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# pandas.SparseDtype

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

  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:
    dtype str, ExtensionDtype, numpy.dtype, type, default numpy.float64

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

    fill_value scalar, optional

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

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

      The default value may be overridden by specifying a `fill_value`.

  Attributes

  None |

  Methods

  None |

  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