import pandas

df = pandas.read_csv("titanic.csv")
print(df.head())

df["age_group"] = pandas.cut(x=df["age"], bins=[0, 5, 18, 60, 100], labels=["toddler", "young", "adult", "senior"])
print(df["age_group"])

Here, we are creating a new column “age_group” in the DataFrame. We are using the pandas.cut() function to discretize the numerical values contained in the “age” column and the discretized values are kept in the “age_group” column.

The x=df[“age”] parameter in the cut() function indicates the column whose values are discretized. The bins parameter represents the bins. And the labels parameter indicates the labels as per the binning.

So, those aged 0 to 5 years should be labeled as toddlers. Those aged 5 to 18 years should be labeled as young. Those who are more than 18, but less than 60 years should be labeled as adults. And the rest should be labeled as a senior. And these discretized values will be kept in a separate column named “age_group”.

The output of the above program will be:

     survived  pclass     sex   age  ...  deck  embark_town  alive  alone
886         0       2    male  27.0  ...   NaN  Southampton     no   True
887         1       1  female  19.0  ...     B  Southampton    yes   True
888         0       3  female   NaN  ...   NaN  Southampton     no  False
889         1       1    male  26.0  ...     C    Cherbourg    yes   True
890         0       3    male  32.0  ...   NaN   Queenstown     no   True

[5 rows x 15 columns]
0      adult
1      adult
2      adult
3      adult
4      adult
       ...  
886    adult
887    adult
888      NaN
889    adult
890    adult
Name: age_group, Length: 891, dtype: category
Categories (4, object): ['toddler' < 'young' < 'adult' < 'senior']