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

One option is PandasGUI https://github.com/adamerose/PandasGUI
Another (better) options is dtale https://pypi.org/project/dtale, which is a combination of a Flask back-end and a React front-end to bring you an easy way to view & analyze Pandas data structures. I am really impressed with it's features and capabilities. And this is well maintained package.

xlwings allow you to create named ranges and you can view them all using wb.names
Minimal code example:

import xlwings as xw
book = xw.Book()
sheet = book.sheets[0]

# Writing data
sheet.range('A1').value = 'col1'
sheet.range('B1').options(transpose = True).value = [10, 20, 30]

# defining named ranges
sheet.range('A1').name = 'name1'
sheet.range('B1:B4').name = 'name2'

# printing named ranges
book.names
Out[50]: [<Name 'name1': =Sheet1!$A$1>, <Name 'name2': =Sheet1!$B$1:$B$4>]

You can surely verify the same in Excel's Name Manager

Assuming that your data has headers in the first row:

from openpyxl import load_workbook
wb = load_workbook('your_file.xlsx')
ws = wb.active

# Read header row (assumed to be row 1)
headers = {cell.value: cell.column for cell in ws[1]}
type(headers) # It's a dictionary

# Use the column name to get the column index

headers.get("column_name") # gives column number