How to pivot pandas DataFrame column to create binary "value table"?

Question

I have the following pandas dataframe:

import pandas as pd
df = pd.read_csv("filename.csv")

df 
     A   B         C         D        E    
0    a  0.469112 -0.282863 -1.509059  cat  
1    c -1.135632  1.212112 -0.173215  dog   
2    e  0.119209 -1.044236 -0.861849  dog   
3    f -2.104569 -0.494929  1.071804  bird   
4    g -2.224569 -0.724929  2.234213  elephant
...

I would like to create more columns based on the identity of categorical values in column E such that the dataframe looks like this:

 df 
         A   B         C         D        cat    dog     bird    elephant ....    
    0    a  0.469112 -0.282863 -1.509059  -1      0       0       0
    1    c -1.135632  1.212112 -0.173215   0     -1       0       0
    2    e  0.119209 -1.044236 -0.861849   0     -1       0       0
    3    f -2.104569 -0.494929  1.071804   0      0      -1       0
    4    g -2.224569 -0.724929  2.234213   0      0       0       0
    ...

That is, I pivot the values for column E to be a binary matrix based on the values of E, giving 1 if the value exists, and 0 for all others where it doesn't (here, I would like it to be -1 or a "negative binary matrix")?

I'm not sure which function in pandas best does this: maybe pandas.DataFrame.unstack()?

Any insight appreciated!

Answer 1

use pd.concat, drop, and get_dummies

pd.concat([df.drop('E', 1), pd.get_dummies(df.E).mul(-1)], axis=1)