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videollama2/lib/python3.10/site-packages/pandas/_libs/arrays.pyi

@@ -0,0 +1,40 @@
+from typing import Sequence
+
+import numpy as np
+
+from pandas._typing import (
+    AxisInt,
+    DtypeObj,
+    Self,
+    Shape,
+)
+
+class NDArrayBacked:
+    _dtype: DtypeObj
+    _ndarray: np.ndarray
+    def __init__(self, values: np.ndarray, dtype: DtypeObj) -> None: ...
+    @classmethod
+    def _simple_new(cls, values: np.ndarray, dtype: DtypeObj): ...
+    def _from_backing_data(self, values: np.ndarray): ...
+    def __setstate__(self, state): ...
+    def __len__(self) -> int: ...
+    @property
+    def shape(self) -> Shape: ...
+    @property
+    def ndim(self) -> int: ...
+    @property
+    def size(self) -> int: ...
+    @property
+    def nbytes(self) -> int: ...
+    def copy(self, order=...): ...
+    def delete(self, loc, axis=...): ...
+    def swapaxes(self, axis1, axis2): ...
+    def repeat(self, repeats: int | Sequence[int], axis: int | None = ...): ...
+    def reshape(self, *args, **kwargs): ...
+    def ravel(self, order=...): ...
+    @property
+    def T(self): ...
+    @classmethod
+    def _concat_same_type(
+        cls, to_concat: Sequence[Self], axis: AxisInt = ...
+    ) -> Self: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/byteswap.pyi

@@ -0,0 +1,5 @@
+def read_float_with_byteswap(data: bytes, offset: int, byteswap: bool) -> float: ...
+def read_double_with_byteswap(data: bytes, offset: int, byteswap: bool) -> float: ...
+def read_uint16_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ...
+def read_uint32_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ...
+def read_uint64_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/groupby.pyi

@@ -0,0 +1,216 @@
+from typing import Literal
+
+import numpy as np
+
+from pandas._typing import npt
+
+def group_median_float64(
+    out: np.ndarray,  # ndarray[float64_t, ndim=2]
+    counts: npt.NDArray[np.int64],
+    values: np.ndarray,  # ndarray[float64_t, ndim=2]
+    labels: npt.NDArray[np.int64],
+    min_count: int = ...,  # Py_ssize_t
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_cumprod(
+    out: np.ndarray,  # float64_t[:, ::1]
+    values: np.ndarray,  # const float64_t[:, :]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    skipna: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_cumsum(
+    out: np.ndarray,  # int64float_t[:, ::1]
+    values: np.ndarray,  # ndarray[int64float_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    skipna: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_shift_indexer(
+    out: np.ndarray,  # int64_t[::1]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    periods: int,
+) -> None: ...
+def group_fillna_indexer(
+    out: np.ndarray,  # ndarray[intp_t]
+    labels: np.ndarray,  # ndarray[int64_t]
+    sorted_labels: npt.NDArray[np.intp],
+    mask: npt.NDArray[np.uint8],
+    limit: int,  # int64_t
+    dropna: bool,
+) -> None: ...
+def group_any_all(
+    out: np.ndarray,  # uint8_t[::1]
+    values: np.ndarray,  # const uint8_t[::1]
+    labels: np.ndarray,  # const int64_t[:]
+    mask: np.ndarray,  # const uint8_t[::1]
+    val_test: Literal["any", "all"],
+    skipna: bool,
+    result_mask: np.ndarray | None,
+) -> None: ...
+def group_sum(
+    out: np.ndarray,  # complexfloatingintuint_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[complexfloatingintuint_t, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    mask: np.ndarray | None,
+    result_mask: np.ndarray | None = ...,
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+) -> None: ...
+def group_prod(
+    out: np.ndarray,  # int64float_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[int64float_t, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    mask: np.ndarray | None,
+    result_mask: np.ndarray | None = ...,
+    min_count: int = ...,
+) -> None: ...
+def group_var(
+    out: np.ndarray,  # floating[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floating, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...,  # Py_ssize_t
+    ddof: int = ...,  # int64_t
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    is_datetimelike: bool = ...,
+    name: str = ...,
+) -> None: ...
+def group_skew(
+    out: np.ndarray,  # float64_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[float64_T, ndim=2]
+    labels: np.ndarray,  # const intp_t[::1]
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_mean(
+    out: np.ndarray,  # floating[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floating, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...,  # Py_ssize_t
+    is_datetimelike: bool = ...,  # bint
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_ohlc(
+    out: np.ndarray,  # floatingintuint_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[floatingintuint_t, ndim=2]
+    labels: np.ndarray,  # const intp_t[:]
+    min_count: int = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_quantile(
+    out: npt.NDArray[np.float64],
+    values: np.ndarray,  # ndarray[numeric, ndim=1]
+    labels: npt.NDArray[np.intp],
+    mask: npt.NDArray[np.uint8],
+    qs: npt.NDArray[np.float64],  # const
+    starts: npt.NDArray[np.int64],
+    ends: npt.NDArray[np.int64],
+    interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
+    result_mask: np.ndarray | None,
+    is_datetimelike: bool,
+) -> None: ...
+def group_last(
+    out: np.ndarray,  # rank_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    mask: npt.NDArray[np.bool_] | None,
+    result_mask: npt.NDArray[np.bool_] | None = ...,
+    min_count: int = ...,  # Py_ssize_t
+    is_datetimelike: bool = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_nth(
+    out: np.ndarray,  # rank_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    mask: npt.NDArray[np.bool_] | None,
+    result_mask: npt.NDArray[np.bool_] | None = ...,
+    min_count: int = ...,  # int64_t
+    rank: int = ...,  # int64_t
+    is_datetimelike: bool = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_rank(
+    out: np.ndarray,  # float64_t[:, ::1]
+    values: np.ndarray,  # ndarray[rank_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    ties_method: Literal["average", "min", "max", "first", "dense"] = ...,
+    ascending: bool = ...,
+    pct: bool = ...,
+    na_option: Literal["keep", "top", "bottom"] = ...,
+    mask: npt.NDArray[np.bool_] | None = ...,
+) -> None: ...
+def group_max(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_min(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    counts: np.ndarray,  # int64_t[::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_idxmin_idxmax(
+    out: npt.NDArray[np.intp],
+    counts: npt.NDArray[np.int64],
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: npt.NDArray[np.intp],
+    min_count: int = ...,
+    is_datetimelike: bool = ...,
+    mask: np.ndarray | None = ...,
+    name: str = ...,
+    skipna: bool = ...,
+    result_mask: np.ndarray | None = ...,
+) -> None: ...
+def group_cummin(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    skipna: bool = ...,
+) -> None: ...
+def group_cummax(
+    out: np.ndarray,  # groupby_t[:, ::1]
+    values: np.ndarray,  # ndarray[groupby_t, ndim=2]
+    labels: np.ndarray,  # const int64_t[:]
+    ngroups: int,
+    is_datetimelike: bool,
+    mask: np.ndarray | None = ...,
+    result_mask: np.ndarray | None = ...,
+    skipna: bool = ...,
+) -> None: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/hashing.pyi

@@ -0,0 +1,9 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def hash_object_array(
+    arr: npt.NDArray[np.object_],
+    key: str,
+    encoding: str = ...,
+) -> npt.NDArray[np.uint64]: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/indexing.pyi

@@ -0,0 +1,17 @@
+from typing import (
+    Generic,
+    TypeVar,
+)
+
+from pandas.core.indexing import IndexingMixin
+
+_IndexingMixinT = TypeVar("_IndexingMixinT", bound=IndexingMixin)
+
+class NDFrameIndexerBase(Generic[_IndexingMixinT]):
+    name: str
+    # in practice obj is either a DataFrame or a Series
+    obj: _IndexingMixinT
+
+    def __init__(self, name: str, obj: _IndexingMixinT) -> None: ...
+    @property
+    def ndim(self) -> int: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/internals.pyi

@@ -0,0 +1,94 @@
+from typing import (
+    Iterator,
+    Sequence,
+    final,
+    overload,
+)
+import weakref
+
+import numpy as np
+
+from pandas._typing import (
+    ArrayLike,
+    Self,
+    npt,
+)
+
+from pandas import Index
+from pandas.core.internals.blocks import Block as B
+
+def slice_len(slc: slice, objlen: int = ...) -> int: ...
+def get_concat_blkno_indexers(
+    blknos_list: list[npt.NDArray[np.intp]],
+) -> list[tuple[npt.NDArray[np.intp], BlockPlacement]]: ...
+def get_blkno_indexers(
+    blknos: np.ndarray,  # int64_t[:]
+    group: bool = ...,
+) -> list[tuple[int, slice | np.ndarray]]: ...
+def get_blkno_placements(
+    blknos: np.ndarray,
+    group: bool = ...,
+) -> Iterator[tuple[int, BlockPlacement]]: ...
+def update_blklocs_and_blknos(
+    blklocs: npt.NDArray[np.intp],
+    blknos: npt.NDArray[np.intp],
+    loc: int,
+    nblocks: int,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+@final
+class BlockPlacement:
+    def __init__(self, val: int | slice | np.ndarray) -> None: ...
+    @property
+    def indexer(self) -> np.ndarray | slice: ...
+    @property
+    def as_array(self) -> np.ndarray: ...
+    @property
+    def as_slice(self) -> slice: ...
+    @property
+    def is_slice_like(self) -> bool: ...
+    @overload
+    def __getitem__(
+        self, loc: slice | Sequence[int] | npt.NDArray[np.intp]
+    ) -> BlockPlacement: ...
+    @overload
+    def __getitem__(self, loc: int) -> int: ...
+    def __iter__(self) -> Iterator[int]: ...
+    def __len__(self) -> int: ...
+    def delete(self, loc) -> BlockPlacement: ...
+    def add(self, other) -> BlockPlacement: ...
+    def append(self, others: list[BlockPlacement]) -> BlockPlacement: ...
+    def tile_for_unstack(self, factor: int) -> npt.NDArray[np.intp]: ...
+
+class Block:
+    _mgr_locs: BlockPlacement
+    ndim: int
+    values: ArrayLike
+    refs: BlockValuesRefs
+    def __init__(
+        self,
+        values: ArrayLike,
+        placement: BlockPlacement,
+        ndim: int,
+        refs: BlockValuesRefs | None = ...,
+    ) -> None: ...
+    def slice_block_rows(self, slicer: slice) -> Self: ...
+
+class BlockManager:
+    blocks: tuple[B, ...]
+    axes: list[Index]
+    _known_consolidated: bool
+    _is_consolidated: bool
+    _blknos: np.ndarray
+    _blklocs: np.ndarray
+    def __init__(
+        self, blocks: tuple[B, ...], axes: list[Index], verify_integrity=...
+    ) -> None: ...
+    def get_slice(self, slobj: slice, axis: int = ...) -> Self: ...
+    def _rebuild_blknos_and_blklocs(self) -> None: ...
+
+class BlockValuesRefs:
+    referenced_blocks: list[weakref.ref]
+    def __init__(self, blk: Block | None = ...) -> None: ...
+    def add_reference(self, blk: Block) -> None: ...
+    def add_index_reference(self, index: Index) -> None: ...
+    def has_reference(self) -> bool: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/join.pyi

@@ -0,0 +1,79 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def inner_join(
+    left: np.ndarray,  # const intp_t[:]
+    right: np.ndarray,  # const intp_t[:]
+    max_groups: int,
+    sort: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def left_outer_join(
+    left: np.ndarray,  # const intp_t[:]
+    right: np.ndarray,  # const intp_t[:]
+    max_groups: int,
+    sort: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def full_outer_join(
+    left: np.ndarray,  # const intp_t[:]
+    right: np.ndarray,  # const intp_t[:]
+    max_groups: int,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def ffill_indexer(
+    indexer: np.ndarray,  # const intp_t[:]
+) -> npt.NDArray[np.intp]: ...
+def left_join_indexer_unique(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> npt.NDArray[np.intp]: ...
+def left_join_indexer(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> tuple[
+    np.ndarray,  # np.ndarray[join_t]
+    npt.NDArray[np.intp],
+    npt.NDArray[np.intp],
+]: ...
+def inner_join_indexer(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> tuple[
+    np.ndarray,  # np.ndarray[join_t]
+    npt.NDArray[np.intp],
+    npt.NDArray[np.intp],
+]: ...
+def outer_join_indexer(
+    left: np.ndarray,  # ndarray[join_t]
+    right: np.ndarray,  # ndarray[join_t]
+) -> tuple[
+    np.ndarray,  # np.ndarray[join_t]
+    npt.NDArray[np.intp],
+    npt.NDArray[np.intp],
+]: ...
+def asof_join_backward_on_X_by_Y(
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # const int64_t[:]
+    right_by_values: np.ndarray,  # const int64_t[:]
+    allow_exact_matches: bool = ...,
+    tolerance: np.number | float | None = ...,
+    use_hashtable: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def asof_join_forward_on_X_by_Y(
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # const int64_t[:]
+    right_by_values: np.ndarray,  # const int64_t[:]
+    allow_exact_matches: bool = ...,
+    tolerance: np.number | float | None = ...,
+    use_hashtable: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
+def asof_join_nearest_on_X_by_Y(
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # const int64_t[:]
+    right_by_values: np.ndarray,  # const int64_t[:]
+    allow_exact_matches: bool = ...,
+    tolerance: np.number | float | None = ...,
+    use_hashtable: bool = ...,
+) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/json.pyi

@@ -0,0 +1,23 @@
+from typing import (
+    Any,
+    Callable,
+)
+
+def ujson_dumps(
+    obj: Any,
+    ensure_ascii: bool = ...,
+    double_precision: int = ...,
+    indent: int = ...,
+    orient: str = ...,
+    date_unit: str = ...,
+    iso_dates: bool = ...,
+    default_handler: None
+    | Callable[[Any], str | float | bool | list | dict | None] = ...,
+) -> str: ...
+def ujson_loads(
+    s: str,
+    precise_float: bool = ...,
+    numpy: bool = ...,
+    dtype: None = ...,
+    labelled: bool = ...,
+) -> Any: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/missing.pyi

@@ -0,0 +1,16 @@
+import numpy as np
+from numpy import typing as npt
+
+class NAType:
+    def __new__(cls, *args, **kwargs): ...
+
+NA: NAType
+
+def is_matching_na(
+    left: object, right: object, nan_matches_none: bool = ...
+) -> bool: ...
+def isposinf_scalar(val: object) -> bool: ...
+def isneginf_scalar(val: object) -> bool: ...
+def checknull(val: object, inf_as_na: bool = ...) -> bool: ...
+def isnaobj(arr: np.ndarray, inf_as_na: bool = ...) -> npt.NDArray[np.bool_]: ...
+def is_numeric_na(values: np.ndarray) -> npt.NDArray[np.bool_]: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/ops.pyi

@@ -0,0 +1,51 @@
+from typing import (
+    Any,
+    Callable,
+    Iterable,
+    Literal,
+    TypeAlias,
+    overload,
+)
+
+import numpy as np
+
+from pandas._typing import npt
+
+_BinOp: TypeAlias = Callable[[Any, Any], Any]
+_BoolOp: TypeAlias = Callable[[Any, Any], bool]
+
+def scalar_compare(
+    values: np.ndarray,  # object[:]
+    val: object,
+    op: _BoolOp,  # {operator.eq, operator.ne, ...}
+) -> npt.NDArray[np.bool_]: ...
+def vec_compare(
+    left: npt.NDArray[np.object_],
+    right: npt.NDArray[np.object_],
+    op: _BoolOp,  # {operator.eq, operator.ne, ...}
+) -> npt.NDArray[np.bool_]: ...
+def scalar_binop(
+    values: np.ndarray,  # object[:]
+    val: object,
+    op: _BinOp,  # binary operator
+) -> np.ndarray: ...
+def vec_binop(
+    left: np.ndarray,  # object[:]
+    right: np.ndarray,  # object[:]
+    op: _BinOp,  # binary operator
+) -> np.ndarray: ...
+@overload
+def maybe_convert_bool(
+    arr: npt.NDArray[np.object_],
+    true_values: Iterable | None = None,
+    false_values: Iterable | None = None,
+    convert_to_masked_nullable: Literal[False] = ...,
+) -> tuple[np.ndarray, None]: ...
+@overload
+def maybe_convert_bool(
+    arr: npt.NDArray[np.object_],
+    true_values: Iterable = ...,
+    false_values: Iterable = ...,
+    *,
+    convert_to_masked_nullable: Literal[True],
+) -> tuple[np.ndarray, np.ndarray]: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/parsers.pyi

@@ -0,0 +1,77 @@
+from typing import (
+    Hashable,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._typing import (
+    ArrayLike,
+    Dtype,
+    npt,
+)
+
+STR_NA_VALUES: set[str]
+DEFAULT_BUFFER_HEURISTIC: int
+
+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 = ...,
+        float_precision: Literal["round_trip", "legacy", "high"] | None = ...,
+        skip_blank_lines: bool = ...,
+        encoding_errors: bytes | str = ...,
+    ) -> 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]]: ...
+
+# _maybe_upcast, na_values are only exposed for testing
+na_values: dict
+
+def _maybe_upcast(
+    arr, use_dtype_backend: bool = ..., dtype_backend: str = ...
+) -> np.ndarray: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/reshape.pyi

@@ -0,0 +1,16 @@
+import numpy as np
+
+from pandas._typing import npt
+
+def unstack(
+    values: np.ndarray,  # reshape_t[:, :]
+    mask: np.ndarray,  # const uint8_t[:]
+    stride: int,
+    length: int,
+    width: int,
+    new_values: np.ndarray,  # reshape_t[:, :]
+    new_mask: np.ndarray,  # uint8_t[:, :]
+) -> None: ...
+def explode(
+    values: npt.NDArray[np.object_],
+) -> tuple[npt.NDArray[np.object_], npt.NDArray[np.int64]]: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/sparse.pyi

@@ -0,0 +1,51 @@
+from typing import Sequence
+
+import numpy as np
+
+from pandas._typing import (
+    Self,
+    npt,
+)
+
+class SparseIndex:
+    length: int
+    npoints: int
+    def __init__(self) -> None: ...
+    @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, y_: SparseIndex) -> Self: ...
+    def make_union(self, y_: SparseIndex) -> Self: ...
+
+class IntIndex(SparseIndex):
+    indices: npt.NDArray[np.int32]
+    def __init__(
+        self, length: int, indices: Sequence[int], check_integrity: bool = ...
+    ) -> None: ...
+
+class BlockIndex(SparseIndex):
+    nblocks: int
+    blocs: np.ndarray
+    blengths: np.ndarray
+    def __init__(
+        self, length: int, blocs: np.ndarray, blengths: np.ndarray
+    ) -> None: ...
+
+    # Override to have correct parameters
+    def intersect(self, other: SparseIndex) -> Self: ...
+    def make_union(self, y: SparseIndex) -> Self: ...
+
+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]]: ...

videollama2/lib/python3.10/site-packages/pandas/_libs/writers.pyi

@@ -0,0 +1,20 @@
+import numpy as np
+
+from pandas._typing import ArrayLike
+
+def write_csv_rows(
+    data: list[ArrayLike],
+    data_index: np.ndarray,
+    nlevels: int,
+    cols: np.ndarray,
+    writer: object,  # _csv.writer
+) -> None: ...
+def convert_json_to_lines(arr: str) -> str: ...
+def max_len_string_array(
+    arr: np.ndarray,  # pandas_string[:]
+) -> int: ...
+def word_len(val: object) -> int: ...
+def string_array_replace_from_nan_rep(
+    arr: np.ndarray,  # np.ndarray[object, ndim=1]
+    nan_rep: object,
+) -> None: ...