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Advanced Pandas

via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

6 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Part of

Python for Business Data Science

Overview

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Most Python users pick up just enough pandas to load a CSV and glance at a few rows, then stop there, leaving most of the library's real power untouched. Data scientist Brett Vanderblock built this course to close that gap and take you well past the basics of DataFrame handling. It moves from foundational setup into the cleaning, combining, and scaling techniques that real, messy datasets actually demand. You'll set up and navigate DataFrames, clean and convert messy real-world values, combine and reshape data from multiple sources, and visualize and summarize what you find. You'll also work with tools that extend pandas beyond its core capabilities, including automated data profiling, geospatial analysis, and frameworks built for datasets too large for pandas alone. By the end of this course, you'll be able to take a messy, multi-source dataset and turn it into a clean, well-organized foundation ready for analysis, visualization, or a decision-maker's next move.

Syllabus

From Beginner to Advanced Pandas
- Before you can clean, transform, or analyze anything, you need a reliable way to load your data and see exactly what you're working with. In this module, you'll set up pandas, build and inspect DataFrames, and configure your workspace so you can navigate any dataset with confidence.
Advanced Calculations
- Real data almost never shows up in the shape you need it. In this module, you'll convert data types, clean up strings and dates, handle missing values, and apply custom functions to get any dataset ready for reliable analysis.
Transforming Dataframes
- A single dataset rarely tells the whole story on its own. In this module, you'll group and summarize data, reshape it into the layout your analysis actually needs, combine multiple dataframes together, and organize numerical values into meaningful categories.
Exploratory Data Analysis and Visualization
- Numbers on their own are hard to reason about, but a chart or a well-chosen statistic can reveal a pattern instantly. In this module, you'll visualize your data directly in pandas and calculate the statistics that reveal how your variables actually behave and relate to one another.
Beyond Pandas
- Pandas has plenty of neighbors worth knowing. In this module, you'll accelerate your exploratory analysis with automated profiling, work with location-based data using a geospatial extension of pandas, and see how pandas' concepts translate to tools built for datasets too large for pandas alone.
Conclusion
- You've covered a lot of ground with pandas, from setting up and navigating DataFrames through cleaning, combining, visualizing, and extending your work with tools beyond pandas itself. In this module, you'll complete a comprehensive final assessment and pick up pointers for where to go next.