DOC: fix code-block ipython highlighting

I noticed some warnings in the travis doc build log in the sense of
"WARNING: Could not parse literal_block as "python". highlighting
skipped."   This was because some of the `.. code-block:: python`
directives contained code that could not be interpreted as python.
And, additionally, these code blocks were also not highlighted
correctly (the `In [1]` prompt were not recognized by the default
python highlighter).     Therefore, I changed those `.. code-block::
python` directives containing IPython prompt to `.. code-block::
ipython`

Author: Joris Van den Bossche <[email protected]>

Closes #12853 from jorisvandenbossche/fix-ipython-highlighting and squashes the following commits:

1e104cc [Joris Van den Bossche] DOC: fix code-block ipython highlighting

Author: jorisvandenbossche

doc/source/advanced.rst

@@ -790,7 +790,7 @@ In float indexes, slicing using floats is allowed
 
 In non-float indexes, slicing using floats will raise a ``TypeError``
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [1]: pd.Series(range(5))[3.5]
    TypeError: the label [3.5] is not a proper indexer for this index type (Int64Index)
@@ -802,7 +802,7 @@ In non-float indexes, slicing using floats will raise a ``TypeError``
 
    Using a scalar float indexer for ``.iloc`` has been removed in 0.18.0, so the following will raise a ``TypeError``
 
-   .. code-block:: python
+   .. code-block:: ipython
 
       In [3]: pd.Series(range(5)).iloc[3.0]
       TypeError: cannot do positional indexing on <class 'pandas.indexes.range.RangeIndex'> with these indexers [3.0] of <type 'float'>

doc/source/basics.rst

@@ -272,7 +272,7 @@ To evaluate single-element pandas objects in a boolean context, use the method
 
    .. code-block:: python
 
-       >>>if df:
+       >>> if df:
             ...
 
    Or
@@ -352,7 +352,7 @@ objects of the same length:
 Trying to compare ``Index`` or ``Series`` objects of different lengths will
 raise a ValueError:
 
-.. code-block:: python
+.. code-block:: ipython
 
     In [55]: pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo', 'bar'])
     ValueError: Series lengths must match to compare

doc/source/computation.rst

@@ -236,7 +236,7 @@ These are created from methods on ``Series`` and ``DataFrame``.
 
 These object provide tab-completion of the avaible methods and properties.
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [14]: r.
    r.agg         r.apply       r.count       r.exclusions  r.max         r.median      r.name        r.skew        r.sum

doc/source/enhancingperf.rst

@@ -68,7 +68,7 @@ Here's the function in pure python:
 
 We achieve our result by using ``apply`` (row-wise):
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [7]: %timeit df.apply(lambda x: integrate_f(x['a'], x['b'], x['N']), axis=1)
    10 loops, best of 3: 174 ms per loop
@@ -125,7 +125,7 @@ is here to distinguish between function versions):
   to be using bleeding edge ipython for paste to play well with cell magics.
 
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [4]: %timeit df.apply(lambda x: integrate_f_plain(x['a'], x['b'], x['N']), axis=1)
    10 loops, best of 3: 85.5 ms per loop
@@ -154,7 +154,7 @@ We get another huge improvement simply by providing type information:
       ...:     return s * dx
       ...:
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [4]: %timeit df.apply(lambda x: integrate_f_typed(x['a'], x['b'], x['N']), axis=1)
    10 loops, best of 3: 20.3 ms per loop
@@ -234,7 +234,7 @@ the rows, applying our ``integrate_f_typed``, and putting this in the zeros arra
     Loops like this would be *extremely* slow in python, but in Cython looping
     over numpy arrays is *fast*.
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [4]: %timeit apply_integrate_f(df['a'].values, df['b'].values, df['N'].values)
    1000 loops, best of 3: 1.25 ms per loop
@@ -284,7 +284,7 @@ advanced cython techniques:
       ...:     return res
       ...:
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [4]: %timeit apply_integrate_f_wrap(df['a'].values, df['b'].values, df['N'].values)
    1000 loops, best of 3: 987 us per loop
@@ -348,7 +348,7 @@ Using ``numba`` to just-in-time compile your code. We simply take the plain pyth
 
 Note that we directly pass ``numpy`` arrays to the numba function. ``compute_numba`` is just a wrapper that provides a nicer interface by passing/returning pandas objects.
 
-.. code-block:: python
+.. code-block:: ipython
 
     In [4]: %timeit compute_numba(df)
     1000 loops, best of 3: 798 us per loop

doc/source/indexing.rst

@@ -297,7 +297,7 @@ Selection By Label
      dfl = pd.DataFrame(np.random.randn(5,4), columns=list('ABCD'), index=pd.date_range('20130101',periods=5))
      dfl
 
-  .. code-block:: python
+  .. code-block:: ipython
 
      In [4]: dfl.loc[2:3]
      TypeError: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'>

doc/source/io.rst

@@ -4375,7 +4375,7 @@ Creating BigQuery Tables
 As of 0.15.2, the gbq module has a function :func:`~pandas.io.gbq.generate_bq_schema` which will
 produce the dictionary representation schema of the specified pandas DataFrame.
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [10]: gbq.generate_bq_schema(df, default_type='STRING')
 
@@ -4633,7 +4633,7 @@ Performance Considerations
 
 This is an informal comparison of various IO methods, using pandas 0.13.1.
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [1]: df = DataFrame(randn(1000000,2),columns=list('AB'))
 
@@ -4648,7 +4648,7 @@ This is an informal comparison of various IO methods, using pandas 0.13.1.
 
 Writing
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [14]: %timeit test_sql_write(df)
    1 loops, best of 3: 6.24 s per loop
@@ -4670,7 +4670,7 @@ Writing
 
 Reading
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [18]: %timeit test_sql_read()
    1 loops, best of 3: 766 ms per loop
@@ -4692,7 +4692,7 @@ Reading
 
 Space on disk (in bytes)
 
-.. code-block:: python
+.. code-block::
 
     25843712 Apr  8 14:11 test.sql
     24007368 Apr  8 14:11 test_fixed.hdf

doc/source/options.rst

@@ -130,7 +130,7 @@ Setting Startup Options in python/ipython Environment
 
 Using startup scripts for the python/ipython environment to import pandas and set options makes working with pandas more efficient.  To do this, create a .py or .ipy script in the startup directory of the desired profile.  An example where the startup folder is in a default ipython profile can be found at:
 
-.. code-block:: python
+.. code-block:: none
 
   $IPYTHONDIR/profile_default/startup
 

doc/source/release.rst

@@ -1521,7 +1521,7 @@ API Changes
   of the future import. You can use ``//`` and ``floordiv`` to do integer
   division.
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [3]: arr = np.array([1, 2, 3, 4])
 

doc/source/remote_data.rst

@@ -192,7 +192,7 @@ every world bank indicator is accessible.
 For example, if you wanted to compare the Gross Domestic Products per capita in
 constant dollars in North America, you would use the ``search`` function:
 
-.. code-block:: python
+.. code-block:: ipython
 
     In [1]: from pandas.io import wb
 
@@ -207,7 +207,7 @@ constant dollars in North America, you would use the ``search`` function:
 Then you would use the ``download`` function to acquire the data from the World
 Bank's servers:
 
-.. code-block:: python
+.. code-block:: ipython
 
     In [3]: dat = wb.download(indicator='NY.GDP.PCAP.KD', country=['US', 'CA', 'MX'], start=2005, end=2008)
 
@@ -230,7 +230,7 @@ Bank's servers:
 The resulting dataset is a properly formatted ``DataFrame`` with a hierarchical
 index, so it is easy to apply ``.groupby`` transformations to it:
 
-.. code-block:: python
+.. code-block:: ipython
 
     In [6]: dat['NY.GDP.PCAP.KD'].groupby(level=0).mean()
     Out[6]:
@@ -243,7 +243,7 @@ index, so it is easy to apply ``.groupby`` transformations to it:
 Now imagine you want to compare GDP to the share of people with cellphone
 contracts around the world.
 
-.. code-block:: python
+.. code-block:: ipython
 
     In [7]: wb.search('cell.*%').iloc[:,:2]
     Out[7]:
@@ -255,7 +255,7 @@ contracts around the world.
 Notice that this second search was much faster than the first one because
 ``pandas`` now has a cached list of available data series.
 
-.. code-block:: python
+.. code-block:: ipython
 
     In [13]: ind = ['NY.GDP.PCAP.KD', 'IT.MOB.COV.ZS']
     In [14]: dat = wb.download(indicator=ind, country='all', start=2011, end=2011).dropna()
@@ -273,7 +273,7 @@ Finally, we use the ``statsmodels`` package to assess the relationship between
 our two variables using ordinary least squares regression. Unsurprisingly,
 populations in rich countries tend to use cellphones at a higher rate:
 
-.. code-block:: python
+.. code-block:: ipython
 
     In [17]: import numpy as np
     In [18]: import statsmodels.formula.api as smf

doc/source/timeseries.rst

@@ -1522,7 +1522,7 @@ If ``Period`` freq is daily or higher (``D``, ``H``, ``T``, ``S``, ``L``, ``U``,
    p + timedelta(minutes=120)
    p + np.timedelta64(7200, 's')
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [1]: p + Minute(5)
    Traceback
@@ -1536,7 +1536,7 @@ If ``Period`` has other freqs, only the same ``offsets`` can be added. Otherwise
    p = Period('2014-07', freq='M')
    p + MonthEnd(3)
 
-.. code-block:: python
+.. code-block:: ipython
 
    In [1]: p + MonthBegin(3)
    Traceback