Whose ratings should you trust?

• FiveThirtyEight Fandango Dataset
• Be Suspicious Of Online Movie Ratings, Especially Fandango’s

Github Repository

• Whose ratings should you trust?
  • Dataset
  • Exploration
    • Fandango Displayed Star Scores vs User Ratings
  • Comparing Fandango Star Ratings to other Sites
    • Rotten Tomatoes
    • MetaCritic
    • IMDB
    • Normalizing the Datasets

Dataset

!wget https://raw.githubusercontent.com/fivethirtyeight/data/master/fandango/fandango_score_comparison.csv -P dataset
!wget https://raw.githubusercontent.com/fivethirtyeight/data/master/fandango/fandango_scrape.csv -P dataset

fandango_score_comparison.csv contains every film that has a Rotten Tomatoes rating, a RT User rating, a Metacritic score, a Metacritic User score, and IMDb score, and at least 30 fan reviews on Fandango. The data from Fandango was pulled on Aug. 24, 2015:

Column	Definition
FILM	The film in question
RottenTomatoes	The Rotten Tomatoes Tomatometer score for the film
RottenTomatoes_User	The Rotten Tomatoes user score for the film
Metacritic	The Metacritic critic score for the film
Metacritic_User	The Metacritic user score for the film
IMDB	The IMDb user score for the film
Fandango_Stars	The number of stars the film had on its Fandango movie page
Fandango_Ratingvalue	The Fandango ratingValue for the film, as pulled from the HTML of each page. This is the actual average score the movie obtained.
RT_norm	The Rotten Tomatoes Tomatometer score for the film , normalized to a 0 to 5 point system
RT_user_norm	The Rotten Tomatoes user score for the film , normalized to a 0 to 5 point system
Metacritic_norm	The Metacritic critic score for the film, normalized to a 0 to 5 point system
Metacritic_user_nom	The Metacritic user score for the film, normalized to a 0 to 5 point system
IMDB_norm	The IMDb user score for the film, normalized to a 0 to 5 point system
RT_norm_round	The Rotten Tomatoes Tomatometer score for the film , normalized to a 0 to 5 point system and rounded to the nearest half-star
RT_user_norm_round	The Rotten Tomatoes user score for the film , normalized to a 0 to 5 point system and rounded to the nearest half-star
Metacritic_norm_round	The Metacritic critic score for the film, normalized to a 0 to 5 point system and rounded to the nearest half-star
Metacritic_user_norm_round	The Metacritic user score for the film, normalized to a 0 to 5 point system and rounded to the nearest half-star
IMDB_norm_round	The IMDb user score for the film, normalized to a 0 to 5 point system and rounded to the nearest half-star
Metacritic_user_vote_count	The number of user votes the film had on Metacritic
IMDB_user_vote_count	The number of user votes the film had on IMDb
Fandango_votes	The number of user votes the film had on Fandango
Fandango_Difference	The difference between the presented Fandango_Stars and the actual Fandango_Ratingvalue

fandango_scrape.csv contains every film we pulled from Fandango:

Column	Definiton
FILM	The movie
STARS	Number of stars presented on Fandango.com
RATING	The Fandango ratingValue for the film, as pulled from the HTML of each page. This is the actual average score the movie obtained.
VOTES	number of people who had reviewed the film at the time we pulled it.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

Exploration

Fandango Displayed Star Scores vs User Ratings

fandango_df = pd.read_csv('dataset/fandango_scrape.csv')
fandango_df.head(5)

	FILM	STARS	RATING	VOTES
0	Fifty Shades of Grey (2015)	4.0	3.9	34846
1	Jurassic World (2015)	4.5	4.5	34390
2	American Sniper (2015)	5.0	4.8	34085
3	Furious 7 (2015)	5.0	4.8	33538
4	Inside Out (2015)	4.5	4.5	15749

fandango_df.info()

<class 'pandas.core.frame.DataFrame'>

• RangeIndex: 510 entries, 0 to 509
• Data columns (total 4 columns):

#	Column	Non-Null Count	Dtype
0	FILM	510 non-null	object
1	STARS	510 non-null	float64
2	RATING	510 non-null	float64
3	VOTES	510 non-null	int64
dtypes: float64(2), int64(1), object(1)
memory usage: 16.1+ KB

fandango_df.describe()

	STARS	RATING	VOTES
count	510.000000	510.000000	510.000000
mean	3.532353	3.351765	1134.364706
std	1.585616	1.512628	3809.952176
min	0.000000	0.000000	0.000000
25%	3.500000	3.025000	2.000000
50%	4.000000	3.800000	17.500000
75%	4.500000	4.300000	183.500000
max	5.000000	5.000000	34846.000000

sns.set(
    style='darkgrid',
    palette='winter'
)

# Find Correlations between Stars, Ratings and Number of Votes
sns.pairplot(
    data=fandango_df
)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_01.webp', bbox_inches='tight')

# how do the columns correlate
fandango_df.corr(numeric_only=True)
# there is a small difference between stars and user ratings

	STARS	RATING	VOTES
STARS	1.000000	0.994870	0.165774
RATING	0.994870	1.000000	0.165147
VOTES	0.165774	0.165147	1.000000

plt.figure(figsize=(10, 4))

sns.scatterplot(
    data=fandango_df,
    x='RATING',
    y='VOTES',
    hue='STARS',
    palette='winter',
    # size='training level'
).set_title('Average Rating vs Number of Votes')

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_02.webp', bbox_inches='tight')

plt.figure(figsize=(12, 6))
plt.title('Correlation between Ratings and Stars')

sns.histplot(
    data=fandango_df,
    x='RATING',
    bins=65,
    hue='STARS',
    palette='tab20',
    kde=False
)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_03.webp', bbox_inches='tight')

# add YEAR column
fandango_df['YEAR'] = fandango_df['FILM'].apply(
    lambda x: x.replace('"','')
).apply(
    lambda x: x.replace('(1)', '(0000)')
).apply(
    lambda x: x[-5:-1]
)

fandango_df['YEAR'].tail(5)

505	2015
506	2015
507	2015
508	1964
509	0000
Name: YEAR, dtype: object

fandango_df['YEAR'].value_counts()

2015	478
2014	23
0000	7
2016	1
1964	1
Name: YEAR, dtype: int64

plt.figure(figsize=(10, 5))
plt.title('Number of Movies by Year')

sns.countplot(
    data=fandango_df,
    x='YEAR',
    hue='STARS',
    palette='winter'
)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_04.webp', bbox_inches='tight')

fandango_years_df = fandango_df.groupby(['YEAR'])
temp_df = fandango_years_df.mean(numeric_only=True).round(1).reset_index()
temp_df.head(5)

	YEAR	STARS	RATING	VOTES
0	0000	1.1	1.1	0.4
1	1964	0.0	0.0	0.0
2	2014	4.1	3.8	3341.4
3	2015	3.5	3.4	1049.5
4	2016	5.0	4.7	11.0

plt.figure(figsize=(10, 4))
plt.title('Average Rating by Year')

plot = sns.barplot(x='RATING', y='STARS' , data=temp_df, palette='winter', hue='YEAR' )

plt.legend(bbox_to_anchor=(1.01,1.01))
plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_05.webp', bbox_inches='tight')

# movies with highest voter count
plt.figure(figsize=(8, 5))
plt.title('Top10 Voted-for Movies Rating')

plot = sns.barplot(
    x='RATING',
    y='STARS',
    data=fandango_df.sort_values(by='VOTES', ascending=False).head(10),
    palette='tab20', hue='FILM' )

plt.legend(bbox_to_anchor=(1.01,1.01))

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_06.webp', bbox_inches='tight')

# how many movies have zero votes
fandango_df[fandango_df['VOTES'] == 0]['FILM'].count()
# 73

# remove all 73 movies nobody voted for
fandango_df_non_zero_votes = fandango_df[fandango_df['VOTES'] != 0]
fandango_df_non_zero_votes.sort_values(by='VOTES', ascending=True).head(5)

	FILM	STARS	RATING	VOTES	YEAR
436	Z For Zachariah (2015)	5.0	5.0	1	2015
415	Buggs Bunny (2015)	4.0	4.0	1	2015
414	Wild Canaries (2015)	3.0	3.0	1	2015
413	The Living (2015)	3.0	3.0	1	2015
412	The Face of an Angel (2015)	3.0	3.0	1	2015

# do ratings and star follow the same distribution?
plt.figure(figsize=(10, 5))
plt.title('Distribution between Stars and Rating')

fandango_df_no_votes = fandango_df_non_zero_votes[['STARS','RATING']]

plot = sns.kdeplot(data=fandango_df_no_votes, multiple="stack", palette='winter')

plot.set_xlim(0,5)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_07.webp', bbox_inches='tight')

# quantify discrepancy between rating and star rating
fandango_df_no_votes['RATE_STAR_DIFF'] = (
    fandango_df_no_votes['STARS'] - fandango_df_no_votes['RATING']
).round(1)

fandango_df_no_votes[['RATING', 'STARS', 'RATE_STAR_DIFF']].head(10)

	RATING	STARS	RATE_STAR_DIFF
0	3.9	4.0	0.1
1	4.5	4.5	0.0
2	4.8	5.0	0.2
3	4.8	5.0	0.2
4	4.5	4.5	0.0
5	4.3	4.5	0.2
6	4.2	4.5	0.3
7	4.0	4.0	0.0
8	4.5	5.0	0.5
9	3.4	3.5	0.1

# what movie has the greatest deviation
fandango_df_no_votes.sort_values(by='RATE_STAR_DIFF', ascending=False).head(10)

	STARS	RATING	RATE_STAR_DIFF
381	5.0	4.0	1.0
190	5.0	4.5	0.5
161	4.0	3.5	0.5
210	4.5	4.0	0.5
209	4.0	3.5	0.5
200	5.0	4.5	0.5
189	4.5	4.0	0.5
169	5.0	4.5	0.5
122	4.0	3.5	0.5
50	4.0	3.5	0.5

# what is the title of movie `381`
fandango_df.iloc[381]

FILM	Turbo Kid (2015)
STARS	5.0
RATING	4.0
VOTES	2
YEAR	2015
Name: 381, dtype: object

plt.figure(figsize=(14, 5))
plt.title('How often do Deviation of a certain value occur?')

plot = sns.countplot(
    data=fandango_df_no_votes,
    x='RATE_STAR_DIFF',
    hue='STARS',
    palette='winter'
)

plot.set_xticks(np.arange(8))

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_08.webp', bbox_inches='tight')
# there are only positive deviations
# and the sum of movies with positive
# deviations is greater than the number
# of movies with a deviation of zero

Comparing Fandango Star Ratings to other Sites

all_sites_df = pd.read_csv('dataset/fandango_score_comparison.csv')
all_sites_df.head(2).transpose()

	0	1
FILM	Avengers: Age of Ultron (2015)	Cinderella (2015)
RottenTomatoes	74	85
RottenTomatoes_User	86	80
Metacritic	66	67
Metacritic_User	7.1	7.5
IMDB	7.8	7.1
Fandango_Stars	5.0	5.0
Fandango_Ratingvalue	4.5	4.5
RT_norm	3.7	4.25
RT_user_norm	4.3	4.0
Metacritic_norm	3.3	3.35
Metacritic_user_nom	3.55	3.75
IMDB_norm	3.9	3.55
RT_norm_round	3.5	4.5
RT_user_norm_round	4.5	4.0
Metacritic_norm_round	3.5	3.5
Metacritic_user_norm_round	3.5	4.0
IMDB_norm_round	4.0	3.5
Metacritic_user_vote_count	1330	249
IMDB_user_vote_count	271107	65709
Fandango_votes	14846	12640
Fandango_Difference	0.5	0.5

Rotten Tomatoes

plt.figure(figsize=(14, 6))

sns.regplot(
    x='RottenTomatoes_User',
    y='RottenTomatoes',
    data=all_sites_df,
    ci=95,
    n_boot=1000,
    color='fuchsia'
)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_09a.webp', bbox_inches='tight')

plt.figure(figsize=(14, 6))

sns.scatterplot(
    x='RottenTomatoes_User',
    y='RottenTomatoes',
    data=all_sites_df,
    hue='Fandango_Stars',
    palette='winter',
    size='Fandango_Ratingvalue'
).set_title('Rotten Tomatoes User and Critics Ratings')

plt.legend(bbox_to_anchor=(1.01,1.01))
plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_09b.webp', bbox_inches='tight')

# quantify discrepancy between critics and user rating
all_sites_df['RottenTomatoes_Critic_User_Diff'] = (
    all_sites_df['RottenTomatoes'] - all_sites_df['RottenTomatoes_User']
).round(1)

all_sites_df[
    ['RottenTomatoes_Critic_User_Diff', 'RottenTomatoes', 'RottenTomatoes_User']
].head(10)

	RottenTomatoes_Critic_User_Diff	RottenTomatoes	RottenTomatoes_User
0	-12	74	86
1	5	85	80
2	-10	80	90
3	-66	18	84
4	-14	14	28
5	1	63	62
6	-11	42	53
7	22	86	64
8	17	99	82
9	2	89	87

# what is the average absolute deviation between user and critic scores?
all_sites_df['RottenTomatoes_Critic_User_Diff'].apply(abs).mean()
# 15.095890410958905

plt.figure(figsize=(10, 5))
plt.title('Distribution of Deviation in User and Critic Ratings')

sns.histplot(
    data=all_sites_df,
    x='RottenTomatoes_Critic_User_Diff',
    bins=20,
    kde=True
)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_09c.webp', bbox_inches='tight')

plt.figure(figsize=(10, 5))
plt.title('Distribution of Deviation in absolute User and Critic Ratings')

sns.histplot(
    x=all_sites_df['RottenTomatoes_Critic_User_Diff'].apply(abs),
    bins=25,
    kde=True
)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_09d.webp', bbox_inches='tight')

# what movies have the largest neg difference in critics - user?
all_sites_df.sort_values(
    by='RottenTomatoes_Critic_User_Diff',
    ascending=True)[
    ['FILM','RottenTomatoes_Critic_User_Diff']
].head(10)

• Movies loved by RT Users but disliked by Critics: | | FILM | RottenTomatoes_Critic_User_Diff | | -- | -- | -- | | 3 | Do You Believe? (2015) | -66 | | 85 | Little Boy (2015) | -61 | | 134 | The Longest Ride (2015) | -42 | | 105 | Hitman: Agent 47 (2015) | -42 | | 125 | The Wedding Ringer (2015) | -39 | | 132 | Max (2015) | -38 | | 19 | Pixels (2015) | -37 | | 15 | Taken 3 (2015) | -37 | | 51 | Entourage (2015) | -36 | | 49 | Terminator Genisys (2015) | -34 |

# what movies have the largest pos difference in critics - user?
all_sites_df.sort_values(by='RottenTomatoes_Critic_User_Diff', ascending=False)[['FILM','RottenTomatoes_Critic_User_Diff']].head(10)

• Movies loved by Critics but disliked by RT Users:

	FILM	RottenTomatoes_Critic_User_Diff
69	Mr. Turner (2014)	42
112	It Follows (2015)	31
115	While We're Young (2015)	31
145	Kumiko, The Treasure Hunter (2015)	24
37	Welcome to Me (2015)	24
40	I'll See You In My Dreams (2015)	24
90	The SpongeBob Movie: Sponge Out of Water (2015)	23
7	Top Five (2014)	22
11	Black Sea (2015)	22
116	Clouds of Sils Maria (2015)	22

plt.figure(figsize=(24, 5))
plt.title('Deviation between User and Critic Ratings')

plot = sns.countplot(
    data=all_sites_df,
    x='RottenTomatoes_Critic_User_Diff'
)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_10.webp', bbox_inches='tight')

plt.figure(figsize=(12, 6))
plt.title('Deviation between User and Critic Ratings')

plot = sns.histplot(
    data=all_sites_df,
    bins=50,
    x='RottenTomatoes_Critic_User_Diff',
    kde=True
)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_11.webp', bbox_inches='tight')

MetaCritic

plt.figure(figsize=(14, 6))

sns.regplot(
    x='Metacritic_User',
    y='Metacritic',
    data=all_sites_df,
    ci=95,
    n_boot=1000,
    color='fuchsia'
)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_12a.webp', bbox_inches='tight')

plt.figure(figsize=(14, 6))

sns.scatterplot(
    x='Metacritic_User',
    y='Metacritic',
    data=all_sites_df,
    hue='Fandango_Stars',
    palette='winter',
    size='Fandango_Ratingvalue'
).set_title('Metacritic User and Displayed Ratings')

plt.legend(bbox_to_anchor=(1.01,1.01))
plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_12b.webp', bbox_inches='tight')

IMDB

plt.figure(figsize=(14, 6))

sns.regplot(
    x='Metacritic_User',
    y='Metacritic',
    data=all_sites_df,
    ci=95,
    n_boot=1000,
    color='fuchsia'
)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_13a.webp', bbox_inches='tight')

plt.figure(figsize=(14, 6))

sns.scatterplot(
    x='IMDB',
    y='Metacritic_User',
    data=all_sites_df,
    hue='Fandango_Stars',
    palette='winter',
    size='Fandango_Ratingvalue'
).set_title('IMDB vs Metacritic User Ratings')

plt.legend(bbox_to_anchor=(1.01,1.01))
plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_13b.webp', bbox_inches='tight')

Normalizing the Datasets

all_sites_df.describe()
# Rotten Tomatoes: 0-100
# Rotten Tomatoes Users: 0-100
# Metacritic: 0-100
# Metacritic User: 0-10
# IMDB: 0-10
# Fandango Stars: 0-5
# Fandango Rating: 0-5

	RottenTomatoes	RottenTomatoes_User	Metacritic	Metacritic_User	IMDB	Fandango_Stars	Fandango_Ratingvalue
count	146.000000	146.000000	146.000000	146.000000	146.000000	146.000000	146.000000
mean	60.849315	63.876712	58.808219	6.519178	6.736986	4.089041	3.845205
std	30.168799	20.024430	19.517389	1.510712	0.958736	0.540386	0.502831
min	5.000000	20.000000	13.000000	2.400000	4.000000	3.000000	2.700000
25%	31.250000	50.000000	43.500000	5.700000	6.300000	3.500000	3.500000
50%	63.500000	66.500000	59.000000	6.850000	6.900000	4.000000	3.900000
75%	89.000000	81.000000	75.000000	7.500000	7.400000	4.500000	4.200000
max	100.000000	94.000000	94.000000	9.600000	8.600000	5.000000	4.800000

# Normalized to the Fandango 0-5 rating system
all_sites_df['RottenTomatoes_Normalized'] = (all_sites_df['RottenTomatoes'] / 20).round(2)
all_sites_df['RottenTomatoes_User_Normalized'] = (all_sites_df['RottenTomatoes_User'] / 20).round(2)
all_sites_df['Metacritic_Normalized'] = (all_sites_df['Metacritic'] / 20).round(2)
all_sites_df['Metacritic_User_Normalized'] = (all_sites_df['Metacritic_User'] / 2).round(2)
all_sites_df['IMDB_Normalized'] = (all_sites_df['IMDB'] / 2).round(2)

all_sites_normed_ratings_df = all_sites_df[[
    'RottenTomatoes_Normalized',
    'RottenTomatoes_User_Normalized',
    'Metacritic_Normalized',
    'Metacritic_User_Normalized',
    'IMDB_Normalized',
    'Fandango_Stars',
    'Fandango_Ratingvalue'
    
]]

all_sites_normed_ratings_df.head(1).T

	0
RottenTomatoes_Normalized	3.70
RottenTomatoes_User_Normalized	4.30
Metacritic_Normalized	3.30
Metacritic_User_Normalized	3.55
IMDB_Normalized	3.90
Fandango_Stars	5.00
Fandango_Ratingvalue	4.50

# move legend in kdeplot
def move_legend(ax, new_loc, **kws):
    old_legend = ax.legend_
    handles = old_legend.legendHandles
    labels = [t.get_text() for t in old_legend.get_texts()]
    title = old_legend.get_title().get_text()
    ax.legend(handles, labels, loc=new_loc, title=title, **kws)

# do all the ratings follow the same distribution?
fig, ax = plt.subplots(figsize=(12, 7))
fig.suptitle('Normalized Rating Distribution for different Platforms')

plot = sns.kdeplot(
    data=all_sites_normed_ratings_df,
    multiple='stack',
    palette='winter',
    clip=[0,5]
)

move_legend(ax, "upper left")
plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_14.webp', bbox_inches='tight')
# the fandango distribution leans towards higher ratings

fig, ax = plt.subplots(figsize=(12,6))
sns.kdeplot(
    clip=[0,5],
    fill=True,
    data=all_sites_normed_ratings_df[
        ['RottenTomatoes_Normalized','Fandango_Stars']
    ]    
)

fig.suptitle('Normalized Rating Distribution')

move_legend(ax, "upper left")
plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_15.webp', bbox_inches='tight')

sns.clustermap(
    all_sites_normed_ratings_df,
    figsize=(10, 15),
    cmap='winter',
    annot=False,
    col_cluster=False
)

plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_16.webp', bbox_inches='tight')
# the clustermap groups movies together by rating. the fandango ratings in the lower right are
# all higher (green instead of a blue hue) compared to the rest of the ratings from other platforms

# compare the top10 worst rated movies
worts_10_df = all_sites_normed_ratings_df.sort_values(
    by='Fandango_Stars', ascending=False
).tail(10)

worts_10_df

	RottenTomatoes_Normalized	RottenTomatoes_User_Normalized	Metacritic_Normalized	Metacritic_User_Normalized	IMDB_Normalized	Fandango_Stars	Fandango_Ratingvalue
67	3.00	1.95	2.95	2.90	2.95	3.0	2.7
101	0.80	1.35	1.50	3.50	2.20	3.0	2.9
136	1.10	1.25	2.10	2.20	2.45	3.0	3.0
135	0.70	1.15	1.55	2.45	2.60	3.0	3.0
115	4.15	2.60	3.80	3.35	3.20	3.0	2.9
107	0.65	1.05	1.85	2.70	2.30	3.0	2.9
112	4.80	3.25	4.15	3.75	3.45	3.0	2.9
113	3.65	2.60	4.05	3.70	3.35	3.0	2.9
48	0.45	1.00	1.35	1.25	2.00	3.0	2.7
82	1.70	1.25	2.55	2.70	2.70	3.0	2.8

# get movie names
all_sites_df[
    ['FILM','Fandango_Stars','RottenTomatoes_User']
].iloc[
    [67, 101, 136, 135, 115, 107, 112, 113, 48, 82]
]

	FILM	Fandango_Stars	RottenTomatoes_User
67	Unfriended (2015)	3.0	39
101	The Gallows (2015)	3.0	27
136	The Woman In Black 2 Angel of Death (2015)	3.0	25
135	The Lazarus Effect (2015)	3.0	23
115	While We're Young (2015)	3.0	52
107	The Vatican Tapes (2015)	3.0	21
112	It Follows (2015)	3.0	65
113	Inherent Vice (2014)	3.0	52
48	Fantastic Four (2015)	3.0	20
82	Blackhat (2015)	3.0	25

# do ratings and star follow the same distribution?
plt.figure(figsize=(10, 5))
plt.title('Worst 10 Movies Rating Distribution')

sns.kdeplot(
    data=worts_10_df,
    x='RottenTomatoes_Normalized',
    clip=[0,5],
    fill=True,
    label='RT Rating'
)
sns.kdeplot(
    data=worts_10_df,
    x='RottenTomatoes_User_Normalized',
    clip=[0,5],
    fill=True,
    label='RT User Rating'
)
sns.kdeplot(
    data=worts_10_df,
    x='Metacritic_Normalized',
    clip=[0,5],
    fill=True,
    label='MC Rating'
)
sns.kdeplot(
    data=worts_10_df,
    x='Metacritic_User_Normalized',
    clip=[0,5],
    fill=True,
    label='MC User Rating'
)
sns.kdeplot(
    data=worts_10_df,
    x='IMDB_Normalized',
    clip=[0,5],
    fill=True,
    label='IMDB User Rating'
)
sns.kdeplot(
    data=worts_10_df,
    x='Fandango_Stars',
    clip=[0,5],
    fill=True,
    label='Fandango Stars'
)
sns.kdeplot(
    data=worts_10_df,
    x='Fandango_Ratingvalue',
    clip=[0,5],
    fill=True,
    label='Fandango Rating'
)

plt.legend(loc=(1.01, 0.57))
plt.xlabel('Normalized Rating [0,5]')
plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_18.webp', bbox_inches='tight')

# fandango groups all movies with ratings below 3 in the 3-star category

grid = sns.PairGrid(
    data=all_sites_normed_ratings_df,
    height=3,
    aspect=2,
    despine=True
)

grid = grid.map_upper(
    sns.regplot,
    scatter_kws={'alpha':0.55},
    line_kws={'color': 'fuchsia'}
)
grid = grid.map_lower(sns.scatterplot)
grid = grid.map_diag(sns.kdeplot, color='mediumspringgreen', fill=True)


plt.savefig('assets/FiveThirtyEight_Fandango_Dataset_17.webp', bbox_inches='tight')