Grouping / Categorizing ages column

Question

I have a dataframe say df with a column 'Ages'

>>> df['Age']
0  22
1  38
2  26
3  35
4  35
5  -1
6  54

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.

Possible duplicate of [pandas create new column based on values from other columns](https://stackoverflow.com/questions/26886653/pandas-create-new-column-based-on-values-from-other-columns) — Shintlor, Oct 11 '18 at 06:31

jezrael · Accepted Answer · 2018-10-11T06:57:29.477

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

So it would look something like this ? `bins= [-1, 0,2,4,13,20,110] labels = ['Unknown', 'Infant','Toddler','Kid','Teen','Adult']` — Anand Siddharth, Oct 11 '18 at 06:57

score 3 · Answer 2 · answered Oct 11 '18 at 06:35

3

Just use:

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

answered Oct 11 '18 at 06:35

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Grouping / Categorizing ages column

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