Move from Excel to Python with Pandas and Jupyter Course

Course Summary

If you work with Excel and have ever thought - "there has to be a better way" then this course is for you! Excel has been used to automate and power businesses and solve business users' problems for years. But there are many drawbacks with relying so heavily on Excel for your data processing needs. In this course, you will see how Python's modern data science libraries will allow you to solve the same problems while avoiding the pitfalls and limitations inherent in Excel.

What students are saying

I just finished the course. Thanks to you I learned much about the topic. Not all things were completely new, but I clarify most of them and now I feel that I really should use them (and I want to, of course) :) I believe that the acquired knowledge will move my Python skills to another level.
-- Tomas Ryba

Source code and course GitHub repository

github.com/talkpython/excel-to-python-course

What's this course about and how is it different?

This course will teach you how to use Python to replace your tedious and error-prone Excel actions. This course is designed to quickly build your Python and pandas knowledge so that you can leverage the power and efficiency of Python in your day to day work. This course is unique in that it shows common Excel tasks and translates them to Python code.

If you have ever been curious about how Python can immediately impact your daily job but have been unsure where to start - this course is for you!

What topics are covered

In this course, you will:

  • Learn about the typical challenges with Excel and how Python is a better alternative for many tasks
  • See all steps needed to install Python and setup your workspace
  • Create and use Conda environments
  • Learn about Windows specific-tricks for efficiently working with Python
  • See how the Jupyter notebook can be a streamlined environment for working with data
  • Learn about the pandas DataFrame and how to use it for data analysis
  • Use pandas to read in CSV and Excel files
  • Efficiently clean and manipulate large sets of data
  • Perform mathematical operations with pandas
  • Discover how to work with dates and other data types in pandas
  • Group and summarize large data sets using powerful pandas commands such as groupby and pivot_table
  • Combine data by merging and concatenating multiple DataFrames
  • Read multiple file types and create formatted Excel workbooks
  • And much more

View the full course outline.

Who is this course for?

This course is designed for anyone that uses Excel in their day to day work and is interested in working more efficiently with Python. If you have tried to learn Python in the past and struggled, this course will help you understand Python using common Excel tasks as a reference.

This course is best for a student that has experience with some basic Python concepts:

  • Importing modules
  • Assigning variables
  • Working with lists and dictionaries
  • Creating and using functions

Note: All software used during this course, including editors, Python language, etc., are 100% free and open source. You won't have to buy anything to take the course.

Concepts backed by concise visuals

While exploring a topic interactively with demos and live code is very engaging, it can mean losing the forest for the trees. That's why when we hit a new topic, we stop and discuss it with concise and clear visuals.

Here's an example of understanding the basic commands for working with a pandas DataFrame.

Example: Concepts backed by concise visuals

Get hands-on for almost every chapter

This course is heavy on practical applications. We will not spend much time talking about language theory. We want to get you up and running with Python quickly. Many of the concepts we cover have natural analogies to processes with Excel which we will use to speed up the learning process. In this course, we'll develop a lot of code using pandas using the Jupyter notebook environment in a way that you can leverage for your own business problems.

You'll have access to all the source code at github.com/talkpython/excel-to-python-course where you can explore, tweak things and leverage for your own projects.

Follow along with subtitles and transcripts

Each course comes with subtitles and full transcripts. The transcripts are available as a separate searchable page for each lecture. They also are available in course-wide search results to help you find just the right lecture.

Each course has subtitles available in the video player.

Who am I? Why should you take my course?

Who is Chris Moffitt? Hi, I'm Chris Moffitt. I am passionate about finding ways to use the power of Python to be more efficient and effective in a business setting. I've been using Python for over 15 years to solve a variety of real-world problems for everything from web development to system administration and most recently data science.

Free office hours keep you from getting stuck

One of the challenges of self-paced online learning is getting stuck. It can be hard to get the help you need to get unstuck.

That's why at Talk Python Training, we offer live, online office hours. You drop in and join a group of fellow students to chat about your course progress and see solutions via screen sharing.

Just visit your account page to see the upcoming office hour schedule.

The time to act is now

If you have struggled with repetitive tasks in Excel and have been looking to learn how to use Python, then this course is designed for you. After completing this course, you will have a fully functional Python environment and have enough pandas knowledge to perform meaningful and impactful analysis.

Dive into Excel to Python and level up your skills. Join today! You've got nothing to lose. Every one of our courses comes with a 2-week money-back guarantee.

Buy for $59 + tax Bundle and save 85% Team Gift

Questions? Send us an email: contact@talkpython.fm

Course Outline: Chapters and Lectures

Welcome to the course
8:12
Welcome!
1:45
Challenges of Excel
1:28
Where do Excel errors come from?
1:46
Why Python?
1:26
Course topics
1:02
Get the source
0:10
Meeting your instructor
0:35
Setup and installations
5:43
Python concepts you'll need
1:00
Working on Windows
0:49
Downloading miniconda
0:12
Installing miniconda
0:37
Launching miniconda
0:23
Creating a conda environment
1:52
Conda quick reference
0:50
Code data concepts
7:11
Getting organized
0:50
Recommended file structure
1:00
Launching Jupyter notebooks
0:21
Auto launch Conda env with Windows terminal
0:43
Create a project with cookiecutter
1:05
Launching Jupyter notebooks
0:59
Organizing within the notebook
0:45
Data compatible with Pandas
0:49
Where does excel fit in?
0:39
Intro to Pandas
32:58
Intro to pandas
0:49
Pandas DataFrame vs Excel worksheets
1:12
Understanding initial data introduction
0:46
Demo: Understanding initial data
4:13
Concept: Understanding your dataframe
0:31
Working with column names
7:46
Diving into the data
2:42
Working with multiple columns
3:00
Concept: Working with columns
1:11
Working with column names
7:46
Concept: Selecting rows and columns
0:47
Advanced autocomplete and help
2:15
Data wrangling with Pandas
21:55
What is data wrangling?
0:31
Pandas data types
0:59
Walk through of data types in Excel
1:44
Pandas' dt, the date time accessor
3:22
String and math manipulations
4:22
Concept: Filtering data
0:44
Examples of boolean filtering in pandas
4:39
Filtering by dates
3:54
Concept: Filtering and cleaning text and dates
1:40
Aggregating, grouping, and merging
26:02
Aggregating merging grouping intro
0:59
Examples of aggregation in excel
1:05
Examples of aggregation in pandas
8:44
Concept: Pandas groupby
0:36
Concept: Pivot tables and crosstab
1:33
Pivot table and crosstab
4:42
Merging data in Excel
2:16
Merging data in Pandas
5:36
Concept: Appending data
0:31
Data I/O (input and output)
20:10
Intro to I/O
0:29
Reading non-tabular Excel files
3:58
Reading Excel and converting data types
4:53
Reading Excel summary
0:41
Demo: Simple Excel saves
2:29
Demo: Multi-sheet Excel workbooks
2:06
Demo: Complex Excel saves
2:26
Excel writer summary
0:26
Excel fails at reading this large file
0:45
Pandas loves large files
1:57
Case study walk through: Sales commissions
35:49
Case study introduction
0:36
Environment setup
0:44
Excel file intro
0:28
Notebook setup for file reading
1:28
Commission rate calculations
3:59
Summary results lead to more questions
0:55
Combining customer sales reps
1:51
Cleaning up region
2:46
Merging sales reps
2:01
Cleaned, joined file
4:33
Excel file review
0:35
Process review
0:32
Excel invoice files
0:49
Excel customer invoice files
1:38
Product commission scenarios
3:07
Product commission variable amounts
2:46
Commission state adjustments
3:19
Final Excel report
2:12
Wrapping up
1:30
Course conclusion and review
10:37
Excel challenges
1:15
Conda review
0:51
File structure for Jupyter
1:29
DataFrame summary
1:05
Data wrangling
1:26
Grouping and joining data
1:34
File I/O
0:39
Practical example
1:16
Thank you and next steps
1:02
Appendix: Python language concepts
46:31
Concept: The shape of a program
1:25
Concept: Variables
0:51
Concept: Truthiness
1:47
Concept: if/else
1:24
Concept: Complex Conditionals
1:31
Concept: for-in
1:41
Concept: Calling functions
0:59
Concept: Creating functions
1:33
Concept: File I/O
1:20
Concept: Imports and importing modules
1:59
Concept: Python Package Index (PyPI)
1:54
Concept: pip
2:26
Concept: Virtual environments
3:53
Concept: Slicing
2:53
Concept: Tuples
1:43
Concept: Named tuples
1:44
Concept: Classes
2:01
Concept: objects vs. classes
1:44
Concept: Inheritance
1:50
Concept: Polymorphism
0:53
Concept: Dictionaries
2:30
Concept: Error Handling
2:38
Concept: lambdas
2:09
Concept: List Comprehensions
2:57
Concept: Want more Foundational Python?
0:46
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Questions? Send us an email: contact@talkpython.fm

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