Exploratory Data Analysis in Python: Steps and Code Explained

In data science, it's very important to understand your data, and that’s where Exploratory Data Analysis (EDA) helps. Exploratory data analysis in Python is a basic method used to look at data and find useful information using charts and numbers. It helps you see patterns, trends, or anything unusual in the data that could affect your results. Python is a popular language for EDA because it is easy to use and has strong tools like Pandas, Matplotlib, and Seaborn. So, this article on EDA Python will show you the main steps to do EDA in Python. It also shows some simple code examples to help you learn and explore your data better.

What is Exploratory Data Analysis?

It is an approach for analyzing data sets to summarize their main characteristics by using visual methods. Exploratory data analysis in Python is vital for understanding the data before applying any machine learning models or statistical tests. It helps in identifying trends, patterns, and anomalies in the data, which can significantly influence the results of your analysis.

Why Use Exploratory Analysis in Python?

In recent times, it has become one of the most popular programming languages for data analysis. Due to its simplicity and the powerful libraries it offers. Libraries such as Pandas, Matplotlib, and Seaborn make it easy to perform exploratory data analysis in Python. With Python, you can handle large datasets, perform complex calculations, and visualize data seamlessly.

Steps for EDA analysis in Python

Let’s check out the different steps involved in Exploratory Data Analysis:

Step 1: Import Libraries

We need to bring in some tools (libraries) to help us work with data and make charts.

import pandas as pd      # To work with tables
import numpy as np       # For numbers and math
import matplotlib.pyplot as plt  # For drawing charts
import seaborn as sns    # For pretty charts

Step 2: Load the Data

Now, in this step of exploratory data analysis using Python, load your data (for example, from a CSV file) into a table called a DataFrame.

data = pd.read_csv('your_dataset.csv')

Step 3: Look at the Data

In this step, examine the structure and content of your dataset.

print(data.head())       # Show first 5 rows
print(data.info())       # Show number of rows, columns, and data types
print(data.describe())   # Show simple stats (like mean, min, max)

Step 4: Clean the Data

Identify and fix any issues in the data.

- Check for the missing values:

print(data.isnull().sum())

- Fill missing values with the average:

data.fillna(data.mean(), inplace=True)

Step 5: Univariate Analysis (Look at One Column)

After completing all the above exploratory data analysis steps in Python. Now, look at one column at a time to understand it.

- Example: See how one column is spread out:

plt.figure(figsize=(10, 6))
sns.histplot(data['your_column'], bins=30, kde=True)
plt.title('Distribution of Your Column')
plt.xlabel('Your Column')
plt.ylabel('Frequency')
plt.show()

Step 6: Bivariate Analysis (Compare Two Columns)

In this step of exploratory data analysis in Python, see how the two columns relate to each other.

- Example: Make a scatter plot:

plt.figure(figsize=(10, 6))
sns.scatterplot(x='column_x', y='column_y', data=data)
plt.title('Scatter Plot of Column X vs Column Y')
plt.xlabel('Column X')
plt.ylabel('Column Y')
plt.show()

Step 7: Multivariate Analysis (Look at Many Columns Together)

See how all columns relate to each other.

- Example: This EDA code in Python show a heatmap of how columns are related:

plt.figure(figsize=(12, 8))
correlation_matrix = data.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()

Step 8: Feature Engineering

Make new columns (features) if needed. This can help in building better models later.

Examples:

• Combine two columns

• Convert text into numbers

• Create a new column from dates, etc.

Step 9: Write Down Your Findings

After writing all the exploratory data analysis Python codes make notes of what you found:

• Which columns are important?

• Any problems in the data?

• Any patterns or trends?

This helps when you build models later.

Conclusion

Exploratory data analysis in Python helps you understand your data better. It generally involves simple steps like loading data, looking at one column at a time (univariate), comparing two columns (bivariate), and exploring many columns together (multivariate). Python libraries like Pandas, Matplotlib, and Seaborn make this easy by helping you see patterns through charts and graphs. Writing down what you learn is important for future use.

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