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from __future__ import annotations
import contextlib
import inspect
import os
import re
from typing import TYPE_CHECKING
import warnings
if TYPE_CHECKING:
from collections.abc import Generator
from types import FrameType
@contextlib.contextmanager
def rewrite_exception(old_name: str, new_name: str) -> Gener... |
""" support pyarrow compatibility across versions """
from __future__ import annotations
from pandas.util.version import Version
try:
import pyarrow as pa
_palv = Version(Version(pa.__version__).base_version)
pa_version_under10p1 = _palv < Version("10.0.1")
pa_version_under11p0 = _palv < Version("11... |
"""
_constants
======
Constants relevant for the Python implementation.
"""
from __future__ import annotations
import platform
import sys
import sysconfig
IS64 = sys.maxsize > 2**32
PY310 = sys.version_info >= (3, 10)
PY311 = sys.version_info >= (3, 11)
PY312 = sys.version_info >= (3, 12)
PY314 = sys.version_info ... |
"""
For compatibility with numpy libraries, pandas functions or methods have to
accept '*args' and '**kwargs' parameters to accommodate numpy arguments that
are not actually used or respected in the pandas implementation.
To ensure that users do not abuse these parameters, validation is performed in
'validators.py' to... |
""" support numpy compatibility across versions """
import warnings
import numpy as np
from pandas.util.version import Version
# numpy versioning
_np_version = np.__version__
_nlv = Version(_np_version)
np_version_lt1p23 = _nlv < Version("1.23")
np_version_gte1p24 = _nlv >= Version("1.24")
np_version_gte1p24p3 = _nl... |
"""
compat
======
Cross-compatible functions for different versions of Python.
Other items:
* platform checker
"""
from __future__ import annotations
import os
import platform
import sys
from typing import TYPE_CHECKING
from pandas.compat._constants import (
IS64,
ISMUSL,
PY310,
PY311,
PY312,
... |
"""
Support pre-0.12 series pickle compatibility.
"""
from __future__ import annotations
import contextlib
import copy
import io
import pickle as pkl
from typing import TYPE_CHECKING
import numpy as np
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import BaseOffset
from pandas import Index
... |
from __future__ import annotations
import importlib
import sys
from typing import TYPE_CHECKING
import warnings
from pandas.util._exceptions import find_stack_level
from pandas.util.version import Version
if TYPE_CHECKING:
import types
# Update install.rst & setup.cfg when updating versions!
VERSIONS = {
... |
"""
Patched ``BZ2File`` and ``LZMAFile`` to handle pickle protocol 5.
"""
from __future__ import annotations
from pickle import PickleBuffer
from pandas.compat._constants import PY310
try:
import bz2
has_bz2 = True
except ImportError:
has_bz2 = False
try:
import lzma
has_lzma = True
except Im... |
__version__="2.3.3"
__git_version__="9c8bc3e55188c8aff37207a74f1dd144980b8874"
|
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from pandas.core.dtypes.missing import remove_na_arraylike
from pandas import (
MultiIndex,
concat,
)
from pandas.plotting._matplotlib.misc import unpack_single_str_list
if TYPE_CHECKING:
from collections.abc import... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Literal,
NamedTuple,
)
import warnings
import matplotlib as mpl
from matplotlib.artist import setp
import numpy as np
from pandas._libs import lib
from pandas.util._decorators import cache_readonly
from pandas.util._exceptions import ... |
from __future__ import annotations
from typing import TYPE_CHECKING
from pandas.plotting._matplotlib.boxplot import (
BoxPlot,
boxplot,
boxplot_frame,
boxplot_frame_groupby,
)
from pandas.plotting._matplotlib.converter import (
deregister,
register,
)
from pandas.plotting._matplotlib.core impo... |
from __future__ import annotations
from collections.abc import (
Collection,
Iterator,
)
import itertools
from typing import (
TYPE_CHECKING,
cast,
)
import warnings
import matplotlib as mpl
import matplotlib.colors
import numpy as np
from pandas._typing import MatplotlibColor as Color
from pandas.ut... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Literal,
final,
)
import numpy as np
from pandas.core.dtypes.common import (
is_integer,
is_list_like,
)
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCIndex,
)
from pandas.core.dtypes.missing impor... |
# being a bit too dynamic
from __future__ import annotations
from math import ceil
from typing import TYPE_CHECKING
import warnings
from matplotlib import ticker
import matplotlib.table
import numpy as np
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.common import is_list_like
from pa... |
from __future__ import annotations
import random
from typing import TYPE_CHECKING
from matplotlib import patches
import matplotlib.lines as mlines
import numpy as np
from pandas.core.dtypes.missing import notna
from pandas.io.formats.printing import pprint_thing
from pandas.plotting._matplotlib.style import get_sta... |
# TODO: Use the fact that axis can have units to simplify the process
from __future__ import annotations
import functools
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import warnings
import numpy as np
from pandas._libs.tslibs import (
BaseOffset,
Period,
to_offset,
)
from pandas._libs.t... |
"""
Plotting public API.
Authors of third-party plotting backends should implement a module with a
public ``plot(data, kind, **kwargs)``. The parameter `data` will contain
the data structure and can be a `Series` or a `DataFrame`. For example,
for ``df.plot()`` the parameter `data` will contain the DataFrame `df`.
In ... |
""" orc compat """
from __future__ import annotations
import io
from types import ModuleType
from typing import (
TYPE_CHECKING,
Any,
Literal,
)
from pandas._libs import lib
from pandas.compat._optional import import_optional_dependency
from pandas.util._validators import check_dtype_backend
from pandas.... |
from __future__ import annotations
from collections import defaultdict
from typing import TYPE_CHECKING
import warnings
import numpy as np
from pandas._libs import (
lib,
parsers,
)
from pandas.compat._optional import import_optional_dependency
from pandas.errors import DtypeWarning
from pandas.util._excepti... |
from pandas.io.parsers.readers import (
TextFileReader,
TextParser,
read_csv,
read_fwf,
read_table,
)
__all__ = ["TextFileReader", "TextParser", "read_csv", "read_fwf", "read_table"]
|
from __future__ import annotations
from typing import TYPE_CHECKING
import warnings
from pandas._libs import lib
from pandas.compat._optional import import_optional_dependency
from pandas.errors import (
ParserError,
ParserWarning,
)
from pandas.util._exceptions import find_stack_level
from pandas.core.dtype... |
from __future__ import annotations
from typing import TYPE_CHECKING
from pandas._libs import lib
from pandas.compat._optional import import_optional_dependency
from pandas.util._validators import check_dtype_backend
from pandas.core.dtypes.inference import is_list_like
from pandas.io.common import stringify_path
i... |
"""
Data IO api
"""
from pandas.io.clipboards import read_clipboard
from pandas.io.excel import (
ExcelFile,
ExcelWriter,
read_excel,
)
from pandas.io.feather_format import read_feather
from pandas.io.gbq import read_gbq
from pandas.io.html import read_html
from pandas.io.json import read_json
from pandas.... |
from __future__ import annotations
from collections import defaultdict
import datetime
import json
from typing import (
TYPE_CHECKING,
Any,
DefaultDict,
cast,
overload,
)
from pandas.io.excel._base import ExcelWriter
from pandas.io.excel._util import (
combine_kwargs,
validate_freeze_panes... |
from __future__ import annotations
from datetime import (
date,
datetime,
time,
timedelta,
)
from typing import (
TYPE_CHECKING,
Any,
Union,
)
from pandas.compat._optional import import_optional_dependency
from pandas.util._decorators import doc
import pandas as pd
from pandas.core.shared... |
from __future__ import annotations
from datetime import time
import math
from typing import TYPE_CHECKING
import numpy as np
from pandas.compat._optional import import_optional_dependency
from pandas.util._decorators import doc
from pandas.core.shared_docs import _shared_docs
from pandas.io.excel._base import Base... |
# pyright: reportMissingImports=false
from __future__ import annotations
from typing import TYPE_CHECKING
from pandas.compat._optional import import_optional_dependency
from pandas.util._decorators import doc
from pandas.core.shared_docs import _shared_docs
from pandas.io.excel._base import BaseExcelReader
if TYPE... |
from __future__ import annotations
from collections.abc import (
Hashable,
Iterable,
MutableMapping,
Sequence,
)
from typing import (
TYPE_CHECKING,
Any,
Callable,
Literal,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dty... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
cast,
)
import numpy as np
from pandas._typing import (
FilePath,
ReadBuffer,
Scalar,
StorageOptions,
)
from pandas.compat._optional import import_optional_dependency
from pandas.util._decorators import doc
import pandas ... |
from pandas.io.excel._base import (
ExcelFile,
ExcelWriter,
read_excel,
)
from pandas.io.excel._odswriter import ODSWriter as _ODSWriter
from pandas.io.excel._openpyxl import OpenpyxlWriter as _OpenpyxlWriter
from pandas.io.excel._util import register_writer
from pandas.io.excel._xlsxwriter import XlsxWrite... |
from __future__ import annotations
import mmap
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import numpy as np
from pandas.compat._optional import import_optional_dependency
from pandas.util._decorators import doc
from pandas.core.shared_docs import _shared_docs
from pandas.io.excel._base import (
... |
from __future__ import annotations
import json
from typing import (
TYPE_CHECKING,
Any,
)
from pandas.io.excel._base import ExcelWriter
from pandas.io.excel._util import (
combine_kwargs,
validate_freeze_panes,
)
if TYPE_CHECKING:
from pandas._typing import (
ExcelWriterIfSheetExists,
... |
""" io on the clipboard """
from __future__ import annotations
from io import StringIO
from typing import TYPE_CHECKING
import warnings
from pandas._libs import lib
from pandas.util._exceptions import find_stack_level
from pandas.util._validators import check_dtype_backend
from pandas.core.dtypes.generic import ABCD... |
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Literal,
)
import numpy as np
from pandas._config import using_string_dtype
from pandas._libs import lib
from pandas.compat import (
pa_version_under18p0,
pa_version_under19p0,
)
from pandas.compat._optional import import_optiona... |
# ruff: noqa: TCH004
from typing import TYPE_CHECKING
if TYPE_CHECKING:
# import modules that have public classes/functions
from pandas.io import (
formats,
json,
stata,
)
# mark only those modules as public
__all__ = ["formats", "json", "stata"]
|
from __future__ import annotations
from typing import Final
magic: Final = (
b"\x00\x00\x00\x00\x00\x00\x00\x00"
b"\x00\x00\x00\x00\xc2\xea\x81\x60"
b"\xb3\x14\x11\xcf\xbd\x92\x08\x00"
b"\x09\xc7\x31\x8c\x18\x1f\x10\x11"
)
align_1_checker_value: Final = b"3"
align_1_offset: Final = 32
align_1_length:... |
"""
Read SAS sas7bdat or xport files.
"""
from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from typing import (
TYPE_CHECKING,
overload,
)
from pandas.util._decorators import doc
from pandas.core.shared_docs import _shared_docs
from pandas.io.common import stringify_path
... |
from pandas.io.sas.sasreader import read_sas
__all__ = ["read_sas"]
|
"""
Read a SAS XPort format file into a Pandas DataFrame.
Based on code from Jack Cushman (github.com/jcushman/xport).
The file format is defined here:
https://support.sas.com/content/dam/SAS/support/en/technical-papers/record-layout-of-a-sas-version-5-or-6-data-set-in-sas-transport-xport-format.pdf
"""
from __futur... |
""" pickle compat """
from __future__ import annotations
import pickle
from typing import (
TYPE_CHECKING,
Any,
)
import warnings
from pandas.compat import pickle_compat as pc
from pandas.util._decorators import doc
from pandas.core.shared_docs import _shared_docs
from pandas.io.common import get_handle
if... |
"""
Printing tools.
"""
from __future__ import annotations
from collections.abc import (
Iterable,
Mapping,
Sequence,
)
import sys
from typing import (
Any,
Callable,
TypeVar,
Union,
)
from unicodedata import east_asian_width
from pandas._config import get_option
from pandas.core.dtypes.i... |
"""
Module for formatting output data in console (to string).
"""
from __future__ import annotations
from shutil import get_terminal_size
from typing import TYPE_CHECKING
import numpy as np
from pandas.io.formats.printing import pprint_thing
if TYPE_CHECKING:
from collections.abc import Iterable
from panda... |
# GH37967: Enable the use of CSS named colors, as defined in
# matplotlib.colors.CSS4_COLORS, when exporting to Excel.
# This data has been copied here, instead of being imported from matplotlib,
# not to have ``to_excel`` methods require matplotlib.
# source: matplotlib._color_data (3.3.3)
from __future__ import annot... |
"""
Module for formatting output data into CSV files.
"""
from __future__ import annotations
from collections.abc import (
Hashable,
Iterable,
Iterator,
Sequence,
)
import csv as csvlib
import os
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import numpy as np
from pandas._libs import... |
"""
Internal module for console introspection
"""
from __future__ import annotations
from shutil import get_terminal_size
def get_console_size() -> tuple[int | None, int | None]:
"""
Return console size as tuple = (width, height).
Returns (None,None) in non-interactive session.
"""
from pandas i... |
"""
Utilities for interpreting CSS from Stylers for formatting non-HTML outputs.
"""
from __future__ import annotations
import re
from typing import (
TYPE_CHECKING,
Callable,
)
import warnings
from pandas.errors import CSSWarning
from pandas.util._exceptions import find_stack_level
if TYPE_CHECKING:
fro... |
"""
:mod:`pandas.io.formats.xml` is a module for formatting data in XML.
"""
from __future__ import annotations
import codecs
import io
from typing import (
TYPE_CHECKING,
Any,
final,
)
import warnings
from pandas.errors import AbstractMethodError
from pandas.util._decorators import (
cache_readonly,
... |
# ruff: noqa: TCH004
from typing import TYPE_CHECKING
if TYPE_CHECKING:
# import modules that have public classes/functions
from pandas.io.formats import style
# and mark only those modules as public
__all__ = ["style"]
|
""" feather-format compat """
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
from pandas._config import using_string_dtype
from pandas._libs import lib
from pandas.compat._optional import import_optional_dependency
from pandas.util._decorators import doc
from pandas.util._valid... |
"""
Table Schema builders
https://specs.frictionlessdata.io/table-schema/
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import warnings
from pandas._libs import lib
from pandas._libs.json import ujson_loads
from pandas._libs.tslibs import timezones
from pandas._l... |
from pandas.io.json._json import (
read_json,
to_json,
ujson_dumps,
ujson_loads,
)
from pandas.io.json._table_schema import build_table_schema
__all__ = [
"ujson_dumps",
"ujson_loads",
"read_json",
"to_json",
"build_table_schema",
]
|
# ---------------------------------------------------------------------
# JSON normalization routines
from __future__ import annotations
from collections import (
abc,
defaultdict,
)
import copy
from typing import (
TYPE_CHECKING,
Any,
DefaultDict,
)
import numpy as np
from pandas._libs.writers i... |
""" Google BigQuery support """
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
import warnings
from pandas.compat._optional import import_optional_dependency
from pandas.util._exceptions import find_stack_level
if TYPE_CHECKING:
from google.auth.credentials import Credentia... |
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from pandas._libs import lib
from pandas._libs.algos import unique_deltas
from pandas._libs.tslibs import (
Timestamp,
get_unit_from_dtype,
periods_per_day,
tz_convert_from_utc,
)
from pandas._libs.tslibs.ccalendar... |
"""
Timeseries API
"""
from pandas._libs.tslibs.parsing import guess_datetime_format
from pandas.tseries import offsets
from pandas.tseries.frequencies import infer_freq
__all__ = ["infer_freq", "offsets", "guess_datetime_format"]
|
from __future__ import annotations
from pandas._libs.tslibs.offsets import (
FY5253,
BaseOffset,
BDay,
BMonthBegin,
BMonthEnd,
BQuarterBegin,
BQuarterEnd,
BusinessDay,
BusinessHour,
BusinessMonthBegin,
BusinessMonthEnd,
BYearBegin,
BYearEnd,
CBMonthBegin,
CBM... |
# ruff: noqa: TCH004
from typing import TYPE_CHECKING
if TYPE_CHECKING:
# import modules that have public classes/functions:
from pandas.tseries import (
frequencies,
offsets,
)
# and mark only those modules as public
__all__ = ["frequencies", "offsets"]
|
from __future__ import annotations
from datetime import (
datetime,
timedelta,
)
import warnings
from dateutil.relativedelta import (
FR,
MO,
SA,
SU,
TH,
TU,
WE,
)
import numpy as np
from pandas.errors import PerformanceWarning
from pandas import (
DateOffset,
DatetimeInd... |
"""
Methods used by Block.replace and related methods.
"""
from __future__ import annotations
import operator
import re
from re import Pattern
from typing import (
TYPE_CHECKING,
Any,
)
import numpy as np
from pandas.core.dtypes.common import (
is_bool,
is_re,
is_re_compilable,
)
from pandas.core... |
"""
datetimelke_accumulations.py is for accumulations of datetimelike extension arrays
"""
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,
*,
... |
"""
masked_reductions.py is for reduction algorithms using a mask-based approach
for missing values.
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Callable,
)
import warnings
import numpy as np
from pandas._libs import missing as libmissing
from pandas.core.nanops import check_b... |
"""
core.array_algos is for algorithms that operate on ndarray and ExtensionArray.
These should:
- Assume that any Index, Series, or DataFrame objects have already been unwrapped.
- Assume that any list arguments have already been cast to ndarray/EA.
- Not depend on Index, Series, or DataFrame, nor import any of these... |
"""
transforms.py is for shape-preserving functions.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from pandas._typing import (
AxisInt,
Scalar,
)
def shift(
values: np.ndarray, periods: int, axis: AxisInt, fill_value: Scal... |
"""
masked_accumulations.py is for accumulation algorithms using a mask-based approach
for missing values.
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Callable,
)
import numpy as np
if TYPE_CHECKING:
from pandas._typing import npt
def _cum_func(
func: Callable,
v... |
"""
EA-compatible analogue to np.putmask
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.cast import infer_dtype_from
from pandas.core.dtypes.common import is_list_like
from pandas.core.arrays import E... |
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from pandas.core.dtypes.missing import (
isna,
na_value_for_dtype,
)
if TYPE_CHECKING:
from pandas._typing import (
ArrayLike,
Scalar,
npt,
)
def quantile_compat(
values: ArrayLike, q... |
"""
Module containing utilities for NDFrame.sample() and .GroupBy.sample()
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCSeries,
)
if TYPE_CHECKING:
from pandas._typing im... |
from __future__ import annotations
import functools
from typing import (
TYPE_CHECKING,
Any,
Callable,
)
if TYPE_CHECKING:
from pandas._typing import Scalar
import numpy as np
from pandas.compat._optional import import_optional_dependency
@functools.cache
def generate_apply_looper(func, nopython=T... |
# Disable type checking for this module since numba's internals
# are not typed, and we use numba's internals via its extension API
# mypy: ignore-errors
"""
Utility classes/functions to let numba recognize
pandas Index/Series/DataFrame
Mostly vendored from https://github.com/numba/numba/blob/main/numba/tests/pdlike_u... |
"""
Numba 1D var kernels that can be shared by
* Dataframe / Series
* groupby
* rolling / expanding
Mirrors pandas/_libs/window/aggregation.pyx
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import numba
import numpy as np
if TYPE_CHECKING:
from pandas._typing import npt
from pandas.co... |
from __future__ import annotations
from typing import TYPE_CHECKING
import numba
if TYPE_CHECKING:
import numpy as np
@numba.jit(
# error: Any? not callable
numba.boolean(numba.int64[:]), # type: ignore[misc]
nopython=True,
nogil=True,
parallel=False,
)
def is_monotonic_increasing(bounds: ... |
from pandas.core._numba.kernels.mean_ import (
grouped_mean,
sliding_mean,
)
from pandas.core._numba.kernels.min_max_ import (
grouped_min_max,
sliding_min_max,
)
from pandas.core._numba.kernels.sum_ import (
grouped_sum,
sliding_sum,
)
from pandas.core._numba.kernels.var_ import (
grouped_v... |
"""
Numba 1D sum kernels that can be shared by
* Dataframe / Series
* groupby
* rolling / expanding
Mirrors pandas/_libs/window/aggregation.pyx
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
import numba
from numba.extending import register_jitable
import numpy as np
if TY... |
"""
Numba 1D min/max kernels that can be shared by
* Dataframe / Series
* groupby
* rolling / expanding
Mirrors pandas/_libs/window/aggregation.pyx
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import numba
import numpy as np
if TYPE_CHECKING:
from pandas._typing import npt
@numba.ji... |
"""
Numba 1D mean kernels that can be shared by
* Dataframe / Series
* groupby
* rolling / expanding
Mirrors pandas/_libs/window/aggregation.pyx
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import numba
import numpy as np
from pandas.core._numba.kernels.shared import is_monotonic_increasi... |
"""Common utilities for Numba operations"""
from __future__ import annotations
import types
from typing import (
TYPE_CHECKING,
Callable,
)
import numpy as np
from pandas.compat._optional import import_optional_dependency
from pandas.errors import NumbaUtilError
GLOBAL_USE_NUMBA: bool = False
def maybe_us... |
"""
data hash pandas / numpy objects
"""
from __future__ import annotations
import itertools
from typing import TYPE_CHECKING
import numpy as np
from pandas._libs.hashing import hash_object_array
from pandas.core.dtypes.common import is_list_like
from pandas.core.dtypes.dtypes import CategoricalDtype
from pandas.co... |
from __future__ import annotations
import functools
import re
import textwrap
from typing import (
TYPE_CHECKING,
Callable,
Literal,
cast,
)
import unicodedata
import warnings
import numpy as np
from pandas._libs import lib
import pandas._libs.missing as libmissing
import pandas._libs.ops as libops
f... |
from __future__ import annotations
import abc
from typing import (
TYPE_CHECKING,
Callable,
Literal,
)
from pandas._libs import lib
if TYPE_CHECKING:
from collections.abc import Sequence
import re
from pandas._typing import Scalar
from pandas import Series
class BaseStringArrayMethods... |
"""
Implementation of pandas.Series.str and its interface.
* strings.accessor.StringMethods : Accessor for Series.str
* strings.base.BaseStringArrayMethods: Mixin ABC for EAs to implement str methods
Most methods on the StringMethods accessor follow the pattern:
1. extract the array from the series (or index)
... |
"""
Templating for ops docstrings
"""
from __future__ import annotations
def make_flex_doc(op_name: str, typ: str) -> str:
"""
Make the appropriate substitutions for the given operation and class-typ
into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring
to attach to a generated metho... |
"""
Missing data handling for arithmetic operations.
In particular, pandas conventions regarding division by zero differ
from numpy in the following ways:
1) np.array([-1, 0, 1], dtype=dtype1) // np.array([0, 0, 0], dtype=dtype2)
gives [nan, nan, nan] for most dtype combinations, and [0, 0, 0] for
th... |
"""
Functions for arithmetic and comparison operations on NumPy arrays and
ExtensionArrays.
"""
from __future__ import annotations
import datetime
from functools import partial
import operator
from typing import (
TYPE_CHECKING,
Any,
)
import warnings
import numpy as np
from pandas._libs import (
NaT,
... |
"""
Boilerplate functions used in defining binary operations.
"""
from __future__ import annotations
from functools import wraps
from typing import (
TYPE_CHECKING,
Callable,
)
from pandas._libs.lib import item_from_zerodim
from pandas._libs.missing import is_matching_na
from pandas.core.dtypes.generic impor... |
"""
Functions for defining unary operations.
"""
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
from pandas.core.dtypes.generic import ABCExtensionArray
if TYPE_CHECKING:
from pandas._typing import ArrayLike
def should_extension_dispatch(left: ArrayLike, right: Any) -> bo... |
"""
Arithmetic operations for PandasObjects
This is not a public API.
"""
from __future__ import annotations
from pandas.core.ops.array_ops import (
arithmetic_op,
comp_method_OBJECT_ARRAY,
comparison_op,
fill_binop,
get_array_op,
logical_op,
maybe_prepare_scalar_for_op,
)
from pandas.core... |
"""
Ops for masked arrays.
"""
from __future__ import annotations
import numpy as np
from pandas._libs import (
lib,
missing as libmissing,
)
def kleene_or(
left: bool | np.ndarray | libmissing.NAType,
right: bool | np.ndarray | libmissing.NAType,
left_mask: np.ndarray | None,
right_mask: np... |
"""
Templates for invalid operations.
"""
from __future__ import annotations
import operator
from typing import TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from pandas._typing import npt
def invalid_comparison(left, right, op) -> npt.NDArray[np.bool_]:
"""
If a comparison has mismatched types an... |
"""
Operator classes for eval.
"""
from __future__ import annotations
from datetime import datetime
from functools import partial
import operator
from typing import (
TYPE_CHECKING,
Callable,
Literal,
)
import numpy as np
from pandas._libs.tslibs import Timestamp
from pandas.core.dtypes.common import (... |
from __future__ import annotations
from pandas.compat._optional import import_optional_dependency
ne = import_optional_dependency("numexpr", errors="warn")
NUMEXPR_INSTALLED = ne is not None
__all__ = ["NUMEXPR_INSTALLED"]
|
__all__ = ["eval"]
from pandas.core.computation.eval import eval
|
from __future__ import annotations
from functools import reduce
import numpy as np
from pandas._config import get_option
def ensure_decoded(s) -> str:
"""
If we have bytes, decode them to unicode.
"""
if isinstance(s, (np.bytes_, bytes)):
s = s.decode(get_option("display.encoding"))
ret... |
"""
Core eval alignment algorithms.
"""
from __future__ import annotations
from functools import (
partial,
wraps,
)
from typing import (
TYPE_CHECKING,
Callable,
)
import warnings
import numpy as np
from pandas.errors import PerformanceWarning
from pandas.util._exceptions import find_stack_level
fr... |
"""
Module for scope operations
"""
from __future__ import annotations
from collections import ChainMap
import datetime
import inspect
from io import StringIO
import itertools
import pprint
import struct
import sys
from typing import TypeVar
import numpy as np
from pandas._libs.tslibs import Timestamp
from pandas.er... |
"""
Top level ``eval`` module.
"""
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,
... |
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