df["age"] = numpy.where(df["age"] > upper_limit, upper_limit, numpy.where(df["age"] < lower_limit, lower_limit, df["age"]))

Please note that we can also use the following Python statement to remove the outliers after detecting them.

df = df[(df["age"] >= lower_limit) & (df["age"] <= upper_limit)]

The output of the given program for the outlier capping will be:

Outliers: 
      survived  pclass     sex    age  ...  deck  embark_town  alive  alone
7           0       3    male   2.00  ...   NaN  Southampton     no  False
10          1       3  female   4.00  ...     G  Southampton    yes  False
16          0       3    male   2.00  ...   NaN   Queenstown     no  False
33          0       2    male  66.00  ...   NaN  Southampton     no   True
43          1       2  female   3.00  ...   NaN    Cherbourg    yes  False
..        ...     ...     ...    ...  ...   ...          ...    ...    ...
829         1       1  female  62.00  ...     B          NaN    yes   True
831         1       2    male   0.83  ...   NaN  Southampton    yes  False
850         0       3    male   4.00  ...   NaN  Southampton     no  False
851         0       3    male  74.00  ...   NaN  Southampton     no   True
869         1       3    male   4.00  ...   NaN  Southampton    yes  False

[62 rows x 15 columns]
   survived  pclass     sex   age  ...  deck  embark_town  alive  alone
0         0       3    male  22.0  ...   NaN  Southampton     no  False
1         1       1  female  38.0  ...     C    Cherbourg    yes  False
2         1       3  female  26.0  ...   NaN  Southampton    yes   True
3         1       1  female  35.0  ...     C  Southampton    yes  False
4         0       3    male  35.0  ...   NaN  Southampton     no   True

[5 rows x 15 columns]