PYTHON

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

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)

pandas replace row values 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

pandas replace values based on condition

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

replace values in a column by condition python

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

replace column values/create new column based on another column values/condition in Pandas

df['New Column'] = np.where(df['A']==0, df['B'], df['A'])

panda replace row values based on condition in some existing column

df['column'].mask(df['column'] == 'original_value', new_value, inplace=True)

replace pandas column values based on condition

d.loc[d["conditionDisplayName"] == "Brand New", "conditionDisplayName"] = 6
d.loc[d["conditionDisplayName"] != "Brand New", "conditionDisplayName"] = 4