Quick Answer: how an Excel user automates the weekly report with Python in two weeks
Give it ten working days at about an hour a day, ending with a script that turns this week's raw exports into the finished report file on a schedule. You do not need a computer science course. You need enough Python to read files, enough pandas to combine and group them, and one script that runs without you.
That is the whole plan. Days 1 to 5 get you reading data: install Python, learn the basics, meet pandas, load your own export, and combine a folder of files into one table. Days 6 to 10 build the report: your pivot table as code, one script from top to bottom, a formatted output file, a real-life example, then a schedule.
Two rules make it work. Every day ends with something running on your own exports, not a tutorial's sample file. And every video below was picked because its title names exactly the job of that day, so you watch it once, close it, and build.
Why Python for a weekly Excel report
A report you rebuild by hand every week is a list of steps you repeat: open the exports, paste them together, fix the columns, build the pivot, format it, save it. Python writes those steps down once as code. Next week the same code runs on the new files, and the report appears without the paste-and-fix morning.
You are in good company. In LearnPath's own path data for the 60 days to 7 October 2026, "python" was the second most common topic learners built a path on, with 58 paths. Plenty of people are making the same move from spreadsheet to script.
Most of what you know from Excel carries over. A sheet becomes a DataFrame, which is pandas' name for a table. A filter becomes a condition in brackets. A pivot table becomes a groupby. SUMIFS becomes a sum over a filtered table. What changes is that every step is written down, so nothing depends on you remembering which column you fixed last Tuesday.
One thing does not carry over: fixing a number by hand in the output. If a total is wrong, the fix goes into the script, never into the finished file. That feels slower the first week. By the third week it is the reason the report takes seconds.
If your Excel side is still shaky, spend an evening on our one-week Excel plan for data analysis first. Knowing what a clean table and a good pivot look like makes every pandas step easier to check.
Before Day 1: collect three weeks of exports and one finished report
Before you open a video, pull three things together: the raw exports behind the last three weekly reports, the finished report from one of those weeks, and a written list of the steps you do by hand. Without your own files the plan turns into tutorial-watching, and tutorials do not build your report.
Put the raw exports into one folder, one subfolder per week if that is how they arrive. Do not clean them by hand. Cleaning is what the script will do.
Keep one finished report as your answer key. On Day 9 the script's output has to match it number by number, and any difference is a step you forgot to write down until proven otherwise.
Then write your manual steps as a plain numbered list. "Open the three regional exports. Paste them under each other. Delete the blank rows. Pivot sales by region and week. Bold the header. Save as Weekly_Report with the date." That list is the outline of your script. Each line becomes a few lines of code by Day 9.
Week 1: learn enough Python and pandas to read your exports
Week 1 is five days of groundwork: installing Python, learning the basics, meeting pandas, loading your real export, and combining a folder of files into one table. There is nothing to show your manager yet. It is the week people want to skip, and it is the reason Week 2 takes hours rather than weeks.
Day 1: install Python and Jupyter, and run your first lines
Start freeCodeCamp's Data Analysis with Python for Excel Users - Full Course (freeCodeCamp.org, 3:57:46), taught by Frank Andrade. Today watch only its opening sections: installing Python and Jupyter Notebook with Anaconda, and the Jupyter interface. It is enough because today's job is a working setup, and the course was built for people coming from Excel.
On your own machine today: install it, open a notebook, and run a few lines: add two numbers, print a sentence, store your report's name in a variable. If the install fights you, solve that today, while there is nothing else to break.
Day 2: learn the Python basics a report script uses
Keep going in the same freeCodeCamp course through its Python basics sections: data types, variables, lists, dictionaries, conditionals, loops, functions and modules. It is enough because those are almost the only parts of the language a reporting script touches.
If that pace is too fast, watch the first part of Programming with Mosh's Python Full Course for Beginners (Programming with Mosh, 2:02:21) instead. Stop once you have covered the same list. Do not finish it this week.
On your own data today: write a list of your export file names, loop over it, and print each one. Write one small function that takes a week number and returns a file name like report_week_41.xlsx. You will use both ideas on Day 5 and Day 7.
Day 3: meet pandas, and see your first pivot table as code
Finish the pandas half of the freeCodeCamp course: creating DataFrames, selecting and adding columns, value_counts(), sort_values(), pivot tables in pandas, plots, and the section titled "Save Plot and Export Pivot Table". It is enough because those sections cover, in order, the moves a weekly report makes.
On your own data today: follow along on the course file, then repeat the pivot table step on any small table you type in yourself. Do not reach for your real export yet. Today is about seeing that a pivot is a function call.
Day 4: load your own export into pandas
Watch Corey Schafer's Python Pandas Tutorial (Part 1): Getting Started with Data Analysis - Installation and Loading Data (Corey Schafer, 23:01). It is enough because the title names today's job exactly: installing pandas and loading data. The rest of his Pandas Tutorials playlist is reference for later.
On your own data today: load one real export. For an Excel file it is one line, and pandas reads .xlsx files through the openpyxl library, so install that too if it asks.
import pandas as pd
df = pd.read_excel("exports/week_40/sales_north.xlsx")
print(df.shape)
print(df.head())
Check the row count against what Excel shows. If the header is on row 3, read_excel takes a header=2 argument. If the file is a CSV, use pd.read_csv instead.
Day 5: combine many export files into one table
Watch Jie Jenn's How To Combine Excel Files With Python (And pandas) (Jie Jenn, 5:14). It is enough because the title is the copy-paste step you do every week, and the video is five minutes long. Spend the rest of the hour on your own folder.
On your own data today: replace the paste-them-under-each-other step with a loop.
import glob
import pandas as pd
files = glob.glob("exports/week_40/*.xlsx")
df = pd.concat([pd.read_excel(f) for f in files], ignore_index=True)
Check that the combined row count equals the sum of the separate files. If one region's export has a slightly different column name, this is the day you find out.
Week 2: build the report script and put it on a schedule
Week 2 turns what you learned into the report: your pivot table as a groupby, one script that runs from start to finish, an output file formatted the way readers expect, a check against a real report, then a schedule. Each day ends with the script doing one more of the steps from your handwritten list.
Day 6: turn your pivot table into a groupby
Watch Corey Schafer's Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data (Corey Schafer, 49:06). It is enough because grouping and aggregating is what your pivot table does. If you want the short version, Alex The Analyst's Group By and Aggregate Functions in Pandas | Python Pandas Tutorials (Alex The Analyst, 11:05) covers the same job.
On your own data today: rebuild the main pivot from your finished report.
summary = df.groupby(["region", "week"], as_index=False)["sales"].sum()
Use your own column names. Then compare three totals against last week's report by hand. If they match, the hardest part of the plan is done.
Day 7: make it one script that runs from top to bottom
Watch John Watson Rooney's Automate Excel Work with Python and Pandas (John Watson Rooney, 21:29). It is enough because its title is the whole goal of this plan, and today is about joining the pieces, not learning new ones.
On your own data today: move your notebook code into one file called weekly_report.py. Read the folder, combine, clean, group, then write the result.
summary.to_excel("output/weekly_report.xlsx", index=False)
Run it from the command line, not the notebook. If it runs on a fresh terminal with no notebook open, it can run on a schedule.
Day 8: format the output the way your readers expect
Watch Tech With Tim's Automate Excel With Python - Python Excel Tutorial (OpenPyXL) (Tech With Tim, 38:02). It is enough because openpyxl is the library that edits a saved workbook, and today's job is the finishing your colleagues notice: bold headers, column widths, number formats.
On your own data today: after to_excel, open the file with openpyxl, bold the header row, widen the columns, and set a number format on the sales column. Keep it plain. The test is whether the file looks like the one you used to send by hand.
Day 9: check it against a real report
Watch Sven Bosau's Automate Excel Reporting Using Python (Real-Life-Example) | Pandas, Plotly, Xlwings Tutorial (Sven Bosau, 12:52). It is enough because the title promises a real-life reporting example, which is a useful sanity check on your own structure. His The Easiest Way to Automate Excel with Python (Sven Bosau, 16:54) is the second angle.
On your own data today: point the script at one of the past weeks you saved before Day 1 and compare its output with that week's finished report, number by number. Every difference is a manual step missing from the script. Add it, rerun, compare again.
Day 10: schedule it so it runs without you
Watch ritvikmath's Schedule Python Tasks (in Windows) : Data Science Code (ritvikmath, 4:06). It is enough because scheduling is one setting, not a new concept, and the title names the exact job on a Windows machine.
On your own data today: schedule weekly_report.py for the morning after the exports usually land. Make the script name the output file by date, so last week's report is never overwritten. Then drop this week's exports in and let it run. If your company's machines are locked down, ask IT before you set this up, not after.
Python in Excel or a standalone script: which one this plan needs
For a report that should build itself every week, a standalone script. Microsoft's "Introduction to Python in Excel" page, checked October 2026, says its calculations run in the Microsoft Cloud and need a qualifying Microsoft 365 subscription and internet access. That suits analysis inside one workbook. A scheduled script suits reading new files and writing a new one.
Here is what Microsoft's page says, checked October 2026. Python in Excel works on Excel for Windows, Excel on the web and Excel for Mac. It is not available on iPad, iPhone or Android. It comes with "a core set of Python libraries provided by Anaconda", and it needs internet access.
If you want to see it before you decide, watch Leila Gharani's Is Python in Excel Actually Useful? (What You Need to Know) (Leila Gharani, 8:18), whose title asks exactly that question, then Kevin Stratvert's Python in Excel - Beginner Tutorial (Kevin Stratvert, 20:06).
The good news is that the choice is not final. The pandas you learn in this plan is the same pandas in both places. Build the scheduled script first, because that is what removes the weekly rebuild. Try Python in Excel later, for one-off analysis inside a workbook someone sends you.
The free reading companion: Automate the Boring Stuff, chapter 14
If you learn better from text, Al Sweigart's Automate the Boring Stuff with Python, Third Edition, is free to read online under a Creative Commons license. Chapter 14 is titled "Excel Spreadsheets". Read it alongside Day 8, when you are working with openpyxl, as the written version of the same job.
You can find it at automatetheboringstuff.com, and chapter 14 on Excel spreadsheets directly. Treat it as a companion, not a second course. Read the part that matches the day, then go back to your own script.
What to skip in these two weeks
Skip anything that does not get your weekly report building itself before the deadline: full beginner courses watched end to end, web frameworks, machine learning, and data visualization beyond one simple chart. Each is real and useful later. None of them turns this week's exports into the finished file, which is the only test right now.
The full courses, end to end. The freeCodeCamp course is nearly four hours, and Mosh's is over two. You watch the parts that match each day and stop. Come back to the rest after the report runs.
Web frameworks and apps. Many Python tutorials end in a website or a game. A report script needs neither. Our guide to learning Python from YouTube covers the wider language when you want it.
Machine learning and data science. They use pandas too, which is why they show up in your search results. They are a different job from rebuilding a report.
Choosing the perfect editor. Jupyter for learning and a plain .py file for the script is enough. Pick a fancier editor in month two.
Switching tools mid-plan. If someone suggests Power BI instead, note it. Power BI is a fine month-three project if dashboards become the job. For now, finish the script.
What it costs to automate the report
Building costs nothing. Python and pandas are free and open source, and every video in this plan is free on YouTube, as is Automate the Boring Stuff online. Python in Excel is the exception: Microsoft says it requires a qualifying Microsoft 365 subscription, checked October 2026. The standalone script in this plan does not need it.
The real cost is your hour a day for ten working days, plus a conversation with IT if your work machine does not let you install software. Ask on Day 1, not Day 10, because install requests can take longer than the build.
Proof at the end: the script and the report it builds
The proof that these ten days worked is not a quiz score. It is a script that turns this week's raw exports into the finished report file, matches a past report number by number, and runs on a schedule while you do something else. That is the deliverable your manager actually asked for.
Show your manager two things. The output file next to last week's hand-built version, with a note that the numbers match. And one sentence on how it runs: exports land in the folder, the script runs in the morning, the report appears. Keep your handwritten list of steps too. It is the documentation for whoever takes over the script.
When the report runs and you want more Python, our list of the best YouTube channels for Python in 2026 is the place to start.
LearnPath builds a path of free YouTube videos for a topic and generates a quiz from each video's transcript, so you find out whether the watching stuck. Free opens step 1 of up to two paths; every later step is Pro, and the completion certificate is Pro-only, so free accounts do not get one. The certificate shows you finished a plan. The script shows you can do the job.
Build a work-ready path on Python before your next weekly report.
Frequently Asked Questions
Six questions that come up when an Excel user is told to automate a weekly report with Python. The short answers below point back to the day in the plan that covers each one, so you can jump straight there when the question comes up in the middle of a build.
Can an Excel user learn enough Python in two weeks to automate a report?
Enough to automate one weekly report, yes. Ten working days at about an hour each covers installing Python, the basics, reading Excel files with pandas, combining exports, grouping, formatting the output and scheduling it. It will not make you a programmer. It will replace one manual rebuild with a script you understand.
Do I need to learn general Python before pandas?
A little, not a lot. You need variables, lists, dictionaries, loops, functions and imports, which is about two days of the plan. Classes, web frameworks and algorithms can wait. Most of a reporting script is pandas calls, so move to pandas on Day 3 even if the basics still feel shaky.
Should I use Python in Excel or a standalone Python script?
For a report that should build itself on a schedule, a standalone script. Microsoft says Python in Excel runs its calculations in the Microsoft Cloud and needs a qualifying Microsoft 365 subscription and internet access. It suits analysis inside one workbook. A script suits reading this week's exports and writing a new file.
What is the Python version of a pivot table?
In pandas it is groupby followed by an aggregate such as sum or mean, or the pivot_table function, which takes the same rows, columns and values idea you already know. The freeCodeCamp course for Excel users covers pivot tables in pandas, and Day 6 of this plan is built around grouping.
Will the script still work when next week's export changes?
If the column names stay the same, yes. That is the point of the plan. If a column the script uses is renamed, pandas stops with an error that names the missing column, which is better than a silent wrong number. Fix the name in one line, rerun, and the report rebuilds as before.
Does LearnPath give a certificate for finishing a Python learning path?
Yes, on the Pro plan; free accounts do not get one. LearnPath builds a path of free YouTube videos for a topic and generates a quiz from each video's transcript. Free opens step 1 of up to two paths, and every later step is Pro. The real proof is still the script that builds your report.
