Today, Let’s move to the next topic in the Data Science Learning Series: *Intro to Pandas DataFrame* *1. What is a DataFrame?* A DataFrame is a 2D tabular data structure in Pandas, similar to an Excel sheet or SQL table. Think of it as: - Rows = Records - Columns = Features/Attributes *2. Creating a DataFrame (Basic Example)* import pandas as pd data = { 'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 28], 'City': ['New York', 'London', 'Paris'] } df = pd.DataFrame(data) print(df) *Output:* Name Age City 0 Alice 25 New York 1 Bob 30 London 2 Charlie 28 Paris *3. Why DataFrame is Important in Data Science?* - You can easily filter, clean, visualize, and model data. - Almost every dataset you import (CSV, Excel, SQL) becomes a DataFrame. *4. Accessing Data in DataFrame* df['Name'] # Access single column df[['Name', 'Age']] # Multiple columns df.iloc[0] # First row df.loc[0, 'Name'] # Specific value *5. Real Data Science Use Case* Imagine analyzing customer data like: df = pd.read_csv("customers.csv") df['Spending'].mean() # Average customer spending df[df['Country'] == 'India'] # Filter Indian customers You can now explore insights, patterns, and prepare data for ML models — all starting with DataFrames! *React with ❤️ once you're ready for the quiz* Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998 Python Cheatsheet: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1660