Python for Data Analysis: Step-By-Step with Projects - Selecting Rows and Setting the Index (1)

The video tutorial covers essential data manipulation techniques in pandas, focusing on selecting specific rows from a DataFrame and setting the index. It explains three main methods: label-based indexing using loc, integer-based indexing using iloc, and Boolean indexing for conditional filtering. The tutorial provides examples to illustrate these methods, highlighting the differences between label-based and integer-based slicing. It also demonstrates how to change index labels to make data selection more intuitive and how to use Boolean arrays for filtering data.

Which method is primarily used for label-based row selection in a DataFrame?

• loc

• iloc

• Boolean indexing

• set_index

What is the main difference between label-based slicing and usual Python slicing?

• Label-based slicing excludes the start label

• Label-based slicing includes both start and stop labels

• Python slicing includes both start and stop labels

• Python slicing excludes the start label

How can you change the index labels to a column of superhero names?

• Using the set_index method

• Using the iloc method

• Using the Boolean indexing

• Using the loc method

What happens if you use loc with a label that doesn't exist in the current index?

• It returns an empty DataFrame

• It returns the first row

• It raises an error

• It defaults to integer position

Which method is used for integer position-based row selection?

• set_index

• iloc

• loc

• Boolean indexing

How does integer-based slicing differ from label-based slicing?

• Label-based slicing excludes the start position

• Integer-based slicing excludes the stop position

• Integer-based slicing includes both start and stop positions

• Label-based slicing includes only the stop position

What is a limitation of using iloc with Boolean indexing?

• It cannot return multiple rows

• It cannot handle multiple conditions

• It requires a mask without an index

• It only works with integer indices

How can you overcome the limitation of iloc with Boolean masks?

• By using set_index

• By converting the mask to integers

• By removing the index from the mask

• By using loc instead

What does the Boolean indexing directly on a DataFrame allow you to do?

• Select rows based on integer positions

• Filter data based on conditions

• Select columns based on labels

• Change index labels

Which of the following is NOT a method for selecting rows in a DataFrame?

• loc

• Boolean indexing

• iloc

• set_index