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Newly added in Oct 2023: The NEW Python in Excel Feature!

(Python in Excel and xlwings are complementary tools - get the best of both tools!)


Excel vs. Python - what is the best tool for Data Science, Business, and Finance?

The answer is: Use Excel and Python together and integrate both tools with xlwings. Get the best of two worlds!


With xlwings, you can use Python Data Science libraries like Numpy, Pandas, Scipy, Matplotlib, Seaborn, and Scikit-learn directly in Excel! You can run Python code in Excel and boost your Excel projects! More and more Professionals and Developers use

  • Excel as Frontend

  • Python as analytical Backend.  


This course is the perfect choice for

  • Experienced Python Coders: Use Excel as Graphical User Interphase (GUI) | Run your Python scripts with Excel | Present your results with Excel Dashboards 

  • Excel Users and complete Python Beginners: Boost your Excel projects with clean and powerful Python code!

  • Mixed Groups: Non-Coders can run and use Python code simply by clicking on buttons in Excel.


Why take this course?

  • You will learn and master the xlwings library from scratch

  • For Excel Users and complete Python Beginners: This course includes a Python Crash Course that is tailor-made for you!

  • It´s the most comprehensive and practical (hands-on) xlwings course on the web

  • It covers three comprehensive real-world projects.

  • Project 1: You will learn how to boost your financial model in Excel by adding a Python Monte Carlo Simulation - Run your Excel calculation 10,000 times with different sets of inputs and analyze the results!

  • Project 2: You will learn to create Bloomberg-like Stock Dashboard Apps with Excel (Graphical User Interface) and Python (analytical Backend).

  • Project 3: You will learn how to use Pandas methods and functions on your datasets directly in Excel.


Why use Excel?

There is no better Graphical User Interface (GUI) and Reporting tool than Excel. Excel is

  • widely spread (750 million users)

  • standardized

  • intuitive to use

  • most users are well-trained

  • it requires low/zero setup

  • it requires low/zero maintenance  

  • and it´s still the best choice for financial models & spreadsheet calculations


Why use Python?

With hundreds of powerful Libraries, Python is the first choice for Data Science, Machine Learning, and advanced analytics in Business and Finance. The Python Ecosystem is way more powerful and versatile than VBA. And it´s cleaner and easier to learn and apply!


Why learn and master xlwings?

xlwings is the perfect tool to integrate Excel and Python! xlwings allows you to

  • Automate Excel from Python e.g. to produce reports or to interact with Jupyter Notebooks.

  • Write macros in Python that you can run from buttons in Excel, e.g. to load data from a database or an external API.

  • Write UDFs (user-defined functions) and leverage the power from NumPy, Pandas, and machine learning libraries.

  • Leverage Python's scientific stack for interactive data analysis using Jupyter Notebooks, NumPy, Pandas, scikit-learn, etc.

  • Use xlwings to automate Excel reports with Python.

  • Write Excel tools with Python instead of VBA and call your code directly from within Excel, e.g. via a button on the sheet.

  • This also works great for prototyping web apps.

  • Write (array) UDFs in a breeze by taking advantage of all the functionality already available in libraries like NumPy and Pandas.

  • Dynamic array formulas are supported.


As always, there is no risk for you as I offer a 30-day money-back guarantee. I am looking forward to seeing you in the course!

(Mac users are welcome! However, please note that 10%-15% of the course content (UDFs) is currently unavailable on Mac!)

Bạn sẽ học được gì

Automate Excel with clean and powerful Python Code

Learn and master the xlwings library from 0 to 100

Use Excel as Graphical User Interface (GUI) and run your Python code with Excel

Create powerful Dashboard Apps with Excel (frontend) and Python (backend)

Use powerful Data Visualization Tools (Matplotlib, Seaborn) in Excel

Learn Python from scratch with a taylor-made Crash Course (For Python beginners)

Write UDFs (user defined functions) and use Numpy, Pandas and Machine Learning Libraries directly in Excel

Write Excel tools with Python instead of VBA and call your code directly from within Excel

Use xlwings to automate Excel reports with Python

Prototype Web apps

Write and use Dynamic Arrays with xlwings

Run your financial model 10,000 times & more with a Python Monte Carlo Simulation

Load (financial) data from Web APIs directly into Excel

Run Python Scripts from within Excel with Run main and RunPython

Replace VBA macros with clean and powerful Python code

Yêu cầu

  • A desktop computer (Windows or Mac) capable of storing and running Python/Anaconda. The course will walk you through installing the necessary free software.
  • An internet connection capable of streaming HD Videos.
  • A working installation of Microsoft Excel.
  • Mac users are welcome. Please note that 10%-15% of the course content (UDFs) work on Windows only!
  • Willingness to code and work with Python.

Nội dung khoá học

15 sections

Getting Started

8 lectures
Introduction (don´t skip!)
03:28
Course Overview (don´t skip!)
02:06
Tips: How to get the most out of this Course (don´t skip!)
05:27
NEW: xlwings vs. the new Python in Excel Feature (added in August 2023)
01:05
FAQ / Your Questions answered
02:12
How to download and install Anaconda for Python coding
06:15
Jupyter Notebooks - let´s get started
12:24
How to work with Jupyter Notebooks
17:25

First Steps with xlwings (Reading and Writing Elements)

11 lectures
Introduction and Downloads
01:28
How to install xlwings
01:38
How to use xlwings as a Data Viewer
07:24
Data Viewer - Update
01:26
How to connect to an Excel Workbook
07:28
How to read and write single Values
05:56
How to assign a name
03:08
How to write Excel Functions with Python
02:58
Range Shortcuts
01:55
Case Study - Bringing it all together
06:59
Homework
04:42

Reading and writing many Values

10 lectures
Section Downloads
00:04
One-dimensional Data Structures
04:45
How to write Values vertically
01:45
Rows and Columns (1dim vs. 2dim)
02:50
How to read two-dimensional Data Structures
02:53
Advanced Reading with expand
03:47
How to write two-dimensional Data Structures
01:24
Range Indexing and Slicing
04:44
Efficiency
03:42
Homework
00:07

Project 1: Monte Carlo Simulations in Excel with Python (Part 1)

7 lectures
Introduction
07:40
Section Downloads
00:05
The Excel Model explained (Part 1)
11:09
The Excel Model explained (Part 2)
05:11
Running a simple Monte Carlo Simulation
14:33
A more advanced and realistic Monte Carlo Simulation
05:16
Final Considerations
03:00

Running Python Scripts in Excel - RunPython

10 lectures
Introduction and Downloads
02:38
Installing the xlwings add-in and other preparations
08:44
Running Python Scripts with "Run main"
04:41
Troubleshooting (Part 1)
04:32
All you need to know about VBA Macros
07:15
Running Python Scripts with "RunPython"
07:11
Troubleshooting (Part 2)
06:56
Run main vs RunPython
04:12
Excursus: Converting Jupyter Notebooks to .py
04:00
Homework
00:09

Project 1: Monte Carlo Simulations in Excel with Python (Part 2)

3 lectures
Introduction and Downloads
01:14
Monte Carlo Simulation with RunPython (Part 1)
08:07
Monte Carlo Simulation with RunPython (Part 2)
08:24

Using Matplotlib and Seaborn in Excel with xlwings

8 lectures
Introduction and Downloads
01:05
How to write a Matplotlib Plot into Excel
03:23
How to update the Plot
04:22
How to change Size and Position (Part 1)
04:42
How to change Size and Position (Part 2)
02:13
How write a Seaborn Plot into Excel
03:54
How to create Excel Charts with Python
03:25
Homework: Adding a Plot to the Monte Carlo Simulation (Project 1)
06:08

Project 2: Build Dashboard Apps with Excel (GUI) and Python (analytical backend)

8 lectures
Introduction and Downloads
02:44
IMPORTANT NOTICE (Update January 23)
01:30
Stock Performance Analysis during COVID-19 with Python & Pandas (Part 1)
08:38
Stock Performance Analysis during COVID-19 with Python & Pandas (Part 2)
07:44
Stock Performance Analysis during COVID-19 with Python & Pandas (Part 3)
08:36
Building a Stock Performance Dashboard App (Part 1)
12:45
Building a Stock Performance Dashboard App (Part 2)
05:50
Improving the Source Code and Errors
14:01

Reading and Writing Data Structures (Numpy, Pandas) & Converters

11 lectures
Section Downloads
00:05
(Default) Converters
04:10
The Numpy Converter
02:56
The Dictionary Converter
03:44
The DataFrame Converter (Part 1)
05:17
The DataFrame Converter (Part 2)
03:42
Data Science Application: Inspecting and Manipulating DataFrames in Excel
05:08
The Pandas Series Converter
04:20
Excursus: How to load Data from Excel into Pandas with pd.read_excel()
11:11
Excursus: Advanced import with pd.read_excel()
08:06
Excursus: How to load Financial Data / Time Series with pd.read_excel()
10:45

User-defined Functions (UDF) and Dynamic Arrays with xlwings (Windows only)

12 lectures
Introduction and Downloads
01:19
Preparations and your first UDF
07:46
How to change the Name and Location of the Python Module
05:27
Troubleshooting (UDF)
06:42
UDFs - Behind the Scenes
03:14
More complex UDFs and the @xw.arg Decorator
08:25
How to create Numpy UDFs
03:14
UDFs and Array Formulas
06:05
How to create Dynamic Arrays with xlwings UDFs
04:47
How to create Pandas UDFs
07:30
How to add Docstrings
03:53
Homework
00:08

Project 3: Use Pandas UDFs in Excel for Data Science and Finance (Windows only)

9 lectures
Introduction and Downloads
02:59
IMPORTANT NOTICE (Update January 23)
01:18
How to load Financial Data from the Web into Excel with the DataReader UDF
04:31
How to resample Time Series in Excel with the resample UDF
03:57
How to calculate Financial Returns with a Pandas/Numpy UDF
03:35
How to get Summary Statistics of a Dataset with the describe UDF
02:26
How to create a Dataset´s Correlation Matrix with the corr UDF
03:16
Taken all together - the Super UDF
09:16
How to perform inner/outer/left/right joins with the merge UDF
07:37

BONUS (added in Oct 2023): The new Python in Excel Feature - Overview

7 lectures
Introduction and Preview
06:08
Python in Excel: How it works & what you need
02:48
Python in Excel: Features & alternative Tools (xlwings)
10:11
How to set up Python in Excel (BETA)
03:11
Troubleshooting
05:36
Simple Operations using the PY Function
12:04
What else you can do with Python in Excel - Outlook
01:01

APPENDIX 1: Python Crash Course for Excel Users

56 lectures
Introduction and Overview
01:46
Section Downloads
00:04
Intro to the Time Value of Money (TVM) Concept (Theory)
06:01
Calculate Future Values (FV) with Python / Compounding
03:29
Calculate Present Values (FV) with Python / Discounting
02:38
Interest Rates and Returns (Theory)
04:26
Calculate Interest Rates and Returns with Python
03:47
Introduction to Variables
05:04
Excursus: How to add inline comments
02:50
Variables and Memory (Theory)
01:57
More on Variables and Memory
06:33
Variables - Dos, Don´ts and Conventions
03:49
The print() Function
04:09
Coding Exercise 1
09:00
TVM Problems with many Cashflows
03:21
Intro to Python Lists
02:22
Zero-based Indexing and negative Indexing in Python (Theory)
02:47
Indexing Lists
03:10
For Loops - Iterating over Lists
07:48
The range Object - another Iterable
04:56
Calculate FV and PV for many Cashflows
07:35
The Net Present Value - NPV (Theory)
07:47
Calculate an Investment Project´s NPV
03:02
Coding Exercise 2
08:41
Data Types in Action
06:07
The Data Type Hierarchy (Theory)
03:30
Excursus: Dynamic Typing in Python
01:38
Build-in Functions
05:52
Integers
03:18
Floats
05:58
How to round Floats (and Integers) with round()
05:10
More on Lists
05:15
Lists and Element-wise Operations
04:19
Slicing Lists
04:33
Slicing Cheat Sheet
00:03
Changing Elements in Lists
02:44
Sorting and Reversing Lists
03:48
Adding and removing Elements from/to Lists
09:33
Mutable vs. immutable Objects (Part 1)
09:04
Mutable vs. immutable Objects (Part 2)
05:12
Coding Exercise 3
11:32
Tuples
06:50
Dictionaries
06:22
Intro to Strings
08:47
String Replacement
04:10
Booleans
02:23
Operators (Theory)
04:37
Comparison, Logical and Membership Operators in Action
08:21
Coding Exercise 4
08:56
Conditional Statements
09:04
Keywords pass, continue and break
09:37
Calculate a Project´s Payback Period
04:35
Defining your first user-defined Function
06:07
What´s the difference between Positional Arguments vs. Keyword Arguments?
05:35
How to work with Default Arguments
05:27
Coding Exercise 5
00:04

APPENDIX 2: Matplotlib, Numpy, Pandas and Seaborn Crash Course

45 lectures
Downloads for this Section
00:04
Matplotlib Introduction
01:23
Line Plots
05:38
Scatter Plots
01:59
Customizing Plots (Part 1)
06:29
Customizing Plots (Part 2)
11:54
Coding Exercise 6
00:04
Modules, Packages and Libraries - No need to reinvent the Wheel
07:52
Numpy Arrays
08:23
Indexing and Slicing Numpy Arrays
03:13
Vectorized Operations with Numpy Arrays
03:56
Changing Elements in Numpy Arrays & Mutability
05:50
View vs. copy - potential Pitfalls when slicing Numpy Arrays
04:45
Numpy Array Methods and Attributes
05:13
Numpy Universal Functions
03:59
Boolean Arrays and Conditional Filtering
04:39
Coding Exercise 7
00:04
How to work with nested Lists
04:21
2-dimensional Numpy Arrays
03:51
How to slice 2-dim Numpy Arrays (Part 1)
05:36
How to slice 2-dim Numpy Arrays (Part 2)
02:03
Recap: Changing Elements in a Numpy Array / slice
03:39
How to perform row-wise and column-wise Operations
04:33
Coding Exercise 8
00:04
Intro to Tabular Data / Pandas
04:19
Create your very first Pandas DataFrame (from csv)
09:09
Pandas Display Options and the methods head() & tail()
06:41
First Data Inspection
11:25
Coding Exercise 9
00:04
Selecting Columns
06:05
Selecting one Column with the "dot notation"
02:16
Zero-based Indexing and Negative Indexing
03:04
Selecting Rows with iloc (position-based indexing)
10:07
Slicing Rows and Columns with iloc (position-based indexing)
04:39
Position-based Indexing Cheat Sheets
00:02
Selecting Rows with loc (label-based indexing)
03:14
Slicing Rows and Columns with loc (label-based indexing)
10:21
Label-based Indexing Cheat Sheets
00:02
Summary, Best Practices and Outlook
06:30
Coding Exercise 10
00:04
First Steps with Pandas Series
03:53
First Steps with Pandas Index Objects
05:57
Importing Time Series Data from csv-files
08:16
Initial Analysis / Visualization of Time Series
05:41
Seaborn Introduction
05:24

What´s next? (outlook and additional resources)

1 lectures
Bonus Lecture
04:43

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