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Jun 11, 2019
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DOC: fix old whatsnew examples + few sphinx warnings #26780

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17bf87e
fix whatsnew code examples
jorisvandenbossche Jun 11, 2019
a988030
fix docstring warnings
jorisvandenbossche Jun 11, 2019
fa83b3e
fix example
jorisvandenbossche Jun 11, 2019
f537061
fix spacing
jorisvandenbossche Jun 11, 2019
0db3d3f
more code-blocks
jorisvandenbossche Jun 11, 2019
a9fe079
allow exception
jorisvandenbossche Jun 11, 2019
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181 changes: 143 additions & 38 deletions doc/source/whatsnew/v0.11.0.rst
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Expand Up @@ -105,27 +105,54 @@ Conversion

Mixed Conversion

.. ipython:: python
:okwarning:
.. code-block:: ipython

df3['D'] = '1.'
df3['E'] = '1'
df3.convert_objects(convert_numeric=True).dtypes
In [12]: df3['D'] = '1.'

# same, but specific dtype conversion
df3['D'] = df3['D'].astype('float16')
df3['E'] = df3['E'].astype('int32')
df3.dtypes
In [13]: df3['E'] = '1'

In [14]: df3.convert_objects(convert_numeric=True).dtypes
Out[14]:
A float32
B float64
C float64
D float64
E int64
dtype: object

# same, but specific dtype conversion
In [15]: df3['D'] = df3['D'].astype('float16')
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Maybe not a big issue since this matches current documentation, but are these types supposed to be exactly the same as the above? Comment suggests so but result does not

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I don't think they are supposed to be exactly the same. convert_objects did a generic conversion, so using the 64 bit version by default. With astype you could specify a specific type.


In [16]: df3['E'] = df3['E'].astype('int32')

In [17]: df3.dtypes
Out[17]:
A float32
B float64
C float64
D float16
E int32
dtype: object

Forcing Date coercion (and setting ``NaT`` when not datelike)

.. ipython:: python
:okwarning:
.. code-block:: ipython

In [18]: import datetime

In [19]: s = pd.Series([datetime.datetime(2001, 1, 1, 0, 0), 'foo', 1.0, 1,
....: pd.Timestamp('20010104'), '20010105'], dtype='O')
....:

import datetime
s = pd.Series([datetime.datetime(2001, 1, 1, 0, 0), 'foo', 1.0, 1,
pd.Timestamp('20010104'), '20010105'], dtype='O')
s.convert_objects(convert_dates='coerce')
In [20]: s.convert_objects(convert_dates='coerce')
Out[20]:
0 2001-01-01
1 NaT
2 NaT
3 NaT
4 2001-01-04
5 2001-01-05
dtype: datetime64[ns]

Dtype Gotchas
~~~~~~~~~~~~~
Expand All @@ -138,11 +165,22 @@ dtypes, they *WILL* be respected, however (:issue:`2837`)

The following will all result in ``int64`` dtypes

.. ipython:: python
.. code-block:: ipython

In [21]: pd.DataFrame([1, 2], columns=['a']).dtypes
Out[21]:
a int64
dtype: object

In [22]: pd.DataFrame({'a': [1, 2]}).dtypes
Out[22]:
a int64
dtype: object

pd.DataFrame([1, 2], columns=['a']).dtypes
pd.DataFrame({'a': [1, 2]}).dtypes
pd.DataFrame({'a': 1}, index=range(2)).dtypes
In [23]: pd.DataFrame({'a': 1}, index=range(2)).dtypes
Out[23]:
a int64
dtype: object

Keep in mind that ``DataFrame(np.array([1,2]))`` **WILL** result in ``int32`` on 32-bit platforms!

Expand All @@ -152,28 +190,95 @@ Keep in mind that ``DataFrame(np.array([1,2]))`` **WILL** result in ``int32`` on
Performing indexing operations on integer type data can easily upcast the data.
The dtype of the input data will be preserved in cases where ``nans`` are not introduced.

.. ipython:: python

dfi = df3.astype('int32')
dfi['D'] = dfi['D'].astype('int64')
dfi
dfi.dtypes

casted = dfi[dfi > 0]
casted
casted.dtypes
.. code-block:: ipython

In [24]: dfi = df3.astype('int32')

In [25]: dfi['D'] = dfi['D'].astype('int64')

In [26]: dfi
Out[26]:
A B C D E
0 0 0 0 1 1
1 -2 0 1 1 1
2 -2 0 2 1 1
3 0 -1 3 1 1
4 1 0 4 1 1
5 0 0 5 1 1
6 0 -1 6 1 1
7 0 0 7 1 1

In [27]: dfi.dtypes
Out[27]:
A int32
B int32
C int32
D int64
E int32
dtype: object

In [28]: casted = dfi[dfi > 0]

In [29]: casted
Out[29]:
A B C D E
0 NaN NaN NaN 1 1
1 NaN NaN 1.0 1 1
2 NaN NaN 2.0 1 1
3 NaN NaN 3.0 1 1
4 1.0 NaN 4.0 1 1
5 NaN NaN 5.0 1 1
6 NaN NaN 6.0 1 1
7 NaN NaN 7.0 1 1

In [30]: casted.dtypes
Out[30]:
A float64
B float64
C float64
D int64
E int32
dtype: object

While float dtypes are unchanged.

.. ipython:: python

df4 = df3.copy()
df4['A'] = df4['A'].astype('float32')
df4.dtypes

casted = df4[df4 > 0]
casted
casted.dtypes
.. code-block:: ipython

In [31]: df4 = df3.copy()

In [32]: df4['A'] = df4['A'].astype('float32')

In [33]: df4.dtypes
Out[33]:
A float32
B float64
C float64
D float16
E int32
dtype: object

In [34]: casted = df4[df4 > 0]

In [35]: casted
Out[35]:
A B C D E
0 NaN NaN NaN 1.0 1
1 NaN 0.567020 1.0 1.0 1
2 NaN 0.276232 2.0 1.0 1
3 NaN NaN 3.0 1.0 1
4 1.933792 NaN 4.0 1.0 1
5 NaN 0.113648 5.0 1.0 1
6 NaN NaN 6.0 1.0 1
7 NaN 0.524988 7.0 1.0 1

In [36]: casted.dtypes
Out[36]:
A float32
B float64
C float64
D float16
E int32
dtype: object

Datetimes Conversion
~~~~~~~~~~~~~~~~~~~~
Expand Down
44 changes: 33 additions & 11 deletions doc/source/whatsnew/v0.13.0.rst
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Expand Up @@ -271,17 +271,39 @@ This is like an ``append`` operation.

A Panel setting operation on an arbitrary axis aligns the input to the Panel

.. ipython:: python
:okwarning:

p = pd.Panel(np.arange(16).reshape(2, 4, 2),
items=['Item1', 'Item2'],
major_axis=pd.date_range('2001/1/12', periods=4),
minor_axis=['A', 'B'], dtype='float64')
p
p.loc[:, :, 'C'] = pd.Series([30, 32], index=p.items)
p
p.loc[:, :, 'C']
.. code-block:: ipython

In [20]: p = pd.Panel(np.arange(16).reshape(2, 4, 2),
....: items=['Item1', 'Item2'],
....: major_axis=pd.date_range('2001/1/12', periods=4),
....: minor_axis=['A', 'B'], dtype='float64')
....:

In [21]: p
Out[21]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 2 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2001-01-12 00:00:00 to 2001-01-15 00:00:00
Minor_axis axis: A to B

In [22]: p.loc[:, :, 'C'] = pd.Series([30, 32], index=p.items)

In [23]: p
Out[23]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 3 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2001-01-12 00:00:00 to 2001-01-15 00:00:00
Minor_axis axis: A to C

In [24]: p.loc[:, :, 'C']
Out[24]:
Item1 Item2
2001-01-12 30.0 32.0
2001-01-13 30.0 32.0
2001-01-14 30.0 32.0
2001-01-15 30.0 32.0

Float64Index API Change
~~~~~~~~~~~~~~~~~~~~~~~
Expand Down
19 changes: 9 additions & 10 deletions doc/source/whatsnew/v0.25.0.rst
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Expand Up @@ -145,29 +145,28 @@ Constructing a :class:`MultiIndex` with ``NaN`` levels or codes value < -1 was a
Now, construction with codes value < -1 is not allowed and ``NaN`` levels' corresponding codes
would be reassigned as -1. (:issue:`19387`)

.. ipython:: python

mi1 = pd.MultiIndex(levels=[[np.nan, None, pd.NaT, 128, 2]],
codes=[[0, -1, 1, 2, 3, 4]])
mi2 = pd.MultiIndex(levels=[[1, 2]], codes=[[0, -2]])

*Previous Behavior*:

.. code-block:: ipython

In [1]: mi1
In [1]: pd.MultiIndex(levels=[[np.nan, None, pd.NaT, 128, 2]],
...: codes=[[0, -1, 1, 2, 3, 4]])
...:
Out[1]: MultiIndex(levels=[[nan, None, NaT, 128, 2]],
codes=[[0, -1, 1, 2, 3, 4]])
In [2]: mi2

In [2]: pd.MultiIndex(levels=[[1, 2]], codes=[[0, -2]])
Out[2]: MultiIndex(levels=[[1, 2]],
codes=[[0, -2]])

*New Behavior*:

.. ipython:: python
:okexcept:

mi1
mi2
pd.MultiIndex(levels=[[np.nan, None, pd.NaT, 128, 2]],
codes=[[0, -1, 1, 2, 3, 4]])
pd.MultiIndex(levels=[[1, 2]], codes=[[0, -2]])


.. _whatsnew_0250.api_breaking.groupby_apply_first_group_once:
Expand Down
3 changes: 3 additions & 0 deletions pandas/core/frame.py
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Expand Up @@ -4000,6 +4000,7 @@ def rename(self, *args, **kwargs):
intent.

Rename columns using a mapping:

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
a c
Expand All @@ -4008,13 +4009,15 @@ def rename(self, *args, **kwargs):
2 3 6

Rename index using a mapping:

>>> df.rename(index={0: "x", 1: "y", 2: "z"})
A B
x 1 4
y 2 5
z 3 6

Cast index labels to a different type:

>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Expand Down
1 change: 1 addition & 0 deletions pandas/core/generic.py
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Expand Up @@ -10867,6 +10867,7 @@ def _doc_parms(cls):
level_output_1=0)

_stat_func_see_also = """

See Also
--------
Series.sum : Return the sum.
Expand Down