replace ages lower than the lower limit with the lower limit. Otherwise, if the age is more than the upper limit, the age will be replaced by the upper limit. If these two conditions are not met, then the age will be as it is.

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 above program for outlier capping will be:

Lower limit of age:  4.0
Upper limit of age:  56.0
Outliers: 
      survived  pclass     sex    age  ...  deck  embark_town  alive  alone
7           0       3    male   2.00  ...   NaN  Southampton     no  False
11          1       1  female  58.00  ...     C  Southampton    yes   True
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
..        ...     ...     ...    ...  ...   ...          ...    ...    ...
824         0       3    male   2.00  ...   NaN  Southampton     no  False
827         1       2    male   1.00  ...   NaN    Cherbourg    yes  False
829         1       1  female  62.00  ...     B          NaN    yes   True
831         1       2    male   0.83  ...   NaN  Southampton    yes  False
851         0       3    male  74.00  ...   NaN  Southampton     no   True

[65 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]