Practice Data Filtering & Sorting with Hollywood Movie Data

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_average and revenue columns.

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.