First, to convert a Categorical column to its numerical codes, you can do this easier with: dataframe['c'].cat.codes.
Further, it is possible to select automatically all columns with a certain dtype in a dataframe using select_dtypes. This way, you can apply above operation on multiple and automatically selected columns.

First making an example dataframe:

CopyIn [75]: df = pd.DataFrame({'col1':[1,2,3,4,5], 'col2':list('abcab'),  'col3':list('ababb')})

In [76]: df['col2'] = df['col2'].astype('category')

In [77]: df['col3'] = df['col3'].astype('category')

In [78]: df.dtypes
Out[78]:
col1       int64
col2    category
col3    category
dtype: object

Then by using select_dtypes to select the columns, and then applying .cat.codes on each of these columns, you can get the following result:

CopyIn [80]: cat_columns = df.select_dtypes(['category']).columns

In [81]: cat_columns
Out[81]: Index([u'col2', u'col3'], dtype='object')

In [83]: df[cat_columns] = df[cat_columns].apply(lambda x: x.cat.codes)

In [84]: df
Out[84]:
   col1  col2  col3
0     1     0     0
1     2     1     1
2     3     2     0
3     4     0     1
4     5     1     1

Note:

1. NaN becomes -1
2. This method is fast because the relationship between code and category is readily available and do not need to be computed.

First, change the type of the column:

df.cc = pd.Categorical(df.cc)

Now the data look similar but are stored categorically. To capture the category codes:

df['code'] = df.cc.codes

Now you have:

   cc  temp  code
0  US  37.0     2
1  CA  12.0     1
2  US  35.0     2
3  AU  20.0     0

If you don't want to modify your DataFrame but simply get the codes(.cat is used to access categorical methods):

df.cc.astype('category').cat.codes

Or use the categorical column as an index:

df2 = pd.DataFrame(df.temp)
df2.index = pd.CategoricalIndex(df.cc)