/Python/Pandas replace values in column based on condition

Pandas 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         1003

Source: stackoverflow.com

Pandas conditional replace values in a series

# np.where function works as follows:
import numpy as np

# E.g. 1 - Set column values based on if another column is greater than or equal to 50
df['X'] = np.where(df['Y'] >= 50, 'yes', 'no')

# E.g. 2 - Replace values over 20000 with 0, otherwise keep original value
df['my_value'] = np.where(df.my_value > 20000, 0, df.my_value)

Source: stackoverflow.com

Pandas replace values based on condition

df.loc[df['First Season'] > 1990, 'First Season'] = 1

Pandas replace data in specific columns with specific values

### replace one value ###
df["column"].replace("US","UK") # you can also use numerical values
### replace more than one value ###
df["column"].replace(["man","woman","child"],[1,2,3]) # you can also use numerical values
#   man ==> 1
# woman ==> 2
# child ==> 3

Replace values in a column by condition python

df.loc[df['employrate'] > 70, 'employrate'] = 7

Source: stackoverflow.com

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