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/sparse.pyi

47 lines
1.4 KiB

from typing import (
Sequence,
TypeVar,
)
import numpy as np
from pandas._typing import npt
SparseIndexT = TypeVar("SparseIndexT", bound="SparseIndex")
class SparseIndex:
length: int
npoints: int
def __init__(self): ...
@property
def ngaps(self) -> int: ...
@property
def nbytes(self) -> int: ...
@property
def indices(self) -> npt.NDArray[np.int32]: ...
def equals(self, other) -> bool: ...
def lookup(self, index: int) -> np.int32: ...
def lookup_array(self, indexer: npt.NDArray[np.int32]) -> npt.NDArray[np.int32]: ...
def to_int_index(self) -> IntIndex: ...
def to_block_index(self) -> BlockIndex: ...
def intersect(self: SparseIndexT, y_: SparseIndex) -> SparseIndexT: ...
def make_union(self: SparseIndexT, y_: SparseIndex) -> SparseIndexT: ...
class IntIndex(SparseIndex):
indices: npt.NDArray[np.int32]
def __init__(
self, length: int, indices: Sequence[int], check_integrity: bool = ...
): ...
class BlockIndex(SparseIndex):
nblocks: int
blocs: np.ndarray
blengths: np.ndarray
def __init__(self, length: int, blocs: np.ndarray, blengths: np.ndarray): ...
def make_mask_object_ndarray(
arr: npt.NDArray[np.object_], fill_value
) -> npt.NDArray[np.bool_]: ...
def get_blocks(
indices: npt.NDArray[np.int32],
) -> tuple[npt.NDArray[np.int32], npt.NDArray[np.int32]]: ...