some new features

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

@@ -0,0 +1,188 @@
+from typing import Optional
+
+import numpy as np
+from packaging.version import Version, parse
+import pandas as pd
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+    cache_readonly,
+    deprecate_kwarg,
+)
+
+__all__ = [
+    "assert_frame_equal",
+    "assert_index_equal",
+    "assert_series_equal",
+    "data_klasses",
+    "frequencies",
+    "is_numeric_dtype",
+    "testing",
+    "cache_readonly",
+    "deprecate_kwarg",
+    "Appender",
+    "Substitution",
+    "is_int_index",
+    "is_float_index",
+    "make_dataframe",
+    "to_numpy",
+    "PD_LT_1_0_0",
+    "get_cached_func",
+    "get_cached_doc",
+    "call_cached_func",
+    "PD_LT_1_4",
+    "PD_LT_2",
+    "MONTH_END",
+    "QUARTER_END",
+    "YEAR_END",
+    "FUTURE_STACK",
+]
+
+version = parse(pd.__version__)
+
+PD_LT_2_2_0 = version < Version("2.1.99")
+PD_LT_2_1_0 = version < Version("2.0.99")
+PD_LT_1_0_0 = version < Version("0.99.0")
+PD_LT_1_4 = version < Version("1.3.99")
+PD_LT_2 = version < Version("1.9.99")
+
+try:
+    from pandas.api.types import is_numeric_dtype
+except ImportError:
+    from pandas.core.common import is_numeric_dtype
+
+try:
+    from pandas.tseries import offsets as frequencies
+except ImportError:
+    from pandas.tseries import frequencies
+
+data_klasses = (pd.Series, pd.DataFrame)
+
+try:
+    import pandas.testing as testing
+except ImportError:
+    import pandas.util.testing as testing
+
+assert_frame_equal = testing.assert_frame_equal
+assert_index_equal = testing.assert_index_equal
+assert_series_equal = testing.assert_series_equal
+
+
+def is_int_index(index: pd.Index) -> bool:
+    """
+    Check if an index is integral
+
+    Parameters
+    ----------
+    index : pd.Index
+        Any numeric index
+
+    Returns
+    -------
+    bool
+        True if is an index with a standard integral type
+    """
+    return (
+        isinstance(index, pd.Index)
+        and isinstance(index.dtype, np.dtype)
+        and np.issubdtype(index.dtype, np.integer)
+    )
+
+
+def is_float_index(index: pd.Index) -> bool:
+    """
+    Check if an index is floating
+
+    Parameters
+    ----------
+    index : pd.Index
+        Any numeric index
+
+    Returns
+    -------
+    bool
+        True if an index with a standard numpy floating dtype
+    """
+    return (
+        isinstance(index, pd.Index)
+        and isinstance(index.dtype, np.dtype)
+        and np.issubdtype(index.dtype, np.floating)
+    )
+
+
+try:
+    from pandas._testing import makeDataFrame as make_dataframe
+except ImportError:
+    import string
+
+    def rands_array(nchars, size, dtype="O"):
+        """
+        Generate an array of byte strings.
+        """
+        rands_chars = np.array(
+            list(string.ascii_letters + string.digits), dtype=(np.str_, 1)
+        )
+        retval = (
+            np.random.choice(rands_chars, size=nchars * np.prod(size))
+            .view((np.str_, nchars))
+            .reshape(size)
+        )
+        if dtype is None:
+            return retval
+        else:
+            return retval.astype(dtype)
+
+    def make_dataframe():
+        """
+        Simple verion of pandas._testing.makeDataFrame
+        """
+        n = 30
+        k = 4
+        index = pd.Index(rands_array(nchars=10, size=n), name=None)
+        data = {
+            c: pd.Series(np.random.randn(n), index=index)
+            for c in string.ascii_uppercase[:k]
+        }
+
+        return pd.DataFrame(data)
+
+
+def to_numpy(po: pd.DataFrame) -> np.ndarray:
+    """
+    Workaround legacy pandas lacking to_numpy
+
+    Parameters
+    ----------
+    po : Pandas obkect
+
+    Returns
+    -------
+    ndarray
+        A numpy array
+    """
+    try:
+        return po.to_numpy()
+    except AttributeError:
+        return po.values
+
+
+def get_cached_func(cached_prop):
+    try:
+        return cached_prop.fget
+    except AttributeError:
+        return cached_prop.func
+
+
+def call_cached_func(cached_prop, *args, **kwargs):
+    f = get_cached_func(cached_prop)
+    return f(*args, **kwargs)
+
+
+def get_cached_doc(cached_prop) -> Optional[str]:
+    return get_cached_func(cached_prop).__doc__
+
+
+MONTH_END = "M" if PD_LT_2_2_0 else "ME"
+QUARTER_END = "Q" if PD_LT_2_2_0 else "QE"
+YEAR_END = "Y" if PD_LT_2_2_0 else "YE"
+FUTURE_STACK = {} if PD_LT_2_1_0 else {"future_stack": True}