Let’s say we are reading the titanic dataset. As we learned from this article, we can use the following code to know the percentage of missing values in each column of the dataset.

import seaborn

df = seaborn.load_dataset("titanic")
print(df.isnull().mean()*100)

The output will show the following:

survived        0.000000
pclass          0.000000
sex             0.000000
age            19.865320
sibsp           0.000000
parch           0.000000
fare            0.000000
embarked        0.224467
class           0.000000
who             0.000000
adult_male      0.000000
deck           77.216611
embark_town     0.224467
alive           0.000000
alone           0.000000
dtype: float64

So, there are many columns in the dataset that have missing values. Now, these missing values can introduce biases and affect …