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