Pandas dataframe add field based on multiple if statements

Question

I am new to Python and Pandas, so this may be an obvious question.

I have a data frame with the age specified in it. I want to create a new field with an age strip. I can use the lambda operator to capture a single operator if / else, but I want to use some, if, for example if age < 18 then 'under 18' elif age < 40 then 'under 40' else '>40'.

I do not think I can do this using lambda, but I'm not sure how to do it differently. I have this code:

import pandas as pd
import numpy as n

d = {'Age' : pd.Series([36., 42., 6., 66., 38.]) }

df = pd.DataFrame(d)

df['Age_Group'] =  df['Age'].map(lambda x: '<18' if x < 19 else '>18')

print(df)

Answer 1

pandas DataFrame provides a good query capability.

What you are trying to do can be done simply:

# Set a default value
df['Age_Group'] = '<40'
# Set Age_Group value for all row indexes which Age are greater than 40
df['Age_Group'][df['Age'] > 40] = '>40'
# Set Age_Group value for all row indexes which Age are greater than 18 and < 40
df['Age_Group'][(df['Age'] > 18) & (df['Age'] < 40)] = '>18'
# Set Age_Group value for all row indexes which Age are less than 18
df['Age_Group'][df['Age'] < 18] = '<18'

DataFrame .

, (, '&' '|')

> 18.

Edit:

DataFrame :

http://pandas.pydata.org/pandas-docs/dev/indexing.html#index-objects

Edit:

, :

>>> d = {'Age' : pd.Series([36., 42., 6., 66., 38.]) }
>>> df = pd.DataFrame(d)
>>> df
   Age
0   36
1   42
2    6
3   66
4   38
>>> df['Age_Group'] = '<40'
>>> df['Age_Group'][df['Age'] > 40] = '>40'
>>> df['Age_Group'][(df['Age'] > 18) & (df['Age'] < 40)] = '>18'
>>> df['Age_Group'][df['Age'] < 18] = '<18'
>>> df
   Age Age_Group
0   36       >18
1   42       >40
2    6       <18
3   66       >40
4   38       >18

Edit:

, [ EdChums].

>>> df['Age_Group'] = '<40'
>>> df.loc[df['Age'] < 40,'Age_Group'] = '<40'
>>> df.loc[(df['Age'] > 18) & (df['Age'] < 40), 'Age_Group'] = '>18'
>>> df.loc[df['Age'] < 18,'Age_Group'] = '<18'
>>> df
   Age Age_Group
0   36       >18
1   42       <40
2    6       <18
3   66       <40
4   38       >18

Answer 2

np.where()

df['Age_group'] = np.where(df.Age<18, 'under 18',
                           np.where(df.Age<40,'under 40', '>40'))