We will create a dataframe to store all of the results we need with rows for each calculation and columns for each genre. The genre column in this dataframe is made up of a string of genre names separated by pipes, or the | character. Jurassic popularity 10865 non-null float64 2.600000e+08 7.3 8 8.585801 4.5 4 plt.title('Average Popularity by Budget Level', fontsize=15) Khan|Vi... Seth In [45]: # scatter plot of the budget versus vote rating http://www.thedivergentseries.movie/#insu Statham|Michelle Question 3: The distribution of revenue in different score rating levels in recent five years. Duplicates Rows mean_high = high['vote_average'].mean() Documentary 6.957312 Learn more. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Missing Values I left out the issue on the later stage. certified by TMDb[1]. Jurassic plt.title('Vote Ratings by Revenue Level', fontsize=15) Read the .csv file into a pandas dataframe. Dallas id 10865 non-null int64 6.951084 0.578849 8 I highly recommend this service to anyone in my shoes. 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. movies.groupby('revenue_adj')['popularity'].value_counts().tail(10) Colin popularity 10865 non-null float64 cast object 5 rows � 21 columns They wrote my entire research paper for me, and it turned out brilliantly. 2.908194 6.5 12 overview Out[4]: id int64 id popularity budget revenue runtime vote_count vote_average release_year high = movies.query('budget_adj >= {}'.format(median_rev)) movies[movies['revenue'].notnull()].count() mean_low = low['vote_average'].mean() In [27]: # should return False movies.groupby('revenue_adj')['vote_average'].value_counts().head(10) Every piece of data has been added by our amazing community dating back to 2008. fig, ax = plt.subplots(figsize=(15,7)) Elizabeth Work fast with our official CLI. First count the zero value in the zero budget dataframe . 3. plt.bar(locations, heights, tick_label=labels) The top three genres are Drama, Comedy, and Thriller. release_date [0.7564478409230605, 0.9790179787846799] Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. What do I need to install? Miller This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. movies.info() Howard|Irrfan old version created under Udacity Nanodegree View Here. Look at general statistics about the dataframe. And I found it's Wikipedia page and there is definitely a budget record in $10 million. Family 5.973175 We can now use these results to find out which genre was the most popular in each year by the mean vote_average. In [1]: # packages import 3.683713e+08 6.3 27
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