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

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

Answer 2

df['one'].divide(df['two'])

---

Code:

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(5,2), columns=list('ab'))
df.loc[[1,3], 'b'] = 0
print(df)

print(df['a'].divide(df['b']))

Result:

    a           b
0   0.517925    0.305973
1   0.900899    0.000000
2   0.414219    0.781512
3   0.516072    0.000000
4   0.841636    0.166157

0    1.692717
1         inf
2    0.530023
3         inf
4    5.065297
dtype: float64

Answer 3

You can always use a try statement:

try:
  z = var1/var2
except ZeroDivisionError:
  print ("0") #As python-3's rule is: Parentheses

OR...

You can also do:

if var1==0:
    if var2==0:
        print("0")
else:
    var3 = var1/var2

Hope this helped! Choose whichever choice you desire (they're both the same anyways).

Answer 4

Two approaches to consider:

Prepare your data so that never has a divide by zero situation, by explicitly coding a "no data" value and testing for that.

Wrap each division that might result in an error with a try/except pair, as described at https://wiki.python.org/moin/HandlingExceptions (which has a divide by zero example to use)

(x,y) = (5,0)
try:
  z = x/y
except ZeroDivisionError:
  print "divide by zero"

I worry about the situation where your data includes a zero that's really a zero (and not a missing value).