clean whitespace

Author: jreback

doc/source/cookbook.rst

@@ -1340,17 +1340,17 @@ Create two DataFrames with one column each from a subset of the unique values wi
 
 .. ipython:: python
 
-   df1.A.dtype 
+   df1.A.dtype
 
 .. ipython:: python
 
-   df2.A.dtype 
+   df2.A.dtype
 
 .. ipython:: python
 
-   df3.A.dtype 
+   df3.A.dtype
 
-The data type of all the columns is Object. Using Categorical columns should improve memory usage.    
+The data type of all the columns is Object. Using Categorical columns should improve memory usage.
 
 .. ipython:: python
 
@@ -1367,17 +1367,17 @@ The data type of all the columns is Object. Using Categorical columns should imp
 .. ipython:: python
 
    dfc3 = dfc1.append(dfc2)
-   dfc3.memory_usage() 
+   dfc3.memory_usage()
 
 .. ipython:: python
 
    df3.A.dtype
 
-The first two Dataframes used a lot less memory because their columns are Categorical, but the column in the third Dataframe has a data type of Object and is using the same amount of memory as before. The appended category columns were converted into an object column because the columns' categories are incompatible: 
+The first two Dataframes used a lot less memory because their columns are Categorical, but the column in the third Dataframe has a data type of Object and is using the same amount of memory as before. The appended category columns were converted into an object column because the columns' categories are incompatible:
 
 .. ipython:: python
 
-   dfc1.A.cat.categories 
+   dfc1.A.cat.categories
 
 .. ipython:: python
 
@@ -1404,12 +1404,10 @@ This time the same categories are specified for both of the starting columns.
    dfc3 = dfc1.append(dfc2)
    dfc3.memory_usage()
 
-The resulting DataFrame's memory usage is far smaller now that the data type of the final column is Category. It worked as expected this time because the code --> category mapping was the same for both of the original columns, which carried over to the final column: 
+The resulting DataFrame's memory usage is far smaller now that the data type of the final column is Category. It worked as expected this time because the code --> category mapping was the same for both of the original columns, which carried over to the final column:
 
 .. ipython:: python
 
    [(code, cat) for code, cat in enumerate(dfc3.A.cat.categories)]
 
 `More information about categorical data <http://pandas.pydata.org/pandas-docs/stable/categorical.html>`__
-
-