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

Attributes

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Methods

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