Merge branch 'main' of github.com:pandas-dev/pandas into issue-37210-…

…to-sql-truncate

LICENSE

@@ -3,7 +3,7 @@ BSD 3-Clause License
 Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
 All rights reserved.
 
-Copyright (c) 2011-2024, Open source contributors.
+Copyright (c) 2011-2025, Open source contributors.
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:

ci/code_checks.sh

@@ -81,18 +81,11 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
         -i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
-        -i "pandas.arrays.NumpyExtensionArray SA01" \
         -i "pandas.arrays.TimedeltaArray PR07,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
-        -i "pandas.core.resample.Resampler.max PR01,RT03,SA01" \
-        -i "pandas.core.resample.Resampler.mean SA01" \
-        -i "pandas.core.resample.Resampler.min PR01,RT03,SA01" \
-        -i "pandas.core.resample.Resampler.prod SA01" \
         -i "pandas.core.resample.Resampler.quantile PR01,PR07" \
-        -i "pandas.core.resample.Resampler.std SA01" \
         -i "pandas.core.resample.Resampler.transform PR01,RT03,SA01" \
-        -i "pandas.core.resample.Resampler.var SA01" \
         -i "pandas.errors.ValueLabelTypeMismatch SA01" \
         -i "pandas.plotting.andrews_curves RT03,SA01" \
         -i "pandas.tseries.offsets.BDay PR02,SA01" \

doc/cheatsheet/README.md

@@ -6,10 +6,12 @@ and pick "PDF" as the format.
 
 This cheat sheet, originally written by Irv Lustig, [Princeton Consultants](https://www.princetonoptimization.com/), was inspired by the [RStudio Data Wrangling Cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf).
 
-| Topic                  | PDF                                                                                                                                                                                                                                     | PPT                                                                                                                                                                                                                                               |
-|------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Pandas_Cheat_Sheet     | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a>    | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet.pptx" target="_parent"><img src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a>     |
-| Pandas_Cheat_Sheet_JA  | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_JA.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a> | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_JA.pptx" target="_parent"><img  src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a> |
+| Topic                  | Language    | PDF                                                                                                                                                                                                                                     | PPT                                                                                                                                                                                                                                               |
+|------------------------|-------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Pandas_Cheat_Sheet     | English     | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a>    | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet.pptx" target="_parent"><img src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a>     |
+| Pandas_Cheat_Sheet_JA  | Japanese    | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_JA.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a> | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_JA.pptx" target="_parent"><img  src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a> |
+| Pandas_Cheat_Sheet_FA  | Persian     | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_FA.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a> | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_FA.pptx" target="_parent"><img  src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a> |
+
 
 
 **Alternative**

doc/source/user_guide/indexing.rst

@@ -858,9 +858,10 @@ and :ref:`Advanced Indexing <advanced>` you may select along more than one axis
 
 .. warning::
 
-   ``iloc`` supports two kinds of boolean indexing. If the indexer is a boolean ``Series``,
-   an error will be raised. For instance, in the following example, ``df.iloc[s.values, 1]`` is ok.
-   The boolean indexer is an array. But ``df.iloc[s, 1]`` would raise ``ValueError``.
+   While ``loc`` supports two kinds of boolean indexing, ``iloc`` only supports indexing with a
+   boolean array. If the indexer is a boolean ``Series``, an error will be raised. For instance,
+   in the following example, ``df.iloc[s.values, 1]`` is ok. The boolean indexer is an array.
+   But ``df.iloc[s, 1]`` would raise ``ValueError``.
 
    .. ipython:: python
 

doc/source/user_guide/visualization.rst

@@ -1210,11 +1210,6 @@ You may set the ``xlabel`` and ``ylabel`` arguments to give the plot custom labe
 for x and y axis. By default, pandas will pick up index name as xlabel, while leaving
 it empty for ylabel.
 
-.. ipython:: python
-   :suppress:
-
-   plt.figure();
-
 .. ipython:: python
 
    df.plot();

doc/source/whatsnew/v3.0.0.rst

@@ -61,6 +61,7 @@ Other enhancements
 - :meth:`pandas.concat` will raise a ``ValueError`` when ``ignore_index=True`` and ``keys`` is not ``None`` (:issue:`59274`)
 - :meth:`str.get_dummies` now accepts a  ``dtype`` parameter to specify the dtype of the resulting DataFrame (:issue:`47872`)
 - Add ``"delete_rows"`` option to ``if_exists`` argument in :meth:`DataFrame.to_sql` deleting all records of the table before inserting data (:issue:`37210`).
+- Implemented :meth:`Series.str.isascii` and :meth:`Series.str.isascii` (:issue:`59091`)
 - Multiplying two :class:`DateOffset` objects will now raise a ``TypeError`` instead of a ``RecursionError`` (:issue:`59442`)
 - Restore support for reading Stata 104-format and enable reading 103-format dta files (:issue:`58554`)
 - Support passing a :class:`Iterable[Hashable]` input to :meth:`DataFrame.drop_duplicates` (:issue:`59237`)

pandas/_libs/index.pyx

@@ -561,23 +561,15 @@ cdef class StringObjectEngine(ObjectEngine):
 
     cdef:
         object na_value
-        bint uses_na
 
     def __init__(self, ndarray values, na_value):
         super().__init__(values)
         self.na_value = na_value
-        self.uses_na = na_value is C_NA
-
-    cdef bint _checknull(self, object val):
-        if self.uses_na:
-            return val is C_NA
-        else:
-            return util.is_nan(val)
 
     cdef _check_type(self, object val):
         if isinstance(val, str):
             return val
-        elif self._checknull(val):
+        elif checknull(val):
             return self.na_value
         else:
             raise KeyError(val)

pandas/_libs/tslibs/nattype.pyx

@@ -704,7 +704,7 @@ class NaTType(_NaT):
         difference between the current timezone and UTC.
 
         Returns
-        --------
+        -------
         timedelta
             The difference between UTC and the local time as a `timedelta` object.
 

pandas/_libs/tslibs/timestamps.pyx

@@ -2217,7 +2217,7 @@ class Timestamp(_Timestamp):
         difference between the current timezone and UTC.
 
         Returns
-        --------
+        -------
         timedelta
             The difference between UTC and the local time as a `timedelta` object.
 

pandas/core/arrays/_arrow_string_mixins.py

@@ -253,6 +253,10 @@ def _str_isalpha(self):
         result = pc.utf8_is_alpha(self._pa_array)
         return self._convert_bool_result(result)
 
+    def _str_isascii(self):
+        result = pc.string_is_ascii(self._pa_array)
+        return self._convert_bool_result(result)
+
     def _str_isdecimal(self):
         result = pc.utf8_is_decimal(self._pa_array)
         return self._convert_bool_result(result)

pandas/core/arrays/arrow/array.py

@@ -2160,7 +2160,7 @@ def interpolate(
         """
         # NB: we return type(self) even if copy=False
         if not self.dtype._is_numeric:
-            raise ValueError("Values must be numeric.")
+            raise TypeError(f"Cannot interpolate with {self.dtype} dtype")
 
         if (
             not pa_version_under13p0

pandas/core/arrays/numpy_.py

@@ -71,6 +71,11 @@ class NumpyExtensionArray(  # type: ignore[misc]
     -------
     None
 
+    See Also
+    --------
+    array : Create an array.
+    Series.to_numpy : Convert a Series to a NumPy array.
+
     Examples
     --------
     >>> pd.arrays.NumpyExtensionArray(np.array([0, 1, 2, 3]))
@@ -287,6 +292,9 @@ def interpolate(
         See NDFrame.interpolate.__doc__.
         """
         # NB: we return type(self) even if copy=False
+        if not self.dtype._is_numeric:
+            raise TypeError(f"Cannot interpolate with {self.dtype} dtype")
+
         if not copy:
             out_data = self._ndarray
         else:

pandas/core/computation/eval.py

@@ -190,8 +190,8 @@ def eval(
 
     .. warning::
 
-        ``eval`` can run arbitrary code which can make you vulnerable to code
-         injection and untrusted data.
+        This function can run arbitrary code which can make you vulnerable to code
+        injection if you pass user input to this function.
 
     Parameters
     ----------

pandas/core/frame.py

@@ -4476,8 +4476,10 @@ def query(self, expr: str, *, inplace: bool = False, **kwargs) -> DataFrame | No
         """
         Query the columns of a DataFrame with a boolean expression.
 
-        This method can run arbitrary code which can make you vulnerable to code
-        injection if you pass user input to this function.
+        .. warning::
+
+            This method can run arbitrary code which can make you vulnerable to code
+            injection if you pass user input to this function.
 
         Parameters
         ----------
@@ -4634,6 +4636,11 @@ def eval(self, expr: str, *, inplace: bool = False, **kwargs) -> Any | None:
         """
         Evaluate a string describing operations on DataFrame columns.
 
+        .. warning::
+
+            This method can run arbitrary code which can make you vulnerable to code
+            injection if you pass user input to this function.
+
         Operates on columns only, not specific rows or elements.  This allows
         `eval` to run arbitrary code, which can make you vulnerable to code
         injection if you pass user input to this function.

pandas/core/resample.py

@@ -1096,6 +1096,13 @@ def prod(
         Series or DataFrame
             Computed prod of values within each group.
 
+        See Also
+        --------
+        core.resample.Resampler.sum : Compute sum of groups, excluding missing values.
+        core.resample.Resampler.mean : Compute mean of groups, excluding missing values.
+        core.resample.Resampler.median : Compute median of groups, excluding missing
+            values.
+
         Examples
         --------
         >>> ser = pd.Series(
@@ -1126,9 +1133,30 @@ def min(
         """
         Compute min value of group.
 
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None``.
+
+        min_count : int, default 0
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` non-NA values are present the result will be NA.
+
         Returns
         -------
         Series or DataFrame
+            Compute the minimum value in the given Series or DataFrame.
+
+        See Also
+        --------
+        core.resample.Resampler.max : Compute max value of group.
+        core.resample.Resampler.mean : Compute mean of groups, excluding missing values.
+        core.resample.Resampler.median : Compute median of groups, excluding missing
+            values.
 
         Examples
         --------
@@ -1160,9 +1188,30 @@ def max(
         """
         Compute max value of group.
 
+        Parameters
+        ----------
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+
+            .. versionchanged:: 2.0.0
+
+                numeric_only no longer accepts ``None``.
+
+        min_count : int, default 0
+            The required number of valid values to perform the operation. If fewer
+            than ``min_count`` non-NA values are present the result will be NA.
+
         Returns
         -------
         Series or DataFrame
+            Computes the maximum value in the given Series or Dataframe.
+
+        See Also
+        --------
+        core.resample.Resampler.min : Compute min value of group.
+        core.resample.Resampler.mean : Compute mean of groups, excluding missing values.
+        core.resample.Resampler.median : Compute median of groups, excluding missing
+            values.
 
         Examples
         --------
@@ -1236,6 +1285,16 @@ def mean(
         DataFrame or Series
             Mean of values within each group.
 
+        See Also
+        --------
+        core.resample.Resampler.median : Compute median of groups, excluding missing
+            values.
+        core.resample.Resampler.sum : Compute sum of groups, excluding missing values.
+        core.resample.Resampler.std : Compute standard deviation of groups, excluding
+            missing values.
+        core.resample.Resampler.var : Compute variance of groups, excluding missing
+            values.
+
         Examples
         --------
 
@@ -1285,6 +1344,14 @@ def std(
         DataFrame or Series
             Standard deviation of values within each group.
 
+        See Also
+        --------
+        core.resample.Resampler.mean : Compute mean of groups, excluding missing values.
+        core.resample.Resampler.median : Compute median of groups, excluding missing
+            values.
+        core.resample.Resampler.var : Compute variance of groups, excluding missing
+            values.
+
         Examples
         --------
 
@@ -1336,6 +1403,14 @@ def var(
         DataFrame or Series
             Variance of values within each group.
 
+        See Also
+        --------
+        core.resample.Resampler.std : Compute standard deviation of groups, excluding
+            missing values.
+        core.resample.Resampler.mean : Compute mean of groups, excluding missing values.
+        core.resample.Resampler.median : Compute median of groups, excluding missing
+            values.
+
         Examples
         --------