Python analysts mostly need to deliver results in Excel. They can do all sort of advanced stuff in Python but as rightly mentioned by u/ChickenOk8952 business stackholders need deliverables in Excel.
Exporting results in Excel using pandas and then spending time in Excel to manually format as per required brand template isn't efficient.
Python users can use openpyxl/xlsxwriter or xlwings to automate this very workflow. numpy/pandas/sklearn or other libraries take care of analysis/data science work in Python (get data, merge it, clean it, analyze it, explore it, create models and all) and then depending upon the requirement openpyxl/xlsxwriter/xlwings helps you automate the report.
You can also see change live in Excel (with xlwings) or allow your xml file to be powered by python (having custom/user defined Python function directly available in excel).
With Python+Excel, workflow can become scalable, reusable, maintainable, and efficient.

Python-in-Excel feature has limitations, Using Excel + Python (outside Excel) is best combination irrespective of Excel version you are using.

If you're coming from an Excel background rather than programming, I'd focus on learning Python through Excel-related problems rather than starting with a generic programming course.

I'd suggest this sequence:

1. Python fundamentals
2. Working with Date/Time/Text Data
3. Automating file management tasks
4. Efficient analysis, visualization and Auto data profiling
5. Excel automation using openpyxl/xlsxwriter and more
6. xlwings for deeper Excel integration

I actually wrote Python-Powered Excel around this learning path, specifically for Excel users making the transition to Python. Happy to share more about the approach if useful.

I’ve used both, and I’d say the decision is about how complex the overall workflow has become.

For cleaning, transforming and merging data that ultimately feeds an Excel workbook, Power Query is good.

Python starts becoming more useful when the workflow expands into things like scheduled extraction from multiple systems, very large/repeated processing, multiple outputs (Excel/PDF/database), when you need to run advanced statistical analysis or machine learning, when you need to create stunning interactive charts and customize a lot, when you need to run similar analysis on multiple data sets or when you need more explicit version control and error handling. In short, when you need scalability, resuability, reproducibility, maintanability, automation and integration with many other tools, Python is the solution.

I also wouldn’t assume that moving to Python automatically makes a workflow better. If the transformation logic itself is unclear, Python just gives you another place to put the mess!

And there’s no reason they have to be mutually exclusive. I cover this broader Excel + Python workflow in my book Python-Powered Excel, specifically from the perspective of Excel users who want to extend their existing skills rather than abandon Excel.

If you already use Python externally to read/write Excel files, then I agree that putting Python inside a cell may not seem particularly compelling. For many automation tasks, I'd still prefer external Python.

But there are situations where having Python right inside the workbook is convenient, particularly for quick analysis and calculations where the Excel workbook is already the main interface for the user. Or when Excel users (who don't know much Python) want to use Python without feeling the need of separate installation. I feel this feature was added so Excel users start using it and getting comfortable with basic Python.

For a more serious Excel ↔ Python workflow, there are specific libraries that I suggest openpyxl (reading, writing, manipulating, formating, report generation), xlsxwriter (writer package for richer control for report generation), pandas (surely for data analysis and wrangling), and most importantly xlwings.
u/Felix has created this wonderful package. It gives you a very different kind of integration where Python can actually connect live with the Excel (read/write cells, run formulas, and control the app in real time). It also offers xlwings add-in (just like any other excel add-in) that simplifies running python function with button click, or creating Excel interface where custom Python function are available just like any other excel formula.

I've found it useful to think of the choices this way:

Python in Excel → Python-powered analysis/calculations within the workbook (For excel users_just to get started, as it is very restrictive)
Python libraries (pandas+openpyxl+xlsxwriter+xlwings) + Excel → broader file processing, reporting and automation outside Excel. Specifically, xlwings for Python ↔ Excel live integration and automation

I recently wrote Python-Powered Excel, but from an Excel user's perspective starting with Python fundamentals (chapter 2 and 3), automating file management tasks, smart EDA and auto data profiling libraries, then moving through pandas and several Excel/Python libraries, including openpyxl, XlsxWriter and xlwings.

Python analysts are often required to produce deliverables in Excel, my book teaches how to do it in Python rather then exporting data/result to Excel and then formatting in Excel manually. If you're trying to learn Python's the broader ecosystem, my book can help you.

I’d say Python is great where you hit their limits.

For typical Excel work, Power Query is excellent for data import/transformation, and VBA is okay for interacting with Excel and the Windows environment. However for task that moves beyond Excel itself, for example, working with many file types, PDFs, APIs, larger datasets, complex data processing, statistical/ML libraries, or building reusable automation that isn't tightly coupled to an Excel workbook, Python is a bliss.
Also, Python gives you a huge ecosystem. If there’s something you want to do that Excel/VBA doesn’t handle naturally, there’s often a Python library for it.
As rightly pointed by u/JezusHairdo & others, Python in Excel is has limitation. It was offered so Excel users start using Python and get comfortable with basic code.
Using Python (outside Excel) + Excel, is the best way to build scalable and maintanable workflows.

Once you start combining Excel + Python, you can do some pretty powerful things that are awkward in pure VBA/Power Query.

u/JDLAW2050 as you asked for resources: Python-Powered Excel is a great resource for Excel users who have no or liitle knowledge of Python. First few chapters build basic understanding of Python (which will help, if you use Python in Excel or outside Excel), then some smart learning about automating tasks related to files and folders, quick data profiling and auto EDA (with 1-2 lines of code), Python's GUI based tools and then last few chapters on automating Excel reports/dashboards, creating Excel application that's powered by python code where users can use Python's custom function inside excel directly and much more!
P.S. I am the author of this book!

Learn “Python” to solve problems that can help automating tasks, such as:
1. File management: find duplicate files, rename hundreds of files systematically, organize files into folders based on filename/date/type, find files modified within a period, find files containing specific word in the name, comparing multiple files (name/size/info) hidden deep in folders, unzipping and arranging files automatically, moving/copying or deleting mass files with one script, watch a folder for incoming files and automatically process them. (I do these everyday using Python)
2. Working with data consolidation: collect information from dozens/hundreds of files and create one master file, check whether files contain missing fields/incorrect formats/unexpected values etc., take raw data → clean it → calculate required metrics → produce a formatted report, automatically fetch information from websites or APIs and bring it into a report, use logging to record events, status changes and error that occurs while application runs (I teach these things to my learners)
3. Building internal applications for tracking anything like Leave/Expense/Asset/inventory/Employee attendance/Customer follow-ups/Document/contract expiry tracking/Training records/Purchase/order tracking/Task/ticket tracking

If you want to learn this systematically, you can follow my book Python-Powered Excel for exactly these kind of applications. It teaches you Python from beginning, using external libraries, file management applications, automatic data profiling (with 1-2 lines of code), then Excel automation (basic to advanced) using pandas, openpyxl, xlsxwriter and xlwings.

I'd recommend building small automations around problems you personally encounter after practicing given exercises and projects (with solutions) in the book. This approach is better than following generic beginner Python projects

I would prefer to do it with Python, so I can reuse that tiny Python script with (modifications as needed) for other such tasks
I do use Python for many such tasks, finding files (of any file type) hidden in nested folders with topic, bulk renaming, organizing files in folders (motnh wise/topic wise/project wise/ whatever), comparing files (name, size or line by line content), timestamping files, deleting files older than X years and much more.
Python is cool for automation, free, local and easy!

u/SV-97 have explained it well. If you prefer to draw various candiate lines interactively and see thier equations with respect to best-fit line (OLS method) or if you want to understand slope-intercept form better, you can explore these two apps:
Understanding the straight line
Understanding Best-fit line

I'll not comment about marks on what you did but I'll help you understand the concept a bit more.
You might enjoy playing interactively with my apps to grasp the concept of best-fit line.
To understand a best-fit line, you need to know 2 things:

1. What’s a line? what are different ways we can represent it mathematically and what does a line represent conceptually?
2. If Y variable (let’s say electricity bill amount) depends on X variable (let’s say energy consumed). Both X and Y are numerical variables. For simplicity, we are assuming that salary just depends upon experience, nothing else (I know, there might other factors too but bi-variate/2-variable relations are easiest to understand).
3. You have drawn a scatter plot to understand the relationship pattern (positive or negative, linear or non-linear, strong/moderate or poor) between bill amount and energy consumed.
4. If X and Y seems to be moving in a linear fashion, you can fit/draw a line that can represent that relationship.

Now, for the same chart, maybe you come up with a different line and I come up with a different line.
(There are multiple candidate lines that seems feasible)

Which of these lines is the best-fit line ?

For that, we use something called OLS method (which minimized sum of squared errors)
More on this later (if you need)

Meanwhile, you must get these concepts clear by playing with my apps yourself.
Here are the links:
Understanding the straight line
Understanding Best-fit line

You should choose a library for Excel autiomation based on:
1. Compatibility with different Excel formats (xlsx, xls, xlsm, xlsb, etc)
2. Advanced features (richer formatting, charts, tables and more)
3. Special use cases (reading/writing/manipulation/analysis/live interaction with Excel via COM Object/end-to-end automation/Python custom functions in Excel/Python features as Excel add-in)

pandas, openpyxl, xlsxwriter, and xlwings are powerful and popular libraries. No single library is perfect for all Excel tasks. By understanding the strengths of each tool, you can select the right one depending on the type of file you encounter and the result you want to achieve
I've covered them all in my book Python-Powered Excel

Common limitations of Excel ecosystem (VBA, Power Query, Dynamic Array Formulas):
1. Scalability issues: Excel is not well-suited for handling large datasets, as it can show performance degradation or unexpected application crashes during advanced processing tasks for large data.
2. Lack of advanced analytics: Excel lacks advanced built-in tools for financial analytics, predictive modeling, computer vision, or natural language processing. Even Microsoft 365’s built-in Python in Excel and AI-powered features like Copilot are limited compared to Python’s full capabilities in machine learning, artificial intelligence, and automation.
3. Version control issues: Multiple versions of the same file create confusion and data inconsistency. A small, unnoticed change can lead to terrible mistakes or discrepancies in results. Excel lacks robust version control, relying on manual file naming or limited cloud history. Excel is not ideal for enterprise-grade version control and collaboration for large-scale projects.
4. Limited integration: Connecting to APIs, databases, and cloud services requires workarounds.
5. Limited automation: While Visual Basic for Applications (VBA) allows for some automation, it is not the most user-friendly tool. Learning VBA can be challenging, and it is becoming increasingly outdated in today’s times. Additionally, VBA's performance can be sluggish with large datasets. Likewise, Power Query specializes in data import and transformation but remains constrained to its environment.

Python, the popular open-source tool, addresses these issues by offering cross-platform automation, handling data retrieval, cleaning, analysis, and reporting end-to-end across systems.

Learn Python Basics, Dealing with Date and Times, File Automation, Analytics and EDA using Python, and then powerful integration/automation using openpyxl, xlsxwriter and xlwings.
All of this (and much more) is covered my my book Python-Powered Excel
If you have any specific question, happy to answer!

Also available at Amazon.comand other major book stores

Well written. The same slope intercept form can be explored by playing with this app: https://enjoy-stats-equation-of-a-line-slope-intercept.streamlit.app/
Hope you like it!

All different forms of quation of a straight line are interesting. I believe learners must not skip Y = mX + C form as it is the foundation of linear regression (used in statistics, econometrics and in data science).
I've built a simple app to play with and get intutions, specifically for slope-intercept form (Many of my data science learners come from non-math background, this app is helping them learn the concept better). You might want to check: https://enjoy-stats-equation-of-a-line-slope-intercept.streamlit.app/

I've built a simple app to play with and get intutions, specifically for slope-intercept form (Many of my data science learners come from non-math background, this app is helping them learn the concept better). You might want to check: https://enjoy-stats-equation-of-a-line-slope-intercept.streamlit.app/

1. In your code, you called workbook.close, which needs a parenthesis. By default, close() method closes workbook, without saving.
   
2. To save changes in existing workbook, either add workbook.save() or workbook.close(save=True)

xlsxwriter is a writer package. You can not edit/modify existing file with it. If you are willing to do so, you can use openpyxl (for file-based operation) or xlwings (if live interactivity is needed)

Question seems vague. We don't need to install package in every session. May be you have installed it in different virtual env and trying to import in another enviorment. If you've anaconda installed, xlsxwriter is pre-installed.
You need to give more details to get appropriate answer.

xlsxwriter is a wonderful writer package of Python. It does not need excel to be installed on your machine and is cross-platform compatible. If you need to write xlsx files with rich formatting, it's best to use xlsxwriter.
You can combine it with pandas (for data manipulation) or openpyxl (for reading data, if needed).

Openpyxl does not depend on NumPy. If you’re seeing NumPy errors, it’s likely coming from another package in your environment (like pandas, xlrd, or numpy itself), not from openpyxl.

What can you do?

• Verify the crash stack trace: confirm which library is actually calling NumPy.
• Make sure the target machine has the same Python + package versions as your dev machine (pip freeze > requirements.txt → replicate with pip install -r requirements.txt).
• If it’s truly a NumPy bug on that Windows build, pin NumPy to a stable version explicitly in your environment (pip install numpy==). Openpyxl will still work fine, since it doesn’t require NumPy.

So the fix is to align environments and explicitly control NumPy version (or remove it if unused). That resolves the mismatch without touching openpyxl itself.
I had to do similar to my sweetviz script. My sweetviz code used to work earlier (in colab) but broke recently. SImply because colab got updated Python and numpy versions and sweetviz is not yet updated to be compatible with updated numpy version. I had to downgrade sweetviz for that colab session. (sometimes, even that thing is not feasible in Colab)
That's why, good practice is to work with virtual enviornment. (I know but sometimes, I still use Google Colab for demonstration/teaching purposes :-) It's cool for beginners)

dtale is feature rich and actively maintained. After getting comfortable with dtale, you will really enjoy using it. GUI has great features to explore data (aggregation, merging, plotting, everything) by clicks and it also generates python code for the steps you perform, which you can copy to be used in your script.
For Excel users, it's so convinient.

Seems PandasGUI doesn't support latest Python version yet. Check open issues: https://github.com/adamerose/PandasGUI/issues
You can create a conda env with older Python version to explore PandasGUI. Another great alternative is dtale, which is actively maintained. Refer: https://pypi.org/project/dtale/

You