2.9.26
Released 2 Nov 2026
Official Fredit Flow website
Data Cleaning Python Scripts That You Review and Test
Fredit Flow 2.9.26 is a Windows agent that writes and runs Python cleaning scripts on your own CSV and JSON files, saves the result to a new file and shows you the diff.
Clean customers.csv: trim spaces, lower-case the e-mails, drop duplicate customers and save customers_clean.csv.
- glob
./*.csv2 files - read_file
./customers.csvsample read - write_file
./clean_customers.pysaved - Shell
your Python command clean_customers.pyexit 0 - read_file
./customers_clean.csv8 rows
Waiting for your OK before the next stepRejectAccept
Windows 10/11
64-bit (x64) only
About 34 MB
Portable .exe is about 25 MB
13 languages
Built into the app
Not needed
Nothing to sign up for
Cloud or local
Or the built-in model
How data cleaning Python scripts work here
Data cleaning Python scripts find and fix errors in a dataset before analysis: wrong types, stray spaces, mixed letter case, duplicate rows and missing values. Fredit Flow writes and runs such a script while you approve each rule and read the diff. The original stays untouched. Cleaned data goes into a new file, and the 4 walkthroughs below show the steps.
Tidy a customer export from your own shop
A shop export called customers.csv has stray spaces, mixed-case e-mails, repeated customers and ages stored as text. Fredit Flow first uses read_file to look at the opening rows, then write_file saves clean_customers.py, a script that trims spaces, lower-cases e-mails, skips repeated addresses and turns ages into numbers. Shell runs it with your own Python command and reports the real exit code and the printed row counts. The output is customers_clean.csv. Your original, customers.csv, stays as it was. In Ask first mode the card pauses before the file is saved, and you spot-check 10 rows of both files by hand before you rely on the result.
Tool trace
read_file ./customers.csv → first rows
write_file ./clean_customers.py → saved
Shell your Python command clean_customers.py → exit 0
read_file ./customers_clean.csv → 8 rowsDecide what to do with missing values
Missing values have no single right answer, and this part of the data cleaning process is your decision. In sales.csv, grep searches for two commas in a row, which finds only blanks in the middle of a line, so the script must also handle blanks at line ends and placeholders such as n/a. You pick a rule per column: drop the row, fill with a fixed value, fill with the median, or keep the gap and flag it. write_file creates clean_sales.py, edit_file changes that script, and in Ask first mode you read the diff. Record each choice. Then read 5 flagged rows in sales_clean.csv yourself.
Tool trace
grep ,, ./sales.csv → 6 lines
write_file ./clean_sales.py → saved
edit_file ./clean_sales.py → +9 lines
Shell your Python command clean_sales.py → exit 0Data wrangling with Python: merge two files on a key
You have orders.csv and customers.csv from your own shop and want one table. read_file shows the header rows of both, so the agent can name the shared customer id, and write_file saves merge_orders.py. Shell runs the script with your own Python command and prints how many orders found a customer and how many did not. Unmatched rows are listed. They go to a separate file instead of disappearing, and the merged table goes to a new file as well. Compare the 2 row counts with the originals, then open a few merged rows to check that each order carries the right customer.
Read the plan before anything runs
Plan only mode writes the cleaning steps and runs nothing, so no command starts and no file changes. For a folder of exports, the plan lists what read_file will inspect, which rules the script will apply and where each output file will go. Plans survive restarts. You read it, ask for a different rule in the chat, and switch to Ask first to carry it out. Later, Cron can re-run the script on a schedule, so 1 new export gets the same rules. The saved Python script is a CSV cleaner in plain text: you can read it, run it on a copy and keep it.
How it differs from a typical chat assistant
Chat assistants suggest steps. Fredit Flow 2.9.26 runs the tools itself, and every action appears in the chat as a card with its real result, such as the command output or the file diff.
| Typical chat assistant | Fredit Flow | |
|---|---|---|
| Actions | Suggests a script and leaves running it to you | Runs the tools and shows each result as a card |
| Your files | Works from text you paste into the chat | Reads files in the chat's working folder |
| Changes | You copy the edits across by hand | Shows a diff before you accept an edit in Ask first |
| Data privacy | Pasted text goes to the provider | With a local model, nothing leaves your PC |
How to start
- Download the setup program, about 34 MB, and compare its SHA-256 checksum with the published manifest.
- Run it on Windows 10 or 11, then connect a model: a cloud provider, a model running locally, or the built-in one.
- Choose the project folder that holds your own CSV or JSON files; the file tools work inside the chat's working folder.
- Ask for one cleaning job in Ask first or Plan only mode, read the diff, run the script on a copy and spot-check the output.
Who uses it and when
Analysts, shop owners and survey owners with a messy export are the first group. Their CSV and JSON files arrive with stray spaces, mixed date formats and repeated customers, and they want a script they can read, rerun and keep. Originals stay as they are.
Students and engineers who prepare data for machine learning are the second group. Messy real world data keeps breaking models, and data preprocessing is the name for cleaning followed by steps such as encoding categories and scaling numbers. Split first. Fredit Flow 2.9.26 writes the script, you check the split.
Anyone whose files contain personal details has one extra step. Prompts and any file text the agent reads go to the model endpoint you choose, so use a model running locally on your PC or remove the personal columns first. Know where prompts go. Work only on data you own or have the right to process.
Common questions
What does "data cleaning Python" mean?
The phrase means writing a Python script that finds and fixes errors in a dataset before analysis, such as wrong types, stray spaces, duplicate rows and missing values. Fredit Flow helps by writing and running that script with you. You approve every rule.
Is freditcode.com the official Fredit Flow website?
Yes, freditcode.com is the official Fredit Flow website. The installer is unsigned. Compare the SHA-256 checksum of your download with the published manifest before you run it, and send any questions through the Send feedback button inside the app.
When is historical data worth cleaning?
It is worth cleaning when old records still feed a report, a decision or a model, and their errors follow rules a script can state. Otherwise, archive them untouched. Keep the raw copy either way, because you can redo a rule only while the original exists.
Which data cleaning best practices should I start with?
Five habits come first. Look at the raw data before you write a rule, write each rule down, and save the result to a new file instead of overwriting the original. Then compare row counts before and after, and spot-check the output by hand.
Does the agent overwrite my original file?
No, not if you ask for a new output file, and the examples here all do. In Ask first mode the agent pauses before it changes anything, so you can read the diff and refuse a write that targets the original. Keep a backup too.
Do my files leave the PC?
The answer is your choice. With a cloud model, the prompt text, including any file text the agent reads, goes to that provider, while a model running locally on your PC sends nothing out. Personal data therefore calls for a local model.
Are the results always right?
No. The agent applies the rules you approve and cannot tell whether a value is true, and no automated cleaning is complete. Read the diff, test the script on a copy and spot-check the cleaned file by hand before you rely on it.
Try it on a copy of your own data
Download Fredit Flow 2.9.26 for Windows 10 or 11, connect a model and point it at a copy of one file you own. No account is needed. Read every diff.
Download Fredit Flow for Windows ↗More about Fredit Flow
Every page is available in 20 languages.
What the agent can do with your files ↗ Cloud, local and built-in models ↗ Step-by-step cleaning workflows ↗ Download and verify the installer ↗ Questions and answers ↗ Privacy and data handling ↗
