DOC: Fix flake8 issues in whatsnew v10* and v11* (#24277)

Author: saurav-chakravorty

doc/source/whatsnew/v0.10.0.rst

@@ -5,11 +5,6 @@ v0.10.0 (December 17, 2012)
 
 {{ header }}
 
-.. 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
@@ -60,7 +55,7 @@ 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.
@@ -77,7 +72,7 @@ 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]:
@@ -187,10 +182,14 @@ 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``
@@ -199,8 +198,8 @@ 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
@@ -211,7 +210,7 @@ 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')
@@ -230,9 +229,9 @@ 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)
 
@@ -249,7 +248,7 @@ 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`).
 
@@ -264,7 +263,7 @@ 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
 
@@ -300,13 +299,16 @@ 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
@@ -322,13 +324,13 @@ 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']")
@@ -361,8 +363,8 @@ 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()

doc/source/whatsnew/v0.10.1.rst

@@ -5,11 +5,6 @@ v0.10.1 (January 22, 2013)
 
 {{ header }}
 
-.. 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
@@ -48,24 +43,27 @@ You may need to upgrade your existing data files. Please visit the
    :suppress:
    :okexcept:
 
+   import os
+
    os.remove('store.h5')
 
 You can designate (and index) certain columns that you want to be able to
 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
@@ -77,16 +75,16 @@ 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')
@@ -99,21 +97,21 @@ 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
@@ -131,7 +129,7 @@ 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]:
@@ -162,26 +160,28 @@ 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**

doc/source/whatsnew/v0.11.0.rst

@@ -5,11 +5,6 @@ v0.11.0 (April 22, 2013)
 
 {{ header }}
 
-.. ipython:: python
-   :suppress:
-
-   from pandas import * # noqa F401, F403
-
 
 This is a major release from 0.10.1 and includes many new features and
 enhancements along with a large number of bug fixes. The methods of Selecting
@@ -79,12 +74,12 @@ Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passe
 
 .. ipython:: python
 
-   df1 = DataFrame(randn(8, 1), columns = ['A'], dtype = 'float32')
+   df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')
    df1
    df1.dtypes
-   df2 = DataFrame(dict( A = Series(randn(8),dtype='float16'),
-                         B = Series(randn(8)),
-                         C = Series(range(8),dtype='uint8') ))
+   df2 = pd.DataFrame({'A': pd.Series(np.random.randn(8), dtype='float16'),
+                       'B': pd.Series(np.random.randn(8)),
+                       'C': pd.Series(range(8), dtype='uint8')})
    df2
    df2.dtypes
 
@@ -127,9 +122,9 @@ Forcing Date coercion (and setting ``NaT`` when not datelike)
 .. ipython:: python
    :okwarning:
 
-   from datetime import datetime
-   s = Series([datetime(2001,1,1,0,0), 'foo', 1.0, 1,
-               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')
 
 Dtype Gotchas
@@ -145,9 +140,9 @@ The following will all result in ``int64`` dtypes
 
 .. ipython:: python
 
-    DataFrame([1,2],columns=['a']).dtypes
-    DataFrame({'a' : [1,2] }).dtypes
-    DataFrame({'a' : 1 }, index=range(2)).dtypes
+    pd.DataFrame([1, 2], columns=['a']).dtypes
+    pd.DataFrame({'a': [1, 2]}).dtypes
+    pd.DataFrame({'a': 1}, index=range(2)).dtypes
 
 Keep in mind that ``DataFrame(np.array([1,2]))`` **WILL** result in ``int32`` on 32-bit platforms!
 
@@ -164,7 +159,7 @@ The dtype of the input data will be preserved in cases where ``nans`` are not in
    dfi
    dfi.dtypes
 
-   casted = dfi[dfi>0]
+   casted = dfi[dfi > 0]
    casted
    casted.dtypes
 
@@ -176,7 +171,7 @@ While float dtypes are unchanged.
    df4['A'] = df4['A'].astype('float32')
    df4.dtypes
 
-   casted = df4[df4>0]
+   casted = df4[df4 > 0]
    casted
    casted.dtypes
 
@@ -190,23 +185,23 @@ Furthermore ``datetime64[ns]`` columns are created by default, when passed datet
 
 .. ipython:: python
 
-   df = DataFrame(randn(6,2),date_range('20010102',periods=6),columns=['A','B'])
-   df['timestamp'] = Timestamp('20010103')
+   df = pd.DataFrame(np.random.randn(6, 2), pd.date_range('20010102', periods=6),
+                     columns=['A', ' B'])
+   df['timestamp'] = pd.Timestamp('20010103')
    df
 
    # datetime64[ns] out of the box
    df.get_dtype_counts()
 
    # use the traditional nan, which is mapped to NaT internally
-   df.loc[df.index[2:4], ['A','timestamp']] = np.nan
+   df.loc[df.index[2:4], ['A', 'timestamp']] = np.nan
    df
 
 Astype conversion on ``datetime64[ns]`` to ``object``, implicitly converts ``NaT`` to ``np.nan``
 
 .. ipython:: python
 
-   import datetime
-   s = Series([datetime.datetime(2001, 1, 2, 0, 0) for i in range(3)])
+   s = pd.Series([datetime.datetime(2001, 1, 2, 0, 0) for i in range(3)])
    s.dtype
    s[1] = np.nan
    s
@@ -250,14 +245,16 @@ Enhancements
 
       .. ipython:: python
 
-          df = DataFrame(dict(A=lrange(5), B=lrange(5)))
-          df.to_hdf('store.h5','table',append=True)
-          read_hdf('store.h5', 'table', where = ['index>2'])
+          df = pd.DataFrame({'A': lrange(5), 'B': lrange(5)})
+          df.to_hdf('store.h5', 'table', append=True)
+          pd.read_hdf('store.h5', 'table', where=['index > 2'])
 
       .. ipython:: python
           :suppress:
           :okexcept:
 
+          import os
+
           os.remove('store.h5')
 
     - provide dotted attribute access to ``get`` from stores, e.g. ``store.df == store['df']``
@@ -271,23 +268,23 @@ Enhancements
 
     .. ipython:: python
 
-        idx = date_range("2001-10-1", periods=5, freq='M')
-        ts = Series(np.random.rand(len(idx)),index=idx)
+        idx = pd.date_range("2001-10-1", periods=5, freq='M')
+        ts = pd.Series(np.random.rand(len(idx)), index=idx)
         ts['2001']
 
-        df = DataFrame(dict(A = ts))
+        df = pd.DataFrame({'A': ts})
         df['2001']
 
   - ``Squeeze`` to possibly remove length 1 dimensions from an object.
 
     .. ipython:: python
 
-       p = Panel(randn(3,4,4),items=['ItemA','ItemB','ItemC'],
-                          major_axis=date_range('20010102',periods=4),
-                          minor_axis=['A','B','C','D'])
+       p = pd.Panel(np.random.randn(3, 4, 4), items=['ItemA', 'ItemB', 'ItemC'],
+                    major_axis=pd.date_range('20010102', periods=4),
+                    minor_axis=['A', 'B', 'C', 'D'])
        p
        p.reindex(items=['ItemA']).squeeze()
-       p.reindex(items=['ItemA'],minor=['B']).squeeze()
+       p.reindex(items=['ItemA'], minor=['B']).squeeze()
 
   - In ``pd.io.data.Options``,
 

setup.cfg

@@ -50,9 +50,6 @@ exclude =
     doc/source/whatsnew/v0.8.0.rst
     doc/source/whatsnew/v0.9.0.rst
     doc/source/whatsnew/v0.9.1.rst
-    doc/source/whatsnew/v0.10.0.rst
-    doc/source/whatsnew/v0.10.1.rst
-    doc/source/whatsnew/v0.11.0.rst
     doc/source/whatsnew/v0.12.0.rst
     doc/source/whatsnew/v0.13.0.rst
     doc/source/whatsnew/v0.13.1.rst