Branagh Khan|Vi... Limitations imdb_id object We use optional third-party analytics cookies to understand how you use so we can build better products. histogram are approximativaty the same. labels = ['low', 'high'] Since each movie can have multiple genres, the simplest way to analyze genre information is to include a movie in the group for each genre it has, even if that means that a movie is included in multiple dataframes. 3.683713e+08 4.955130 27 Descriptive Statistics Summary 124 Action Univers You signed in with another tab or window. plt.xlabel('Revenue Level', fontsize=12) homepage object vote_count int64 The Movie Database (TMDb) is a popular, user editable database for movies and TV shows. Finally, a higher average budget does appear to have a very slight association with a higher average vote rating, but more statistical analysis will need to be performed to prove anything. Ratings are on a scale of 1-5 and have been obtained from the official GroupLens website. 4 168259 tt2820852 9.335014 190000000 1506249360 Furious 7 plt.ylabel('Average Vote Rating', fontsize=15); 1. Out[28]: False You can try it for yourself here. 2.504192e+08 1.232098 16 In this step we will inspect the dataset, in order to undestand it's properties and structures: Convert release_date in datetime Object 2.716921e+08 7.3 27 32 254470 tt2848292 3.877764 29000000 287506194 Howard|Irrfan (movies['budget_adj'] == 0).all() There are some odd characters in the ‘cast’ column. Steinfeld|Br... Hope. As a data science newbie and self-learner, this definitely encouraged me a lot. After engaging in a lot, I got pass for just one time and Udacity reviewer rated it as very great job, including questions digging and data wrangling. cols = ['budget', 'budget_adj', 'revenue', 'revenue_adj'] Out[56]: revenue_adj popularity In [19]: movies= (movies.drop('genres', axis=1) As the table shown above, we can find outliers in popularity data because the max value is 32.99 while other quantiles are just around 0.2–0.7. Hardy|Charlize Out[30]: False grouped= movies.groupby(['release_year', 'genres']).count()['popularity'] Khan|Vi... popularity respectively of 0.7420684714824547 and 0.9989869505300212. In [16]: budget 181294 /search - Text based search is the most common way. imdb_id 10855 non-null object genres 10842 non-null object budget int64 Name: budget, dtype: int64 Action 5.859801 high = movies.query('revenue_adj >= {}'.format(median_rev)) Name: vote_average, dtype: int64 This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. In [44]: # comparison of the median popularity of movies with low and high revenue calculated? 1 76341 tt1392190 28.419936 150000000 378436354 My friend sent me a link to to tis site. plt.ylabel("Popularity", fontsize=18); MacFarlane 2.370705 0.462609 12 plt.ylabel('Vote Average', fontsize=12) 0.969398 5.3 20 dtype: int64 movies.groupby('budget_adj')['popularity'].value_counts().head(10) Out[41]: 7.033402e+07 76851 The first line in each file contains headers that describe what is in each column. We're There are 56 years, from 1960–2015, in this dataset. Filter by your subscribed streaming services and find something to watch. movies[item] = movies[item].replace({0:movies[item].mean()}) The most highly rated movie genre by year varied, but the Documentary genre was the genre that had the highest average rating across the most years. 3.155006e+08 4.965391 27 Statham|Michelle But under the risk that these zero values may be just small values and record as “0”, I preceded to take a look for some zero data to decide whether it is just a missing value or small value. Udacity--Project-Investigate-TMDB-Movies-Dataset. Out[50]: budget_adj vote_average original_title 10865 non-null object In [6]: # Number of columns for the movie dataset Since some movie have a huge amount of budget and revenue and the fact that we fill many missing values with the mean, Introduction And I found it's Wikipedia page and there is definitely a budget record in $10 million. movies.drop_duplicates(inplace=True) Number of Distinct Observations Out[49]: budget_adj vote_average Colin movies= (movies.drop('production_companies', axis=1) release_date 5909 genres object mean_high = high['vote_average'].mean() movies_by_genres.plot(kind='bar', alpha=.7, figsize=(15,6)) Questions in the projects are as follows: In this process the main idea is to take a quick glance on the data set, find the potential unreasonable data value, unnecessary variables for my research question, null data or duplicates, and then make data clearing decisions. budget_adj 10865 non-null float64 The Documentary genre is the most highly rated movie genre on average, and the Adventure genre has the highest budgets and revenues according to this dataset. It’s kind of huge amounts. Instead, we will create separate dataframes for each individual genre, including a movie if the genre is included in its list of genres. Fisher|Adam D... high = movies.query('revenue_adj >= {}'.format(median_rev)) Out[22]: 76851 most popular movies genre. Trevorrow Is there a way to download the entire dataset to be used locally? revenue_adj float64 Seth Every piece of data has been added by our amazing community dating back to 2008. You can leave them as is. # plottting the bar chart of movies by genre My clearing goal is to keep the data integrity from the original one, although there are some null values in keywords and production companies columns, it is still useful for analyze, and in fact the number of their null values are not very huge, so I just kept both of them. Advantage: 1. movies[movies['revenue_adj'] == 0].count()['revenue_adj'] plt.title('Vote Ratings by Revenue Level', fontsize=15) dtypes: float64(4), int64(6), object(11) 8.585801 4.5 4 6.951084 0.578849 8 movies.revenue.value_counts().head() plt.xlabel('Movie Genre', fontsize=12) What a huge amounts! tagline cast object # Udacity--Project-Investigate-TMDB-Movies-Dataset Hello Everyone! We can now use these results to find out which genre was the most popular in each year by the mean vote_average. #_df = df.loc[df.genres.str.contains('')], 'Proportion of the Total Number of Movies in Each Genre'. 2.000000e+07 126 124 Action Amblin Ent The factors/criteria used for their calculations. id imdb_id popularity budget revenue original_title cast director runtime genres product id 10865 non-null int64 database TMDb. Entertain dropna (axis = 0, inplace = True, subset = ['genres']) In [12]: df. Chris Pratt|Bryce In [40]: heights = [mean_low, mean_high] World How to create a spam filter using Bayes’ theorem? 4.250000e+08 6.4 25 Fiction IMDb Dataset Details Each dataset is contained in a gzipped, tab-separated-values (TSV) formatted file in the UTF-8 character set. (movies['revenue_adj'] == 0).all() The online documentations of pandas, numpy, and matplotlib. Please note that the explanations for the executed code will precede the code itself throughout the report. Droping Extraneous Columns Data columns (total 21 columns): Additionally, the highest and lowest vote ratings of all the dataset were with lower budget movies. In [59]: # scatter plot of the movies budget versus popularity limitations in this analysis, the process of categorizing the movie with low and high revenue and budget using the median. 1.443191e+09 7.637767 9 Colin .stack().reset_index(level=1,drop=True) Check out the dataset status after dropping null values so far. (movies['revenue'] == 0).all() In [30]: # should return False The genre column in this dataframe is made up of a string of genre names separated by pipes, or the | character. (Remark: The dataset on Kaggle page may be updated to new version.) Howard|Irrfan Learn more. If not, the only way is to use the TMDB API for every call my app gets? mean_high = high['vote_average'].mean() No problem. Star wars, Avtar have made gross profits above 2.5 Billion dollars. plt.ylabel('Average Vote Rating', fontsize=15); Whitaker... 8.102293 6.9 45 low = movies.query('budget_adj < {}'.format(median_rev)) fig, ax = plt.subplots(figsize=(15,7)) tagline 7997 Deletion of duplicates You will need an installation of Python, plus the following libraries: Out[48]: 2.677536e+07 69433 The mean of the budget_adj column across all years for each dataframe. Now customize the name of a clipboard to store your clips. The data now with 10703 entries and 17 columns. runtime 247 If I did drop them, it would have lost a lot of useful information! This data set contains information about 10,000 movies collected from The Movie Database (TMDb). Chris print(heights) plt.xticks(fontsize=10) Ted is In [33]: # unique genres movies existing in the dataframe 1. movies.shape[1] dtype: object If you continue browsing the site, you agree to the use of cookies on this website. In [41]: # counting the movie revenue unique values GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Hence, I assume the zero value in revenue and budget column are missing in the dataset. Music 6.302175 In [53]: # mean rating for each revenue level revenue_adj 4840 For full project reports, codes and dataset files, see my Github repository. Entertain median_rev = movies['revenue_adj'].median() plt.scatter(movies['budget_adj'], movies['popularity'], linewidth=5) Conclusions 2.600000e+08 7.3 8 George plt.xticks(fontsize=10) 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. You signed in with another tab or window. 5.006696 0.317091 27 Khan|Vi... These metrics can be seen as how successful these movies are. mean_low = low['popularity'].mean() Chris locations = [1,2] Believe in Out[44]: (6.0, 6.3) relation between the genres and vote avarage, we found that, the Documentary recieves the highest rating. A ‘\N’ is used to denote that a particular field is missing or null for that title/name. shadow of In case I drop too many raw data to keep the data integrity, I decide to retain these rows and replace zero values with null values. Mystery 5.986585 id popularity budget revenue runtime vote_count vote_average release_year We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. Out[63]: budget_adj popularity History 6.417070 is just the Universa Dallas If you continue browsing the site, you agree to the use of cookies on this website. Budget may help account for higher ratings, and revenue allows us to calculate profit. Colin The [Movie Database TMDB]( is a community built movie and TV database. Then, replace zero values with null values in the budget and revenue column with replace(0, np.NaN). beginning. James|Kate runtime int64 mean_high = high['popularity'].mean() The Public Domain Review, an Interview with Editor Adam Green, Beginner’s Guide to Data Analysis using numpy and pandas. Steven Byrne|Nic... This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue,cast,release year. 2.500000e+07 4255 plt.title('Average Popularity by Revenue Level', fontsize=15) Lily James|Cate In this step we will clean the dataset by removing columns that are irrelevant for our analysis, convert the release date .stack().reset_index(level=1,drop=True) From 1960 to 2015 gross profit has increased from 1.25 billion dollars to 17.5 billion dollars. 25% 10596.000000 0.207575 0.000000e+00 0.000000e+00 90.000000 17.000000 5.400000 1995.000000 runtime 10865 non-null int64 Pratt|Bryce Out[24]: 69433 Khan|Vi... 4.519285 0.464188 9 0.969398 0.520430 20 before filling those values with the mean. 2.827124e+09 7.1 64 1.309053 4.8 8 plt.xlabel('Revenue Level', fontsize=15)

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