Grouping / Categorizing ages column

Question

I have a dataframe say df. df has a column 'Ages'

>>> df['Age']

I want to group this ages and create a new column something like this

If age >= 0 & age < 2 then AgeGroup = Infant
If age >= 2 & age < 4 then AgeGroup = Toddler
If age >= 4 & age < 13 then AgeGroup = Kid
If age >= 13 & age < 20 then AgeGroup = Teen
and so on .....

How can I achieve this using Pandas library.

I tried doing this something like this

X_train_data['AgeGroup'][ X_train_data.Age < 13 ] = 'Kid'
X_train_data['AgeGroup'][ X_train_data.Age < 3 ] = 'Toddler'
X_train_data['AgeGroup'][ X_train_data.Age < 1 ] = 'Infant'

but doing this i get this warning

/Users/Anand/miniconda3/envs/learn/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
This is separate from the ipykernel package so we can avoid doing imports until
/Users/Anand/miniconda3/envs/learn/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

How to avoid this warning and do it in a better way.

Asked By: Anand Siddharth

||

Source

Answer 1

Use pandas.cut with parameter right=False for not includes the rightmost edge of bins:

X_train_data = pd.DataFrame({'Age':[0,2,4,13,35,-1,54]})

bins= [0,2,4,13,20,110]
labels = ['Infant','Toddler','Kid','Teen','Adult']
X_train_data['AgeGroup'] = pd.cut(X_train_data['Age'], bins=bins, labels=labels, right=False)
print (X_train_data)
   Age AgeGroup
0    0   Infant
1    2  Toddler
2    4      Kid
3   13     Teen
4   35    Adult
5   -1      NaN
6   54    Adult

Last for replace missing value use add_categories with fillna:

X_train_data['AgeGroup'] = X_train_data['AgeGroup'].cat.add_categories('unknown')
                                                   .fillna('unknown')
print (X_train_data)
   Age AgeGroup
0    0   Infant
1    2  Toddler
2    4      Kid
3   13     Teen
4   35    Adult
5   -1  unknown
6   54    Adult

bins= [-1,0,2,4,13,20, 110]
labels = ['unknown','Infant','Toddler','Kid','Teen', 'Adult']
X_train_data['AgeGroup'] = pd.cut(X_train_data['Age'], bins=bins, labels=labels, right=False)

print (X_train_data)
   Age AgeGroup
0    0   Infant
1    2  Toddler
2    4      Kid
3   13     Teen
4   35    Adult
5   -1  unknown
6   54    Adult

Answered By: jezrael

Answer 2

Just use:

X_train_data.loc[(X_train_data.Age < 13),  'AgeGroup'] = 'Kid'

Answered By: quest

Grouping / Categorizing ages column

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