A PyQT GUI application for converting InfoLease report outputs into Excel files. Handles parsing and summarizing. Learns where files are meant to be store and compiles monthly and yearly summaries.
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InfoLeaseExtract/venv/Lib/site-packages/pandas/_libs/parsers.pyi

71 lines
2.2 KiB

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