Python for Data Analysis: Step-By-Step with Projects - Tackling Missing Data (Imputing with Statistics) and Missing Indi

This lesson covers methods to impute missing data using statistics like mean, median, and mode. It demonstrates how to use pandas and scikit-learn's SimpleImputer for this purpose, treating numerical and categorical columns differently. The lesson also explains how to mark missing data with indicators, providing a comprehensive guide to handling missing values in datasets.

What is the primary advantage of using statistical measures like mean or median for imputing missing data?

• They are easier to calculate.

• They make the data more representative of the original dataset.

• They are faster to compute than other methods.

• They require less computational power.

Which method in pandas is used to calculate the mean of numerical columns?

• mean()

• average()

• sum()

• median()

How can you verify that missing values in a column have been filled with the mean in pandas?

• By printing the entire dataframe.

• By checking the column's data type.

• By using the describe() method.

• By using the value_counts() method with dropna=False.

What is the most frequent value used for in categorical data imputation?

• To calculate the mean of a column.

• To replace missing categorical values.

• To replace missing numerical values.

• To determine the data type of a column.

Which method is used to select rows with the most frequent value in a pandas dataframe?

• iloc

• loc

• filter

• select

What is the main benefit of using SimpleImputer from scikit-learn?

• It is faster than pandas.

• It integrates well with machine learning pipelines.

• It requires less memory.

• It is easier to use than pandas.

Which strategy in SimpleImputer is used to replace missing data with the median?

• constant

• mean

• median

• most_frequent

What additional feature does SimpleImputer offer when marking missing data?

• It can add a missing indicator column.

• It can delete rows with missing data.

• It can convert missing data to zeros.

• It can highlight missing data in red.

Why might it be useful to add a missing data indicator?

• To reduce the number of columns.

• To make the dataset more colorful.

• To provide additional information for analysis.

• To increase the size of the dataset.

What is the purpose of using two square brackets when filtering a single column in pandas?

• To convert the column into a list.

• To ensure the result is a dataframe.

• To increase the speed of the operation.

• To reduce memory usage.