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!
