Python for Excel Users First Steps
Published by O'Reilly Media, Inc.
Spreadsheets are the tool of choice for data analysis and reporting among a majority of business professionals, who are well-acquainted with common data processes like sorting, filtering, and aggregation. But spreadsheets have their limitations, which templates and complex formulas fail to solve.
For quick, dynamic, and repeatable data processes, Python is a helpful addition to your skillset. Join expert George Mount to bridge the gap between spreadsheets and Python. You’ll discover how to pivot your existing knowledge about data into a new application, as you learn Python equivalents of common spreadsheet tasks, how Python can augment and automate common data preparation and manipulation tasks often done in spreadsheets, and more. The thought of learning a programming language may be intimidating, but as a spreadsheet user, you know more about data and coding than you think.
What you’ll learn and how you can apply it
By the end of this live online course, you’ll understand:
- Python’s powerful universe of open source packages and tools
- The Python equivalents of common spreadsheet tasks such as PivotTables and lookups
- Where Python can augment and automate common data preparation and manipulation tasks often done in spreadsheets
- The role of variables, objects, and functions in Python
And you’ll be able to:
- Load, view, and write spreadsheet files from Python
- Perform common data wrangling tasks such as sorting, filtering, and aggregation
- Navigate and execute code in Jupyter notebooks
- Identify, install, and implement useful packages for your needs
This live event is for you because...
- You're an analyst responsible for collecting, analyzing, and interpreting data for business insights.
- You want to learn how to use Python and its most common packages inside Jupyter notebooks.
- You’d like to speed up, automate, and validate your reporting and analysis using open source software.
- You’re a spreadsheet user interested in learning more about data science or software development.
Prerequisites
- Download files from this Github repo
- A computer with the Anaconda distribution of Python installed (instructions)
- A working knowledge of basic tasks and functions in Excel, including sorting and filtering, IF statements, conditional aggregates like SUMIF() and COUNTIF(), and PivotTables and VLOOKUP()
- No prior programming knowledge required (As an Excel user, you already know more programming than you realize.)
Recommended preparation:
- Read “Preliminaries” and “Python Language Basics, IPython, and Jupyter Notebooks” (chapters 1 and 2 in Python for Data Analysis, second edition)
Recommended follow-up:
- Read Advancing into Analytics (book)
- Read Automate the Boring Stuff with Python, second edition (book)
- Read Python for Data Analysis, second edition (book)
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
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
- Q&A
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
- Q&A
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
- Q&A
Your Instructor
George Mount
George Mount is the founder and CEO of Stringfest Analytics, a consulting firm specializing in analytics education and upskilling. He has worked with leading bootcamps, learning platforms and practice organizations to help individuals excel at analytics.
George regularly blogs and speaks on data analysis, data education and workforce development and is the author of Advancing into Analytics: From Excel to Python and R (O'Reilly Media, 2021) and _Modern Data Analytics in Excel: Using Power Query, Power Pivot and More for Enhanced Data Analytics _(O'Reilly Media, 2024). He is a recipient of the Microsoft Most Valuable Professional (MVP) award for exceptional technical expertise and community advocacy in the field of Excel.
George holds a bachelor’s degree in economics from Hillsdale College and master’s degrees in finance and information systems from Case Western Reserve University. He resides in Cleveland, Ohio.