__init__.pyi

"""Pandas public API"""
from pathlib import Path
from typing import (
    IO,
    Any,
    Callable,
    Dict,
    List,
    Mapping,
    Optional,
    Sequence,
    Tuple,
    Type,
    TypeVar,
    Union,
    overload,
)

import numpy as _np
from typing_extensions import Literal

from . import testing
from .core.arrays.integer import Int8Dtype as Int8Dtype
from .core.arrays.integer import Int16Dtype as Int16Dtype
from .core.arrays.integer import Int32Dtype as Int32Dtype
from .core.arrays.integer import Int64Dtype as Int64Dtype
from .core.arrays.integer import UInt8Dtype as UInt8Dtype
from .core.arrays.integer import UInt16Dtype as UInt16Dtype
from .core.arrays.integer import UInt32Dtype as UInt32Dtype
from .core.arrays.integer import UInt64Dtype as UInt64Dtype
from .core.frame import DataFrame as DataFrame
from .core.frame import _AxisType, _ListLike
from .core.indexes import Index as Index
from .core.indexes import MultiIndex as MultiIndex
from .core.series import Series as Series

def concat(
    dataframes: Union[Sequence[DataFrame], Mapping[str, DataFrame]],
    axis: _AxisType = ...,
    sort: Optional[bool] = ...,
    ignore_index: bool = ...,
) -> DataFrame: ...
def cut(arr: _np.ndarray, bins: int) -> Tuple[Union[Series, _np.ndarray], _np.ndarray]: ...
def get_dummies(df: Union[DataFrame, Series], columns: Optional[_ListLike] = ...) -> DataFrame: ...
@overload
def isna(obj: Union[float, str]) -> bool: ...
@overload
def isna(obj: DataFrame) -> DataFrame: ...
@overload
def isna(obj: Series) -> Series[bool]: ...
@overload
def isna(obj: Union[Index, _np.ndarray]) -> _np.ndarray[_np.bool_]: ...
@overload
def isnull(obj: Union[None, float, str]) -> bool: ...
@overload
def isnull(obj: DataFrame) -> DataFrame: ...
@overload
def isnull(obj: Series) -> Series[bool]: ...
@overload
def isnull(obj: Union[Index, _np.ndarray]) -> _np.ndarray[_np.bool_]: ...
@overload
def merge(left: DataFrame, right: DataFrame, on: str = ...) -> DataFrame: ...
@overload
def merge(
    left: DataFrame, right: DataFrame, left_on: str, right_on: str, how: str
) -> DataFrame: ...
@overload
def merge(
    left: DataFrame, right: DataFrame, left_on: List[str], right_on: List[str], how: str
) -> DataFrame: ...
@overload
def merge(
    left: DataFrame,
    right: DataFrame,
    left_index: bool = ...,
    right_index: bool = ...,
    how: str = ...,
) -> DataFrame: ...
def read_parquet(
    path: Union[str, Path, IO],
    engine: Literal["auto", "pyarrow", "fastparquet"] = ...,
    columns: Optional[List[str]] = ...,
    **kwargs: Any,
) -> DataFrame: ...
def read_csv(
    filepath_or_buffer: Union[str, Path, IO],
    sep: str = ...,
    delimiter: Optional[str] = ...,  # only an alias to sep
    header: Optional[Union[int, List[int], Literal["infer"]]] = ...,
    names: Optional[List[str]] = ...,
    index_col: Optional[Union[str, int, List[str], Tuple[str, ...], Sequence[int], bool]] = ...,
    usecols: Optional[Union[List[str], List[int], Callable]] = ...,
    squeeze: bool = ...,
    prefix: Optional[str] = ...,
    mangle_dupe_cols: bool = ...,
    dtype: Optional[Union[Type, str, Mapping[str, Union[str, Type]]]] = ...,
    engine: Optional[Union[Literal["c"], Literal["python"]]] = ...,
    converters: Dict[Union[str, int], Callable] = ...,
    true_values: Optional[List] = ...,
    false_values: Optional[List] = ...,
    skipinitialspace: bool = ...,
    skiprows: Optional[Union[int, _ListLike, Callable]] = ...,
    skipfooter: int = ...,
    nrows: Optional[int] = ...,
    na_values: Optional[Union[str, List[str]]] = ...,
    keep_default_na: bool = ...,
    na_filter: bool = ...,
    verbose: bool = ...,
    skip_blank_line: bool = ...,
    parse_dates: Union[bool, List[int], List[str], List[List[int]], Dict[str, List[int]]] = ...,
    infer_datetime_format: bool = ...,
    keep_date_col: bool = ...,
    date_parser: Optional[Callable] = ...,
    dayfirst: bool = ...,
    cache_dates: bool = ...,
    iterator: bool = ...,
    chunksize: Optional[int] = ...,
    compression: Optional[Literal["infer", "gzip", "bz3", "zip", "xz"]] = ...,
    thousands: Optional[str] = ...,
    decimal: Optional[str] = ...,
    lineterminator: Optional[str] = ...,
    quotechar: Optional[str] = ...,
    quoting: Optional[Literal[0, 1, 2, 3]] = ...,
    doublequote: bool = ...,
    escapechar: Optional[str] = ...,
    comment: Optional[str] = ...,
    encoding: Optional[str] = ...,
    dialect: Any = ...,  # TODO str or csv.Dialect Optional
    error_bad_lines: bool = ...,
    warn_bad_lines: bool = ...,
    delim_whitespace: bool = ...,
    low_memory: bool = ...,
    memory_map: bool = ...,
    float_precision: Optional[str] = ...,
) -> DataFrame: ...
def read_sql(
    sql: Union[str, Any],
    con: Union[str, Any] = ...,
    index_col: Optional[Union[str, List[str]]] = ...,
    coerce_float: bool = ...,
    params: Optional[Union[List[str], Tuple[str, ...], Dict[str, str]]] = ...,
    parse_dates: Optional[Union[List[str], Dict[str, str], Dict[str, Dict[str, Any]]]] = ...,
    columns: List[str] = ...,
    chunksize: int = ...,
) -> DataFrame: ...
def read_feather(p: Union[Path, IO]) -> DataFrame: ...
def read_json(
    path_or_buf: str = ...,
    orient: Optional[Literal["split", "records", "index", "columns", "values", "table"]] = ...,
    typ: Literal["frame", "series"] = ...,
    dtype: Optional[Union[bool, Dict[str, str]]] = ...,
    convert_axes: Optional[bool] = ...,
    convert_dates: Optional[Union[bool, List[str]]] = ...,
    keep_default_dates: Optional[bool] = ...,
    numpy: Optional[bool] = ...,
    precise_float: Optional[bool] = ...,
    date_unit: Optional[str] = ...,
    encoding: str = ...,
    lines: bool = ...,
    chunksize: Optional[int] = ...,
    compression: Optional[Literal["infer", "gzip", "bz3", "zip", "xz"]] = ...,
    nrows: Optional[int] = ...,
) -> Union[DataFrame, Series]: ...
def to_numeric(
    arg: Union[int, float, List, Tuple, _np.ndarray, Series],
    errors: Literal["ignore", "raise", "coerce"] = ...,
    downcast: Literal["integer", "signed", "unsigned", "float"] = ...,
) -> Union[Series, _np.ndarray]: ...
def unique(values: Series) -> _np.ndarray: ...