711 行
19 KiB
Python
711 行
19 KiB
Python
"""
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Misc tools for implementing data structures
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Note: pandas.core.common is *not* part of the public API.
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"""
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from __future__ import annotations
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import builtins
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from collections import (
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abc,
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defaultdict,
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)
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import contextlib
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from functools import partial
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import inspect
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Collection,
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Hashable,
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Iterable,
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Iterator,
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Sequence,
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cast,
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overload,
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)
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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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from pandas._typing import (
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AnyArrayLike,
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ArrayLike,
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NpDtype,
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RandomState,
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T,
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)
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from pandas.util._exceptions import find_stack_level
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from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
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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_extension_array_dtype,
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is_integer,
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)
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from pandas.core.dtypes.generic import (
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ABCExtensionArray,
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ABCIndex,
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ABCSeries,
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)
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from pandas.core.dtypes.inference import iterable_not_string
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from pandas.core.dtypes.missing import isna
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if TYPE_CHECKING:
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from pandas import Index
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def flatten(line):
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"""
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Flatten an arbitrarily nested sequence.
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Parameters
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----------
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line : sequence
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The non string sequence to flatten
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Notes
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-----
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This doesn't consider strings sequences.
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Returns
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-------
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flattened : generator
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"""
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for element in line:
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if iterable_not_string(element):
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yield from flatten(element)
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else:
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yield element
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def consensus_name_attr(objs):
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name = objs[0].name
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for obj in objs[1:]:
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try:
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if obj.name != name:
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name = None
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except ValueError:
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name = None
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return name
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def is_bool_indexer(key: Any) -> bool:
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"""
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Check whether `key` is a valid boolean indexer.
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Parameters
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----------
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key : Any
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Only list-likes may be considered boolean indexers.
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All other types are not considered a boolean indexer.
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For array-like input, boolean ndarrays or ExtensionArrays
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with ``_is_boolean`` set are considered boolean indexers.
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Returns
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-------
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bool
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Whether `key` is a valid boolean indexer.
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Raises
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------
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ValueError
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When the array is an object-dtype ndarray or ExtensionArray
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and contains missing values.
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See Also
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--------
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check_array_indexer : Check that `key` is a valid array to index,
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and convert to an ndarray.
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"""
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if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or (
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is_array_like(key) and is_extension_array_dtype(key.dtype)
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):
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if key.dtype == np.object_:
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key_array = np.asarray(key)
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if not lib.is_bool_array(key_array):
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na_msg = "Cannot mask with non-boolean array containing NA / NaN values"
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if lib.infer_dtype(key_array) == "boolean" and isna(key_array).any():
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# Don't raise on e.g. ["A", "B", np.nan], see
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# test_loc_getitem_list_of_labels_categoricalindex_with_na
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raise ValueError(na_msg)
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return False
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return True
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elif is_bool_dtype(key.dtype):
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return True
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elif isinstance(key, list):
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# check if np.array(key).dtype would be bool
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if len(key) > 0:
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if type(key) is not list:
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# GH#42461 cython will raise TypeError if we pass a subclass
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key = list(key)
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return lib.is_bool_list(key)
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return False
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def cast_scalar_indexer(val, warn_float: bool = False):
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"""
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To avoid numpy DeprecationWarnings, cast float to integer where valid.
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Parameters
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----------
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val : scalar
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warn_float : bool, default False
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If True, issue deprecation warning for a float indexer.
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Returns
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-------
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outval : scalar
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"""
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# assumes lib.is_scalar(val)
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if lib.is_float(val) and val.is_integer():
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if warn_float:
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warnings.warn(
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"Indexing with a float is deprecated, and will raise an IndexError "
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"in pandas 2.0. You can manually convert to an integer key instead.",
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FutureWarning,
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stacklevel=find_stack_level(),
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)
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return int(val)
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return val
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def not_none(*args):
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"""
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Returns a generator consisting of the arguments that are not None.
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"""
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return (arg for arg in args if arg is not None)
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def any_none(*args) -> bool:
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"""
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Returns a boolean indicating if any argument is None.
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"""
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return any(arg is None for arg in args)
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def all_none(*args) -> bool:
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"""
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Returns a boolean indicating if all arguments are None.
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"""
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return all(arg is None for arg in args)
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def any_not_none(*args) -> bool:
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"""
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Returns a boolean indicating if any argument is not None.
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"""
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return any(arg is not None for arg in args)
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def all_not_none(*args) -> bool:
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"""
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Returns a boolean indicating if all arguments are not None.
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"""
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return all(arg is not None for arg in args)
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def count_not_none(*args) -> int:
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"""
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Returns the count of arguments that are not None.
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"""
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return sum(x is not None for x in args)
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@overload
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def asarray_tuplesafe(
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values: ArrayLike | list | tuple | zip, dtype: NpDtype | None = ...
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) -> np.ndarray:
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# ExtensionArray can only be returned when values is an Index, all other iterables
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# will return np.ndarray. Unfortunately "all other" cannot be encoded in a type
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# signature, so instead we special-case some common types.
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...
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@overload
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def asarray_tuplesafe(values: Iterable, dtype: NpDtype | None = ...) -> ArrayLike:
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...
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def asarray_tuplesafe(values: Iterable, dtype: NpDtype | None = None) -> ArrayLike:
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if not (isinstance(values, (list, tuple)) or hasattr(values, "__array__")):
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values = list(values)
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elif isinstance(values, ABCIndex):
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return values._values
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if isinstance(values, list) and dtype in [np.object_, object]:
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return construct_1d_object_array_from_listlike(values)
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try:
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with warnings.catch_warnings():
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# Can remove warning filter once NumPy 1.24 is min version
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warnings.simplefilter("ignore", np.VisibleDeprecationWarning)
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result = np.asarray(values, dtype=dtype)
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except ValueError:
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# Using try/except since it's more performant than checking is_list_like
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# over each element
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# error: Argument 1 to "construct_1d_object_array_from_listlike"
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# has incompatible type "Iterable[Any]"; expected "Sized"
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return construct_1d_object_array_from_listlike(values) # type: ignore[arg-type]
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if issubclass(result.dtype.type, str):
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result = np.asarray(values, dtype=object)
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if result.ndim == 2:
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# Avoid building an array of arrays:
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values = [tuple(x) for x in values]
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result = construct_1d_object_array_from_listlike(values)
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return result
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def index_labels_to_array(
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labels: np.ndarray | Iterable, dtype: NpDtype | None = None
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) -> np.ndarray:
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"""
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Transform label or iterable of labels to array, for use in Index.
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Parameters
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----------
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dtype : dtype
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If specified, use as dtype of the resulting array, otherwise infer.
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Returns
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-------
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array
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"""
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if isinstance(labels, (str, tuple)):
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labels = [labels]
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if not isinstance(labels, (list, np.ndarray)):
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try:
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labels = list(labels)
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except TypeError: # non-iterable
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labels = [labels]
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labels = asarray_tuplesafe(labels, dtype=dtype)
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return labels
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def maybe_make_list(obj):
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if obj is not None and not isinstance(obj, (tuple, list)):
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return [obj]
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return obj
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def maybe_iterable_to_list(obj: Iterable[T] | T) -> Collection[T] | T:
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"""
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If obj is Iterable but not list-like, consume into list.
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"""
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if isinstance(obj, abc.Iterable) and not isinstance(obj, abc.Sized):
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return list(obj)
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obj = cast(Collection, obj)
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return obj
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def is_null_slice(obj) -> bool:
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"""
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We have a null slice.
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"""
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return (
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isinstance(obj, slice)
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and obj.start is None
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and obj.stop is None
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and obj.step is None
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)
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def is_true_slices(line) -> list[bool]:
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"""
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Find non-trivial slices in "line": return a list of booleans with same length.
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"""
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return [isinstance(k, slice) and not is_null_slice(k) for k in line]
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# TODO: used only once in indexing; belongs elsewhere?
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def is_full_slice(obj, line: int) -> bool:
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"""
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We have a full length slice.
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"""
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return (
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isinstance(obj, slice)
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and obj.start == 0
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and obj.stop == line
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and obj.step is None
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)
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def get_callable_name(obj):
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# typical case has name
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if hasattr(obj, "__name__"):
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return getattr(obj, "__name__")
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# some objects don't; could recurse
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if isinstance(obj, partial):
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return get_callable_name(obj.func)
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# fall back to class name
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if callable(obj):
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return type(obj).__name__
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# everything failed (probably because the argument
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# wasn't actually callable); we return None
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# instead of the empty string in this case to allow
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# distinguishing between no name and a name of ''
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return None
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def apply_if_callable(maybe_callable, obj, **kwargs):
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"""
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Evaluate possibly callable input using obj and kwargs if it is callable,
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otherwise return as it is.
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Parameters
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----------
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maybe_callable : possibly a callable
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obj : NDFrame
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**kwargs
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"""
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if callable(maybe_callable):
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return maybe_callable(obj, **kwargs)
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return maybe_callable
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def standardize_mapping(into):
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"""
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Helper function to standardize a supplied mapping.
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Parameters
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----------
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into : instance or subclass of collections.abc.Mapping
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Must be a class, an initialized collections.defaultdict,
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or an instance of a collections.abc.Mapping subclass.
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Returns
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-------
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mapping : a collections.abc.Mapping subclass or other constructor
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a callable object that can accept an iterator to create
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the desired Mapping.
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See Also
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--------
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DataFrame.to_dict
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Series.to_dict
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"""
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if not inspect.isclass(into):
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if isinstance(into, defaultdict):
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return partial(defaultdict, into.default_factory)
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into = type(into)
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if not issubclass(into, abc.Mapping):
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raise TypeError(f"unsupported type: {into}")
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elif into == defaultdict:
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raise TypeError("to_dict() only accepts initialized defaultdicts")
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return into
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@overload
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def random_state(state: np.random.Generator) -> np.random.Generator:
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...
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@overload
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def random_state(
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state: int | ArrayLike | np.random.BitGenerator | np.random.RandomState | None,
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) -> np.random.RandomState:
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...
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def random_state(state: RandomState | None = None):
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"""
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Helper function for processing random_state arguments.
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Parameters
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----------
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state : int, array-like, BitGenerator, Generator, np.random.RandomState, None.
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If receives an int, array-like, or BitGenerator, passes to
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np.random.RandomState() as seed.
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If receives an np.random RandomState or Generator, just returns that unchanged.
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If receives `None`, returns np.random.
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If receives anything else, raises an informative ValueError.
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.. versionchanged:: 1.1.0
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array-like and BitGenerator object now passed to np.random.RandomState()
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as seed
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Default None.
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Returns
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-------
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np.random.RandomState or np.random.Generator. If state is None, returns np.random
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"""
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if (
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is_integer(state)
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or is_array_like(state)
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or isinstance(state, np.random.BitGenerator)
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):
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# error: Argument 1 to "RandomState" has incompatible type "Optional[Union[int,
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# Union[ExtensionArray, ndarray[Any, Any]], Generator, RandomState]]"; expected
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# "Union[None, Union[Union[_SupportsArray[dtype[Union[bool_, integer[Any]]]],
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# Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]],
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# Sequence[Sequence[_SupportsArray[dtype[Union[bool_, integer[Any]]]]]],
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# Sequence[Sequence[Sequence[_SupportsArray[dtype[Union[bool_,
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# integer[Any]]]]]]],
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# Sequence[Sequence[Sequence[Sequence[_SupportsArray[dtype[Union[bool_,
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# integer[Any]]]]]]]]], Union[bool, int, Sequence[Union[bool, int]],
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# Sequence[Sequence[Union[bool, int]]], Sequence[Sequence[Sequence[Union[bool,
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# int]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int]]]]]]],
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# BitGenerator]"
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return np.random.RandomState(state) # type: ignore[arg-type]
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elif isinstance(state, np.random.RandomState):
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return state
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elif isinstance(state, np.random.Generator):
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return state
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elif state is None:
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return np.random
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else:
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raise ValueError(
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"random_state must be an integer, array-like, a BitGenerator, Generator, "
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"a numpy RandomState, or None"
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)
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def pipe(
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obj, func: Callable[..., T] | tuple[Callable[..., T], str], *args, **kwargs
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) -> T:
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"""
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|
Apply a function ``func`` to object ``obj`` either by passing obj as the
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first argument to the function or, in the case that the func is a tuple,
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interpret the first element of the tuple as a function and pass the obj to
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that function as a keyword argument whose key is the value of the second
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element of the tuple.
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|
Parameters
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----------
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func : callable or tuple of (callable, str)
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Function to apply to this object or, alternatively, a
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``(callable, data_keyword)`` tuple where ``data_keyword`` is a
|
|
string indicating the keyword of ``callable`` that expects the
|
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object.
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*args : iterable, optional
|
|
Positional arguments passed into ``func``.
|
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**kwargs : dict, optional
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A dictionary of keyword arguments passed into ``func``.
|
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|
Returns
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-------
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object : the return type of ``func``.
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"""
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if isinstance(func, tuple):
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func, target = func
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if target in kwargs:
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msg = f"{target} is both the pipe target and a keyword argument"
|
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raise ValueError(msg)
|
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kwargs[target] = obj
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return func(*args, **kwargs)
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else:
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return func(obj, *args, **kwargs)
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|
|
|
|
def get_rename_function(mapper):
|
|
"""
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|
Returns a function that will map names/labels, dependent if mapper
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is a dict, Series or just a function.
|
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"""
|
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def f(x):
|
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if x in mapper:
|
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return mapper[x]
|
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else:
|
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return x
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return f if isinstance(mapper, (abc.Mapping, ABCSeries)) else mapper
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|
|
|
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def convert_to_list_like(
|
|
values: Hashable | Iterable | AnyArrayLike,
|
|
) -> list | AnyArrayLike:
|
|
"""
|
|
Convert list-like or scalar input to list-like. List, numpy and pandas array-like
|
|
inputs are returned unmodified whereas others are converted to list.
|
|
"""
|
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if isinstance(values, (list, np.ndarray, ABCIndex, ABCSeries, ABCExtensionArray)):
|
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return values
|
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elif isinstance(values, abc.Iterable) and not isinstance(values, str):
|
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return list(values)
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|
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return [values]
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|
|
|
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@contextlib.contextmanager
|
|
def temp_setattr(obj, attr: str, value) -> Iterator[None]:
|
|
"""Temporarily set attribute on an object.
|
|
|
|
Args:
|
|
obj: Object whose attribute will be modified.
|
|
attr: Attribute to modify.
|
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value: Value to temporarily set attribute to.
|
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Yields:
|
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obj with modified attribute.
|
|
"""
|
|
old_value = getattr(obj, attr)
|
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setattr(obj, attr, value)
|
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try:
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yield obj
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finally:
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|
setattr(obj, attr, old_value)
|
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|
|
|
|
def require_length_match(data, index: Index) -> None:
|
|
"""
|
|
Check the length of data matches the length of the index.
|
|
"""
|
|
if len(data) != len(index):
|
|
raise ValueError(
|
|
"Length of values "
|
|
f"({len(data)}) "
|
|
"does not match length of index "
|
|
f"({len(index)})"
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)
|
|
|
|
|
|
# the ufuncs np.maximum.reduce and np.minimum.reduce default to axis=0,
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|
# whereas np.min and np.max (which directly call obj.min and obj.max)
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|
# default to axis=None.
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|
_builtin_table = {
|
|
builtins.sum: np.sum,
|
|
builtins.max: np.maximum.reduce,
|
|
builtins.min: np.minimum.reduce,
|
|
}
|
|
|
|
_cython_table = {
|
|
builtins.sum: "sum",
|
|
builtins.max: "max",
|
|
builtins.min: "min",
|
|
np.all: "all",
|
|
np.any: "any",
|
|
np.sum: "sum",
|
|
np.nansum: "sum",
|
|
np.mean: "mean",
|
|
np.nanmean: "mean",
|
|
np.prod: "prod",
|
|
np.nanprod: "prod",
|
|
np.std: "std",
|
|
np.nanstd: "std",
|
|
np.var: "var",
|
|
np.nanvar: "var",
|
|
np.median: "median",
|
|
np.nanmedian: "median",
|
|
np.max: "max",
|
|
np.nanmax: "max",
|
|
np.min: "min",
|
|
np.nanmin: "min",
|
|
np.cumprod: "cumprod",
|
|
np.nancumprod: "cumprod",
|
|
np.cumsum: "cumsum",
|
|
np.nancumsum: "cumsum",
|
|
}
|
|
|
|
|
|
def get_cython_func(arg: Callable) -> str | None:
|
|
"""
|
|
if we define an internal function for this argument, return it
|
|
"""
|
|
return _cython_table.get(arg)
|
|
|
|
|
|
def is_builtin_func(arg):
|
|
"""
|
|
if we define a builtin function for this argument, return it,
|
|
otherwise return the arg
|
|
"""
|
|
return _builtin_table.get(arg, arg)
|
|
|
|
|
|
def fill_missing_names(names: Sequence[Hashable | None]) -> list[Hashable]:
|
|
"""
|
|
If a name is missing then replace it by level_n, where n is the count
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
Parameters
|
|
----------
|
|
names : list-like
|
|
list of column names or None values.
|
|
|
|
Returns
|
|
-------
|
|
list
|
|
list of column names with the None values replaced.
|
|
"""
|
|
return [f"level_{i}" if name is None else name for i, name in enumerate(names)]
|
|
|
|
|
|
def resolve_numeric_only(numeric_only: bool | None | lib.NoDefault) -> bool:
|
|
"""Determine the Boolean value of numeric_only.
|
|
|
|
See GH#46560 for details on the deprecation.
|
|
|
|
Parameters
|
|
----------
|
|
numeric_only : bool, None, or lib.no_default
|
|
Value passed to the method.
|
|
|
|
Returns
|
|
-------
|
|
Resolved value of numeric_only.
|
|
"""
|
|
if numeric_only is lib.no_default:
|
|
# Methods that behave like numeric_only=True and only got the numeric_only
|
|
# arg in 1.5.0 default to lib.no_default
|
|
result = True
|
|
elif numeric_only is None:
|
|
# Methods that had the numeric_only arg prior to 1.5.0 and try all columns
|
|
# first default to None
|
|
result = False
|
|
else:
|
|
result = numeric_only
|
|
return result
|
|
|
|
|
|
def deprecate_numeric_only_default(
|
|
cls: type, name: str, deprecate_none: bool = False
|
|
) -> None:
|
|
"""Emit FutureWarning message for deprecation of numeric_only.
|
|
|
|
See GH#46560 for details on the deprecation.
|
|
|
|
Parameters
|
|
----------
|
|
cls : type
|
|
pandas type that is generating the warning.
|
|
name : str
|
|
Name of the method that is generating the warning.
|
|
deprecate_none : bool, default False
|
|
Whether to also warn about the deprecation of specifying ``numeric_only=None``.
|
|
"""
|
|
if name in ["all", "any"]:
|
|
arg_name = "bool_only"
|
|
else:
|
|
arg_name = "numeric_only"
|
|
|
|
msg = (
|
|
f"The default value of {arg_name} in {cls.__name__}.{name} is "
|
|
"deprecated. In a future version, it will default to False. "
|
|
)
|
|
if deprecate_none:
|
|
msg += f"In addition, specifying '{arg_name}=None' is deprecated. "
|
|
msg += (
|
|
f"Select only valid columns or specify the value of {arg_name} to silence "
|
|
"this warning."
|
|
)
|
|
|
|
warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
|