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:
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:
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