Pandas Data Analysis

Master data manipulation and analysis with Python's pandas library.

Duration≈2 hours

Lessons9 total

Modules3

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Prerequisites

•
Python Programming Fundamentals
•
NumPy for Quants

What you'll learn

Clear, practical outcomes for this course

Master the fundamentals of pandas Series and DataFrames.

Learn data manipulation techniques including filtering, updating, and indexing.

Perform advanced data analysis using aggregation and grouping operations.

Combine multiple datasets using merging and concatenation.

Handle time-series data effectively for temporal analysis.

Course Curriculum

3 Modules • 9 Lessons

🐼

Introduction & Series

Getting started with pandas and working with Series

Introduction to pandas

Learn about pandas, its benefits, and how to install and import it.

10 min

Basics of pandas Series

Understand pandas Series, creation methods, and attributes.

15 min

pandas Series Operations

Perform arithmetic operations and use various Series methods.

15 min

📊

DataFrames

Working with 2D data structures

Basics of pandas DataFrame

Learn about the pandas DataFrame structure, how to create one, and its core attributes.

25 min

pandas DataFrame Filtering

Learn how to filter data in a pandas DataFrame using various methods.

10 min

pandas DataFrame Manipulation

Learn how to update, add, delete, and change the index of a pandas DataFrame.

15 min

🚀

Advanced Operations

Aggregation, Merging, and Time Series

Aggregation and GroupBy in pandas

intermediate

Learn how to aggregate and group data in pandas for powerful data analysis.

10 min

Merging and Concatenating pandas DataFrames

intermediate

Learn how to combine multiple DataFrames using merge and concat operations.

20 min

Working with time-series data in pandas

intermediate

Learn how to handle dates and time-series data using pandas.

10 min