Jupyter Notebook setup and usage in Data Analysis Python

Introduction

Jupyter Notebook lets you write and run code step-by-step. It helps you see results right away and explain your work clearly.

When you want to explore data interactively and see charts immediately.

When you need to explain your data analysis with code and notes together.

When you want to test small pieces of code before building a bigger program.

When you want to share your work with others in an easy-to-read format.

When you want to combine code, text, and images in one place.

Syntax

Data Analysis Python

1. Install Jupyter with: pip install notebook
2. Start Jupyter Notebook by running: jupyter notebook
3. Create a new notebook and write Python code in cells.
4. Run a cell by pressing Shift + Enter.

You need Python installed before installing Jupyter Notebook.

Each cell can contain code or text (Markdown).

Examples

Write Python code in a cell and run it to see output below.

Data Analysis Python

# This is a code cell
print('Hello, Jupyter!')

Use Markdown cells to add explanations or titles.

Data Analysis Python

## This is a Markdown cell
# You can write notes like this.

Run calculations and see results immediately.

Data Analysis Python

# Run math calculations
2 + 3 * 4

Sample Program

This code creates a small table of fruits and their counts, shows the table, and draws a bar chart.

Data Analysis Python

# Sample Jupyter Notebook code
import pandas as pd
import matplotlib.pyplot as plt

# Create a simple data table
data = {'Fruits': ['Apple', 'Banana', 'Cherry'], 'Count': [10, 15, 7]}
df = pd.DataFrame(data)

# Show the table
display(df)

# Plot a bar chart
plt.bar(df['Fruits'], df['Count'])
plt.title('Fruit Counts')
plt.xlabel('Fruit')
plt.ylabel('Count')
plt.show()

Important Notes

Use Shift + Enter to run a cell and move to the next one.

Save your notebook often to avoid losing work.

You can export notebooks as HTML or PDF to share with others.

Summary

Jupyter Notebook helps you write and run code step-by-step.

You can mix code, text, and visuals in one place.

It is great for learning, exploring data, and sharing results.