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Backport PR #42386 on branch 1.3.x (DOC fix the incorrect doc style in 1.2.1) #42445

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Merged

Backport PR #42386 on branch 1.3.x (DOC fix the incorrect doc style in 1.2.1) #42445

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90 changes: 52 additions & 38 deletions doc/source/whatsnew/v1.0.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -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'
Expand All @@ -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):
Expand Down Expand Up @@ -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
Expand All @@ -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*
Expand All @@ -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*
Expand All @@ -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*
Expand All @@ -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*
Expand All @@ -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*
Expand Down Expand Up @@ -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*
Expand All @@ -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
Expand Down Expand Up @@ -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``
Expand Down
32 changes: 19 additions & 13 deletions doc/source/whatsnew/v1.2.1.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -52,30 +52,34 @@ DataFrame / Series combination) would ignore the indices, only match
the inputs by shape, and use the index/columns of the first DataFrame for
the result:

.. code-block:: python
.. code-block:: ipython
>>> df1 = pd.DataFrame({"a": [1, 2], "b": [3, 4]}, index=[0, 1])
... df2 = pd.DataFrame({"a": [1, 2], "b": [3, 4]}, index=[1, 2])
>>> df1
In [1]: df1 = pd.DataFrame({"a": [1, 2], "b": [3, 4]}, index=[0, 1])
In [2]: df2 = pd.DataFrame({"a": [1, 2], "b": [3, 4]}, index=[1, 2])
In [3]: df1
Out[3]:
a b
0 1 3
1 2 4
>>> df2
In [4]: df2
Out[4]:
a b
1 1 3
2 2 4
>>> np.add(df1, df2)
In [5]: np.add(df1, df2)
Out[5]:
a b
0 2 6
1 4 8
This contrasts with how other pandas operations work, which first align
the inputs:

.. code-block:: python
.. code-block:: ipython
>>> df1 + df2
In [6]: df1 + df2
Out[6]:
a b
0 NaN NaN
1 3.0 7.0
Expand All @@ -94,20 +98,22 @@ objects (eg ``np.add(s1, s2)``) already aligns and continues to do so.
To avoid the warning and keep the current behaviour of ignoring the indices,
convert one of the arguments to a NumPy array:

.. code-block:: python
.. code-block:: ipython
>>> np.add(df1, np.asarray(df2))
In [7]: np.add(df1, np.asarray(df2))
Out[7]:
a b
0 2 6
1 4 8
To obtain the future behaviour and silence the warning, you can align manually
before passing the arguments to the ufunc:

.. code-block:: python
.. code-block:: ipython
>>> df1, df2 = df1.align(df2)
>>> np.add(df1, df2)
In [8]: df1, df2 = df1.align(df2)
In [9]: np.add(df1, df2)
Out[9]:
a b
0 NaN NaN
1 3.0 7.0
Expand Down