Data Analysis Report

Prepared By: Zahiruddin Zahidanishah

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Netflix, Hulu, Prime Video and Disney+

A collection of movies found on these streaming platforms

Content

The dataset is an amalgamation of:

1. data that was scraped, which comprised a comprehensive list of movies available on various streaming platforms
2. IMDb dataset
</ol>


Data visualization of the above can be found on:

https://public.tableau.com/profile/ruchi.bhatia#!/vizhome/Moviesavailableonstreamingplatforms/Moviesavailableonstreamingapplications

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
import seaborn as sns
from sklearn.preprocessing import StandardScaler, normalize
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from jupyterthemes import jtplot
jtplot.style(theme='monokai', context='notebook', ticks=True, grid=False) 

Dataset

The dataset was download from the Kaggle dataset website; https://www.kaggle.com/ruchi798/movies-on-netflix-prime-video-hulu-and-disney.

data = pd.read_csv('MoviesOnStreamingPlatforms_updated.csv')

Total Movies Based on Year Released

data1 = data.groupby('Year').sum()
data1.reset_index(level=0, inplace=True)

fig = make_subplots(rows=4, cols=1,
                    shared_xaxes=True,
                    vertical_spacing=0.05)

fig.add_trace(go.Scatter(x=data1['Year'], y=data1['Netflix'],name='Netflix'),
              row=1, col=1)

fig.add_trace(go.Scatter(x=data1['Year'], y=data1['Hulu'],name='Hulu'),
              row=2, col=1)

fig.add_trace(go.Scatter(x=data1['Year'], y=data1['Prime Video'],name='Prime Video'),
              row=3, col=1)

fig.add_trace(go.Scatter(x=data1['Year'], y=data1['Disney+'],name='Disney+'),
              row=4, col=1)

fig.update_layout(height=600, width=600,
                  title_text="Summary of Movies Produced Based on Years Released")
fig.show()

Total Movies Runtime

bar_plots = [
    go.Bar(x = data1['Year'], y = data1['Runtime'],name='Runtime')
           ]
layout = go.Layout(
title=go.layout.Title(text='Total Runtime Annually',x=0.5),
yaxis_title='Total Runtime (hrs.)',xaxis_tickmode='array')
fig = go.Figure(data=bar_plots, layout=layout)
fig.show()

Based on the above graphs shows that movies released in year 2017 recorded the highest total runtime ie 125.822k min.

IMDB Ratings

data2 = data.groupby('IMDb').sum()
data2.reset_index(level=0, inplace=True)

fig = make_subplots(rows=4, cols=1,
                    shared_xaxes=True,
                    vertical_spacing=0.05)

fig.add_trace(go.Scatter(x=data2['IMDb'], y=data2['Netflix'], name="Netflix"),
              row=1, col=1)

fig.add_trace(go.Scatter(x=data2['IMDb'], y=data2['Hulu'], name="Hulu"),
              row=2, col=1)

fig.add_trace(go.Scatter(x=data2['IMDb'], y=data2['Prime Video'], name="Prime Video"),
              row=3, col=1)

fig.add_trace(go.Scatter(x=data2['IMDb'], y=data2['Disney+'], name="Disney+"),
              row=4, col=1)

fig.update_layout(height=600, width=600,
                  title_text="Summary of IMDb Ratings")
fig.show()

Based on the above graphs shows that in general all movies streaming in Netflix, Hulu, Prime Video and Disney+ have IMDB ratings between 4 to 8.

Rottens Tomatoes Ratings

data3 = data.groupby('Rotten Tomatoes').sum()
data3.reset_index(level=0, inplace=True)
data3['Rotten Tomatoes'] = data3['Rotten Tomatoes'].str.replace("%","").astype(int)
data3['Rotten Tomatoes'] = data3['Rotten Tomatoes']/1
data3 = data3.sort_values(by='Rotten Tomatoes', ascending=False)

fig = make_subplots(rows=4, cols=1,
                    shared_xaxes=True,
                    vertical_spacing=0.05)

fig.add_trace(go.Scatter(x=data3['Rotten Tomatoes'], y=data3['Netflix'], name="Netflix"),
              row=1, col=1)

fig.add_trace(go.Scatter(x=data3['Rotten Tomatoes'], y=data3['Hulu'], name="Hulu"),
              row=2, col=1)

fig.add_trace(go.Scatter(x=data3['Rotten Tomatoes'], y=data3['Prime Video'], name="Prime Video"),
              row=3, col=1)

fig.add_trace(go.Scatter(x=data3['Rotten Tomatoes'], y=data3['Disney+'], name="Disney+"),
              row=4, col=1)

fig.update_layout(height=600, width=600,
                  title_text="Summary of Rotten Tomatoes Ratings")
fig.show()

Movies Runtime

data4 = data.groupby('Runtime').sum()
data4.reset_index(level=0, inplace=True)

fig = make_subplots(rows=4, cols=1,
                    shared_xaxes=True,
                    vertical_spacing=0.05)

fig.add_trace(go.Scatter(x=data4['Runtime'], y=data4['Netflix'], name="Netflix"),
              row=1, col=1)

fig.add_trace(go.Scatter(x=data4['Runtime'], y=data4['Hulu'], name="Hulu"),
              row=2, col=1)

fig.add_trace(go.Scatter(x=data4['Runtime'], y=data4['Prime Video'], name="Prime Video"),
              row=3, col=1)

fig.add_trace(go.Scatter(x=data4['Runtime'], y=data4['Disney+'], name="Disney+"),
              row=4, col=1)

fig.update_layout(height=600, width=600,
                  title_text="Movies Runtime Summary")
fig.show()

Based on the above graphs shows that Netflix, Hulu, Prime Video and Disney+ have most movies with 90 min. runtime.

Movies Age Ratings

data5 = data.groupby('Age').sum()
data5.reset_index(level=0, inplace=True)

fig = make_subplots(rows=4, cols=1,
                    shared_xaxes=True,
                    vertical_spacing=0.05)

fig.add_trace(go.Scatter(x=data5['Age'], y=data5['Netflix'], name="Netflix"),
              row=1, col=1)

fig.add_trace(go.Scatter(x=data5['Age'], y=data5['Hulu'], name="Hulu"),
              row=2, col=1)

fig.add_trace(go.Scatter(x=data5['Age'], y=data5['Prime Video'], name="Prime Video"),
              row=3, col=1)

fig.add_trace(go.Scatter(x=data5['Age'], y=data5['Disney+'], name="Disney+"),
              row=4, col=1)

fig.update_layout(height=600, width=600,
                  title_text="Movies Age Ratings Summary")
fig.show()