Pixar's Movies

Posted on Dim 23 septembre 2018 in Data Analysis

Analyze Pixar's Movies

The dataset represent Pixar's movies that contains information on each movie's critics ratings, revenue figures, production costs, Oscar results, and more.

The goal is to analyze the Data set with Data Visualization.

In [2]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [3]:
pixar_movies = pd.read_csv("PixarMovies.csv")
print(pixar_movies.shape[0])
print(pixar_movies.shape[1])
cols = pixar_movies.columns.values
for el in cols:
    print(pixar_movies[el].dtypes)
pixar_movies.describe()

15
16
int64
object
int64
int64
float64
int64
float64
float64
float64
float64
float64
object
object
int64
float64
float64
Out[3]:
Year Released Length RT Score IMDB Score Metacritic Score Opening Weekend Worldwide Gross Domestic Gross Adjusted Domestic Gross International Gross Production Budget Oscars Nominated Oscars Won
count 15.000000 15.000000 15.000000 15.000000 15.000000 15.000000 15.000000 15.000000 15.000000 15.000000 15.000000 14.000000 14.000000
mean 2006.066667 101.533333 89.333333 7.846667 82.800000 67.990667 612.486667 258.506667 318.448000 353.986667 133.400000 2.857143 0.785714
std 5.933761 9.927355 16.451950 0.655599 12.119642 23.270468 190.193934 66.518284 73.321064 135.061615 59.696614 2.032700 0.801784
min 1995.000000 81.000000 39.000000 6.300000 57.000000 29.140000 362.000000 162.800000 194.430000 170.200000 30.000000 0.000000 0.000000
25% 2002.000000 96.000000 85.000000 7.300000 75.000000 58.755000 503.150000 215.100000 261.350000 256.050000 91.000000 1.000000 0.000000
50% 2007.000000 102.000000 96.000000 8.000000 88.000000 66.300000 559.900000 245.900000 318.900000 336.600000 150.000000 3.000000 1.000000
75% 2010.500000 109.000000 98.500000 8.300000 92.000000 76.450000 704.200000 280.750000 361.165000 427.800000 182.500000 5.000000 1.500000
max 2015.000000 116.000000 100.000000 8.800000 96.000000 110.310000 1063.200000 415.000000 457.460000 648.200000 200.000000 6.000000 2.000000
In [4]:
pixar_movies.head(17)
Out[4]:
Year Released Movie Length RT Score IMDB Score Metacritic Score Opening Weekend Worldwide Gross Domestic Gross Adjusted Domestic Gross International Gross Domestic % International % Production Budget Oscars Nominated Oscars Won
0 1995 Toy Story 81 100 8.3 92 29.14 362.0 191.8 356.21 170.2 52.98% 47.02% 30 3.0 0.0
1 1998 A Bug's Life 96 92 7.2 77 33.26 363.4 162.8 277.18 200.6 44.80% 55.20% 45 1.0 0.0
2 1999 Toy Story 2 92 100 7.9 88 57.39 485.0 245.9 388.43 239.2 50.70% 49.32% 90 1.0 0.0
3 2001 Monsters, Inc. 90 96 8.1 78 62.58 528.8 255.9 366.12 272.9 48.39% 51.61% 115 3.0 1.0
4 2003 Finding Nemo 104 99 8.2 90 70.25 895.6 339.7 457.46 555.9 37.93% 62.07% 94 4.0 1.0
5 2004 The Incredibles 115 97 8.0 90 70.47 631.4 261.4 341.28 370.0 41.40% 58.60% 92 4.0 2.0
6 2006 Cars 116 74 7.2 73 60.12 462.0 244.1 302.59 217.9 52.84% 47.16% 70 2.0 0.0
7 2007 Ratatouille 111 96 8.0 96 47.00 623.7 206.4 243.65 417.3 33.09% 66.91% 150 5.0 1.0
8 2008 WALL-E 97 96 8.4 94 63.10 521.3 223.8 253.11 297.5 42.93% 57.07% 180 6.0 1.0
9 2009 Up 96 98 8.3 88 68.11 731.3 293.0 318.90 438.3 40.07% 59.93% 175 5.0 2.0
10 2010 Toy Story 3 103 99 8.4 92 110.31 1063.2 415.0 423.88 648.2 39.03% 60.97% 200 5.0 2.0
11 2011 Cars 2 113 39 6.3 57 109.00 559.9 191.5 194.43 368.4 34.20% 65.80% 200 0.0 0.0
12 2012 Brave 100 78 7.2 69 66.30 539.0 237.3 243.39 301.7 44.03% 55.97% 185 1.0 1.0
13 2013 Monsters University 107 78 7.4 65 82.43 743.6 268.5 269.59 475.1 36.11% 63.89% 200 0.0 0.0
14 2015 Inside Out 102 98 8.8 93 90.40 677.1 340.5 340.50 336.6 50.29% 49.71% 175 NaN NaN

Data Cleaning

In [5]:
pixar_movies["Domestic %"] = pixar_movies["Domestic %"].str.rstrip('%').astype('float')
pixar_movies["International %"] = pixar_movies["International %"].str.rstrip('%').astype('float')

Change Scale of IMDB Score

In [6]:
pixar_movies["IMDB Score"] = pixar_movies["IMDB Score"] * 10
In [7]:
filtered_pixar = pixar_movies.dropna()
In [8]:
filtered_pixar.set_index("Movie", inplace = True)
pixar_movies.set_index("Movie", inplace = True)
pixar_movies.head()
Out[8]:
Year Released Length RT Score IMDB Score Metacritic Score Opening Weekend Worldwide Gross Domestic Gross Adjusted Domestic Gross International Gross Domestic % International % Production Budget Oscars Nominated Oscars Won
Movie
Toy Story 1995 81 100 83.0 92 29.14 362.0 191.8 356.21 170.2 52.98 47.02 30 3.0 0.0
A Bug's Life 1998 96 92 72.0 77 33.26 363.4 162.8 277.18 200.6 44.80 55.20 45 1.0 0.0
Toy Story 2 1999 92 100 79.0 88 57.39 485.0 245.9 388.43 239.2 50.70 49.32 90 1.0 0.0
Monsters, Inc. 2001 90 96 81.0 78 62.58 528.8 255.9 366.12 272.9 48.39 51.61 115 3.0 1.0
Finding Nemo 2003 104 99 82.0 90 70.25 895.6 339.7 457.46 555.9 37.93 62.07 94 4.0 1.0

Data Visualization, Line Plots : compare scores of different review sites

In [9]:
critics_reviews = pixar_movies[["RT Score","IMDB Score","Metacritic Score"]]
critics_reviews.plot()
plt.show()
In [10]:
critics_reviews.plot(figsize = (10,6))
plt.show()

Data Visualization, Box Plot : how is distributed scores

In [12]:
critics_reviews.plot(kind = "box", figsize = (9,5))
Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fbd13822cf8>

Data Visualization, Stacked Bar Plot : Revenue made in US and Internationally

In [16]:
revenue_proportions = filtered_pixar[["Domestic %","International %"]]
revenue_proportions.plot(kind = "bar", stacked = True)
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fdf6527ebe0>

Data Analysis