Branching in Pandas with multiple conditions

Question

Basic decision-making logic I managed to complete but, ironically, struggling with something very basic. 80% of cases my code is catching but asking help with the remaining 20%. Not even sure if this called branching or simply a decision tree, but it is beginners' stuff.

Small sample of my data:

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'Part ID' : [ 'Power Cord', 'Cat5 cable', 'Laptop', 'Hard Disk', 'Laptop Case', 'USB drive'],
    'Part Serial Number' : [111222, 999444, 888333, 141417, np.NaN, 222666], 
    'Mother s/n': [100111, 200112, 888333, 888333, 888333, np.NaN],
    })
df['Part Serial Number'] = df['Part Serial Number'].astype('Int64')
df['Mother s/n'] = df['Mother s/n'].astype('Int64')
df

This is my code:

df['Is mother s/n known?'] = np.where(df['Mother s/n'].isin(df['Part Serial Number']), 'Yes', 'No')
df

and it gives following output:

As you can see in the image, some of the results should be different. How to branch my code with Pandas, to achieve it, please?

Answer 1

You can use select to choose between multiple conditions (not just between two as in where):

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'Part ID' : [ 'Power Cord', 'Cat5 cable', 'Laptop', 'Hard Disk', 'Laptop Case', 'USB drive'],
    'Part Serial Number' : [111222, 999444, 888333, 141417, np.NaN, 222666], 
    'Mother s/n': [100111, 200112, 888333, 888333, 888333, np.NaN],
    })
df['Part Serial Number'] = df['Part Serial Number'].astype('Int64')
df['Mother s/n'] = df['Mother s/n'].astype('Int64')

conditions = [df['Mother s/n'].eq(df['Part Serial Number']).fillna(False).astype(bool),
              df['Mother s/n'].fillna(-1).isin(df['Part Serial Number']),
              df['Mother s/n'].isna()]
choices = ['Self', 'Yes', 'Mother s/n unknown']
df['Is mother s/n known?'] = np.select(conditions, choices, 'No')

Result:

       Part ID  Part Serial Number  Mother s/n Is mother s/n known?
0   Power Cord              111222      100111                   No
1   Cat5 cable              999444      200112                   No
2       Laptop              888333      888333                 Self
3    Hard Disk              141417      888333                  Yes
4  Laptop Case                <NA>      888333                  Yes
5    USB drive              222666        <NA>   Mother s/n unknown