If/else with Python Pandas Dataframe

Question

Apologies for the noob question but I struggle with python conditionals.

Have the following dataframe:

id  bonus
1   1.5
2   1.12
3   1.09
4   0.9
5   0.74
6   0.83

I have upper and lower limit variables:

upper_limit = 1.2
lower limit = 0.8

Trying to write a conditional that: 1) Checks if the bonus is above or below the threshold 2) Creates a new column that ensures the value doesn't go above or below the thresholds. If the bonus value is within range, it doesn't change.

Should look like:

id  bonus   bonus_capped
1   1.5     1.2
2   1.12    1.12
3   1.09    1.09
4   0.9     0.9
5   0.74    0.8
6   0.83    0.83

My code is:

conditions = [df["bonus"] > upper_limit, df["bonus"] < lower_limit]
choices = [upper_limit, lower_limit]

df["bonus_capped"] = np.select(conditions, choices)

print(df)

but the output I'm getting is only addressing one condition and returning zeros for the rest. What am I missing?

id  bonus   bonus_capped
1   1.5     0
2   1.12    0
3   1.09    0
4   0.9     0
5   0.74    0.8
6   0.83    0

your code works for me - additionaly it returns 0 because that's teh default - try np.select(conditions, choices,default=df['bonus']) — Umar.H
– Umar.H, Commented Apr 16, 2020 at 18:40

Eric M · Accepted Answer · 2020-04-16 18:41:14Z

3

This will be easy using numpy.clip:

import numpy as np

df['bonus_capped'] = np.clip(df['bonus'], 0.8, 1.2)

where 0.8 and 1.2 are your lower and upper limits respectively.

edited Apr 16, 2020 at 18:41

answered Apr 16, 2020 at 18:36

Eric M

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Comments

BENY · Accepted Answer · 2020-04-16 18:37:50Z

2

In pandas

upper_limit = 1.2
lower_limit = 0.8
df.bonus.clip(lower_limit,upper_limit)
0    1.20
1    1.12
2    1.09
3    0.90
4    0.80
5    0.83
Name: bonus, dtype: float64
#df.bonus=df.bonus.clip(lower_limit,upper_limit)

answered Apr 16, 2020 at 18:37

BENY

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Comments

Mayank Porwal · Accepted Answer · 2020-04-16 18:45:39Z

By going with the method you were trying:

In [1100]: col         = 'bonus' 
      ...: conditions  = [ df['bonus'] < 0.8 , df['bonus'] > 1.2] 
      ...: choices     = [0.8, 1.2]                                                                                                                                                                         

In [1102]: df['bonus_capped'] = np.select(conditions, choices, default=df['bonus'])                                                                                                                         

In [1103]: df                                                                                                                                                                                               
Out[1103]: 
   id  bonus  bonus_capped
0   1   1.50          1.20
1   2   1.12          1.12
2   3   1.09          1.09
3   4   0.9           0.9
4   5   0.74          0.80
5   6   0.83          0.83

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