How do you do an EDA analysis?
How to Perform EDA

  1. Import Libraries and Load Dataset. Python Code:
  2. Check for Missing Values. auto.isnull().sum()
  3. Visualizing the Missing Values. With the help of heatmap, we can see the amount of data that is missing from the attribute.
  4. Asking Analytical Questions and Visualizations. This is the most important step in EDA.

An initial step in Exploratory Data Analysis (EDA) is to examine how the values of different variables are distributed. Graphical approaches for examining the distribution of the data include histograms, boxplots, cumulative distribution functions, and quantile-quantile (Q-Q) plots.Many people start exploratory analysis using interactive visualization techniques, such as scatterplot matrices, 2-variable graphing, box and whisker plots, bubble charts etc. Often it can be easier to see relations this way, rather than jumping into the statistics.

What are the techniques used in EDA : EDA techniques may include calculating summary statistics, visualizing data distributions, identifying outliers, exploring relationships between variables, and performing hypothesis testing.

What are the 4 types of exploratory data analysis

The four types of EDA are univariate non-graphical, multivariate non- graphical, univariate graphical, and multivariate graphical.

How to do EDA using Excel : Here's a step-by-step incremental approach for conducting EDA in Excel:

  1. Step 1: Importing Data.
  2. Step 2: Data Cleaning and Preprocessing.
  3. Step 3: Descriptive Statistics.
  4. Step 4: Data Visualization.
  5. Step 5: Data Relationships.
  6. Step 6: Hypothesis Testing.
  7. Step 7: Documentation and Reporting.
  8. Step 8: Iterative Analysis.

In conclusion, there are several different types of exploratory data analysis, including univariate, bivariate, and multivariate EDA. Within each of these types, there are both graphical and non-graphical methods for exploring the data.

An EDA report must thoroughly explain the dataset's variables, their correlation, and any preprocessing performed on the dataset to make it suitable for applying a machine learning algorithm for further use in the organization.

How to do EDA in Excel

Here's a step-by-step incremental approach for conducting EDA in Excel:

  1. Step 1: Importing Data.
  2. Step 2: Data Cleaning and Preprocessing.
  3. Step 3: Descriptive Statistics.
  4. Step 4: Data Visualization.
  5. Step 5: Data Relationships.
  6. Step 6: Hypothesis Testing.
  7. Step 7: Documentation and Reporting.
  8. Step 8: Iterative Analysis.

Exploratory Data Analysis Techniques

  • Univariate Non-Graphical. This is the simplest type of EDA, where data has a single variable.
  • Univariate Graphical. Non-graphical techniques do not present the complete picture of data.
  • Multivariate Non-Graphical. Multivariate data consists of several variables.
  • Multivariate Graphical.

The three main types of EDA are univariate, bivariate, and multivariate EDA. Let's break down what each of these means: Univariate EDA involves looking at a single variable at a time. Univariate EDA can help you understand the data distribution and identify any outliers.

Steps Involved in Exploratory Data Analysis

  1. Data Collection. Data collection is an essential part of exploratory data analysis.
  2. Data Cleaning. Data cleaning refers to the process of removing unwanted variables and values from your dataset and getting rid of any irregularities in it.
  3. Univariate Analysis.
  4. Bivariate Analysis.

How to perform EDA with Python : Exploratory data analysis (EDA) steps.

  1. Importing necessary libraries.
  2. Load the dataset.
  3. Viewing the dataset.
  4. Check for duplication.
  5. Data preparation (Handling missing values and outliers)
  6. Analyzing the data.(univariate, bivariate and multivariate analysis)
  7. Visualization.

What is an example of EDA : There are dress shoes, hiking boots, sandals, etc. Using EDA, you are open to the fact that any number of people might buy any number of different types of shoes. You visualize the data using exploratory data analysis to find that most customers buy 1-3 different types of shoes.

How to do EDA in Python

Table of contents

  1. Step 1: Import Python Libraries.
  2. Step 2: Reading Dataset.
  3. Step 3: Data Reduction.
  4. Step 4: Feature Engineering.
  5. Step 5: Creating Features.
  6. Step 6: Data Cleaning/Wrangling.
  7. Step 7: EDA Exploratory Data Analysis.
  8. Step 8: S tatistics Summary.


It's the same in analytics: confronted with a new dataset, you never know what you are going to find. This chapter is about exploring and describing a dataset so that we know what questions to ask of it. The process is referred to as exploratory data analysis, or EDA.ETL refers to the process of extracting data from multiple sources, transforming it into a consistent format, and loading it into a target database for further analysis. On the other hand, EDA focuses on examining and understanding raw datasets to gain insights before any transformation or modeling takes place.

What is the best language for EDA : Both Python and R are widely used for EDA, and each has its strengths: Python for EDA: Versatility: Python is a general-purpose programming language with a rich ecosystem of libraries and packa.