Python for Data Analysis: Step-By-Step with Projects - Combining Datasets

This video tutorial covers how to manipulate dataframes using pandas in Python. It begins with an introduction to merging and concatenating dataframes, followed by creating simple dataframes and merging them using the pandas merge function. The tutorial explains different types of merges, including inner, left, right, and outer joins. It also demonstrates how to concatenate dataframes using the concat function. Finally, the video shows how to combine hero dataframes using both merge and concat functions, providing practical examples and explanations throughout.

What function in pandas is used to merge two dataframes based on a common column?

• concat

• merge

• join

• append

Which type of merge returns only the intersection of two dataframes?

• Left

• Right

• Inner

• Outer

In a left merge, which dataframe's values are fully retained?

• Neither dataframe

• Both dataframes

• Left dataframe

• Right dataframe

What is the result of an outer merge?

• Only right dataframe values

• Only left dataframe values

• Union of both dataframes

• Intersection of both dataframes

What parameter in the concat function allows you to ignore the original indexes of dataframes?

• keys

• levels

• ignore_index

• axis

What is the default axis for concatenation in pandas?

• 3 (time-wise)

• 1 (column-wise)

• 2 (depth-wise)

• 0 (row-wise)

Which function is used to concatenate dataframes along a specified axis?

• append

• merge

• join

• concat

Which function is used to load dataframes from pickle files in pandas?

• read_csv

• read_pickle

• read_excel

• read_json

When combining dataframes with different column names as keys, which parameters are used in the merge function?

• axis and ignore_index

• on and how

• keys and levels

• left_on and right_on

What type of merge should be used to get only the heroes present in both hero info and hero powers dataframes?

• Inner

• Outer

• Right

• Left