How to change multiple Pandas DF columns to categorical without a loop
<p>I have a DataFrame where I want to change several columns from type 'object' to 'category'.</p>

<p>I can change several columns at the same time for float, </p>

<pre><code>dftest[['col3', 'col4', 'col5', 'col6']] = \
    dftest[['col3', 'col4', 'col5', 'col6']].astype(float)
</code></pre>

<p>For 'category' I can not do it the same, I need to do one by one (or in a loop like <a href="http://stackoverflow.com/a/28910914/427129">here</a>).</p>

<pre><code>for col in ['col1', 'col2']:
    dftest[col] = dftest[col].astype('category')
</code></pre>

<p>Question: Is there any way of doing the change for all wanted columns at once like in the 'float' example?</p>

<p>If I try to do several columns at the same time I have:</p>

<pre><code>dftest[['col1','col2']] = dftest[['col1','col2']].astype('category')
## NotImplementedError: &gt; 1 ndim Categorical are not supported at this time
</code></pre>

<p>My current working test code: </p>

<pre><code>import numpy as np
import pandas as pd 

factors= np.array([
        ['a', 'xx'],
        ['a', 'xx'],
        ['ab', 'xx'],
        ['ab', 'xx'],
        ['ab', 'yy'],
        ['cc', 'yy'],
        ['cc', 'zz'],
        ['d', 'zz'],
        ['d', 'zz'],
        ['g', 'zz'] 
        ])

values = np.random.randn(10,4).round(2)

dftest = pd.DataFrame(np.hstack([factors,values]), 
                  columns = ['col1', 'col2', 'col3', 'col4', 'col5', 'col6'])

#dftest[['col1','col2']] = dftest[['col1','col2']].astype('category')
## NotImplementedError: &gt; 1 ndim Categorical are not supported at this time

## it works with individual astype
#dftest['col2'] = dftest['col2'].astype('category')
#dftest['col1'] = dftest['col1'].astype('category')

print(dftest)

## doing a loop
for col in ['col1', 'col2']:
    dftest[col] = dftest[col].astype('category')


dftest[['col3', 'col4', 'col5', 'col6']] = \
    dftest[['col3', 'col4', 'col5', 'col6']].astype(float)

dftest.dtypes
</code></pre>

<hr>

<p>output:</p>

<pre><code>col1    category
col2    category
col3     float64
col4     float64
col5     float64
col6     float64
dtype: object
</code></pre>

<p>== [update] ==</p>

<p>I don't have a problem using the loop now that I know the trick, but I asked the question because I wanted to learn/understand WHY I need to do a loop for 'category' and not for float, if there is no other way of doing it.</p>

<p>you can do it this way:</p>

<pre><code>In [99]: pd.concat([dftest[['col1', 'col2']].apply(lambda x: x.astype('category')), dftest.ix[:, 'col3':].astype('float')], axis=1)
Out[99]:
  col1 col2  col3  col4  col5  col6
0    a   xx  0.30  2.28  0.84  0.31
1    a   xx -0.13  2.04  2.62  0.49
2   ab   xx -0.34 -0.32 -1.87  1.49
3   ab   xx -1.18 -0.57 -0.57  0.87
4   ab   yy  0.66  0.65  0.96  0.07
5   cc   yy  0.88  2.43  0.76  1.93
6   cc   zz  1.81 -1.40 -2.29 -0.13
7    d   zz -0.05  0.60 -0.78 -0.28
8    d   zz -0.36  0.98  0.23 -0.17
9    g   zz -1.31 -0.84  0.02  0.47

In [100]: pd.concat([dftest[['col1', 'col2']].apply(lambda x: x.astype('category')), dftest.ix[:, 'col3':].astype('float')], axis=1).dtypes
Out[100]:
col1    category
col2    category
col3     float64
col4     float64
col5     float64
col6     float64
dtype: object
</code></pre>

<p>but it won't be <strong>much</strong> faster, as <code>apply()</code> method uses looping under the hood</p>

python pandas numpy dataframe categorization