pandas update with condition
import pandas as pd
import numpy as np
df = pd.DataFrame({'value':np.arange(1000000)})
# Solution 1 - Fastest :
df['value'] = np.where(df['value'] > 20000, 0, df['value'])
# Solution 2:
df.loc[df['value'] > 20000, 'value'] = 0
# Solution 3:
df['value'] = df['value'].mask(df['value'] > 20000, 0)
# Solution 4 - Slowest, note that df.where applies where condition is wrong:
df['a'] = df.where(df.a <= 20000, 0)change pandas column value 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 1003compute value based on condition of existing column dataframe
# create a list of our conditions
conditions = [
(df['likes_count'] <= 2),
(df['likes_count'] > 2) & (df['likes_count'] <= 9),
(df['likes_count'] > 9) & (df['likes_count'] <= 15),
(df['likes_count'] > 15)
]
# create a list of the values we want to assign for each condition
values = ['tier_4', 'tier_3', 'tier_2', 'tier_1']
# create a new column and use np.select to assign values to it using our lists as arguments
df['tier'] = np.select(conditions, values)
# display updated DataFrame
df.head()Copyright © 2021 Codeinu
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