import seaborn
from sklearn.preprocessing import StandardScaler

df = seaborn.load_dataset("titanic")

standard_scaler = StandardScaler()
standard_scaler.fit(df[["age"]])
print("Mean age: ", standard_scaler.mean_)
print("Variance: ", standard_scaler.var_)
df[["age"]] = standard_scaler.transform(df[["age"]])

print(df.head())

print("Mean age after standardization: ", df["age"].mean())
print("Variance after standardization: ", df["age"].var())

Here, we are using the StandardScaler class from the sklearn.preprocessing module to standardize data. The output after standardization will be:

Mean age:  [29.69911765]
Variance:  [210.72357975]
   survived  pclass     sex       age  ...  deck  embark_town  alive  alone
0         0       3    male -0.530377  ...   NaN  Southampton     no  False
1         1       1  female  0.571831  ...     C    Cherbourg    yes  False
2         1       3  female -0.254825  ...   NaN  Southampton    yes   True
3         1       1  female  0.365167  ...     C  Southampton    yes  False
4         0       3    male  0.365167  ...   NaN  Southampton     no   True

[5 rows x 15 columns]
Mean age after standardization:  2.388378943731429e-16
Variance after standardization:  1.0014025245441796

As we can see, after standardization, the mean age is very close to 0 and the variance is close to 1.