📊 Exploratory Data Analysis (EDA): A Step-by-Step Guide

✅ What is EDA?

Exploratory Data Analysis (EDA) is the process of analyzing datasets to summarize their main characteristics, often using visual methods. It helps you understand the data before applying any machine learning or statistical models.

🪜 Step-by-Step EDA Process

Step 1: Understand the Dataset

Load the data using tools like pandas.

Check structure:

Number of rows and columns

Data types of each column

Sample rows (df.head())

python

Copy

Edit

import pandas as pd

df = pd.read_csv("your_dataset.csv")

print(df.shape)

print(df.dtypes)

df.head()

Step 2: Handle Missing Values

Check for null or missing values.

Decide how to handle them:

Drop (df.dropna())

Fill (df.fillna(value))

Impute (mean, median, mode)

python

Copy

Edit

df.isnull().sum()

df = df.fillna(df.mean())  # Example: fill numeric NaNs with column mean

Step 3: Summary Statistics

Use .describe() to get basic statistics:

Count, mean, std, min, max, percentiles

python

Copy

Edit

df.describe()

Use .value_counts() for categorical data.

Step 4: Univariate Analysis (Single Variable)

Visualize distributions:

Histograms for numeric variables

Bar charts for categorical variables

python

Copy

Edit

import seaborn as sns

import matplotlib.pyplot as plt

sns.histplot(df['age'], kde=True)

plt.show()

df['gender'].value_counts().plot(kind='bar')

plt.show()

Step 5: Bivariate Analysis (Two Variables)

Understand relationships between variables:

Numerical vs. Numerical → Scatter plots, correlation matrix

Categorical vs. Numerical → Box plots

Categorical vs. Categorical → Crosstab, stacked bar charts

python

Copy

Edit

sns.scatterplot(x='height', y='weight', data=df)

sns.boxplot(x='gender', y='income', data=df)

Step 6: Correlation Analysis

Check how numerical variables are related

Use a correlation matrix and heatmap

python

Copy

Edit

corr = df.corr()

sns.heatmap(corr, annot=True, cmap='coolwarm')

plt.show()

Step 7: Outlier Detection

Use box plots and z-scores to detect outliers

Decide whether to keep, remove, or transform them

python

Copy

Edit

sns.boxplot(x=df['salary'])

Step 8: Feature Engineering (Optional)

Create new variables based on existing ones

Binning, transformations, encoding categories

Useful for improving model performance later

Step 9: Document Insights

Summarize key findings:

Trends

Anomalies

Relationships

Use markdown, notebooks, or dashboards

🛠️ Tools Commonly Used in EDA

Tool/Library Purpose

pandas Data manipulation and cleaning

numpy Numerical operations

matplotlib Basic plotting

seaborn Advanced statistical plots

plotly Interactive visualizations

Jupyter Interactive exploration and reporting

🏁 Conclusion

Exploratory Data Analysis (EDA) is a crucial first step in any data science project. It helps you:

Understand your data deeply

Uncover patterns and anomalies

Prepare the data for modeling

Good EDA leads to better models and smarter decisions.

Learn Data Science Course in Hyderabad

Read More

Data Wrangling Techniques Every Data Scientist Should Know

How to Handle Missing Data in Data Science

The Art of Data Cleaning: Why It Matters

Data Analysis and Visualization in Data Science

Visit Our Quality Thought Training Institute in Hyderabad

Get Directions