Hi Data enthusiast !
-Exploratory data analysis (EDA) is the first basic step performed on data by a data analyst or data scientist .

**What is exploratory data analysis?**

This is basically a process used by data scientists to analyze and investigate data sets and summarize their main characteristics , often employing data visualizatons method.

It can help determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns and check assumptions.

        **Importance of EDA**

1.Identify patterns and relationships: EDA helps to identify patterns and relationships between different variables in the data. This can help to generate hypotheses and guide further analysis.
2.Detect outliers and errors: EDA can help to identify outliers and errors in the data, which can then be corrected or removed before further analysis.

1. Assess data quality: EDA can help to assess the quality of the data and determine if it is suitable for analysis. This includes checking for missing values, inconsistencies, and data formatting issues.
   4.Understand the data distribution: EDA can help to understand the distribution of the data and its characteristics such as mean, median, and standard deviation. This can help to identify potential biases in the data.
   5.Communicate insights: EDA can help to communicate insights and findings to others in a clear and concise manner. This can be especially important in interdisciplinary teams where people may have different levels of technical expertise.

   ** Types of EDA**
   1.Univariate -Univariate analysis involves examining the distribution and characteristics of a single variable.
   2.Bivariate – This analysis involves examining the relationship between two variables. .

2. Multivariate analysis - This analysis involves examining the relationship between two or more variables .

Techniques for EDA
The most common techniques used for EDA are:
1.Box plots
2.Histogram
3.Bar chart

1. Line graph
2. Stem and leaf plot 6.Pareto chart
3. Heat maps 8.Scatter plot

Exploratory Data Analysis can be done using several tools eg R and python.
In this guide we will focus on EDA in python.

Python is a popular programming language used for EDA due to its rich ecosystem of libraries and tools. Here are the basic steps for EDA in Python:

Importing Libraries: The first step is to import the necessary libraries such as pandas, numpy, matplotlib, seaborn, etc.

Loading Data: The next step is to load the data into a pandas dataframe.

Data Exploration: Once the data is loaded, you can start exploring the data by using various pandas functions like head(), tail(), describe(), info() etc.

Data Cleaning: This step involves identifying and handling missing values, removing duplicates, handling outliers, and converting data types if necessary.

Data Visualization: Data visualization is a powerful tool for EDA, and Python offers several libraries like matplotlib, seaborn, and plotly for creating visualizations. You can create different types of plots like scatter plots, histograms, bar plots, etc.

Correlation Analysis: Correlation analysis helps you identify relationships between variables. You can use pandas functions like corr() and heatmap from seaborn library for this purpose.

Feature Engineering: Feature engineering involves creating new features from the existing ones to improve the model's performance. You can use pandas functions like apply(), map() and lambda functions to create new features.

Conclusion: Finally, you can draw conclusions and insights from your analysis and share your findings with others.
Here is a little 'cheat sheet' to help you get started.

IMPORT pandas,numpy,matplotlib,seaborn and the data

.head()-first five observations

.tail()-last five obserations

.shape -no of rows and columns

.info()-columns and their corresponding data

.describe()-summary statistics

.quality.unique-insights from dependant variable

.corr()-find correlation

annot=true - correlation in grid-cells

boxplot-check minimun,quatiles,maximum
check linearity-distribution graph
pairplot