pandas.SparseArray — pandas 0.24.2 documentation (original) (raw)
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 containfill_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 dtypeis 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. |
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Attributes
| T | Returns the SparseArray. |
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| 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 |
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| 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 | | | ----------- | |