tmdb movie dataset

By getting a total number and the maximum length of words representing genre tags in all dataframe genre column values, we can initialize a properly sized array to utilize numpy's efficiency better. Python for Data Analysis: Data Wrangling With pandas, numpy, and ipython (2nd ed.). In [9]: # informations about the dataset However, some columns are with a lot of null values like homepage, tagline, keywords and production_companies, especially the homepage and tagline column are even not necessary for answering the questions, so I decide to drop both of the columns on the stage, and I kept the keywords and production_companies in case I drop too much data. 0 135397 tt0369610 32.985763 150000000 1513528810 Pictures We will use the Python libraries NumPy, pandas, and Matplotlib to make your analysis easier.

Among the budget data in zero values, I randomly chose Mr. Holmes and google searched it. Branagh Do movies with highest budget have more popularity? This analysis looks into the relations that genre, release year, and budget (adjusted for inflation) have with a movies' overall rating and profit based on the data from The Movie Database (TMDb), which includes information, classifications, and statistics about nearly 11,000 movies. 0 135397 tt0369610 32.985763 150000000 1513528810 Howard|Irrfan Spielberg As a data science newbie and self-learner, this definitely encouraged me a lot. std 92134.091971 1.000231 3.091428e+07 1.170083e+08 31.382701 575.644627 0.935138 12.813260 For more information, see our Privacy Statement. movies[movies['budget'] == 0].count()['budget'] Do movies with high budget get the highest rating? It contains 6016 rows in zero values, so I also decide to keep these rows and replace zero values with null values.

Pratt|Bryce 2.504192e+08 5.8 16 Colin Vin Diesel|Paul Walker|Jason Statham|Michelle ... Deckard Shaw seeks revenge against Dominic Tor... Universal Pictures|Original Film|Media Rights ... What is the most highly rated movie genre in each year? Khan|Vi... Kendrick|Rebel IMDb Dataset Details Each dataset is contained in a gzipped, tab-separated-values (TSV) formatted file in the UTF-8 character set. And it has no upper-bond. We've analyzed the dataset, in order the answer different research questions: - Most popular movies by genre, - relations between movie popularity and rating with the production budget and revenue. 3.683713e+08 6.3 27 In [51]: # comparison of the median popularity of movies with low and high revenue I'm new to the TMDB.

vote_average 10865 non-null float64 Horror 5.444786 download the GitHub extension for Visual Studio, Project_TMDB_Movies_Dataset_Analysis.html, Git for windows - for terminal application using Git Bash, Python using Anaconda (latest version for windows). movies.head() Antoine homepage plt.xlabel('Revenue Level', fontsize=15) plt.ylabel("count", fontsize=18); In [21]: # inspecting the movies and budget columns If nothing happens, download Xcode and try again.

This may be because documentaries are more serious productions that tend to be polished and because less of them are produced.

Usability. 32 254470 tt2848292 3.877764 29000000 287506194 low = movies.query('revenue_adj < {}'.format(median_rev))


movies.revenue_adj.value_counts().head() Name: budget, dtype: int64

World There are some odd characters … General Properties In [49]: # 10 first values 8.585801 4.5 4 From the table above, there are totally 10866 entries and total 21 columns. 1. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Do movies with highest revenue have more popularity? MovieLens 20M Dataset: This dataset includes 20 million ratings and 465,000 tag applications, applied to 27,000 movies by 138,000 users. The datatypes of each column Summit Entertainment|Mandeville Films|Red Wago... Harrison Ford|Mark Hamill|Carrie Fisher|Adam D... Thirty years after defeating the Galactic Empi... Lucasfilm|Truenorth Productions|Bad Robot. is just the 2.827124e+09 7.1 64 The movie basic information contained like cast, director, keywords, runtime, genres, etc. Now customize the name of a clipboard to store your clips. First count the zero value in the zero budget dataframe . In [46]: # mean rating for each revenue level

To begin getting all of this information, we need to know the full range of years in the overall dataset. Star Wars: In [20]: # splitting into row the production_companies columns release_year 56 release_year 10865 non-null int64 In [40]: heights = [mean_low, mean_high] In [6]: # Number of columns for the movie dataset Chris If nothing happens, download GitHub Desktop and try again. These metrics can be seen as how successful these movies are. Mark Wahlberg|Seth TV Movie 5.651250 Another Budget may help account for higher ratings, and revenue allows us to calculate profit., heights, tick_label=labels) movies_by_genres = movies.groupby('genres')['popularity'].mean() World production_companies Action 5.859801 Retrieved September 15, 2018, from min 5.000000 0.000065 0.000000e+00 0.000000e+00 0.000000 10.000000 1.500000 1960.000000

Int64Index: 10865 entries, 0 to 10865 This seems not the best way to fix those columns since the mean is not always the best measure of center.

They wrote my entire research paper for me, and it turned out brilliantly. Looks like you’ve clipped this slide to already. revenue 10865 non-null int64 In [27]: # should return False In [22]: # inspecting the movies and budget columns 6. Pratt|Bryce Udacity Data Analyst Nanodegree P2: Investigate [TMDb Movie] dataset Author: Mouhamadou GUEYE Date: May 26, 2019 Table of contents Introduction Data Wrangling Exploratory Data Analysis Conclusions Introduction In this project we will analyze the dataset associated with the informations about 10000 movies collected from the movie database TMDb. You will need an installation of Python, plus the following libraries: 10.296367 0.222776 18 McAdams|Forest Question 1: Number of movie released year by year, Question 2: Keywords Trends by Generation. Out[7]: 1 budget_adj 2614 View the Project Here. 'cast', 'homepage', 'director', 'tagline', 'keywords', 'overview', Out[36]: genres runtime int64 According Kaggle introduction page, the data contains information that are provided from The Movie Database (TMDb). Data columns (total 21 columns): Their API also provides access to data on many additional movies, actors and actresses, crew members, and TV shows. the median should not be the best for categoring the movie. Udacity Data Analyst Nanodegree Project 2. dtype. locations = [1,2] As of this date, Scribd will manage your SlideShare account and any content you may have on SlideShare, and Scribd's General Terms of Use and Privacy Policy will apply. We then split the elements of the genre array by the | delimiter and store the individual genres in a new array. We can see that, the film with high budget seem to be more popular than the ones with low budget, with an average popularity Let's see which movie genres are the most profitible per year on average and then visualize the overarching information.


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