np select with multiple conditions

1conditions = [
2    df['gender'].eq('male') & df['pet1'].eq(df['pet2']),
3    df['gender'].eq('female') & df['pet1'].isin(['cat', 'dog'])
4]
5
6choices = [5,5]
7
8df['points'] = np.select(conditions, choices, default=0)
9
10print(df)
11     gender      pet1      pet2  points
120      male       dog       dog       5
131      male       cat       cat       5
142      male       dog       cat       0
153    female       cat  squirrel       5
164    female       dog       dog       5
175    female  squirrel       cat       0
186  squirrel       dog       cat       0
19

1#Using .assign you can make multiple
2#operations that depend on the
3#previous ones without the need
4#of creating intermediate variables
5import pandas as pd
6
7df = pd.DataFrame({
8    'name': ['alice','bob','charlie','daniel'],
9    'age': [25,66,56,78]
10})
11
12df.assign(
13    is_senior = lambda dataframe: dataframe['age'].map(lambda age: True if age >= 65 else False) 
14).assign(
15    name_uppercase = lambda dataframe: dataframe['name'].map(lambda name: name.upper()),
16).assign(
17    name_uppercase_double = lambda dataframe: dataframe['name_uppercase'].map(lambda name: name.upper()+"-"+name.upper())
18)
19