np.select with multiple conditions

conditions = [
    df['gender'].eq('male') & df['pet1'].eq(df['pet2']),
    df['gender'].eq('female') & df['pet1'].isin(['cat', 'dog'])
]

choices = [5,5]

df['points'] = np.select(conditions, choices, default=0)

print(df)
     gender      pet1      pet2  points
0      male       dog       dog       5
1      male       cat       cat       5
2      male       dog       cat       0
3    female       cat  squirrel       5
4    female       dog       dog       5
5    female  squirrel       cat       0
6  squirrel       dog       cat       0

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3.67

9

Awgiedawgie 440220 points

                                    #Using .assign you can make multiple
#operations that depend on the
#previous ones without the need
#of creating intermediate variables
import pandas as pd

df = pd.DataFrame({
    'name': ['alice','bob','charlie','daniel'],
    'age': [25,66,56,78]
})

df.assign(
    is_senior = lambda dataframe: dataframe['age'].map(lambda age: True if age &gt;= 65 else False) 
).assign(
    name_uppercase = lambda dataframe: dataframe['name'].map(lambda name: name.upper()),
).assign(
    name_uppercase_double = lambda dataframe: dataframe['name_uppercase'].map(lambda name: name.upper()+&quot;-&quot;+name.upper())
)

Thank you! 9

3.67 (9 Votes)

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