id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
173,180 | from __future__ import annotations
from datetime import datetime
import functools
from itertools import zip_longest
import operator
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Hashable,
Iterable,
Literal,
NoReturn,
Sequence,
TypeVar,
cast,
final,
over... | When checking if our dtype is comparable with another, we need to unpack CategoricalDtype to look at its categories.dtype. Parameters ---------- other : Index Returns ------- Index |
173,181 | from __future__ import annotations
from datetime import datetime
import functools
from itertools import zip_longest
import operator
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Hashable,
Iterable,
Literal,
NoReturn,
Sequence,
TypeVar,
cast,
final,
over... | null |
173,182 | from __future__ import annotations
import datetime as dt
import operator
from typing import (
TYPE_CHECKING,
Hashable,
)
import warnings
import numpy as np
import pytz
from pandas._libs import (
NaT,
Period,
Timestamp,
index as libindex,
lib,
)
from pandas._libs.tslibs import (
Resolutio... | This is called upon unpickling, rather than the default which doesn't have arguments and breaks __new__ |
173,183 | from __future__ import annotations
import datetime as dt
import operator
from typing import (
TYPE_CHECKING,
Hashable,
)
import warnings
import numpy as np
import pytz
from pandas._libs import (
NaT,
Period,
Timestamp,
index as libindex,
lib,
)
from pandas._libs.tslibs import (
Resolutio... | Return a fixed frequency DatetimeIndex with business day as the default. Parameters ---------- start : str or datetime-like, default None Left bound for generating dates. end : str or datetime-like, default None Right bound for generating dates. periods : int, default None Number of periods to generate. freq : str, Tim... |
173,184 | from __future__ import annotations
import datetime as dt
import operator
from typing import (
TYPE_CHECKING,
Hashable,
)
import warnings
import numpy as np
import pytz
from pandas._libs import (
NaT,
Period,
Timestamp,
index as libindex,
lib,
)
from pandas._libs.tslibs import (
Resolutio... | null |
173,185 | from __future__ import annotations
import numpy as np
from pandas._typing import AxisInt
AxisInt = int
def shift(values: np.ndarray, periods: int, axis: AxisInt, fill_value) -> np.ndarray:
new_values = values
if periods == 0 or values.size == 0:
return new_values.copy()
# make sure array sent to... | null |
173,186 | from __future__ import annotations
import functools
from typing import (
TYPE_CHECKING,
cast,
overload,
)
import numpy as np
from pandas._libs import (
algos as libalgos,
lib,
)
from pandas._typing import (
ArrayLike,
AxisInt,
npt,
)
from pandas.core.dtypes.cast import maybe_promote
from... | Specialized version for 1D arrays. Differences compared to `take_nd`: - Assumes input array has already been converted to numpy array / EA - Assumes indexer is already guaranteed to be intp dtype ndarray - Only works for 1D arrays To ensure the lowest possible overhead. Note: similarly to `take_nd`, this function assum... |
173,187 | from __future__ import annotations
import functools
from typing import (
TYPE_CHECKING,
cast,
overload,
)
import numpy as np
from pandas._libs import (
algos as libalgos,
lib,
)
from pandas._typing import (
ArrayLike,
AxisInt,
npt,
)
from pandas.core.dtypes.cast import maybe_promote
from... | Specialized Cython take which sets NaN values in one pass. |
173,188 | from __future__ import annotations
import functools
from typing import (
TYPE_CHECKING,
cast,
overload,
)
import numpy as np
from pandas._libs import (
algos as libalgos,
lib,
)
from pandas._typing import (
ArrayLike,
AxisInt,
npt,
)
from pandas.core.dtypes.cast import maybe_promote
from... | null |
173,189 | from __future__ import annotations
import operator
import re
from typing import (
Any,
Pattern,
)
import numpy as np
from pandas._typing import (
ArrayLike,
Scalar,
npt,
)
from pandas.core.dtypes.common import (
is_re,
is_re_compilable,
is_scalar,
)
from pandas.core.dtypes.missing import... | Compare two array-like inputs of the same shape or two scalar values Calls operator.eq or re.search, depending on regex argument. If regex is True, perform an element-wise regex matching. Parameters ---------- a : array-like b : scalar or regex pattern regex : bool mask : np.ndarray[bool] Returns ------- mask : array-l... |
173,190 | from __future__ import annotations
import operator
import re
from typing import (
Any,
Pattern,
)
import numpy as np
from pandas._typing import (
ArrayLike,
Scalar,
npt,
)
from pandas.core.dtypes.common import (
is_re,
is_re_compilable,
is_scalar,
)
from pandas.core.dtypes.missing import... | Parameters ---------- values : ArrayLike Object dtype. rx : re.Pattern value : Any mask : np.ndarray[bool], optional Notes ----- Alters values in-place. |
173,191 | from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._libs import iNaT
from pandas.core.dtypes.missing import isna
def _cum_func(
func: Callable,
values: np.ndarray,
*,
skipna: bool = True,
):
def cummin(values: np.ndarray, *, skipna: bool = True):
return _c... | null |
173,192 | from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._libs import iNaT
from pandas.core.dtypes.missing import isna
def _cum_func(
func: Callable,
values: np.ndarray,
*,
skipna: bool = True,
):
"""
Accumulations for 1D datetimelike arrays.
Parameters
... | null |
173,193 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
import numpy as np
from pandas._libs import lib
from pandas._typing import (
ArrayLike,
npt,
)
from pandas.compat import np_version_under1p21
from pandas.core.dtypes.cast import infer_dtype_from
from pandas.core.dtypes.common ... | ExtensionArray-compatible implementation of np.putmask. The main difference is we do not handle repeating or truncating like numpy. Parameters ---------- values: np.ndarray or ExtensionArray mask : np.ndarray[bool] We assume extract_bool_array has already been called. value : Any |
173,194 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
import numpy as np
from pandas._libs import lib
from pandas._typing import (
ArrayLike,
npt,
)
from pandas.compat import np_version_under1p21
from pandas.core.dtypes.cast import infer_dtype_from
from pandas.core.dtypes.common ... | np.putmask will truncate or repeat if `new` is a listlike with len(new) != len(values). We require an exact match. Parameters ---------- values : np.ndarray mask : np.ndarray[bool] new : Any |
173,195 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
import numpy as np
from pandas._libs import lib
from pandas._typing import (
ArrayLike,
npt,
)
from pandas.compat import np_version_under1p21
from pandas.core.dtypes.cast import infer_dtype_from
from pandas.core.dtypes.common ... | Validate mask and check if this putmask operation is a no-op. |
173,196 | from __future__ import annotations
import numpy as np
from pandas._typing import (
ArrayLike,
Scalar,
npt,
)
from pandas.compat.numpy import np_percentile_argname
from pandas.core.dtypes.missing import (
isna,
na_value_for_dtype,
)
def quantile_with_mask(
values: np.ndarray,
mask: npt.NDArra... | Compute the quantiles of the given values for each quantile in `qs`. Parameters ---------- values : np.ndarray or ExtensionArray qs : np.ndarray[float64] interpolation : str Returns ------- np.ndarray or ExtensionArray |
173,197 | from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._typing import npt
from pandas.core.dtypes.common import (
is_bool_dtype,
is_float_dtype,
is_integer_dtype,
)
def _cum_func(
func: Callable,
values: np.ndarray,
mask: npt.NDArray[np.bool_],
*,
s... | null |
173,198 | from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._typing import npt
from pandas.core.dtypes.common import (
is_bool_dtype,
is_float_dtype,
is_integer_dtype,
)
def _cum_func(
func: Callable,
values: np.ndarray,
mask: npt.NDArray[np.bool_],
*,
s... | null |
173,199 | from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._libs import missing as libmissing
from pandas._typing import (
AxisInt,
npt,
)
from pandas.core.nanops import check_below_min_count
def _reductions(
func: Callable,
values: np.ndarray,
mask: npt.NDArray[np... | null |
173,200 | from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._libs import missing as libmissing
from pandas._typing import (
AxisInt,
npt,
)
from pandas.core.nanops import check_below_min_count
def _reductions(
func: Callable,
values: np.ndarray,
mask: npt.NDArray[np... | null |
173,201 | from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._libs import missing as libmissing
from pandas._typing import (
AxisInt,
npt,
)
from pandas.core.nanops import check_below_min_count
def _minmax(
func: Callable,
values: np.ndarray,
mask: npt.NDArray[np.boo... | null |
173,202 | from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._libs import missing as libmissing
from pandas._typing import (
AxisInt,
npt,
)
from pandas.core.nanops import check_below_min_count
def _minmax(
func: Callable,
values: np.ndarray,
mask: npt.NDArray[np.boo... | null |
173,203 | from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._libs import missing as libmissing
from pandas._typing import (
AxisInt,
npt,
)
from pandas.core.nanops import check_below_min_count
def _reductions(
func: Callable,
values: np.ndarray,
mask: npt.NDArray[np... | null |
173,204 | from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._libs import missing as libmissing
from pandas._typing import (
AxisInt,
npt,
)
from pandas.core.nanops import check_below_min_count
def _reductions(
func: Callable,
values: np.ndarray,
mask: npt.NDArray[np... | null |
173,205 | from __future__ import annotations
from typing import Callable
import numpy as np
from pandas._libs import missing as libmissing
from pandas._typing import (
AxisInt,
npt,
)
from pandas.core.nanops import check_below_min_count
def _reductions(
func: Callable,
values: np.ndarray,
mask: npt.NDArray[np... | null |
173,206 | from __future__ import annotations
import operator
def radd(left, right):
return right + left | null |
173,207 | from __future__ import annotations
import operator
def rsub(left, right):
return right - left | null |
173,208 | from __future__ import annotations
import operator
def rmul(left, right):
return right * left | null |
173,209 | from __future__ import annotations
import operator
def rdiv(left, right):
return right / left | null |
173,210 | from __future__ import annotations
import operator
def rtruediv(left, right):
return right / left | null |
173,211 | from __future__ import annotations
import operator
def rfloordiv(left, right):
return right // left | null |
173,212 | from __future__ import annotations
import operator
def rmod(left, right):
# check if right is a string as % is the string
# formatting operation; this is a TypeError
# otherwise perform the op
if isinstance(right, str):
typ = type(left).__name__
raise TypeError(f"{typ} cannot perform th... | null |
173,213 | from __future__ import annotations
import operator
def rdivmod(left, right):
return divmod(right, left) | null |
173,214 | from __future__ import annotations
import operator
def rpow(left, right):
return right**left | null |
173,215 | from __future__ import annotations
import operator
def rand_(left, right):
return operator.and_(right, left) | null |
173,216 | from __future__ import annotations
import operator
def ror_(left, right):
return operator.or_(right, left) | null |
173,217 | from __future__ import annotations
import operator
def rxor(left, right):
return operator.xor(right, left) | null |
173,218 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | null |
173,219 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | Check whether `key` is a valid boolean indexer. Parameters ---------- key : Any Only list-likes may be considered boolean indexers. All other types are not considered a boolean indexer. For array-like input, boolean ndarrays or ExtensionArrays with ``_is_boolean`` set are considered boolean indexers. Returns ------- bo... |
173,220 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | Disallow indexing with a float key, even if that key is a round number. Parameters ---------- val : scalar Returns ------- outval : scalar |
173,221 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | Returns a boolean indicating if all arguments are None. |
173,222 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | Transform label or iterable of labels to array, for use in Index. Parameters ---------- dtype : dtype If specified, use as dtype of the resulting array, otherwise infer. Returns ------- array |
173,223 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | null |
173,224 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | We have an empty slice, e.g. no values are selected. |
173,225 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | Find non-trivial slices in "line": return a list of booleans with same length. |
173,226 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | We have a full length slice. |
173,227 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | Evaluate possibly callable input using obj and kwargs if it is callable, otherwise return as it is. Parameters ---------- maybe_callable : possibly a callable obj : NDFrame **kwargs |
173,228 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | Apply a function ``func`` to object ``obj`` either by passing obj as the first argument to the function or, in the case that the func is a tuple, interpret the first element of the tuple as a function and pass the obj to that function as a keyword argument whose key is the value of the second element of the tuple. Para... |
173,229 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | Returns a function that will map names/labels, dependent if mapper is a dict, Series or just a function. |
173,230 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | Temporarily set attribute on an object. Args: obj: Object whose attribute will be modified. attr: Attribute to modify. value: Value to temporarily set attribute to. Yields: obj with modified attribute. |
173,231 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | if we define an internal function for this argument, return it |
173,232 | from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
import contextlib
from functools import partial
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
Collection,
Generator,
Hashable,
Iterable,
Sequence,
cast,
ov... | if we define a builtin function for this argument, return it, otherwise return the arg |
173,233 | from __future__ import annotations
import copy
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Callable,
Hashable,
Literal,
cast,
final,
no_type_check,
)
import warnings
import numpy as np
from pandas._libs import lib
from pandas._libs.tslibs import (
BaseOffset,
Inco... | Create a TimeGrouper and return our resampler. |
173,234 | from __future__ import annotations
import copy
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Callable,
Hashable,
Literal,
cast,
final,
no_type_check,
)
import warnings
import numpy as np
from pandas._libs import lib
from pandas._libs.tslibs import (
BaseOffset,
Inco... | Return our appropriate resampler when grouping as well. |
173,235 | from __future__ import annotations
import copy
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Callable,
Hashable,
Literal,
cast,
final,
no_type_check,
)
import warnings
import numpy as np
from pandas._libs import lib
from pandas._libs.tslibs import (
BaseOffset,
Inco... | null |
173,236 | from __future__ import annotations
import copy
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Callable,
Hashable,
Literal,
cast,
final,
no_type_check,
)
import warnings
import numpy as np
from pandas._libs import lib
from pandas._libs.tslibs import (
BaseOffset,
Inco... | Adjust the provided `first` and `last` Periods to the respective Period of the given offset that encompasses them. Parameters ---------- first : pd.Period The beginning Period of the range to be adjusted. last : pd.Period The ending Period of the range to be adjusted. freq : pd.DateOffset The freq to which the Periods ... |
173,237 | from __future__ import annotations
import copy
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Callable,
Hashable,
Literal,
cast,
final,
no_type_check,
)
import warnings
import numpy as np
from pandas._libs import lib
from pandas._libs.tslibs import (
BaseOffset,
Inco... | null |
173,238 | from __future__ import annotations
import copy
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Callable,
Hashable,
Literal,
cast,
final,
no_type_check,
)
import warnings
import numpy as np
from pandas._libs import lib
from pandas._libs.tslibs import (
BaseOffset,
Inco... | Warn for deprecation of args and kwargs in resample functions. Parameters ---------- cls : type Class to warn about. kernel : str Operation name. args : tuple or None args passed by user. Will be None if and only if kernel does not have args. kwargs : dict or None kwargs passed by user. Will be None if and only if kern... |
173,239 | from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
algos,
lib,
)
from pandas._typing import (
ArrayLike,
Axis,
AxisInt,
F,
npt,
)
from pandas.... | Validate the size of the values passed to ExtensionArray.fillna. |
173,240 | from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
algos,
lib,
)
from pandas._typing import (
ArrayLike,
Axis,
AxisInt,
F,
npt,
)
from pandas.... | Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True Parameters ---------- arr : ArrayLike values_to_mask: list, tuple, or scalar Returns ------- np.ndarray[bool] |
173,241 | from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
algos,
lib,
)
from pandas._typing import (
ArrayLike,
Axis,
AxisInt,
F,
npt,
)
from pandas.... | Wrapper to dispatch to either interpolate_2d or _interpolate_2d_with_fill. Notes ----- Alters 'data' in-place. |
173,242 | from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
algos,
lib,
)
from pandas._typing import (
ArrayLike,
Axis,
AxisInt,
F,
npt,
)
from pandas.... | Wrapper to handle datetime64 and timedelta64 dtypes. |
173,243 | from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
algos,
lib,
)
from pandas._typing import (
ArrayLike,
Axis,
AxisInt,
F,
npt,
)
from pandas.... | null |
173,244 | from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
algos,
lib,
)
from pandas._typing import (
ArrayLike,
Axis,
AxisInt,
F,
npt,
)
from pandas.... | null |
173,245 | from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
algos,
lib,
)
from pandas._typing import (
ArrayLike,
Axis,
AxisInt,
F,
npt,
)
from pandas.... | null |
173,246 | from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
algos,
lib,
)
from pandas._typing import (
ArrayLike,
Axis,
AxisInt,
F,
npt,
)
from pandas.... | null |
173,247 | from __future__ import annotations
import ast
from functools import (
partial,
reduce,
)
from keyword import iskeyword
import tokenize
from typing import (
Callable,
TypeVar,
)
import numpy as np
from pandas.compat import PY39
from pandas.errors import UndefinedVariableError
import pandas.core.common as... | Compose a collection of tokenization functions. Parameters ---------- source : str A Python source code string f : callable This takes a tuple of (toknum, tokval) as its argument and returns a tuple with the same structure but possibly different elements. Defaults to the composition of ``_rewrite_assign``, ``_replace_b... |
173,248 | from __future__ import annotations
import ast
from functools import (
partial,
reduce,
)
from keyword import iskeyword
import tokenize
from typing import (
Callable,
TypeVar,
)
import numpy as np
from pandas.compat import PY39
from pandas.errors import UndefinedVariableError
import pandas.core.common as... | Factory for a type checking function of type ``t`` or tuple of types. |
173,249 | from __future__ import annotations
import ast
from functools import (
partial,
reduce,
)
from keyword import iskeyword
import tokenize
from typing import (
Callable,
TypeVar,
)
import numpy as np
from pandas.compat import PY39
from pandas.errors import UndefinedVariableError
import pandas.core.common as... | Filter out AST nodes that are subclasses of ``superclass``. |
173,250 | from __future__ import annotations
import ast
from functools import (
partial,
reduce,
)
from keyword import iskeyword
import tokenize
from typing import (
Callable,
TypeVar,
)
import numpy as np
from pandas.compat import PY39
from pandas.errors import UndefinedVariableError
import pandas.core.common as... | Decorator to disallow certain nodes from parsing. Raises a NotImplementedError instead. Returns ------- callable |
173,251 | from __future__ import annotations
import ast
from functools import (
partial,
reduce,
)
from keyword import iskeyword
import tokenize
from typing import (
Callable,
TypeVar,
)
import numpy as np
from pandas.compat import PY39
from pandas.errors import UndefinedVariableError
import pandas.core.common as... | Decorator to add default implementation of ops. |
173,252 | from __future__ import annotations
import datetime
import inspect
from io import StringIO
import itertools
import pprint
import struct
import sys
from typing import (
ChainMap,
TypeVar,
)
import numpy as np
from pandas._libs.tslibs import Timestamp
from pandas.errors import UndefinedVariableError
def _replacer(... | Return the padded hexadecimal id of ``obj``. |
173,253 | from __future__ import annotations
import datetime
import inspect
from io import StringIO
import itertools
import pprint
import struct
import sys
from typing import (
ChainMap,
TypeVar,
)
import numpy as np
from pandas._libs.tslibs import Timestamp
from pandas.errors import UndefinedVariableError
class StringI... | Return a prettier version of obj. Parameters ---------- obj : object Object to pretty print Returns ------- str Pretty print object repr |
173,254 | from __future__ import annotations
import abc
from typing import TYPE_CHECKING
from pandas.errors import NumExprClobberingError
from pandas.core.computation.align import (
align_terms,
reconstruct_object,
)
from pandas.core.computation.ops import (
MATHOPS,
REDUCTIONS,
)
from pandas.io.formats import pr... | Attempt to prevent foot-shooting in a helpful way. Parameters ---------- expr : Expr Terms can contain |
173,255 | from __future__ import annotations
from io import StringIO
from keyword import iskeyword
import token
import tokenize
from typing import (
Hashable,
Iterator,
)
def create_valid_python_identifier(name: str) -> str:
"""
Create valid Python identifiers from any string.
Check if name contains any speci... | Function to emulate the cleaning of a backtick quoted name. The purpose for this function is to see what happens to the name of identifier if it goes to the process of being parsed a Python code inside a backtick quoted string and than being cleaned (removed of any special characters). Parameters ---------- name : hash... |
173,256 | from __future__ import annotations
import operator
import warnings
import numpy as np
from pandas._config import get_option
from pandas._typing import FuncType
from pandas.util._exceptions import find_stack_level
from pandas.core.computation.check import NUMEXPR_INSTALLED
from pandas.core.ops import roperator
if NUMEXP... | null |
173,257 | from __future__ import annotations
import operator
import warnings
import numpy as np
from pandas._config import get_option
from pandas._typing import FuncType
from pandas.util._exceptions import find_stack_level
from pandas.core.computation.check import NUMEXPR_INSTALLED
from pandas.core.ops import roperator
_TEST_MOD... | Keeps track of whether numexpr was used. Stores an additional ``True`` for every successful use of evaluate with numexpr since the last ``get_test_result``. |
173,258 | from __future__ import annotations
import operator
import warnings
import numpy as np
from pandas._config import get_option
from pandas._typing import FuncType
from pandas.util._exceptions import find_stack_level
from pandas.core.computation.check import NUMEXPR_INSTALLED
from pandas.core.ops import roperator
_TEST_RES... | Get test result and reset test_results. |
173,259 | from __future__ import annotations
import ast
from functools import partial
from typing import Any
import numpy as np
from pandas._libs.tslibs import (
Timedelta,
Timestamp,
)
from pandas._typing import npt
from pandas.errors import UndefinedVariableError
from pandas.core.dtypes.common import is_list_like
impor... | Validate that the where statement is of the right type. The type may either be String, Expr, or list-like of Exprs. Parameters ---------- w : String term expression, Expr, or list-like of Exprs. Returns ------- where : The original where clause if the check was successful. Raises ------ TypeError : An invalid data type... |
173,260 | from __future__ import annotations
from datetime import datetime
from functools import partial
import operator
from typing import (
Callable,
Iterable,
Iterator,
Literal,
)
import numpy as np
from pandas._libs.tslibs import Timestamp
from pandas.core.dtypes.common import (
is_list_like,
is_scala... | Compute the vectorized membership of ``x in y`` if possible, otherwise use Python. |
173,261 | from __future__ import annotations
from datetime import datetime
from functools import partial
import operator
from typing import (
Callable,
Iterable,
Iterator,
Literal,
)
import numpy as np
from pandas._libs.tslibs import Timestamp
from pandas.core.dtypes.common import (
is_list_like,
is_scala... | Compute the vectorized membership of ``x not in y`` if possible, otherwise use Python. |
173,262 | from __future__ import annotations
from datetime import datetime
from functools import partial
import operator
from typing import (
Callable,
Iterable,
Iterator,
Literal,
)
import numpy as np
from pandas._libs.tslibs import Timestamp
from pandas.core.dtypes.common import (
is_list_like,
is_scala... | Cast an expression inplace. Parameters ---------- terms : Op The expression that should cast. acceptable_dtypes : list of acceptable numpy.dtype Will not cast if term's dtype in this list. dtype : str or numpy.dtype The dtype to cast to. |
173,263 | from __future__ import annotations
from datetime import datetime
from functools import partial
import operator
from typing import (
Callable,
Iterable,
Iterator,
Literal,
)
import numpy as np
from pandas._libs.tslibs import Timestamp
from pandas.core.dtypes.common import (
is_list_like,
is_scala... | null |
173,264 | from __future__ import annotations
from datetime import datetime
from functools import partial
import operator
from typing import (
Callable,
Iterable,
Iterator,
Literal,
)
import numpy as np
from pandas._libs.tslibs import Timestamp
from pandas.core.dtypes.common import (
is_list_like,
is_scala... | null |
173,265 | from __future__ import annotations
from functools import reduce
import numpy as np
from pandas._config import get_option
The provided code snippet includes necessary dependencies for implementing the `ensure_decoded` function. Write a Python function `def ensure_decoded(s) -> str` to solve the following problem:
If we... | If we have bytes, decode them to unicode. |
173,266 | from __future__ import annotations
import tokenize
from typing import TYPE_CHECKING
import warnings
from pandas.util._exceptions import find_stack_level
from pandas.util._validators import validate_bool_kwarg
from pandas.core.dtypes.common import is_extension_array_dtype
from pandas.core.computation.engines import ENGI... | Evaluate a Python expression as a string using various backends. The following arithmetic operations are supported: ``+``, ``-``, ``*``, ``/``, ``**``, ``%``, ``//`` (python engine only) along with the following boolean operations: ``|`` (or), ``&`` (and), and ``~`` (not). Additionally, the ``'pandas'`` parser allows t... |
173,267 | from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Callable,
Sequence,
)
import warnings
import numpy as np
from pandas.errors import PerformanceWarning
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.generic ... | null |
173,268 | from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Callable,
Sequence,
)
import warnings
import numpy as np
from pandas.errors import PerformanceWarning
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.generic ... | Align a set of terms. |
173,269 | from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Callable,
Sequence,
)
import warnings
import numpy as np
from pandas.errors import PerformanceWarning
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.generic ... | Reconstruct an object given its type, raw value, and possibly empty (None) axes. Parameters ---------- typ : object A type obj : object The value to use in the type constructor axes : dict The axes to use to construct the resulting pandas object Returns ------- ret : typ An object of type ``typ`` with the value `obj` a... |
173,270 | from __future__ import annotations
from typing import (
Callable,
final,
)
import warnings
from pandas.util._decorators import doc
from pandas.util._exceptions import find_stack_level
class Callable(BaseTypingInstance):
def py__call__(self, arguments):
"""
def x() -> Callable[[Callable[... | Add delegated names to a class using a class decorator. This provides an alternative usage to directly calling `_add_delegate_accessors` below a class definition. Parameters ---------- delegate : object The class to get methods/properties & doc-strings. accessors : Sequence[str] List of accessor to add. typ : {'propert... |
173,271 | from __future__ import annotations
from typing import (
Callable,
final,
)
import warnings
from pandas.util._decorators import doc
from pandas.util._exceptions import find_stack_level
def _register_accessor(name, cls):
def register_dataframe_accessor(name):
from pandas import DataFrame
return _registe... | null |
173,272 | from __future__ import annotations
from typing import (
Callable,
final,
)
import warnings
from pandas.util._decorators import doc
from pandas.util._exceptions import find_stack_level
def _register_accessor(name, cls):
"""
Register a custom accessor on {klass} objects.
Parameters
----------
... | null |
173,273 | from __future__ import annotations
from typing import (
Callable,
final,
)
import warnings
from pandas.util._decorators import doc
from pandas.util._exceptions import find_stack_level
def _register_accessor(name, cls):
def register_index_accessor(name):
from pandas import Index
return _register_access... | null |
173,274 | from __future__ import annotations
import numpy as np
from pandas._libs.lib import i8max
from pandas._libs.tslibs import (
BaseOffset,
OutOfBoundsDatetime,
Timedelta,
Timestamp,
iNaT,
)
from pandas._typing import npt
def _generate_range_overflow_safe(
endpoint: int, periods: int, stride: int, si... | Generate a range of dates or timestamps with the spans between dates described by the given `freq` DateOffset. Parameters ---------- start : Timedelta, Timestamp or None First point of produced date range. end : Timedelta, Timestamp or None Last point of produced date range. periods : int or None Number of periods in p... |
173,275 | from __future__ import annotations
from csv import QUOTE_NONNUMERIC
from functools import partial
import operator
from shutil import get_terminal_size
from typing import (
TYPE_CHECKING,
Hashable,
Iterator,
Literal,
Sequence,
TypeVar,
cast,
overload,
)
import numpy as np
from pandas._con... | null |
173,276 | from __future__ import annotations
from csv import QUOTE_NONNUMERIC
from functools import partial
import operator
from shutil import get_terminal_size
from typing import (
TYPE_CHECKING,
Hashable,
Iterator,
Literal,
Sequence,
TypeVar,
cast,
overload,
)
import numpy as np
from pandas._con... | Helper for membership check for ``key`` in ``cat``. This is a helper method for :method:`__contains__` and :class:`CategoricalIndex.__contains__`. Returns True if ``key`` is in ``cat.categories`` and the location of ``key`` in ``categories`` is in ``container``. Parameters ---------- cat : :class:`Categorical`or :class... |
173,277 | from __future__ import annotations
from csv import QUOTE_NONNUMERIC
from functools import partial
import operator
from shutil import get_terminal_size
from typing import (
TYPE_CHECKING,
Hashable,
Iterator,
Literal,
Sequence,
TypeVar,
cast,
overload,
)
import numpy as np
from pandas._con... | utility routine to turn values into codes given the specified categories If `values` is known to be a Categorical, use recode_for_categories instead. |
173,278 | from __future__ import annotations
import numbers
from typing import (
TYPE_CHECKING,
cast,
)
import numpy as np
from pandas._libs import (
lib,
missing as libmissing,
)
from pandas._typing import (
Dtype,
DtypeObj,
type_t,
)
from pandas.core.dtypes.common import (
is_list_like,
is_n... | Coerce the input values array to numpy arrays with a mask. Parameters ---------- values : 1D list-like mask : bool 1D array, optional copy : bool, default False if True, copy the input Returns ------- tuple of (values, mask) |
173,279 | from __future__ import annotations
from datetime import (
datetime,
timedelta,
)
from functools import wraps
import operator
from typing import (
TYPE_CHECKING,
Any,
Callable,
Iterator,
Literal,
Sequence,
TypeVar,
Union,
cast,
final,
overload,
)
import warnings
import... | For PeriodArray methods, dispatch to DatetimeArray and re-wrap the results in PeriodArray. We cannot use ._ndarray directly for the affected methods because the i8 data has different semantics on NaT values. |
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