Merge branch 'main' of https://github.com/ShisuiUzumaki/pandas

Author: ShisuiUzumaki

.github/workflows/wheels.yml

@@ -86,7 +86,8 @@ jobs:
           activate-environment: test
           channels: conda-forge, anaconda
           channel-priority: true
-          mamba-version: "*"
+          # mamba fails to solve, also we really don't need this since we're just installing python
+          # mamba-version: "*"
 
       - name: Test wheels (Windows 64-bit only)
         if: ${{ matrix.buildplat[1] == 'win_amd64' }}
@@ -154,7 +155,8 @@ jobs:
           python-version: '3.8'
           channels: conda-forge
           channel-priority: true
-          mamba-version: "*"
+          # mamba fails to solve, also we really don't need this since we're just installing python
+          # mamba-version: "*"
 
       - name: Build sdist
         run: |

.pre-commit-config.yaml

@@ -92,7 +92,7 @@ repos:
         args: [--disable=all, --enable=redefined-outer-name]
         stages: [manual]
 -   repo: https://github.com/PyCQA/isort
-    rev: 5.11.4
+    rev: 5.12.0
     hooks:
     -   id: isort
 -   repo: https://github.com/asottile/pyupgrade

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) 2011-2022, Open source contributors.
+Copyright (c) 2011-2023, Open source contributors.
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:

asv_bench/benchmarks/indexing.py

@@ -8,6 +8,7 @@
 import numpy as np
 
 from pandas import (
+    NA,
     CategoricalIndex,
     DataFrame,
     Index,
@@ -83,6 +84,37 @@ def time_loc_slice(self, index, index_structure):
         self.data.loc[:800000]
 
 
+class NumericMaskedIndexing:
+    monotonic_list = list(range(10**6))
+    non_monotonic_list = (
+        list(range(50)) + [54, 53, 52, 51] + list(range(55, 10**6 - 1))
+    )
+
+    params = [
+        ("Int64", "UInt64", "Float64"),
+        (True, False),
+    ]
+    param_names = ["dtype", "monotonic"]
+
+    def setup(self, dtype, monotonic):
+
+        indices = {
+            True: Index(self.monotonic_list, dtype=dtype),
+            False: Index(self.non_monotonic_list, dtype=dtype).append(
+                Index([NA], dtype=dtype)
+            ),
+        }
+        self.data = indices[monotonic]
+        self.indexer = np.arange(300, 1_000)
+        self.data_dups = self.data.append(self.data)
+
+    def time_get_indexer(self, dtype, monotonic):
+        self.data.get_indexer(self.indexer)
+
+    def time_get_indexer_dups(self, dtype, monotonic):
+        self.data.get_indexer_for(self.indexer)
+
+
 class NonNumericSeriesIndexing:
 
     params = [

asv_bench/benchmarks/indexing_engines.py

@@ -1,5 +1,8 @@
 """
-Benchmarks in this file depend exclusively on code in _libs/
+Benchmarks in this file depend mostly on code in _libs/
+
+We have to created masked arrays to test the masked engine though. The
+array is unpacked on the Cython level.
 
 If a PR does not edit anything in _libs, it is very unlikely that benchmarks
 in this file will be affected.
@@ -9,6 +12,8 @@
 
 from pandas._libs import index as libindex
 
+from pandas.core.arrays import BaseMaskedArray
+
 
 def _get_numeric_engines():
     engine_names = [
@@ -30,6 +35,26 @@ def _get_numeric_engines():
     ]
 
 
+def _get_masked_engines():
+    engine_names = [
+        ("MaskedInt64Engine", "Int64"),
+        ("MaskedInt32Engine", "Int32"),
+        ("MaskedInt16Engine", "Int16"),
+        ("MaskedInt8Engine", "Int8"),
+        ("MaskedUInt64Engine", "UInt64"),
+        ("MaskedUInt32Engine", "UInt32"),
+        ("MaskedUInt16engine", "UInt16"),
+        ("MaskedUInt8Engine", "UInt8"),
+        ("MaskedFloat64Engine", "Float64"),
+        ("MaskedFloat32Engine", "Float32"),
+    ]
+    return [
+        (getattr(libindex, engine_name), dtype)
+        for engine_name, dtype in engine_names
+        if hasattr(libindex, engine_name)
+    ]
+
+
 class NumericEngineIndexing:
 
     params = [
@@ -80,6 +105,61 @@ def time_get_loc_near_middle(self, engine_and_dtype, index_type, unique, N):
         self.data.get_loc(self.key_middle)
 
 
+class MaskedNumericEngineIndexing:
+
+    params = [
+        _get_masked_engines(),
+        ["monotonic_incr", "monotonic_decr", "non_monotonic"],
+        [True, False],
+        [10**5, 2 * 10**6],  # 2e6 is above SIZE_CUTOFF
+    ]
+    param_names = ["engine_and_dtype", "index_type", "unique", "N"]
+
+    def setup(self, engine_and_dtype, index_type, unique, N):
+        engine, dtype = engine_and_dtype
+
+        if index_type == "monotonic_incr":
+            if unique:
+                arr = np.arange(N * 3, dtype=dtype.lower())
+            else:
+                values = list([1] * N + [2] * N + [3] * N)
+                arr = np.array(values, dtype=dtype.lower())
+            mask = np.zeros(N * 3, dtype=np.bool_)
+        elif index_type == "monotonic_decr":
+            if unique:
+                arr = np.arange(N * 3, dtype=dtype.lower())[::-1]
+            else:
+                values = list([1] * N + [2] * N + [3] * N)
+                arr = np.array(values, dtype=dtype.lower())[::-1]
+            mask = np.zeros(N * 3, dtype=np.bool_)
+        else:
+            assert index_type == "non_monotonic"
+            if unique:
+                arr = np.zeros(N * 3, dtype=dtype.lower())
+                arr[:N] = np.arange(N * 2, N * 3, dtype=dtype.lower())
+                arr[N:] = np.arange(N * 2, dtype=dtype.lower())
+
+            else:
+                arr = np.array([1, 2, 3] * N, dtype=dtype.lower())
+            mask = np.zeros(N * 3, dtype=np.bool_)
+            mask[-1] = True
+
+        self.data = engine(BaseMaskedArray(arr, mask))
+        # code belows avoids populating the mapping etc. while timing.
+        self.data.get_loc(2)
+
+        self.key_middle = arr[len(arr) // 2]
+        self.key_early = arr[2]
+
+    def time_get_loc(self, engine_and_dtype, index_type, unique, N):
+        self.data.get_loc(self.key_early)
+
+    def time_get_loc_near_middle(self, engine_and_dtype, index_type, unique, N):
+        # searchsorted performance may be different near the middle of a range
+        #  vs near an endpoint
+        self.data.get_loc(self.key_middle)
+
+
 class ObjectEngineIndexing:
 
     params = [("monotonic_incr", "monotonic_decr", "non_monotonic")]

ci/code_checks.sh

@@ -83,7 +83,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
     $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX04,GL01,GL02,GL03,GL04,GL05,GL06,GL07,GL09,GL10,PR03,PR04,PR05,PR06,PR08,PR09,PR10,RT01,RT02,RT04,RT05,SA02,SA03,SA04,SS01,SS02,SS03,SS04,SS05,SS06
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
-    MSG='Partially validate docstrings (EX01)' ; echo $MSG
+    MSG='Partially validate docstrings (EX01)' ;  echo $MSG
     $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX01 --ignore_functions \
         pandas.Series.index \
         pandas.Series.dtype \
@@ -187,7 +187,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.show_versions \
         pandas.test \
         pandas.NaT \
-        pandas.Timestamp.unit \
         pandas.Timestamp.as_unit \
         pandas.Timestamp.ctime \
         pandas.Timestamp.date \
@@ -574,7 +573,73 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.DataFrame.sparse.to_coo \
         pandas.DataFrame.to_gbq \
         pandas.DataFrame.style \
-        pandas.DataFrame.__dataframe__ \
+        pandas.DataFrame.__dataframe__
+    RET=$(($RET + $?)) ; echo $MSG "DONE"
+
+    MSG='Partially validate docstrings (EX02)' ;  echo $MSG
+    $BASE_DIR/scripts/validate_docstrings.py --format=actions --errors=EX02 --ignore_functions \
+        pandas.DataFrame.copy \
+        pandas.DataFrame.plot.line \
+        pandas.DataFrame.std \
+        pandas.DataFrame.var \
+        pandas.Index.factorize \
+        pandas.Period.strftime \
+        pandas.Series.copy \
+        pandas.Series.factorize \
+        pandas.Series.floordiv \
+        pandas.Series.plot.line \
+        pandas.Series.rfloordiv \
+        pandas.Series.sparse.density \
+        pandas.Series.sparse.npoints \
+        pandas.Series.sparse.sp_values \
+        pandas.Series.std \
+        pandas.Series.var \
+        pandas.Timestamp.fromtimestamp \
+        pandas.api.types.infer_dtype \
+        pandas.api.types.is_bool_dtype \
+        pandas.api.types.is_categorical_dtype \
+        pandas.api.types.is_complex_dtype \
+        pandas.api.types.is_datetime64_any_dtype \
+        pandas.api.types.is_datetime64_dtype \
+        pandas.api.types.is_datetime64_ns_dtype \
+        pandas.api.types.is_datetime64tz_dtype \
+        pandas.api.types.is_dict_like \
+        pandas.api.types.is_file_like \
+        pandas.api.types.is_float_dtype \
+        pandas.api.types.is_hashable \
+        pandas.api.types.is_int64_dtype \
+        pandas.api.types.is_integer_dtype \
+        pandas.api.types.is_interval_dtype \
+        pandas.api.types.is_iterator \
+        pandas.api.types.is_list_like \
+        pandas.api.types.is_named_tuple \
+        pandas.api.types.is_numeric_dtype \
+        pandas.api.types.is_object_dtype \
+        pandas.api.types.is_period_dtype \
+        pandas.api.types.is_re \
+        pandas.api.types.is_re_compilable \
+        pandas.api.types.is_signed_integer_dtype \
+        pandas.api.types.is_sparse \
+        pandas.api.types.is_string_dtype \
+        pandas.api.types.is_timedelta64_dtype \
+        pandas.api.types.is_timedelta64_ns_dtype \
+        pandas.api.types.is_unsigned_integer_dtype \
+        pandas.core.groupby.DataFrameGroupBy.take \
+        pandas.core.groupby.SeriesGroupBy.take \
+        pandas.factorize \
+        pandas.io.formats.style.Styler.concat \
+        pandas.io.formats.style.Styler.export \
+        pandas.io.formats.style.Styler.set_td_classes \
+        pandas.io.formats.style.Styler.use \
+        pandas.io.json.build_table_schema \
+        pandas.merge_ordered \
+        pandas.option_context \
+        pandas.plotting.andrews_curves \
+        pandas.plotting.autocorrelation_plot \
+        pandas.plotting.lag_plot \
+        pandas.plotting.parallel_coordinates \
+        pandas.plotting.radviz \
+        pandas.tseries.frequencies.to_offset
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
 fi

doc/source/development/contributing_docstring.rst

@@ -67,8 +67,6 @@ case of pandas, the NumPy docstring convention is followed. These conventions ar
 explained in this document:
 
 * `numpydoc docstring guide <https://numpydoc.readthedocs.io/en/latest/format.html>`_
-  (which is based in the original `Guide to NumPy/SciPy documentation
-  <https://github.com/numpy/numpy/blob/main/doc/HOWTO_DOCUMENT.rst.txt>`_)
 
 numpydoc is a Sphinx extension to support the NumPy docstring convention.
 

doc/source/development/maintaining.rst

@@ -465,7 +465,8 @@ which will be triggered when the tag is pushed.
 
 7. Download all wheels from the Anaconda repository where MacPython uploads them:
    https://anaconda.org/multibuild-wheels-staging/pandas/files?version=<version>
-   to the ``dist/`` directory in the local pandas copy.
+   to the ``dist/`` directory in the local pandas copy. You can use the script
+   ``scripts/download_wheels.sh`` to download all wheels at once.
 
 8. Upload wheels to PyPI:
 

doc/source/reference/frame.rst

@@ -83,6 +83,7 @@ Binary operator functions
 .. autosummary::
    :toctree: api/
 
+   DataFrame.__add__
    DataFrame.add
    DataFrame.sub
    DataFrame.mul

doc/source/whatsnew/v1.1.4.rst

@@ -31,7 +31,7 @@ Fixed regressions
 - Fixed regression in setitem with :meth:`DataFrame.iloc` which raised error when trying to set a value while filtering with a boolean list (:issue:`36741`)
 - Fixed regression in setitem with a Series getting aligned before setting the values (:issue:`37427`)
 - Fixed regression in :attr:`MultiIndex.is_monotonic_increasing` returning wrong results with ``NaN`` in at least one of the levels (:issue:`37220`)
-- Fixed regression in inplace arithmetic operation on a Series not updating the parent DataFrame (:issue:`36373`)
+- Fixed regression in inplace arithmetic operation (`+=`) on a Series not updating the parent DataFrame/Series (:issue:`36373`)
 
 .. ---------------------------------------------------------------------------