Pandas create new column based on first unique values of existing column

user_5369777 0
Viewed 1862

I'm trying to add a new column to a dataframe with only unique values from an existing column. There will be fewer rows in the new column maybe with np.nan values where duplicates would have been.

import pandas as pd
import numpy as np

df = pd.DataFrame({'a':[1,2,3,4,5], 'b':[3,4,3,4,5]})
df

    a   b
0   1   3
1   2   4
2   3   3
3   4   4
4   5   5

Goal:

    a   b   c
0   1   3   3
1   2   4   4
2   3   3   nan
3   4   4   nan
4   5   5   5

I've tried:

df['c'] = np.where(df['b'].unique(), df['b'], np.nan)

It throws: operands could not be broadcast together with shapes (3,) (5,) ()

pandas unique python-3.x numpy python
2 Answers

mask + duplicated

You can use Pandas methods for masking a series:

df['c'] = df['b'].mask(df['b'].duplicated())

print(df)

   a  b    c
0  1  3  3.0
1  2  4  4.0
2  3  3  NaN
3  4  4  NaN
4  5  5  5.0
user_92095460

Use duplicated with np.where:

df['c'] = np.where(df['b'].duplicated(),np.nan,df['b'])

Or:

df['c'] = df['b'].where(~df['b'].duplicated(),np.nan)

print(df)
   a  b    c
0  1  3  3.0
1  2  4  4.0
2  3  3  NaN
3  4  4  NaN
4  5  5  5.0
user_83208610
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