Analysing Pixar Movies

Pixar Animation is one of the most well-known animation studios in the world, with hits like Toy Story 3, Finding Nemo, Monster’s Inc, and A Bug’s Life, Pixar’s movies are adored by kids for their charming characters and by adults for their wit

In this project, I’ll explore the ups and downs of Pixar over the years primarily using data visualization. Data visualization is especially useful in this case since our sample size, only 15, is low and we can glean more general insights from exploring the data visually.

Analysis

You can visit my Github repo for the complete code and the dataset.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# reading the dataset
pixar=pd.read_csv("C:/Users/hp/Downloads/PixarMovies.csv")
pixar

The DataSet

Here are some of the columns in this dataset, PixarMovies.csv:

Year Released — the year the movie was released.
Movie — the name of the movie.
RT Score — the Rotten Tomatoes rating for the movie.
IMDB Score — the IMDB rating for the movie.
Metacritic Score — the Metacritic rating for the movie.
Opening Weekend — the amount of revenue the movie made on opening weekend (in millions of dollars).
Worldwide Gross — the total amount of revenue the movie has made to date.
Production Budget — the amount of money spent to produce the film (in millions of dollars).
Oscars Won — the number of Oscar awards the movie won.

Cleaning the data

#cleaning data removing % from domestic % columns and converting it to float
#making score out of 100 for imdb instead of 10
pixar['Domestic %']=pixar['Domestic %'].str.rstrip('%').astype('float')
pixar['International %']=pixar['International %'].str.rstrip('%').astype('float')
pixar['IMDB Score']=pixar['IMDB Score']*10

Adding new features to the dataset

# Adding new features
pixar['Profit']=pixar['Worldwide Gross']-pixar['Production Budget'] 
pixar['Domestic Profit']=pixar['Domestic Gross']-pixar['Production Budget']
pixar['International Profit']=pixar['Profit']-pixar['Domestic Profit']

Data Visualisation

How do the major review site rate Pixar movies?

# now analysisng critics score
critics_review=pixar[['RT Score','IMDB Score','Metacritic Score']]
critics_review.plot(figsize=(10,6),grid=False,linewidth=2)
plt.title("Critics Review")
plt.show()

From the previous plot, it seems like the review site Rotten Tomatoes gives Pixar consistently higher ratings. Let’s generate a box plot to explore the question:

How are the average ratings from each review site across all the movies distributed?

#analysing through boxplot
critics_review.boxplot(figsize=(10,6),grid=False)
plt.title("Critics Review")
plt.show()

From the above plot, it becomes clear that Rotten Tomatoes have consistently given higher scores.The most spread out scores are given by Metacritic ranging from right under 60 to right under 100.IMDB gives an average rating of 80.

The Profit Scenario

#The Profit Senario
pixar.sort_values('Profit')[['Profit','International Profit','Domestic Profit']]
.plot(kind='bar',figsize=(15,6),grid=False)
plt.title("The Profit Senario")
plt.show()

Pixar studios have a terrific reputation when it comes to business, every single movie released has made profits, this is incredible.Toy Story 3 being the most successful movie in terms of business, followed by Finding Nemo. Movies which did not do well in the domestic market were still profitable due to its international presence worldwide. Cars 2 failed to impress in the Domestic market but did well overseas.

The International market

# how pixar took up the international market 
pixar_year=pixar.set_index("Year Released")
pixar_year[['Domestic %','International %']]
.plot(kind='bar',figsize=(15,6),grid=False)
plt.title("Comparing Domestic market vs International market year on year")
plt.show()

Does Pixar have a worldwide reach?

Pixar always had a wider reach, from 2001 Pixar made more Profits from the overseas market than their domestic market. Their overseas popularity has always increased year on year. This says a lot about their worldwide popularity.

Let’s draw a stacked bar chart for better understanding:

# comparing domestic vs international collection
pixar[['Domestic %','International %']].plot(kind='bar',figsize=(15,6),stacked=True,grid=False)
plt.title("Domestic collection vs International collection in %")
plt.show()

Spend sometimes visually exploring it. You’ll notice that there’s been a general decrease in the proportion of revenue that was made domestically. 

Opening Weekend Collection

#Comparing Production Budget vs Opening Weekend Collection
op=pixar[['Opening Weekend','Production Budget']]
op.sort_values('Opening Weekend').plot(kind='bar',figsize=(15,6),grid=False)
plt.title("Comparing Production Budget vs Opening Weekend Collection")
plt.show()

The plot shows a very interesting result. Almost all movies collect more than 50 % of their cost in the opening weekend. 

The big stage: Oscars

#The big stage: Oscars
pixar[['Oscars Nominated','Oscars Won']]
.plot(kind='bar',figsize=(15,6),grid=False)
plt.title("The big stage: Oscars")
plt.show()

9 out 16 movies produced by Pixar have one at least one oscar award. Isn’t that great feet.The movie WALL-E has received a maximum of 6 oscar nominations followed by Toy Story 3, Up, Ratatouille with each 5 nominations.

Toy Story 3, Up along with The Incredibles have won 2 Oscars.

Cars 2 and Monster University are the only movies which did not receive any oscar nominations.

Conclusion

The study brings into light the strong presence of Pixar in the international market. It’s growing popularity. The profit machine for its shareholders and receiving accolades at the Oscars.

Thank you