I’m pleased to present a new O’Reilly Online Learning session entitled “Python for Excel Users: First Steps” on Friday October 30th at 12p Eastern.
The session is free to attend for all subscribers to the fantastic O’Reilly Online Learning platform. Check your employer or university for an institutional account, or really, consider signing up — and not just for this session! You’ll get access to an untold number of similarly insightful workshops and a vast multimedia library.
Bridging the gap from Excel to Python
Excel users intuitively understand how to work with data: they can sort, filter, group and join it. They know which arrangements make for easy analysis, and which arrangements mean lots of cleanup.
As it turns out, research indicates that relating what you’ve learned to what you already know is an immensely powerful learning approach. As put by Peter C. Brown and colleagues in the fantastic book Make it Stick: The Science of Successful Learning:
The more you can explain about the way your new learning relates to your prior knowledge, the stronger your grasp of the new learning wil be, and the more connections you create that will help you remember it later.
Brown, Roediger et al., Make It Stick: The Science of Successful Learning
In this workshop, we will explicitly relate what you know about working with data in Excel into Python. For example, you’ll compare Excel ranges to
numpy arrays, Tables to
pandas DataFrames and
VLOOKUP() to left outer joins.
This will expedite your learning and tighten your comprehension. In addition, we’ll carve a straight learning path toward data analysis, visualization and reproducible research. This is no laundry-list of Python concepts and techniques.
Because we’ll be focusing specifically on Python with the need and experiences of the Excel user in mind, we’ll be able to look more critically at the “how’s” and “why’s” of Python, object-oriented programming and open-source software. This ability to contextualize and choose the right analytics solution(s) for the job is more important than any one tool!
Up and running from spreadsheets to Python (55 minutes)
- Presentation: Welcome to Planet Python—What is Python, and when would you use it instead of a spreadsheet?; hello from Jupyter—navigating and executing Python code from Jupyter notebooks; from spreadsheet cells and ranges to Python lists and dictionaries—creating, inspecting, and manipulating lists and dictionaries
- Jupyter Notebook exercise: Assign, index, and subset variables in Python
Break (5 minutes)
Working with tabular data (55 minutes)
- Presentation: From spreadsheet tables to Python DataFrames—creating, inspecting, and manipulating DataFrames, importing spreadsheet data into Python; from lookups and PivotTables to pandas manipulation—manipulating tabular data in pandas (sorting, summarizing, merging, reshaping, exporting to spreadsheets)
- Jupyter Notebook exercise: Manipulate and analyze tabular data with pandas
Break (5 minutes)
Python for data analysis (60 minutes)
- Presentation: Data visualization with seaborn—visualizing univariate and bivariate distributions (bar charts, histograms, scatter plots, line charts), customizing plots and themes; from “That’s hard in spreadsheets” to “That’s easy in Python!”—conducting an end-to-end data analysis project (appending, transposing, summarizing, and visualizing a set of CSV files)
- Jupyter Notebook exercise: Build an end-to-end Python data analysis project from spreadsheet data
Excited? Me too! And so are lots of other people, so you should register now
You can also view the preliminary slides, datasets and demo notes at the course’s GitHub repo.
I hope to see you there — the more attendees I get, the more awesome programs I can provide.