Initial commit: 首次建仓，建立目录结构

dashboard/venv/lib/python3.12/site-packages/pandas/compat/__init__.py

@@ -0,0 +1,179 @@
+"""
+compat
+======
+
+Cross-compatible functions for different versions of Python.
+
+Other items:
+* platform checker
+"""
+
+from __future__ import annotations
+
+import os
+import platform
+import sys
+from typing import TYPE_CHECKING
+
+from pandas.compat._constants import (
+    CHAINED_WARNING_DISABLED,
+    IS64,
+    ISMUSL,
+    PY312,
+    PY314,
+    PYPY,
+    WASM,
+)
+from pandas.compat.numpy import is_numpy_dev
+from pandas.compat.pyarrow import (
+    HAS_PYARROW,
+    PYARROW_INSTALLED,
+    PYARROW_MIN_VERSION,
+    pa_version_under14p0,
+    pa_version_under14p1,
+    pa_version_under16p0,
+    pa_version_under17p0,
+    pa_version_under18p0,
+    pa_version_under19p0,
+    pa_version_under20p0,
+    pa_version_under21p0,
+    pa_version_under22p0,
+    pa_version_under23p0,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import F
+
+
+def set_function_name(f: F, name: str, cls: type) -> F:
+    """
+    Bind the name/qualname attributes of the function.
+    """
+    f.__name__ = name
+    f.__qualname__ = f"{cls.__name__}.{name}"
+    f.__module__ = cls.__module__
+    return f
+
+
+def is_platform_little_endian() -> bool:
+    """
+    Checking if the running platform is little endian.
+
+    Returns
+    -------
+    bool
+        True if the running platform is little endian.
+    """
+    return sys.byteorder == "little"
+
+
+def is_platform_windows() -> bool:
+    """
+    Checking if the running platform is windows.
+
+    Returns
+    -------
+    bool
+        True if the running platform is windows.
+    """
+    return sys.platform in ["win32", "cygwin"]
+
+
+def is_platform_linux() -> bool:
+    """
+    Checking if the running platform is linux.
+
+    Returns
+    -------
+    bool
+        True if the running platform is linux.
+    """
+    return sys.platform == "linux"
+
+
+def is_platform_mac() -> bool:
+    """
+    Checking if the running platform is mac.
+
+    Returns
+    -------
+    bool
+        True if the running platform is mac.
+    """
+    return sys.platform == "darwin"
+
+
+def is_platform_arm() -> bool:
+    """
+    Checking if the running platform use ARM architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("arm64", "aarch64") or platform.machine().startswith(
+        "armv"
+    )
+
+
+def is_platform_power() -> bool:
+    """
+    Checking if the running platform use Power architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("ppc64", "ppc64le")
+
+
+def is_platform_riscv64() -> bool:
+    """
+    Checking if the running platform use riscv64 architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses riscv64 architecture.
+    """
+    return platform.machine() == "riscv64"
+
+
+def is_ci_environment() -> bool:
+    """
+    Checking if running in a continuous integration environment by checking
+    the PANDAS_CI environment variable.
+
+    Returns
+    -------
+    bool
+        True if the running in a continuous integration environment.
+    """
+    return os.environ.get("PANDAS_CI", "0") == "1"
+
+
+__all__ = [
+    "CHAINED_WARNING_DISABLED",
+    "HAS_PYARROW",
+    "IS64",
+    "ISMUSL",
+    "PY312",
+    "PY314",
+    "PYARROW_INSTALLED",
+    "PYARROW_MIN_VERSION",
+    "PYPY",
+    "WASM",
+    "is_numpy_dev",
+    "pa_version_under14p0",
+    "pa_version_under14p1",
+    "pa_version_under16p0",
+    "pa_version_under17p0",
+    "pa_version_under18p0",
+    "pa_version_under19p0",
+    "pa_version_under20p0",
+    "pa_version_under21p0",
+    "pa_version_under22p0",
+    "pa_version_under23p0",
+]

dashboard/venv/lib/python3.12/site-packages/pandas/compat/_constants.py

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+"""
+_constants
+======
+
+Constants relevant for the Python implementation.
+"""
+
+from __future__ import annotations
+
+import platform
+import sys
+import sysconfig
+
+IS64 = sys.maxsize > 2**32
+
+PY312 = sys.version_info >= (3, 12)
+PY314 = sys.version_info >= (3, 14)
+PYPY = platform.python_implementation() == "PyPy"
+WASM = (sys.platform == "emscripten") or (platform.machine() in ["wasm32", "wasm64"])
+ISMUSL = "musl" in (sysconfig.get_config_var("HOST_GNU_TYPE") or "")
+# the refcount for self in a chained __setitem__/.(i)loc indexing/method call
+REF_COUNT = 2 if PY314 else 3
+REF_COUNT_IDX = 2
+REF_COUNT_METHOD = 1 if PY314 else 2
+CHAINED_WARNING_DISABLED = PYPY
+
+
+__all__ = [
+    "IS64",
+    "ISMUSL",
+    "PY312",
+    "PY314",
+    "PYPY",
+    "WASM",
+]

dashboard/venv/lib/python3.12/site-packages/pandas/compat/_optional.py

@@ -0,0 +1,191 @@
+from __future__ import annotations
+
+import importlib
+import sys
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+    overload,
+)
+import warnings
+
+from pandas.util._exceptions import find_stack_level
+
+from pandas.util.version import Version
+
+if TYPE_CHECKING:
+    import types
+
+# Update install.rst, actions-311-minimum_versions.yaml,
+# deps_minimum.toml & pyproject.toml when updating versions!
+
+VERSIONS = {
+    "adbc-driver-postgresql": "1.2.0",
+    "adbc-driver-sqlite": "1.2.0",
+    "bs4": "4.12.3",
+    "bottleneck": "1.4.2",
+    "fastparquet": "2024.11.0",
+    "fsspec": "2024.10.0",
+    "html5lib": "1.1",
+    "hypothesis": "6.116.0",
+    "gcsfs": "2024.10.0",
+    "jinja2": "3.1.5",
+    "lxml.etree": "5.3.0",
+    "matplotlib": "3.9.3",
+    "numba": "0.60.0",
+    "numexpr": "2.10.2",
+    "odfpy": "1.4.1",
+    "openpyxl": "3.1.5",
+    "psycopg2": "2.9.10",  # (dt dec pq3 ext lo64)
+    "pymysql": "1.1.1",
+    "pyarrow": "13.0.0",
+    "pyiceberg": "0.8.1",
+    "pyreadstat": "1.2.8",
+    "pytest": "8.3.4",
+    "python-calamine": "0.3.0",
+    "pytz": "2020.1",  # keep this pinned (https://github.com/pandas-dev/pandas/pull/65133)
+    "pyxlsb": "1.0.10",
+    "s3fs": "2024.10.0",
+    "scipy": "1.14.1",
+    "sqlalchemy": "2.0.36",
+    "tables": "3.10.1",
+    "tabulate": "0.9.0",
+    "xarray": "2024.10.0",
+    "xlrd": "2.0.1",
+    "xlsxwriter": "3.2.0",
+    "zstandard": "0.23.0",
+    "qtpy": "2.4.2",
+    "pyqt5": "5.15.9",
+}
+
+# A mapping from import name to package name (on PyPI) for packages where
+# these two names are different.
+
+INSTALL_MAPPING = {
+    "bs4": "beautifulsoup4",
+    "bottleneck": "Bottleneck",
+    "jinja2": "Jinja2",
+    "lxml.etree": "lxml",
+    "odf": "odfpy",
+    "python_calamine": "python-calamine",
+    "sqlalchemy": "SQLAlchemy",
+    "tables": "pytables",
+}
+
+
+def get_version(module: types.ModuleType) -> str:
+    version = getattr(module, "__version__", None)
+
+    if version is None:
+        raise ImportError(f"Can't determine version for {module.__name__}")
+    if module.__name__ == "psycopg2":
+        # psycopg2 appends " (dt dec pq3 ext lo64)" to it's version
+        version = version.split()[0]
+    return version
+
+
+@overload
+def import_optional_dependency(
+    name: str,
+    extra: str = ...,
+    min_version: str | None = ...,
+    *,
+    errors: Literal["raise"] = ...,
+) -> types.ModuleType: ...
+
+
+@overload
+def import_optional_dependency(
+    name: str,
+    extra: str = ...,
+    min_version: str | None = ...,
+    *,
+    errors: Literal["warn", "ignore"],
+) -> types.ModuleType | None: ...
+
+
+def import_optional_dependency(
+    name: str,
+    extra: str = "",
+    min_version: str | None = None,
+    *,
+    errors: Literal["raise", "warn", "ignore"] = "raise",
+) -> types.ModuleType | None:
+    """
+    Import an optional dependency.
+
+    By default, if a dependency is missing an ImportError with a nice
+    message will be raised. If a dependency is present, but too old,
+    we raise.
+
+    Parameters
+    ----------
+    name : str
+        The module name.
+    extra : str
+        Additional text to include in the ImportError message.
+    errors : str {'raise', 'warn', 'ignore'}
+        What to do when a dependency is not found or its version is too old.
+
+        * raise : Raise an ImportError
+        * warn : Only applicable when a module's version is to old.
+          Warns that the version is too old and returns None
+        * ignore: If the module is not installed, return None, otherwise,
+          return the module, even if the version is too old.
+          It's expected that users validate the version locally when
+          using ``errors="ignore"`` (see. ``io/html.py``)
+    min_version : str, default None
+        Specify a minimum version that is different from the global pandas
+        minimum version required.
+    Returns
+    -------
+    maybe_module : Optional[ModuleType]
+        The imported module, when found and the version is correct.
+        None is returned when the package is not found and `errors`
+        is False, or when the package's version is too old and `errors`
+        is ``'warn'`` or ``'ignore'``.
+    """
+    assert errors in {"warn", "raise", "ignore"}
+
+    package_name = INSTALL_MAPPING.get(name)
+    install_name = package_name if package_name is not None else name
+
+    msg = (
+        f"`Import {install_name}` failed. {extra} "
+        f"Use pip or conda to install the {install_name} package."
+    )
+    try:
+        module = importlib.import_module(name)
+    except ImportError as err:
+        if errors == "raise":
+            raise ImportError(msg) from err
+        return None
+
+    # Handle submodules: if we have submodule, grab parent module from sys.modules
+    parent = name.split(".", maxsplit=1)[0]
+    if parent != name:
+        install_name = parent
+        module_to_get = sys.modules[install_name]
+    else:
+        module_to_get = module
+    minimum_version = min_version if min_version is not None else VERSIONS.get(parent)
+    if minimum_version:
+        version = get_version(module_to_get)
+        if version and Version(version) < Version(minimum_version):
+            msg = (
+                f"Pandas requires version '{minimum_version}' or newer of '{parent}' "
+                f"(version '{version}' currently installed)."
+            )
+            if errors == "warn":
+                warnings.warn(
+                    msg,
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return None
+            elif errors == "raise":
+                raise ImportError(msg)
+            else:
+                return None
+
+    return module

dashboard/venv/lib/python3.12/site-packages/pandas/compat/numpy/__init__.py

@@ -0,0 +1,50 @@
+"""support numpy compatibility across versions"""
+
+import warnings
+
+import numpy as np
+
+from pandas.util.version import Version
+
+# numpy versioning
+_np_version = np.__version__
+_nlv = Version(_np_version)
+np_version_gt2 = _nlv >= Version("2.0.0")
+np_version_gt2_3 = _nlv >= Version("2.3.0")
+np_version_gt2_5 = _nlv >= Version("2.5.0")
+is_numpy_dev = _nlv.dev is not None
+_min_numpy_ver = "1.26.0"
+
+
+if _nlv < Version(_min_numpy_ver):
+    raise ImportError(
+        f"Please upgrade numpy to >= {_min_numpy_ver} to use this pandas version.\n"
+        f"Your numpy version is {_np_version}."
+    )
+
+
+np_long: type
+np_ulong: type
+
+if np_version_gt2:
+    try:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                r".*In the future `np\.long` will be defined as.*",
+                FutureWarning,
+            )
+            np_long = np.long
+            np_ulong = np.ulong
+    except AttributeError:
+        np_long = np.int_
+        np_ulong = np.uint
+else:
+    np_long = np.int_
+    np_ulong = np.uint
+
+
+__all__ = [
+    "_np_version",
+    "is_numpy_dev",
+]

dashboard/venv/lib/python3.12/site-packages/pandas/compat/numpy/function.py

@@ -0,0 +1,376 @@
+"""
+For compatibility with numpy libraries, pandas functions or methods have to
+accept '*args' and '**kwargs' parameters to accommodate numpy arguments that
+are not actually used or respected in the pandas implementation.
+
+To ensure that users do not abuse these parameters, validation is performed in
+'validators.py' to make sure that any extra parameters passed correspond ONLY
+to those in the numpy signature. Part of that validation includes whether or
+not the user attempted to pass in non-default values for these extraneous
+parameters. As we want to discourage users from relying on these parameters
+when calling the pandas implementation, we want them only to pass in the
+default values for these parameters.
+
+This module provides a set of commonly used default arguments for functions and
+methods that are spread throughout the codebase. This module will make it
+easier to adjust to future upstream changes in the analogous numpy signatures.
+"""
+
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    TypeVar,
+    cast,
+    overload,
+)
+
+import numpy as np
+from numpy import ndarray
+
+from pandas._libs.lib import (
+    is_bool,
+    is_integer,
+)
+from pandas.errors import UnsupportedFunctionCall
+from pandas.util._validators import (
+    validate_args,
+    validate_args_and_kwargs,
+    validate_kwargs,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        AxisInt,
+    )
+
+    AxisNoneT = TypeVar("AxisNoneT", Axis, None)
+
+
+class CompatValidator:
+    def __init__(
+        self,
+        defaults,
+        fname=None,
+        method: str | None = None,
+        max_fname_arg_count=None,
+    ) -> None:
+        self.fname = fname
+        self.method = method
+        self.defaults = defaults
+        self.max_fname_arg_count = max_fname_arg_count
+
+    def __call__(
+        self,
+        args,
+        kwargs,
+        fname=None,
+        max_fname_arg_count=None,
+        method: str | None = None,
+    ) -> None:
+        if not args and not kwargs:
+            return None
+
+        fname = self.fname if fname is None else fname
+        max_fname_arg_count = (
+            self.max_fname_arg_count
+            if max_fname_arg_count is None
+            else max_fname_arg_count
+        )
+        method = self.method if method is None else method
+
+        if method == "args":
+            validate_args(fname, args, max_fname_arg_count, self.defaults)
+        elif method == "kwargs":
+            validate_kwargs(fname, kwargs, self.defaults)
+        elif method == "both":
+            validate_args_and_kwargs(
+                fname, args, kwargs, max_fname_arg_count, self.defaults
+            )
+        else:
+            raise ValueError(f"invalid validation method '{method}'")
+
+
+ARGMINMAX_DEFAULTS = {"out": None}
+validate_argmin = CompatValidator(
+    ARGMINMAX_DEFAULTS, fname="argmin", method="both", max_fname_arg_count=1
+)
+validate_argmax = CompatValidator(
+    ARGMINMAX_DEFAULTS, fname="argmax", method="both", max_fname_arg_count=1
+)
+
+
+def process_skipna(skipna: bool | ndarray | None, args) -> tuple[bool, Any]:
+    if isinstance(skipna, ndarray) or skipna is None:
+        args = (skipna, *args)
+        skipna = True
+
+    return skipna, args
+
+
+def validate_argmin_with_skipna(skipna: bool | ndarray | None, args, kwargs) -> bool:
+    """
+    If 'Series.argmin' is called via the 'numpy' library, the third parameter
+    in its signature is 'out', which takes either an ndarray or 'None', so
+    check if the 'skipna' parameter is either an instance of ndarray or is
+    None, since 'skipna' itself should be a boolean
+    """
+    skipna, args = process_skipna(skipna, args)
+    validate_argmin(args, kwargs)
+    return skipna
+
+
+def validate_argmax_with_skipna(skipna: bool | ndarray | None, args, kwargs) -> bool:
+    """
+    If 'Series.argmax' is called via the 'numpy' library, the third parameter
+    in its signature is 'out', which takes either an ndarray or 'None', so
+    check if the 'skipna' parameter is either an instance of ndarray or is
+    None, since 'skipna' itself should be a boolean
+    """
+    skipna, args = process_skipna(skipna, args)
+    validate_argmax(args, kwargs)
+    return skipna
+
+
+ARGSORT_DEFAULTS: dict[str, int | str | None] = {}
+ARGSORT_DEFAULTS["axis"] = -1
+ARGSORT_DEFAULTS["kind"] = "quicksort"
+ARGSORT_DEFAULTS["order"] = None
+ARGSORT_DEFAULTS["kind"] = None
+ARGSORT_DEFAULTS["stable"] = None
+
+
+validate_argsort = CompatValidator(
+    ARGSORT_DEFAULTS, fname="argsort", max_fname_arg_count=0, method="both"
+)
+
+# two different signatures of argsort, this second validation for when the
+# `kind` param is supported
+ARGSORT_DEFAULTS_KIND: dict[str, int | None] = {}
+ARGSORT_DEFAULTS_KIND["axis"] = -1
+ARGSORT_DEFAULTS_KIND["order"] = None
+ARGSORT_DEFAULTS_KIND["stable"] = None
+validate_argsort_kind = CompatValidator(
+    ARGSORT_DEFAULTS_KIND, fname="argsort", max_fname_arg_count=0, method="both"
+)
+
+
+def validate_argsort_with_ascending(ascending: bool | int | None, args, kwargs) -> bool:
+    """
+    If 'Categorical.argsort' is called via the 'numpy' library, the first
+    parameter in its signature is 'axis', which takes either an integer or
+    'None', so check if the 'ascending' parameter has either integer type or is
+    None, since 'ascending' itself should be a boolean
+    """
+    if is_integer(ascending) or ascending is None:
+        args = (ascending, *args)
+        ascending = True
+
+    validate_argsort_kind(args, kwargs, max_fname_arg_count=3)
+    ascending = cast(bool, ascending)
+    return ascending
+
+
+CLIP_DEFAULTS: dict[str, Any] = {"out": None}
+validate_clip = CompatValidator(
+    CLIP_DEFAULTS, fname="clip", method="both", max_fname_arg_count=3
+)
+
+
+@overload
+def validate_clip_with_axis(axis: ndarray, args, kwargs) -> None: ...
+
+
+@overload
+def validate_clip_with_axis(axis: AxisNoneT, args, kwargs) -> AxisNoneT: ...
+
+
+def validate_clip_with_axis(
+    axis: ndarray | AxisNoneT, args, kwargs
+) -> AxisNoneT | None:
+    """
+    If 'NDFrame.clip' is called via the numpy library, the third parameter in
+    its signature is 'out', which can takes an ndarray, so check if the 'axis'
+    parameter is an instance of ndarray, since 'axis' itself should either be
+    an integer or None
+    """
+    if isinstance(axis, ndarray):
+        args = (axis, *args)
+        # error: Incompatible types in assignment (expression has type "None",
+        # variable has type "Union[ndarray[Any, Any], str, int]")
+        axis = None  # type: ignore[assignment]
+
+    validate_clip(args, kwargs)
+    # error: Incompatible return value type (got "Union[ndarray[Any, Any],
+    # str, int]", expected "Union[str, int, None]")
+    return axis  # type: ignore[return-value]
+
+
+CUM_FUNC_DEFAULTS: dict[str, Any] = {}
+CUM_FUNC_DEFAULTS["dtype"] = None
+CUM_FUNC_DEFAULTS["out"] = None
+validate_cum_func = CompatValidator(
+    CUM_FUNC_DEFAULTS, method="both", max_fname_arg_count=1
+)
+validate_cumsum = CompatValidator(
+    CUM_FUNC_DEFAULTS, fname="cumsum", method="both", max_fname_arg_count=1
+)
+
+
+def validate_cum_func_with_skipna(skipna: bool, args, kwargs, name) -> bool:
+    """
+    If this function is called via the 'numpy' library, the third parameter in
+    its signature is 'dtype', which takes either a 'numpy' dtype or 'None', so
+    check if the 'skipna' parameter is a boolean or not
+    """
+    if not is_bool(skipna):
+        args = (skipna, *args)
+        skipna = True
+    elif isinstance(skipna, np.bool_):
+        skipna = bool(skipna)
+
+    validate_cum_func(args, kwargs, fname=name)
+    return skipna
+
+
+ALLANY_DEFAULTS: dict[str, bool | None] = {}
+ALLANY_DEFAULTS["dtype"] = None
+ALLANY_DEFAULTS["out"] = None
+ALLANY_DEFAULTS["keepdims"] = False
+ALLANY_DEFAULTS["axis"] = None
+validate_all = CompatValidator(
+    ALLANY_DEFAULTS, fname="all", method="both", max_fname_arg_count=1
+)
+validate_any = CompatValidator(
+    ALLANY_DEFAULTS, fname="any", method="both", max_fname_arg_count=1
+)
+
+LOGICAL_FUNC_DEFAULTS = {"out": None, "keepdims": False}
+validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method="kwargs")
+
+MINMAX_DEFAULTS = {"axis": None, "dtype": None, "out": None, "keepdims": False}
+validate_min = CompatValidator(
+    MINMAX_DEFAULTS, fname="min", method="both", max_fname_arg_count=1
+)
+validate_max = CompatValidator(
+    MINMAX_DEFAULTS, fname="max", method="both", max_fname_arg_count=1
+)
+
+
+REPEAT_DEFAULTS: dict[str, Any] = {"axis": None}
+validate_repeat = CompatValidator(
+    REPEAT_DEFAULTS, fname="repeat", method="both", max_fname_arg_count=1
+)
+
+ROUND_DEFAULTS: dict[str, Any] = {"out": None}
+validate_round = CompatValidator(
+    ROUND_DEFAULTS, fname="round", method="both", max_fname_arg_count=1
+)
+
+STAT_FUNC_DEFAULTS: dict[str, Any | None] = {}
+STAT_FUNC_DEFAULTS["dtype"] = None
+STAT_FUNC_DEFAULTS["out"] = None
+
+SUM_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+SUM_DEFAULTS["axis"] = None
+SUM_DEFAULTS["keepdims"] = False
+SUM_DEFAULTS["initial"] = None
+
+PROD_DEFAULTS = SUM_DEFAULTS.copy()
+
+MEAN_DEFAULTS = SUM_DEFAULTS.copy()
+
+MEDIAN_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+MEDIAN_DEFAULTS["overwrite_input"] = False
+MEDIAN_DEFAULTS["keepdims"] = False
+
+STAT_FUNC_DEFAULTS["keepdims"] = False
+
+validate_stat_func = CompatValidator(STAT_FUNC_DEFAULTS, method="kwargs")
+validate_sum = CompatValidator(
+    SUM_DEFAULTS, fname="sum", method="both", max_fname_arg_count=1
+)
+validate_prod = CompatValidator(
+    PROD_DEFAULTS, fname="prod", method="both", max_fname_arg_count=1
+)
+validate_mean = CompatValidator(
+    MEAN_DEFAULTS, fname="mean", method="both", max_fname_arg_count=1
+)
+validate_median = CompatValidator(
+    MEDIAN_DEFAULTS, fname="median", method="both", max_fname_arg_count=1
+)
+
+STAT_DDOF_FUNC_DEFAULTS: dict[str, bool | None] = {}
+STAT_DDOF_FUNC_DEFAULTS["dtype"] = None
+STAT_DDOF_FUNC_DEFAULTS["out"] = None
+STAT_DDOF_FUNC_DEFAULTS["keepdims"] = False
+validate_stat_ddof_func = CompatValidator(STAT_DDOF_FUNC_DEFAULTS, method="kwargs")
+
+TAKE_DEFAULTS: dict[str, str | None] = {}
+TAKE_DEFAULTS["out"] = None
+TAKE_DEFAULTS["mode"] = "raise"
+validate_take = CompatValidator(TAKE_DEFAULTS, fname="take", method="kwargs")
+
+
+TRANSPOSE_DEFAULTS = {"axes": None}
+validate_transpose = CompatValidator(
+    TRANSPOSE_DEFAULTS, fname="transpose", method="both", max_fname_arg_count=0
+)
+
+
+def validate_groupby_func(name: str, args, kwargs, allowed=None) -> None:
+    """
+    'args' and 'kwargs' should be empty, except for allowed kwargs because all
+    of their necessary parameters are explicitly listed in the function
+    signature
+    """
+    if allowed is None:
+        allowed = []
+
+    kwargs = set(kwargs) - set(allowed)
+
+    if len(args) + len(kwargs) > 0:
+        raise UnsupportedFunctionCall(
+            "numpy operations are not valid with groupby. "
+            f"Use .groupby(...).{name}() instead"
+        )
+
+
+def validate_minmax_axis(axis: AxisInt | None, ndim: int = 1) -> None:
+    """
+    Ensure that the axis argument passed to min, max, argmin, or argmax is zero
+    or None, as otherwise it will be incorrectly ignored.
+
+    Parameters
+    ----------
+    axis : int or None
+    ndim : int, default 1
+
+    Raises
+    ------
+    ValueError
+    """
+    if axis is None:
+        return
+    if axis >= ndim or (axis < 0 and ndim + axis < 0):
+        raise ValueError(f"`axis` must be fewer than the number of dimensions ({ndim})")
+
+
+_validation_funcs = {
+    "median": validate_median,
+    "mean": validate_mean,
+    "min": validate_min,
+    "max": validate_max,
+    "sum": validate_sum,
+    "prod": validate_prod,
+}
+
+
+def validate_func(fname, args, kwargs) -> None:
+    if fname not in _validation_funcs:
+        return validate_stat_func(args, kwargs, fname=fname)
+
+    validation_func = _validation_funcs[fname]
+    return validation_func(args, kwargs)

dashboard/venv/lib/python3.12/site-packages/pandas/compat/pickle_compat.py

@@ -0,0 +1,143 @@
+"""
+Pickle compatibility to pandas version 1.0
+"""
+
+from __future__ import annotations
+
+import contextlib
+import io
+import pickle
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.tslibs import BaseOffset
+
+from pandas.core.arrays import (
+    DatetimeArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.internals import BlockManager
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+
+# If classes are moved, provide compat here.
+_class_locations_map = {
+    # Re-routing unpickle block logic to go through _unpickle_block instead
+    # for pandas <= 1.3.5
+    ("pandas.core.internals.blocks", "new_block"): (
+        "pandas._libs.internals",
+        "_unpickle_block",
+    ),
+    # Avoid Cython's warning "contradiction to Python 'class private name' rules"
+    ("pandas._libs.tslibs.nattype", "__nat_unpickle"): (
+        "pandas._libs.tslibs.nattype",
+        "_nat_unpickle",
+    ),
+    # 50775, remove Int64Index, UInt64Index & Float64Index from codebase
+    ("pandas.core.indexes.numeric", "Int64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.indexes.numeric", "UInt64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.indexes.numeric", "Float64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.arrays.sparse.dtype", "SparseDtype"): (
+        "pandas.core.dtypes.dtypes",
+        "SparseDtype",
+    ),
+}
+
+
+# our Unpickler sub-class to override methods and some dispatcher
+# functions for compat and uses a non-public class of the pickle module.
+class Unpickler(pickle._Unpickler):
+    def find_class(self, module: str, name: str) -> Any:
+        key = (module, name)
+        module, name = _class_locations_map.get(key, key)
+        return super().find_class(module, name)
+
+    dispatch = pickle._Unpickler.dispatch.copy()
+
+    def load_reduce(self) -> None:
+        stack = self.stack  # type: ignore[attr-defined]
+        args = stack.pop()
+        func = stack[-1]
+
+        try:
+            stack[-1] = func(*args)
+        except TypeError:
+            # If we have a deprecated function,
+            # try to replace and try again.
+            if args and isinstance(args[0], type) and issubclass(args[0], BaseOffset):
+                # TypeError: object.__new__(Day) is not safe, use Day.__new__()
+                cls = args[0]
+                stack[-1] = cls.__new__(*args)
+                return
+            elif args and issubclass(args[0], PeriodArray):
+                cls = args[0]
+                stack[-1] = NDArrayBacked.__new__(*args)
+                return
+            raise
+
+    dispatch[pickle.REDUCE[0]] = load_reduce  # type: ignore[assignment]
+
+    def load_newobj(self) -> None:
+        args = self.stack.pop()  # type: ignore[attr-defined]
+        cls = self.stack.pop()  # type: ignore[attr-defined]
+
+        # compat
+        if issubclass(cls, DatetimeArray) and not args:
+            arr = np.array([], dtype="M8[ns]")
+            obj = cls.__new__(cls, arr, arr.dtype)
+        elif issubclass(cls, TimedeltaArray) and not args:
+            arr = np.array([], dtype="m8[ns]")
+            obj = cls.__new__(cls, arr, arr.dtype)
+        elif cls is BlockManager and not args:
+            obj = cls.__new__(cls, (), [], False)
+        else:
+            obj = cls.__new__(cls, *args)
+        self.append(obj)  # type: ignore[attr-defined]
+
+    dispatch[pickle.NEWOBJ[0]] = load_newobj  # type: ignore[assignment]
+
+
+def loads(
+    bytes_object: bytes,
+    *,
+    fix_imports: bool = True,
+    encoding: str = "ASCII",
+    errors: str = "strict",
+) -> Any:
+    """
+    Analogous to pickle._loads.
+    """
+    fd = io.BytesIO(bytes_object)
+    return Unpickler(
+        fd, fix_imports=fix_imports, encoding=encoding, errors=errors
+    ).load()
+
+
+@contextlib.contextmanager
+def patch_pickle() -> Generator[None]:
+    """
+    Temporarily patch pickle to use our unpickler.
+    """
+    orig_loads = pickle.loads
+    try:
+        setattr(pickle, "loads", loads)
+        yield
+    finally:
+        setattr(pickle, "loads", orig_loads)

dashboard/venv/lib/python3.12/site-packages/pandas/compat/pyarrow.py

@@ -0,0 +1,95 @@
+"""support pyarrow compatibility across versions"""
+
+from __future__ import annotations
+
+import sys
+from typing import Any
+
+from pandas.util.version import Version
+
+PYARROW_MIN_VERSION = "13.0.0"
+try:
+    import pyarrow as pa
+
+    _palv = Version(Version(pa.__version__).base_version)
+    pa_version_under14p0 = _palv < Version("14.0.0")
+    pa_version_under14p1 = _palv < Version("14.0.1")
+    pa_version_under15p0 = _palv < Version("15.0.0")
+    pa_version_under16p0 = _palv < Version("16.0.0")
+    pa_version_under17p0 = _palv < Version("17.0.0")
+    pa_version_under18p0 = _palv < Version("18.0.0")
+    pa_version_under19p0 = _palv < Version("19.0.0")
+    pa_version_under20p0 = _palv < Version("20.0.0")
+    pa_version_under21p0 = _palv < Version("21.0.0")
+    pa_version_under22p0 = _palv < Version("22.0.0")
+    pa_version_under23p0 = _palv < Version("23.0.0")
+    PYARROW_INSTALLED = True
+    HAS_PYARROW = _palv >= Version(PYARROW_MIN_VERSION)
+except ImportError:
+    pa_version_under14p0 = True
+    pa_version_under14p1 = True
+    pa_version_under15p0 = True
+    pa_version_under16p0 = True
+    pa_version_under17p0 = True
+    pa_version_under18p0 = True
+    pa_version_under19p0 = True
+    pa_version_under20p0 = True
+    pa_version_under21p0 = True
+    pa_version_under22p0 = True
+    pa_version_under23p0 = True
+    PYARROW_INSTALLED = False
+    HAS_PYARROW = False
+
+
+def _safe_fill_null(
+    arr: pa.Array | pa.ChunkedArray, fill_value: Any
+) -> pa.Array | pa.ChunkedArray:
+    """
+    Safe wrapper for pyarrow.compute.fill_null with fallback for Windows + pyarrow 21.
+
+    pyarrow 21.0.0 on Windows has a bug in fill_null that incorrectly fills null values.
+    This function uses a fallback implementation for that specific case, otherwise uses
+    the standard pyarrow.compute.fill_null.
+
+    Parameters
+    ----------
+    arr : pyarrow.Array | pyarrow.ChunkedArray
+        Input array with potential null values.
+    fill_value : Any
+        Value to fill nulls with.
+
+    Returns
+    -------
+    pyarrow.Array | pyarrow.ChunkedArray
+        Array with nulls filled with fill_value.
+    """
+    import pyarrow.compute as pc
+
+    is_windows = sys.platform in ["win32", "cygwin"]
+    use_fallback = (
+        HAS_PYARROW and is_windows and not pa_version_under21p0 and pa_version_under22p0
+    )
+    if not use_fallback or isinstance(fill_value, (pa.Array, pa.ChunkedArray)):
+        return pc.fill_null(arr, fill_value)
+
+    fill_scalar = pa.scalar(fill_value, type=arr.type)
+
+    if pa.types.is_duration(arr.type):
+
+        def fill_null_duration(arr: pa.Array, fill_scalar: pa.Scalar) -> pa.Array:
+            mask = pc.is_null(arr)
+            zero_duration = pa.scalar(0, type=arr.type)
+            arr_zeroed = pc.if_else(mask, zero_duration, arr)
+            return pc.if_else(mask, fill_scalar, arr_zeroed)
+
+        if isinstance(arr, pa.ChunkedArray):
+            return pa.chunked_array(
+                [fill_null_duration(chunk, fill_scalar) for chunk in arr.chunks]
+            )
+        return fill_null_duration(arr, fill_scalar)
+
+    if isinstance(arr, pa.ChunkedArray):
+        return pa.chunked_array(
+            [pc.if_else(pc.is_null(chunk), fill_scalar, chunk) for chunk in arr.chunks]
+        )
+    return pc.if_else(pc.is_null(arr), fill_scalar, arr)