Convert SAS7BDAT to CSV

A .sas7bdat file is a SAS dataset — the native table format of SAS, the statistics platform used across pharma, government, and research. A SAS license costs thousands, but you don’t need one to get your data out. There’s no in-browser converter for this format here yet, so this guide covers the free routes that actually work. Fastest option with no software: the Google Colab recipe below. If you already use Python or R, jump to those sections.

No installation: convert in Google Colab

This route needs nothing but a browser and a Google account — it works on a Chromebook or a library computer.

1. Open Colab — Go to colab.research.google.com, sign in with any Google account, and click New Notebook.
2. Upload your file — Click the folder icon in the left sidebar, then the upload button, and select your .sas7bdat file.
3. Run the code — Paste the snippet below into the cell and press the run button.
4. Download the result — Refresh the file sidebar, right-click output.csv, and choose Download.

import pandas as pd

df = pd.read_sas("yourfile.sas7bdat")  # replace with your uploaded filename
df.to_csv("output.csv", index=False)

read_sas is built into pandas, so there’s nothing to install. Your uploaded file lives in a temporary private session that disappears when you close the tab — but it is on Google’s servers, so for confidential data use a desktop option below instead.

Free desktop apps (no code)

SAS Universal Viewer

The official free viewer from SAS itself (Windows only, download from support.sas.com). Open your dataset with File → Open, then use the Table menu → Save As and pick CSV as the file type. Being an official SAS tool, it’s the safest bet for files that other readers choke on.

jamovi

A free, cross-platform statistics app that imports .sas7bdat (and .xpt) files directly. Open the file in jamovi, then export the data as CSV. The better choice if you also need to run analyses on the data afterward, since you can do that without ever leaving the app.

Python

The pandas call is one line, with no extra packages needed:

import pandas as pd

df = pd.read_sas("data.sas7bdat")
df.to_csv("output.csv", index=False)

If the output contains garbled text, the file was written with a legacy encoding — this is THE common failure with older SAS files. The fix:

df = pd.read_sas("data.sas7bdat", encoding="latin1")

The same read_sas call also reads .xpt (SAS transport) files, so no separate tooling is needed for those.

R

The haven package reads SAS datasets directly:

install.packages("haven")  # first time only
df <- haven::read_sas("data.sas7bdat")
write.csv(df, "output.csv", row.names = FALSE)

Which method should I use?