parameters:

dtypestr, ExtensionDtype, numpy.dtype, type, default numpy.float64

The dtype of the underlying array storing the non-fill value values.

fill_valuescalar, optional

The scalar value not stored in the SparseArray. By default, this depends on dtype.

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

The default value may be overridden by specifying a fill_value.

See also

arrays.SparseArray: The array structure that uses SparseDtype for data representation.

Examples

>>> ser = pd.Series([1, 0, 0], dtype=pd.SparseDtype(dtype=int, fill_value=0))
>>> ser
0    1
1    0
2    0
dtype: Sparse[int64, 0]
>>> ser.sparse.density
0.3333333333333333

Attributes

None

Methods

None