Exploratory Data Analysis (EDA) is a crucial step in the data analysis process where you visually and statistically summarize, explore, and interpret the main characteristics, patterns, and trends in a dataset. Let's break down the steps involved in performing EDA on the Indian Premier League (IPL) dataset:
Importing Libraries: You start by importing libraries like pandas, numpy, matplotlib, and seaborn to handle data manipulation, numerical computations, and visualization.
Loading the Dataset: Load the IPL dataset into a Pandas DataFrame using the pd.read_csv() function.
Basic Exploration: This step involves understanding the basic structure of the dataset. You can use functions like df.head() to display the first few rows, df.describe() to get summary statistics of numerical columns, and df.info() to get information about column data types and missing values.
Data Cleaning: Before diving into analysis, it's important to clean the data. This step includes identifying and handling missing values (df.isnull().sum()), and removing duplicate rows (df.drop_duplicates()).
Exploratory Analysis: In this phase, you generate visualizations to gain insights into the data. For example, you can create a bar plot using sns.countplot() to show the number of matches played in each IPL season. This gives an overview of the tournament's growth over the years.
Correlation Analysis: You can use a correlation heatmap to visualize the relationships between numerical variables. This helps in understanding how variables are related and whether there are any strong correlations.
Feature Engineering: Sometimes, creating new features or transforming existing ones can provide more insights. For instance, calculating the run rate (total_runs divided by overs) gives an idea about a team's scoring rate during a match.
Advanced Visualizations: More complex visualizations like line plots or scatter plots can be used to explore patterns over time or relationships between variables in more detail. For example, a line plot could show how the total runs scored by different teams have changed over different seasons