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"""
Expressions
-----------
Offer fast expression evaluation through numexpr
"""
from __future__ import annotations
import operator
from typing import TYPE_CHECKING
import warnings
import numpy as np
from pandas._config import get_option
from pandas.util._exceptions import find_stack_level
from pandas.core impor... |
"""
Engine classes for :func:`~pandas.eval`
"""
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,... |
"""
:func:`~pandas.eval` source string parsing functions
"""
from __future__ import annotations
from io import StringIO
from keyword import iskeyword
import token
import tokenize
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from collections.abc import (
Hashable,
Iterator,
)
# A token v... |
"""Common utilities for Numba operations with groupby ops"""
from __future__ import annotations
import functools
import inspect
from typing import (
TYPE_CHECKING,
Any,
Callable,
)
import numpy as np
from pandas.compat._optional import import_optional_dependency
from pandas.core.util.numba_ import (
... |
"""
Provide basic components for groupby.
"""
from __future__ import annotations
import dataclasses
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from collections.abc import Hashable
@dataclasses.dataclass(order=True, frozen=True)
class OutputKey:
label: Hashable
position: int
# special case to p... |
from __future__ import annotations
import numpy as np
from pandas.core.algorithms import unique1d
from pandas.core.arrays.categorical import (
Categorical,
CategoricalDtype,
recode_for_categories,
)
def recode_for_groupby(
c: Categorical, sort: bool, observed: bool
) -> tuple[Categorical, Categorica... |
from pandas.core.groupby.generic import (
DataFrameGroupBy,
NamedAgg,
SeriesGroupBy,
)
from pandas.core.groupby.groupby import GroupBy
from pandas.core.groupby.grouper import Grouper
__all__ = [
"DataFrameGroupBy",
"NamedAgg",
"SeriesGroupBy",
"GroupBy",
"Grouper",
]
|
from __future__ import annotations
from collections.abc import Iterable
from typing import (
TYPE_CHECKING,
Literal,
cast,
)
import numpy as np
from pandas.util._decorators import (
cache_readonly,
doc,
)
from pandas.core.dtypes.common import (
is_integer,
is_list_like,
)
if TYPE_CHECKI... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
import numpy as np
from pandas._libs.lib import infer_dtype
from pandas._libs.tslibs import iNaT
from pandas.errors import NoBufferPresent
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.dtypes import Ba... |
"""
Utility functions and objects for implementing the interchange API.
"""
from __future__ import annotations
import typing
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.dtypes import (
ArrowDtype,
CategoricalDtype,
DatetimeTZDtype,
)
import pandas as pd
if typing.TYPE_CHEC... |
from __future__ import annotations
from collections import abc
from typing import TYPE_CHECKING
from pandas.core.interchange.column import PandasColumn
from pandas.core.interchange.dataframe_protocol import DataFrame as DataFrameXchg
from pandas.core.interchange.utils import maybe_rechunk
if TYPE_CHECKING:
from ... |
"""
A verbatim copy (vendored) of the spec from https://github.com/data-apis/dataframe-api
"""
from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
import enum
from typing import (
TYPE_CHECKING,
Any,
TypedDict,
)
if TYPE_CHECKING:
from collections.abc import (
... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
from pandas.core.interchange.dataframe_protocol import (
Buffer,
DlpackDeviceType,
)
if TYPE_CHECKING:
import numpy as np
import pyarrow as pa
class PandasBuffer(Buffer):
"""
Data in the buffer is guarante... |
from __future__ import annotations
import ctypes
import re
from typing import Any
import numpy as np
from pandas._config import using_string_dtype
from pandas.compat._optional import import_optional_dependency
from pandas.errors import SettingWithCopyError
import pandas as pd
from pandas.core.interchange.dataframe... |
from pandas._libs import (
NaT,
Period,
Timedelta,
Timestamp,
)
from pandas._libs.missing import NA
from pandas.core.dtypes.dtypes import (
ArrowDtype,
CategoricalDtype,
DatetimeTZDtype,
IntervalDtype,
PeriodDtype,
)
from pandas.core.dtypes.missing import (
isna,
isnull,
... |
from __future__ import annotations
from typing import TYPE_CHECKING
import weakref
if TYPE_CHECKING:
from pandas.core.generic import NDFrame
class Flags:
"""
Flags that apply to pandas objects.
Parameters
----------
obj : Series or DataFrame
The object these flags are associated wit... |
from __future__ import annotations
from datetime import (
datetime,
timedelta,
)
from typing import TYPE_CHECKING
import warnings
import numpy as np
from pandas._libs import index as libindex
from pandas._libs.tslibs import (
BaseOffset,
NaT,
Period,
Resolution,
Tick,
)
from pandas._libs.... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Literal,
cast,
)
import numpy as np
from pandas._libs import index as libindex
from pandas.util._decorators import (
cache_readonly,
doc,
)
from pandas.core.dtypes.common import is_scalar
from pandas.core.dtypes.conc... |
from __future__ import annotations
import textwrap
from typing import (
TYPE_CHECKING,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
lib,
)
from pandas.errors import InvalidIndexError
from pandas.core.dtypes.cast import find_common_type
from pandas.core.algorithms import safe_sort
from... |
""" implement the TimedeltaIndex """
from __future__ import annotations
from typing import TYPE_CHECKING
import warnings
from pandas._libs import (
index as libindex,
lib,
)
from pandas._libs.tslibs import (
Resolution,
Timedelta,
to_offset,
)
from pandas._libs.tslibs.timedeltas import disallow_am... |
"""
datetimelike delegation
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
cast,
)
import warnings
import numpy as np
from pandas._libs import lib
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.common import (
is_integer_dtype,
is_list_like,
... |
"""
frozen (immutable) data structures to support MultiIndexing
These are used for:
- .names (FrozenList)
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
NoReturn,
)
from pandas.core.base import PandasObject
from pandas.io.formats.printing import pprint_thing
if TYPE_CHECKING:
... |
"""
Shared methods for Index subclasses backed by ExtensionArray.
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Callable,
TypeVar,
)
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.generic import ABCDataFrame
from pandas.core.indexes.base import In... |
"""
Misc tools for implementing data structures
Note: pandas.core.common is *not* part of the public API.
"""
from __future__ import annotations
import builtins
from collections import (
abc,
defaultdict,
)
from collections.abc import (
Collection,
Generator,
Hashable,
Iterable,
Sequence,
... |
"""
timedelta support tools
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
overload,
)
import warnings
import numpy as np
from pandas._libs import lib
from pandas._libs.tslibs import (
NaT,
NaTType,
)
from pandas._libs.tslibs.timedeltas import (
Timedelta,
disallow... |
from __future__ import annotations
from datetime import (
datetime,
time,
)
from typing import TYPE_CHECKING
import warnings
import numpy as np
from pandas._libs.lib import is_list_like
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.generic import (
ABCIndex,
ABCSeries,... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Literal,
)
import warnings
import numpy as np
from pandas._libs import (
lib,
missing as libmissing,
)
from pandas.util._exceptions import find_stack_level
from pandas.util._validators import check_dtype_backend
from pandas.core.... |
"""
accessor.py contains base classes for implementing accessor properties
that can be mixed into or pinned onto other pandas classes.
"""
from __future__ import annotations
from typing import (
Callable,
final,
)
import warnings
from pandas.util._decorators import doc
from pandas.util._exceptions import fi... |
from pandas.core.reshape.concat import concat
from pandas.core.reshape.encoding import (
from_dummies,
get_dummies,
)
from pandas.core.reshape.melt import (
lreshape,
melt,
wide_to_long,
)
from pandas.core.reshape.merge import (
merge,
merge_asof,
merge_ordered,
)
from pandas.core.reshap... |
from __future__ import annotations
from collections import defaultdict
from collections.abc import (
Hashable,
Iterable,
)
import itertools
from typing import (
TYPE_CHECKING,
cast,
)
import numpy as np
from pandas._libs import missing as libmissing
from pandas._libs.sparse import IntIndex
from pand... |
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from pandas.core.dtypes.common import is_list_like
if TYPE_CHECKING:
from pandas._typing import NumpyIndexT
def cartesian_product(X) -> list[np.ndarray]:
"""
Numpy version of itertools.product.
Sometimes faster ... |
from __future__ import annotations
import re
from typing import TYPE_CHECKING
import numpy as np
from pandas.util._decorators import Appender
from pandas.core.dtypes.common import is_list_like
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.missing import notna
import pandas.core.algori... |
"""
Low-dependency indexing utilities.
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.common import (
is_array_like,
is_bool_dtype,
is_integer,
is_integer_dtype,
is_list_like,
)
fro... |
"""Indexer objects for computing start/end window bounds for rolling operations"""
from __future__ import annotations
from datetime import timedelta
import numpy as np
from pandas._libs.tslibs import BaseOffset
from pandas._libs.window.indexers import calculate_variable_window_bounds
from pandas.util._decorators imp... |
from pandas.core.indexers.utils import (
check_array_indexer,
check_key_length,
check_setitem_lengths,
disallow_ndim_indexing,
is_empty_indexer,
is_list_like_indexer,
is_scalar_indexer,
is_valid_positional_slice,
length_of_indexer,
maybe_convert_indices,
unpack_1tuple,
un... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
NamedTuple,
)
from pandas.core.dtypes.common import is_1d_only_ea_dtype
if TYPE_CHECKING:
from collections.abc import Iterator
from pandas._libs.internals import BlockPlacement
from pandas._typing import ArrayLike
from p... |
"""
This is a pseudo-public API for downstream libraries. We ask that downstream
authors
1) Try to avoid using internals directly altogether, and failing that,
2) Use only functions exposed here (or in core.internals)
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from ... |
"""
Base class for the internal managers. Both BlockManager and ArrayManager
inherit from this class.
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Literal,
cast,
final,
)
import numpy as np
from pandas._config import (
using_copy_on_write,
warn_copy_on_w... |
from pandas.core.internals.api import make_block # 2023-09-18 pyarrow uses this
from pandas.core.internals.array_manager import (
ArrayManager,
SingleArrayManager,
)
from pandas.core.internals.base import (
DataManager,
SingleDataManager,
)
from pandas.core.internals.concat import concatenate_managers
... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
cast,
)
import warnings
import numpy as np
from pandas._libs import (
NaT,
algos as libalgos,
internals as libinternals,
lib,
)
from pandas._libs.missing import NA
from pandas.util._decorators import cache_readonly
from pa... |
"""
Reversed Operations not available in the stdlib operator module.
Defining these instead of using lambdas allows us to reference them by name.
"""
from __future__ import annotations
import operator
def radd(left, right):
return right + left
def rsub(left, right):
return right - left
def rmul(left, rig... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Literal,
overload,
)
import warnings
import numpy as np
from pandas._libs import (
lib,
missing as libmissing,
)
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.cast import maybe_box_native
from p... |
"""
Module responsible for execution of NDFrame.describe() method.
Method NDFrame.describe() delegates actual execution to function describe_ndframe().
"""
from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from typing import (
TYPE_CHECKING,
Callable,
cast,
)
import nump... |
"""
Implementation of nlargest and nsmallest.
"""
from __future__ import annotations
from collections.abc import (
Hashable,
Sequence,
)
from typing import (
TYPE_CHECKING,
cast,
final,
)
import numpy as np
from pandas._libs import algos as libalgos
from pandas.core.dtypes.common import (
i... |
"""
Methods that can be shared by many array-like classes or subclasses:
Series
Index
ExtensionArray
"""
from __future__ import annotations
import operator
from typing import Any
import numpy as np
from pandas._libs import lib
from pandas._libs.ops_dispatch import maybe_dispatch_ufunc_to_dunder_op
from ... |
from __future__ import annotations
from typing import ClassVar
import numpy as np
from pandas.core.dtypes.base import register_extension_dtype
from pandas.core.dtypes.common import is_float_dtype
from pandas.core.arrays.numeric import (
NumericArray,
NumericDtype,
)
class FloatingDtype(NumericDtype):
... |
from __future__ import annotations
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Literal,
)
import numpy as np
from pandas._libs import lib
from pandas.compat import (
pa_version_under10p1,
pa_version_under11p0,
pa_version_under13p0,
pa_version_under17p0... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Literal,
)
import numpy as np
from pandas._libs import lib
from pandas._libs.tslibs import is_supported_dtype
from pandas.compat.numpy import function as nv
from pandas.core.dtypes.astype import astype_array
from pandas.core.dty... |
from __future__ import annotations
import numbers
from typing import (
TYPE_CHECKING,
ClassVar,
cast,
)
import numpy as np
from pandas._libs import (
lib,
missing as libmissing,
)
from pandas.core.dtypes.common import is_list_like
from pandas.core.dtypes.dtypes import register_extension_dtype
fr... |
from __future__ import annotations
import operator
import re
from typing import (
TYPE_CHECKING,
Callable,
Union,
)
import warnings
import numpy as np
from pandas._libs import (
lib,
missing as libmissing,
)
from pandas.compat import (
pa_version_under10p1,
pa_version_under13p0,
pa_ve... |
from __future__ import annotations
from typing import ClassVar
import numpy as np
from pandas.core.dtypes.base import register_extension_dtype
from pandas.core.dtypes.common import is_integer_dtype
from pandas.core.arrays.numeric import (
NumericArray,
NumericDtype,
)
class IntegerDtype(NumericDtype):
... |
"""
Helper functions to generate range-like data for DatetimeArray
(and possibly TimedeltaArray/PeriodArray)
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from pandas._libs.lib import i8max
from pandas._libs.tslibs import (
BaseOffset,
OutOfBoundsDatetime,
Tim... |
from pandas.core.arrays.arrow.accessors import (
ListAccessor,
StructAccessor,
)
from pandas.core.arrays.arrow.array import ArrowExtensionArray
__all__ = ["ArrowExtensionArray", "StructAccessor", "ListAccessor"]
|
from __future__ import annotations
import json
from typing import TYPE_CHECKING
import pyarrow
from pandas.compat import pa_version_under14p1
from pandas.core.dtypes.dtypes import (
IntervalDtype,
PeriodDtype,
)
from pandas.core.arrays.interval import VALID_CLOSED
if TYPE_CHECKING:
from pandas._typing... |
"""Accessors for arrow-backed data."""
from __future__ import annotations
from abc import (
ABCMeta,
abstractmethod,
)
from typing import (
TYPE_CHECKING,
cast,
)
from pandas.compat import (
pa_version_under10p1,
pa_version_under11p0,
)
from pandas.core.dtypes.common import is_list_like
if ... |
from __future__ import annotations
import numpy as np
import pyarrow
def pyarrow_array_to_numpy_and_mask(
arr, dtype: np.dtype
) -> tuple[np.ndarray, np.ndarray]:
"""
Convert a primitive pyarrow.Array to a numpy array and boolean mask based
on the buffers of the Array.
At the moment pyarrow.Bool... |
from pandas.core.arrays.arrow import ArrowExtensionArray
from pandas.core.arrays.base import (
ExtensionArray,
ExtensionOpsMixin,
ExtensionScalarOpsMixin,
)
from pandas.core.arrays.boolean import BooleanArray
from pandas.core.arrays.categorical import Categorical
from pandas.core.arrays.datetimes import Dat... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
import numpy as np
from pandas._libs import lib
from pandas.errors import LossySetitemError
from pandas.core.dtypes.cast import np_can_hold_element
from pandas.core.dtypes.common import is_numeric_dtype
if TYPE_CHECKING:
from... |
"""
Interaction with scipy.sparse matrices.
Currently only includes to_coo helpers.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
from pandas._libs import lib
from pandas.core.dtypes.missing import notna
from pandas.core.algorithms import factorize
from pandas.core.indexes.api import Mult... |
"""Sparse accessor"""
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.cast import find_common_type
from pandas.core.dtypes.dtypes import SparseDtype
from pandas.core.accessor import (
P... |
from pandas.core.arrays.sparse.accessor import (
SparseAccessor,
SparseFrameAccessor,
)
from pandas.core.arrays.sparse.array import (
BlockIndex,
IntIndex,
SparseArray,
make_sparse_index,
)
__all__ = [
"BlockIndex",
"IntIndex",
"make_sparse_index",
"SparseAccessor",
"SparseA... |
from __future__ import annotations
from functools import wraps
from typing import (
TYPE_CHECKING,
Any,
Literal,
cast,
overload,
)
import numpy as np
from pandas._libs import lib
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import is_supported_dtype
from pandas._typing i... |
from __future__ import annotations
import numbers
from typing import (
TYPE_CHECKING,
Any,
Callable,
)
import numpy as np
from pandas._libs import (
lib,
missing as libmissing,
)
from pandas.errors import AbstractMethodError
from pandas.util._decorators import cache_readonly
from pandas.core.dty... |
from __future__ import annotations
import functools
from typing import (
TYPE_CHECKING,
Any,
Callable,
)
import numpy as np
from pandas.compat._optional import import_optional_dependency
from pandas.core.util.numba_ import jit_user_function
if TYPE_CHECKING:
from pandas._typing import Scalar
@fun... |
"""Common utility functions for rolling operations"""
from __future__ import annotations
from collections import defaultdict
from typing import cast
import numpy as np
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCSeries,
)
from pandas.core.indexes.api import MultiIndex
def flex_binary_moment(... |
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from pandas.compat._optional import import_optional_dependency
def generate_online_numba_ewma_func(
nopython: bool,
nogil: bool,
parallel: bool,
):
"""
Generate a numba jitted groupby ewma function specified ... |
from pandas.core.window.ewm import (
ExponentialMovingWindow,
ExponentialMovingWindowGroupby,
)
from pandas.core.window.expanding import (
Expanding,
ExpandingGroupby,
)
from pandas.core.window.rolling import (
Rolling,
RollingGroupby,
Window,
)
__all__ = [
"Expanding",
"ExpandingGr... |
"""Any shareable docstring components for rolling/expanding/ewm"""
from __future__ import annotations
from textwrap import dedent
from pandas.core.shared_docs import _shared_docs
_shared_docs = dict(**_shared_docs)
def create_section_header(header: str) -> str:
"""Create numpydoc section header"""
return f... |
from pandas.core.dtypes.dtypes import SparseDtype
from pandas.core.arrays.sparse import SparseArray
__all__ = ["SparseArray", "SparseDtype"]
|
""" basic inference routines """
from __future__ import annotations
from collections import abc
from numbers import Number
import re
from re import Pattern
from typing import TYPE_CHECKING
import numpy as np
from pandas._libs import lib
if TYPE_CHECKING:
from collections.abc import Hashable
from pandas._t... |
from pandas.core.dtypes.common import (
is_any_real_numeric_dtype,
is_array_like,
is_bool,
is_bool_dtype,
is_categorical_dtype,
is_complex,
is_complex_dtype,
is_datetime64_any_dtype,
is_datetime64_dtype,
is_datetime64_ns_dtype,
is_datetime64tz_dtype,
is_dict_like,
is_... |
"""
Extend pandas with custom array types.
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
TypeVar,
cast,
overload,
)
import numpy as np
from pandas._libs import missing as libmissing
from pandas._libs.hashtable import object_hash
from pandas._libs.properties impor... |
""" define generic base classes for pandas objects """
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Type,
cast,
)
if TYPE_CHECKING:
from pandas import (
Categorical,
CategoricalIndex,
DataFrame,
DatetimeIndex,
Index,
IntervalInd... |
"""
Functions for implementing 'astype' methods according to pandas conventions,
particularly ones that differ from numpy.
"""
from __future__ import annotations
import inspect
from typing import (
TYPE_CHECKING,
overload,
)
import warnings
import numpy as np
from pandas._libs import lib
from pandas._libs.ts... |
"""
Utility functions related to concat.
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
cast,
)
import warnings
import numpy as np
from pandas._libs import lib
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.astype import astype_array
from pandas.core... |
"""
config for datetime formatting
"""
from __future__ import annotations
from pandas._config import config as cf
pc_date_dayfirst_doc = """
: boolean
When True, prints and parses dates with the day first, eg 20/01/2005
"""
pc_date_yearfirst_doc = """
: boolean
When True, prints and parses dates with the yea... |
"""
Helpers for configuring locale settings.
Name `localization` is chosen to avoid overlap with builtin `locale` module.
"""
from __future__ import annotations
from contextlib import contextmanager
import locale
import platform
import re
import subprocess
from typing import TYPE_CHECKING
from pandas._config.config ... |
"""
Unopinionated display configuration.
"""
from __future__ import annotations
import locale
import sys
from pandas._config import config as cf
# -----------------------------------------------------------------------------
# Global formatting options
_initial_defencoding: str | None = None
def detect_console_en... |
"""
pandas._config is considered explicitly upstream of everything else in pandas,
should have no intra-pandas dependencies.
importing `dates` and `display` ensures that keys needed by _libs
are initialized.
"""
__all__ = [
"config",
"detect_console_encoding",
"get_option",
"set_option",
"reset_opt... |
from __future__ import annotations
import os
import warnings
__docformat__ = "restructuredtext"
# Let users know if they're missing any of our hard dependencies
_hard_dependencies = ("numpy", "pytz", "dateutil")
_missing_dependencies = []
for _dependency in _hard_dependencies:
try:
__import__(_dependenc... |
"""
Helpers for sharing tests between DataFrame/Series
"""
from __future__ import annotations
from typing import TYPE_CHECKING
from pandas import DataFrame
if TYPE_CHECKING:
from pandas._typing import DtypeObj
def get_dtype(obj) -> DtypeObj:
if isinstance(obj, DataFrame):
# Note: we are assuming on... |
from __future__ import annotations
import gzip
import io
import pathlib
import tarfile
from typing import (
TYPE_CHECKING,
Any,
Callable,
)
import uuid
import zipfile
from pandas.compat import (
get_bz2_file,
get_lzma_file,
)
from pandas.compat._optional import import_optional_dependency
import p... |
from __future__ import annotations
from contextlib import contextmanager
import os
from pathlib import Path
import tempfile
from typing import (
IO,
TYPE_CHECKING,
Any,
)
import uuid
from pandas._config import using_copy_on_write
from pandas.compat import (
PYPY,
WARNING_CHECK_DISABLED,
)
from pa... |
from __future__ import annotations
from decimal import Decimal
import operator
import os
from sys import byteorder
from typing import (
TYPE_CHECKING,
Callable,
ContextManager,
)
import warnings
import numpy as np
from pandas._config import using_string_dtype
from pandas._config.localization import (
... |
from __future__ import annotations
from contextlib import (
contextmanager,
nullcontext,
)
import inspect
import re
import sys
from typing import (
TYPE_CHECKING,
Literal,
cast,
)
import warnings
from pandas.compat import PY311
if TYPE_CHECKING:
from collections.abc import (
Generator... |
"""
Hypothesis data generator helpers.
"""
from datetime import datetime
from hypothesis import strategies as st
from hypothesis.extra.dateutil import timezones as dateutil_timezones
from hypothesis.extra.pytz import timezones as pytz_timezones
from pandas.compat import is_platform_windows
import pandas as pd
from ... |
from __future__ import annotations
from collections.abc import (
Hashable,
Iterator,
Mapping,
MutableMapping,
Sequence,
)
from datetime import (
date,
datetime,
timedelta,
tzinfo,
)
from os import PathLike
import sys
from typing import (
TYPE_CHECKING,
Any,
Callable,
... |
"""
Public API for extending pandas objects.
"""
from pandas._libs.lib import no_default
from pandas.core.dtypes.base import (
ExtensionDtype,
register_extension_dtype,
)
from pandas.core.accessor import (
register_dataframe_accessor,
register_index_accessor,
register_series_accessor,
)
from pand... |
"""
Public API for DataFrame interchange protocol.
"""
from pandas.core.interchange.dataframe_protocol import DataFrame
from pandas.core.interchange.from_dataframe import from_dataframe
__all__ = ["from_dataframe", "DataFrame"]
|
"""
Public toolkit API.
"""
from pandas._libs.lib import infer_dtype
from pandas.core.dtypes.api import * # noqa: F403
from pandas.core.dtypes.concat import union_categoricals
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
DatetimeTZDtype,
IntervalDtype,
PeriodDtype,
)
__all__ = [
"in... |
"""
Public API classes that store intermediate results useful for type-hinting.
"""
from pandas._libs import NaTType
from pandas._libs.missing import NAType
from pandas.core.groupby import (
DataFrameGroupBy,
SeriesGroupBy,
)
from pandas.core.resample import (
DatetimeIndexResamplerGroupby,
PeriodInde... |
""" public toolkit API """
from pandas.api import (
extensions,
indexers,
interchange,
types,
typing,
)
__all__ = [
"interchange",
"extensions",
"indexers",
"types",
"typing",
]
|
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