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.nanint 0bool False datetime64 pd.NaTtimedelta64 pd.NaTThe fill value is potentiall specified in three ways. In order of precedence, these are
- The fill_value argument
dtype.fill_valueif fill_value is None and dtype is aSparseDtypedata.dtype.fill_valueif fill_value is None and dtype is not aSparseDtypeand 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
regsions of
fill-valuevalues 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
regsions 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_valuesandself.fill_value.- copy : bool, default False
Whether to explicitly copy the incoming data array.
Attributes
TReturns the SparseArray. densityThe percent of non- fill_valuepoints, as decimal.dtypeAn instance of ‘ExtensionDtype’. fill_valueElements in data that are fill_value are not stored. kindThe kind of sparse index for this array. nbytesThe number of bytes needed to store this object in memory. ndimExtension Arrays are only allowed to be 1-dimensional. npointsThe number of non- fill_valuepoints.shapeReturn a tuple of the array dimensions. sp_indexThe SparseIndex containing the location of non- fill_valuepoints.sp_valuesAn ndarray containing the non- fill_valuevalues.valuesDense values Methods
all([axis])Tests whether all elements evaluate True any([axis])Tests whether at least one of elements evaluate True argsort([ascending, kind])Return the indices that would sort this array. astype([dtype, copy])Change the dtype of a SparseArray. copy([deep])Return a copy of the array. cumsum([axis])Cumulative sum of non-NA/null values. dropna()Return ExtensionArray without NA values factorize([na_sentinel])Encode the extension array as an enumerated type. fillna([value, method, limit])Fill missing values with value. get_values()Convert SparseArray to a NumPy array. isna()A 1-D array indicating if each value is missing. map(mapper)Map categories using input correspondence (dict, Series, or function). mean([axis])Mean of non-NA/null values repeat(repeats[, axis])Repeat elements of a ExtensionArray. searchsorted(v[, side, sorter])Find indices where elements should be inserted to maintain order. shift([periods, fill_value])Shift values by desired number. sum([axis])Sum of non-NA/null values take(indices[, allow_fill, fill_value])Take elements from an array. to_dense()Convert SparseArray to a NumPy array. transpose(*axes)Returns the SparseArray. unique()Compute the ExtensionArray of unique values. value_counts([dropna])Returns a Series containing counts of unique values. nonzero