Project : Movies Data Analysis

Data Analysis & Data Presentation

(Movies Dataset)

Done on May 2020

Step 1 : Data Preprocessing and Linear Analysis

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
pd.options.display.max_columns = 25
pd.options.display.float_format = '{:.2f}'.format

## Read the csv file; change date col as release_date
df = pd.read_csv("movies_complete.csv", parse_dates= ["release_date"])

## Observe the columns of database
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 44691 entries, 0 to 44690
Data columns (total 22 columns):
 #   Column                 Non-Null Count  Dtype         
---  ------                 --------------  -----         
 0   id                     44691 non-null  int64         
 1   title                  44691 non-null  object        
 2   tagline                20284 non-null  object        
 3   release_date           44657 non-null  datetime64[ns]
 4   genres                 42586 non-null  object        
 5   belongs_to_collection  4463 non-null   object        
 6   original_language      44681 non-null  object        
 7   budget_musd            8854 non-null   float64       
 8   revenue_musd           7385 non-null   float64       
 9   production_companies   33356 non-null  object        
 10  production_countries   38835 non-null  object        
 11  vote_count             44691 non-null  float64       
 12  vote_average           42077 non-null  float64       
 13  popularity             44691 non-null  float64       
 14  runtime                43179 non-null  float64       
 15  overview               43740 non-null  object        
 16  spoken_languages       41094 non-null  object        
 17  poster_path            44467 non-null  object        
 18  cast                   42502 non-null  object        
 19  cast_size              44691 non-null  int64         
 20  crew_size              44691 non-null  int64         
 21  director               43960 non-null  object        
dtypes: datetime64[ns](1), float64(6), int64(3), object(12)
memory usage: 7.5+ MB

df.head(5)

	id	title	tagline	release_date	genres	belongs_to_collection	original_language	budget_musd	revenue_musd	production_companies	production_countries	vote_count	vote_average	popularity	runtime	overview	spoken_languages	poster_path	cast	cast_size	crew_size	director
0	862	Toy Story	NaN	1995-10-30	Animation|Comedy|Family	Toy Story Collection	en	30.00	373.55	Pixar Animation Studios	United States of America	5415.00	7.70	21.95	81.00	Led by Woody, Andy's toys live happily in his ...	English	<img src='http://image.tmdb.org/t/p/w185//uXDf...	Tom Hanks|Tim Allen|Don Rickles|Jim Varney|Wal...	13	106	John Lasseter
1	8844	Jumanji	Roll the dice and unleash the excitement!	1995-12-15	Adventure|Fantasy|Family	NaN	en	65.00	262.80	TriStar Pictures|Teitler Film|Interscope Commu...	United States of America	2413.00	6.90	17.02	104.00	When siblings Judy and Peter discover an encha...	English|Français	<img src='http://image.tmdb.org/t/p/w185//vgpX...	Robin Williams|Jonathan Hyde|Kirsten Dunst|Bra...	26	16	Joe Johnston
2	15602	Grumpier Old Men	Still Yelling. Still Fighting. Still Ready for...	1995-12-22	Romance|Comedy	Grumpy Old Men Collection	en	nan	nan	Warner Bros.|Lancaster Gate	United States of America	92.00	6.50	11.71	101.00	A family wedding reignites the ancient feud be...	English	<img src='http://image.tmdb.org/t/p/w185//1FSX...	Walter Matthau|Jack Lemmon|Ann-Margret|Sophia ...	7	4	Howard Deutch
3	31357	Waiting to Exhale	Friends are the people who let you be yourself...	1995-12-22	Comedy|Drama|Romance	NaN	en	16.00	81.45	Twentieth Century Fox Film Corporation	United States of America	34.00	6.10	3.86	127.00	Cheated on, mistreated and stepped on, the wom...	English	<img src='http://image.tmdb.org/t/p/w185//4wjG...	Whitney Houston|Angela Bassett|Loretta Devine|...	10	10	Forest Whitaker
4	11862	Father of the Bride Part II	Just When His World Is Back To Normal... He's ...	1995-02-10	Comedy	Father of the Bride Collection	en	nan	76.58	Sandollar Productions|Touchstone Pictures	United States of America	173.00	5.70	8.39	106.00	Just when George Banks has recovered from his ...	English	<img src='http://image.tmdb.org/t/p/w185//lf9R...	Steve Martin|Diane Keaton|Martin Short|Kimberl...	12	7	Charles Shyer

Features:

• id: The ID of the movie (clear/unique identifier).
• title: The Official Title of the movie.
• tagline: The tagline of the movie.
• release_date: Theatrical Release Date of the movie.
• genres: Genres associated with the movie.
• belongs_to_collection: Gives information on the movie series/franchise the particular film belongs to.
• original_language: The language in which the movie was originally shot in.
• budget_musd: The budget of the movie in million dollars.
• revenue_musd: The total revenue of the movie in million dollars.
• production_companies: Production companies involved with the making of the movie.
• production_countries: Countries where the movie was shot/produced in.
• vote_count: The number of votes by users, as counted by TMDB.
• vote_average: The average rating of the movie.
• popularity: The Popularity Score assigned by TMDB.
• runtime: The runtime of the movie in minutes.
• overview: A brief blurb of the movie.
• spoken_languages: Spoken languages in the film.
• poster_path: The URL of the poster image.
• cast: (Main) Actors appearing in the movie.
• cast_size: number of Actors appearing in the movie.
• director: Director of the movie.
• crew_size: Size of the film crew (incl. director, excl. actors).

## Statistical representation of the dataset
df.describe()

	id	budget_musd	revenue_musd	vote_count	vote_average	popularity	runtime	cast_size	crew_size
count	44691.00	8854.00	7385.00	44691.00	42077.00	44691.00	43179.00	44691.00	44691.00
mean	107186.24	21.67	68.97	111.65	6.00	2.96	97.57	12.48	10.31
std	111806.36	34.36	146.61	495.32	1.28	6.04	34.65	12.12	15.89
min	2.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00	0.00
25%	26033.50	2.00	2.41	3.00	5.30	0.40	86.00	6.00	2.00
50%	59110.00	8.20	16.87	10.00	6.10	1.15	95.00	10.00	6.00
75%	154251.00	25.00	67.64	35.00	6.80	3.77	107.00	15.00	12.00
max	469172.00	380.00	2787.97	14075.00	10.00	547.49	1256.00	313.00	435.00

## Which movies-genres has highest reviews
ratings = df.groupby(['id', 'genres'], as_index=False)['vote_average'].aggregate(np.mean)
ratings.head()

	id	genres	vote_average
0	2	Drama|Crime	7.10
1	3	Drama|Comedy	7.10
2	5	Crime|Comedy	6.50
3	6	Action|Thriller|Crime	6.40
4	11	Adventure|Action|Science Fiction	8.10

## How many have highest runtime over the period?
import seaborn as sns
sns.distplot(df[(df['runtime'] <300) & (df['runtime']>0)]['runtime'])

<matplotlib.axes._subplots.AxesSubplot at 0x125f36b7188>

## How many movies in years have been released?
released = df.groupby('release_date')['title'].count()
released.plot(figsize=(20,8))

<matplotlib.axes._subplots.AxesSubplot at 0x12581733a08>

Step 2 : Data Processing & Analysis

Identifying Best & Worst Movies

Setting crieteria for selection : budget, revenue, voting & popularity

## Arrange best 50 movies in descending order of profit then return
df_best.head(50).sort_values(['profit_musd','return'],ascending = False)

	poster_path	title	budget_musd	revenue_musd	vote_count	vote_average	popularity	profit_musd	return
0	<img src='http://image.tmdb.org/t/p/w185//uXDf...	Toy Story	30.00	373.55	5415.00	7.70	21.95	343.55	12.45
46	<img src='http://image.tmdb.org/t/p/w185//GQP6...	Se7en	33.00	327.31	5915.00	8.10	18.46	294.31	9.92
9	<img src='http://image.tmdb.org/t/p/w185//z0lj...	GoldenEye	58.00	352.19	1194.00	6.60	14.69	294.19	6.07
47	<img src='http://image.tmdb.org/t/p/w185//kZ1f...	Pocahontas	55.00	346.08	1509.00	6.70	13.28	291.08	6.29
33	<img src='http://image.tmdb.org/t/p/w185//kf34...	Babe	30.00	254.13	756.00	6.00	14.40	224.13	8.47
1	<img src='http://image.tmdb.org/t/p/w185//vgpX...	Jumanji	65.00	262.80	2413.00	6.90	17.02	197.80	4.04
18	<img src='http://image.tmdb.org/t/p/w185//wcin...	Ace Ventura: When Nature Calls	30.00	212.39	1128.00	6.10	8.21	182.39	7.08
31	<img src='http://image.tmdb.org/t/p/w185//2F9K...	Twelve Monkeys	29.50	168.84	2470.00	7.40	12.30	139.34	5.72
5	<img src='http://image.tmdb.org/t/p/w185//lbf2...	Heat	60.00	187.44	1886.00	7.70	17.92	127.44	3.12
16	<img src='http://image.tmdb.org/t/p/w185//1ryN...	Sense and Sensibility	16.50	135.00	364.00	7.20	10.67	118.50	8.18
43	<img src='http://image.tmdb.org/t/p/w185//eUdh...	Mortal Kombat	18.00	122.20	452.00	5.40	10.87	104.20	6.79
20	<img src='http://image.tmdb.org/t/p/w185//enHk...	Get Shorty	30.25	115.10	305.00	6.40	12.67	84.85	3.81
3	<img src='http://image.tmdb.org/t/p/w185//4wjG...	Waiting to Exhale	16.00	81.45	34.00	6.10	3.86	65.45	5.09
15	<img src='http://image.tmdb.org/t/p/w185//13jm...	Casino	52.00	116.11	1343.00	7.80	10.14	64.11	2.23
24	<img src='http://image.tmdb.org/t/p/w185//rl5R...	Leaving Las Vegas	3.60	49.80	365.00	7.10	10.33	46.20	13.83
10	<img src='http://image.tmdb.org/t/p/w185//yObO...	The American President	62.00	107.88	199.00	6.50	6.32	45.88	1.74
8	<img src='http://image.tmdb.org/t/p/w185//gV1V...	Sudden Death	35.00	64.35	174.00	5.50	5.23	29.35	1.84
35	<img src='http://image.tmdb.org/t/p/w185//njn4...	Dead Man Walking	11.00	39.36	350.00	7.30	6.89	28.36	3.58
49	<img src='http://image.tmdb.org/t/p/w185//6Dr8...	The Usual Suspects	6.00	23.34	3334.00	8.10	16.30	17.34	3.89
26	<img src='http://image.tmdb.org/t/p/w185//yQ8x...	Now and Then	12.00	27.40	91.00	6.60	8.68	15.40	2.28
45	<img src='http://image.tmdb.org/t/p/w185//kFVx...	How To Make An American Quilt	10.00	23.57	38.00	6.50	4.49	13.57	2.36
44	<img src='http://image.tmdb.org/t/p/w185//aedh...	To Die For	20.00	21.28	177.00	6.70	10.45	1.28	1.06
17	<img src='http://image.tmdb.org/t/p/w185//xhU6...	Four Rooms	4.00	4.30	539.00	6.50	9.03	0.30	1.07
28	<img src='http://image.tmdb.org/t/p/w185//zIIW...	The City of Lost Children	18.00	1.74	308.00	7.60	9.82	-16.26	0.10
22	<img src='http://image.tmdb.org/t/p/w185//9LaH...	Assassins	50.00	30.30	394.00	6.00	11.07	-19.70	0.61
19	<img src='http://image.tmdb.org/t/p/w185//jWBD...	Money Train	60.00	35.43	224.00	5.40	7.34	-24.57	0.59
13	<img src='http://image.tmdb.org/t/p/w185//yzMS...	Nixon	44.00	13.68	72.00	7.10	5.09	-30.32	0.31
14	<img src='http://image.tmdb.org/t/p/w185//hYde...	Cutthroat Island	98.00	10.02	137.00	5.70	7.28	-87.98	0.10
2	<img src='http://image.tmdb.org/t/p/w185//1FSX...	Grumpier Old Men	nan	nan	92.00	6.50	11.71	nan	nan
4	<img src='http://image.tmdb.org/t/p/w185//lf9R...	Father of the Bride Part II	nan	76.58	173.00	5.70	8.39	nan	nan
6	<img src='http://image.tmdb.org/t/p/w185//z1oN...	Sabrina	58.00	nan	141.00	6.20	6.68	nan	nan
7	<img src='http://image.tmdb.org/t/p/w185//6yox...	Tom and Huck	nan	nan	45.00	5.40	2.56	nan	nan
11	<img src='http://image.tmdb.org/t/p/w185//4rRf...	Dracula: Dead and Loving It	nan	nan	210.00	5.70	5.43	nan	nan
12	<img src='http://image.tmdb.org/t/p/w185//tpoa...	Balto	nan	11.35	423.00	7.10	12.14	nan	nan
21	<img src='http://image.tmdb.org/t/p/w185//fdYw...	Copycat	nan	nan	199.00	6.50	10.70	nan	nan
23	<img src='http://image.tmdb.org/t/p/w185//kImK...	Powder	nan	nan	143.00	6.30	12.13	nan	nan
25	<img src='http://image.tmdb.org/t/p/w185//6u14...	Othello	nan	nan	33.00	7.00	1.85	nan	nan
27	<img src='http://image.tmdb.org/t/p/w185//tYOF...	Persuasion	nan	nan	36.00	7.40	2.23	nan	nan
29	<img src='http://image.tmdb.org/t/p/w185//x6SZ...	Shanghai Triad	nan	nan	17.00	6.50	1.10	nan	nan
30	<img src='http://image.tmdb.org/t/p/w185//vzAm...	Dangerous Minds	nan	180.00	249.00	6.40	9.48	nan	nan
32	<img src='http://image.tmdb.org/t/p/w185//hSfI...	Wings of Courage	nan	nan	4.00	6.80	0.75	nan	nan
34	<img src='http://image.tmdb.org/t/p/w185//8Nme...	Carrington	nan	nan	16.00	6.40	1.49	nan	nan
36	<img src='http://image.tmdb.org/t/p/w185//lKOA...	Across the Sea of Time	nan	nan	2.00	3.50	0.11	nan	nan
37	<img src='http://image.tmdb.org/t/p/w185//sdes...	It Takes Two	nan	nan	149.00	6.10	7.78	nan	nan
38	<img src='http://image.tmdb.org/t/p/w185//8AwV...	Clueless	12.00	nan	828.00	6.90	9.88	nan	nan
39	<img src='http://image.tmdb.org/t/p/w185//kGPs...	Cry, the Beloved Country	nan	0.68	13.00	6.70	0.89	nan	nan
40	<img src='http://image.tmdb.org/t/p/w185//aN2B...	Richard III	nan	nan	50.00	6.90	4.56	nan	nan
41	<img src='http://image.tmdb.org/t/p/w185//gCqf...	Dead Presidents	10.00	nan	80.00	6.60	9.88	nan	nan
42	<img src='http://image.tmdb.org/t/p/w185//wkf0...	Restoration	19.00	nan	30.00	6.30	10.98	nan	nan
48	<img src='http://image.tmdb.org/t/p/w185//f9ed...	When Night Is Falling	nan	nan	10.00	5.90	3.30	nan	nan

## Top 5 movies HTML Link and Title
top_5 = df_best.iloc[:5, :2]
top_5

	poster_path	title
0	<img src='http://image.tmdb.org/t/p/w185//uXDf...	Toy Story
1	<img src='http://image.tmdb.org/t/p/w185//vgpX...	Jumanji
2	<img src='http://image.tmdb.org/t/p/w185//1FSX...	Grumpier Old Men
3	<img src='http://image.tmdb.org/t/p/w185//4wjG...	Waiting to Exhale
4	<img src='http://image.tmdb.org/t/p/w185//lf9R...	Father of the Bride Part II

HTML(top_5.to_html(escape=False))

	poster_path	title
0		Toy Story
1		Jumanji
2		Grumpier Old Men
3		Waiting to Exhale
4		Father of the Bride Part II

## High budget movies with high rate of return and high profits
df_best.loc[df_best.budget_musd >= 300].sort_values(by = "return", ascending = False)

	poster_path	title	budget_musd	revenue_musd	vote_count	vote_average	popularity	profit_musd	return
11743	<img src='http://image.tmdb.org/t/p/w185//oVh3...	Pirates of the Caribbean: At World's End	300.00	961.00	4627.00	6.90	31.36	661.00	3.20
16986	<img src='http://image.tmdb.org/t/p/w185//keGf...	Pirates of the Caribbean: On Stranger Tides	380.00	1045.71	5068.00	6.40	27.89	665.71	2.75

Searching as per Priorities

Search 1: Science Fiction Action Movie with Bruce Willis (with high Rating)

HTML(bruce.to_html(escape=False))

	poster_path	vote_average
title
The Fifth Element		7.30
Looper		6.60
Armageddon		6.50
Surrogates		5.90
G.I. Joe: Retaliation		5.40
Vice		4.10

: Seach 2: Most Successful Pixar Studio Movies between 2010 and 2015 (high Revenue)

HTML(pixar.head(5).to_html(escape=False))

	poster_path	revenue_musd	release_date
title
Toy Story 3		1066.97	2010-06-16
Inside Out		857.61	2015-06-09
Monsters University		743.56	2013-06-20
Cars 2		559.85	2011-06-11
Brave		538.98	2012-06-21

Search 3: Action or Thriller Movie in English and minimum Rating of 7.5 (latest one)

HTML(next_mov.to_html(escape=False))

	poster_path	genres	vote_average	vote_count	release_date
title
Descendants 2		TV Movie|Family|Action|Comedy|Music|Adventure	7.50	171.00	2017-07-21
Dunkirk		Action|Drama|History|Thriller|War	7.50	2712.00	2017-07-19
The Book of Henry		Thriller|Drama|Crime	7.60	84.00	2017-06-16
Guardians of the Galaxy Vol. 2		Action|Adventure|Comedy|Science Fiction	7.60	4858.00	2017-04-19
Logan		Action|Drama|Science Fiction	7.60	6310.00	2017-02-28
The Thinning		Thriller	7.60	92.00	2016-10-12
11.22.63		Drama|History|Thriller	8.00	213.00	2016-02-15
Star Wars: The Force Awakens		Action|Adventure|Science Fiction|Fantasy	7.50	7993.00	2015-12-15
London Spy		Romance|Crime|Drama|Mystery|Thriller	8.80	12.00	2015-11-09
Spotlight		Drama|Thriller|History	7.80	2751.00	2015-11-06
Room		Drama|Thriller	8.10	2838.00	2015-10-16
The Fear of 13		Thriller|Documentary	8.00	45.00	2015-10-15
3 ½ Minutes, 10 Bullets		Thriller|Documentary	7.80	30.00	2015-10-02
An Inspector Calls		Drama|Crime|Thriller	7.60	42.00	2015-09-13
Kung Fury		Action|Comedy|Science Fiction|Fantasy	7.60	762.00	2015-05-22
Kingsman: The Secret Service		Crime|Comedy|Action|Adventure	7.60	6069.00	2015-01-29
Black Mirror: White Christmas		Drama|Horror|Mystery|Science Fiction|Thriller|TV Movie	8.30	211.00	2014-12-16
The Imitation Game		History|Drama|Thriller|War	8.00	5895.00	2014-11-14
Big Hero 6		Adventure|Family|Animation|Action|Comedy	7.80	6289.00	2014-10-24
Nightcrawler		Crime|Drama|Thriller	7.60	3475.00	2014-10-23

Seach 4: Most Successful Franchises

franchises[franchises[("vote_count", "mean")] >=1000].nlargest(20, ("vote_average", "mean"))

	title	budget_musd		revenue_musd		vote_average	popularity	ROI	vote_count
	count	sum	mean	sum	mean	mean	mean	median	mean
belongs_to_collection
The Lord of the Rings Collection	3	266.00	88.67	2916.54	972.18	8.03	30.27	11.73	8253.00
The Godfather Collection	3	73.00	24.33	429.38	143.13	7.97	31.64	3.66	3677.00
Blade Runner Collection	1	28.00	28.00	33.14	33.14	7.90	96.27	1.18	3833.00
The Man With No Name Collection	3	2.00	0.67	35.50	11.83	7.83	14.17	25.00	1422.67
The Dark Knight Collection	3	585.00	195.00	2463.72	821.24	7.80	57.42	4.34	9681.00
Guardians of the Galaxy Collection	2	370.00	185.00	1636.74	818.37	7.75	119.31	4.43	7436.00
Kill Bill Collection	2	60.00	30.00	333.11	166.55	7.70	23.40	5.55	4576.00
Kingsman Collection	1	81.00	81.00	414.35	414.35	7.60	28.22	5.12	6069.00
How to Train Your Dragon Collection	2	310.00	155.00	1104.00	552.00	7.55	13.34	3.60	3741.00
Harry Potter Collection	8	1280.00	160.00	7707.37	963.42	7.54	26.25	6.17	5983.25
Toy Story Collection	3	320.00	106.67	1937.89	645.96	7.53	18.82	5.53	4679.67
Back to the Future Collection	3	99.00	33.00	957.64	319.21	7.50	17.50	8.30	4381.00
Trainspotting Collection	2	22.00	11.00	57.90	28.95	7.45	18.50	3.21	1766.00
Deadpool Collection	1	58.00	58.00	783.11	783.11	7.40	187.86	13.50	11444.00
The Incredibles Collection	1	92.00	92.00	631.44	631.44	7.40	22.22	6.86	5290.00
Star Wars Collection	8	854.35	106.79	7434.49	929.31	7.37	23.41	8.24	5430.38
The Avengers Collection	2	500.00	250.00	2924.96	1462.48	7.35	63.63	5.96	9454.00
The Lego Movie Collection	2	140.00	70.00	781.11	390.56	7.35	16.74	5.86	2300.00
Tangled Collection	2	260.00	260.00	591.79	591.79	7.25	12.32	2.28	1901.00
The Space Odyssey Series	2	38.50	19.25	109.10	54.55	7.25	14.99	3.99	1682.50

Seach 5: Most Successful Directors

plt.figure(figsize = (12, 8))
df.director.value_counts().head(20).plot(kind='bar', fontsize = 15)
plt.title("Most Active Directors",fontsize = 20)
plt.ylabel("Number of Movies", fontsize = 15)
plt.show()

plt.figure(figsize = (12, 8))
df.groupby("director").revenue_musd.sum().nlargest(20).plot(kind='bar', fontsize = 15)
plt.title("Total Revenue",fontsize = 20)
plt.ylabel("Revenue (in MUSD)", fontsize = 15)
plt.show()

Search 6: Most Successful Actors

act.columns = ["Actor"]

act = act.merge(df[["title", "revenue_musd", "vote_average", "popularity"]],
                how = "left", left_index = True, right_index = True)

act.Actor.value_counts().head(20)

Bess Flowers          240
Christopher Lee       148
John Wayne            125
Samuel L. Jackson     122
Michael Caine         110
John Carradine        109
Gérard Depardieu      109
Donald Sutherland     108
Jackie Chan           108
Frank Welker          107
Robert De Niro        104
Steve Buscemi         101
Danny Trejo           101
Irving Bacon           98
John Goodman           97
Christopher Walken     96
Danny Glover           95
John Hurt              93
Susan Sarandon         92
Dennis Hopper          92
Name: Actor, dtype: int64

plt.figure(figsize = (12, 8))
act.Actor.value_counts().head(20).plot(kind='bar', fontsize = 15)
plt.title("Most Active Actors",fontsize = 20)
plt.ylabel("Number of Movies", fontsize = 15)
plt.show()

agg.nlargest(10, "Total_Movies")

	Total_Revenue	Mean_Revenue	Mean_Rating	Mean_Pop	Total_Movies
Actor
Bess Flowers	368.91	14.76	6.18	2.03	240
Christopher Lee	9417.05	324.73	5.91	4.75	148
John Wayne	236.09	11.24	5.71	3.09	125
Samuel L. Jackson	17109.62	213.87	6.27	11.70	122
Michael Caine	8053.40	191.75	6.27	8.27	110
Gérard Depardieu	1247.61	95.97	6.05	3.70	109
John Carradine	255.84	19.68	5.55	2.43	109
Donald Sutherland	5390.77	138.22	6.23	7.00	108
Jackie Chan	4699.19	146.85	6.28	5.86	108
Frank Welker	13044.15	326.10	6.31	9.57	107

agg.nlargest(10, "Total_Revenue")

	Total_Revenue	Mean_Revenue	Mean_Rating	Mean_Pop	Total_Movies
Actor
Stan Lee	19414.96	647.17	6.51	29.94	48
Samuel L. Jackson	17109.62	213.87	6.27	11.70	122
Warwick Davis	13256.03	662.80	6.29	13.09	34
Frank Welker	13044.15	326.10	6.31	9.57	107
John Ratzenberger	12596.13	449.86	6.48	10.96	46
Jess Harnell	12234.61	611.73	6.44	10.92	35
Hugo Weaving	11027.58	459.48	6.47	10.97	40
Ian McKellen	11015.59	478.94	6.35	15.45	44
Johnny Depp	10653.76	217.42	6.44	12.38	69
Alan Rickman	10612.63	353.75	6.72	10.40	45

plt.figure(figsize = (12, 8))
agg.Total_Revenue.nlargest(10).plot(kind='bar', fontsize = 15)
plt.title("Total Revenue",fontsize = 20)
plt.ylabel("Revenue (in MUSD)", fontsize = 15)
plt.show()