IPL Data Analysis Python Project
IPL Data Analysis Python Project using Pandas. The project has its data in the form of a CSV file. This CSV file contains all the information about the matches played between the participating teams of IPL.
Data Science is all about finding valuable information from the given dataset. Using this data, we tried to find out the following information from IPL DataFrame.
- How many matches have been played in each season?
- Who won the maximum matches?
- Who won the match by maximum runs?
- Match runs by the maximum wickets
- The Most valuable player in all the IPL season?
- Match Won by minimum wickets?
- The most successful team in all the season.
Pandas, time, sqlalchemy, and matplotlib modules are used for various purposes in this Pandas Project. YouTube video has detailed working of the project.
This project has been divided into four major parts
- Read Data from CSV file
- Data Visualization
- Graph Plotting
- Export data from data frames
Source code of IPL data analysis project
# project name : IPL analysis
# made by : rakesh kumar
# email : rakesh@binarynote.com
# session : 2020-21
import pandas as pd
import time
import sqlalchemy
import matplotlib.pyplot as plt
df = pd.DataFrame()
csv_file = "C:/Users/rakesh/Desktop/IP_notes/12IP/Projects/matches.csv"
def introduction():
msg = '''
Cricket is a bat-and-ball game played between two teams of eleven players each on a cricket field,
at the centre of which is a rectangular 20-metre (22-yard) pitch with a target at each end called
the wicket (a set of three wooden stumps upon which two bails sit). Each phase of play is called
an innings, during which one team bats, attempting to score as many runs as possible, whilst their
opponents bowl and field, attempting to minimise the number of runs scored. When each innings ends,
the teams usually swap roles for the next innings (i.e. the team that previously batted will
bowl/field, and vice versa). The teams each bat for one or two innings, depending on the type of
match. The winning team is the one that scores the most runs, including any extras gained
(except when the result is not a win/loss result). Source: https://en.wikipedia.org/wiki/Cricket
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 = '''
IPL Data Analysis 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\nD A T A A N A L Y S I S M E N U ')
print('_'*100,'\n')
print('1. Show Whole DataFrame')
print('2. Show Columns')
print('3. Show Top Rows')
print('4. Show Bottom Rows')
print('5. Show Specific Column')
print('6. Add a New Record')
print('7. Add a New Column')
print('8. Delete a Column')
print('9. Delete a Record')
print('10. Match win By Maximum Runs')
print('11. Match win By Minimum Runs')
print('12. Match win By Maximum Wickets')
print('13. Match win By Minimum Wickets')
print('14. Top 5 Match Winners Team')
print('15. Top 10 man of the macth Player')
print('16. Data Summery')
print('17. Exit (Move to main menu)')
ch = int(input('\n\nEnter your choice:'))
if ch == 1:
print(df)
wait = input('\n\n\n Press any key to continuee.....')
if ch == 2:
print(df.columns)
wait = input('\n\n\n Press any key to continuee.....')
if ch == 3:
n = int(input('Enter Total rows you want to show :'))
print(df.head(n))
wait = input('\n\n\n Press any key to continuee.....')
if ch == 4:
n = int(input('Enter Total rows you want to show :'))
print(df.tail(n))
wait = input('\n\n\n Press any key to continuee.....')
if ch == 5:
print(df.columns)
col_name = input('Enter Column Name that You want to print : ')
print(df[col_name])
wait = input('\n\n\n Press any key to continuee.....')
if ch == 6:
a = input('Enter Match ID :')
b = input('Enter IPL season :')
c = input('Enter City Name :')
d = input('Enter Match date :')
e = input('Enter Team 1 Name :')
f = input('Enter Team 2 Name :')
g = input('Enter Toss Winner :')
h = input('Enter Toss Decision :')
i = input('Enter Result (Normal /tie/ DL ) :')
j = input('Enter Duckwoth Lewis Method applied (0 for No, 1 for Yes ) :) :')
k = input('Enter Winner Team :')
l = input('Enter Win By runs :')
m = input('Enter Win By Wickets :')
n = input('Enter Man of the Match Player :')
data = {'id': a, 'season': b, 'city': c,
'date': d, 'team1': e, 'team2': f, 'toss_winner': g,
'toss_decision':h,'result':i,'dl_applied':j,'winner':k,
'win_by_runs':l,'win_by_wickets':m,'player_of_match':n
}
df = df.append(data, ignore_index=True)
print(df)
wait = input('\n\n\n Press any key to continuee.....')
if ch == 7:
col_name = input('Enter new column name :')
col_value = 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()
if ch == 10:
df1= df.sort_values('win_by_runs',ascending=False).head(1)
print(df1)
wait = input('\n\n\n Press any key to continue....')
if ch == 11:
df1 = df.sort_values('win_by_runs').head(1)
print(df1)
wait = input('\n\n\n Press any key to continue....')
if ch == 12: #macth won by maximum wicket
df1 = df.sort_values('win_by_wickets',ascending=False).head(1)
print(df1)
wait = input('\n\n\n Press any key to continue....')
if ch == 13: # macth won by minimum wicket
df1 = df.sort_values('win_by_wickets').head(1)
print(df1)
wait = input('\n\n\n Press any key to continue....')
if ch == 14: #most macth winner team
df1 = df.groupby('winner').count()
df2=df1.sort_values('result',ascending=False).head(5)
print(df2)
wait = input('\n\n\n Press any key to continue....')
if ch == 15: # most man of the macth player
df1 = df.groupby('player_of_match').count()
df2 = df1.sort_values('result', ascending=False).head(10)
print(df2)
wait = input('\n\n\n Press any key to continue....')
if ch == 16:
print(df.describe())
wait = input('\n\n\n Press any key to continue....')
if ch == 17:
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('\n1. Season wise Matches - Line Graph')
print('\n2. Season wise Matches - Bar Graph')
print('\n3. Season wise Matches - Horizontal Bar Graph')
print('\n4. Most Successful Team - Bar Graph')
print('\n5. Most Successful Player - Bar Graph')
print('\n6. Exit (Move to main menu)\n')
ch = int(input('Enter your choice:'))
if ch == 1:
g = df.groupby('season')
x = df['season'].unique()
y = g['season'].count()
#plt.xticks(rotation='vertical')
plt.xlabel('Season')
plt.ylabel('Matches')
plt.title('Season wise Matches')
plt.grid(True)
plt.plot(x, y) #line graph
plt.show()
if ch == 2:
g = df.groupby('season')
x = df['season'].unique()
y = g['season'].count()
#plt.xticks(rotation='vertical')
plt.xlabel('Season')
plt.ylabel('Matches')
plt.title('Season wise Matches')
plt.bar(x, y) #bar graph
plt.grid(True)
plt.show()
if ch == 3:
g = df.groupby('season')
x = df['season'].unique()
y = g['season'].count()
#plt.xticks(rotation='vertical')
plt.xlabel('Season')
plt.ylabel('Matches')
plt.title('Season wise Matches')
plt.grid(True)
plt.barh(x, y)
plt.show()
if ch == 4:
g = df.groupby('winner')
x = df['winner'].unique()
y = g['winner'].count()
plt.xticks(rotation='vertical')
plt.xlabel('winner')
plt.ylabel('Matches')
plt.title('Most Successful Team')
plt.grid(True)
plt.barh(x, y)
plt.show()
if ch == 5:
g = df.groupby('player_of_match')
x = df['player_of_match'].unique()
y = g['player_of_match'].count()
plt.xticks(rotation='vertical')
plt.xlabel('Player Name')
plt.ylabel('Total Man of the Match')
plt.title('Most successful Player')
plt.grid(True)
plt.barh(x, y)
plt.show()
if ch ==6:
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\nE X P O R T M E N U ')
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/ipl_data_backup.csv')
print('\n\nCheck your new file "ipl_data_backup.csv" on C: Drive.....')
wait = input('\n\n\n Press any key to continuee.....')
if ch == 2:
df.to_excel('c:/backup/ipl_data_backup.xlsx')
print('\n\nCheck your new file "ipl_data_backup.xlsx" on C: Drive.....')
wait = input('\n\n\n Press any key to continuee.....')
if ch == 3:
engine = sqlalchemy.create_engine(
'mysql+pymysql://root:@localhost:3306/davschool')
df.to_sql(name='ipl_data_backup', con=engine,
index=False, if_exists='replace')
print('\n\nPlease check DAVSCHOOL database for ipl_data_backup table.....')
wait = input('\n\n\n Press any key to continuee.....')
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(
'\n\n Press any key to continue....')
if choice == 2:
data_analysis_menu()
wait = input('\n\n Press any key to continue....')
if choice == 3:
graph()
wait = input('\n\n Press any key to continue....')
if choice == 4:
export_menu()
wait = input(
'\n\n Press any key to continue....')
if choice == 5:
break
clear()
made_by()
# call your main menu
main_menu()
IPL_data_analysis – Click me to download
Few output screenshot of the above code are
Season wise IPL Match Summery in the form of horizontal Bar Graph
Most successful Team in All IPL Seasons
How to Run Download IPL Data Analysis Pandas Project
- Unzip the downloaded zip file into a folder.
- This folder contains 2 files a) ipl_data_analysis.py b) ipl_data.csv file
- Make changes in the Python file for CSV file location on your system
- Open Python File in IDLE/IDE and hit the run button to check its output
Final conclusion
Though IPL Data Analysis Python Project is complete for beginners still it has a few limitations that advanced pandas users can overcome very easily. Matplotlib has been used for graph plotting that needs all the data in a refined format. Seaburn python module has built-in capabilities for better graph plotting.
Please feel free to contact me for any queries.
