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?

jpp · Accepted Answer · 2018-04-25 23:52:58Z

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

Martijn Pieters · Accepted Answer · 2018-04-25 21:03:49Z

3

Use separate assignments. You have different conditions to filter on, you can combine the address and the two name* filters with & (but put parentheses around each test):

df1.loc[(df1.Address.isin(['NJ'])) & (df1.name1 isin ('john', 'bob')), 'name1'] = 'N/A'
df1.loc[(df1.Address.isin(['NJ'])) & (df1.name2 isin ('mayer', 'dylan')), 'name2'] = 'N/A'

You can always store the boolean filters in a variable first:

nj_address = df1.Address.isin(['NJ'])
name1_filter = df1.name1 isin ('john', 'bob')
name2_filter = df1.name2 isin ('mayer', 'dylan')
df1.loc[nj_address & name1_filter, 'name1'] = 'N/A'
df1.loc[nj_address & name2_filter, 'name2'] = 'N/A'

answered Apr 25, 2018 at 21:03

Martijn Pieters

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