id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
173,480 | 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,
... | For each index in each level the function returns lengths of indexes. Parameters ---------- levels : list of lists List of values on for level. sentinel : string, optional Value which states that no new index starts on there. Returns ------- Returns list of maps. For each level returns map of indexes (key is index in r... |
173,481 | 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,
... | Appends lines to a buffer. Parameters ---------- buf The buffer to write to lines The lines to append. |
173,482 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | Wrap result set of query in a DataFrame. |
173,483 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | Execute the given SQL query using the provided connection object. Parameters ---------- sql : string SQL query to be executed. con : SQLAlchemy connection or sqlite3 connection If a DBAPI2 object, only sqlite3 is supported. params : list or tuple, optional, default: None List of parameters to pass to execute method. Re... |
173,484 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | null |
173,485 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | null |
173,486 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | Read SQL database table into a DataFrame. Given a table name and a SQLAlchemy connectable, returns a DataFrame. This function does not support DBAPI connections. Parameters ---------- table_name : str Name of SQL table in database. con : SQLAlchemy connectable or str A database URI could be provided as str. SQLite DBAP... |
173,487 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | null |
173,488 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | null |
173,489 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | Read SQL query into a DataFrame. Returns a DataFrame corresponding to the result set of the query string. Optionally provide an `index_col` parameter to use one of the columns as the index, otherwise default integer index will be used. Parameters ---------- sql : str SQL query or SQLAlchemy Selectable (select or text o... |
173,490 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | null |
173,491 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | null |
173,492 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | Read SQL query or database table into a DataFrame. This function is a convenience wrapper around ``read_sql_table`` and ``read_sql_query`` (for backward compatibility). It will delegate to the specific function depending on the provided input. A SQL query will be routed to ``read_sql_query``, while a database table nam... |
173,493 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | Write records stored in a DataFrame to a SQL database. Parameters ---------- frame : DataFrame, Series name : str Name of SQL table. con : SQLAlchemy connectable(engine/connection) or database string URI or sqlite3 DBAPI2 connection Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2... |
173,494 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | return our implementation |
173,495 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | null |
173,496 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from contextlib import (
ExitStack,
contextmanager,
)
from datetime import (
date,
datetime,
time,
)
from functools import partial
import re
from typing import (
TYPE_CHECKING,
Any,
Iterator,
Literal,... | Get the SQL db table schema for the given frame. Parameters ---------- frame : DataFrame name : str name of SQL table keys : string or sequence, default: None columns to use a primary key con: an open SQL database connection object or a SQLAlchemy connectable Using SQLAlchemy makes it possible to use any DB supported b... |
173,497 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import warnings
from pandas._libs.json import loads
from pandas._libs.tslibs import timezones
from pandas._typing import (
DtypeObj,
JSONSerializable,
)
from pandas.util._exceptions import find_stack_level
from panda... | Create a Table schema from ``data``. Parameters ---------- data : Series, DataFrame index : bool, default True Whether to include ``data.index`` in the schema. primary_key : bool or None, default True Column names to designate as the primary key. The default `None` will set `'primaryKey'` to the index level or levels i... |
173,498 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
cast,
)
import warnings
from pandas._libs.json import loads
from pandas._libs.tslibs import timezones
from pandas._typing import (
DtypeObj,
JSONSerializable,
)
from pandas.util._exceptions import find_stack_level
from panda... | Builds a DataFrame from a given schema Parameters ---------- json : A JSON table schema precise_float : bool Flag controlling precision when decoding string to double values, as dictated by ``read_json`` Returns ------- df : DataFrame Raises ------ NotImplementedError If the JSON table schema contains either timezone o... |
173,499 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from collections import abc
from io import StringIO
from itertools import islice
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generic,
Literal,
Mapping,
TypeVar,
over... | null |
173,500 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from collections import abc
from io import StringIO
from itertools import islice
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generic,
Literal,
Mapping,
TypeVar,
over... | null |
173,501 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from collections import abc
from io import StringIO
from itertools import islice
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generic,
Literal,
Mapping,
TypeVar,
over... | null |
173,502 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from collections import abc
from io import StringIO
from itertools import islice
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generic,
Literal,
Mapping,
TypeVar,
over... | null |
173,503 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from collections import abc
from io import StringIO
from itertools import islice
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generic,
Literal,
Mapping,
TypeVar,
over... | null |
173,504 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from collections import abc
from io import StringIO
from itertools import islice
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generic,
Literal,
Mapping,
TypeVar,
over... | null |
173,505 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from collections import abc
from io import StringIO
from itertools import islice
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generic,
Literal,
Mapping,
TypeVar,
over... | null |
173,506 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
from collections import abc
from io import StringIO
from itertools import islice
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Generic,
Literal,
Mapping,
TypeVar,
over... | Convert a JSON string to pandas object. Parameters ---------- path_or_buf : a valid JSON str, path object or file-like object Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: ``file://localhost/pa... |
173,507 | from __future__ import annotations
from collections import (
abc,
defaultdict,
)
import copy
import sys
from typing import (
Any,
DefaultDict,
Iterable,
)
import numpy as np
from pandas._libs.writers import convert_json_to_lines
from pandas._typing import (
IgnoreRaise,
Scalar,
)
import pand... | Normalize semi-structured JSON data into a flat table. Parameters ---------- data : dict or list of dicts Unserialized JSON objects. record_path : str or list of str, default None Path in each object to list of records. If not passed, data will be assumed to be an array of records. meta : list of paths (str or list of ... |
173,508 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | null |
173,509 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | Ensure that an index / column name is a str (python 3); otherwise they may be np.string dtype. Non-string dtypes are passed through unchanged. https://github.com/pandas-dev/pandas/issues/13492 |
173,510 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | store this object, close it if we opened it |
173,511 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | Read from the store, close it if we opened it. Retrieve pandas object stored in file, optionally based on where criteria. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrust... |
173,512 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | null |
173,513 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | for a tz-aware type, return an encoded zone |
173,514 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | null |
173,515 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | null |
173,516 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | coerce the values to a DatetimeIndex if tz is set preserve the input shape if possible Parameters ---------- values : ndarray or Index tz : str or tzinfo coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray |
173,517 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | null |
173,518 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | null |
173,519 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | null |
173,520 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | null |
173,521 | from __future__ import annotations
from contextlib import suppress
import copy
from datetime import (
date,
tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from types import TracebackType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Final,
Hashable,
Itera... | Prior to 0.10.1, we named values blocks like: values_block_0 an the name values_0, adjust the given name if necessary. Parameters ---------- name : str version : Tuple[int, int, int] Returns ------- str |
173,522 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
import codecs
from collections import defaultdict
import dataclasses
import functools
import gzip
from io import (
BufferedIOBase,
BytesIO,
RawIOBase,
StringIO,
TextIOBase,
TextIOWrapper,
)
import mmap
import os
... | converts an absolute native path to a FILE URL. Parameters ---------- path : a path in native format Returns ------- a valid FILE URL |
173,523 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
import codecs
from collections import defaultdict
import dataclasses
import functools
import gzip
from io import (
BufferedIOBase,
BytesIO,
RawIOBase,
StringIO,
TextIOBase,
TextIOWrapper,
)
import mmap
import os
... | Check whether or not the `columns` parameter could be converted into a MultiIndex. Parameters ---------- columns : array-like Object which may or may not be convertible into a MultiIndex index_col : None, bool or list, optional Column or columns to use as the (possibly hierarchical) index Returns ------- bool : Whether... |
173,524 | from __future__ import annotations
from abc import (
ABC,
abstractmethod,
)
import codecs
from collections import defaultdict
import dataclasses
import functools
import gzip
from io import (
BufferedIOBase,
BytesIO,
RawIOBase,
StringIO,
TextIOBase,
TextIOWrapper,
)
import mmap
import os
... | Rename column names if duplicates exist. Currently the renaming is done by appending a period and an autonumeric, but a custom pattern may be supported in the future. Examples -------- >>> dedup_names(["x", "y", "x", "x"], is_potential_multiindex=False) ['x', 'y', 'x.1', 'x.2'] |
173,525 | from __future__ import annotations
import io
import os
from typing import (
Any,
Literal,
)
import warnings
from warnings import catch_warnings
from pandas._libs import lib
from pandas._typing import (
DtypeBackend,
FilePath,
ReadBuffer,
StorageOptions,
WriteBuffer,
)
from pandas.compat._opt... | File handling for PyArrow. |
173,526 | from __future__ import annotations
import io
import os
from typing import (
Any,
Literal,
)
import warnings
from warnings import catch_warnings
from pandas._libs import lib
from pandas._typing import (
DtypeBackend,
FilePath,
ReadBuffer,
StorageOptions,
WriteBuffer,
)
from pandas.compat._opt... | Write a DataFrame to the parquet format. Parameters ---------- df : DataFrame path : str, path object, file-like object, or None, default None String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``write()`` function. If None, the result is returned as bytes. If a string, i... |
173,527 | from __future__ import annotations
import io
import os
from typing import (
Any,
Literal,
)
import warnings
from warnings import catch_warnings
from pandas._libs import lib
from pandas._typing import (
DtypeBackend,
FilePath,
ReadBuffer,
StorageOptions,
WriteBuffer,
)
from pandas.compat._opt... | Load a parquet object from the file path, returning a DataFrame. 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, g... |
173,528 | 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 ABCDataFrame
from pandas import (
... | r""" Read text from clipboard and pass to read_csv. Parameters ---------- sep : str, default '\s+' A string or regex delimiter. The default of '\s+' denotes one or more whitespace characters. dtype_backend : {"numpy_nullable", "pyarrow"}, defaults to NumPy backed DataFrames Which dtype_backend to use, e.g. whether a Da... |
173,529 | 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 ABCDataFrame
from pandas import (
... | Attempt to write text representation of object to the system clipboard The clipboard can be then pasted into Excel for example. Parameters ---------- obj : the object to write to the clipboard excel : bool, defaults to True if True, use the provided separator, writing in a csv format for allowing easy pasting into exce... |
173,530 | from __future__ import annotations
import io
from types import ModuleType
from typing import (
Any,
Literal,
)
from pandas._libs import lib
from pandas._typing import (
DtypeBackend,
FilePath,
ReadBuffer,
WriteBuffer,
)
from pandas.compat._optional import import_optional_dependency
from pandas.u... | Load an ORC object from the file path, returning a DataFrame. 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... |
173,531 | from __future__ import annotations
import io
from types import ModuleType
from typing import (
Any,
Literal,
)
from pandas._libs import lib
from pandas._typing import (
DtypeBackend,
FilePath,
ReadBuffer,
WriteBuffer,
)
from pandas.compat._optional import import_optional_dependency
from pandas.u... | Write a DataFrame to the ORC format. .. versionadded:: 1.5.0 Parameters ---------- df : DataFrame The dataframe to be written to ORC. Raises NotImplementedError if dtype of one or more columns is category, unsigned integers, intervals, periods or sparse. path : str, file-like object or None, default None If a string, i... |
173,532 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | Convert from SIF to datetime. https://www.stata.com/help.cgi?datetime Parameters ---------- dates : Series The Stata Internal Format date to convert to datetime according to fmt fmt : str The format to convert to. Can be, tc, td, tw, tm, tq, th, ty Returns Returns ------- converted : Series The converted dates Examples... |
173,533 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | Convert from datetime to SIF. https://www.stata.com/help.cgi?datetime Parameters ---------- dates : Series Series or array containing datetime.datetime or datetime64[ns] to convert to the Stata Internal Format given by fmt fmt : str The format to convert to. Can be, tc, td, tw, tm, tq, th, ty |
173,534 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | Checks the dtypes of the columns of a pandas DataFrame for compatibility with the data types and ranges supported by Stata, and converts if necessary. Parameters ---------- data : DataFrame The DataFrame to check and convert Notes ----- Numeric columns in Stata must be one of int8, int16, int32, float32 or float64, wit... |
173,535 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | null |
173,536 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | null |
173,537 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | Take a char string and pads it with null bytes until it's length chars. |
173,538 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | Convert from one of the stata date formats to a type in TYPE_MAP. |
173,539 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | null |
173,540 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | Convert dtype types to stata types. Returns the byte of the given ordinal. See TYPE_MAP and comments for an explanation. This is also explained in the dta spec. 1 - 244 are strings of this length Pandas Stata 251 - for int8 byte 252 - for int16 int 253 - for int32 long 254 - for float32 float 255 - for double double If... |
173,541 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | Map numpy dtype to stata's default format for this type. Not terribly important since users can change this in Stata. Semantics are object -> "%DDs" where DD is the length of the string. If not a string, raise ValueError float64 -> "%10.0g" float32 -> "%9.0g" int64 -> "%9.0g" int32 -> "%12.0g" int16 -> "%8.0g" int8 -> ... |
173,542 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | Converts dtype types to stata types. Returns the byte of the given ordinal. See TYPE_MAP and comments for an explanation. This is also explained in the dta spec. 1 - 2045 are strings of this length Pandas Stata 32768 - for object strL 65526 - for int8 byte 65527 - for int16 int 65528 - for int32 long 65529 - for float3... |
173,543 | from __future__ import annotations
from collections import abc
import datetime
from io import BytesIO
import os
import struct
import sys
from types import TracebackType
from typing import (
IO,
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Final,
Hashable,
Sequence,
cast,
)
import warnings
f... | Takes a bytes instance and pads it with null bytes until it's length chars. |
173,544 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | Add engine to the excel writer registry.io.excel. You must use this method to integrate with ``to_excel``. Parameters ---------- klass : ExcelWriter |
173,545 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | Return the default reader/writer for the given extension. Parameters ---------- ext : str The excel file extension for which to get the default engine. mode : str {'reader', 'writer'} Whether to get the default engine for reading or writing. Either 'reader' or 'writer' Returns ------- str The default engine for the ext... |
173,546 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | null |
173,547 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | null |
173,548 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | null |
173,549 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | null |
173,550 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | null |
173,551 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | Convert `usecols` into a compatible format for parsing in `parsers.py`. Parameters ---------- usecols : object The use-columns object to potentially convert. Returns ------- converted : object The compatible format of `usecols`. |
173,552 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | null |
173,553 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | null |
173,554 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | null |
173,555 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | Forward fill blank entries in row but only inside the same parent index. Used for creating headers in Multiindex. Parameters ---------- row : list List of items in a single row. control_row : list of bool Helps to determine if particular column is in same parent index as the previous value. Used to stop propagation of ... |
173,556 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | Pop the header name for MultiIndex parsing. Parameters ---------- row : list The data row to parse for the header name. index_col : int, list The index columns for our data. Assumed to be non-null. Returns ------- header_name : str The extracted header name. trimmed_row : list The original data row with the header name... |
173,557 | from __future__ import annotations
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Literal,
MutableMapping,
Sequence,
TypeVar,
overload,
)
from pandas.compat._optional import import_optional_dependency
from pandas.core.dtypes.common import (
is_integer,... | Used to combine two sources of kwargs for the backend engine. Use of kwargs is deprecated, this function is solely for use in 1.3 and should be removed in 1.4/2.0. Also _base.ExcelWriter.__new__ ensures either engine_kwargs or kwargs must be None or empty respectively. Parameters ---------- engine_kwargs: dict kwargs t... |
173,558 | from __future__ import annotations
import abc
import datetime
from functools import partial
from io import BytesIO
import os
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Iterable,
List,
Literal,
Mapping,
Sequence,
Uni... | null |
173,559 | from __future__ import annotations
import abc
import datetime
from functools import partial
from io import BytesIO
import os
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Iterable,
List,
Literal,
Mapping,
Sequence,
Uni... | null |
173,560 | from __future__ import annotations
import abc
import datetime
from functools import partial
from io import BytesIO
import os
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Iterable,
List,
Literal,
Mapping,
Sequence,
Uni... | null |
173,561 | from __future__ import annotations
import abc
import datetime
from functools import partial
from io import BytesIO
import os
from textwrap import fill
from types import TracebackType
from typing import (
IO,
Any,
Callable,
Hashable,
Iterable,
List,
Literal,
Mapping,
Sequence,
Uni... | Inspect the path or content of an excel file and get its format. Adopted from xlrd: https://github.com/python-excel/xlrd. Parameters ---------- content_or_path : str or file-like object Path to file or content of file to inspect. May be a URL. {storage_options} Returns ------- str or None Format of file if it can be de... |
173,562 | from __future__ import annotations
import sys
def removesuffix(string: str, suffix: str) -> str:
if string.endswith(suffix):
return string[: -len(suffix)]
return string | null |
173,563 | from __future__ import annotations
import sys
def removeprefix(string: str, prefix: str) -> str:
if string.startswith(prefix):
return string[len(prefix) :]
return string | null |
173,564 | from __future__ import annotations
import codecs
import json
import locale
import os
import platform
import struct
import sys
from pandas._typing import JSONSerializable
from pandas.compat._optional import (
VERSIONS,
get_version,
import_optional_dependency,
)
def _get_sys_info() -> dict[str, JSONSerializab... | Provide useful information, important for bug reports. It comprises info about hosting operation system, pandas version, and versions of other installed relative packages. Parameters ---------- as_json : str or bool, default False * If False, outputs info in a human readable form to the console. * If str, it will be co... |
173,565 | from __future__ import annotations
from functools import wraps
import inspect
from textwrap import dedent
from typing import (
Any,
Callable,
Mapping,
cast,
)
import warnings
from pandas._libs.properties import cache_readonly
from pandas._typing import (
F,
T,
)
from pandas.util._exceptions impo... | Return a new function that emits a deprecation warning on use. To use this method for a deprecated function, another function `alternative` with the same signature must exist. The deprecated function will emit a deprecation warning, and in the docstring it will contain the deprecation directive with the provided versio... |
173,566 | from __future__ import annotations
from functools import wraps
import inspect
from textwrap import dedent
from typing import (
Any,
Callable,
Mapping,
cast,
)
import warnings
from pandas._libs.properties import cache_readonly
from pandas._typing import (
F,
T,
)
from pandas.util._exceptions impo... | Decorator to deprecate a keyword argument of a function. Parameters ---------- old_arg_name : str Name of argument in function to deprecate new_arg_name : str or None Name of preferred argument in function. Use None to raise warning that ``old_arg_name`` keyword is deprecated. mapping : dict or callable If mapping is p... |
173,567 | from __future__ import annotations
from functools import wraps
import inspect
from textwrap import dedent
from typing import (
Any,
Callable,
Mapping,
cast,
)
import warnings
from pandas._libs.properties import cache_readonly
from pandas._typing import (
F,
T,
)
from pandas.util._exceptions impo... | Decorator to deprecate a use of non-keyword arguments of a function. Parameters ---------- version : str, optional The version in which positional arguments will become keyword-only. If None, then the warning message won't specify any particular version. allowed_args : list, optional In case of list, it must be the lis... |
173,568 | from __future__ import annotations
from functools import wraps
import inspect
from textwrap import dedent
from typing import (
Any,
Callable,
Mapping,
cast,
)
import warnings
from pandas._libs.properties import cache_readonly
from pandas._typing import (
F,
T,
)
from pandas.util._exceptions impo... | A decorator take docstring templates, concatenate them and perform string substitution on it. This decorator will add a variable "_docstring_components" to the wrapped callable to keep track the original docstring template for potential usage. If it should be consider as a template, it will be saved as a string. Otherw... |
173,569 | from __future__ import annotations
from functools import wraps
import inspect
from textwrap import dedent
from typing import (
Any,
Callable,
Mapping,
cast,
)
import warnings
from pandas._libs.properties import cache_readonly
from pandas._typing import (
F,
T,
)
from pandas.util._exceptions impo... | null |
173,570 | from __future__ import annotations
from typing import (
Iterable,
Sequence,
TypeVar,
overload,
)
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.common import (
is_bool,
is_integer,
)
def _check_arg_length(fname, args, max_fname_arg_count, compat_args):
"""
Checks... | Checks whether the length of the `*args` argument passed into a function has at most `len(compat_args)` arguments and whether or not all of these elements in `args` are set to their default values. Parameters ---------- fname : str The name of the function being passed the `*args` parameter args : tuple The `*args` par... |
173,571 | from __future__ import annotations
from typing import (
Iterable,
Sequence,
TypeVar,
overload,
)
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.common import (
is_bool,
is_integer,
)
def _check_arg_length(fname, args, max_fname_arg_count, compat_args):
"""
Checks... | Checks whether parameters passed to the *args and **kwargs argument in a function `fname` are valid parameters as specified in `*compat_args` and whether or not they are set to their default values. Parameters ---------- fname: str The name of the function being passed the `**kwargs` parameter args: tuple The `*args` p... |
173,572 | from __future__ import annotations
from typing import (
Iterable,
Sequence,
TypeVar,
overload,
)
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.common import (
is_bool,
is_integer,
)
def clean_fill_method(method: str | None, allow_nearest: bool = False):
# asfreq is... | Validate the keyword arguments to 'fillna'. This checks that exactly one of 'value' and 'method' is specified. If 'method' is specified, this validates that it's a valid method. Parameters ---------- value, method : object The 'value' and 'method' keyword arguments for 'fillna'. validate_scalar_dict_value : bool, defau... |
173,573 | from __future__ import annotations
from typing import (
Iterable,
Sequence,
TypeVar,
overload,
)
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.common import (
is_bool,
is_integer,
)
BoolishT = TypeVar("BoolishT", bool, int)
def validate_ascending(ascending: BoolishT) -... | null |
173,574 | from __future__ import annotations
from typing import (
Iterable,
Sequence,
TypeVar,
overload,
)
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.common import (
is_bool,
is_integer,
)
BoolishT = TypeVar("BoolishT", bool, int)
class Sequence(_Collection[_T_co], Reversible... | null |
173,575 | from __future__ import annotations
from typing import (
Iterable,
Sequence,
TypeVar,
overload,
)
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.common import (
is_bool,
is_integer,
)
BoolishT = TypeVar("BoolishT", bool, int)
def validate_bool_kwarg(
value: BoolishNon... | Validate ``ascending`` kwargs for ``sort_index`` method. |
173,576 | from __future__ import annotations
from typing import (
Iterable,
Sequence,
TypeVar,
overload,
)
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.common import (
is_bool,
is_integer,
)
The provided code snippet includes necessary dependencies for implementing the `validat... | Check that the `closed` argument is among [None, "left", "right"] Parameters ---------- closed : {None, "left", "right"} Returns ------- left_closed : bool right_closed : bool Raises ------ ValueError : if argument is not among valid values |
173,577 | from __future__ import annotations
from typing import (
Iterable,
Sequence,
TypeVar,
overload,
)
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.common import (
is_bool,
is_integer,
)
The provided code snippet includes necessary dependencies for implementing the `validat... | Check that the `inclusive` argument is among {"both", "neither", "left", "right"}. Parameters ---------- inclusive : {"both", "neither", "left", "right"} Returns ------- left_right_inclusive : tuple[bool, bool] Raises ------ ValueError : if argument is not among valid values |
173,578 | from __future__ import annotations
from typing import (
Iterable,
Sequence,
TypeVar,
overload,
)
import numpy as np
from pandas._libs import lib
from pandas.core.dtypes.common import (
is_bool,
is_integer,
)
The provided code snippet includes necessary dependencies for implementing the `validat... | Check that we have an integer between -length and length, inclusive. Standardize negative loc to within [0, length]. The exceptions we raise on failure match np.insert. |
173,579 | from __future__ import annotations
import contextlib
import inspect
import os
import re
from typing import Generator
import warnings
class Generator(Iterator[_T_co], Generic[_T_co, _T_contra, _V_co]):
def __next__(self) -> _T_co: ...
def send(self, __value: _T_contra) -> _T_co: ...
def throw(
self,... | Rewrite the message of an exception. |
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