Welcome to the Pandas basics tutorial! Pandas is a powerful library for data analysis and manipulation in Python. Let's explore its core features and how to get started.

Core Data Structures

Pandas provides two primary data structures:

  • Series: One-dimensional labeled array (think of it as a column in a spreadsheet)
  • DataFrame: Two-dimensional labeled data structure (like a table in a relational database)
import pandas as pd
# Example: Creating a Series
s = pd.Series([1, 2, 3], name="Numbers")
# Example: Creating a DataFrame
df = pd.DataFrame({"Name": ["Alice", "Bob"], "Age": [25, 30]})

Basic Operations

Learn how to perform common tasks with Pandas:

  • Reading data: Use pd.read_csv() or pd.read_excel() to load datasets
  • Data selection: Access columns with df['column_name'] or rows with df.loc[]
  • Data filtering: Use boolean indexing like df[df['Age'] > 25]

Need more practice? Check out our Data Wrangling Tutorial for hands-on exercises!

Data Cleaning

Pandas makes it easy to handle missing data and duplicates:

  • Use df.dropna() to remove missing values
  • Use df.fillna() to fill missing values
  • Use df.drop_duplicates() to clean up repeated entries

Data Visualization

Create visualizations with Pandas' built-in tools:

  • Use df.plot() for quick graphs
  • Use sns (Seaborn) for more advanced plots
Pandas Basics

Next Steps

Ready to level up your Pandas skills? Explore these topics:

Happy coding! 🚀