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			78 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
			
		
		
	
	
			78 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
from typing import (
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    Hashable,
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    Literal,
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)
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import numpy as np
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from pandas._typing import (
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    ArrayLike,
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    Dtype,
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    npt,
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)
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STR_NA_VALUES: set[str]
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DEFAULT_BUFFER_HEURISTIC: int
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def sanitize_objects(
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    values: npt.NDArray[np.object_],
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    na_values: set,
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) -> int: ...
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class TextReader:
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    unnamed_cols: set[str]
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    table_width: int  # int64_t
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    leading_cols: int  # int64_t
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    header: list[list[int]]  # non-negative integers
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    def __init__(
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        self,
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        source,
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        delimiter: bytes | str = ...,  # single-character only
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        header=...,
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        header_start: int = ...,  # int64_t
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        header_end: int = ...,  # uint64_t
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        index_col=...,
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        names=...,
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        tokenize_chunksize: int = ...,  # int64_t
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        delim_whitespace: bool = ...,
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        converters=...,
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        skipinitialspace: bool = ...,
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        escapechar: bytes | str | None = ...,  # single-character only
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        doublequote: bool = ...,
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        quotechar: str | bytes | None = ...,  # at most 1 character
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        quoting: int = ...,
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        lineterminator: bytes | str | None = ...,  # at most 1 character
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        comment=...,
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        decimal: bytes | str = ...,  # single-character only
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        thousands: bytes | str | None = ...,  # single-character only
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        dtype: Dtype | dict[Hashable, Dtype] = ...,
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        usecols=...,
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        error_bad_lines: bool = ...,
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        warn_bad_lines: bool = ...,
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        na_filter: bool = ...,
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        na_values=...,
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        na_fvalues=...,
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        keep_default_na: bool = ...,
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        true_values=...,
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        false_values=...,
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        allow_leading_cols: bool = ...,
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        skiprows=...,
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        skipfooter: int = ...,  # int64_t
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        verbose: bool = ...,
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        float_precision: Literal["round_trip", "legacy", "high"] | None = ...,
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        skip_blank_lines: bool = ...,
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        encoding_errors: bytes | str = ...,
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    ) -> None: ...
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    def set_noconvert(self, i: int) -> None: ...
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    def remove_noconvert(self, i: int) -> None: ...
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    def close(self) -> None: ...
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    def read(self, rows: int | None = ...) -> dict[int, ArrayLike]: ...
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    def read_low_memory(self, rows: int | None) -> list[dict[int, ArrayLike]]: ...
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# _maybe_upcast, na_values are only exposed for testing
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na_values: dict
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def _maybe_upcast(
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    arr, use_dtype_backend: bool = ..., dtype_backend: str = ...
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) -> np.ndarray: ...
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