Dividing data-frame columns : TypeError: 'int' object is not iterable

Question

I have a dataframe df that looks like:

           A       B
0          0    4140
1   0.142857    1071
2          0    1196
3   0.090909    2110
4   0.083333    1926
5   0.166667    1388
6          0    3081
7          0    1149
8          0    1600
9   0.058824    1873
10         0    3960
:          :       :
19         0    4315
20         0    2007
21  0.086957    3323
22  0.166667    1084
23       0.5    2703
24         0    1214
25         0    1955
26         0    6750
27         0    3240
28         0    1437
29         0    1701

I am trying to use the following line of code is trying to produce a new column which divides A/B if A is greater than 0 (else populate with 0) and then mutiply by 90:

df['new_column'] = np.where(df['A'] = 0, 0.0, df['A'].divide(df['B']))*90.0

However I get the error for the line:

 result[:] = [tuple(x) for x in values]

TypeError: 'int' object is not iterable

The desired output for new_column is:

           A     B   new_column
0          0  4140            0
1   0.142857  1071   0.01200479
2          0  1196            0
3   0.090909  2110  0.003877635
4   0.083333  1926  0.003894065
5   0.166667  1388  0.010806938
6          0  3081            0
7          0  1149            0
8          0  1600            0
9   0.058824  1873  0.002826567
10         0  3960            0
 :         :     :            :
19         0  4315            0
20         0  2007            0
21  0.086957  3323   0.00235514
22  0.166667  1084  0.013837666
23       0.5  2703  0.016648169
24         0  1214            0
25         0  1955            0
26         0  6750            0
27         0  3240            0
28         0  1437            0
29         0  1701            0

sentence · Accepted Answer · 2019-05-17 21:16:23Z

2

Note that numpy.where works as:

numpy.where(condition[, x, y])

Therefore:

import pandas as pd

df = pd.DataFrame({'A':[0,0.142857,0,0.090909],
                   'B':[4140,1071,1196,2110]})

df['new_column'] = np.where(df['A'] > 0, df['A']*90/df['B'], 0)

output

           A       B    new_column
0   0.000000    4140    0.000000
1   0.142857    1071    0.012005
2   0.000000    1196    0.000000
3   0.090909    2110    0.003878

edited May 17, 2019 at 21:16

answered May 17, 2019 at 21:13

sentence

9,0414 gold badges36 silver badges41 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

sentence Over a year ago

@d_kennetz because if A is less than or equal to 0, the value has to be 0.

Collectives™ on Stack Overflow

Dividing data-frame columns : TypeError: 'int' object is not iterable

1 Answer 1

1 Comment

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

1 Comment

Your Answer

Sign up or log in

Post as a guest

Related