Data Analysis with Python: Test_Cricket_Analysis

Features of the Dataset:

• Player: Name of the player,
• Span: Duration of Test Career,
• Mat: No. of matches played,
• Inns: No. of innings played,
• Balls: Total no. of balls bowled,
• Runs: No. of runs conceded,
• Wkts: No. of wickets taken altogether,
• BBI: Best balling figure in an innings,
• BBM: Best balling figure in a match (2 innings),
• Ave: Average meaning average no. of runs conceded per wicket,
• Econ: Economy Rate, Econ= (Total runs conceded)/(Total over bowled),
• SR: Strike Rate, SR means the average no. of balls needed to bowl per wicket,
• 5: Shows the no. of 5-wicket wholes in an innings,
• 10: Shows the number of times this bowler has taken ten wickets in a match.
  
  

Objectives:

Data Analysis:

• Using Python's different bulit-in libraries such as pandas, numpy etc.,
• Read different types of files with Pandas Dataframe. (.csv file, .xlsx file, etc.)

Data Manipulation:

• Creating and naming the new data frame in Pandas,
• Find the number of rows and columns in the dataframe,
• Find the data statistics of the dataset,
• Find the data types and missing values,
• Rename the column names,
• Remove unnecessary columns.
• Analyzing the data and find answers of different kinds of questions that arises on mind while dealing with the dataset.
• Arrange the dataset in a custom column order and making it more understandable and so on.
  
  

DataSet Reference: https://stats.espncricinfo.com/ci/content/records/93276.html