Nested if statements with .loc in pandas / python

Question

I am using if in a conditional statement like the below code. If address is NJ then the value of name column is changed to 'N/A'.

df1.loc[df1.Address.isin(['NJ']), 'name'] = 'N/A'

How do I do the same, if I have 'nested if statements' like below?

# this not code just representing the logic
if address isin ('NJ', 'NY'):
    if name1 isin ('john', 'bob'):
        name1 = 'N/A' 
    if name2 isin ('mayer', 'dylan'):
        name2 = 'N/A'

Can I achieve above logic using df.loc? Or is there any other way to do it?

Answer 1

Separate assignments, as shown by @MartijnPeiters, are a good idea for a small number of conditions.

For a large number of conditions, consider using numpy.select to separate your conditions and choices. This should make your code more readable and easier to maintain.

For example:

import pandas as pd, numpy as np

df = pd.DataFrame({'address': ['NY', 'CA', 'NJ', 'NY', 'WS'],
                   'name1': ['john', 'mayer', 'dylan', 'bob', 'mary'],
                   'name2': ['mayer', 'dylan', 'mayer', 'bob', 'bob']})

address_mask = df['address'].isin(('NJ', 'NY'))

conditions = [address_mask & df['name1'].isin(('john', 'bob')),
              address_mask & df['name2'].isin(('mayer', 'dylan'))]

choices = ['Option 1', 'Option 2']

df['result'] = np.select(conditions, choices)

print(df)

  address  name1  name2    result
0      NY   john  mayer  Option 1
1      CA  mayer  dylan         0
2      NJ  dylan  mayer  Option 2
3      NY    bob    bob  Option 1
4      WS   mary    bob         0