Pandas - Convert a categorical column to binary encoded form

Question

I have a dataset that looks like so -

     yyyy      month        tmax         tmin
0    1908    January         5.0         -1.4
1    1908   February         7         


        

Answer 1

I think you need get_dummies:

df = pd.get_dummies(df['month'])

And if need add new columns to original and remove month use join with pop:

df2 = df.join(pd.get_dummies(df.pop('month')))
print (df2.head())
   yyyy  tmax  tmin  April  August  December  February  January  July  June  \
0  1908   5.0  -1.4      0       0         0         0        1     0     0   
1  1908   7.3   1.9      0       0         0         1        0     0     0   
2  1908   6.2   0.3      0       0         0         0        0     0     0   
3  1908   7.4   2.1      1       0         0         0        0     0     0   
4  1908  16.5   7.7      0       0         0         0        0     0     0   

   March  May  November  October  September  
0      0    0         0        0          0  
1      0    0         0        0          0  
2      1    0         0        0          0  
3      0    0         0        0          0  
4      0    1         0        0          0  

If NOT need remove column month:

df2 = df.join(pd.get_dummies(df['month']))
print (df2.head())
   yyyy     month  tmax  tmin  April  August  December  February  January  \
0  1908   January   5.0  -1.4      0       0         0         0        1   
1  1908  February   7.3   1.9      0       0         0         1        0   
2  1908     March   6.2   0.3      0       0         0         0        0   
3  1908     April   7.4   2.1      1       0         0         0        0   
4  1908       May  16.5   7.7      0       0         0         0        0   

   July  June  March  May  November  October  September  
0     0     0      0    0         0        0          0  
1     0     0      0    0         0        0          0  
2     0     0      1    0         0        0          0  
3     0     0      0    0         0        0          0  
4     0     0      0    1         0        0          0  

If need sort columns there is more possible solutions - use reindex or reindex_axis:

months = ['January', 'February', 'March','April' ,'May',  'June', 'July', 'August', 'September','October', 'November','December']

df1 = pd.get_dummies(df['month']).reindex_axis(months, 1)
print (df1.head())
   January  February  March  April  May  June  July  August  September  \
0        1         0      0      0    0     0     0       0          0   
1        0         1      0      0    0     0     0       0          0   
2        0         0      1      0    0     0     0       0          0   
3        0         0      0      1    0     0     0       0          0   
4        0         0      0      0    1     0     0       0          0   

   October  November  December  
0        0         0         0  
1        0         0         0  
2        0         0         0  
3        0         0         0  
4        0         0         0  

df1 = pd.get_dummies(df['month']).reindex(columns=months)
print (df1.head())
   January  February  March  April  May  June  July  August  September  \
0        1         0      0      0    0     0     0       0          0   
1        0         1      0      0    0     0     0       0          0   
2        0         0      1      0    0     0     0       0          0   
3        0         0      0      1    0     0     0       0          0   
4        0         0      0      0    1     0     0       0          0   

   October  November  December  
0        0         0         0  
1        0         0         0  
2        0         0         0  
3        0         0         0  
4        0         0         0  

Or convert column month to ordered categorical:

df1 = pd.get_dummies(df['month'].astype('category', categories=months, ordered=True))
print (df1.head())
   January  February  March  April  May  June  July  August  September  \
0        1         0      0      0    0     0     0       0          0   
1        0         1      0      0    0     0     0       0          0   
2        0         0      1      0    0     0     0       0          0   
3        0         0      0      1    0     0     0       0          0   
4        0         0      0      0    1     0     0       0          0   

   October  November  December  
0        0         0         0  
1        0         0         0  
2        0         0         0  
3        0         0         0  
4        0         0         0  

Answer 2

IIUC,

You can use assign, ** unpacking operator, and pd.get_dummies:

df.assign(**pd.get_dummies(df['month']))

Output:

    yyyy      month  tmax  tmin  April  August  December  February  January  \
0   1908    January   5.0  -1.4      0       0         0         0        1   
1   1908   February   7.3   1.9      0       0         0         1        0   
2   1908      March   6.2   0.3      0       0         0         0        0   
3   1908      April   7.4   2.1      1       0         0         0        0   
4   1908        May  16.5   7.7      0       0         0         0        0   
5   1908       June  17.7   8.7      0       0         0         0        0   
6   1908       July  20.1  11.0      0       0         0         0        0   
7   1908     August  17.5   9.7      0       1         0         0        0   
8   1908  September  16.3   8.4      0       0         0         0        0   
9   1908    October  14.6   8.0      0       0         0         0        0   
10  1908   November   9.6   3.4      0       0         0         0        0   
11  1908   December   5.8  -0.3      0       0         1         0        0   
12  1909    January   5.0   0.1      0       0         0         0        1   
13  1909   February   5.5  -0.3      0       0         0         1        0   
14  1909      March   5.6  -0.3      0       0         0         0        0   
15  1909      April  12.2   3.3      1       0         0         0        0   
16  1909        May  14.7   4.8      0       0         0         0        0   
17  1909       June  15.0   7.5      0       0         0         0        0   
18  1909       July  17.3  10.8      0       0         0         0        0   
19  1909     August  18.8  10.7      0       1         0         0        0   
20  1909  September  14.5   8.1      0       0         0         0        0   
21  1909    October  12.9   6.9      0       0         0         0        0   
22  1909   November   7.5   1.7      0       0         0         0        0   
23  1909   December   5.3   0.4      0       0         1         0        0   
24  1910    January   5.2  -0.5      0       0         0         0        1   

    July  June  March  May  November  October  September  
0      0     0      0    0         0        0          0  
1      0     0      0    0         0        0          0  
2      0     0      1    0         0        0          0  
3      0     0      0    0         0        0          0  
4      0     0      0    1         0        0          0  
5      0     1      0    0         0        0          0  
6      1     0      0    0         0        0          0  
7      0     0      0    0         0        0          0  
8      0     0      0    0         0        0          1  
9      0     0      0    0         0        1          0  
10     0     0      0    0         1        0          0  
11     0     0      0    0         0        0          0  
12     0     0      0    0         0        0          0  
13     0     0      0    0         0        0          0  
14     0     0      1    0         0        0          0  
15     0     0      0    0         0        0          0  
16     0     0      0    1         0        0          0  
17     0     1      0    0         0        0          0  
18     1     0      0    0         0        0          0  
19     0     0      0    0         0        0          0  
20     0     0      0    0         0        0          1  
21     0     0      0    0         0        1          0  
22     0     0      0    0         1        0          0  
23     0     0      0    0         0        0          0  
24     0     0      0    0         0        0          0