Pandas Project for Class 12 Students – Movies Analysis
Pandas project for class 12 students– Movies analysis. Movie Analysis Python Pandas project is based on the data collected by the popular movie review website TMDB.com. Data file in CSV format downloaded from kaggle.com and analyzed.
Movies are integral part of our life and it is very hard to find out good movies from the world cinema. The data collected in our CSV file is collected through the popular API avaialable in TMDB.com and the rating of these movies are based on the rating given by its viewers.
Movies analysis Python Pandas project is going to solve this simple problem of the viewers. Based on different criteria, we tried to analyze this data in Python Pandas
Python Modules used in Python Pandas Project
- Python Pandas 1.0.5 – for different data analysis work
- sqlalchemy – for exporting data from data frame to SQL table
- matplotlib – for Data visualization using Python
- time – for introductory and end animation using its sleep( ) function
This Python Pandas project is divided into four major parts.
- Data Read part
- Data Analysis
- Graphical Representation
- Backup of Data
Source code
# project name : Popular movies of IMDB.com
# made by : rakesh kumar
# roll no : your roll no
# session : your session
# website : https://cbsetoday.com
import pandas as pd
import numpy as np
import time
import sqlalchemy
import matplotlib.pyplot as plt
df = pd.DataFrame()
csv_file = "C:/Users/rakesh/Desktop/IP_notes/12IP/Projects/imbd_updated.csv"
def introduction():
msg = '''
Movies are integral part of our life. We love to watch movies but it is very hard to find out good
movies from the world cinema.
TMDB.org is a crowd-sourced movie information database used by many film-related consoles,
sites and apps, such as XBMC, MythTV and Plex. Dozens of media managers, mobile apps and
social sites make use of its API.
TMDb lists some 80,000 films at time of writing, which is considerably fewer than IMDb.
While not as complete as IMDb, it holds extensive information for most popular/Hollywood films.
This is dataset of the 10,000 most popular movies across the world has been fetched through the
read API. TMDB's free API provides for developers and their team to programmatically fetch and
use TMDb's data. Their API is to use as long as you attribute TMDb as the source of the data
and/or images. Also, they update their API from time to time.
This data set is fetched using exception handling process so the data set contains some null
values as there are missing fields in the tmdb database. Thought it's good for a young analyst
to deal with messing value.
In this project we are going to analyse the same dataset using Python Pandas on windows machine but the
project can be run on any machine support Python and Pandas. Besides pandas we also used matplotlib python
module for visualization of this dataset.
The whole project is divided into four major parts ie reading, analysis, visualization and export. all these
part are further divided into menus for easy navigation
NOTE: Python is case-SENSITIVE so type exact Column Name wherever required.
If you have any query or suggestions please contact me at rakesh@binarynote.com \n\n\n\n'''
for x in msg:
print(x, end='')
time.sleep(0.002)
wait = input('Press any key to continue.....')
def made_by():
msg = '''
Movie Recommendation made by : xyx
Roll No : 1234
School Name : Your school name
session : 2020-21
Thanks for evaluating my Project.
\n\n\n
'''
for x in msg:
print(x, end='')
time.sleep(0.002)
wait = input('Press any key to continue.....')
def read_csv_file():
df = pd.read_csv(csv_file)
print(df)
# name of function : clear
# purpose : clear output screen
def clear():
for x in range(65):
print()
def data_analysis_menu():
df = pd.read_csv(csv_file)
while True:
clear()
print('\n\nData Analysis MENU ')
print('_'*100)
print('1. Show Whole DataFrame')
print('2. Show Columns')
print('3. Show Top Rows')
print('4. Row Bottom Rows')
print('5. Show Specific Column\n')
print('6. Add a New Record\n')
print('7. Add a New Column\n')
print('8. Delete a Column\n')
print('9. Delete a Record\n')
print('10. Update a Record\n')
print('11. Rating Wise Report \n')
print('12. Language wise Report \n')
print('13. Data Summery\n')
print('14. Exit (Move to main menu)\n')
ch = int(input('Enter your choice:'))
if ch == 1:
print(df)
wait = input()
if ch == 2:
print(df.columns)
wait = input()
if ch == 3:
n = int(input('Enter Total rows you want to show :'))
print(df.head(n))
wait = input()
if ch == 4:
n = int(input('Enter Total rows you want to show :'))
print(df.tail(n))
wait = input()
if ch == 5:
print(df.columns)
col_name = input('Enter Column Name that You want to print : ')
print(df[col_name])
wait = input()
if ch==6:
a = input('Enter Index Number :')
b = input('Enter New Movie Name :')
c = input(' Enter Movie Overview :')
d= int(input('Enter Movie Language :'))
e = int(input('Enter Vote Count :'))
f = int(input('Enter Vote Average :'))
data={'index':a,'title':b,'overview':c,'language':d,'vote_count':e,'vote_average':f}
df = df.append(data,ignore_index=True)
print(df)
wait=input()
if ch==7:
col_name = input('Enter new column name :')
col_value = int(input('Enter default column value :'))
df[col_name]=col_value
print(df)
print('\n\n\n Press any key to continue....')
wait=input()
if ch==8:
col_name =input('Enter column Name to delete :')
del df[col_name]
print(df)
print('\n\n\n Press any key to continue....')
wait=input()
if ch==9:
index_no =int(input('Enter the Index Number that You want to delete :'))
df = df.drop(df.index[index_no])
print(df)
print('\n\n\n Press any key to continue....')
wait = input()
#update a record - this is to be cover
if ch == 10:
index_no = int(
input('Enter the Index Number that You want to update :'))
df = df.drop(df.index[index_no])
print(df)
print('\n\n\n Press any key to continue....')
wait = input()
if ch==11:
g = df.sort_values(by=['vote_average','vote_count'],ascending=False)
clear()
print('Top 20 Movies Based on Rating')
print('-'*120)
print(g.head(20))
print('\n\n\n Press any key to continue....')
wait=input()
if ch==12:
df1=df.language.unique()
print('Available Languages :',df1)
print('\n\n')
lang1 =input('Enter Language Type :')
df1=df[df.language==lang1]
clear()
print('Top 20 Movies Based on Rating | Language :',lang1)
print('-'*120)
print(df1.sort_values(by='vote_average', ascending=False).head(20))
print('\n\n\n Press any key to continue....')
wait = input()
if ch==13:
print(df.describe())
print("\n\n\nPress any key to continue....")
wait=input()
if ch == 14:
break
# name of function : graph
# purpose : To generate a Graph menu
def graph():
df = pd.read_csv(csv_file)
while True:
clear()
print('\nGRAPH MENU ')
print('_'*100)
print('1. Whole Data LINE Graph\n')
print('2. Whole Data Bar Graph\n')
print('3. Whole Data Bar Graph- Horizontal\n')
print('4. Whole Data Scatter Graph\n')
print('5. Exit (Move to main menu)\n')
ch = int(input('Enter your choice:'))
if ch == 1:
g = df.groupby('language')
x = df['language'].unique()
y = g['language'].count()
plt.xticks(rotation='vertical')
plt.xlabel('Language')
plt.ylabel('Total Movies')
plt.title('Language wise movies count')
plt.grid(True)
plt.plot(x, y)
plt.show()
if ch == 2:
g = df.groupby('language')
x = df['language'].unique()
y = g['language'].count()
#plt.xticks(rotation='vertical')
plt.xlabel('Language')
plt.ylabel('Total Movies')
plt.title('Language wise movies count')
plt.bar(x, y)
plt.grid(True)
plt.show()
if ch == 3:
g = df.groupby('language')
x = df['language'].unique()
y = g['language'].count()
#plt.xticks(rotation='vertical')
plt.xlabel('Language')
plt.ylabel('Total Movies')
plt.title('Language wise movies count')
plt.barh(x, y)
plt.grid(True)
plt.show()
if ch == 4:
g = df.groupby('language')
x = df['language'].unique()
y = g['language'].count()
#plt.xticks(rotation='vertical')
plt.xlabel('Language')
plt.ylabel('Total Movies')
plt.title('Language wise movies count')
plt.grid(True)
plt.scatter(x, y)
plt.show()
wait = input()
if ch == 5:
break
# function name : export_menu
# purpose : function to generate export menu
def export_menu():
df = pd.read_csv(csv_file)
while True:
clear()
print('\n\nEXPORT MENU ')
print('_'*100)
print()
print('1. CSV File\n')
print('2. Excel File\n')
print('3. MySQL Table\n')
print('4. Exit (Move to main menu)')
ch = int(input('Enter your Choice : '))
if ch == 1:
df.to_csv('c:/backup/newMovies.csv')
print('\n\nCheck your new file "newMovies.csv" on C: Drive.....')
wait = input()
if ch == 2:
df.to_excel('c:/backup/newMovies.xlsx')
print('\n\nCheck your new file "newMovies.xlsx" on C: Drive.....')
wait = input()
if ch == 3:
engine = sqlalchemy.create_engine(
'mysql+pymysql://root:@localhost:3306/davschool')
df.to_sql(name='movies', con=engine,
index=False, if_exists='replace')
print('\n\nPlease check DAVSCHOOL database for movies table.....')
wait = input()
if ch == 4:
break
def main_menu():
clear()
introduction()
while True:
clear()
print('MAIN MENU ')
print('_'*100)
print()
print('1. Read CSV File\n')
print('2. Data Analysis Menu\n')
print('3. Graph Menu\n')
print('4. Export Data\n')
print('5. Exit\n')
choice = int(input('Enter your choice :'))
if choice == 1:
read_csv_file()
wait = input()
if choice == 2:
data_analysis_menu()
wait = input()
if choice == 3:
graph()
wait = input()
if choice == 4:
export_menu()
wait = input()
if choice == 5:
break
clear()
made_by()
# call your main menu
main_menu()
Download Source Code
How to Run Movies Analysis Project
- Unzip the downloaded file into a folder. This folder contains the python project file and its CSV file
- set the Path of CSV file in your Python project
- Install the required Python Modules using the inbuilt pip command
- Run Python Project
Output produced by Python Pandas project for class 12 student – Movies analysis
To understand the working of this project you are required to check this youtube video.
Conclusion
Through the Pandas project for class 12 students, we tried to analyze the very basic information available in this dataset. You can extend the working of the project as per your requirement. There is nothing encrypted in this project. If you have any queries, please feel free to contact us.
