How to deal with "divide by zero" with pandas dataframes when manipulating columns? [duplicate]

Question

I'm working with hundreds of pandas dataframes. A typical dataframe is as follows:

import pandas as pd
import numpy as np
data = 'filename.csv'
df = pd.DataFrame(data)
df 

        one       two     three  four   five
a  0.469112 -0.282863 -1.509059  bar   True
b  0.932424  1.224234  7.823421  bar  False
c -1.135632  1.212112 -0.173215  bar  False
d  0.232424  2.342112  0.982342  unbar True
e  0.119209 -1.044236 -0.861849  bar   True
f -2.104569 -0.494929  1.071804  bar  False
....

There are certain operations whereby I'm dividing between columns values, e.g.

df['one']/df['two'] 

However, there are times where I am dividing by zero, or perhaps both

df['one'] = 0
df['two'] = 0

Naturally, this outputs the error:

ZeroDivisionError: division by zero

I would prefer for 0/0 to actually mean "there's nothing here", as this is often what such a zero means in a dataframe.

(a) How would I code this to mean "divide by zero" is 0 ?

(b) How would I code this to "pass" if divide by zero is encountered?

Answer 1

It would probably be more useful to use a dataframe that actually has zero in the denominator (see the last row of column two).

        one       two     three   four   five
a  0.469112 -0.282863 -1.509059    bar   True
b  0.932424  1.224234  7.823421    bar  False
c -1.135632  1.212112 -0.173215    bar  False
d  0.232424  2.342112  0.982342  unbar   True
e  0.119209 -1.044236 -0.861849    bar   True
f -2.104569  0.000000  1.071804    bar  False

>>> df.one / df.two
a   -1.658442
b    0.761639
c   -0.936904
d    0.099237
e   -0.114159
f        -inf  # <<< Note division by zero
dtype: float64

When one of the values is zero, you should get inf or -inf in the result. One way to convert these values is as follows:

df['result'] = df.one.div(df.two)

df.loc[~np.isfinite(df['result']), 'result'] = np.nan  # Or = 0 per part a) of question.
# or df.loc[np.isinf(df['result']), ...

>>> df
        one       two     three   four   five    result
a  0.469112 -0.282863 -1.509059    bar   True -1.658442
b  0.932424  1.224234  7.823421    bar  False  0.761639
c -1.135632  1.212112 -0.173215    bar  False -0.936904
d  0.232424  2.342112  0.982342  unbar   True  0.099237
e  0.119209 -1.044236 -0.861849    bar   True -0.114159
f -2.104569  0.000000  1.071804    bar  False       NaN