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)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)Copyright © 2021 Codeinu
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