Data Manipulation in Python: A Pandas Crash Course

When data manipulation and preparation accounts for up to 80% of a data scientist’s work, learning data cleansing techniques that take raw data to a final product for analysis as efficiently as possible is essential for success.
After 9 hours of on-demand learning, your data wrangling workflow will undertake a huge overhaul. Be prepared to:
Clean and manipulate your data in half the time
Achieve better results by spending more time problem-solving and less time data wrangling
Add next-level data visualization and animations to your portfolio
Gain valuable up-to-date skills with the latest version of Python and industry-standard Pandas library.
Best of all, you can start from scratch.
After setting up your development environment, you will then delve into Pandas, DataFrames: how to make them, load them, save them, merge them, group them, aggregate them, pivot them, resample, filter, map and manipulate them so that you'll always know the right tool for the job.
Plus! Samuel guides you into the world of time series data and visualizations —and all of it is done using real-world datasets and downloadable notebooks so you can follow along.

Performing data analysis with Python’s Pandas library can help you do a lot, but it does have one downside -- its huge size. As you dive deeper into the Pandas library, you'll quickly see that the sheer number of functions that Pandas provides can be overwhelming.
Where should you start? What's the correct function in a specific use case? What's the most efficient way of approaching a specific problem?
Thankfully, together, we can help you beat this obstacle head-on.
After completion, you will feel comfortable delving into complex and heterogeneous datasets knowing with absolute confidence that you can produce a useful result for the next stage of data analysis.

Plus, save yourself time by avoiding incomplete Pandas documentation in order to surpass common pitfalls.