Python for Data Analysis: Step-By-Step with Projects - Tackling Missing Data (Imputing with Constant)

The video tutorial covers strategies for imputing missing values in datasets, focusing on using constant values. It introduces two methods: the fillna method in Pandas and the SimpleImputer in Scikit-learn. The tutorial explains how to handle numerical and categorical columns differently, using specific constants for each. It provides step-by-step instructions for implementing these methods in Python, highlighting the advantages of each approach.

Why is imputing missing values often preferred over dropping them?

• It reduces dataset size.

• It retains useful information.

• It simplifies the dataset.

• It increases computational speed.

Which library provides the fillna method for imputing missing values?

• pandas

• scikit-learn

• NumPy

• TensorFlow

What constant value is used to fill missing numerical data in the example?

• -9999

• -999

• 0

• 999

What is the purpose of using the select_dtypes method in pandas?

• To delete columns

• To merge data frames

• To filter columns by data type

• To sort the data frame

Which library provides the SimpleImputer class?

• pandas

• Matplotlib

• NumPy

• scikit-learn

What is the first step in using the SimpleImputer?

• Visualize the data

• Import the data

• Set up the SimpleImputer object

• Transform the data

What is the strategy used in SimpleImputer to replace missing values?

• Constant

• Median

• Mean

• Mode

What is the final step in using the SimpleImputer?

• Delete missing values

• Visualize the data

• Update the original data frame

• Export the data

What string is used to fill missing categorical data in the example?

• Null

• Missing

• Empty

• Unknown

What will the next lesson focus on regarding missing data?

• Exporting data

• Visualizing missing data

• Imputing with statistics

• Dropping missing data