Exploratory Data Analysis

Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.

  • Import Libraries
  • Load & Read Data
  • Examine Data
  • Descriptive Statistical Analysis
  • Analyze Data
What is Exploratory Data Analysis?

Exploratory Data Analysis (EDA) is like being a detective for data. Imagine you have a giant puzzle made of numbers and information. EDA is how you look at all those pieces to find exciting things and understand what’s happening.

EDA is a unique tool in data science, which is all about making sense of large datasets. Data analysts use EDA to discover cool stuff hidden in the data. It’s like how explorers search for hidden treasures, but instead of jungles, they’re exploring data jungles!

Think of EDA as a magnifying glass for numbers. Using EDA, you can spot trends and patterns that happen repeatedly. It’s like noticing that you do better in maths when it’s sunny outside—that’s a trend! EDA also helps find strange things in the data, like numbers that don’t fit in, just like a puzzle piece that doesn’t belong.

Businesses also use EDA to make intelligent decisions. Imagine you have a lemonade stand and want to know the best time to sell lemonade. EDA could help determine if more people buy lemonade on sunny or rainy days. Then you’d know when to deal the most!

Types of Exploratory Data Analysis


Consider that you are attempting to comprehend the evolution of a single object while examining it in detail. Think about how many new goods a firm develops each month over a year. We’re only focusing on this aspect – how many products they make – and we’re not trying to determine if one thing is directly causing another.
So, we collect all this information about the products they create monthly for twelve months. In this type of analysis, we’re not investigating reasons or connections but simply watching and recording how one thing changes.


Bivariate analysis is like connecting puzzle pieces to understand how two things, like an employee’s age and their financial situation (how much they earn and spend), might affect something important about their job, like how happy they are or how well they’re doing. It’s about seeing if these two factors fit together and tell us a story about the employee’s work situation. It helps us determine if age and money have anything to do with how things are going at work for the employee.


Multivariate analysis involves studying more than two variables at once, like comparing the type of product and quantity sold with factors such as product price, advertising costs, and discounts. These variables can be numbers or categories. The analysis results can be shown in numbers, charts, or graphs, divided into non-graphical (like tables) and graphical (like plots) representations.

Steps Involved in Exploratory Data Analysis

The critical components of an EDA are the primary processes taken to complete the EDA. These are the following:

Data Gathering

We produce a lot of information on everything we do in today’s society, including maintaining our health, participating in sports, creating things, and traveling. Companies are aware that it’s critical to interpret this data correctly. However, they must first gather it from various sources, including asking inquiries, scouring social media, utilizing specialized tools, and getting feedback from others. They can use it for other things if they can access enough accurate information.

Importing Essential Libraries and Loading Data
To begin exploring your data, you’ll need some tools. Think of these as your data investigation kit. You’ll utilize R’s ggplot, Leaflet, and lattice or Python’s Matplotlib, Pandas, and Seaborn. You can better see and comprehend your data with the aid of these libraries. It’s time to load your data after your tools are prepared. It’s like finding a treasure box full of data when you do this. It’s easier than it seems, and you do it with code. Consider it like opening a book to read.
Comprehensive Data Examination

Now that you’ve opened the treasure box, it’s time to look into its contents. Consider it as if numbers and words were used in place of colors while viewing an image. You should first determine what each of those numbers means. You’ll learn how many items were sold when they were sold and perhaps even who purchased them. It’s similar to labeling every component of the picture.

Identifying and Comprehending All Variables

Think of your data like a recipe with many ingredients. Each ingredient is variable and comes together to make the final dish. To comprehend the recipe, you must first grasp what each component performs. The same may be said about your data. You’ll learn what each variable signifies, whether it’s a person’s age, the price of a product, or the location where something was purchased. This assists you in comprehending the tale that your data is attempting to portray.

Dataset Preparation

Before plunging in, it’s a good idea to clean up a little. Assume you’re preparing for a picnic. You’d clean up the space, dispose of junk, and ensure everything is positioned correctly. Tidying up your data is like that. You’ll remove empty spots, fix mistakes, and organize everything neatly. This makes working more accessible and helps you focus on the important stuff.

Descriptive Statistical Analysis and Determining Related Variables

It’s now time to crunch some data. You will use numbers to describe your data, like counting your friends. You’ll learn about the average, highest, and lowest values. This gives you a summary of your data’s story. You’ll also examine how certain factors are connected. It’s similar to determining whether your favorite cuisine is more prevalent when it’s chilly outside.

In-Depth Data Analysis and Selecting the Best Statistical Methods

Let us now delve further. It’s like going on a treasure hunt in a maze. You’ll use different methods to uncover secrets in your data. Use charts, graphs, or fancy maths formulas depending on the data type and what you want. It’s like using special tools to unlock hidden doors in a maze.

Results from Visualization and Analysis

After your detective work, you’ll have a bunch of findings. These are like puzzle pieces that fit together to create a picture. You might notice patterns or trends, like people buying more on sunny days. These findings help you understand what’s happening and why. It’s like solving a puzzle and revealing a beautiful picture at the end. These results are valuable for making intelligent choices.

Exploratory Data Analysis Tools


People explore data using Python for tasks like spotting missing values and summarising datasets. Python’s simple syntax works with helpful libraries like Matplotlib and Seaborn for data visualization and analysis. Some tools, like D-Tale, and Pandas Profiling, automate the process, making it easier for everyone to uncover insights from data.


The R programming language is famous among data scientists and statisticians. It helps in doing extensive EDA by making statistical observations and analysing data. R is an open-source programming language for statistical computing and graphics. Aside from the regularly used libraries like gplot and Lattice, there are several sophisticated R libraries for automated EDA, such as Data Explorer, SmartEDA, GGally, etc.


MATLAB is a well-known industrial product amongst engineers. Using MATLAB for exploratory information evaluation (EDA) therefore needs a basic familiarity of the MATLAB computer language.

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