Merge branch 'master' into 2d-object-dont-copy

Author: irgolic

azure-pipelines.yml

@@ -21,12 +21,12 @@ jobs:
 - template: ci/azure/posix.yml
   parameters:
     name: macOS
-    vmImage: macOS-10.14
+    vmImage: macOS-10.15
 
 - template: ci/azure/windows.yml
   parameters:
     name: Windows
-    vmImage: vs2017-win2016
+    vmImage: windows-2019
 
 - job: py38_32bit
   pool:

ci/deps/actions-39-slow.yaml

@@ -14,25 +14,29 @@ dependencies:
 
   # pandas dependencies
   - beautifulsoup4
-  - fsspec>=0.7.4, <2021.6.0
+  - bottleneck
+  - fsspec>=0.8.0, <2021.6.0
+  - gcsfs
   - html5lib
+  - jinja2
   - lxml
   - matplotlib
+  - moto>=1.3.14
+  - flask
   - numexpr
   - numpy
   - openpyxl
-  - patsy
-  - psycopg2
-  - pymysql
+  - pyarrow
   - pytables
   - python-dateutil
   - pytz
-  - s3fs>=0.4.0
-  - moto>=1.3.14
+  - s3fs>=0.4.2
   - scipy
   - sqlalchemy
   - xlrd
   - xlsxwriter
-  - moto
-  - flask
-  - numba
+  - xlwt
+  - pyreadstat
+  - pip
+  - pip:
+    - pyxlsb

ci/deps/actions-39.yaml

@@ -12,11 +12,30 @@ dependencies:
   - hypothesis>=5.5.3
 
   # pandas dependencies
+  - beautifulsoup4
+  - bottleneck
+  - fsspec>=0.8.0, <2021.6.0
+  - gcsfs
+  - html5lib
+  - jinja2
+  - lxml
+  - matplotlib
+  - moto>=1.3.14
+  - flask
+  - numexpr
   - numpy
+  - openpyxl
+  - pyarrow
+  - pytables
   - python-dateutil
   - pytz
-
-  # optional dependencies
-  - pytables
+  - s3fs>=0.4.2
   - scipy
-  - pyarrow=1.0
+  - sqlalchemy
+  - xlrd
+  - xlsxwriter
+  - xlwt
+  - pyreadstat
+  - pip
+  - pip:
+    - pyxlsb

doc/source/development/contributing_codebase.rst

@@ -812,7 +812,21 @@ Changes should be reflected in the release notes located in ``doc/source/whatsne
 This file contains an ongoing change log for each release.  Add an entry to this file to
 document your fix, enhancement or (unavoidable) breaking change.  Make sure to include the
 GitHub issue number when adding your entry (using ``:issue:`1234``` where ``1234`` is the
-issue/pull request number).
+issue/pull request number). Your entry should be written using full sentences and proper
+grammar.
+
+When mentioning parts of the API, use a Sphinx ``:func:``, ``:meth:``, or ``:class:``
+directive as appropriate. Not all public API functions and methods have a
+documentation page; ideally links would only be added if they resolve. You can
+usually find similar examples by checking the release notes for one of the previous
+versions.
+
+If your code is a bugfix, add your entry to the relevant bugfix section. Avoid
+adding to the ``Other`` section; only in rare cases should entries go there.
+Being as concise as possible, the description of the bug should include how the
+user may encounter it and an indication of the bug itself, e.g.
+"produces incorrect results" or "incorrectly raises". It may be necessary to also
+indicate the new behavior.
 
 If your code is an enhancement, it is most likely necessary to add usage
 examples to the existing documentation.  This can be done following the section

doc/source/user_guide/style.ipynb

@@ -1190,9 +1190,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In version 0.20.0 the ability to customize the bar chart further was given. You can now have the `df.style.bar` be centered on zero or midpoint value (in addition to the already existing way of having the min value at the left side of the cell), and you can pass a list of `[color_negative, color_positive]`.\n",
+    "Additional keyword arguments give more control on centering and positioning, and you can pass a list of `[color_negative, color_positive]` to highlight lower and higher values.\n",
     "\n",
-    "Here's how you can change the above with the new `align='mid'` option:"
+    "Here's how you can change the above with the new `align` option, combined with setting `vmin` and `vmax` limits, the `width` of the figure, and underlying css `props` of cells, leaving space to display the text and the bars:"
    ]
   },
   {
@@ -1201,7 +1201,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df2.style.bar(subset=['A', 'B'], align='mid', color=['#d65f5f', '#5fba7d'])"
+    "df2.style.bar(align=0, vmin=-2.5, vmax=2.5, color=['#d65f5f', '#5fba7d'],\n",
+    "              width=60, props=\"width: 120px; border-right: 1px solid black;\").format('{:.3f}', na_rep=\"\")"
    ]
   },
   {
@@ -1225,28 +1226,31 @@
     "\n",
     "# Test series\n",
     "test1 = pd.Series([-100,-60,-30,-20], name='All Negative')\n",
-    "test2 = pd.Series([10,20,50,100], name='All Positive')\n",
-    "test3 = pd.Series([-10,-5,0,90], name='Both Pos and Neg')\n",
+    "test2 = pd.Series([-10,-5,0,90], name='Both Pos and Neg')\n",
+    "test3 = pd.Series([10,20,50,100], name='All Positive')\n",
+    "test4 = pd.Series([100, 103, 101, 102], name='Large Positive')\n",
+    "\n",
     "\n",
     "head = \"\"\"\n",
     "<table>\n",
     "    <thead>\n",
     "        <th>Align</th>\n",
     "        <th>All Negative</th>\n",
-    "        <th>All Positive</th>\n",
     "        <th>Both Neg and Pos</th>\n",
+    "        <th>All Positive</th>\n",
+    "        <th>Large Positive</th>\n",
     "    </thead>\n",
     "    </tbody>\n",
     "\n",
     "\"\"\"\n",
     "\n",
-    "aligns = ['left','zero','mid']\n",
+    "aligns = ['left', 'right', 'zero', 'mid', 'mean', 99]\n",
     "for align in aligns:\n",
     "    row = \"<tr><th>{}</th>\".format(align)\n",
-    "    for series in [test1,test2,test3]:\n",
+    "    for series in [test1,test2,test3, test4]:\n",
     "        s = series.copy()\n",
     "        s.name=''\n",
-    "        row += \"<td>{}</td>\".format(s.to_frame().style.bar(align=align, \n",
+    "        row += \"<td>{}</td>\".format(s.to_frame().style.hide_index().bar(align=align, \n",
     "                                                           color=['#d65f5f', '#5fba7d'], \n",
     "                                                           width=100).render()) #testn['width']\n",
     "    row += '</tr>'\n",

doc/source/whatsnew/v1.0.0.rst

@@ -338,19 +338,20 @@ maps labels to their new names along the default axis, is allowed to be passed b
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df = pd.DataFrame([[1]])
-   >>> df.rename({0: 1}, {0: 2})
+   In [1]: df = pd.DataFrame([[1]])
+   In [2]: df.rename({0: 1}, {0: 2})
+   Out[2]:
    FutureWarning: ...Use named arguments to resolve ambiguity...
       2
    1  1
 
 *pandas 1.0.0*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df.rename({0: 1}, {0: 2})
+   In [3]: df.rename({0: 1}, {0: 2})
    Traceback (most recent call last):
    ...
    TypeError: rename() takes from 1 to 2 positional arguments but 3 were given
@@ -359,26 +360,28 @@ Note that errors will now be raised when conflicting or potentially ambiguous ar
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df.rename({0: 1}, index={0: 2})
+   In [4]: df.rename({0: 1}, index={0: 2})
+   Out[4]:
       0
    1  1
 
-   >>> df.rename(mapper={0: 1}, index={0: 2})
+   In [5]: df.rename(mapper={0: 1}, index={0: 2})
+   Out[5]:
       0
    2  1
 
 *pandas 1.0.0*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df.rename({0: 1}, index={0: 2})
+   In [6]: df.rename({0: 1}, index={0: 2})
    Traceback (most recent call last):
    ...
    TypeError: Cannot specify both 'mapper' and any of 'index' or 'columns'
 
-   >>> df.rename(mapper={0: 1}, index={0: 2})
+   In [7]: df.rename(mapper={0: 1}, index={0: 2})
    Traceback (most recent call last):
    ...
    TypeError: Cannot specify both 'mapper' and any of 'index' or 'columns'
@@ -405,12 +408,12 @@ Extended verbose info output for :class:`~pandas.DataFrame`
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df = pd.DataFrame({"int_col": [1, 2, 3],
+   In [1]: df = pd.DataFrame({"int_col": [1, 2, 3],
    ...                    "text_col": ["a", "b", "c"],
    ...                    "float_col": [0.0, 0.1, 0.2]})
-   >>> df.info(verbose=True)
+   In [2]: df.info(verbose=True)
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 3 entries, 0 to 2
    Data columns (total 3 columns):
@@ -440,14 +443,16 @@ Extended verbose info output for :class:`~pandas.DataFrame`
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> pd.array(["a", None])
+   In [1]: pd.array(["a", None])
+   Out[1]:
    <PandasArray>
    ['a', None]
    Length: 2, dtype: object
 
-   >>> pd.array([1, None])
+   In [2]: pd.array([1, None])
+   Out[2]:
    <PandasArray>
    [1, None]
    Length: 2, dtype: object
@@ -470,15 +475,17 @@ As a reminder, you can specify the ``dtype`` to disable all inference.
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> a = pd.array([1, 2, None], dtype="Int64")
-   >>> a
+   In [1]: a = pd.array([1, 2, None], dtype="Int64")
+   In [2]: a
+   Out[2]:
    <IntegerArray>
    [1, 2, NaN]
    Length: 3, dtype: Int64
 
-   >>> a[2]
+   In [3]: a[2]
+   Out[3]:
    nan
 
 *pandas 1.0.0*
@@ -499,9 +506,10 @@ will now raise.
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-    >>> np.asarray(a, dtype="float")
+    In [1]: np.asarray(a, dtype="float")
+    Out[1]:
     array([ 1.,  2., nan])
 
 *pandas 1.0.0*
@@ -525,9 +533,10 @@ will now be ``pd.NA`` instead of ``np.nan`` in presence of missing values
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-    >>> pd.Series(a).sum(skipna=False)
+    In [1]: pd.Series(a).sum(skipna=False)
+    Out[1]:
     nan
 
 *pandas 1.0.0*
@@ -543,9 +552,10 @@ integer dtype for the values.
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> pd.Series([2, 1, 1, None], dtype="Int64").value_counts().dtype
+   In [1]: pd.Series([2, 1, 1, None], dtype="Int64").value_counts().dtype
+   Out[1]:
    dtype('int64')
 
 *pandas 1.0.0*
@@ -565,15 +575,17 @@ Comparison operations on a :class:`arrays.IntegerArray` now returns a
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> a = pd.array([1, 2, None], dtype="Int64")
-   >>> a
+   In [1]: a = pd.array([1, 2, None], dtype="Int64")
+   In [2]: a
+   Out[2]:
    <IntegerArray>
    [1, 2, NaN]
    Length: 3, dtype: Int64
 
-   >>> a > 1
+   In [3]: a > 1
+   Out[3]:
    array([False,  True, False])
 
 *pandas 1.0.0*
@@ -640,9 +652,10 @@ scalar values in the result are instances of the extension dtype's scalar type.
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df.resample("2D").agg(lambda x: 'a').A.dtype
+   In [1]> df.resample("2D").agg(lambda x: 'a').A.dtype
+   Out[1]:
    CategoricalDtype(categories=['a', 'b'], ordered=False)
 
 *pandas 1.0.0*
@@ -657,9 +670,10 @@ depending on how the results are cast back to the original dtype.
 
 *pandas 0.25.x*
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> df.resample("2D").agg(lambda x: 'c')
+   In [1] df.resample("2D").agg(lambda x: 'c')
+   Out[1]:
 
         A
    0  NaN
@@ -871,10 +885,10 @@ matplotlib directly rather than :meth:`~DataFrame.plot`.
 
 To use pandas formatters with a matplotlib plot, specify
 
-.. code-block:: python
+.. code-block:: ipython
 
-   >>> import pandas as pd
-   >>> pd.options.plotting.matplotlib.register_converters = True
+   In [1]: import pandas as pd
+   In [2]: pd.options.plotting.matplotlib.register_converters = True
 
 Note that plots created by :meth:`DataFrame.plot` and :meth:`Series.plot` *do* register the converters
 automatically. The only behavior change is when plotting a date-like object via ``matplotlib.pyplot.plot``