Adding dummy columns to the original dataframe

Question

I have a dataframe looks like this:

             EXEC_FULLNAME   YEAR BECAMECEO
CO_PER_ROL
5622        Ira A. Eichner   1992  19550101
5622        Ira A. Eichner   1993  19550101
5622        Ira A. Eichner   1994  19550101
5623       David P. Storch   1994  19961009
5623       David P. Storch   1995  19961009
5623       David P. Storch   1996  19961009

For the YEAR column, I want to add year columns (1993, 1994..., 2009) to the original dataframe. For example, if a YEAR value for a row is 1992, then the value in the 1992 column should be 1 otherwise 0 for that row.

I used a for loop, but it seems to run forever as I have a large dataset.

Answer 1

In [77]: df = pd.concat([df, pd.get_dummies(df['YEAR'])], axis=1); df
Out[77]: 
      JOINED_CO GENDER    EXEC_FULLNAME  GVKEY  YEAR    CONAME  BECAMECEO  \
5622        NaN   MALE   Ira A. Eichner   1004  1992  AAR CORP   19550101   
5622        NaN   MALE   Ira A. Eichner   1004  1993  AAR CORP   19550101   
5622        NaN   MALE   Ira A. Eichner   1004  1994  AAR CORP   19550101   
5622        NaN   MALE   Ira A. Eichner   1004  1995  AAR CORP   19550101   
5622        NaN   MALE   Ira A. Eichner   1004  1996  AAR CORP   19550101   
5622        NaN   MALE   Ira A. Eichner   1004  1997  AAR CORP   19550101   
5622        NaN   MALE   Ira A. Eichner   1004  1998  AAR CORP   19550101   
5623        NaN   MALE  David P. Storch   1004  1992  AAR CORP   19961009   
5623        NaN   MALE  David P. Storch   1004  1993  AAR CORP   19961009   
5623        NaN   MALE  David P. Storch   1004  1994  AAR CORP   19961009   
5623        NaN   MALE  David P. Storch   1004  1995  AAR CORP   19961009   
5623        NaN   MALE  David P. Storch   1004  1996  AAR CORP   19961009   

      REJOIN   LEFTOFC    LEFTCO  RELEFT    REASON  PAGE  1992  1993  1994  \
5622     NaN  19961001  19990531     NaN  RESIGNED    79     1     0     0   
5622     NaN  19961001  19990531     NaN  RESIGNED    79     0     1     0   
5622     NaN  19961001  19990531     NaN  RESIGNED    79     0     0     1   
5622     NaN  19961001  19990531     NaN  RESIGNED    79     0     0     0   
5622     NaN  19961001  19990531     NaN  RESIGNED    79     0     0     0   
5622     NaN  19961001  19990531     NaN  RESIGNED    79     0     0     0   
5622     NaN  19961001  19990531     NaN  RESIGNED    79     0     0     0   
5623     NaN       NaN       NaN     NaN       NaN    57     1     0     0   
5623     NaN       NaN       NaN     NaN       NaN    57     0     1     0   
5623     NaN       NaN       NaN     NaN       NaN    57     0     0     1   
5623     NaN       NaN       NaN     NaN       NaN    57     0     0     0   
5623     NaN       NaN       NaN     NaN       NaN    57     0     0     0   

      1995  1996  1997  1998  
5622     0     0     0     0  
5622     0     0     0     0  
5622     0     0     0     0  
5622     1     0     0     0  
5622     0     1     0     0  
5622     0     0     1     0  
5622     0     0     0     1  
5623     0     0     0     0  
5623     0     0     0     0  
5623     0     0     0     0  
5623     1     0     0     0  
5623     0     1     0     0  

If you'd like to delete the YEAR column, then you could follow this up with del df['YEAR']. Or, drop the YEAR column from df before calling concat:

df = pd.concat([df.drop('YEAR', axis=1), pd.get_dummies(df['YEAR'])], axis=1)

Answer 2

Another way is to use str.get_dummies(). It works with string values, so convert to string first.

dummies = df['YEAR'].astype(str).str.get_dummies()
df = pd.concat([df.drop(columns='YEAR'), dummies], axis=1)

---

Another method is to use OneHotEncoder from sklearn.preprocessing.

from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder()
df[ohe.get_feature_names_out()] = ohe.fit_transform(df[['YEAR']]).toarray()