# 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:

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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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