Merge remote-tracking branch 'upstream/master' into multiindex_at_fix

Author: rhshadrach

asv_bench/benchmarks/arithmetic.py

@@ -67,7 +67,7 @@ def time_series_op_with_fill_value_no_nas(self):
         self.ser.add(self.ser, fill_value=4)
 
 
-class MixedFrameWithSeriesAxis0:
+class MixedFrameWithSeriesAxis:
     params = [
         [
             "eq",
@@ -78,7 +78,7 @@ class MixedFrameWithSeriesAxis0:
             "gt",
             "add",
             "sub",
-            "div",
+            "truediv",
             "floordiv",
             "mul",
             "pow",
@@ -87,15 +87,19 @@ class MixedFrameWithSeriesAxis0:
     param_names = ["opname"]
 
     def setup(self, opname):
-        arr = np.arange(10 ** 6).reshape(100, -1)
+        arr = np.arange(10 ** 6).reshape(1000, -1)
         df = DataFrame(arr)
         df["C"] = 1.0
         self.df = df
         self.ser = df[0]
+        self.row = df.iloc[0]
 
     def time_frame_op_with_series_axis0(self, opname):
         getattr(self.df, opname)(self.ser, axis=0)
 
+    def time_frame_op_with_series_axis1(self, opname):
+        getattr(operator, opname)(self.df, self.ser)
+
 
 class Ops:
 

asv_bench/benchmarks/groupby.py

@@ -660,4 +660,62 @@ def function(values):
         self.grouper.transform(function, engine="cython")
 
 
+class AggEngine:
+    def setup(self):
+        N = 10 ** 3
+        data = DataFrame(
+            {0: [str(i) for i in range(100)] * N, 1: list(range(100)) * N},
+            columns=[0, 1],
+        )
+        self.grouper = data.groupby(0)
+
+    def time_series_numba(self):
+        def function(values, index):
+            total = 0
+            for i, value in enumerate(values):
+                if i % 2:
+                    total += value + 5
+                else:
+                    total += value * 2
+            return total
+
+        self.grouper[1].agg(function, engine="numba")
+
+    def time_series_cython(self):
+        def function(values):
+            total = 0
+            for i, value in enumerate(values):
+                if i % 2:
+                    total += value + 5
+                else:
+                    total += value * 2
+            return total
+
+        self.grouper[1].agg(function, engine="cython")
+
+    def time_dataframe_numba(self):
+        def function(values, index):
+            total = 0
+            for i, value in enumerate(values):
+                if i % 2:
+                    total += value + 5
+                else:
+                    total += value * 2
+            return total
+
+        self.grouper.agg(function, engine="numba")
+
+    def time_dataframe_cython(self):
+        def function(values):
+            total = 0
+            for i, value in enumerate(values):
+                if i % 2:
+                    total += value + 5
+                else:
+                    total += value * 2
+            return total
+
+        self.grouper.agg(function, engine="cython")
+
+
 from .pandas_vb_common import setup  # noqa: F401 isort:skip

asv_bench/benchmarks/io/parsers.py

@@ -2,7 +2,7 @@
 
 try:
     from pandas._libs.tslibs.parsing import (
-        _concat_date_cols,
+        concat_date_cols,
         _does_string_look_like_datetime,
     )
 except ImportError:
@@ -39,4 +39,4 @@ def setup(self, value, dim):
             )
 
     def time_check_concat(self, value, dim):
-        _concat_date_cols(self.object)
+        concat_date_cols(self.object)

asv_bench/benchmarks/rolling.py

@@ -150,19 +150,18 @@ def time_quantile(self, constructor, window, dtype, percentile, interpolation):
         self.roll.quantile(percentile, interpolation=interpolation)
 
 
-class PeakMemFixed:
-    def setup(self):
-        N = 10
-        arr = 100 * np.random.random(N)
-        self.roll = pd.Series(arr).rolling(10)
-
-    def peakmem_fixed(self):
-        # GH 25926
-        # This is to detect memory leaks in rolling operations.
-        # To save time this is only ran on one method.
-        # 6000 iterations is enough for most types of leaks to be detected
-        for x in range(6000):
-            self.roll.max()
+class PeakMemFixedWindowMinMax:
+
+    params = ["min", "max"]
+
+    def setup(self, operation):
+        N = int(1e6)
+        arr = np.random.random(N)
+        self.roll = pd.Series(arr).rolling(2)
+
+    def peakmem_fixed(self, operation):
+        for x in range(5):
+            getattr(self.roll, operation)()
 
 
 class ForwardWindowMethods:

asv_bench/benchmarks/stat_ops.py

@@ -11,8 +11,8 @@ class FrameOps:
     param_names = ["op", "dtype", "axis"]
 
     def setup(self, op, dtype, axis):
-        if op == "mad" and dtype == "Int64" and axis == 1:
-            # GH-33036
+        if op == "mad" and dtype == "Int64":
+            # GH-33036, GH#33600
             raise NotImplementedError
         values = np.random.randn(100000, 4)
         if dtype == "Int64":

ci/deps/azure-36-minimum_versions.yaml

@@ -1,6 +1,5 @@
 name: pandas-dev
 channels:
-  - defaults
   - conda-forge
 dependencies:
   - python=3.6.1
@@ -19,12 +18,12 @@ dependencies:
   - jinja2=2.8
   - numba=0.46.0
   - numexpr=2.6.2
-  - numpy=1.13.3
+  - numpy=1.15.4
   - openpyxl=2.5.7
   - pytables=3.4.3
   - python-dateutil=2.7.3
   - pytz=2017.2
-  - scipy=0.19.0
+  - scipy=1.2
   - xlrd=1.1.0
   - xlsxwriter=0.9.8
   - xlwt=1.2.0

ci/deps/azure-37-numpydev.yaml

@@ -14,8 +14,7 @@ dependencies:
   - pytz
   - pip
   - pip:
-    - cython==0.29.16
-    # GH#33507 cython 3.0a1 is causing TypeErrors 2020-04-13
+    - cython>=0.29.16
     - "git+git://github.com/dateutil/dateutil.git"
     - "-f https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com"
     - "--pre"

ci/deps/azure-macos-36.yaml

@@ -19,7 +19,7 @@ dependencies:
   - matplotlib=2.2.3
   - nomkl
   - numexpr
-  - numpy=1.14
+  - numpy=1.15.4
   - openpyxl
   - pyarrow>=0.13.0
   - pytables

ci/setup_env.sh

@@ -128,7 +128,7 @@ conda list pandas
 echo "[Build extensions]"
 python setup.py build_ext -q -i -j2
 
-# XXX: Some of our environments end up with old versions of pip (10.x)
+# TODO: Some of our environments end up with old versions of pip (10.x)
 # Adding a new enough version of pip to the requirements explodes the
 # solve time. Just using pip to update itself.
 # - py35_macos

conda.recipe/meta.yaml

@@ -20,12 +20,12 @@ requirements:
     - cython
     - numpy
     - setuptools >=3.3
-    - python-dateutil >=2.5.0
+    - python-dateutil >=2.7.3
     - pytz
   run:
     - python {{ python }}
     - {{ pin_compatible('numpy') }}
-    - python-dateutil >=2.5.0
+    - python-dateutil >=2.7.3
     - pytz
 
 test:

doc/source/development/contributing.rst

@@ -581,7 +581,7 @@ do not make sudden changes to the code that could have the potential to break
 a lot of user code as a result, that is, we need it to be as *backwards compatible*
 as possible to avoid mass breakages.
 
-Additional standards are outlined on the `pandas code style guide <code_style>`_
+Additional standards are outlined on the :ref:`pandas code style guide <code_style>`
 
 Optional dependencies
 ---------------------

doc/source/getting_started/install.rst

@@ -220,7 +220,7 @@ Dependencies
 Package                                                          Minimum supported version
 ================================================================ ==========================
 `setuptools <https://setuptools.readthedocs.io/en/latest/>`__    24.2.0
-`NumPy <https://www.numpy.org>`__                                1.13.3
+`NumPy <https://www.numpy.org>`__                                1.15.4
 `python-dateutil <https://dateutil.readthedocs.io/en/stable/>`__ 2.7.3
 `pytz <https://pypi.org/project/pytz/>`__                        2017.2
 ================================================================ ==========================

doc/source/reference/general_utility_functions.rst

@@ -35,9 +35,12 @@ Exceptions and warnings
 .. autosummary::
    :toctree: api/
 
+   errors.AccessorRegistrationWarning
    errors.DtypeWarning
    errors.EmptyDataError
    errors.OutOfBoundsDatetime
+   errors.MergeError
+   errors.NumbaUtilError
    errors.ParserError
    errors.ParserWarning
    errors.PerformanceWarning

doc/source/reference/groupby.rst

@@ -36,8 +36,10 @@ Function application
 
    GroupBy.apply
    GroupBy.agg
-   GroupBy.aggregate
-   GroupBy.transform
+   SeriesGroupBy.aggregate
+   DataFrameGroupBy.aggregate
+   SeriesGroupBy.transform
+   DataFrameGroupBy.transform
    GroupBy.pipe
 
 Computations / descriptive stats

doc/source/user_guide/basics.rst

@@ -1781,6 +1781,31 @@ used to sort a pandas object by its index levels.
    # Series
    unsorted_df['three'].sort_index()
 
+.. _basics.sort_index_key:
+
+.. versionadded:: 1.1.0
+
+Sorting by index also supports a ``key`` parameter that takes a callable
+function to apply to the index being sorted. For `MultiIndex` objects,
+the key is applied per-level to the levels specified by `level`.
+
+.. ipython:: python
+
+   s1 = pd.DataFrame({
+       "a": ['B', 'a', 'C'],
+       "b": [1, 2, 3],
+       "c": [2, 3, 4]
+   }).set_index(list("ab"))
+   s1
+
+.. ipython:: python
+
+   s1.sort_index(level="a")
+   s1.sort_index(level="a", key=lambda idx: idx.str.lower())
+
+For information on key sorting by value, see :ref:`value sorting
+<basics.sort_value_key>`.
+
 .. _basics.sort_values:
 
 By values
@@ -1813,6 +1838,39 @@ argument:
    s.sort_values()
    s.sort_values(na_position='first')
 
+.. _basics.sort_value_key:
+
+.. versionadded:: 1.1.0
+
+Sorting also supports a ``key`` parameter that takes a callable function
+to apply to the values being sorted.
+
+.. ipython:: python
+
+   s1 = pd.Series(['B', 'a', 'C'])
+
+.. ipython:: python
+
+   s1.sort_values()
+   s1.sort_values(key=lambda x: x.str.lower())
+
+`key` will be given the :class:`Series` of values and should return a ``Series``
+or array of the same shape with the transformed values. For `DataFrame` objects,
+the key is applied per column, so the key should still expect a Series and return
+a Series, e.g.
+
+.. ipython:: python
+
+   df = pd.DataFrame({"a": ['B', 'a', 'C'], "b": [1, 2, 3]})
+
+.. ipython:: python
+
+   df.sort_values(by='a')
+   df.sort_values(by='a', key=lambda col: col.str.lower())
+
+The name or type of each column can be used to apply different functions to
+different columns.
+
 .. _basics.sort_indexes_and_values:
 
 By indexes and values

doc/source/user_guide/computation.rst

@@ -318,8 +318,8 @@ We provide a number of common statistical functions:
     :meth:`~Rolling.kurt`, Sample kurtosis (4th moment)
     :meth:`~Rolling.quantile`, Sample quantile (value at %)
     :meth:`~Rolling.apply`, Generic apply
-    :meth:`~Rolling.cov`, Unbiased covariance (binary)
-    :meth:`~Rolling.corr`, Correlation (binary)
+    :meth:`~Rolling.cov`, Sample covariance (binary)
+    :meth:`~Rolling.corr`, Sample correlation (binary)
 
 .. _computation.window_variance.caveats:
 
@@ -341,6 +341,8 @@ We provide a number of common statistical functions:
    sample variance under the circumstances would result in a biased estimator
    of the variable we are trying to determine.
 
+   The same caveats apply to using any supported statistical sample methods.
+
 .. _stats.rolling_apply:
 
 Rolling apply
@@ -380,8 +382,8 @@ and their default values are set to ``False``, ``True`` and ``False`` respective
 .. note::
 
    In terms of performance, **the first time a function is run using the Numba engine will be slow**
-   as Numba will have some function compilation overhead. However, ``rolling`` objects will cache
-   the function and subsequent calls will be fast. In general, the Numba engine is performant with
+   as Numba will have some function compilation overhead. However, the compiled functions are cached,
+   and subsequent calls will be fast. In general, the Numba engine is performant with
    a larger amount of data points (e.g. 1+ million).
 
 .. code-block:: ipython
@@ -870,12 +872,12 @@ Method summary
     :meth:`~Expanding.max`, Maximum
     :meth:`~Expanding.std`, Sample standard deviation
     :meth:`~Expanding.var`, Sample variance
-    :meth:`~Expanding.skew`, Unbiased skewness (3rd moment)
-    :meth:`~Expanding.kurt`, Unbiased kurtosis (4th moment)
+    :meth:`~Expanding.skew`, Sample skewness (3rd moment)
+    :meth:`~Expanding.kurt`, Sample kurtosis (4th moment)
     :meth:`~Expanding.quantile`, Sample quantile (value at %)
     :meth:`~Expanding.apply`, Generic apply
-    :meth:`~Expanding.cov`, Unbiased covariance (binary)
-    :meth:`~Expanding.corr`, Correlation (binary)
+    :meth:`~Expanding.cov`, Sample covariance (binary)
+    :meth:`~Expanding.corr`, Sample correlation (binary)
 
 .. note::
 
@@ -884,6 +886,8 @@ Method summary
    windows. See :ref:`this section <computation.window_variance.caveats>` for more
    information.
 
+   The same caveats apply to using any supported statistical sample methods.
+
 .. currentmodule:: pandas
 
 Aside from not having a ``window`` parameter, these functions have the same