id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
173,380 | from __future__ import annotations
from typing import (
Any,
TypeVar,
cast,
overload,
)
from numpy import ndarray
from pandas._libs.lib import (
is_bool,
is_integer,
)
from pandas._typing import (
Axis,
AxisInt,
)
from pandas.errors import UnsupportedFunctionCall
from pandas.util._valida... | If 'NDFrame.clip' is called via the numpy library, the third parameter in its signature is 'out', which can takes an ndarray, so check if the 'axis' parameter is an instance of ndarray, since 'axis' itself should either be an integer or None |
173,381 | from __future__ import annotations
from typing import (
Any,
TypeVar,
cast,
overload,
)
from numpy import ndarray
from pandas._libs.lib import (
is_bool,
is_integer,
)
from pandas._typing import (
Axis,
AxisInt,
)
from pandas.errors import UnsupportedFunctionCall
from pandas.util._valida... | If this function is called via the 'numpy' library, the third parameter in its signature is 'dtype', which takes either a 'numpy' dtype or 'None', so check if the 'skipna' parameter is a boolean or not |
173,382 | from __future__ import annotations
from typing import (
Any,
TypeVar,
cast,
overload,
)
from numpy import ndarray
from pandas._libs.lib import (
is_bool,
is_integer,
)
from pandas._typing import (
Axis,
AxisInt,
)
from pandas.errors import UnsupportedFunctionCall
from pandas.util._valida... | If this function is called via the 'numpy' library, the third parameter in its signature is 'axis', which takes either an ndarray or 'None', so check if the 'convert' parameter is either an instance of ndarray or is None |
173,383 | from __future__ import annotations
from typing import (
Any,
TypeVar,
cast,
overload,
)
from numpy import ndarray
from pandas._libs.lib import (
is_bool,
is_integer,
)
from pandas._typing import (
Axis,
AxisInt,
)
from pandas.errors import UnsupportedFunctionCall
from pandas.util._valida... | 'args' and 'kwargs' should be empty, except for allowed kwargs because all of their necessary parameters are explicitly listed in the function signature |
173,384 | from __future__ import annotations
from typing import (
Any,
TypeVar,
cast,
overload,
)
from numpy import ndarray
from pandas._libs.lib import (
is_bool,
is_integer,
)
from pandas._typing import (
Axis,
AxisInt,
)
from pandas.errors import UnsupportedFunctionCall
from pandas.util._valida... | 'args' and 'kwargs' should be empty because all of their necessary parameters are explicitly listed in the function signature |
173,385 | from __future__ import annotations
from typing import (
Any,
TypeVar,
cast,
overload,
)
from numpy import ndarray
from pandas._libs.lib import (
is_bool,
is_integer,
)
from pandas._typing import (
Axis,
AxisInt,
)
from pandas.errors import UnsupportedFunctionCall
from pandas.util._valida... | Ensure that the axis argument passed to min, max, argmin, or argmax is zero or None, as otherwise it will be incorrectly ignored. Parameters ---------- axis : int or None ndim : int, default 1 Raises ------ ValueError |
173,386 | from __future__ import annotations
import contextlib
import copy
import io
import pickle as pkl
from typing import Generator
import numpy as np
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import BaseOffset
from pandas import Index
from pandas.core.arrays import (
DatetimeArray,
Period... | null |
173,387 | from __future__ import annotations
import contextlib
import copy
import io
import pickle as pkl
from typing import Generator
import numpy as np
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import BaseOffset
from pandas import Index
from pandas.core.arrays import (
DatetimeArray,
Period... | null |
173,388 | from __future__ import annotations
import contextlib
import copy
import io
import pickle as pkl
from typing import Generator
import numpy as np
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import BaseOffset
from pandas import Index
from pandas.core.arrays import (
DatetimeArray,
Period... | null |
173,389 | from __future__ import annotations
import contextlib
import copy
import io
import pickle as pkl
from typing import Generator
import numpy as np
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import BaseOffset
from pandas import Index
from pandas.core.arrays import (
DatetimeArray,
Period... | Temporarily patch pickle to use our unpickler. |
173,390 | from __future__ import annotations
import bz2
from pickle import PickleBuffer
from pandas.compat._constants import PY310
The provided code snippet includes necessary dependencies for implementing the `flatten_buffer` function. Write a Python function `def flatten_buffer( b: bytes | bytearray | memoryview | PickleB... | Return some 1-D `uint8` typed buffer. Coerces anything that does not match that description to one that does without copying if possible (otherwise will copy). |
173,391 | from __future__ import annotations
import io
from typing import (
Any,
Callable,
Sequence,
)
from pandas._libs import lib
from pandas._typing import (
TYPE_CHECKING,
CompressionOptions,
ConvertersArg,
DtypeArg,
DtypeBackend,
FilePath,
ParseDatesArg,
ReadBuffer,
StorageOpt... | Extract raw XML data. The method accepts three input types: 1. filepath (string-like) 2. file-like object (e.g. open file object, StringIO) 3. XML string or bytes This method turns (1) into (2) to simplify the rest of the processing. It returns input types (2) and (3) unchanged. |
173,392 | from __future__ import annotations
import io
from typing import (
Any,
Callable,
Sequence,
)
from pandas._libs import lib
from pandas._typing import (
TYPE_CHECKING,
CompressionOptions,
ConvertersArg,
DtypeArg,
DtypeBackend,
FilePath,
ParseDatesArg,
ReadBuffer,
StorageOpt... | Convert extracted raw data. This method will return underlying data of extracted XML content. The data either has a `read` attribute (e.g. a file object or a StringIO/BytesIO) or is a string or bytes that is an XML document. |
173,393 | from __future__ import annotations
import io
from typing import (
Any,
Callable,
Sequence,
)
from pandas._libs import lib
from pandas._typing import (
TYPE_CHECKING,
CompressionOptions,
ConvertersArg,
DtypeArg,
DtypeBackend,
FilePath,
ParseDatesArg,
ReadBuffer,
StorageOpt... | r""" Read XML document into a ``DataFrame`` object. .. versionadded:: 1.3.0 Parameters ---------- path_or_buffer : str, path object, or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a ``read()`` function. The string can be any valid XML string or a path. The ... |
173,394 | from __future__ import annotations
from collections import abc
from datetime import (
datetime,
timedelta,
)
import sys
from typing import cast
import numpy as np
from pandas._typing import (
CompressionOptions,
FilePath,
ReadBuffer,
)
from pandas.errors import (
EmptyDataError,
OutOfBoundsD... | Convert to Timestamp if possible, otherwise to datetime.datetime. SAS float64 lacks precision for more than ms resolution so the fit to datetime.datetime is ok. Parameters ---------- sas_datetimes : {Series, Sequence[float]} Dates or datetimes in SAS unit : {str} "d" if the floats represent dates, "s" for datetimes Ret... |
173,395 | from __future__ import annotations
from abc import (
ABCMeta,
abstractmethod,
)
from types import TracebackType
from typing import (
TYPE_CHECKING,
Hashable,
overload,
)
from pandas._typing import (
CompressionOptions,
FilePath,
ReadBuffer,
)
from pandas.util._decorators import doc
from ... | null |
173,396 | from __future__ import annotations
from abc import (
ABCMeta,
abstractmethod,
)
from types import TracebackType
from typing import (
TYPE_CHECKING,
Hashable,
overload,
)
from pandas._typing import (
CompressionOptions,
FilePath,
ReadBuffer,
)
from pandas.util._decorators import doc
from ... | null |
173,397 | from __future__ import annotations
from abc import (
ABCMeta,
abstractmethod,
)
from types import TracebackType
from typing import (
TYPE_CHECKING,
Hashable,
overload,
)
from pandas._typing import (
CompressionOptions,
FilePath,
ReadBuffer,
)
from pandas.util._decorators import doc
from ... | Read SAS files stored as either XPORT or SAS7BDAT format files. Parameters ---------- filepath_or_buffer : str, path object, or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``read()`` function. The string could be a URL. Valid URL schemes include ht... |
173,398 | from __future__ import annotations
from collections import abc
from datetime import datetime
import struct
import warnings
import numpy as np
from pandas._typing import (
CompressionOptions,
DatetimeNaTType,
FilePath,
ReadBuffer,
)
from pandas.util._decorators import Appender
from pandas.util._exception... | Given a date in xport format, return Python date. |
173,399 | from __future__ import annotations
from collections import abc
from datetime import datetime
import struct
import warnings
import numpy as np
from pandas._typing import (
CompressionOptions,
DatetimeNaTType,
FilePath,
ReadBuffer,
)
from pandas.util._decorators import Appender
from pandas.util._exception... | Parameters ---------- s: str Fixed-length string to split parts: list of (name, length) pairs Used to break up string, name '_' will be filtered from output. Returns ------- Dict of name:contents of string at given location. |
173,400 | from __future__ import annotations
from collections import abc
from datetime import datetime
import struct
import warnings
import numpy as np
from pandas._typing import (
CompressionOptions,
DatetimeNaTType,
FilePath,
ReadBuffer,
)
from pandas.util._decorators import Appender
from pandas.util._exception... | null |
173,401 | from __future__ import annotations
from collections import abc
from datetime import datetime
import struct
import warnings
import numpy as np
from pandas._typing import (
CompressionOptions,
DatetimeNaTType,
FilePath,
ReadBuffer,
)
from pandas.util._decorators import Appender
from pandas.util._exception... | Parse a vector of float values representing IBM 8 byte floats into native 8 byte floats. |
173,402 | from __future__ import annotations
from collections import abc
import numbers
import re
from typing import (
TYPE_CHECKING,
Iterable,
Literal,
Pattern,
Sequence,
cast,
)
from pandas._libs import lib
from pandas._typing import (
BaseBuffer,
DtypeBackend,
FilePath,
ReadBuffer,
)
fr... | Replace extra whitespace inside of a string with a single space. Parameters ---------- s : str or unicode The string from which to remove extra whitespace. regex : re.Pattern The regular expression to use to remove extra whitespace. Returns ------- subd : str or unicode `s` with all extra whitespace replaced with a sin... |
173,403 | from __future__ import annotations
from collections import abc
import numbers
import re
from typing import (
TYPE_CHECKING,
Iterable,
Literal,
Pattern,
Sequence,
cast,
)
from pandas._libs import lib
from pandas._typing import (
BaseBuffer,
DtypeBackend,
FilePath,
ReadBuffer,
)
fr... | Try to read from a url, file or string. Parameters ---------- obj : str, unicode, path object, or file-like object Returns ------- raw_text : str |
173,404 | from __future__ import annotations
from collections import abc
import numbers
import re
from typing import (
TYPE_CHECKING,
Iterable,
Literal,
Pattern,
Sequence,
cast,
)
from pandas._libs import lib
from pandas._typing import (
BaseBuffer,
DtypeBackend,
FilePath,
ReadBuffer,
)
fr... | Build an xpath expression to simulate bs4's ability to pass in kwargs to search for attributes when using the lxml parser. Parameters ---------- attrs : dict A dict of HTML attributes. These are NOT checked for validity. Returns ------- expr : unicode An XPath expression that checks for the given HTML attributes. |
173,405 | from __future__ import annotations
from collections import abc
import numbers
import re
from typing import (
TYPE_CHECKING,
Iterable,
Literal,
Pattern,
Sequence,
cast,
)
from pandas._libs import lib
from pandas._typing import (
BaseBuffer,
DtypeBackend,
FilePath,
ReadBuffer,
)
fr... | r""" Read HTML tables into a ``list`` of ``DataFrame`` objects. Parameters ---------- io : str, path object, or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a string ``read()`` function. The string can represent a URL or the HTML itself. Note that lxml only ... |
173,406 | from __future__ import annotations
from pathlib import Path
from typing import (
TYPE_CHECKING,
Sequence,
)
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
fr... | Load an SPSS file from the file path, returning a DataFrame. Parameters ---------- path : str or Path File path. usecols : list-like, optional Return a subset of the columns. If None, return all columns. convert_categoricals : bool, default is True Convert categorical columns into pd.Categorical. dtype_backend : {"nump... |
173,407 | from __future__ import annotations
import pickle
from typing import Any
import warnings
from pandas._typing import (
CompressionOptions,
FilePath,
ReadPickleBuffer,
StorageOptions,
WriteBuffer,
)
from pandas.compat import pickle_compat as pc
from pandas.util._decorators import doc
from pandas.core.s... | Pickle (serialize) object to file. Parameters ---------- obj : any object Any python object. filepath_or_buffer : str, path object, or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``write()`` function. Also accepts URL. URL has to be of S3 or GCS. {... |
173,408 | from __future__ import annotations
import pickle
from typing import Any
import warnings
from pandas._typing import (
CompressionOptions,
FilePath,
ReadPickleBuffer,
StorageOptions,
WriteBuffer,
)
from pandas.compat import pickle_compat as pc
from pandas.util._decorators import doc
from pandas.core.s... | Load pickled pandas object (or any object) from file. .. warning:: Loading pickled data received from untrusted sources can be unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__. Parameters ---------- filepath_or_buffer : str, path object, or file-like object String, path object (implementing ``os.Pat... |
173,409 | from __future__ import annotations
from typing import (
Hashable,
Sequence,
)
from pandas._libs import lib
from pandas._typing import (
DtypeBackend,
FilePath,
ReadBuffer,
StorageOptions,
WriteBuffer,
)
from pandas.compat._optional import import_optional_dependency
from pandas.util._decorato... | Write a DataFrame to the binary Feather format. Parameters ---------- df : DataFrame path : str, path object, or file-like object {storage_options} .. versionadded:: 1.2.0 **kwargs : Additional keywords passed to `pyarrow.feather.write_feather`. .. versionadded:: 1.1.0 |
173,410 | from __future__ import annotations
from typing import (
Hashable,
Sequence,
)
from pandas._libs import lib
from pandas._typing import (
DtypeBackend,
FilePath,
ReadBuffer,
StorageOptions,
WriteBuffer,
)
from pandas.compat._optional import import_optional_dependency
from pandas.util._decorato... | Load a feather-format object from the file path. Parameters ---------- path : str, path object, or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``read()`` function. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For fi... |
173,411 | from __future__ import annotations
from collections import (
abc,
defaultdict,
)
import csv
from io import StringIO
import re
import sys
from typing import (
IO,
TYPE_CHECKING,
DefaultDict,
Hashable,
Iterator,
List,
Literal,
Mapping,
Sequence,
cast,
)
import numpy as np
f... | null |
173,412 | from __future__ import annotations
from collections import (
abc,
defaultdict,
)
import csv
from io import StringIO
import re
import sys
from typing import (
IO,
TYPE_CHECKING,
DefaultDict,
Hashable,
Iterator,
List,
Literal,
Mapping,
Sequence,
cast,
)
import numpy as np
f... | Validate the 'skipfooter' parameter. Checks whether 'skipfooter' is a non-negative integer. Raises a ValueError if that is not the case. Parameters ---------- skipfooter : non-negative integer The number of rows to skip at the end of the file. Returns ------- validated_skipfooter : non-negative integer The original inp... |
173,413 | from __future__ import annotations
from collections import defaultdict
from copy import copy
import csv
import datetime
from enum import Enum
import itertools
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
List,
Mapping,
Sequence,
Tuple,
cast,
final,
... | null |
173,414 | from __future__ import annotations
from collections import defaultdict
from copy import copy
import csv
import datetime
from enum import Enum
import itertools
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
List,
Mapping,
Sequence,
Tuple,
cast,
final,
... | null |
173,415 | from __future__ import annotations
from collections import defaultdict
from copy import copy
import csv
import datetime
from enum import Enum
import itertools
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
List,
Mapping,
Sequence,
Tuple,
cast,
final,
... | Get the NaN values for a given column. Parameters ---------- col : str The name of the column. na_values : array-like, dict The object listing the NaN values as strings. na_fvalues : array-like, dict The object listing the NaN values as floats. keep_default_na : bool If `na_values` is a dict, and the column is not mapp... |
173,416 | from __future__ import annotations
from collections import defaultdict
from copy import copy
import csv
import datetime
from enum import Enum
import itertools
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
List,
Mapping,
Sequence,
Tuple,
cast,
final,
... | Check whether or not the 'parse_dates' parameter is a non-boolean scalar. Raises a ValueError if that is the case. |
173,417 | from __future__ import annotations
from collections import defaultdict
from copy import copy
import csv
import datetime
from enum import Enum
import itertools
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
List,
Mapping,
Sequence,
Tuple,
cast,
final,
... | null |
173,418 | from __future__ import annotations
from collections import defaultdict
from typing import (
TYPE_CHECKING,
Hashable,
Mapping,
Sequence,
)
import warnings
import numpy as np
from pandas._libs import (
lib,
parsers,
)
from pandas._typing import (
ArrayLike,
DtypeArg,
DtypeObj,
Read... | Concatenate chunks of data read with low_memory=True. The tricky part is handling Categoricals, where different chunks may have different inferred categories. |
173,419 | from __future__ import annotations
from collections import defaultdict
from typing import (
TYPE_CHECKING,
Hashable,
Mapping,
Sequence,
)
import warnings
import numpy as np
from pandas._libs import (
lib,
parsers,
)
from pandas._typing import (
ArrayLike,
DtypeArg,
DtypeObj,
Read... | Ensure we have either None, a dtype object, or a dictionary mapping to dtype objects. |
173,420 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | null |
173,421 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | null |
173,422 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | null |
173,423 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | null |
173,424 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | null |
173,425 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | null |
173,426 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | null |
173,427 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | null |
173,428 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | null |
173,429 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | null |
173,430 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | r""" Read a table of fixed-width formatted lines into DataFrame. Also supports optionally iterating or breaking of the file into chunks. Additional help can be found in the `online docs for IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_. Parameters ---------- filepath_or_buffer : str, path... |
173,431 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | Converts lists of lists/tuples into DataFrames with proper type inference and optional (e.g. string to datetime) conversion. Also enables iterating lazily over chunks of large files Parameters ---------- data : file-like object or list delimiter : separator character to use dialect : str or csv.Dialect instance, option... |
173,432 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | null |
173,433 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | Extract concrete csv dialect instance. Returns ------- csv.Dialect or None |
173,434 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | Merge default kwargs in TextFileReader with dialect parameters. Parameters ---------- dialect : csv.Dialect Concrete csv dialect. See csv.Dialect documentation for more details. defaults : dict Keyword arguments passed to TextFileReader. Returns ------- kwds : dict Updated keyword arguments, merged with dialect paramet... |
173,435 | from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Literal,
NamedTuple,
Sequence,
overload,
)
import warnings
import numpy as np
from pandas._libs... | Check whether skipfooter is compatible with other kwargs in TextFileReader. Parameters ---------- kwds : dict Keyword arguments passed to TextFileReader. Raises ------ ValueError If skipfooter is not compatible with other parameters. |
173,436 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
from pandas.compat._optional import import_optional_dependency
def _try_import():
# since pandas is a dependency of pandas-gbq
# we need to import on first use
msg = (
"pandas-gbq is required to load data from Goog... | Load data from Google BigQuery. This function requires the `pandas-gbq package <https://pandas-gbq.readthedocs.io>`__. See the `How to authenticate with Google BigQuery <https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__ guide for authentication instructions. Parameters ---------- query : str SQL... |
173,437 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
)
from pandas.compat._optional import import_optional_dependency
def _try_import():
# since pandas is a dependency of pandas-gbq
# we need to import on first use
msg = (
"pandas-gbq is required to load data from Goog... | null |
173,438 | from __future__ import annotations
from contextlib import contextmanager
import copy
from functools import partial
import operator
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generator,
Hashable,
Sequence,
overload,
)
import numpy as np
from pandas._config import get_option
from panda... | Color background in a range according to the data or a gradient map |
173,439 | from __future__ import annotations
from contextlib import contextmanager
import copy
from functools import partial
import operator
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generator,
Hashable,
Sequence,
overload,
)
import numpy as np
from pandas._config import get_option
from panda... | Return an array of css props based on condition of data values within given range. |
173,440 | from __future__ import annotations
from contextlib import contextmanager
import copy
from functools import partial
import operator
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generator,
Hashable,
Sequence,
overload,
)
import numpy as np
from pandas._config import get_option
from panda... | Return an array of css strings based on the condition of values matching an op. |
173,441 | from __future__ import annotations
from contextlib import contextmanager
import copy
from functools import partial
import operator
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generator,
Hashable,
Sequence,
overload,
)
import numpy as np
from pandas._config import get_option
from panda... | Draw bar chart in data cells using HTML CSS linear gradient. Parameters ---------- data : Series or DataFrame Underling subset of Styler data on which operations are performed. align : str in {"left", "right", "mid", "zero", "mean"}, int, float, callable Method for how bars are structured or scalar value of centre poin... |
173,442 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | Template to return container with information for a <td></td> or <th></th> element. |
173,443 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | Recursively reduce the number of rows and columns to satisfy max elements. Parameters ---------- rn, cn : int The number of input rows / columns max_elements : int The number of allowable elements max_rows, max_cols : int, optional Directly specify an initial maximum rows or columns before compression. scaling_factor :... |
173,444 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | Given an index, find the level length for each element. Parameters ---------- index : Index Index or columns to determine lengths of each element sparsify : bool Whether to hide or show each distinct element in a MultiIndex max_index : int The maximum number of elements to analyse along the index due to trimming hidden... |
173,445 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | Index -> {(idx_row, idx_col): bool}). |
173,446 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | looks for multiple CSS selectors and separates them: [{'selector': 'td, th', 'props': 'a:v;'}] ---> [{'selector': 'td', 'props': 'a:v;'}, {'selector': 'th', 'props': 'a:v;'}] |
173,447 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | Allows formatters to be expressed as str, callable or None, where None returns a default formatting function. wraps with na_rep, and precision where they are available. |
173,448 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | Ensure that a slice doesn't reduce to a Series or Scalar. Any user-passed `subset` should have this called on it to make sure we're always working with DataFrames. |
173,449 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | Convert css-string to sequence of tuples format if needed. 'color:red; border:1px solid black;' -> [('color', 'red'), ('border','1px solid red')] |
173,450 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | Returns a consistent levels arg for use in ``hide_index`` or ``hide_columns``. Parameters ---------- level : int, str, list Original ``level`` arg supplied to above methods. obj: Either ``self.index`` or ``self.columns`` Returns ------- list : refactored arg with a list of levels to hide |
173,451 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | Indicate whether LaTeX {tabular} should be wrapped with a {table} environment. Parses the `table_styles` and detects any selectors which must be included outside of {tabular}, i.e. indicating that wrapping must occur, and therefore return True, or if a caption exists and requires similar. |
173,452 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | Return the first 'props' 'value' from ``tables_styles`` identified by ``selector``. Examples -------- >>> table_styles = [{'selector': 'foo', 'props': [('attr','value')]}, ... {'selector': 'bar', 'props': [('attr', 'overwritten')]}, ... {'selector': 'bar', 'props': [('a1', 'baz'), ('a2', 'ignore')]}] >>> _parse_latex_t... |
173,453 | from __future__ import annotations
from collections import defaultdict
from functools import partial
import re
from typing import (
Any,
Callable,
DefaultDict,
Dict,
List,
Optional,
Sequence,
Tuple,
TypedDict,
Union,
)
from uuid import uuid4
import numpy as np
from pandas._config... | r""" Refactor the cell `display_value` if a 'colspan' or 'rowspan' attribute is present. 'rowspan' and 'colspan' do not occur simultaneouly. If they are detected then the `display_value` is altered to a LaTeX `multirow` or `multicol` command respectively, with the appropriate cell-span. ``wrap`` is used to enclose the ... |
173,454 | from __future__ import annotations
import re
from typing import (
Callable,
Generator,
Iterable,
Iterator,
)
import warnings
from pandas.errors import CSSWarning
from pandas.util._exceptions import find_stack_level
class Callable(BaseTypingInstance):
def py__call__(self, arguments):
"""
... | Wrapper to expand shorthand property into top, right, bottom, left properties Parameters ---------- side : str The border side to expand into properties Returns ------- function: Return to call when a 'border(-{side}): {value}' string is encountered |
173,455 | from __future__ import annotations
import re
from typing import (
Callable,
Generator,
Iterable,
Iterator,
)
import warnings
from pandas.errors import CSSWarning
from pandas.util._exceptions import find_stack_level
class Callable(BaseTypingInstance):
def py__call__(self, arguments):
"""
... | Wrapper to expand 'border' property into border color, style, and width properties Parameters ---------- side : str The border side to expand into properties Returns ------- function: Return to call when a 'border(-{side}): {value}' string is encountered |
173,456 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from typing import (
TYPE_CHECKING,
Iterator,
Sequence,
)
import numpy as np
from pandas.core.dtypes.generic import ABCMultiIndex
The provided code snippet includes necessary dependencies for implementing the `_split_into_f... | Extract full and short captions from caption string/tuple. Parameters ---------- caption : str or tuple, optional Either table caption string or tuple (full_caption, short_caption). If string is provided, then it is treated as table full caption, while short_caption is considered an empty string. Returns ------- full_c... |
173,457 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from typing import (
TYPE_CHECKING,
Iterator,
Sequence,
)
import numpy as np
from pandas.core.dtypes.generic import ABCMultiIndex
class Sequence(_Collection[_T_co], Reversible[_T_co], Generic[_T_co]):
def __getitem__(se... | Carry out string replacements for special symbols. Parameters ---------- row : list List of string, that may contain special symbols. Returns ------- list list of strings with the special symbols replaced. |
173,458 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from typing import (
TYPE_CHECKING,
Iterator,
Sequence,
)
import numpy as np
from pandas.core.dtypes.generic import ABCMultiIndex
class Sequence(_Collection[_T_co], Reversible[_T_co], Generic[_T_co]):
def __getitem__(se... | Convert elements in ``crow`` to bold. |
173,459 | from __future__ import annotations
import sys
from typing import (
Any,
Callable,
Dict,
Iterable,
Mapping,
Sequence,
TypeVar,
Union,
)
from pandas._config import get_option
from pandas.core.dtypes.inference import is_sequence
def justify(texts: Iterable[str], max_len: int, mode: str = "r... | Glues together two sets of strings using the amount of space requested. The idea is to prettify. ---------- space : int number of spaces for padding lists : str list of str which being joined strlen : callable function used to calculate the length of each str. Needed for unicode handling. justfunc : callable function u... |
173,460 | from __future__ import annotations
import sys
from typing import (
Any,
Callable,
Dict,
Iterable,
Mapping,
Sequence,
TypeVar,
Union,
)
from pandas._config import get_option
from pandas.core.dtypes.inference import is_sequence
def pprint_thing(
thing: Any,
_nest_lvl: int = 0,
... | null |
173,461 | from __future__ import annotations
import sys
from typing import (
Any,
Callable,
Dict,
Iterable,
Mapping,
Sequence,
TypeVar,
Union,
)
from pandas._config import get_option
from pandas.core.dtypes.inference import is_sequence
def pprint_thing(
thing: Any,
_nest_lvl: int = 0,
... | null |
173,462 | from __future__ import annotations
import sys
from typing import (
Any,
Callable,
Dict,
Iterable,
Mapping,
Sequence,
TypeVar,
Union,
)
from pandas._config import get_option
from pandas.core.dtypes.inference import is_sequence
def _pprint_seq(
seq: Sequence, _nest_lvl: int = 0, max_se... | Return the formatted obj as a unicode string Parameters ---------- obj : object must be iterable and support __getitem__ formatter : callable string formatter for an element is_justify : bool should justify the display name : name, optional defaults to the class name of the obj indent_for_name : bool, default True Whet... |
173,463 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
import sys
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Iterable,
Iterator,
Mapping,
Sequence,
)
from pandas._config import get_option
from pandas._typing import (
Dtype,
WriteBuffer,
)
fro... | Make string of specified length, padding to the right if necessary. Parameters ---------- s : Union[str, Dtype] String to be formatted. space : int Length to force string to be of. Returns ------- str String coerced to given length. Examples -------- >>> pd.io.formats.info._put_str("panda", 6) 'panda ' >>> pd.io.format... |
173,464 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
import sys
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Iterable,
Iterator,
Mapping,
Sequence,
)
from pandas._config import get_option
from pandas._typing import (
Dtype,
WriteBuffer,
)
fro... | Return size in human readable format. Parameters ---------- num : int Size in bytes. size_qualifier : str Either empty, or '+' (if lower bound). Returns ------- str Size in human readable format. Examples -------- >>> _sizeof_fmt(23028, '') '22.5 KB' >>> _sizeof_fmt(23028, '+') '22.5+ KB' |
173,465 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
import sys
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Iterable,
Iterator,
Mapping,
Sequence,
)
from pandas._config import get_option
from pandas._typing import (
Dtype,
WriteBuffer,
)
fro... | Get memory usage based on inputs and display options. |
173,466 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
import sys
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Iterable,
Iterator,
Mapping,
Sequence,
)
from pandas._config import get_option
from pandas._typing import (
Dtype,
WriteBuffer,
)
fro... | Create mapping between datatypes and their number of occurrences. |
173,467 | from __future__ import annotations
from shutil import get_terminal_size
from typing import (
TYPE_CHECKING,
Iterable,
)
import numpy as np
from pandas.io.formats.printing import pprint_thing
def _binify(cols: list[int], line_width: int) -> list[int]:
adjoin_width = 1
bins = []
curr_width = 0
i_... | null |
173,468 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | Get the parameters used to repr(dataFrame) calls using DataFrame.to_string. Supplying these parameters to DataFrame.to_string is equivalent to calling ``repr(DataFrame)``. This is useful if you want to adjust the repr output. .. versionadded:: 1.4.0 Example ------- >>> import pandas as pd >>> >>> df = pd.DataFrame([[1,... |
173,469 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | Get the parameters used to repr(Series) calls using Series.to_string. Supplying these parameters to Series.to_string is equivalent to calling ``repr(series)``. This is useful if you want to adjust the series repr output. .. versionadded:: 1.4.0 Example ------- >>> import pandas as pd >>> >>> ser = pd.Series([1, 2, 3, 4... |
173,470 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | Perform serialization. Write to buf or return as string if buf is None. |
173,471 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | Format an array for printing. Parameters ---------- values formatter float_format na_rep digits space justify decimal leading_space : bool, optional, default True Whether the array should be formatted with a leading space. When an array as a column of a Series or DataFrame, we do want the leading space to pad between c... |
173,472 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | Return a formatter callable taking a datetime64 as input and providing a string as output |
173,473 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | given values and a date_format, return a string format |
173,474 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | Return a formatter function for a range of timedeltas. These will all have the same format argument If box, then show the return in quotes |
173,475 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | null |
173,476 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | Separates the real and imaginary parts from the complex number, and executes the _trim_zeros_float method on each of those. |
173,477 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | Trims trailing zeros after a decimal point, leaving just one if necessary. |
173,478 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | null |
173,479 | from __future__ import annotations
from contextlib import contextmanager
from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
from decimal import Decimal
from functools import partial
from io import StringIO
import math
import re
from shutil import get_terminal_size
from typing import (
IO,
TYPE_CHECKING,
... | Format float representation in DataFrame with SI notation. Parameters ---------- accuracy : int, default 3 Number of decimal digits after the floating point. use_eng_prefix : bool, default False Whether to represent a value with SI prefixes. Returns ------- None Examples -------- >>> df = pd.DataFrame([1e-9, 1e-3, 1, 1... |
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