{"id":3627,"date":"2026-10-26T12:48:41","date_gmt":"2026-10-26T04:48:41","guid":{"rendered":"http:\/\/www.pltpro.net\/blog\/?p=3627"},"modified":"2026-10-26T12:48:41","modified_gmt":"2026-10-26T04:48:41","slug":"how-to-write-an-excel-file-in-jupyter-notebook-44de-92477d","status":"publish","type":"post","link":"http:\/\/www.pltpro.net\/blog\/2026\/10\/26\/how-to-write-an-excel-file-in-jupyter-notebook-44de-92477d\/","title":{"rendered":"How to write an Excel file in Jupyter Notebook?"},"content":{"rendered":"

Hey guys, if you\u2019ve 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\u2019ve probably hit a point where you think, \u201cWait, how do I actually save this as an Excel file my team can open without them having to install a million random libraries?\u201d No cap, this used to stress me out back when I was the new guy at the Notebook supply shop\u2014wait, right, I work for a Notebook provider, so let\u2019s 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\u2019re breaking down how to write an Excel file right in Jupyter Notebook, no weird hoops, no confusing code that breaks mid-save. Let\u2019s dive in. Notebook<\/a><\/p>\n

<\/p>\n

First, before we even touch code, let\u2019s get one thing straight: Jupyter itself doesn\u2019t have a built-in Excel writer. You need a third-party library, and the two big ones here are pandas<\/code> and openpyxl<\/code>. Wait, why those two? Pandas is the bread and butter for anyone working with tabular data\u2014if you\u2019ve 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<\/code> files (the modern Excel format, not the old .xls<\/code> which is like a 2000s flip phone). You\u2019ll need to install these first if you haven\u2019t already\u2014trust me, skipping this step will save you 10 minutes of Googling \u201cwhy won\u2019t my code run\u201d.<\/p>\n

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:
\n!pip install pandas openpyxl
\nThat\u2019s it. Hit shift+enter, and when that finishes, you\u2019re ready to go. No overcomplicating, no \u201cmake sure your path is right\u201d garbage yet\u2014we\u2019ll start with the simplest case so you can test it out.<\/p>\n

Let\u2019s say you already have a DataFrame. Maybe it\u2019s a list of monthly sales, or survey responses, whatever. For this example, we\u2019ll make a dummy DataFrame so you can copy-paste and test immediately. Type this in a new cell:
\nimport pandas as pd<\/p>\n

Dummy sales data\u2014replace this with your actual DataFrame!<\/h1>\n

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

See what your DataFrame looks like to check for typos<\/h1>\n

print(df)
\nRun 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):
\ndf.to_excel("monthly_sales.xlsx", index=False)
\nWait, hold up\u2014what\u2019s the index=False<\/code> for? By default, pandas adds a leftmost column with row numbers (0, 1, 2, etc.) that\u2019s super annoying for Excel users who just want to see their actual data. That line tells pandas \u201cdon\u2019t add that random index column, just write the actual headers and data\u201d. 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\u2019ve done that.<\/p>\n

Now hit shift+enter on that line. Wait, nothing showed up? That\u2019s 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\u2014you know, that little folder icon. Refresh it, and you\u2019ll see monthly_sales.xlsx<\/code> pop up right there. Click it, and it should download automatically, or open in whatever Excel viewer you have. That\u2019s 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\u2019s super common for reports, and a lot of guides skip that part. Let\u2019s fix that.<\/p>\n

To write multiple sheets, you can\u2019t use the regular to_excel<\/code> on each DataFrame\u2014you need to create an ExcelWriter object first, which is like a blank binder that holds all your sheets. Here\u2019s how that works. Let\u2019s make a second dummy DataFrame for customer feedback:<\/p>\n

Second DataFrame for a new sheet<\/h1>\n

feedback_data = {
\n"Month": ["January", "February", "March", "April", "May"],
\n"Average Rating": [4.2, 4.5, 4.1, 4.7, 4.8],
\n"Complaints": [3, 2, 5, 1, 0]
\n}
\nfeedback_df = pd.DataFrame(feedback_data)
\nNow, create the Excel writer. The engine="openpyxl"<\/code> part here is important\u2014if you don\u2019t specify that, pandas might use an older engine that doesn\u2019t work right for multiple sheets. Run this next cell:<\/p>\n

Create writer object for multiple sheets<\/h1>\n

with pd.ExcelWriter("sales_and_feedback.xlsx", engine="openpyxl") as writer:
\n# Write first sheet, name it "Sales Data"
\ndf.to_excel(writer, sheet_name="Sales Data", index=False)
\n# Write second sheet, name it "Customer Feedback"
\nfeedback_df.to_excel(writer, sheet_name="Customer Feedback", index=False)
\nThat with<\/code> 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\u2014you\u2019ll 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.<\/p>\n

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\u2019s data without overwriting the old sheets. That\u2019s a trick a lot of people don\u2019t know, so let\u2019s cover that too. First, we need to load the existing Excel file, because openpyxl can edit existing files, not just make new ones. Let\u2019s say we have that sales_and_feedback.xlsx<\/code> file we made earlier, and we want to add a June sales sheet. First, make the new June data:<\/p>\n

New data for June<\/h1>\n

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

Add a new sheet to an existing Excel file<\/h1>\n

with pd.ExcelWriter("sales_and_feedback.xlsx", engine="openpyxl", mode="a", if_sheet_exists="replace") as writer:
\njune_df.to_excel(writer, sheet_name="June Sales", index=False)
\nWait, what\u2019s if_sheet_exists="replace"<\/code>? If you didn\u2019t have that, and a sheet named \u201cJune Sales\u201d already existed, pandas would throw an error. Using \u201creplace\u201d means if the sheet is there, it\u2019ll overwrite it\u2014you can also use \u201cappend\u201d if you want to add rows to an existing sheet, but for new sheets, \u201creplace\u201d works great. Test this, then open the Excel file, and you\u2019ll see a third sheet for June sales. Game changer for updating reports without redoing everything.<\/p>\n

Now, let\u2019s talk about common mistakes that trip people up, because I\u2019ve made all of these. First, forgetting to install openpyxl. If you run to_excel<\/code> and get an error that says \u201cMissing optional dependency \u2018openpyxl\u2019\u201d, that\u2019s exactly what happened. Just re-run the pip install line we had earlier, or run !pip install openpyxl<\/code> alone if you only need to install that. Second, using the wrong file extension. If you name your file something like sales.xls<\/code>, pandas will try to write the old Excel format, which has a limit of 65,536 rows\u2014total garbage if you have more data than that. Always use .xlsx<\/code> for modern files, it can handle millions of rows. Third, not using index=False<\/code>. I can\u2019t stress this enough\u2014 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.<\/p>\n

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\u2019s more advanced. If you\u2019re just looking to get data out of Jupyter into Excel fast, the pandas<\/code> method is more than enough\u2014no need to get fancy unless you have to.<\/p>\n

Wait, also, what if you\u2019re 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\u2019d use something like r"C:\\Users\\YourName\\Documents\\monthly_sales.xlsx"<\/code>\u2014the r<\/code> is there to escape backslashes, which is a weird Python thing but it works. On Mac, it\u2019s something like \/Users\/YourName\/Documents\/monthly_sales.xlsx<\/code>. Just make sure the path exists, otherwise you\u2019ll get a \u201cfile not found\u201d error.<\/p>\n

Now, let\u2019s circle back to the shop I mentioned earlier\u2014we know how annoying it is when your tools don\u2019t work together, or you have to jump through hoops to get your work to where it needs to be. Whether you\u2019re 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\u2019re looking for reliable gear to run Jupyter smoothly\u2014laptops that don\u2019t lag when you have big DataFrames, monitors that make reading spreadsheets easier, or even just tips on other data workflow hacks, we\u2019ve got you covered. Our team knows exactly what data folks need, no overpriced junk, just tools that work when you need them. If you\u2019re ready to stock up on gear to make your data work even better, reach out to our sales team to chat through your needs\u2014we\u2019re here to help make your workflow less stressful.<\/p>\n

<\/p>\n

Wait, one last quick check: if you test all these steps, do they work? Let\u2019s recap to make sure: install pandas and openpyxl, load your data into a DataFrame, use to_excel<\/code> for single sheets, use ExcelWriter<\/code> for multiple, and remember those small details like index=False<\/code> and .xlsx<\/code> extension. It\u2019s all straightforward, no black magic. I\u2019ve used this exact process a hundred times for everything from small survey datasets to 100k-row sales files, and it\u2019s never failed me.<\/p>\n

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

\n

Jiangyin Kingma Printing Co., Ltd.<\/a>
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\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Hey guys, if you\u2019ve ever been deep in a Jupyter Notebook session, cranking out data analysis, … How to write an Excel file in Jupyter Notebook?<\/span>Read more<\/a><\/p>\n","protected":false},"author":196,"featured_media":3627,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[3590],"class_list":["post-3627","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-notebook-45b5-93b556"],"_links":{"self":[{"href":"http:\/\/www.pltpro.net\/blog\/wp-json\/wp\/v2\/posts\/3627","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.pltpro.net\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.pltpro.net\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.pltpro.net\/blog\/wp-json\/wp\/v2\/users\/196"}],"replies":[{"embeddable":true,"href":"http:\/\/www.pltpro.net\/blog\/wp-json\/wp\/v2\/comments?post=3627"}],"version-history":[{"count":0,"href":"http:\/\/www.pltpro.net\/blog\/wp-json\/wp\/v2\/posts\/3627\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/www.pltpro.net\/blog\/wp-json\/wp\/v2\/posts\/3627"}],"wp:attachment":[{"href":"http:\/\/www.pltpro.net\/blog\/wp-json\/wp\/v2\/media?parent=3627"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.pltpro.net\/blog\/wp-json\/wp\/v2\/categories?post=3627"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.pltpro.net\/blog\/wp-json\/wp\/v2\/tags?post=3627"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}