Tidying Up: Removing Duplicates in Video Game Sales Data

Let's start off by identifying how many duplicate values we need to conquer.

Find out and enter the total number of duplicate values in the dataframe.

Find out the number of Unique and Duplicate values in the Address column.

Choose the correct option from below.

Note : The order is ( Unique Address Count, Duplicate Address Count ).

Your task is to identify and extract the distinct duplicate values from the Avatar column.

Step 1: Identify all the duplicate values in the Avatar column and store them in a variable named duplicate_avatars.

Step 2: From the duplicate_avatars variable, extract the unique values of these duplicates and store them in a new variable named duplicate_avatar_names.

Your result should match the following output :

You need to identify duplicate values where both the columns Length of Membership and Yearly Amount Spent match.

Your result should match the following output :

Create a new column named Is_Duplicate_Address to flag rows that have duplicate values in the Address column.

Your result should match the following output :

Drop all the duplicate values present in the Address column and store the result in a new variable called df_unique_addresses.

Your result should match the following output :

Drop all the duplicate values other than the first occurrence from the Length of Membership column and store the result in a new variable called df_cleaned_first.

Your result should match the following output :

Is there any column with 0 duplicate values? Let's find out!

Your task is to find out list of columns who have 0 duplicate values.

Choose the correct option :

Identify and enter the name of the column that has maximum duplicates!