Merge branch '33141-pandas-cut' of github.com:mabelvj/pandas into 33141-pandas-cut

Author: mabelvj

README.md

@@ -7,6 +7,7 @@
 # pandas: powerful Python data analysis toolkit
 [![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/)
 [![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/anaconda/pandas/)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134)
 [![Package Status](https://img.shields.io/pypi/status/pandas.svg)](https://pypi.org/project/pandas/)
 [![License](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/master/LICENSE)
 [![Travis Build Status](https://travis-ci.org/pandas-dev/pandas.svg?branch=master)](https://travis-ci.org/pandas-dev/pandas)

asv_bench/asv.conf.json

@@ -39,7 +39,7 @@
     // followed by the pip installed packages).
     "matrix": {
         "numpy": [],
-        "Cython": [],
+        "Cython": ["0.29.16"],
         "matplotlib": [],
         "sqlalchemy": [],
         "scipy": [],

asv_bench/benchmarks/array.py

@@ -9,6 +9,11 @@ def setup(self):
         self.values_float = np.array([1.0, 0.0, 1.0, 0.0])
         self.values_integer = np.array([1, 0, 1, 0])
         self.values_integer_like = [1, 0, 1, 0]
+        self.data = np.array([True, False, True, False])
+        self.mask = np.array([False, False, True, False])
+
+    def time_constructor(self):
+        pd.arrays.BooleanArray(self.data, self.mask)
 
     def time_from_bool_array(self):
         pd.array(self.values_bool, dtype="boolean")
@@ -21,3 +26,16 @@ def time_from_integer_like(self):
 
     def time_from_float_array(self):
         pd.array(self.values_float, dtype="boolean")
+
+
+class IntegerArray:
+    def setup(self):
+        self.values_integer = np.array([1, 0, 1, 0])
+        self.data = np.array([1, 2, 3, 4], dtype="int64")
+        self.mask = np.array([False, False, True, False])
+
+    def time_constructor(self):
+        pd.arrays.IntegerArray(self.data, self.mask)
+
+    def time_from_integer_array(self):
+        pd.array(self.values_integer, dtype="Int64")

ci/code_checks.sh

@@ -292,10 +292,6 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
     pytest -q --doctest-modules pandas/core/generic.py
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
-    MSG='Doctests groupby.py' ; echo $MSG
-    pytest -q --doctest-modules pandas/core/groupby/groupby.py -k"-cumcount -describe -pipe"
-    RET=$(($RET + $?)) ; echo $MSG "DONE"
-
     MSG='Doctests series.py' ; echo $MSG
     pytest -q --doctest-modules pandas/core/series.py
     RET=$(($RET + $?)) ; echo $MSG "DONE"
@@ -318,6 +314,10 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
     pytest -q --doctest-modules pandas/core/dtypes/
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
+    MSG='Doctests groupby' ; echo $MSG
+    pytest -q --doctest-modules pandas/core/groupby/
+    RET=$(($RET + $?)) ; echo $MSG "DONE"
+
     MSG='Doctests indexes' ; echo $MSG
     pytest -q --doctest-modules pandas/core/indexes/
     RET=$(($RET + $?)) ; echo $MSG "DONE"

ci/deps/azure-36-minimum_versions.yaml

@@ -22,7 +22,7 @@ dependencies:
   - numpy=1.13.3
   - openpyxl=2.5.7
   - pytables=3.4.2
-  - python-dateutil=2.6.1
+  - python-dateutil=2.7.3
   - pytz=2017.2
   - scipy=0.19.0
   - xlrd=1.1.0

ci/deps/azure-37-numpydev.yaml

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

ci/deps/azure-macos-36.yaml

@@ -23,7 +23,7 @@ dependencies:
   - openpyxl
   - pyarrow>=0.13.0
   - pytables
-  - python-dateutil==2.6.1
+  - python-dateutil==2.7.3
   - pytz
   - xarray
   - xlrd

doc/source/getting_started/install.rst

@@ -221,7 +221,7 @@ Package                                                          Minimum support
 ================================================================ ==========================
 `setuptools <https://setuptools.readthedocs.io/en/latest/>`__    24.2.0
 `NumPy <https://www.numpy.org>`__                                1.13.3
-`python-dateutil <https://dateutil.readthedocs.io/en/stable/>`__ 2.6.1
+`python-dateutil <https://dateutil.readthedocs.io/en/stable/>`__ 2.7.3
 `pytz <https://pypi.org/project/pytz/>`__                        2017.2
 ================================================================ ==========================
 

doc/source/getting_started/intro_tutorials/03_subset_data.rst

@@ -23,7 +23,7 @@
                     <div class="card-body">
                         <p class="card-text">
 
-This tutorial uses the titanic data set, stored as CSV. The data
+This tutorial uses the Titanic data set, stored as CSV. The data
 consists of the following data columns:
 
 -  PassengerId: Id of every passenger.
@@ -72,7 +72,7 @@ How do I select specific columns from a ``DataFrame``?
     <ul class="task-bullet">
         <li>
 
-I’m interested in the age of the titanic passengers.
+I’m interested in the age of the Titanic passengers.
 
 .. ipython:: python
 
@@ -111,7 +111,7 @@ the number of rows is returned.
     <ul class="task-bullet">
         <li>
 
-I’m interested in the age and sex of the titanic passengers.
+I’m interested in the age and sex of the Titanic passengers.
 
 .. ipython:: python
 
@@ -198,7 +198,7 @@ can be used to filter the ``DataFrame`` by putting it in between the
 selection brackets ``[]``. Only rows for which the value is ``True``
 will be selected.
 
-We now from before that the original titanic ``DataFrame`` consists of
+We know from before that the original Titanic ``DataFrame`` consists of
 891 rows. Let’s have a look at the amount of rows which satisfy the
 condition by checking the ``shape`` attribute of the resulting
 ``DataFrame`` ``above_35``:
@@ -212,7 +212,7 @@ condition by checking the ``shape`` attribute of the resulting
     <ul class="task-bullet">
         <li>
 
-I’m interested in the titanic passengers from cabin class 2 and 3.
+I’m interested in the Titanic passengers from cabin class 2 and 3.
 
 .. ipython:: python
 

doc/source/user_guide/io.rst

@@ -285,14 +285,18 @@ chunksize : int, default ``None``
 Quoting, compression, and file format
 +++++++++++++++++++++++++++++++++++++
 
-compression : {``'infer'``, ``'gzip'``, ``'bz2'``, ``'zip'``, ``'xz'``, ``None``}, default ``'infer'``
+compression : {``'infer'``, ``'gzip'``, ``'bz2'``, ``'zip'``, ``'xz'``, ``None``, ``dict``}, default ``'infer'``
   For on-the-fly decompression of on-disk data. If 'infer', then use gzip,
   bz2, zip, or xz if filepath_or_buffer is a string ending in '.gz', '.bz2',
   '.zip', or '.xz', respectively, and no decompression otherwise. If using 'zip',
   the ZIP file must contain only one data file to be read in.
-  Set to ``None`` for no decompression.
+  Set to ``None`` for no decompression. Can also be a dict with key ``'method'``
+  set to one of {``'zip'``, ``'gzip'``, ``'bz2'``}, and other keys set to
+  compression settings. As an example, the following could be passed for
+  faster compression: ``compression={'method': 'gzip', 'compresslevel': 1}``.
 
   .. versionchanged:: 0.24.0 'infer' option added and set to default.
+  .. versionchanged:: 1.1.0 dict option extended to support ``gzip`` and ``bz2``.
 thousands : str, default ``None``
   Thousands separator.
 decimal : str, default ``'.'``
@@ -3347,6 +3351,12 @@ The compression type can be an explicit parameter or be inferred from the file e
 If 'infer', then use ``gzip``, ``bz2``, ``zip``, or ``xz`` if filename ends in ``'.gz'``, ``'.bz2'``, ``'.zip'``, or
 ``'.xz'``, respectively.
 
+The compression parameter can also be a ``dict`` in order to pass options to the
+compression protocol. It must have a ``'method'`` key set to the name
+of the compression protocol, which must be one of
+{``'zip'``, ``'gzip'``, ``'bz2'``}. All other key-value pairs are passed to
+the underlying compression library.
+
 .. ipython:: python
 
    df = pd.DataFrame({
@@ -3383,6 +3393,15 @@ The default is to 'infer':
    rt = pd.read_pickle("s1.pkl.bz2")
    rt
 
+Passing options to the compression protocol in order to speed up compression:
+
+.. ipython:: python
+
+   df.to_pickle(
+       "data.pkl.gz",
+       compression={"method": "gzip", 'compresslevel': 1}
+   )
+
 .. ipython:: python
    :suppress:
 

doc/source/user_guide/timeseries.rst

@@ -786,6 +786,15 @@ Furthermore, if you have a ``Series`` with datetimelike values, then you can
 access these properties via the ``.dt`` accessor, as detailed in the section
 on :ref:`.dt accessors<basics.dt_accessors>`.
 
+.. versionadded:: 1.1.0
+
+You may obtain the year, week and day components of the ISO year from the ISO 8601 standard:
+
+.. ipython:: python
+
+   idx = pd.date_range(start='2019-12-29', freq='D', periods=4)
+   idx.to_series().dt.isocalendar()
+
 .. _timeseries.offsets:
 
 DateOffset objects

doc/source/whatsnew/index.rst

@@ -3,7 +3,7 @@
 {{ header }}
 
 *************
-Release Notes
+Release notes
 *************
 
 This is the list of changes to pandas between each release. For full details,

doc/source/whatsnew/v0.10.0.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0100:
 
-v0.10.0 (December 17, 2012)
----------------------------
+Version 0.10.0 (December 17, 2012)
+----------------------------------
 
 {{ header }}
 
@@ -490,7 +490,7 @@ Updated PyTables support
 however, query terms using the prior (undocumented) methodology are unsupported. You must read in the entire
 file and write it out using the new format to take advantage of the updates.
 
-N dimensional Panels (experimental)
+N dimensional panels (experimental)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Adding experimental support for Panel4D and factory functions to create n-dimensional named panels.

doc/source/whatsnew/v0.10.1.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0101:
 
-v0.10.1 (January 22, 2013)
----------------------------
+Version 0.10.1 (January 22, 2013)
+---------------------------------
 
 {{ header }}
 

doc/source/whatsnew/v0.11.0.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0110:
 
-v0.11.0 (April 22, 2013)
-------------------------
+Version 0.11.0 (April 22, 2013)
+-------------------------------
 
 {{ header }}
 

doc/source/whatsnew/v0.12.0.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0120:
 
-v0.12.0 (July 24, 2013)
-------------------------
+Version 0.12.0 (July 24, 2013)
+------------------------------
 
 {{ header }}
 
@@ -177,8 +177,8 @@ API changes
     ``__repr__``). Plus string safety throughout. Now employed in many places
     throughout the pandas library. (:issue:`4090`, :issue:`4092`)
 
-I/O enhancements
-~~~~~~~~~~~~~~~~
+IO enhancements
+~~~~~~~~~~~~~~~
 
   - ``pd.read_html()`` can now parse HTML strings, files or urls and return
     DataFrames, courtesy of @cpcloud. (:issue:`3477`, :issue:`3605`, :issue:`3606`, :issue:`3616`).

doc/source/whatsnew/v0.13.0.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0130:
 
-v0.13.0 (January 3, 2014)
----------------------------
+Version 0.13.0 (January 3, 2014)
+--------------------------------
 
 {{ header }}
 

doc/source/whatsnew/v0.13.1.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0131:
 
-v0.13.1 (February 3, 2014)
---------------------------
+Version 0.13.1 (February 3, 2014)
+---------------------------------
 
 {{ header }}
 

doc/source/whatsnew/v0.14.0.rst

@@ -473,7 +473,7 @@ Some other enhancements to the sql functions include:
 
 .. _whatsnew_0140.slicers:
 
-MultiIndexing using slicers
+Multiindexing using slicers
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In 0.14.0 we added a new way to slice MultiIndexed objects.
@@ -904,7 +904,7 @@ There are no experimental changes in 0.14.0
 
 .. _whatsnew_0140.bug_fixes:
 
-Bug Fixes
+Bug fixes
 ~~~~~~~~~
 
 - Bug in Series ValueError when index doesn't match data (:issue:`6532`)

doc/source/whatsnew/v0.15.0.rst

@@ -600,7 +600,7 @@ Rolling/expanding moments improvements
 
 .. _whatsnew_0150.sql:
 
-Improvements in the sql io module
+Improvements in the SQL io module
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 - Added support for a ``chunksize`` parameter to ``to_sql`` function. This allows DataFrame to be written in chunks and avoid packet-size overflow errors (:issue:`8062`).

doc/source/whatsnew/v0.18.0.rst

@@ -1197,7 +1197,7 @@ Performance improvements
 
 .. _whatsnew_0180.bug_fixes:
 
-Bug Fixes
+Bug fixes
 ~~~~~~~~~
 
 - Bug in ``GroupBy.size`` when data-frame is empty. (:issue:`11699`)

doc/source/whatsnew/v0.18.1.rst

@@ -380,7 +380,7 @@ New behavior:
 
 .. _whatsnew_0181.numpy_compatibility:
 
-numpy function compatibility
+NumPy function compatibility
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Compatibility between pandas array-like methods (e.g. ``sum`` and ``take``) and their ``numpy``

doc/source/whatsnew/v0.19.0.rst

@@ -377,15 +377,15 @@ For ``MultiIndex``, values are dropped if any level is missing by default. Speci
 
 .. _whatsnew_0190.gbq:
 
-Google BigQuery Enhancements
+Google BigQuery enhancements
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 - The :func:`read_gbq` method has gained the ``dialect`` argument to allow users to specify whether to use BigQuery's legacy SQL or BigQuery's standard SQL. See the `docs <https://pandas-gbq.readthedocs.io/en/latest/reading.html>`__ for more details (:issue:`13615`).
 - The :func:`~DataFrame.to_gbq` method now allows the DataFrame column order to differ from the destination table schema (:issue:`11359`).
 
 .. _whatsnew_0190.errstate:
 
-Fine-grained numpy errstate
+Fine-grained NumPy errstate
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Previous versions of pandas would permanently silence numpy's ufunc error handling when ``pandas`` was imported. Pandas did this in order to silence the warnings that would arise from using numpy ufuncs on missing data, which are usually represented as ``NaN`` s. Unfortunately, this silenced legitimate warnings arising in non-pandas code in the application. Starting with 0.19.0, pandas will use the ``numpy.errstate`` context manager to silence these warnings in a more fine-grained manner, only around where these operations are actually used in the pandas code base. (:issue:`13109`, :issue:`13145`)
@@ -1185,7 +1185,7 @@ the result of calling :func:`read_csv` without the ``chunksize=`` argument
 
 .. _whatsnew_0190.sparse:
 
-Sparse Changes
+Sparse changes
 ^^^^^^^^^^^^^^
 
 These changes allow pandas to handle sparse data with more dtypes, and for work to make a smoother experience with data handling.