Deep Learning - Computer Vision for Beginners Using PyTorch - Imputation

The video tutorial covers the concept of data imputation, focusing on handling null values in data frames using the Pandas library. It explains the importance of imputation to avoid data loss, demonstrates how to import data, identify null values, and calculate the mean for imputation. The tutorial also shows how to use the fillna method to replace null values with the mean, ensuring data integrity. The session concludes with a brief introduction to upcoming topics, including the Matplotlib library.

Why is it not advisable to delete rows or columns with null values in large datasets?

• It can lead to data redundancy.

• It may result in significant data loss.

• It can cause data duplication.

• It increases the complexity of data analysis.

What is the first step in handling null values in a data frame?

• Import the data and create a data frame.

• Change the data type of the column.

• Calculate the median of the column.

• Delete all rows with null values.

Which method is used to fill null values in a Pandas data frame?

• dropna

• fillna

• fillnull

• replace

What does the 'inplace' parameter do when using the fillna method?

• It deletes the null values from the data frame.

• It creates a new data frame with filled values.

• It fills null values with zeros.

• It changes the data frame in place without returning a new one.

What is the mean value used to fill the null values in the 'number combined' column?

• 22.45

• 20.78

• 18.92

• 15.34