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merged 4 commits into from
Dec 14, 2018
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DOC: Fix flake8 issues in whatsnew v10* and v11* #24277

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a3f42af
DOC: Flake-8 Fixes for whatsnew v0.10.*
saurav-chakravorty Dec 14, 2018
3cd4a27
DOC: Fix Flake8 issues with whatsnew v0.11.0
saurav-chakravorty Dec 14, 2018
e43d1ec
DOC: added header back
saurav-chakravorty Dec 14, 2018
1ed46ec
Merge remote-tracking branch 'upstream/master' into whatsnew-10
saurav-chakravorty Dec 14, 2018
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56 changes: 28 additions & 28 deletions doc/source/whatsnew/v0.10.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -3,13 +3,6 @@
v0.10.0 (December 17, 2012)
---------------------------

{{ header }}
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do we still need the {{ header }} ? cc @datapythonista

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Yes, it should be in all pages. It is where we do the import pandas as pd and set some options. But even in pages where there is no code, I need it there because with the new sphinx theme, I'll be controlling the navigation with this.

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I added it back. It was a mistake. I deleted with the include block.


.. ipython:: python
:suppress:

from pandas import * # noqa F401, F403


This is a major release from 0.9.1 and includes many new features and
enhancements along with a large number of bug fixes. There are also a number of
Expand Down Expand Up @@ -60,7 +53,7 @@ talking about:
# deprecated now
df - df[0]
# Change your code to
df.sub(df[0], axis=0) # align on axis 0 (rows)
df.sub(df[0], axis=0) # align on axis 0 (rows)

You will get a deprecation warning in the 0.10.x series, and the deprecated
functionality will be removed in 0.11 or later.
Expand All @@ -77,7 +70,7 @@ labeled the aggregated group with the end of the interval: the next day).

In [1]: dates = pd.date_range('1/1/2000', '1/5/2000', freq='4h')

In [2]: series = Series(np.arange(len(dates)), index=dates)
In [2]: series = pd.Series(np.arange(len(dates)), index=dates)

In [3]: series
Out[3]:
Expand Down Expand Up @@ -187,10 +180,14 @@ labeled the aggregated group with the end of the interval: the next day).

.. ipython:: python

data= 'a,b,c\n1,Yes,2\n3,No,4'
import io

data = ('a,b,c\n'
'1,Yes,2\n'
'3,No,4')
print(data)
pd.read_csv(StringIO(data), header=None)
pd.read_csv(StringIO(data), header=None, prefix='X')
pd.read_csv(io.StringIO(data), header=None)
pd.read_csv(io.StringIO(data), header=None, prefix='X')

- Values like ``'Yes'`` and ``'No'`` are not interpreted as boolean by default,
though this can be controlled by new ``true_values`` and ``false_values``
Expand All @@ -199,8 +196,8 @@ labeled the aggregated group with the end of the interval: the next day).
.. ipython:: python

print(data)
pd.read_csv(StringIO(data))
pd.read_csv(StringIO(data), true_values=['Yes'], false_values=['No'])
pd.read_csv(io.StringIO(data))
pd.read_csv(io.StringIO(data), true_values=['Yes'], false_values=['No'])

- The file parsers will not recognize non-string values arising from a
converter function as NA if passed in the ``na_values`` argument. It's better
Expand All @@ -211,7 +208,7 @@ labeled the aggregated group with the end of the interval: the next day).

.. ipython:: python

s = Series([np.nan, 1., 2., np.nan, 4])
s = pd.Series([np.nan, 1., 2., np.nan, 4])
s
s.fillna(0)
s.fillna(method='pad')
Expand All @@ -230,9 +227,9 @@ Convenience methods ``ffill`` and ``bfill`` have been added:
.. ipython:: python

def f(x):
return Series([ x, x**2 ], index = ['x', 'x^2'])
return pd.Series([x, x**2], index=['x', 'x^2'])

s = Series(np.random.rand(5))
s = pd.Series(np.random.rand(5))
s
s.apply(f)

Expand All @@ -249,7 +246,7 @@ Convenience methods ``ffill`` and ``bfill`` have been added:

.. ipython:: python

get_option("display.max_rows")
pd.get_option("display.max_rows")

- to_string() methods now always return unicode strings (:issue:`2224`).

Expand All @@ -264,7 +261,7 @@ representation across multiple rows by default:

.. ipython:: python

wide_frame = DataFrame(randn(5, 16))
wide_frame = pd.DataFrame(np.random.randn(5, 16))

wide_frame

Expand Down Expand Up @@ -300,13 +297,16 @@ Updated PyTables Support
:suppress:
:okexcept:

import os

os.remove('store.h5')

.. ipython:: python

store = HDFStore('store.h5')
df = DataFrame(randn(8, 3), index=date_range('1/1/2000', periods=8),
columns=['A', 'B', 'C'])
store = pd.HDFStore('store.h5')
df = pd.DataFrame(np.random.randn(8, 3),
index=pd.date_range('1/1/2000', periods=8),
columns=['A', 'B', 'C'])
df

# appending data frames
Expand All @@ -322,13 +322,13 @@ Updated PyTables Support
.. ipython:: python
:okwarning:

wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
major_axis=date_range('1/1/2000', periods=5),
minor_axis=['A', 'B', 'C', 'D'])
wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
major_axis=pd.date_range('1/1/2000', periods=5),
minor_axis=['A', 'B', 'C', 'D'])
wp

# storing a panel
store.append('wp',wp)
store.append('wp', wp)

# selecting via A QUERY
store.select('wp', "major_axis>20000102 and minor_axis=['A','B']")
Expand Down Expand Up @@ -361,8 +361,8 @@ Updated PyTables Support
.. ipython:: python

df['string'] = 'string'
df['int'] = 1
store.append('df',df)
df['int'] = 1
store.append('df', df)
df1 = store.select('df')
df1
df1.get_dtype_counts()
Expand Down
57 changes: 27 additions & 30 deletions doc/source/whatsnew/v0.10.1.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -3,13 +3,6 @@
v0.10.1 (January 22, 2013)
---------------------------

{{ header }}
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restore the header here too


.. ipython:: python
:suppress:

from pandas import * # noqa F401, F403


This is a minor release from 0.10.0 and includes new features, enhancements,
and bug fixes. In particular, there is substantial new HDFStore functionality
Expand Down Expand Up @@ -47,6 +40,7 @@ You may need to upgrade your existing data files. Please visit the
.. ipython:: python
:suppress:
:okexcept:
import os
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I think we need a blank line before the import here


os.remove('store.h5')

Expand All @@ -55,17 +49,18 @@ perform queries on a table, by passing a list to ``data_columns``

.. ipython:: python

store = HDFStore('store.h5')
df = DataFrame(randn(8, 3), index=date_range('1/1/2000', periods=8),
columns=['A', 'B', 'C'])
store = pd.HDFStore('store.h5')
df = pd.DataFrame(np.random.randn(8, 3),
index=pd.date_range('1/1/2000', periods=8),
columns=['A', 'B', 'C'])
df['string'] = 'foo'
df.loc[df.index[4:6], 'string'] = np.nan
df.loc[df.index[7:9], 'string'] = 'bar'
df['string2'] = 'cool'
df

# on-disk operations
store.append('df', df, data_columns = ['B','C','string','string2'])
store.append('df', df, data_columns=['B', 'C', 'string', 'string2'])
store.select('df', "B>0 and string=='foo'")

# this is in-memory version of this type of selection
Expand All @@ -77,16 +72,16 @@ Retrieving unique values in an indexable or data column.

# note that this is deprecated as of 0.14.0
# can be replicated by: store.select_column('df','index').unique()
store.unique('df','index')
store.unique('df','string')
store.unique('df', 'index')
store.unique('df', 'string')

You can now store ``datetime64`` in data columns

.. ipython:: python

df_mixed = df.copy()
df_mixed['datetime64'] = Timestamp('20010102')
df_mixed.loc[df_mixed.index[3:4], ['A','B']] = np.nan
df_mixed = df.copy()
df_mixed['datetime64'] = pd.Timestamp('20010102')
df_mixed.loc[df_mixed.index[3:4], ['A', 'B']] = np.nan

store.append('df_mixed', df_mixed)
df_mixed1 = store.select('df_mixed')
Expand All @@ -99,21 +94,21 @@ columns, this is equivalent to passing a

.. ipython:: python

store.select('df',columns = ['A','B'])
store.select('df', columns=['A', 'B'])

``HDFStore`` now serializes MultiIndex dataframes when appending tables.

.. code-block:: ipython

In [19]: index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
....: ['one', 'two', 'three']],
....: labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
....: [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
....: names=['foo', 'bar'])
In [19]: index = pd.MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
....: ['one', 'two', 'three']],
....: labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
....: [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
....: names=['foo', 'bar'])
....:

In [20]: df = DataFrame(np.random.randn(10, 3), index=index,
....: columns=['A', 'B', 'C'])
In [20]: df = pd.DataFrame(np.random.randn(10, 3), index=index,
....: columns=['A', 'B', 'C'])
....:

In [21]: df
Expand All @@ -131,7 +126,7 @@ columns, this is equivalent to passing a
two -3.207595 -1.535854 0.409769
three -0.673145 -0.741113 -0.110891

In [22]: store.append('mi',df)
In [22]: store.append('mi', df)

In [23]: store.select('mi')
Out[23]:
Expand Down Expand Up @@ -162,26 +157,28 @@ combined result, by using ``where`` on a selector table.

.. ipython:: python

df_mt = DataFrame(randn(8, 6), index=date_range('1/1/2000', periods=8),
columns=['A', 'B', 'C', 'D', 'E', 'F'])
df_mt = pd.DataFrame(np.random.randn(8, 6),
index=pd.date_range('1/1/2000', periods=8),
columns=['A', 'B', 'C', 'D', 'E', 'F'])
df_mt['foo'] = 'bar'

# you can also create the tables individually
store.append_to_multiple({ 'df1_mt' : ['A','B'], 'df2_mt' : None }, df_mt, selector = 'df1_mt')
store.append_to_multiple({'df1_mt': ['A', 'B'], 'df2_mt': None},
df_mt, selector='df1_mt')
store

# indiviual tables were created
store.select('df1_mt')
store.select('df2_mt')

# as a multiple
store.select_as_multiple(['df1_mt','df2_mt'], where = [ 'A>0','B>0' ], selector = 'df1_mt')
store.select_as_multiple(['df1_mt', 'df2_mt'], where=['A>0', 'B>0'],
selector='df1_mt')

.. ipython:: python
:suppress:

store.close()
import os
os.remove('store.h5')

**Enhancements**
Expand Down
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