Data Manipulation in Python: A Pandas Crash Course

Learn how to use Python and Pandas for data analysis and data manipulation. Transform, clean and merge data with Python.

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Learn from Industry Leaders

SuperDataScience team is a leader in the Udemy marketplace

1M+

students

50,000+

five-star reviews

4.5+

rating in 49 courses

Master Top Industry Tools

Pandas is one of the most popular Python libraries in data science today. Data scientists at Google, Facebook, JP Morgan, and nearly every other major company that analyzes data use Pandas.

Hands-On Learning

From the basics of setting up Python and a development environment to advance data manipulation and visualization techniques, go hands-on with Python and Pandas using real-world datasets and problems.

Learn From A Data & Python Pro

Top Udemy instructor Ph.D. Samuel Hinton uses powerful examples to help you master basic and advanced data manipulation techniques using Pandas.

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A Curriculum Built for Data Science

  • Creating and loading Pandas DataFrames
  • Saving and serializing DataFrames in and out of multiple formats.
  • Detecting and intelligently filling missing values.
  • Displaying your data with basic plots, 2D and multidimensional visualizations.
  • Performing basic DataFrame manipulations: indexing, labeling, ordering slicing, filtering and more.
  • Performing advanced Pandas DataFrame manipulations: multiIndexing, stacking, hierarchical indexing, pivoting, melting and more.
  • Carrying out DataFrame grouping: aggregation, imputation, and more.
  • Mastering time series manipulations: reindexing, resampling, rolling functions, method chaining and filtering, and more.
  • Merging Pandas DataFrames
  • Learn the common pitfalls and traps that ensnare beginners and how to avoid them. 

Why Should I Take This Course?

New Pandas Release v.1.0.0

Start from Scratch

Global & Industry-Wide Recognized Tools

When data manipulation and preparation accounts for up to 80% of your work as a data scientist, learning data cleansing techniques that take raw data to a final product for analysis as efficiently as possible is essential for success.
Data analysis with Python library Pandas makes it easier for you to achieve better results, increase your productivity, spend more time problem-solving and less time data-wrangling, and allows you to communicate your insights more effectively.

Top Programming Language for Data Science

Python 3 Only

1D, 2D & Multidimensional Visualizations

68% of all data scientists use Python, making it the most popular programming language in the industry and the most in-demand in the US. And Pandas is one of its most widely used libraries in the data science industry— making it a must-have skill for any data scientist in the job market today.

Become a Top Python Data Scientist

According to ZipRecruiter, Data Scientist specializing in Python programming earn can earn up to $187,000 in the U.S.

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Average salary

$121,762
for a Python Data Scientist in the U.S.

What You Will Get

Expert Instructors

  • Learn from the Industry Leaders
  • 1M+ students
  • 50,000 five-star reviews
  • 49 Courses Rated 4.5+

On-Demand Lifetime Content

  • Learn at your own pace with on-demand video
  • Enjoy lifetime access to the course
  • Available to view on mobiles and TV

Intensive Content

  • Python and coding environment setup covered
  • 9 hours of on-demand video content
  • 4 articles
  • 49 downloadable resources

Career Credentials

  • Receive a certificate of completion
  • Build portfolio-ready projects
  • Real-world datasets and applications

People Talk About Us

The lectures explain everything really clearly and in a logical order, and the visual presentation is fantastic. Super professional production! It’s great being able to do the activities alongside the presentations, reinforcing things as I go. Highly recommend!

Alexandra Oliver

Samuel is a great speaker and the content is laid out very well. He answers questions quickly and clearly. Thanks again!

Clayton Van Hovel

The course has been really useful so far in getting my head around pandas. There’s so much you can do with it and it’s so much easier to digest with the lectures than trawling through the docs!

Sam Fletcher

It is just so well explained. This is a relatively new field for me, and it is so reassuring to get clear information and guidance without being spoken down to. Sam is clearly passionate about what he does and the tools he has discovered.

Toni Tuck

Spend More Time Problem-Solving

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.

Avoid Common Pandas Pitfalls

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.

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