Python for Data Science

In This Section, You Will Learn:

• • Why Python is the #1 language for data
• • Setting up your Python environment
• • Essential libraries (Pandas, NumPy)
• • Working with DataFrames (the most important concept)
• • Basic Python operations for data
• • Using AI to write Python code for you

Why Python for Data Science?

• Python is the #1 language because:
• • Reads like English (easy to learn)
• • Huge library ecosystem for data
• • Free and open source
• • Used by all major companies
• • Best AI/ML support
• Comparison:
• • Excel: Good for small data (thousands of rows)
• • Python: Essential for big data (millions of rows)
• • SQL: Good for databases
• • R: Alternative to Python (less popular now)
• The Reality: 90%+ of data science job posts require Python

Setting Up Your Environment

• Option 1: Google Colab (Recommended for Beginners)
• • Free, runs in browser
• • No installation needed
• • Go to: colab.research.google.com
• • Sign in with Google account
• • Click 'New Notebook' and start coding!
• Option 2: Local Installation
• • Download Anaconda (anaconda.com)
• • Includes Python + all data science libraries
• • Use Jupyter Notebook (comes with Anaconda)
• Option 3: VS Code
• • Professional editor
• • Install Python extension
• • Great for larger projects
• Start with Google Colab. It's free and works instantly.

Essential Libraries

• Pandas (Most Important!):
• • 'Excel on Steroids'
• • Load, clean, analyze data
• • Work with millions of rows easily
• • Import: import pandas as pd
• NumPy:
• • Fast mathematical operations
• • Foundation for all other libraries
• • Import: import numpy as np
• Matplotlib & Seaborn:
• • Create visualizations
• • Charts, graphs, plots
• • Import: import matplotlib.pyplot as plt
• Scikit-Learn:
• • Machine Learning library
• • Pre-built ML algorithms
• • Will use later in course

Understanding DataFrames

• A DataFrame is like a spreadsheet in Python:
• • Rows = Records (each customer, each order)
• • Columns = Features (name, age, purchase amount)
• • Each cell = One data point
• Creating a DataFrame:
• df = pd.read_csv('sales_data.csv')
• Basic Operations:
• df.head() → See first 5 rows
• df.info() → See column types and missing values
• df.describe() → Get statistics (mean, min, max)
• df.shape → See (rows, columns)
• df['column_name'] → Access one column
• df[df['price'] > 1000] → Filter rows
• This is the foundation of all data analysis!

Using AI to Write Python Code

• You don't need to memorize everything!
• Use AI to write code for you:
• Prompt: 'Write Python code to load a CSV file called sales.csv and show the first 10 rows'
• Prompt: 'How do I find all customers who spent more than Rs. 5000 in Pandas?'
• Prompt: 'Write code to calculate the average order value by city'
• Debugging with AI:
• • Copy your error message
• • Paste into ChatGPT/Gemini
• • Ask: 'What does this error mean and how do I fix it?'
• The New Workflow:
• 1. Know WHAT you want to do
• 2. Ask AI to write the code
• 3. Understand and adjust the code
• 4. Run and verify results
• This is how professional data scientists work now!