Exploratory data analysis in Python with IPL match dataset:

Importing libraries:

Importing libraries in python

 
Importing dataset:

Importing dataset

 
Viewing first five rows of dataset using df.head( ):

Viewing first five rows

 
Number of rows and colums in the data set using df.shpe:

Number of rows and columns

 
Overall information of the dataset using df.info:

overall information about the dataset

 
Names of the columns using df.columns:

Name of the columns

 
Finding out NaN values using df.isna().any():

Finding NaN values

Statistically describing the dataset using df.describe:

Statistically describing the dataset

 
Counting the number of matches using df[‘id’].count():

Checking the unique values in the season column using df.[‘season’].unique( ):

 
Particular column and value can be filtered using loc function:

 
To locate a cell or to slice the dataset iloc is used:

 
Team which won by maximum wickets is located by iloc:

 
Team which won by maximum runs is located by iloc:

 
Team which won by minimum wickets is located by iloc:
 

To find the year in which maximum number of matches are played using countplot:

 
To find which team has won maximum number of matches:

 
To find the probability of winning:

 
To find number of times the respective teams have won:

 
To find the number of times the players have won man of the match:
 

To find the cities and the number of matches played: