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May 14, 2021
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DOC: freeze old whatsnew notes part 1 #6856 #41464

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DOC: freeze old whatsnew notes #6856
fangchenli May 14, 2021
9db9315
fix typo
fangchenli May 14, 2021
939b3e8
fix extra space
fangchenli May 14, 2021
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fangchenli May 14, 2021
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6 changes: 0 additions & 6 deletions doc/source/whatsnew/v0.5.0.rst
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Expand Up @@ -6,12 +6,6 @@ Version 0.5.0 (October 24, 2011)

{{ header }}

.. ipython:: python
:suppress:

from pandas import * # noqa F401, F403


New features
~~~~~~~~~~~~

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6 changes: 0 additions & 6 deletions doc/source/whatsnew/v0.6.0.rst
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Expand Up @@ -5,12 +5,6 @@ Version 0.6.0 (November 25, 2011)

{{ header }}

.. ipython:: python
:suppress:

from pandas import * # noqa F401, F403


New features
~~~~~~~~~~~~
- :ref:`Added <reshaping.melt>` ``melt`` function to ``pandas.core.reshape``
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111 changes: 92 additions & 19 deletions doc/source/whatsnew/v0.7.0.rst
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Expand Up @@ -31,10 +31,22 @@ New features

- Handle differently-indexed output values in ``DataFrame.apply`` (:issue:`498`)

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

df = pd.DataFrame(np.random.randn(10, 4))
df.apply(lambda x: x.describe())
In [1]: df = pd.DataFrame(np.random.randn(10, 4))
In [2]: df.apply(lambda x: x.describe())
Out[2]:
0 1 2 3
count 10.000000 10.000000 10.000000 10.000000
mean 0.190912 -0.395125 -0.731920 -0.403130
std 0.730951 0.813266 1.112016 0.961912
min -0.861849 -2.104569 -1.776904 -1.469388
25% -0.411391 -0.698728 -1.501401 -1.076610
50% 0.380863 -0.228039 -1.191943 -1.004091
75% 0.658444 0.057974 -0.034326 0.461706
max 1.212112 0.577046 1.643563 1.071804

[8 rows x 4 columns]

- :ref:`Add<advanced.reorderlevels>` ``reorder_levels`` method to Series and
DataFrame (:issue:`534`)
Expand Down Expand Up @@ -116,13 +128,31 @@ One of the potentially riskiest API changes in 0.7.0, but also one of the most
important, was a complete review of how **integer indexes** are handled with
regard to label-based indexing. Here is an example:

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

s = pd.Series(np.random.randn(10), index=range(0, 20, 2))
s
s[0]
s[2]
s[4]
In [3]: s = pd.Series(np.random.randn(10), index=range(0, 20, 2))
In [4]: s
Out[4]:
0 -1.294524
2 0.413738
4 0.276662
6 -0.472035
8 -0.013960
10 -0.362543
12 -0.006154
14 -0.923061
16 0.895717
18 0.805244
Length: 10, dtype: float64

In [5]: s[0]
Out[5]: -1.2945235902555294

In [6]: s[2]
Out[6]: 0.41373810535784006

In [7]: s[4]
Out[7]: 0.2766617129497566

This is all exactly identical to the behavior before. However, if you ask for a
key **not** contained in the Series, in versions 0.6.1 and prior, Series would
Expand Down Expand Up @@ -235,22 +265,65 @@ slice to a Series when getting and setting values via ``[]`` (i.e. the
``__getitem__`` and ``__setitem__`` methods). The behavior will be the same as
passing similar input to ``ix`` **except in the case of integer indexing**:

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

s = pd.Series(np.random.randn(6), index=list('acegkm'))
s
s[['m', 'a', 'c', 'e']]
s['b':'l']
s['c':'k']
In [8]: s = pd.Series(np.random.randn(6), index=list('acegkm'))

In [9]: s
Out[9]:
a -1.206412
c 2.565646
e 1.431256
g 1.340309
k -1.170299
m -0.226169
Length: 6, dtype: float64

In [10]: s[['m', 'a', 'c', 'e']]
Out[10]:
m -0.226169
a -1.206412
c 2.565646
e 1.431256
Length: 4, dtype: float64

In [11]: s['b':'l']
Out[11]:
c 2.565646
e 1.431256
g 1.340309
k -1.170299
Length: 4, dtype: float64

In [12]: s['c':'k']
Out[12]:
c 2.565646
e 1.431256
g 1.340309
k -1.170299
Length: 4, dtype: float64

In the case of integer indexes, the behavior will be exactly as before
(shadowing ``ndarray``):

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

s = pd.Series(np.random.randn(6), index=range(0, 12, 2))
s[[4, 0, 2]]
s[1:5]
In [13]: s = pd.Series(np.random.randn(6), index=range(0, 12, 2))

In [14]: s[[4, 0, 2]]
Out[14]:
4 0.132003
0 0.410835
2 0.813850
Length: 3, dtype: float64

In [15]: s[1:5]
Out[15]:
2 0.813850
4 0.132003
6 -0.827317
8 -0.076467
Length: 4, dtype: float64

If you wish to do indexing with sequences and slicing on an integer index with
label semantics, use ``ix``.
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90 changes: 68 additions & 22 deletions doc/source/whatsnew/v0.7.3.rst
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Expand Up @@ -51,21 +51,37 @@ NA boolean comparison API change
Reverted some changes to how NA values (represented typically as ``NaN`` or
``None``) are handled in non-numeric Series:

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

series = pd.Series(["Steve", np.nan, "Joe"])
series == "Steve"
series != "Steve"
In [1]: series = pd.Series(["Steve", np.nan, "Joe"])

In [2]: series == "Steve"
Out[2]:
0 True
1 False
2 False
Length: 3, dtype: bool

In [3]: series != "Steve"
Out[3]:
0 False
1 True
2 True
Length: 3, dtype: bool

In comparisons, NA / NaN will always come through as ``False`` except with
``!=`` which is ``True``. *Be very careful* with boolean arithmetic, especially
negation, in the presence of NA data. You may wish to add an explicit NA
filter into boolean array operations if you are worried about this:

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

In [4]: mask = series == "Steve"

mask = series == "Steve"
series[mask & series.notnull()]
In [5]: series[mask & series.notnull()]
Out[5]:
0 Steve
Length: 1, dtype: object

While propagating NA in comparisons may seem like the right behavior to some
users (and you could argue on purely technical grounds that this is the right
Expand All @@ -80,21 +96,51 @@ Other API changes
When calling ``apply`` on a grouped Series, the return value will also be a
Series, to be more consistent with the ``groupby`` behavior with DataFrame:

.. ipython:: python
:okwarning:

df = pd.DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
"C": np.random.randn(8),
"D": np.random.randn(8),
}
)
df
grouped = df.groupby("A")["C"]
grouped.describe()
grouped.apply(lambda x: x.sort_values()[-2:]) # top 2 values
.. code-block:: ipython

In [6]: df = pd.DataFrame(
...: {
...: "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
...: "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
...: "C": np.random.randn(8),
...: "D": np.random.randn(8),
...: }
...: )
...:

In [7]: df
Out[7]:
A B C D
0 foo one 0.469112 -0.861849
1 bar one -0.282863 -2.104569
2 foo two -1.509059 -0.494929
3 bar three -1.135632 1.071804
4 foo two 1.212112 0.721555
5 bar two -0.173215 -0.706771
6 foo one 0.119209 -1.039575
7 foo three -1.044236 0.271860

[8 rows x 4 columns]

In [8]: grouped = df.groupby("A")["C"]

In [9]: grouped.describe()
Out[9]:
count mean std min 25% 50% 75% max
A
bar 3.0 -0.530570 0.526860 -1.135632 -0.709248 -0.282863 -0.228039 -0.173215
foo 5.0 -0.150572 1.113308 -1.509059 -1.044236 0.119209 0.469112 1.212112

[2 rows x 8 columns]

In [10]: grouped.apply(lambda x: x.sort_values()[-2:]) # top 2 values
Out[10]:
A
bar 1 -0.282863
5 -0.173215
foo 0 0.469112
4 1.212112
Name: C, Length: 4, dtype: float64


.. _whatsnew_0.7.3.contributors:
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