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 aSparseDtype
data.dtype.fill_value
if fill_value is None and dtype is not aSparseDtype
and 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-value
values 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_values
andself.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. nonzero