Welcome to the second video in our Python for Data Analysis learning series! In this session, we'll dive into the fundamentals of data manipulation using Pandas and NumPy. Let's get started!

Key Concepts 📚

  • Pandas DataFrame: The core data structure for tabular data
    Pandas DataFrame
  • Data Cleaning: Handling missing values, duplicates, and data types
  • Vectorized Operations: Efficient numerical computations with NumPy
  • Indexing & Selection: Accessing data with .loc and .iloc

Practical Examples 💻

import pandas as pd
# Load a sample dataset
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/gapminder.csv")
# Display first 5 rows
df.head()
💡 Tip: Click here for a visual guide on DataFrame operations
Data Frame Exploration

Common Pitfalls ⚠️

  • Avoiding index shifting after row deletion
  • Understanding broadcasting rules in NumPy
  • Properly using dtypes to optimize memory usage

For deeper insights, check out our Data Wrangling video next!

Data Wrangling Process

Practice exercises:

  1. Convert a CSV file to a DataFrame
  2. Remove duplicates from a dataset
  3. Use vectorized operations to calculate GDP growth rates

Let us know if you need help with any of these tasks! 🤝