Posted in

How to write an Excel file in Jupyter Notebook?

by janice•

Hey guys, if you’ve ever been deep in a Jupyter Notebook session, cranking out data analysis, cleaning messy datasets, or even dumping survey results you just pulled from Google Forms, you’ve probably hit a point where you think, “Wait, how do I actually save this as an Excel file my team can open without them having to install a million random libraries?” No cap, this used to stress me out back when I was the new guy at the Notebook supply shop—wait, right, I work for a Notebook provider, so let’s keep that real. We sell all sorts of tools for data folks: cheap but reliable laptops, portable monitors, and yes, even the occasional guide to make your work life way less chaotic. Today we’re breaking down how to write an Excel file right in Jupyter Notebook, no weird hoops, no confusing code that breaks mid-save. Let’s dive in. Notebook

First, before we even touch code, let’s get one thing straight: Jupyter itself doesn’t have a built-in Excel writer. You need a third-party library, and the two big ones here are pandas and openpyxl. Wait, why those two? Pandas is the bread and butter for anyone working with tabular data—if you’ve ever loaded a CSV or cleaned a DataFrame, you already know pandas, so it makes sense to use it to handle Excel files too. Openpyxl is the workhorse that actually lets pandas write .xlsx files (the modern Excel format, not the old .xls which is like a 2000s flip phone). You’ll need to install these first if you haven’t already—trust me, skipping this step will save you 10 minutes of Googling “why won’t my code run”.

Installing them is easy, just pop open a cell in your Jupyter Notebook and run these two lines. No need to mess around with command prompts unless you really want to. First line installs pandas, second installs openpyxl:
!pip install pandas openpyxl
That’s it. Hit shift+enter, and when that finishes, you’re ready to go. No overcomplicating, no “make sure your path is right” garbage yet—we’ll start with the simplest case so you can test it out.

Let’s say you already have a DataFrame. Maybe it’s a list of monthly sales, or survey responses, whatever. For this example, we’ll make a dummy DataFrame so you can copy-paste and test immediately. Type this in a new cell:
import pandas as pd

Dummy sales data—replace this with your actual DataFrame!

data = {
"Month": ["January", "February", "March", "April", "May"],
"Product A Sales": [1200, 1500, 1350, 1700, 1900],
"Product B Sales": [800, 950, 900, 1100, 1250]
}
df = pd.DataFrame(data)

See what your DataFrame looks like to check for typos

print(df)
Run that, and you should get a nice little table with months and sales numbers. Perfect. Now, to turn this into an Excel file. The code is one line (well, one function):
df.to_excel("monthly_sales.xlsx", index=False)
Wait, hold up—what’s the index=False for? By default, pandas adds a leftmost column with row numbers (0, 1, 2, etc.) that’s super annoying for Excel users who just want to see their actual data. That line tells pandas “don’t add that random index column, just write the actual headers and data”. If you forget that, your Excel file will have an extra column of numbers, and your team will side-eye you for wasting their time. Trust me, I’ve done that.

Now hit shift+enter on that line. Wait, nothing showed up? That’s not a glitch! Jupyter just runs the code silently here. How do you check if it worked? Go to the file browser on the left side of Jupyter—you know, that little folder icon. Refresh it, and you’ll see monthly_sales.xlsx pop up right there. Click it, and it should download automatically, or open in whatever Excel viewer you have. That’s the most basic, no-fuss way to write an Excel file in Jupyter. But wait, what if you need more than one sheet? Like, maybe you have sales data and customer feedback, and you want both in the same Excel file? That’s super common for reports, and a lot of guides skip that part. Let’s fix that.

To write multiple sheets, you can’t use the regular to_excel on each DataFrame—you need to create an ExcelWriter object first, which is like a blank binder that holds all your sheets. Here’s how that works. Let’s make a second dummy DataFrame for customer feedback:

Second DataFrame for a new sheet

feedback_data = {
"Month": ["January", "February", "March", "April", "May"],
"Average Rating": [4.2, 4.5, 4.1, 4.7, 4.8],
"Complaints": [3, 2, 5, 1, 0]
}
feedback_df = pd.DataFrame(feedback_data)
Now, create the Excel writer. The engine="openpyxl" part here is important—if you don’t specify that, pandas might use an older engine that doesn’t work right for multiple sheets. Run this next cell:

Create writer object for multiple sheets

with pd.ExcelWriter("sales_and_feedback.xlsx", engine="openpyxl") as writer:
# Write first sheet, name it "Sales Data"
df.to_excel(writer, sheet_name="Sales Data", index=False)
# Write second sheet, name it "Customer Feedback"
feedback_df.to_excel(writer, sheet_name="Customer Feedback", index=False)
That with statement is just a Python thing that makes sure the file saves properly, no corruption, even if something breaks mid-code. Once that runs, check your file browser again—you’ll have a new Excel file with two separate sheets. Open it up, click the tabs at the bottom, and boom, all your data is there. No need to make separate files, no copying and pasting, this is way faster.

Wait, but what if you need to add a new sheet to an existing Excel file? Like, say you already made the monthly sales file last month, and now you want to add June’s data without overwriting the old sheets. That’s a trick a lot of people don’t know, so let’s cover that too. First, we need to load the existing Excel file, because openpyxl can edit existing files, not just make new ones. Let’s say we have that sales_and_feedback.xlsx file we made earlier, and we want to add a June sales sheet. First, make the new June data:

New data for June

june_data = {
"Product A Sales": [2100],
"Product B Sales": [1400]
}
june_df = pd.DataFrame(june_data, index=[5]) # Use index 5 to match the previous rows
Now, to add it to the existing file, we use mode="a" for append, and if_sheet_exists to tell pandas what to do if the sheet already exists. Run this:

Add a new sheet to an existing Excel file

with pd.ExcelWriter("sales_and_feedback.xlsx", engine="openpyxl", mode="a", if_sheet_exists="replace") as writer:
june_df.to_excel(writer, sheet_name="June Sales", index=False)
Wait, what’s if_sheet_exists="replace"? If you didn’t have that, and a sheet named “June Sales” already existed, pandas would throw an error. Using “replace” means if the sheet is there, it’ll overwrite it—you can also use “append” if you want to add rows to an existing sheet, but for new sheets, “replace” works great. Test this, then open the Excel file, and you’ll see a third sheet for June sales. Game changer for updating reports without redoing everything.

Now, let’s talk about common mistakes that trip people up, because I’ve made all of these. First, forgetting to install openpyxl. If you run to_excel and get an error that says “Missing optional dependency ‘openpyxl’”, that’s exactly what happened. Just re-run the pip install line we had earlier, or run !pip install openpyxl alone if you only need to install that. Second, using the wrong file extension. If you name your file something like sales.xls, pandas will try to write the old Excel format, which has a limit of 65,536 rows—total garbage if you have more data than that. Always use .xlsx for modern files, it can handle millions of rows. Third, not using index=False. I can’t stress this enough— that random index column is the number one complaint I hear from teams when they get Excel files from data folks. Adding that extra step of deleting the index in Excel is a waste of time, so just skip it in the code.

Another quick pro tip: if you need to format the Excel file a little, like making the header row bold, or adjusting column widths, you can use openpyxl directly after writing. For example, if you wanted to bold the headers in your sales sheet, you could add a few lines with openpyxl, but that’s more advanced. If you’re just looking to get data out of Jupyter into Excel fast, the pandas method is more than enough—no need to get fancy unless you have to.

Wait, also, what if you’re using a shared Jupyter environment, like one your team uses together? The file you save will go to the same folder where your Jupyter notebook is saved, so make sure everyone knows where to look, or use a full file path if you need to save it somewhere specific. For example, if you want to save the file to your Documents folder on Windows, you’d use something like r"C:\Users\YourName\Documents\monthly_sales.xlsx"—the r is there to escape backslashes, which is a weird Python thing but it works. On Mac, it’s something like /Users/YourName/Documents/monthly_sales.xlsx. Just make sure the path exists, otherwise you’ll get a “file not found” error.

Now, let’s circle back to the shop I mentioned earlier—we know how annoying it is when your tools don’t work together, or you have to jump through hoops to get your work to where it needs to be. Whether you’re a data analyst updating monthly reports, a small business owner tracking inventory, or a student cramming for a stats project, Jupyter is such a flexible tool, and being able to write Excel files right there cuts out so of extra steps. If you’re looking for reliable gear to run Jupyter smoothly—laptops that don’t lag when you have big DataFrames, monitors that make reading spreadsheets easier, or even just tips on other data workflow hacks, we’ve got you covered. Our team knows exactly what data folks need, no overpriced junk, just tools that work when you need them. If you’re ready to stock up on gear to make your data work even better, reach out to our sales team to chat through your needs—we’re here to help make your workflow less stressful.

Wait, one last quick check: if you test all these steps, do they work? Let’s recap to make sure: install pandas and openpyxl, load your data into a DataFrame, use to_excel for single sheets, use ExcelWriter for multiple, and remember those small details like index=False and .xlsx extension. It’s all straightforward, no black magic. I’ve used this exact process a hundred times for everything from small survey datasets to 100k-row sales files, and it’s never failed me.

Brochure and Booklet Reference
McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference, 51-56.
OpenPyXL Documentation. (n.d.). OpenPyXL: A Python library to read/write Excel 2010 xlsx/xlsm/xltx/xltm files.

Jiangyin Kingma Printing Co., Ltd.
As one of the most professional notebook manufacturers and suppliers in China, we also support customized service. Please feel free to wholesale bulk high quality notebook in stock here from our factory. Welcome to contact us for quotation.
Address: 803 Binjiang West Road, Jiangyin City, Jiangsu Province
E-mail: 275695527@qq.com
WebSite: https://www.kingmaprinting.com/