I am trying to map values of my columns. For gender the following mapping works:
df['gender'] = df['gender'].map({'female': 0, 'male': 1, 'other': 2})
Similarly I would like to map age column based on the range they belong to, so that, each decade is a separate class. Here is my not working code:
df['age'] = df['age'].map({
'25 < age < 36': 1,
'35 < age < 46': 2,
'45 < age < 56': 3
})
How can I do the mapping correctly? Thanks in advance.
A possible solution, which is base on numpy.where:
np.where((df['age'] > 25) & (df['age'] < 36), 1,
np.where((df['age'] > 35) & (df['age'] < 46), 2,
np.where((df['age'] > 45) & (df['age'] < 56), 3, 0)))