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 potentiall 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 regsions 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

T	Returns the SparseArray.
density	The percent of non- fill_value points, as decimal.
dtype	An instance of ‘ExtensionDtype’.
fill_value	Elements in data that are fill_value are not stored.
kind	The kind of sparse index for this array.
nbytes	The number of bytes needed to store this object in memory.
ndim	Extension Arrays are only allowed to be 1-dimensional.
npoints	The number of non- fill_value points.
shape	Return a tuple of the array dimensions.
sp_index	The SparseIndex containing the location of non- fill_value points.
sp_values	An ndarray containing the non- fill_value values.
values	Dense 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.

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