change column value based on another column pandas
# Changes the 'is_electric' column based on value in the 'type' column
# If the 'type' column == 'electric' then the 'is_electric' becomes 'YES'
df['is_electric']= df['type'].apply(lambda x: 'YES' if (x == 'electric') else 'NO')replace column values pandas
df['column'] = df['column'].str.replace(',','-')
dfpandas replace values in column based on condition
In [41]:
df.loc[df['First Season'] > 1990, 'First Season'] = 1
df
Out[41]:
Team First Season Total Games
0 Dallas Cowboys 1960 894
1 Chicago Bears 1920 1357
2 Green Bay Packers 1921 1339
3 Miami Dolphins 1966 792
4 Baltimore Ravens 1 326
5 San Franciso 49ers 1950 1003replace value column by another if missing pandas
import numpy as np
df['column'] = np.where(df['column'].isnull(), 'value_if_true', 'value_if_false')pandas replace values from another dataframe
In [36]:
df['Group'] = df['Group'].map(df1.set_index('Group')['Hotel'])
df
Out[36]:
Date Group Family Bonus
0 2011-06-09 Jamel Laavin 456
1 2011-07-09 Frank Grendy 679
2 2011-09-10 Luxy Fantol 431
3 2011-11-02 Frank Gondow 569Copyright © 2021 Codeinu
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