This project extracts movie data from LetterBoxd and performs Exploratory Data Analysis to understand trends in movies and different movie genres.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inlineimport warnings
warnings.filterwarnings('ignore')df = pd.read_json("feb2021.json")df.head()
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| title | year | duration | genre | rating | language | viewers | |
|---|---|---|---|---|---|---|---|
| 0 | Parasite | 2019.0 | 133 | [comedy, drama, thriller] | 4.6 | Korean | Weighted average of 4.60 based on 705,993 ratings |
| 1 | Call Me by Your Name | 2017.0 | 132 | [drama, romance] | 4.1 | English | Weighted average of 4.09 based on 355,937 ratings |
| 2 | Hereditary | 2018.0 | 127 | [thriller, horror, mystery] | 4.0 | English | Weighted average of 3.99 based on 365,955 ratings |
| 3 | Spirited Away | 2001.0 | 125 | [fantasy, animation, family] | 4.5 | Japanese | Weighted average of 4.47 based on 386,917 ratings |
| 4 | Moonlight | 2016.0 | 111 | [drama] | 4.2 | English | Weighted average of 4.20 based on 347,384 ratings |
df.shape(94051, 7)
df.columnsIndex(['title', 'year', 'duration', 'genre', 'rating', 'language', 'viewers'], dtype='object')
df.isnull().sum()title 0
year 3379
duration 0
genre 0
rating 70251
language 586
viewers 70251
dtype: int64
df = df.loc[~(df.year.isnull())]df = df.loc[~(df.rating.isnull())]df = df.loc[~(df.language.isnull())]df.isnull().sum()title 0
year 0
duration 0
genre 0
rating 0
language 0
viewers 0
dtype: int64
df.columnsIndex(['title', 'year', 'duration', 'genre', 'rating', 'language', 'viewers'], dtype='object')
df.year = df.year.astype('int')df.year = df.year.astype('category')df.year.value_counts()2019 1247
2018 1218
2017 1196
2016 1090
2015 1003
...
1895 8
1894 5
1893 1
1888 1
1886 1
Name: year, Length: 131, dtype: int64
df.loc[(df.duration=="")]
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| title | year | duration | genre | rating | language | viewers | |
|---|---|---|---|---|---|---|---|
| 715 | The King's Man | 2021 | [action, adventure, comedy] | 3.3 | English | Weighted average of 3.29 based on 50 ratings | |
| 969 | The Matrix Resurrections | 2021 | [action, adventure, science fiction] | 3.8 | English | Weighted average of 3.76 based on 97 ratings | |
| 1958 | Coming 2 America | 2021 | [family, comedy] | 3.3 | English | Weighted average of 3.27 based on 57 ratings | |
| 2270 | Minions: The Rise of Gru | 2021 | [family, action, adventure, comedy, animation] | 2.6 | English | Weighted average of 2.65 based on 237 ratings | |
| 2891 | Hotel Transylvania 4 | 2021 | [adventure, animation, family, fantasy] | 3.3 | English | Weighted average of 3.30 based on 113 ratings | |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 41910 | Murdered | 2012 | [crime] | 3.0 | French | Weighted average of 3.05 based on 30 ratings | |
| 42148 | New Moon | 2010 | [action] | 2.9 | English | Weighted average of 2.90 based on 31 ratings | |
| 42179 | Uthaman | 2001 | [] | 3.1 | Malayalam | Weighted average of 3.08 based on 32 ratings | |
| 42906 | Daniël Arends: Joko 79 | 2008 | [comedy] | 3.4 | Dutch | Weighted average of 3.42 based on 30 ratings | |
| 45660 | Simiocracia (Crónica de la gran resaca económica) | 2012 | [comedy, animation, documentary] | 3.3 | Spanish | Weighted average of 3.25 based on 32 ratings |
851 rows × 7 columns
len(df.loc[(df.duration=="")])851
df =df.loc[~(df.duration=="")]df.duration = df.duration.astype("int")df.duration.describe()count 22793.000000
mean 84.253631
std 69.173581
min 1.000000
25% 65.000000
50% 90.000000
75% 103.000000
max 3708.000000
Name: duration, dtype: float64
df.columnsIndex(['title', 'year', 'duration', 'genre', 'rating', 'language', 'viewers'], dtype='object')
df.genre.head()0 [comedy, drama, thriller]
1 [drama, romance]
2 [thriller, horror, mystery]
3 [fantasy, animation, family]
4 [drama]
Name: genre, dtype: object
myGenre = set()df.shape(22793, 7)
df = df.reset_index()df.drop("index", axis=1, inplace=True)for i in range(0,len(df)):
for movGen in df.loc[i].genre:
myGenre.add(movGen)df.head()
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| title | year | duration | genre | rating | language | viewers | |
|---|---|---|---|---|---|---|---|
| 0 | Parasite | 2019 | 133 | [comedy, drama, thriller] | 4.6 | Korean | Weighted average of 4.60 based on 705,993 ratings |
| 1 | Call Me by Your Name | 2017 | 132 | [drama, romance] | 4.1 | English | Weighted average of 4.09 based on 355,937 ratings |
| 2 | Hereditary | 2018 | 127 | [thriller, horror, mystery] | 4.0 | English | Weighted average of 3.99 based on 365,955 ratings |
| 3 | Spirited Away | 2001 | 125 | [fantasy, animation, family] | 4.5 | Japanese | Weighted average of 4.47 based on 386,917 ratings |
| 4 | Moonlight | 2016 | 111 | [drama] | 4.2 | English | Weighted average of 4.20 based on 347,384 ratings |
myGenre{'action',
'adventure',
'animation',
'comedy',
'crime',
'documentary',
'drama',
'family',
'fantasy',
'history',
'horror',
'music',
'mystery',
'romance',
'science fiction',
'thriller',
'tv movie',
'war',
'western'}
len(myGenre)19
for col in myGenre:
df[col] = 0df.head()
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| title | year | duration | genre | rating | language | viewers | thriller | tv movie | mystery | ... | action | music | history | comedy | science fiction | romance | crime | animation | war | adventure | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Parasite | 2019 | 133 | [comedy, drama, thriller] | 4.6 | Korean | Weighted average of 4.60 based on 705,993 ratings | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | Call Me by Your Name | 2017 | 132 | [drama, romance] | 4.1 | English | Weighted average of 4.09 based on 355,937 ratings | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | Hereditary | 2018 | 127 | [thriller, horror, mystery] | 4.0 | English | Weighted average of 3.99 based on 365,955 ratings | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | Spirited Away | 2001 | 125 | [fantasy, animation, family] | 4.5 | Japanese | Weighted average of 4.47 based on 386,917 ratings | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | Moonlight | 2016 | 111 | [drama] | 4.2 | English | Weighted average of 4.20 based on 347,384 ratings | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 26 columns
for i in range(0,len(df)):
for x in df.loc[i]["genre"]:
df.at[i, x]=1df.head()
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| title | year | duration | genre | rating | language | viewers | thriller | tv movie | mystery | ... | action | music | history | comedy | science fiction | romance | crime | animation | war | adventure | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Parasite | 2019 | 133 | [comedy, drama, thriller] | 4.6 | Korean | Weighted average of 4.60 based on 705,993 ratings | 1 | 0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | Call Me by Your Name | 2017 | 132 | [drama, romance] | 4.1 | English | Weighted average of 4.09 based on 355,937 ratings | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 2 | Hereditary | 2018 | 127 | [thriller, horror, mystery] | 4.0 | English | Weighted average of 3.99 based on 365,955 ratings | 1 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | Spirited Away | 2001 | 125 | [fantasy, animation, family] | 4.5 | Japanese | Weighted average of 4.47 based on 386,917 ratings | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 4 | Moonlight | 2016 | 111 | [drama] | 4.2 | English | Weighted average of 4.20 based on 347,384 ratings | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 26 columns
df.columnsIndex(['title', 'year', 'duration', 'genre', 'rating', 'language', 'viewers',
'thriller', 'tv movie', 'mystery', 'documentary', 'western', 'family',
'horror', 'drama', 'fantasy', 'action', 'music', 'history', 'comedy',
'science fiction', 'romance', 'crime', 'animation', 'war', 'adventure'],
dtype='object')
df.rating = df.rating.astype("float")df.rating.describe()count 22793.000000
mean 3.137270
std 0.429866
min 1.000000
25% 2.900000
50% 3.200000
75% 3.400000
max 4.600000
Name: rating, dtype: float64
df.language.value_counts().head(10)English 13344
French 1276
Japanese 1078
Spanish 952
German 644
Portuguese 588
Italian 547
Chinese 340
Hindi 315
Korean 303
Name: language, dtype: int64
df.viewers.head()0 Weighted average of 4.60 based on 705,993 ratings
1 Weighted average of 4.09 based on 355,937 ratings
2 Weighted average of 3.99 based on 365,955 ratings
3 Weighted average of 4.47 based on 386,917 ratings
4 Weighted average of 4.20 based on 347,384 ratings
Name: viewers, dtype: object
df.viewers = df.viewers.apply(lambda x: int(x.split()[-2].replace(",","")))df.viewers.describe()count 22793.000000
mean 2443.237705
std 18353.907074
min 30.000000
25% 50.000000
50% 102.000000
75% 324.000000
max 705993.000000
Name: viewers, dtype: float64
fig = plt.figure(figsize=(20,6))
axis1 = fig.add_subplot(1,2,1)
axis2 = fig.add_subplot(1,2,2)
sns.boxplot(x = "rating", data=df, ax=axis1)
sns.boxplot(x = "duration", data=df, ax=axis2)
plt.show()
sns.boxplot(x = "viewers", data=df)
plt.show()
df.columnsIndex(['title', 'year', 'duration', 'genre', 'rating', 'language', 'viewers',
'thriller', 'tv movie', 'mystery', 'documentary', 'western', 'family',
'horror', 'drama', 'fantasy', 'action', 'music', 'history', 'comedy',
'science fiction', 'romance', 'crime', 'animation', 'war', 'adventure'],
dtype='object')
df.columns[0:7]Index(['title', 'year', 'duration', 'genre', 'rating', 'language', 'viewers'], dtype='object')
df.columns[7:]Index(['thriller', 'tv movie', 'mystery', 'documentary', 'western', 'family',
'horror', 'drama', 'fantasy', 'action', 'music', 'history', 'comedy',
'science fiction', 'romance', 'crime', 'animation', 'war', 'adventure'],
dtype='object')
pd.melt(df, id_vars = df.columns[0:7], value_vars=df.columns[7:])
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| title | year | duration | genre | rating | language | viewers | variable | value | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Parasite | 2019 | 133 | [comedy, drama, thriller] | 4.6 | Korean | 705993 | thriller | 1 |
| 1 | Call Me by Your Name | 2017 | 132 | [drama, romance] | 4.1 | English | 355937 | thriller | 0 |
| 2 | Hereditary | 2018 | 127 | [thriller, horror, mystery] | 4.0 | English | 365955 | thriller | 1 |
| 3 | Spirited Away | 2001 | 125 | [fantasy, animation, family] | 4.5 | Japanese | 386917 | thriller | 0 |
| 4 | Moonlight | 2016 | 111 | [drama] | 4.2 | English | 347384 | thriller | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 433062 | Streets of Rio | 2007 | 97 | [drama] | 3.1 | English | 30 | adventure | 0 |
| 433063 | Whose Dominion? | 2017 | 10 | [history, documentary] | 2.9 | English | 41 | adventure | 0 |
| 433064 | Lazania | 2019 | 90 | [comedy] | 2.4 | Persian (Farsi) | 33 | adventure | 0 |
| 433065 | Prisoners | 2019 | 101 | [comedy] | 2.5 | Persian (Farsi) | 30 | adventure | 0 |
| 433066 | Muraren | 2002 | 95 | [] | 3.5 | English | 33 | adventure | 0 |
433067 rows × 9 columns
df_pivot = pd.melt(df, id_vars = df.columns[0:7], value_vars=df.columns[7:])df_pivot
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| title | year | duration | genre | rating | language | viewers | variable | value | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Parasite | 2019 | 133 | [comedy, drama, thriller] | 4.6 | Korean | 705993 | thriller | 1 |
| 1 | Call Me by Your Name | 2017 | 132 | [drama, romance] | 4.1 | English | 355937 | thriller | 0 |
| 2 | Hereditary | 2018 | 127 | [thriller, horror, mystery] | 4.0 | English | 365955 | thriller | 1 |
| 3 | Spirited Away | 2001 | 125 | [fantasy, animation, family] | 4.5 | Japanese | 386917 | thriller | 0 |
| 4 | Moonlight | 2016 | 111 | [drama] | 4.2 | English | 347384 | thriller | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 433062 | Streets of Rio | 2007 | 97 | [drama] | 3.1 | English | 30 | adventure | 0 |
| 433063 | Whose Dominion? | 2017 | 10 | [history, documentary] | 2.9 | English | 41 | adventure | 0 |
| 433064 | Lazania | 2019 | 90 | [comedy] | 2.4 | Persian (Farsi) | 33 | adventure | 0 |
| 433065 | Prisoners | 2019 | 101 | [comedy] | 2.5 | Persian (Farsi) | 30 | adventure | 0 |
| 433066 | Muraren | 2002 | 95 | [] | 3.5 | English | 33 | adventure | 0 |
433067 rows × 9 columns
df_pivot = df_pivot.loc[df_pivot.value==1]df_pivot.head()
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| title | year | duration | genre | rating | language | viewers | variable | value | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Parasite | 2019 | 133 | [comedy, drama, thriller] | 4.6 | Korean | 705993 | thriller | 1 |
| 2 | Hereditary | 2018 | 127 | [thriller, horror, mystery] | 4.0 | English | 365955 | thriller | 1 |
| 7 | 1917 | 2019 | 119 | [thriller, war, drama, action] | 4.1 | English | 358009 | thriller | 1 |
| 8 | Gone Girl | 2014 | 149 | [drama, thriller, mystery] | 4.0 | English | 445648 | thriller | 1 |
| 10 | The Lighthouse | 2019 | 109 | [thriller, fantasy, drama, horror] | 4.1 | English | 304207 | thriller | 1 |
df_pivot.shape(39384, 9)
df_pivot.loc[df_pivot.title=="Soul"]
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| title | year | duration | genre | rating | language | viewers | variable | value | |
|---|---|---|---|---|---|---|---|---|---|
| 113982 | Soul | 2020 | 102 | [family, drama, music, comedy, animation, fant... | 4.1 | English | 281921 | family | 1 |
| 159568 | Soul | 2020 | 102 | [family, drama, music, comedy, animation, fant... | 4.1 | English | 281921 | drama | 1 |
| 182361 | Soul | 2020 | 102 | [family, drama, music, comedy, animation, fant... | 4.1 | English | 281921 | fantasy | 1 |
| 227947 | Soul | 2020 | 102 | [family, drama, music, comedy, animation, fant... | 4.1 | English | 281921 | music | 1 |
| 273533 | Soul | 2020 | 102 | [family, drama, music, comedy, animation, fant... | 4.1 | English | 281921 | comedy | 1 |
| 364705 | Soul | 2020 | 102 | [family, drama, music, comedy, animation, fant... | 4.1 | English | 281921 | animation | 1 |
df_pivot.drop(["genre", "value"], axis=1, inplace=True)df_pivot.head()
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| title | year | duration | rating | language | viewers | variable | |
|---|---|---|---|---|---|---|---|
| 0 | Parasite | 2019 | 133 | 4.6 | Korean | 705993 | thriller |
| 2 | Hereditary | 2018 | 127 | 4.0 | English | 365955 | thriller |
| 7 | 1917 | 2019 | 119 | 4.1 | English | 358009 | thriller |
| 8 | Gone Girl | 2014 | 149 | 4.0 | English | 445648 | thriller |
| 10 | The Lighthouse | 2019 | 109 | 4.1 | English | 304207 | thriller |
df_pivot.rename(columns={"variable": "genre"}, inplace=True)df = df_pivotdef annotate_graph_both(ax):
total_height = 0
for bar in ax.patches:
total_height=total_height+bar.get_height()
# for bar in ax.patches:
# ax.annotate(format((bar.get_height()/total_height )* 100, '.1f')+" %",
# (bar.get_x() + bar.get_width() / 2, bar.get_height()/2),
# ha='center', va='center',
# size=10, xytext=(0, 8),
# textcoords='offset points')
for bar in ax.patches:
ax.annotate(format((bar.get_height()), '.2f'),
(bar.get_x() + bar.get_width() / 2, bar.get_height()),
ha='center', va='center',
size=10, xytext=(0, 8),
textcoords='offset points')
return axgrouped_rating_median = df.loc[:,['genre', 'rating']].groupby(['genre']).median().sort_values(by='rating')
grouped_rating_median = pd.DataFrame(grouped_rating_median)
grouped_rating_median = grouped_rating_median.reset_index()
grouped_rating_median
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| genre | rating | |
|---|---|---|
| 0 | horror | 2.8 |
| 1 | thriller | 2.9 |
| 2 | science fiction | 2.9 |
| 3 | action | 3.0 |
| 4 | adventure | 3.0 |
| 5 | tv movie | 3.0 |
| 6 | romance | 3.1 |
| 7 | mystery | 3.1 |
| 8 | western | 3.1 |
| 9 | family | 3.1 |
| 10 | crime | 3.1 |
| 11 | comedy | 3.1 |
| 12 | fantasy | 3.1 |
| 13 | war | 3.2 |
| 14 | drama | 3.2 |
| 15 | animation | 3.3 |
| 16 | history | 3.3 |
| 17 | music | 3.4 |
| 18 | documentary | 3.4 |
grouped_rating_mean = df.loc[:,['genre', 'rating']].groupby(['genre']).mean().sort_values(by='rating')
grouped_rating_mean = pd.DataFrame(grouped_rating_mean)
grouped_rating_mean = grouped_rating_mean.reset_index()
grouped_rating_mean
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| genre | rating | |
|---|---|---|
| 0 | horror | 2.800131 |
| 1 | science fiction | 2.889947 |
| 2 | thriller | 2.918565 |
| 3 | action | 2.938684 |
| 4 | adventure | 2.986467 |
| 5 | family | 2.991188 |
| 6 | tv movie | 3.015921 |
| 7 | fantasy | 3.041466 |
| 8 | mystery | 3.053564 |
| 9 | comedy | 3.057474 |
| 10 | romance | 3.078985 |
| 11 | crime | 3.104104 |
| 12 | western | 3.125157 |
| 13 | drama | 3.192025 |
| 14 | war | 3.228829 |
| 15 | animation | 3.239053 |
| 16 | history | 3.295238 |
| 17 | music | 3.354450 |
| 18 | documentary | 3.410717 |
fig = plt.figure(figsize=(20,8), dpi=600)
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
sns.despine()
fig1=sns.barplot(x="genre",y="rating",data=grouped_rating_median, order=grouped_rating_median.genre, ax=ax1)
annotate_graph_both(fig1)
fig1.set_ylabel("median rating")
sns.despine()
fig2=sns.barplot(x="genre",y="rating",data=grouped_rating_mean, order=grouped_rating_mean.genre, ax=ax2)
annotate_graph_both(fig2)
fig2.set_ylabel("avg. rating")
plt.show()
COMEDY IS HEAVILY AFFECTED BY OUTLIERS
fig = plt.figure(figsize=(20,6))
sns.boxplot(x = "genre", y="rating" ,data=df, order=grouped.index)
plt.show()
fig = plt.figure(figsize=(20,6))
sns.violinplot(x = "genre", y="rating" ,data=df, order=grouped_rating_median.index)
plt.show()
#--------------------------------------------- DURATOIN -------------------------------------------------#grouped_duration_median = df.loc[:,['genre', 'duration']].groupby(['genre']).median().sort_values(by='duration')
grouped_duration_median = pd.DataFrame(grouped_duration_median)
grouped_duration_median = grouped_duration_median.reset_index()
grouped_duration_median
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| genre | duration | |
|---|---|---|
| 0 | animation | 11 |
| 1 | documentary | 78 |
| 2 | horror | 88 |
| 3 | tv movie | 89 |
| 4 | family | 89 |
| 5 | fantasy | 89 |
| 6 | science fiction | 89 |
| 7 | comedy | 91 |
| 8 | music | 92 |
| 9 | western | 93 |
| 10 | mystery | 93 |
| 11 | adventure | 94 |
| 12 | crime | 95 |
| 13 | thriller | 95 |
| 14 | action | 96 |
| 15 | drama | 97 |
| 16 | romance | 99 |
| 17 | war | 102 |
| 18 | history | 106 |
grouped_duration_mean = df.loc[:,['genre', 'duration']].groupby(['genre']).mean().sort_values(by='duration')
grouped_duration_mean = pd.DataFrame(grouped_duration_mean)
grouped_duration_mean = grouped_duration_mean.reset_index()
grouped_duration_mean
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| genre | duration | |
|---|---|---|
| 0 | animation | 49.370629 |
| 1 | documentary | 76.599204 |
| 2 | fantasy | 77.324976 |
| 3 | horror | 79.642826 |
| 4 | family | 80.514555 |
| 5 | science fiction | 81.720459 |
| 6 | comedy | 85.384798 |
| 7 | tv movie | 88.597368 |
| 8 | western | 89.503145 |
| 9 | music | 90.194379 |
| 10 | adventure | 94.015736 |
| 11 | thriller | 96.223235 |
| 12 | mystery | 99.488470 |
| 13 | romance | 99.847783 |
| 14 | action | 100.359537 |
| 15 | drama | 101.025998 |
| 16 | crime | 101.373134 |
| 17 | war | 103.414414 |
| 18 | history | 112.403880 |
fig = plt.figure(figsize=(20,8), dpi=600)
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
sns.despine()
fig1=sns.barplot(x="genre",y="duration",data=grouped_duration_median, order=grouped_duration_median.genre, ax=ax1)
annotate_graph_both(fig1)
fig1.set_ylabel("median duration")
sns.despine()
fig2=sns.barplot(x="genre",y="duration",data=grouped_duration_mean, order=grouped_duration_mean.genre, ax=ax2)
annotate_graph_both(fig2)
fig2.set_ylabel("avg. duration")
plt.show()
fig = plt.figure(figsize=(20,6))
sns.boxplot(x = "genre", y="duration" ,data=df.loc[~(df.duration>300)], order=grouped_rating_median.genre)
plt.show()
fig = plt.figure(figsize=(20,6))
sns.violinplot(x = "genre", y="duration" ,data=df.loc[~(df.duration>300)], order=grouped_rating_median.genre)
plt.show()
df.columnsIndex(['title', 'year', 'duration', 'rating', 'language', 'viewers', 'genre'], dtype='object')
fig = plt.figure(figsize=(20,6), dpi=300)
# axis1 = fig.add_subplot(1,2,1)
# axis2 = fig.add_subplot(1,2,2)
sns.scatterplot(hue = "genre", y="rating",x="year", size="duration",data=df, sizes=(10,250))
# sns.boxplot(x = "duration", data=df, ax=axis2)
plt.show()
#----------------------------------------------------------------------------------------------------------------#grouped_viewers_mean = df.loc[:,['genre', 'viewers']].groupby(['genre']).mean().sort_values(by='viewers')
grouped_viewers_mean = pd.DataFrame(grouped_viewers_mean)
grouped_viewers_mean = grouped_viewers_mean.reset_index()
grouped_viewers_mean
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| genre | viewers | |
|---|---|---|
| 0 | documentary | 378.595886 |
| 1 | tv movie | 652.826316 |
| 2 | western | 1712.000000 |
| 3 | music | 2240.846604 |
| 4 | horror | 2624.518535 |
| 5 | comedy | 2695.007110 |
| 6 | drama | 3087.450755 |
| 7 | war | 3091.150901 |
| 8 | animation | 3097.246907 |
| 9 | romance | 3221.518977 |
| 10 | crime | 3540.656716 |
| 11 | history | 3729.504409 |
| 12 | thriller | 4492.473424 |
| 13 | family | 5471.936271 |
| 14 | mystery | 5710.663522 |
| 15 | action | 6085.403806 |
| 16 | fantasy | 7251.688525 |
| 17 | science fiction | 8458.423280 |
| 18 | adventure | 10093.222659 |
grouped_viewers_median = df.loc[:,['genre', 'viewers']].groupby(['genre']).median().sort_values(by='viewers')
grouped_viewers_median = pd.DataFrame(grouped_viewers_median)
grouped_viewers_median = grouped_viewers_median.reset_index()
grouped_viewers_median
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| genre | viewers | |
|---|---|---|
| 0 | documentary | 80.5 |
| 1 | music | 99.0 |
| 2 | tv movie | 101.0 |
| 3 | animation | 109.0 |
| 4 | western | 125.5 |
| 5 | drama | 127.0 |
| 6 | comedy | 129.0 |
| 7 | war | 142.0 |
| 8 | romance | 143.0 |
| 9 | history | 145.0 |
| 10 | horror | 146.0 |
| 11 | family | 153.0 |
| 12 | mystery | 157.5 |
| 13 | action | 165.0 |
| 14 | thriller | 166.5 |
| 15 | science fiction | 171.0 |
| 16 | crime | 171.5 |
| 17 | adventure | 179.0 |
| 18 | fantasy | 191.0 |
fig = plt.figure(figsize=(20,8), dpi=600)
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
sns.despine()
fig1=sns.barplot(x="genre",y="viewers",data=grouped_viewers_median, order=grouped_viewers_median.genre, ax=ax1)
annotate_graph_both(fig1)
fig1.set_ylabel("median viewers")
sns.despine()
fig2=sns.barplot(x="genre",y="viewers",data=grouped_viewers_mean, order=grouped_viewers_mean.genre, ax=ax2)
annotate_graph_both(fig2)
fig2.set_ylabel("avg. viewers")
plt.show()
Documentries are highest rated but least viewed. Appraently, "documentries" enojoy an eclectic group who more often than not like what they see and rate it very highly.