Merge branch 'master' into forward-indexer

Author: AlexKirko

ci/code_checks.sh

@@ -327,7 +327,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
     MSG='Validate correct capitalization among titles in documentation' ; echo $MSG
-    $BASE_DIR/scripts/validate_rst_title_capitalization.py $BASE_DIR/doc/source/development/contributing.rst
+    $BASE_DIR/scripts/validate_rst_title_capitalization.py $BASE_DIR/doc/source/development/contributing.rst $BASE_DIR/doc/source/reference
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
 fi

doc/source/development/code_style.rst

@@ -18,8 +18,8 @@ consistent code format throughout the project. For details see the
 Patterns
 ========
 
-foo.__class__
--------------
+Using foo.__class__
+-------------------
 
 
 pandas uses 'type(foo)' instead 'foo.__class__' as it is making the code more
@@ -47,8 +47,8 @@ String formatting
 Concatenated strings
 --------------------
 
-f-strings
-~~~~~~~~~
+Using f-strings
+~~~~~~~~~~~~~~~
 
 pandas uses f-strings formatting instead of '%' and '.format()' string formatters.
 

doc/source/development/contributing.rst

@@ -295,7 +295,7 @@ Below is a brief overview on how to set-up a virtual environment with Powershell
 under Windows. For details please refer to the
 `official virtualenv user guide <https://virtualenv.pypa.io/en/stable/userguide/#activate-script>`__
 
-Use an ENV_DIR of your choice. We'll use ~\virtualenvs\pandas-dev where
+Use an ENV_DIR of your choice. We'll use ~\\virtualenvs\\pandas-dev where
 '~' is the folder pointed to by either $env:USERPROFILE (Powershell) or
 %USERPROFILE% (cmd.exe) environment variable. Any parent directories
 should already exist.

doc/source/development/extending.rst

@@ -139,7 +139,7 @@ and comments contain guidance for properly implementing the interface.
 
 .. _extending.extension.operator:
 
-:class:`~pandas.api.extensions.ExtensionArray` Operator Support
+:class:`~pandas.api.extensions.ExtensionArray` operator support
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. versionadded:: 0.24.0

doc/source/development/policies.rst

@@ -6,7 +6,7 @@ Policies
 
 .. _policies.version:
 
-Version Policy
+Version policy
 ~~~~~~~~~~~~~~
 
 .. versionchanged:: 1.0.0
@@ -48,7 +48,7 @@ deprecation removed in the next next major release (2.0.0).
 These policies do not apply to features marked as **experimental** in the documentation.
 pandas may change the behavior of experimental features at any time.
 
-Python Support
+Python support
 ~~~~~~~~~~~~~~
 
 pandas will only drop support for specific Python versions (e.g. 3.6.x, 3.7.x) in

doc/source/development/roadmap.rst

@@ -152,7 +152,7 @@ We'd like to fund improvements and maintenance of these tools to
 
 .. _roadmap.evolution:
 
-Roadmap Evolution
+Roadmap evolution
 -----------------
 
 pandas continues to evolve. The direction is primarily determined by community

doc/source/getting_started/intro_tutorials/09_timeseries.rst

@@ -96,7 +96,7 @@ objects. In pandas we call these datetime objects similar to
 
         pd.read_csv("../data/air_quality_no2_long.csv", parse_dates=["datetime"])
 
-Why are these :class:`pandas.Timestamp` objects useful. Let’s illustrate the added
+Why are these :class:`pandas.Timestamp` objects useful? Let’s illustrate the added
 value with some example cases.
 
    What is the start and end date of the time series data set working
@@ -106,7 +106,7 @@ value with some example cases.
 
     air_quality["datetime"].min(), air_quality["datetime"].max()
 
-Using :class:`pandas.Timestamp` for datetimes enable us to calculate with date
+Using :class:`pandas.Timestamp` for datetimes enables us to calculate with date
 information and make them comparable. Hence, we can use this to get the
 length of our time series:
 
@@ -122,7 +122,7 @@ from the standard Python library and defining a time duration.
     <div class="d-flex flex-row gs-torefguide">
         <span class="badge badge-info">To user guide</span>
 
-The different time concepts supported by pandas are explained in the user guide section on :ref:`time related concepts <timeseries.overview>`.
+The various time concepts supported by pandas are explained in the user guide section on :ref:`time related concepts <timeseries.overview>`.
 
 .. raw:: html
 
@@ -157,7 +157,7 @@ accessible by the ``dt`` accessor.
 
 An overview of the existing date properties is given in the
 :ref:`time and date components overview table <timeseries.components>`. More details about the ``dt`` accessor
-to return datetime like properties is explained in a dedicated section on the  :ref:`dt accessor <basics.dt_accessors>`.
+to return datetime like properties are explained in a dedicated section on the  :ref:`dt accessor <basics.dt_accessors>`.
 
 .. raw:: html
 
@@ -353,7 +353,7 @@ Make a plot of the daily mean :math:`NO_2` value in each of the stations.
     <div class="d-flex flex-row gs-torefguide">
         <span class="badge badge-info">To user guide</span>
 
-More details on the power of time series ``resampling`` is provided in the user gudie section on :ref:`resampling <timeseries.resampling>`.
+More details on the power of time series ``resampling`` is provided in the user guide section on :ref:`resampling <timeseries.resampling>`.
 
 .. raw:: html
 
@@ -366,7 +366,7 @@ More details on the power of time series ``resampling`` is provided in the user
 
 -  Valid date strings can be converted to datetime objects using
    ``to_datetime`` function or as part of read functions.
--  Datetime objects in pandas supports calculations, logical operations
+-  Datetime objects in pandas support calculations, logical operations
    and convenient date-related properties using the ``dt`` accessor.
 -  A ``DatetimeIndex`` contains these date-related properties and
    supports convenient slicing.
@@ -382,7 +382,7 @@ More details on the power of time series ``resampling`` is provided in the user
     <div class="d-flex flex-row gs-torefguide">
         <span class="badge badge-info">To user guide</span>
 
-A full overview on time series is given in the pages on :ref:`time series and date functionality <timeseries>`.
+A full overview on time series is given on the pages on :ref:`time series and date functionality <timeseries>`.
 
 .. raw:: html
 

doc/source/reference/frame.rst

@@ -251,7 +251,7 @@ Combining / joining / merging
    DataFrame.merge
    DataFrame.update
 
-Time series-related
+Time Series-related
 ~~~~~~~~~~~~~~~~~~~
 .. autosummary::
    :toctree: api/

doc/source/reference/indexing.rst

@@ -328,7 +328,7 @@ DatetimeIndex
 
    DatetimeIndex
 
-Time/Date components
+Time/date components
 ~~~~~~~~~~~~~~~~~~~~
 .. autosummary::
    :toctree: api/

doc/source/reference/series.rst

@@ -110,7 +110,7 @@ Binary operator functions
    Series.product
    Series.dot
 
-Function application, groupby & window
+Function application, GroupBy & window
 --------------------------------------
 .. autosummary::
    :toctree: api/
@@ -249,7 +249,7 @@ Combining / joining / merging
    Series.replace
    Series.update
 
-Time series-related
+Time Series-related
 -------------------
 .. autosummary::
    :toctree: api/

doc/source/whatsnew/v1.1.0.rst

@@ -87,6 +87,7 @@ Other enhancements
 - Positional slicing on a :class:`IntervalIndex` now supports slices with ``step > 1`` (:issue:`31658`)
 - :class:`Series.str` now has a `fullmatch` method that matches a regular expression against the entire string in each row of the series, similar to `re.fullmatch` (:issue:`32806`).
 - :meth:`DataFrame.sample` will now also allow array-like and BitGenerator objects to be passed to ``random_state`` as seeds (:issue:`32503`)
+- :meth:`MultiIndex.union` will now raise `RuntimeWarning` if the object inside are unsortable, pass `sort=False` to suppress this warning (:issue:`33015`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/_libs/lib.pyx

@@ -4,6 +4,7 @@ from fractions import Fraction
 from numbers import Number
 
 import sys
+import warnings
 
 import cython
 from cython import Py_ssize_t
@@ -286,7 +287,11 @@ def fast_unique_multiple(list arrays, sort: bool = True):
         try:
             uniques.sort()
         except TypeError:
-            # TODO: RuntimeWarning?
+            warnings.warn(
+                "The values in the array are unorderable. "
+                "Pass `sort=False` to suppress this warning.",
+                RuntimeWarning,
+            )
             pass
 
     return uniques

pandas/_libs/tslibs/conversion.pyx

@@ -44,8 +44,8 @@ from pandas._libs.tslibs.tzconversion cimport (
 # ----------------------------------------------------------------------
 # Constants
 
-NS_DTYPE = np.dtype('M8[ns]')
-TD_DTYPE = np.dtype('m8[ns]')
+DT64NS_DTYPE = np.dtype('M8[ns]')
+TD64NS_DTYPE = np.dtype('m8[ns]')
 
 
 # ----------------------------------------------------------------------
@@ -105,11 +105,11 @@ def ensure_datetime64ns(arr: ndarray, copy: bool=True):
 
     ivalues = arr.view(np.int64).ravel()
 
-    result = np.empty(shape, dtype=NS_DTYPE)
+    result = np.empty(shape, dtype=DT64NS_DTYPE)
     iresult = result.ravel().view(np.int64)
 
     if len(iresult) == 0:
-        result = arr.view(NS_DTYPE)
+        result = arr.view(DT64NS_DTYPE)
         if copy:
             result = result.copy()
         return result
@@ -145,7 +145,7 @@ def ensure_timedelta64ns(arr: ndarray, copy: bool=True):
     result : ndarray with dtype timedelta64[ns]
 
     """
-    return arr.astype(TD_DTYPE, copy=copy)
+    return arr.astype(TD64NS_DTYPE, copy=copy)
     # TODO: check for overflows when going from a lower-resolution to nanos
 
 

pandas/_libs/writers.pyx

@@ -112,7 +112,7 @@ def convert_json_to_lines(arr: object) -> str:
             if not in_quotes:
                 num_open_brackets_seen -= 1
 
-    return narr.tostring().decode('utf-8')
+    return narr.tobytes().decode('utf-8')
 
 
 # stata, pytables

pandas/core/arrays/categorical.py

@@ -378,7 +378,7 @@ def __init__(
             old_codes = (
                 values._values.codes if isinstance(values, ABCSeries) else values.codes
             )
-            codes = _recode_for_categories(
+            codes = recode_for_categories(
                 old_codes, values.dtype.categories, dtype.categories
             )
 
@@ -572,13 +572,13 @@ def _from_inferred_categories(
         if known_categories:
             # Recode from observation order to dtype.categories order.
             categories = dtype.categories
-            codes = _recode_for_categories(inferred_codes, cats, categories)
+            codes = recode_for_categories(inferred_codes, cats, categories)
         elif not cats.is_monotonic_increasing:
             # Sort categories and recode for unknown categories.
             unsorted = cats.copy()
             categories = cats.sort_values()
 
-            codes = _recode_for_categories(inferred_codes, unsorted, categories)
+            codes = recode_for_categories(inferred_codes, unsorted, categories)
             dtype = CategoricalDtype(categories, ordered=False)
         else:
             dtype = CategoricalDtype(cats, ordered=False)
@@ -727,7 +727,7 @@ def _set_dtype(self, dtype: CategoricalDtype) -> "Categorical":
         We don't do any validation here. It's assumed that the dtype is
         a (valid) instance of `CategoricalDtype`.
         """
-        codes = _recode_for_categories(self.codes, self.categories, dtype.categories)
+        codes = recode_for_categories(self.codes, self.categories, dtype.categories)
         return type(self)(codes, dtype=dtype, fastpath=True)
 
     def set_ordered(self, value, inplace=False):
@@ -849,7 +849,7 @@ def set_categories(self, new_categories, ordered=None, rename=False, inplace=Fal
                 # remove all _codes which are larger and set to -1/NaN
                 cat._codes[cat._codes >= len(new_dtype.categories)] = -1
         else:
-            codes = _recode_for_categories(
+            codes = recode_for_categories(
                 cat.codes, cat.categories, new_dtype.categories
             )
             cat._codes = codes
@@ -2034,7 +2034,7 @@ def __setitem__(self, key, value):
                     "without identical categories"
                 )
             if not self.categories.equals(value.categories):
-                new_codes = _recode_for_categories(
+                new_codes = recode_for_categories(
                     value.codes, value.categories, self.categories
                 )
                 value = Categorical.from_codes(new_codes, dtype=self.dtype)
@@ -2298,7 +2298,7 @@ def equals(self, other):
                 # fastpath to avoid re-coding
                 other_codes = other._codes
             else:
-                other_codes = _recode_for_categories(
+                other_codes = recode_for_categories(
                     other.codes, other.categories, self.categories
                 )
             return np.array_equal(self._codes, other_codes)
@@ -2667,7 +2667,7 @@ def _get_codes_for_values(values, categories):
     return coerce_indexer_dtype(t.lookup(vals), cats)
 
 
-def _recode_for_categories(codes: np.ndarray, old_categories, new_categories):
+def recode_for_categories(codes: np.ndarray, old_categories, new_categories):
     """
     Convert a set of codes for to a new set of categories
 
@@ -2685,7 +2685,7 @@ def _recode_for_categories(codes: np.ndarray, old_categories, new_categories):
     >>> old_cat = pd.Index(['b', 'a', 'c'])
     >>> new_cat = pd.Index(['a', 'b'])
     >>> codes = np.array([0, 1, 1, 2])
-    >>> _recode_for_categories(codes, old_cat, new_cat)
+    >>> recode_for_categories(codes, old_cat, new_cat)
     array([ 1,  0,  0, -1], dtype=int8)
     """
     if len(old_categories) == 0: