How to create new column in DataFrame based on other columns in Python Pandas?

Question

I have DataFrame like below:

data = pd.DataFrame({"col1" : ["a", "b", "c"], 
                     "binary" : [0, 1, 0]})

I would like to create and add new column in this DataFrame called "new" where if in column "binary" is 1 then I want to have data from "col1" in my created column "new", but if in "binary" column is 0 then I want to have np.NaN. Below is the result which I really need based on my description and DatFrame above.

lots of ways to do this properly, `import numpy as np;df['new'] = np.where(df['binary'].eq(1), df['col1'],np.nan)` — Umar.H, Dec 09 '20 at 14:44

sophocles · Accepted Answer · 2020-12-09T14:54:18.613

0

you can use where from the numpy package and , and do:

import pandas as pd
import numpy as np
df = pd.DataFrame({"col1" : ["a", "b", "c"], "binary" : [0, 1, 0]})
df['new'] = np.where(df['binary']==1,df['col1'],np.nan)

which prints:

print(df)
  col1  binary  new
0    a       0  NaN
1    b       1    b
2    c       0  NaN

edited Dec 09 '20 at 14:54

answered Dec 09 '20 at 14:45

sophocles

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After your code i have this Error: TypeError: invalid type promotion – dingaro Dec 09 '20 at 14:51
did you ```import pandas as pd```? It runs smoothly on mine, as shown above. – sophocles Dec 09 '20 at 14:53
1

ok I already know, it was a mistake in data type of my column, Thank you! I choose your answer as the best – dingaro Dec 09 '20 at 15:00

How to create new column in DataFrame based on other columns in Python Pandas?

1 Answers1