All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.
All our activities include solutions with explanations on how they work and why we chose them.
Write the value of the movie with the maximum runtime as a floating point number. For example, 676.0
Select from the following options, what is the earliest date found in the release_date column?
Identify the highest value present in the vote_count column. Please write it as a floating point number, for example, 12751.0
Select the first 5 movies from this df using .iloc[] method and store the result in the variable first_5_movies
Enter the name of the 10th movie.
Find the 2nd, 6th, and 11th movies or series and extract only the columns at index positions 0 (title) and 12 (release_date). Store the result in variable selected_movies.
This activity modifies the original df by sorting it in ascending order based on the release_date column.
Filter the movies with original_language equals to English, and sort them by vote_average in descending order. Store your result in df_english_movies.
Find the movies that have a runtime value greater than 120 and vote_average greather than 7. Store your result in the variable df_runtime_above_120.
Sort the df, genres in ascending order and revenue in descending order. Store the result in the variable df_genres_revenue.
Find only the Action genre movies or series and sort them by the vote_average column in descending order and the revenue column in ascending order.
Filter the df to display movies that have a budget greater than $50,000,000 and a runtime longer than 120 minutes. Show only the original_title, budget, and runtime columns. Store your result in the variable df_budget_runtime.
What are the top three movies in the df with a vote_average over 7 in the Action or Adventure genres?
Note - Sort them by ascending order of the
vote_averageandrevenuecolumns.
Filter the df with Star Wars in the title, and sort them by the release_date column in ascending order and the revenue column in descending order. Store your result in the variable df_star_wars.
Select only the original_title, budget, revenue, and vote_average columns with a budget over $100,000,000. Store the result in the variable df_high_budget.
Note - Don't use comma (,).
Select only the movies or series with a runtime greater than 120 minutes and a vote_average greater than 7 and sort them by the runtime column in descending order. Store the result in the variable df_run_vote
Select the top 10 movies or series with a vote_count greater than 1000 and a vote_average greater than 7. Show only the original_title, vote_count, and vote_average columns. Store the result in the variable df_top_10.
Store the result in the variable df_high.
Note - Don't use comma (,)
Write down the earliest release_date of the movie that is not in English. To find the earliest date, sort the release_date column in ascending order.
Identify the movies with a runtime greater than 150 minutes, vote_average greater than 7, and popularity greater than 20 and sort the df by the vote_average column in descending order. Store the result in the variable df_long_movies.
Sort the df by the runtime column in descending order and select movies in the Action or Adventure or Science Fiction genres. Store the result in the variable df_action_sci_adv.
Sort the df by the revenue column in descending order and select movies with a budget less than 20,000,000 and revenue greater than 100,000,000. Store the result in the variable df_budget_revenue.
