Merge branch 'master' of https://github.com/pandas-dev/pandas into bug-clear-cache

Author: jbrockmendel

.pre-commit-config.yaml

@@ -30,15 +30,3 @@ repos:
     -   id: isort
         language: python_venv
         exclude: ^pandas/__init__\.py$|^pandas/core/api\.py$
--   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.730
-    hooks:
-     -  id: mypy
-        args:
-          # As long as a some files are excluded from check-untyped-defs
-          # we have to exclude it from the pre-commit hook as the configuration
-          # is based on modules but the hook runs on files.
-          - --no-check-untyped-defs
-          - --follow-imports
-          - skip
-        files: pandas/

ci/deps/azure-37-locale_slow.yaml

@@ -18,7 +18,7 @@ dependencies:
   - lxml
   - matplotlib=3.0.0
   - numpy=1.16.*
-  - openpyxl=2.5.7
+  - openpyxl=2.6.0
   - python-dateutil
   - python-blosc
   - pytz=2017.3

ci/deps/azure-37-minimum_versions.yaml

@@ -19,7 +19,7 @@ dependencies:
   - numba=0.46.0
   - numexpr=2.6.8
   - numpy=1.16.5
-  - openpyxl=2.5.7
+  - openpyxl=2.6.0
   - pytables=3.4.4
   - python-dateutil=2.7.3
   - pytz=2017.3

doc/source/development/contributing.rst

@@ -204,6 +204,7 @@ You will need `Build Tools for Visual Studio 2017
 	You DO NOT need to install Visual Studio 2019.
 	You only need "Build Tools for Visual Studio 2019" found by
 	scrolling down to "All downloads" -> "Tools for Visual Studio 2019".
+	In the installer, select the "C++ build tools" workload.
 
 **Mac OS**
 

doc/source/getting_started/install.rst

@@ -274,7 +274,7 @@ html5lib                  1.0.1              HTML parser for read_html (see :ref
 lxml                      4.3.0              HTML parser for read_html (see :ref:`note <optional_html>`)
 matplotlib                2.2.3              Visualization
 numba                     0.46.0             Alternative execution engine for rolling operations
-openpyxl                  2.5.7              Reading / writing for xlsx files
+openpyxl                  2.6.0              Reading / writing for xlsx files
 pandas-gbq                0.12.0             Google Big Query access
 psycopg2                  2.7                PostgreSQL engine for sqlalchemy
 pyarrow                   0.15.0             Parquet, ORC, and feather reading / writing

doc/source/getting_started/overview.rst

@@ -9,9 +9,9 @@ Package overview
 **pandas** is a `Python <https://www.python.org>`__ package providing fast,
 flexible, and expressive data structures designed to make working with
 "relational" or "labeled" data both easy and intuitive. It aims to be the
-fundamental high-level building block for doing practical, **real world** data
+fundamental high-level building block for doing practical, **real-world** data
 analysis in Python. Additionally, it has the broader goal of becoming **the
-most powerful and flexible open source data analysis / manipulation tool
+most powerful and flexible open source data analysis/manipulation tool
 available in any language**. It is already well on its way toward this goal.
 
 pandas is well suited for many different kinds of data:
@@ -21,7 +21,7 @@ pandas is well suited for many different kinds of data:
   - Ordered and unordered (not necessarily fixed-frequency) time series data.
   - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and
     column labels
-  - Any other form of observational / statistical data sets. The data actually
+  - Any other form of observational / statistical data sets. The data
     need not be labeled at all to be placed into a pandas data structure
 
 The two primary data structures of pandas, :class:`Series` (1-dimensional)
@@ -57,7 +57,7 @@ Here are just a few of the things that pandas does well:
     Excel files, databases, and saving / loading data from the ultrafast **HDF5
     format**
   - **Time series**-specific functionality: date range generation and frequency
-    conversion, moving window statistics, date shifting and lagging.
+    conversion, moving window statistics, date shifting, and lagging.
 
 Many of these principles are here to address the shortcomings frequently
 experienced using other languages / scientific research environments. For data
@@ -101,12 +101,12 @@ fashion.
 
 Also, we would like sensible default behaviors for the common API functions
 which take into account the typical orientation of time series and
-cross-sectional data sets. When using ndarrays to store 2- and 3-dimensional
+cross-sectional data sets. When using the N-dimensional array (ndarrays) to store 2- and 3-dimensional
 data, a burden is placed on the user to consider the orientation of the data
 set when writing functions; axes are considered more or less equivalent (except
 when C- or Fortran-contiguousness matters for performance). In pandas, the axes
 are intended to lend more semantic meaning to the data; i.e., for a particular
-data set there is likely to be a "right" way to orient the data. The goal,
+data set, there is likely to be a "right" way to orient the data. The goal,
 then, is to reduce the amount of mental effort required to code up data
 transformations in downstream functions.
 
@@ -148,8 +148,8 @@ pandas possible. Thanks to `all of our contributors <https://github.com/pandas-d
 If you're interested in contributing, please visit the :ref:`contributing guide <contributing>`.
 
 pandas is a `NumFOCUS <https://www.numfocus.org/open-source-projects/>`__ sponsored project.
-This will help ensure the success of development of pandas as a world-class open-source
-project, and makes it possible to `donate <https://pandas.pydata.org/donate.html>`__ to the project.
+This will help ensure the success of the development of pandas as a world-class open-source
+project and makes it possible to `donate <https://pandas.pydata.org/donate.html>`__ to the project.
 
 Project governance
 ------------------

doc/source/user_guide/computation.rst

@@ -361,6 +361,9 @@ compute the mean absolute deviation on a rolling basis:
    @savefig rolling_apply_ex.png
    s.rolling(window=60).apply(mad, raw=True).plot(style='k')
 
+Using the Numba engine
+~~~~~~~~~~~~~~~~~~~~~~
+
 .. versionadded:: 1.0
 
 Additionally, :meth:`~Rolling.apply` can leverage `Numba <https://numba.pydata.org/>`__

doc/source/user_guide/enhancingperf.rst

@@ -373,6 +373,13 @@ nicer interface by passing/returning pandas objects.
 
 In this example, using Numba was faster than Cython.
 
+Numba as an argument
+~~~~~~~~~~~~~~~~~~~~
+
+Additionally, we can leverage the power of `Numba <https://numba.pydata.org/>`__
+by calling it as an argument in :meth:`~Rolling.apply`. See :ref:`Computation tools
+<stats.rolling_apply>` for an extensive example.
+
 Vectorize
 ~~~~~~~~~
 

doc/source/user_guide/sparse.rst

@@ -87,14 +87,15 @@ The :attr:`SparseArray.dtype` property stores two pieces of information
    sparr.dtype
 
 
-A :class:`SparseDtype` may be constructed by passing each of these
+A :class:`SparseDtype` may be constructed by passing only a dtype
 
 .. ipython:: python
 
    pd.SparseDtype(np.dtype('datetime64[ns]'))
 
-The default fill value for a given NumPy dtype is the "missing" value for that dtype,
-though it may be overridden.
+in which case a default fill value will be used (for NumPy dtypes this is often the
+"missing" value for that dtype). To override this default an explicit fill value may be
+passed instead
 
 .. ipython:: python
 

doc/source/whatsnew/v1.1.2.rst

@@ -15,7 +15,9 @@ including other versions of pandas.
 Fixed regressions
 ~~~~~~~~~~~~~~~~~
 - Regression in :meth:`DatetimeIndex.intersection` incorrectly raising ``AssertionError`` when intersecting against a list (:issue:`35876`)
+- Fix regression in updating a column inplace (e.g. using ``df['col'].fillna(.., inplace=True)``) (:issue:`35731`)
 - Performance regression for :meth:`RangeIndex.format` (:issue:`35712`)
+- Regression in :meth:`DataFrame.replace` where a ``TypeError`` would be raised when attempting to replace elements of type :class:`Interval` (:issue:`35931`)
 -
 
 
@@ -29,7 +31,7 @@ Bug fixes
 - Bug in :class:`Series` constructor raising a ``TypeError`` when constructing sparse datetime64 dtypes (:issue:`35762`)
 - Bug in :meth:`DataFrame.apply` with ``result_type="reduce"`` returning with incorrect index (:issue:`35683`)
 - Bug in :meth:`DateTimeIndex.format` and :meth:`PeriodIndex.format` with ``name=True`` setting the first item to ``"None"`` where it should bw ``""`` (:issue:`35712`)
--
+- Bug in :meth:`Float64Index.__contains__` incorrectly raising ``TypeError`` instead of returning ``False`` (:issue:`35788`)
 
 .. ---------------------------------------------------------------------------