1529 lines
49 KiB
Python
1529 lines
49 KiB
Python
"""
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SparseArray data structure
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"""
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from collections import abc
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import numbers
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import operator
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from typing import Any, Callable, Sequence, Type, TypeVar, Union
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import warnings
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import numpy as np
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from pandas._libs import lib
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import pandas._libs.sparse as splib
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from pandas._libs.sparse import BlockIndex, IntIndex, SparseIndex
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from pandas._libs.tslibs import NaT
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from pandas._typing import Scalar
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from pandas.compat.numpy import function as nv
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from pandas.errors import PerformanceWarning
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from pandas.core.dtypes.cast import (
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astype_nansafe,
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construct_1d_arraylike_from_scalar,
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find_common_type,
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infer_dtype_from_scalar,
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maybe_box_datetimelike,
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)
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from pandas.core.dtypes.common import (
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is_array_like,
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is_bool_dtype,
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is_datetime64_any_dtype,
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is_datetime64tz_dtype,
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is_dtype_equal,
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is_integer,
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is_object_dtype,
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is_scalar,
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is_string_dtype,
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pandas_dtype,
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)
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from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
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from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna
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import pandas.core.algorithms as algos
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from pandas.core.arraylike import OpsMixin
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from pandas.core.arrays import ExtensionArray
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from pandas.core.arrays.sparse.dtype import SparseDtype
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from pandas.core.base import PandasObject
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import pandas.core.common as com
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from pandas.core.construction import extract_array, sanitize_array
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from pandas.core.indexers import check_array_indexer
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from pandas.core.missing import interpolate_2d
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from pandas.core.nanops import check_below_min_count
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import pandas.core.ops as ops
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import pandas.io.formats.printing as printing
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# ----------------------------------------------------------------------------
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# Array
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SparseArrayT = TypeVar("SparseArrayT", bound="SparseArray")
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_sparray_doc_kwargs = {"klass": "SparseArray"}
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def _get_fill(arr: "SparseArray") -> np.ndarray:
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"""
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Create a 0-dim ndarray containing the fill value
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Parameters
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----------
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arr : SparseArray
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Returns
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-------
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fill_value : ndarray
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0-dim ndarray with just the fill value.
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Notes
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-----
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coerce fill_value to arr dtype if possible
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int64 SparseArray can have NaN as fill_value if there is no missing
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"""
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try:
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return np.asarray(arr.fill_value, dtype=arr.dtype.subtype)
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except ValueError:
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return np.asarray(arr.fill_value)
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def _sparse_array_op(
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left: "SparseArray", right: "SparseArray", op: Callable, name: str
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) -> Any:
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"""
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Perform a binary operation between two arrays.
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Parameters
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----------
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left : Union[SparseArray, ndarray]
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right : Union[SparseArray, ndarray]
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op : Callable
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The binary operation to perform
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name str
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Name of the callable.
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Returns
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-------
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SparseArray
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"""
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if name.startswith("__"):
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# For lookups in _libs.sparse we need non-dunder op name
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name = name[2:-2]
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# dtype used to find corresponding sparse method
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ltype = left.dtype.subtype
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rtype = right.dtype.subtype
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if not is_dtype_equal(ltype, rtype):
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subtype = find_common_type([ltype, rtype])
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ltype = SparseDtype(subtype, left.fill_value)
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rtype = SparseDtype(subtype, right.fill_value)
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# TODO(GH-23092): pass copy=False. Need to fix astype_nansafe
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left = left.astype(ltype)
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right = right.astype(rtype)
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dtype = ltype.subtype
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else:
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dtype = ltype
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# dtype the result must have
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result_dtype = None
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if left.sp_index.ngaps == 0 or right.sp_index.ngaps == 0:
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with np.errstate(all="ignore"):
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result = op(left.to_dense(), right.to_dense())
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fill = op(_get_fill(left), _get_fill(right))
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if left.sp_index.ngaps == 0:
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index = left.sp_index
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else:
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index = right.sp_index
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elif left.sp_index.equals(right.sp_index):
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with np.errstate(all="ignore"):
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result = op(left.sp_values, right.sp_values)
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fill = op(_get_fill(left), _get_fill(right))
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index = left.sp_index
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else:
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if name[0] == "r":
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left, right = right, left
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name = name[1:]
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if name in ("and", "or", "xor") and dtype == "bool":
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opname = f"sparse_{name}_uint8"
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# to make template simple, cast here
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left_sp_values = left.sp_values.view(np.uint8)
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right_sp_values = right.sp_values.view(np.uint8)
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result_dtype = bool
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else:
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opname = f"sparse_{name}_{dtype}"
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left_sp_values = left.sp_values
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right_sp_values = right.sp_values
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sparse_op = getattr(splib, opname)
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with np.errstate(all="ignore"):
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result, index, fill = sparse_op(
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left_sp_values,
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left.sp_index,
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left.fill_value,
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right_sp_values,
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right.sp_index,
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right.fill_value,
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)
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if result_dtype is None:
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result_dtype = result.dtype
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return _wrap_result(name, result, index, fill, dtype=result_dtype)
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def _wrap_result(name, data, sparse_index, fill_value, dtype=None):
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"""
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wrap op result to have correct dtype
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"""
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if name.startswith("__"):
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# e.g. __eq__ --> eq
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name = name[2:-2]
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if name in ("eq", "ne", "lt", "gt", "le", "ge"):
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dtype = bool
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fill_value = lib.item_from_zerodim(fill_value)
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if is_bool_dtype(dtype):
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# fill_value may be np.bool_
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fill_value = bool(fill_value)
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return SparseArray(
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data, sparse_index=sparse_index, fill_value=fill_value, dtype=dtype
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)
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class SparseArray(OpsMixin, PandasObject, ExtensionArray):
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"""
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An ExtensionArray for storing sparse data.
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.. versionchanged:: 0.24.0
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Implements the ExtensionArray interface.
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Parameters
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----------
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data : array-like
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A dense array of values to store in the SparseArray. This may contain
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`fill_value`.
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sparse_index : SparseIndex, optional
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index : Index
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fill_value : scalar, optional
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Elements in `data` that are `fill_value` are not stored in the
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SparseArray. For memory savings, this should be the most common value
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in `data`. By default, `fill_value` depends on the dtype of `data`:
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=========== ==========
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data.dtype na_value
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=========== ==========
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float ``np.nan``
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int ``0``
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bool False
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datetime64 ``pd.NaT``
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timedelta64 ``pd.NaT``
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=========== ==========
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The fill value is potentially specified in three ways. In order of
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precedence, these are
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1. The `fill_value` argument
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2. ``dtype.fill_value`` if `fill_value` is None and `dtype` is
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a ``SparseDtype``
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3. ``data.dtype.fill_value`` if `fill_value` is None and `dtype`
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is not a ``SparseDtype`` and `data` is a ``SparseArray``.
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kind : {'integer', 'block'}, default 'integer'
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The type of storage for sparse locations.
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* 'block': Stores a `block` and `block_length` for each
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contiguous *span* of sparse values. This is best when
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sparse data tends to be clumped together, with large
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regions of ``fill-value`` values between sparse values.
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* 'integer': uses an integer to store the location of
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each sparse value.
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dtype : np.dtype or SparseDtype, optional
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The dtype to use for the SparseArray. For numpy dtypes, this
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determines the dtype of ``self.sp_values``. For SparseDtype,
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this determines ``self.sp_values`` and ``self.fill_value``.
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copy : bool, default False
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Whether to explicitly copy the incoming `data` array.
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Attributes
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----------
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None
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Methods
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-------
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None
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Examples
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--------
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>>> from pandas.arrays import SparseArray
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>>> arr = SparseArray([0, 0, 1, 2])
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>>> arr
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[0, 0, 1, 2]
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Fill: 0
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IntIndex
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Indices: array([2, 3], dtype=int32)
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"""
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_subtyp = "sparse_array" # register ABCSparseArray
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_hidden_attrs = PandasObject._hidden_attrs | frozenset(["get_values"])
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_sparse_index: SparseIndex
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def __init__(
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self,
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data,
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sparse_index=None,
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index=None,
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fill_value=None,
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kind="integer",
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dtype=None,
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copy=False,
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):
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if fill_value is None and isinstance(dtype, SparseDtype):
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fill_value = dtype.fill_value
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if isinstance(data, type(self)):
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# disable normal inference on dtype, sparse_index, & fill_value
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if sparse_index is None:
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sparse_index = data.sp_index
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if fill_value is None:
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fill_value = data.fill_value
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if dtype is None:
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dtype = data.dtype
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# TODO: make kind=None, and use data.kind?
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data = data.sp_values
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# Handle use-provided dtype
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if isinstance(dtype, str):
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# Two options: dtype='int', regular numpy dtype
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# or dtype='Sparse[int]', a sparse dtype
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try:
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dtype = SparseDtype.construct_from_string(dtype)
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except TypeError:
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dtype = pandas_dtype(dtype)
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if isinstance(dtype, SparseDtype):
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if fill_value is None:
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fill_value = dtype.fill_value
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dtype = dtype.subtype
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if index is not None and not is_scalar(data):
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raise Exception("must only pass scalars with an index")
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if is_scalar(data):
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if index is not None and data is None:
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data = np.nan
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if index is not None:
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npoints = len(index)
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elif sparse_index is None:
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npoints = 1
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else:
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npoints = sparse_index.length
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dtype = infer_dtype_from_scalar(data)[0]
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data = construct_1d_arraylike_from_scalar(data, npoints, dtype)
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if dtype is not None:
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dtype = pandas_dtype(dtype)
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# TODO: disentangle the fill_value dtype inference from
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# dtype inference
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if data is None:
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# TODO: What should the empty dtype be? Object or float?
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data = np.array([], dtype=dtype)
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if not is_array_like(data):
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try:
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# probably shared code in sanitize_series
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data = sanitize_array(data, index=None)
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except ValueError:
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# NumPy may raise a ValueError on data like [1, []]
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# we retry with object dtype here.
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if dtype is None:
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dtype = object
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data = np.atleast_1d(np.asarray(data, dtype=dtype))
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else:
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raise
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if copy:
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# TODO: avoid double copy when dtype forces cast.
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data = data.copy()
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if fill_value is None:
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fill_value_dtype = data.dtype if dtype is None else dtype
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if fill_value_dtype is None:
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fill_value = np.nan
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else:
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fill_value = na_value_for_dtype(fill_value_dtype)
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if isinstance(data, type(self)) and sparse_index is None:
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sparse_index = data._sparse_index
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sparse_values = np.asarray(data.sp_values, dtype=dtype)
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elif sparse_index is None:
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data = extract_array(data, extract_numpy=True)
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if not isinstance(data, np.ndarray):
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# EA
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if is_datetime64tz_dtype(data.dtype):
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warnings.warn(
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f"Creating SparseArray from {data.dtype} data "
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"loses timezone information. Cast to object before "
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"sparse to retain timezone information.",
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UserWarning,
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stacklevel=2,
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)
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data = np.asarray(data, dtype="datetime64[ns]")
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data = np.asarray(data)
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sparse_values, sparse_index, fill_value = make_sparse(
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data, kind=kind, fill_value=fill_value, dtype=dtype
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)
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else:
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sparse_values = np.asarray(data, dtype=dtype)
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if len(sparse_values) != sparse_index.npoints:
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raise AssertionError(
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f"Non array-like type {type(sparse_values)} must "
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"have the same length as the index"
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)
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self._sparse_index = sparse_index
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self._sparse_values = sparse_values
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self._dtype = SparseDtype(sparse_values.dtype, fill_value)
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@classmethod
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def _simple_new(
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cls: Type[SparseArrayT],
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sparse_array: np.ndarray,
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sparse_index: SparseIndex,
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dtype: SparseDtype,
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) -> SparseArrayT:
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new = object.__new__(cls)
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new._sparse_index = sparse_index
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new._sparse_values = sparse_array
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new._dtype = dtype
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return new
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|
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@classmethod
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def from_spmatrix(cls, data):
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"""
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Create a SparseArray from a scipy.sparse matrix.
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.. versionadded:: 0.25.0
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Parameters
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----------
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data : scipy.sparse.sp_matrix
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This should be a SciPy sparse matrix where the size
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of the second dimension is 1. In other words, a
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sparse matrix with a single column.
|
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Returns
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-------
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SparseArray
|
|
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Examples
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--------
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>>> import scipy.sparse
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>>> mat = scipy.sparse.coo_matrix((4, 1))
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>>> pd.arrays.SparseArray.from_spmatrix(mat)
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[0.0, 0.0, 0.0, 0.0]
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Fill: 0.0
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IntIndex
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Indices: array([], dtype=int32)
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"""
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length, ncol = data.shape
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|
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if ncol != 1:
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raise ValueError(f"'data' must have a single column, not '{ncol}'")
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|
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# our sparse index classes require that the positions be strictly
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# increasing. So we need to sort loc, and arr accordingly.
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data = data.tocsc()
|
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data.sort_indices()
|
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arr = data.data
|
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idx = data.indices
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zero = np.array(0, dtype=arr.dtype).item()
|
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dtype = SparseDtype(arr.dtype, zero)
|
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index = IntIndex(length, idx)
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return cls._simple_new(arr, index, dtype)
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|
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def __array__(self, dtype=None) -> np.ndarray:
|
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fill_value = self.fill_value
|
|
|
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if self.sp_index.ngaps == 0:
|
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# Compat for na dtype and int values.
|
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return self.sp_values
|
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if dtype is None:
|
|
# Can NumPy represent this type?
|
|
# If not, `np.result_type` will raise. We catch that
|
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# and return object.
|
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if is_datetime64_any_dtype(self.sp_values.dtype):
|
|
# However, we *do* special-case the common case of
|
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# a datetime64 with pandas NaT.
|
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if fill_value is NaT:
|
|
# Can't put pd.NaT in a datetime64[ns]
|
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fill_value = np.datetime64("NaT")
|
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try:
|
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dtype = np.result_type(self.sp_values.dtype, type(fill_value))
|
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except TypeError:
|
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dtype = object
|
|
|
|
out = np.full(self.shape, fill_value, dtype=dtype)
|
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out[self.sp_index.to_int_index().indices] = self.sp_values
|
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return out
|
|
|
|
def __setitem__(self, key, value):
|
|
# I suppose we could allow setting of non-fill_value elements.
|
|
# TODO(SparseArray.__setitem__): remove special cases in
|
|
# ExtensionBlock.where
|
|
msg = "SparseArray does not support item assignment via setitem"
|
|
raise TypeError(msg)
|
|
|
|
@classmethod
|
|
def _from_sequence(cls, scalars, *, dtype=None, copy=False):
|
|
return cls(scalars, dtype=dtype)
|
|
|
|
@classmethod
|
|
def _from_factorized(cls, values, original):
|
|
return cls(values, dtype=original.dtype)
|
|
|
|
# ------------------------------------------------------------------------
|
|
# Data
|
|
# ------------------------------------------------------------------------
|
|
@property
|
|
def sp_index(self):
|
|
"""
|
|
The SparseIndex containing the location of non- ``fill_value`` points.
|
|
"""
|
|
return self._sparse_index
|
|
|
|
@property
|
|
def sp_values(self) -> np.ndarray:
|
|
"""
|
|
An ndarray containing the non- ``fill_value`` values.
|
|
|
|
Examples
|
|
--------
|
|
>>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)
|
|
>>> s.sp_values
|
|
array([1, 2])
|
|
"""
|
|
return self._sparse_values
|
|
|
|
@property
|
|
def dtype(self):
|
|
return self._dtype
|
|
|
|
@property
|
|
def fill_value(self):
|
|
"""
|
|
Elements in `data` that are `fill_value` are not stored.
|
|
|
|
For memory savings, this should be the most common value in the array.
|
|
"""
|
|
return self.dtype.fill_value
|
|
|
|
@fill_value.setter
|
|
def fill_value(self, value):
|
|
self._dtype = SparseDtype(self.dtype.subtype, value)
|
|
|
|
@property
|
|
def kind(self) -> str:
|
|
"""
|
|
The kind of sparse index for this array. One of {'integer', 'block'}.
|
|
"""
|
|
if isinstance(self.sp_index, IntIndex):
|
|
return "integer"
|
|
else:
|
|
return "block"
|
|
|
|
@property
|
|
def _valid_sp_values(self):
|
|
sp_vals = self.sp_values
|
|
mask = notna(sp_vals)
|
|
return sp_vals[mask]
|
|
|
|
def __len__(self) -> int:
|
|
return self.sp_index.length
|
|
|
|
@property
|
|
def _null_fill_value(self):
|
|
return self._dtype._is_na_fill_value
|
|
|
|
def _fill_value_matches(self, fill_value):
|
|
if self._null_fill_value:
|
|
return isna(fill_value)
|
|
else:
|
|
return self.fill_value == fill_value
|
|
|
|
@property
|
|
def nbytes(self) -> int:
|
|
return self.sp_values.nbytes + self.sp_index.nbytes
|
|
|
|
@property
|
|
def density(self):
|
|
"""
|
|
The percent of non- ``fill_value`` points, as decimal.
|
|
|
|
Examples
|
|
--------
|
|
>>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
|
|
>>> s.density
|
|
0.6
|
|
"""
|
|
return float(self.sp_index.npoints) / float(self.sp_index.length)
|
|
|
|
@property
|
|
def npoints(self) -> int:
|
|
"""
|
|
The number of non- ``fill_value`` points.
|
|
|
|
Examples
|
|
--------
|
|
>>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
|
|
>>> s.npoints
|
|
3
|
|
"""
|
|
return self.sp_index.npoints
|
|
|
|
def isna(self):
|
|
# If null fill value, we want SparseDtype[bool, true]
|
|
# to preserve the same memory usage.
|
|
dtype = SparseDtype(bool, self._null_fill_value)
|
|
return type(self)._simple_new(isna(self.sp_values), self.sp_index, dtype)
|
|
|
|
def fillna(self, value=None, method=None, limit=None):
|
|
"""
|
|
Fill missing values with `value`.
|
|
|
|
Parameters
|
|
----------
|
|
value : scalar, optional
|
|
method : str, optional
|
|
|
|
.. warning::
|
|
|
|
Using 'method' will result in high memory use,
|
|
as all `fill_value` methods will be converted to
|
|
an in-memory ndarray
|
|
|
|
limit : int, optional
|
|
|
|
Returns
|
|
-------
|
|
SparseArray
|
|
|
|
Notes
|
|
-----
|
|
When `value` is specified, the result's ``fill_value`` depends on
|
|
``self.fill_value``. The goal is to maintain low-memory use.
|
|
|
|
If ``self.fill_value`` is NA, the result dtype will be
|
|
``SparseDtype(self.dtype, fill_value=value)``. This will preserve
|
|
amount of memory used before and after filling.
|
|
|
|
When ``self.fill_value`` is not NA, the result dtype will be
|
|
``self.dtype``. Again, this preserves the amount of memory used.
|
|
"""
|
|
if (method is None and value is None) or (
|
|
method is not None and value is not None
|
|
):
|
|
raise ValueError("Must specify one of 'method' or 'value'.")
|
|
|
|
elif method is not None:
|
|
msg = "fillna with 'method' requires high memory usage."
|
|
warnings.warn(msg, PerformanceWarning)
|
|
filled = interpolate_2d(np.asarray(self), method=method, limit=limit)
|
|
return type(self)(filled, fill_value=self.fill_value)
|
|
|
|
else:
|
|
new_values = np.where(isna(self.sp_values), value, self.sp_values)
|
|
|
|
if self._null_fill_value:
|
|
# This is essentially just updating the dtype.
|
|
new_dtype = SparseDtype(self.dtype.subtype, fill_value=value)
|
|
else:
|
|
new_dtype = self.dtype
|
|
|
|
return self._simple_new(new_values, self._sparse_index, new_dtype)
|
|
|
|
def shift(self, periods=1, fill_value=None):
|
|
|
|
if not len(self) or periods == 0:
|
|
return self.copy()
|
|
|
|
if isna(fill_value):
|
|
fill_value = self.dtype.na_value
|
|
|
|
subtype = np.result_type(fill_value, self.dtype.subtype)
|
|
|
|
if subtype != self.dtype.subtype:
|
|
# just coerce up front
|
|
arr = self.astype(SparseDtype(subtype, self.fill_value))
|
|
else:
|
|
arr = self
|
|
|
|
empty = self._from_sequence(
|
|
[fill_value] * min(abs(periods), len(self)), dtype=arr.dtype
|
|
)
|
|
|
|
if periods > 0:
|
|
a = empty
|
|
b = arr[:-periods]
|
|
else:
|
|
a = arr[abs(periods) :]
|
|
b = empty
|
|
return arr._concat_same_type([a, b])
|
|
|
|
def _first_fill_value_loc(self):
|
|
"""
|
|
Get the location of the first missing value.
|
|
|
|
Returns
|
|
-------
|
|
int
|
|
"""
|
|
if len(self) == 0 or self.sp_index.npoints == len(self):
|
|
return -1
|
|
|
|
indices = self.sp_index.to_int_index().indices
|
|
if not len(indices) or indices[0] > 0:
|
|
return 0
|
|
|
|
diff = indices[1:] - indices[:-1]
|
|
return np.searchsorted(diff, 2) + 1
|
|
|
|
def unique(self):
|
|
uniques = list(algos.unique(self.sp_values))
|
|
fill_loc = self._first_fill_value_loc()
|
|
if fill_loc >= 0:
|
|
uniques.insert(fill_loc, self.fill_value)
|
|
return type(self)._from_sequence(uniques, dtype=self.dtype)
|
|
|
|
def _values_for_factorize(self):
|
|
# Still override this for hash_pandas_object
|
|
return np.asarray(self), self.fill_value
|
|
|
|
def factorize(self, na_sentinel=-1):
|
|
# Currently, ExtensionArray.factorize -> Tuple[ndarray, EA]
|
|
# The sparsity on this is backwards from what Sparse would want. Want
|
|
# ExtensionArray.factorize -> Tuple[EA, EA]
|
|
# Given that we have to return a dense array of codes, why bother
|
|
# implementing an efficient factorize?
|
|
codes, uniques = algos.factorize(np.asarray(self), na_sentinel=na_sentinel)
|
|
uniques = SparseArray(uniques, dtype=self.dtype)
|
|
return codes, uniques
|
|
|
|
def value_counts(self, dropna=True):
|
|
"""
|
|
Returns a Series containing counts of unique values.
|
|
|
|
Parameters
|
|
----------
|
|
dropna : boolean, default True
|
|
Don't include counts of NaN, even if NaN is in sp_values.
|
|
|
|
Returns
|
|
-------
|
|
counts : Series
|
|
"""
|
|
from pandas import Index, Series
|
|
|
|
keys, counts = algos.value_counts_arraylike(self.sp_values, dropna=dropna)
|
|
fcounts = self.sp_index.ngaps
|
|
if fcounts > 0 and (not self._null_fill_value or not dropna):
|
|
mask = isna(keys) if self._null_fill_value else keys == self.fill_value
|
|
if mask.any():
|
|
counts[mask] += fcounts
|
|
else:
|
|
keys = np.insert(keys, 0, self.fill_value)
|
|
counts = np.insert(counts, 0, fcounts)
|
|
|
|
if not isinstance(keys, ABCIndexClass):
|
|
keys = Index(keys)
|
|
return Series(counts, index=keys)
|
|
|
|
# --------
|
|
# Indexing
|
|
# --------
|
|
|
|
def __getitem__(self, key):
|
|
|
|
if isinstance(key, tuple):
|
|
if len(key) > 1:
|
|
raise IndexError("too many indices for array.")
|
|
key = key[0]
|
|
|
|
if is_integer(key):
|
|
return self._get_val_at(key)
|
|
elif isinstance(key, tuple):
|
|
data_slice = self.to_dense()[key]
|
|
elif isinstance(key, slice):
|
|
# special case to preserve dtypes
|
|
if key == slice(None):
|
|
return self.copy()
|
|
# TODO: this logic is surely elsewhere
|
|
# TODO: this could be more efficient
|
|
indices = np.arange(len(self), dtype=np.int32)[key]
|
|
return self.take(indices)
|
|
else:
|
|
# TODO: I think we can avoid densifying when masking a
|
|
# boolean SparseArray with another. Need to look at the
|
|
# key's fill_value for True / False, and then do an intersection
|
|
# on the indices of the sp_values.
|
|
if isinstance(key, SparseArray):
|
|
if is_bool_dtype(key):
|
|
key = key.to_dense()
|
|
else:
|
|
key = np.asarray(key)
|
|
|
|
key = check_array_indexer(self, key)
|
|
|
|
if com.is_bool_indexer(key):
|
|
|
|
return self.take(np.arange(len(key), dtype=np.int32)[key])
|
|
elif hasattr(key, "__len__"):
|
|
return self.take(key)
|
|
else:
|
|
raise ValueError(f"Cannot slice with '{key}'")
|
|
|
|
return type(self)(data_slice, kind=self.kind)
|
|
|
|
def _get_val_at(self, loc):
|
|
n = len(self)
|
|
if loc < 0:
|
|
loc += n
|
|
|
|
if loc >= n or loc < 0:
|
|
raise IndexError("Out of bounds access")
|
|
|
|
sp_loc = self.sp_index.lookup(loc)
|
|
if sp_loc == -1:
|
|
return self.fill_value
|
|
else:
|
|
val = self.sp_values[sp_loc]
|
|
val = maybe_box_datetimelike(val, self.sp_values.dtype)
|
|
return val
|
|
|
|
def take(self, indices, *, allow_fill=False, fill_value=None) -> "SparseArray":
|
|
if is_scalar(indices):
|
|
raise ValueError(f"'indices' must be an array, not a scalar '{indices}'.")
|
|
indices = np.asarray(indices, dtype=np.int32)
|
|
|
|
if indices.size == 0:
|
|
result = np.array([], dtype="object")
|
|
kwargs = {"dtype": self.dtype}
|
|
elif allow_fill:
|
|
result = self._take_with_fill(indices, fill_value=fill_value)
|
|
kwargs = {}
|
|
else:
|
|
result = self._take_without_fill(indices)
|
|
kwargs = {"dtype": self.dtype}
|
|
|
|
return type(self)(result, fill_value=self.fill_value, kind=self.kind, **kwargs)
|
|
|
|
def _take_with_fill(self, indices, fill_value=None) -> np.ndarray:
|
|
if fill_value is None:
|
|
fill_value = self.dtype.na_value
|
|
|
|
if indices.min() < -1:
|
|
raise ValueError(
|
|
"Invalid value in 'indices'. Must be between -1 "
|
|
"and the length of the array."
|
|
)
|
|
|
|
if indices.max() >= len(self):
|
|
raise IndexError("out of bounds value in 'indices'.")
|
|
|
|
if len(self) == 0:
|
|
# Empty... Allow taking only if all empty
|
|
if (indices == -1).all():
|
|
dtype = np.result_type(self.sp_values, type(fill_value))
|
|
taken = np.empty_like(indices, dtype=dtype)
|
|
taken.fill(fill_value)
|
|
return taken
|
|
else:
|
|
raise IndexError("cannot do a non-empty take from an empty axes.")
|
|
|
|
# sp_indexer may be -1 for two reasons
|
|
# 1.) we took for an index of -1 (new)
|
|
# 2.) we took a value that was self.fill_value (old)
|
|
sp_indexer = self.sp_index.lookup_array(indices)
|
|
new_fill_indices = indices == -1
|
|
old_fill_indices = (sp_indexer == -1) & ~new_fill_indices
|
|
|
|
if self.sp_index.npoints == 0 and old_fill_indices.all():
|
|
# We've looked up all valid points on an all-sparse array.
|
|
taken = np.full(
|
|
sp_indexer.shape, fill_value=self.fill_value, dtype=self.dtype.subtype
|
|
)
|
|
|
|
elif self.sp_index.npoints == 0:
|
|
# Avoid taking from the empty self.sp_values
|
|
_dtype = np.result_type(self.dtype.subtype, type(fill_value))
|
|
taken = np.full(sp_indexer.shape, fill_value=fill_value, dtype=_dtype)
|
|
else:
|
|
taken = self.sp_values.take(sp_indexer)
|
|
|
|
# Fill in two steps.
|
|
# Old fill values
|
|
# New fill values
|
|
# potentially coercing to a new dtype at each stage.
|
|
|
|
m0 = sp_indexer[old_fill_indices] < 0
|
|
m1 = sp_indexer[new_fill_indices] < 0
|
|
|
|
result_type = taken.dtype
|
|
|
|
if m0.any():
|
|
result_type = np.result_type(result_type, type(self.fill_value))
|
|
taken = taken.astype(result_type)
|
|
taken[old_fill_indices] = self.fill_value
|
|
|
|
if m1.any():
|
|
result_type = np.result_type(result_type, type(fill_value))
|
|
taken = taken.astype(result_type)
|
|
taken[new_fill_indices] = fill_value
|
|
|
|
return taken
|
|
|
|
def _take_without_fill(self, indices) -> Union[np.ndarray, "SparseArray"]:
|
|
to_shift = indices < 0
|
|
indices = indices.copy()
|
|
|
|
n = len(self)
|
|
|
|
if (indices.max() >= n) or (indices.min() < -n):
|
|
if n == 0:
|
|
raise IndexError("cannot do a non-empty take from an empty axes.")
|
|
else:
|
|
raise IndexError("out of bounds value in 'indices'.")
|
|
|
|
if to_shift.any():
|
|
indices[to_shift] += n
|
|
|
|
if self.sp_index.npoints == 0:
|
|
# edge case in take...
|
|
# I think just return
|
|
out = np.full(
|
|
indices.shape,
|
|
self.fill_value,
|
|
dtype=np.result_type(type(self.fill_value)),
|
|
)
|
|
arr, sp_index, fill_value = make_sparse(out, fill_value=self.fill_value)
|
|
return type(self)(arr, sparse_index=sp_index, fill_value=fill_value)
|
|
|
|
sp_indexer = self.sp_index.lookup_array(indices)
|
|
taken = self.sp_values.take(sp_indexer)
|
|
fillable = sp_indexer < 0
|
|
|
|
if fillable.any():
|
|
# TODO: may need to coerce array to fill value
|
|
result_type = np.result_type(taken, type(self.fill_value))
|
|
taken = taken.astype(result_type)
|
|
taken[fillable] = self.fill_value
|
|
|
|
return taken
|
|
|
|
def searchsorted(self, v, side="left", sorter=None):
|
|
msg = "searchsorted requires high memory usage."
|
|
warnings.warn(msg, PerformanceWarning, stacklevel=2)
|
|
if not is_scalar(v):
|
|
v = np.asarray(v)
|
|
v = np.asarray(v)
|
|
return np.asarray(self, dtype=self.dtype.subtype).searchsorted(v, side, sorter)
|
|
|
|
def copy(self: SparseArrayT) -> SparseArrayT:
|
|
values = self.sp_values.copy()
|
|
return self._simple_new(values, self.sp_index, self.dtype)
|
|
|
|
@classmethod
|
|
def _concat_same_type(
|
|
cls: Type[SparseArrayT], to_concat: Sequence[SparseArrayT]
|
|
) -> SparseArrayT:
|
|
fill_value = to_concat[0].fill_value
|
|
|
|
values = []
|
|
length = 0
|
|
|
|
if to_concat:
|
|
sp_kind = to_concat[0].kind
|
|
else:
|
|
sp_kind = "integer"
|
|
|
|
if sp_kind == "integer":
|
|
indices = []
|
|
|
|
for arr in to_concat:
|
|
idx = arr.sp_index.to_int_index().indices.copy()
|
|
idx += length # TODO: wraparound
|
|
length += arr.sp_index.length
|
|
|
|
values.append(arr.sp_values)
|
|
indices.append(idx)
|
|
|
|
data = np.concatenate(values)
|
|
indices = np.concatenate(indices)
|
|
sp_index = IntIndex(length, indices)
|
|
|
|
else:
|
|
# when concatenating block indices, we don't claim that you'll
|
|
# get an identical index as concating the values and then
|
|
# creating a new index. We don't want to spend the time trying
|
|
# to merge blocks across arrays in `to_concat`, so the resulting
|
|
# BlockIndex may have more blocks.
|
|
blengths = []
|
|
blocs = []
|
|
|
|
for arr in to_concat:
|
|
idx = arr.sp_index.to_block_index()
|
|
|
|
values.append(arr.sp_values)
|
|
blocs.append(idx.blocs.copy() + length)
|
|
blengths.append(idx.blengths)
|
|
length += arr.sp_index.length
|
|
|
|
data = np.concatenate(values)
|
|
blocs = np.concatenate(blocs)
|
|
blengths = np.concatenate(blengths)
|
|
|
|
sp_index = BlockIndex(length, blocs, blengths)
|
|
|
|
return cls(data, sparse_index=sp_index, fill_value=fill_value)
|
|
|
|
def astype(self, dtype=None, copy=True):
|
|
"""
|
|
Change the dtype of a SparseArray.
|
|
|
|
The output will always be a SparseArray. To convert to a dense
|
|
ndarray with a certain dtype, use :meth:`numpy.asarray`.
|
|
|
|
Parameters
|
|
----------
|
|
dtype : np.dtype or ExtensionDtype
|
|
For SparseDtype, this changes the dtype of
|
|
``self.sp_values`` and the ``self.fill_value``.
|
|
|
|
For other dtypes, this only changes the dtype of
|
|
``self.sp_values``.
|
|
|
|
copy : bool, default True
|
|
Whether to ensure a copy is made, even if not necessary.
|
|
|
|
Returns
|
|
-------
|
|
SparseArray
|
|
|
|
Examples
|
|
--------
|
|
>>> arr = pd.arrays.SparseArray([0, 0, 1, 2])
|
|
>>> arr
|
|
[0, 0, 1, 2]
|
|
Fill: 0
|
|
IntIndex
|
|
Indices: array([2, 3], dtype=int32)
|
|
|
|
>>> arr.astype(np.dtype('int32'))
|
|
[0, 0, 1, 2]
|
|
Fill: 0
|
|
IntIndex
|
|
Indices: array([2, 3], dtype=int32)
|
|
|
|
Using a NumPy dtype with a different kind (e.g. float) will coerce
|
|
just ``self.sp_values``.
|
|
|
|
>>> arr.astype(np.dtype('float64'))
|
|
... # doctest: +NORMALIZE_WHITESPACE
|
|
[0.0, 0.0, 1.0, 2.0]
|
|
Fill: 0.0
|
|
IntIndex
|
|
Indices: array([2, 3], dtype=int32)
|
|
|
|
Use a SparseDtype if you wish to be change the fill value as well.
|
|
|
|
>>> arr.astype(SparseDtype("float64", fill_value=np.nan))
|
|
... # doctest: +NORMALIZE_WHITESPACE
|
|
[nan, nan, 1.0, 2.0]
|
|
Fill: nan
|
|
IntIndex
|
|
Indices: array([2, 3], dtype=int32)
|
|
"""
|
|
if is_dtype_equal(dtype, self._dtype):
|
|
if not copy:
|
|
return self
|
|
else:
|
|
return self.copy()
|
|
dtype = self.dtype.update_dtype(dtype)
|
|
subtype = dtype._subtype_with_str
|
|
# TODO copy=False is broken for astype_nansafe with int -> float, so cannot
|
|
# passthrough copy keyword: https://github.com/pandas-dev/pandas/issues/34456
|
|
sp_values = astype_nansafe(self.sp_values, subtype, copy=True)
|
|
if sp_values is self.sp_values and copy:
|
|
sp_values = sp_values.copy()
|
|
|
|
return self._simple_new(sp_values, self.sp_index, dtype)
|
|
|
|
def map(self, mapper):
|
|
"""
|
|
Map categories using input correspondence (dict, Series, or function).
|
|
|
|
Parameters
|
|
----------
|
|
mapper : dict, Series, callable
|
|
The correspondence from old values to new.
|
|
|
|
Returns
|
|
-------
|
|
SparseArray
|
|
The output array will have the same density as the input.
|
|
The output fill value will be the result of applying the
|
|
mapping to ``self.fill_value``
|
|
|
|
Examples
|
|
--------
|
|
>>> arr = pd.arrays.SparseArray([0, 1, 2])
|
|
>>> arr.map(lambda x: x + 10)
|
|
[10, 11, 12]
|
|
Fill: 10
|
|
IntIndex
|
|
Indices: array([1, 2], dtype=int32)
|
|
|
|
>>> arr.map({0: 10, 1: 11, 2: 12})
|
|
[10, 11, 12]
|
|
Fill: 10
|
|
IntIndex
|
|
Indices: array([1, 2], dtype=int32)
|
|
|
|
>>> arr.map(pd.Series([10, 11, 12], index=[0, 1, 2]))
|
|
[10, 11, 12]
|
|
Fill: 10
|
|
IntIndex
|
|
Indices: array([1, 2], dtype=int32)
|
|
"""
|
|
# this is used in apply.
|
|
# We get hit since we're an "is_extension_type" but regular extension
|
|
# types are not hit. This may be worth adding to the interface.
|
|
if isinstance(mapper, ABCSeries):
|
|
mapper = mapper.to_dict()
|
|
|
|
if isinstance(mapper, abc.Mapping):
|
|
fill_value = mapper.get(self.fill_value, self.fill_value)
|
|
sp_values = [mapper.get(x, None) for x in self.sp_values]
|
|
else:
|
|
fill_value = mapper(self.fill_value)
|
|
sp_values = [mapper(x) for x in self.sp_values]
|
|
|
|
return type(self)(sp_values, sparse_index=self.sp_index, fill_value=fill_value)
|
|
|
|
def to_dense(self):
|
|
"""
|
|
Convert SparseArray to a NumPy array.
|
|
|
|
Returns
|
|
-------
|
|
arr : NumPy array
|
|
"""
|
|
return np.asarray(self, dtype=self.sp_values.dtype)
|
|
|
|
_internal_get_values = to_dense
|
|
|
|
# ------------------------------------------------------------------------
|
|
# IO
|
|
# ------------------------------------------------------------------------
|
|
def __setstate__(self, state):
|
|
"""Necessary for making this object picklable"""
|
|
if isinstance(state, tuple):
|
|
# Compat for pandas < 0.24.0
|
|
nd_state, (fill_value, sp_index) = state
|
|
sparse_values = np.array([])
|
|
sparse_values.__setstate__(nd_state)
|
|
|
|
self._sparse_values = sparse_values
|
|
self._sparse_index = sp_index
|
|
self._dtype = SparseDtype(sparse_values.dtype, fill_value)
|
|
else:
|
|
self.__dict__.update(state)
|
|
|
|
def nonzero(self):
|
|
if self.fill_value == 0:
|
|
return (self.sp_index.to_int_index().indices,)
|
|
else:
|
|
return (self.sp_index.to_int_index().indices[self.sp_values != 0],)
|
|
|
|
# ------------------------------------------------------------------------
|
|
# Reductions
|
|
# ------------------------------------------------------------------------
|
|
|
|
def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
|
|
method = getattr(self, name, None)
|
|
|
|
if method is None:
|
|
raise TypeError(f"cannot perform {name} with type {self.dtype}")
|
|
|
|
if skipna:
|
|
arr = self
|
|
else:
|
|
arr = self.dropna()
|
|
|
|
# we don't support these kwargs.
|
|
# They should only be present when called via pandas, so do it here.
|
|
# instead of in `any` / `all` (which will raise if they're present,
|
|
# thanks to nv.validate
|
|
kwargs.pop("filter_type", None)
|
|
kwargs.pop("numeric_only", None)
|
|
kwargs.pop("op", None)
|
|
return getattr(arr, name)(**kwargs)
|
|
|
|
def all(self, axis=None, *args, **kwargs):
|
|
"""
|
|
Tests whether all elements evaluate True
|
|
|
|
Returns
|
|
-------
|
|
all : bool
|
|
|
|
See Also
|
|
--------
|
|
numpy.all
|
|
"""
|
|
nv.validate_all(args, kwargs)
|
|
|
|
values = self.sp_values
|
|
|
|
if len(values) != len(self) and not np.all(self.fill_value):
|
|
return False
|
|
|
|
return values.all()
|
|
|
|
def any(self, axis=0, *args, **kwargs):
|
|
"""
|
|
Tests whether at least one of elements evaluate True
|
|
|
|
Returns
|
|
-------
|
|
any : bool
|
|
|
|
See Also
|
|
--------
|
|
numpy.any
|
|
"""
|
|
nv.validate_any(args, kwargs)
|
|
|
|
values = self.sp_values
|
|
|
|
if len(values) != len(self) and np.any(self.fill_value):
|
|
return True
|
|
|
|
return values.any().item()
|
|
|
|
def sum(self, axis: int = 0, min_count: int = 0, *args, **kwargs) -> Scalar:
|
|
"""
|
|
Sum of non-NA/null values
|
|
|
|
Parameters
|
|
----------
|
|
axis : int, default 0
|
|
Not Used. NumPy compatibility.
|
|
min_count : int, default 0
|
|
The required number of valid values to perform the summation. If fewer
|
|
than ``min_count`` valid values are present, the result will be the missing
|
|
value indicator for subarray type.
|
|
*args, **kwargs
|
|
Not Used. NumPy compatibility.
|
|
|
|
Returns
|
|
-------
|
|
scalar
|
|
"""
|
|
nv.validate_sum(args, kwargs)
|
|
valid_vals = self._valid_sp_values
|
|
sp_sum = valid_vals.sum()
|
|
if self._null_fill_value:
|
|
if check_below_min_count(valid_vals.shape, None, min_count):
|
|
return na_value_for_dtype(self.dtype.subtype, compat=False)
|
|
return sp_sum
|
|
else:
|
|
nsparse = self.sp_index.ngaps
|
|
if check_below_min_count(valid_vals.shape, None, min_count - nsparse):
|
|
return na_value_for_dtype(self.dtype.subtype, compat=False)
|
|
return sp_sum + self.fill_value * nsparse
|
|
|
|
def cumsum(self, axis=0, *args, **kwargs):
|
|
"""
|
|
Cumulative sum of non-NA/null values.
|
|
|
|
When performing the cumulative summation, any non-NA/null values will
|
|
be skipped. The resulting SparseArray will preserve the locations of
|
|
NaN values, but the fill value will be `np.nan` regardless.
|
|
|
|
Parameters
|
|
----------
|
|
axis : int or None
|
|
Axis over which to perform the cumulative summation. If None,
|
|
perform cumulative summation over flattened array.
|
|
|
|
Returns
|
|
-------
|
|
cumsum : SparseArray
|
|
"""
|
|
nv.validate_cumsum(args, kwargs)
|
|
|
|
if axis is not None and axis >= self.ndim: # Mimic ndarray behaviour.
|
|
raise ValueError(f"axis(={axis}) out of bounds")
|
|
|
|
if not self._null_fill_value:
|
|
return SparseArray(self.to_dense()).cumsum()
|
|
|
|
return SparseArray(
|
|
self.sp_values.cumsum(),
|
|
sparse_index=self.sp_index,
|
|
fill_value=self.fill_value,
|
|
)
|
|
|
|
def mean(self, axis=0, *args, **kwargs):
|
|
"""
|
|
Mean of non-NA/null values
|
|
|
|
Returns
|
|
-------
|
|
mean : float
|
|
"""
|
|
nv.validate_mean(args, kwargs)
|
|
valid_vals = self._valid_sp_values
|
|
sp_sum = valid_vals.sum()
|
|
ct = len(valid_vals)
|
|
|
|
if self._null_fill_value:
|
|
return sp_sum / ct
|
|
else:
|
|
nsparse = self.sp_index.ngaps
|
|
return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)
|
|
|
|
# ------------------------------------------------------------------------
|
|
# Ufuncs
|
|
# ------------------------------------------------------------------------
|
|
|
|
_HANDLED_TYPES = (np.ndarray, numbers.Number)
|
|
|
|
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
|
|
out = kwargs.get("out", ())
|
|
|
|
for x in inputs + out:
|
|
if not isinstance(x, self._HANDLED_TYPES + (SparseArray,)):
|
|
return NotImplemented
|
|
|
|
# for binary ops, use our custom dunder methods
|
|
result = ops.maybe_dispatch_ufunc_to_dunder_op(
|
|
self, ufunc, method, *inputs, **kwargs
|
|
)
|
|
if result is not NotImplemented:
|
|
return result
|
|
|
|
if len(inputs) == 1:
|
|
# No alignment necessary.
|
|
sp_values = getattr(ufunc, method)(self.sp_values, **kwargs)
|
|
fill_value = getattr(ufunc, method)(self.fill_value, **kwargs)
|
|
|
|
if isinstance(sp_values, tuple):
|
|
# multiple outputs. e.g. modf
|
|
arrays = tuple(
|
|
self._simple_new(
|
|
sp_value, self.sp_index, SparseDtype(sp_value.dtype, fv)
|
|
)
|
|
for sp_value, fv in zip(sp_values, fill_value)
|
|
)
|
|
return arrays
|
|
elif is_scalar(sp_values):
|
|
# e.g. reductions
|
|
return sp_values
|
|
|
|
return self._simple_new(
|
|
sp_values, self.sp_index, SparseDtype(sp_values.dtype, fill_value)
|
|
)
|
|
|
|
result = getattr(ufunc, method)(*[np.asarray(x) for x in inputs], **kwargs)
|
|
if out:
|
|
if len(out) == 1:
|
|
out = out[0]
|
|
return out
|
|
|
|
if type(result) is tuple:
|
|
return tuple(type(self)(x) for x in result)
|
|
elif method == "at":
|
|
# no return value
|
|
return None
|
|
else:
|
|
return type(self)(result)
|
|
|
|
def __abs__(self):
|
|
return np.abs(self)
|
|
|
|
# ------------------------------------------------------------------------
|
|
# Ops
|
|
# ------------------------------------------------------------------------
|
|
|
|
def _arith_method(self, other, op):
|
|
op_name = op.__name__
|
|
|
|
if isinstance(other, SparseArray):
|
|
return _sparse_array_op(self, other, op, op_name)
|
|
|
|
elif is_scalar(other):
|
|
with np.errstate(all="ignore"):
|
|
fill = op(_get_fill(self), np.asarray(other))
|
|
result = op(self.sp_values, other)
|
|
|
|
if op_name == "divmod":
|
|
left, right = result
|
|
lfill, rfill = fill
|
|
return (
|
|
_wrap_result(op_name, left, self.sp_index, lfill),
|
|
_wrap_result(op_name, right, self.sp_index, rfill),
|
|
)
|
|
|
|
return _wrap_result(op_name, result, self.sp_index, fill)
|
|
|
|
else:
|
|
other = np.asarray(other)
|
|
with np.errstate(all="ignore"):
|
|
# TODO: look into _wrap_result
|
|
if len(self) != len(other):
|
|
raise AssertionError(
|
|
f"length mismatch: {len(self)} vs. {len(other)}"
|
|
)
|
|
if not isinstance(other, SparseArray):
|
|
dtype = getattr(other, "dtype", None)
|
|
other = SparseArray(other, fill_value=self.fill_value, dtype=dtype)
|
|
return _sparse_array_op(self, other, op, op_name)
|
|
|
|
def _cmp_method(self, other, op) -> "SparseArray":
|
|
if not is_scalar(other) and not isinstance(other, type(self)):
|
|
# convert list-like to ndarray
|
|
other = np.asarray(other)
|
|
|
|
if isinstance(other, np.ndarray):
|
|
# TODO: make this more flexible than just ndarray...
|
|
if len(self) != len(other):
|
|
raise AssertionError(f"length mismatch: {len(self)} vs. {len(other)}")
|
|
other = SparseArray(other, fill_value=self.fill_value)
|
|
|
|
if isinstance(other, SparseArray):
|
|
op_name = op.__name__.strip("_")
|
|
return _sparse_array_op(self, other, op, op_name)
|
|
else:
|
|
with np.errstate(all="ignore"):
|
|
fill_value = op(self.fill_value, other)
|
|
result = op(self.sp_values, other)
|
|
|
|
return type(self)(
|
|
result,
|
|
sparse_index=self.sp_index,
|
|
fill_value=fill_value,
|
|
dtype=np.bool_,
|
|
)
|
|
|
|
_logical_method = _cmp_method
|
|
|
|
def _unary_method(self, op) -> "SparseArray":
|
|
fill_value = op(np.array(self.fill_value)).item()
|
|
values = op(self.sp_values)
|
|
dtype = SparseDtype(values.dtype, fill_value)
|
|
return type(self)._simple_new(values, self.sp_index, dtype)
|
|
|
|
def __pos__(self) -> "SparseArray":
|
|
return self._unary_method(operator.pos)
|
|
|
|
def __neg__(self) -> "SparseArray":
|
|
return self._unary_method(operator.neg)
|
|
|
|
def __invert__(self) -> "SparseArray":
|
|
return self._unary_method(operator.invert)
|
|
|
|
# ----------
|
|
# Formatting
|
|
# -----------
|
|
def __repr__(self) -> str:
|
|
pp_str = printing.pprint_thing(self)
|
|
pp_fill = printing.pprint_thing(self.fill_value)
|
|
pp_index = printing.pprint_thing(self.sp_index)
|
|
return f"{pp_str}\nFill: {pp_fill}\n{pp_index}"
|
|
|
|
def _formatter(self, boxed=False):
|
|
# Defer to the formatter from the GenericArrayFormatter calling us.
|
|
# This will infer the correct formatter from the dtype of the values.
|
|
return None
|
|
|
|
|
|
def make_sparse(arr: np.ndarray, kind="block", fill_value=None, dtype=None):
|
|
"""
|
|
Convert ndarray to sparse format
|
|
|
|
Parameters
|
|
----------
|
|
arr : ndarray
|
|
kind : {'block', 'integer'}
|
|
fill_value : NaN or another value
|
|
dtype : np.dtype, optional
|
|
copy : bool, default False
|
|
|
|
Returns
|
|
-------
|
|
(sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar)
|
|
"""
|
|
assert isinstance(arr, np.ndarray)
|
|
|
|
if arr.ndim > 1:
|
|
raise TypeError("expected dimension <= 1 data")
|
|
|
|
if fill_value is None:
|
|
fill_value = na_value_for_dtype(arr.dtype)
|
|
|
|
if isna(fill_value):
|
|
mask = notna(arr)
|
|
else:
|
|
# cast to object comparison to be safe
|
|
if is_string_dtype(arr.dtype):
|
|
arr = arr.astype(object)
|
|
|
|
if is_object_dtype(arr.dtype):
|
|
# element-wise equality check method in numpy doesn't treat
|
|
# each element type, eg. 0, 0.0, and False are treated as
|
|
# same. So we have to check the both of its type and value.
|
|
mask = splib.make_mask_object_ndarray(arr, fill_value)
|
|
else:
|
|
mask = arr != fill_value
|
|
|
|
length = len(arr)
|
|
if length != len(mask):
|
|
# the arr is a SparseArray
|
|
indices = mask.sp_index.indices
|
|
else:
|
|
indices = mask.nonzero()[0].astype(np.int32)
|
|
|
|
index = make_sparse_index(length, indices, kind)
|
|
sparsified_values = arr[mask]
|
|
if dtype is not None:
|
|
sparsified_values = astype_nansafe(sparsified_values, dtype=dtype)
|
|
# TODO: copy
|
|
return sparsified_values, index, fill_value
|
|
|
|
|
|
def make_sparse_index(length, indices, kind):
|
|
|
|
if kind == "block" or isinstance(kind, BlockIndex):
|
|
locs, lens = splib.get_blocks(indices)
|
|
index = BlockIndex(length, locs, lens)
|
|
elif kind == "integer" or isinstance(kind, IntIndex):
|
|
index = IntIndex(length, indices)
|
|
else: # pragma: no cover
|
|
raise ValueError("must be block or integer type")
|
|
return index
|