Top IMDB Shows Analysis(EDA)

This is an Exploratory Data Analysis of the top IMDB TV shows

import numpy as np import pandas as pd import os for dirname, _, filenames in os.walk('/work/imdb_tvshows.csv'): for filename in filenames: print(os.path.join(dirname, filename))

Reading the data after importing the necessary libraries

df = pd.read_csv("/work/imdb_tvshows.csv")

Data info

df.head(4)

Titleobject

Aboutobject

The Family Man

A working man from the National Investigation Agency tries to protect the nation from terrorism, but he also needs to keep his family safe from his secret job.

Lucifer

Lucifer Morningstar has decided he's had enough of being the dutiful servant in Hell and decides to spend some time on Earth to better understand humanity. He settles in Los Angeles - the City of Angels.

The Handmaid's Tale

Set in a dystopian future, a woman is forced to live as a concubine under a fundamentalist theocratic dictatorship.

StartUp

A desperate banker, a Haitian-American gang lord and a Cuban-American hacker are forced to work together to unwittingly create their version of the American dream - organized crime 2.0.

df.tail(4)

Titleobject

Aboutobject

2996

Ryan Hansen Solves Crimes on Television

A comedy centered around six 30-something friends who each think the other has it better.

2997

Doubt

Romantic anthology web series revolving around 12 stories of 12 months of young people on their paths to love.

2998

Friends with Better Lives

nan

2999

Love Daily

nan

This returns a random sample of rows/columns

df.sample(4)

Titleobject

Aboutobject

2408

Chacha Vidhayak Hain Humare

Through a combination of God-given talent and Boston grit, Cam Calloway and Reggie Vaughn have achieved fame and fortune that neither could have imagined growing up in one of the toughest ...

2651

Dark Tourist

A pair of newlyweds move in next door to a veteran married couple of 25 years.

2231

Holey Moley

A programmer's mysterious disappearance leads to the reunion of old friends and the discovery that the strange stories she left behind may hold clues to an impending technological crisis.

2707

Trolls: The Beat Goes On!

Adventures of an anti-terrorism special forces team.

df.rename(columns = {'EpisodeDuration(in Minutes)':'runtime'}, inplace = True)

df.shape

df.isna().sum()

print([col for col in df.columns if df[col].isna().sum()>0])

['About', 'runtime', 'Actors']

The features above have missing values, imputation can be done but we aren't building a model. Hence imputing those features would result in misleading insights.

#finding Dulpicates df.duplicated().sum()

df.dropna(inplace=True)

df.describe()

runtimefloat64

Ratingfloat64

count

2831.0

mean

47.257859413634755

7.3305898975626995

std

44.240044547498826

1.0015872411933102

min

1.0

1.6

25%

29.0

6.8

50%

43.0

7.5

75%

60.0

8.0

max

853.0

9.7

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2831 entries, 0 to 2969
Data columns (total 8 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   Title    2831 non-null   object 
 1   About    2831 non-null   object 
 2   runtime  2831 non-null   float64
 3   Genres   2831 non-null   object 
 4   Actors   2831 non-null   object 
 5   Rating   2831 non-null   float64
 6   Votes    2831 non-null   int64  
 7   Years    2831 non-null   object 
dtypes: float64(2), int64(1), object(5)
memory usage: 199.1+ KB

Univariate Analysis

import plotly.express as px

df["Title"].value_counts() titles =df["Title"].value_counts().index[:10] title_occurence =df["Title"].value_counts().values[:10] fig = px.bar(x = titles,y = title_occurence,labels={"x":"Show Title","y":"Repeated"},title="Shows With Same Titles",template="ggplot2",color_discrete_sequence =['green']*len(df)) #Top 10 Movies fig.show()

run_t = df["runtime"].value_counts().index rt_count =df["runtime"].value_counts().values fi =px.bar(y=run_t,x=rt_count,labels={"x":"No of Movies","y":"Runtime"},title="Runtime (Minutes) ") fi.update_layout(yaxis=dict(autorange="reversed"),title_x=0.5)

#Top 6 Genres genre = df["Genres"].value_counts() fig = px.pie(values = genre.values[:6],names=genre.index[:6],title="Top 6 Genres") fig.update_layout(title_x=0.432)

!pip install wordcloud

Collecting wordcloud
  Downloading wordcloud-1.8.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (458 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 458.6/458.6 KB 34.9 MB/s eta 0:00:00
Requirement already satisfied: matplotlib in /shared-libs/python3.9/py/lib/python3.9/site-packages (from wordcloud) (3.6.0)
Requirement already satisfied: pillow in /shared-libs/python3.9/py/lib/python3.9/site-packages (from wordcloud) (9.2.0)
Requirement already satisfied: numpy>=1.6.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from wordcloud) (1.23.4)
Requirement already satisfied: contourpy>=1.0.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->wordcloud) (1.0.5)
Requirement already satisfied: kiwisolver>=1.0.1 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->wordcloud) (1.4.4)
Requirement already satisfied: fonttools>=4.22.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->wordcloud) (4.37.4)
Requirement already satisfied: cycler>=0.10 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from matplotlib->wordcloud) (0.11.0)
Requirement already satisfied: packaging>=20.0 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib->wordcloud) (21.3)
Requirement already satisfied: python-dateutil>=2.7 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib->wordcloud) (2.8.2)
Requirement already satisfied: pyparsing>=2.2.1 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from matplotlib->wordcloud) (3.0.9)
Requirement already satisfied: six>=1.5 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib->wordcloud) (1.16.0)
Installing collected packages: wordcloud
Successfully installed wordcloud-1.8.2.2
WARNING: You are using pip version 22.0.4; however, version 22.3.1 is available.
You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.

#World CLoud import matplotlib.pyplot as plt from wordcloud import WordCloud genre = genre genre.index=genre.index.map(str) wordcloud = WordCloud(background_color="white").generate_from_frequencies(genre) plt.figure(figsize = (12,8),dpi=50) plt.imshow(wordcloud) plt.axis("off") plt.show()

genre

#Top 6 Genres ratings = df["Rating"].value_counts() ratings.index=ratings.index.map(str) wordcloud = WordCloud().generate_from_frequencies(ratings) plt.imshow(wordcloud) plt.axis("off") plt.show()

#Top 15 Shows top_15 = df.sort_values(["Votes","Rating"],ascending=False)[:15] top_15.loc[:,["Title","Years","Genres","Rating","Votes"]]

Titleobject

Game of Thrones6.7%

Breaking Bad6.7%

13 others86.7%

Yearsobject

2011–20196.7%

2008–20136.7%

13 others86.7%

Game of Thrones

2011–2019

Breaking Bad

2008–2013

The Walking Dead

2010–2022

Stranger Things

2016–

122

Sherlock

2010–2017

The Big Bang Theory

2007–2019

Dexter

2006–2021

How I Met Your Mother

2005–2014

True Detective

2014–2019

Lost

2004–2010

#Top Least Rated Shows top_voted = df.sort_values("Votes",ascending=False) top_voted.sort_values("Rating")

Titleobject

Kingdom0.1%

The Office0.1%

2789 others99.8%

Aboutobject

Spin-off of 0.1%

Set in the 1960s, this British medical drama follows the staff and patients of St Aidan's Royal Free Hospital.0%

2828 others99.9%

1667

A Little Late with Lilly Singh

Set in the 1960s, this British medical drama follows the staff and patients of St Aidan's Royal Free Hospital.

837

Metástasis

A Colombian remake of the U.S. TV show Breaking Bad. After he is diagnosed with cancer, Walter Blanco teams up with José Miguel Rosas in order to sell crystal meth.

1338

Ben 10

Ben 10 is the fifth iteration of the Ben 10 franchise. It serves as a reboot of the television series loosely based upon the original series.

2522

La rosa de Guadalupe

Stories about people who get into problems who use the help of Virgin Mary and where a rose appears and their problems are solved.

236

Keeping Up with the Kardashians

A peek inside the exploits and privileged private lives of the blended Kardashian-Jenner family, including sisters Kim, Kourtney, Khloé, Kendall and Kylie.

1286

The Inbetweeners

Four high school friends are not cool or popular, but not total dorks, either.

2287

Small Fortune

Follows a group of best friends living, working and playing in the gritty, diverse New York neighborhood of Washington Heights. Though fiercely proud of where they come from, the show ...

2847

The Only Way Is Essex

Stinky the garbage truck and Dirty the backhoe loader are best friends who have lots of adventures together. They find themselves in some strange situations, and try to find resourceful solutions to problems.

1356

The Bachelor

In the near future, Norway is occupied by Russia on behalf of the European Union, due to the fact that the newly elected environmental friendly Norwegian government has stopped the all important oil- and gas-production in the North Sea.

2679

The Talk

Jack Driscoll moves back to the town on the west coast of Ireland where he was born. He takes over his retired father's Garda post, and solves different cases.

.css-9i38tn{letter-spacing:inherit;-webkit-text-decoration:none;text-decoration:none;}This is an Exploratory Data Analysis of the top IMDB TV shows

Reading the data after importing the necessary libraries

Data info

This returns a random sample of rows/columns

The features above have missing values, imputation can be done but we aren't building a model. Hence imputing those features would result in misleading insights.

Univariate Analysis

This is an Exploratory Data Analysis of the top IMDB TV shows