This is a remote course delivered over two half-day sessions via Zoom, for a small group of approx. 10 people.

The course focuses on best practices and advanced features of the pandas library for data analysis to improve your efficiency when using this tool.

Intended audience: quants, analysts, BI professionals, data scientists, developers and researchers who use the pandas library for data analysis, already grasp the basics of pandas and would like to take their knowledge to the next level. This is a course best suited for "advanced beginners" or intermediate pandas users, who want to master the best practices.

New to Python and pandas? I also offer introductory courses on Python for Data Analysis, please get in touch if you're interested in that instead.

Learning objectives:

By attending this course you'll learn about:

• How to choose the best data types in your DataFrames for efficient usage (memory and speed)
• Best practices to build complex, multi-step data transformation pipelines
• How to write complex aggregation queries, e.g. groupby operations on multiple columns
• Everything you wanted to know about the hierarchical index (but were afraid to ask)
• The ecosystem of other useful packages to integrate with pandas for specific needs

Syllabus overview:

• Efficient data modelling

Deep dive into pandas data types, choosing data types when loading the data, data type conversions, best practices to improve your memory usage when dealing with larger data sets

• Efficient data pipelines

Understanding immutability, method chaining to improve readability, best practices to develop and maintain complex data transformation and query pipelines

• Efficient slicing and dicing

Complex aggregations (groupby) on multiple columns, mastering the hierarchical (multi-level) index, index stacking and unstacking, pivoting and reshaping

• Beyond pandas - Overview on the pandas ecosystem

Overview and examples of usage of other libraries built on top of pandas to address specific needs in data preparation, data analysis and data visualisation

Course schedule, delivery and teaching style

This course is delivered over two half-day sessions on 6-7 December 2021, 2pm-5:30pm UK time (GMT). We'll have comfort breaks during the sessions as appropriate.

You'll need your computer/laptop with a recent Anaconda pre-installed with Python 3.8+. I'll check-in with you before the course with set-up and joining instructions to make sure you have everything you need.

This is an online only course via Zoom, you'll need an adequate bandwidth for live video conferencing. You can use Zoom via web, but you'd probably have a better experience with the app.

The course is hands-on and offers plenty of demos/examples/exercises: expect to read and write code. Whenever theory is needed, I explain it with a focus on the main intuitions and motivations to keep it practical.

By signing up, you'll receive

• Access to the live sessions on 6-7 December 2021 via Zoom
• Set-up and joining instructions before the course
• Teaching material: code examples, demos and exercises in the form of Jupyter notebooks
• Additional exercises: take-home exercises that you can use after the course to further improve your understanding
• A Certificate of Professional Development after course completion
• An invite for a retrospective group call to discuss progress and problems (1-hour call, circa 2-3 weeks after the training, complimentary)

About the instructor

I'm a Data Science consultant, corporate trainer and author based in London, UK. I specialise in the Python for Data Science (PyData) software stack.

With 20 years of experience in the tech industry, I provide consulting, coaching and training services in the data science space through my company Bonzanini Consulting Ltd.

Backed by a PhD in Information Retrieval, I specialise in text analytics applications, and I’ve enjoyed working on a broad range of information management and data science projects, including flight safety, social media data, behavioural data, biomedical data and recruitment data.

I’m the author of “Mastering Social Media Mining with Python“, “Data Analysis with Python” and “Practical Python Data Science Techniques“, published by Packt Publishing.

I currently serve as chair of the PyData London meetup and conference series (largest Python user group in Europe) and I regularly speak at international tech events.

Refunds and Change Policy

Eventbrite offers you a 100% refund for cancellations up to 7 days before the event. Should you need to cancel I'm happy to talk to you to see if we can figure something out.

I can move a ticket to another colleague if needed, simply get in touch.

In the unlikely event that we have to cancel the course then you will be offered either a 100% refund or the option of moving attendance to another future workshop. Bonzanini Consulting Ltd and Marco Bonzanini are not liable for any other costs incurred.

Questions? Get in touch

If you have questions please contact Marco@BonzaniniConsulting.com.

Check out Marco's blog for a list of past public presentations and workshops. I also curate the newsletter Musings on Data where I share my thoughts and recommendations on Data Science, once every few weeks (it also includes announcements of future trainings, so sign up for early access and discount codes).