Deep Learning - Crash Course 2023 - Cleaning and Examining the data

The video tutorial covers handling missing values in pandas dataframes, including detection, dropping, and imputation techniques. It explains querying and slicing dataframes, using conditional operators for data selection, and applying functions efficiently with the apply method. The tutorial also discusses data examination using describe and value_counts methods.

What are the two methods to deal with missing values in a Pandas DataFrame?

How can you check the number of missing values in each column of a DataFrame?

What is the output of the isnull method when applied to a DataFrame?

What happens to the original DataFrame when you use the dropna method without the inplace parameter?

How can you drop columns with null values in a DataFrame?

Describe how to extract a specific column from a DataFrame.

Explain the concept of imputation in the context of missing values.

What is the purpose of the describe method in Pandas?

How do you create a new column based on conditions applied to another column?

What is the significance of using the apply method in Pandas?