DOC: Spellcheck of merging.rst, reshaping.rst and timeseries.rst

doc/source/basics.rst

@@ -2220,7 +2220,7 @@ For example, to select ``bool`` columns:
 
    df.select_dtypes(include=[bool])
 
-You can also pass the name of a dtype in the `numpy dtype hierarchy
+You can also pass the name of a dtype in the `NumPy dtype hierarchy
 <http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`__:
 
 .. ipython:: python

doc/source/enhancingperf.rst

@@ -28,14 +28,14 @@ For many use cases writing pandas in pure Python and NumPy is sufficient. In som
 computationally heavy applications however, it can be possible to achieve sizeable
 speed-ups by offloading work to `cython <http://cython.org/>`__.
 
-This tutorial assumes you have refactored as much as possible in python, for example
+This tutorial assumes you have refactored as much as possible in Python, for example
 trying to remove for loops and making use of NumPy vectorization, it's always worth
 optimising in Python first.
 
 This tutorial walks through a "typical" process of cythonizing a slow computation.
 We use an `example from the cython documentation <http://docs.cython.org/src/quickstart/cythonize.html>`__
 but in the context of pandas. Our final cythonized solution is around 100 times
-faster than the pure python.
+faster than the pure Python.
 
 .. _enhancingperf.pure:
 
@@ -52,7 +52,7 @@ We have a DataFrame to which we want to apply a function row-wise.
                       'x': 'x'})
    df
 
-Here's the function in pure python:
+Here's the function in pure Python:
 
 .. ipython:: python
 
@@ -173,7 +173,7 @@ Using ndarray
 
 It's calling series... a lot! It's creating a Series from each row, and get-ting from both
 the index and the series (three times for each row). Function calls are expensive
-in python, so maybe we could minimize these by cythonizing the apply part.
+in Python, so maybe we could minimize these by cythonizing the apply part.
 
 .. note::
 
@@ -231,7 +231,7 @@ the rows, applying our ``integrate_f_typed``, and putting this in the zeros arra
 
 .. note::
 
-    Loops like this would be *extremely* slow in python, but in Cython looping
+    Loops like this would be *extremely* slow in Python, but in Cython looping
     over NumPy arrays is *fast*.
 
 .. code-block:: ipython

doc/source/indexing.rst

@@ -84,7 +84,7 @@ of multi-axis indexing.
   ``length-1`` of the axis), but may also be used with a boolean
   array.  ``.iloc`` will raise ``IndexError`` if a requested
   indexer is out-of-bounds, except *slice* indexers which allow
-  out-of-bounds indexing.  (this conforms with python/numpy *slice*
+  out-of-bounds indexing.  (this conforms with Python/NumPy *slice*
   semantics).  Allowed inputs are:
 
   - An integer e.g. ``5``.
@@ -1517,7 +1517,7 @@ The :meth:`~pandas.DataFrame.lookup` Method
 
 Sometimes you want to extract a set of values given a sequence of row labels
 and column labels, and the ``lookup`` method allows for this and returns a
-numpy array.  For instance:
+NumPy array.  For instance:
 
 .. ipython:: python
 

doc/source/io.rst

@@ -775,7 +775,7 @@ The simplest case is to just pass in ``parse_dates=True``:
    df = pd.read_csv('foo.csv', index_col=0, parse_dates=True)
    df
 
-   # These are python datetime objects
+   # These are Python datetime objects
    df.index
 
 It is often the case that we may want to store date and time data separately,