Data Science and Machine Learning (Theory and Projects) A to Z - Pandas for Data Manipulation: Pandas Missing Values

The video tutorial covers handling missing values in pandas, including the use of NaN and None. It explains how to fill missing values using the fillna function and how to drop them with dropna. The tutorial also touches on advanced techniques like using averages or regression for imputation, and previews the next video on implicit and explicit indices in pandas.

What is the default return type of any function in Python that is sometimes treated as NaN in Pandas?

• Null

• None

• Zero

• Empty

In the context of data frames, what happens when a new column is added but some values are missing?

• The missing values are ignored.

• The missing values are filled with the previous row's value.

• The missing values are left as NaN.

• The missing values are automatically filled with zeros.

Which function in Pandas is used to fill missing values with a specified value?

• fillna

• fillmissing

• replace

• fillvalue

What is the potential downside of using the dropna function in Pandas?

• It can create new missing values.

• It can slow down the computation.

• It can cause data loss.

• It can lead to data duplication.

What is one method mentioned for handling missing values in Scikit-learn?

• Using a mode

• Using a fixed value

• Using a median

• Using an average

What is the main topic of the next video as mentioned in the transcript?

• Handling missing values

• Data cleaning

• Data visualization

• Implicit and explicit indices

Which functions are mentioned as tools to handle index confusion in Pandas?

• concat and append

• merge and join

• fillna and dropna

• loc and iloc