How to replace rows with character value by integers in a column in pandas dataframe? - python-3.x

Question

I am working on one large dataset, the problem am facing is that there are columns that have all integer values, however, as the dataset is uncleaned there are a few rows where there are 'characters' along with integers. Here am trying to illustrate the problem with a small pandas dataframe example,
I have the following dataframe:
Index
l1
l2
l3
0
1
123
23
1
2
Z3V
343
2
3
321
21
3
4
AZ34
345
4
5
432
3
With dataframe code :
l1,l2,l3 = [1,2,3,4,5], [123, 'Z3V', 321, 'AZ34', 432], [23,343,21,345,3]
data = pd.DataFrame(zip(l1,l2,l3), columns=['l1', 'l2', 'l3'])
print(data)
Here as you can see, column 'l2' at rows index 1 and 3 have 'characters' along with integers. I want to find such rows in this particular column and print them. Later I want to replace them with integer values like 100 or something similar integer. i.e. those numbers that I am replacing with will be different for example, am replacing instances of 'Z3V' with 100 and instances of 'AZ34' with 101. My point is to replace characters containing values with integers. Now, if in 'l2' column, 'Z3V' occurs again, there too, I will replace it with 100.
Expected output :
Index
l1
l2
l3
0
1
123
23
1
2
100
343
2
3
321
21
3
4
101
345
4
5
432
3
As you can see, the two instances where there were characters have been replaced with 100 and 101 respectively
How to get this expected output ?

Answer 1

You could do:
import pandas as pd
import numpy as np
# setup
l1, l2, l3 = [1, 2, 3, 4, 5, 6], [123, 'Z3V', 321, 'AZ34', 432, 'Z3V'], [23, 343, 21, 345, 3, 3]
data = pd.DataFrame(zip(l1, l2, l3), columns=['l1', 'l2', 'l3'])
# find all non numeric values across the whole DataFrame
mask = data.applymap(np.isreal)
rows, cols = np.where(~mask)
# create the replacement dictionary
replacements = {k: i for i, k in enumerate(np.unique(data.values[rows, cols]), 100)}
# apply the replacements
res = data.replace(replacements)
print(res)
Output
l1 l2 l3
0 1 123 23
1 2 101 343
2 3 321 21
3 4 100 345
4 5 432 3
5 6 101 3
Note that I added an extra row to verify the desire behaviour, now the data DataFrame looks like:
l1 l2 l3
0 1 123 23
1 2 Z3V 343
2 3 321 21
3 4 AZ34 345
4 5 432 3
5 6 Z3V 3
By changing this line:
# create the replacement dictionary
replacements = {k: i for i, k in enumerate(np.unique(data.values[rows, cols]), 100)}
you can change the replacement values as you see fit.