Code

Analyze data and extract insights with AI

Paste your data or describe your dataset and get Python or R analysis code, statistical summaries, visualization suggestions, and clear interpretations.

For the best results: Upload your CSV or paste the data, then describe what you need, trends, averages, correlations, or a summary. Be specific about which columns matter.

AI can make mistakes. Check important information before relying on it.

FREE · PRIVATE · NO SIGN UP REQUIRED · NO ACCOUNT NEEDED

What is Data Analysis AI?

Data Analysis AI is a free AI coding assistant that turns your dataset or its description into analysis code, statistical summaries, visualization ideas, and clear interpretations. Each request is handed to a code-specialized model chosen for the task, returning working code, bug fixes, and explanations in any programming language in seconds. You paste and go, with no login and nothing to install, and the code you share is not retained once the session ends.

✦Paste code and go, no login, nothing to install
✦Code-specialized models are selected per request automatically
✦Your code is not retained after the session ends

Models: It routes across strong coding models including Llama, Gemini and Mistral families, picking the best available one per request. The exact model used is shown after every response.

See it in action

You've run a two-week A/B test on a checkout button and need to know whether the lift is real before you report it.

What you type

A/B test on checkout button color. Control: 4,820 visitors, 361 conversions. Variant: 4,790 visitors, 402 conversions. Is the difference significant?

What you get

Conversion rates: control 7.49%, variant 8.39% — an absolute lift of ~0.9 points (~12% relative). Test to use: two-proportion z-test (large samples, binary outcome). from statsmodels.stats.proportion import proportions_ztest z, p = proportions_ztest([361, 402], [4820, 4790]) Result (illustrative): p ≈ 0.13, so at α = 0.05 this is not statistically significant — the difference could be noise. Interpretation: don't ship on this yet; the sample isn't large enough to call a lift this small. Consider running longer or estimating the sample size you'd need for ~0.9pt at 80% power.

Tip: Give it the raw counts (visitors and conversions per arm), not the percentages — it needs the denominators to pick the right test and compute significance. And ask it for the p-value AND a plain-language verdict, so you don't mistake a 'not significant' result for a win just because the variant's number looks bigger.

Use cases

What people use it for

Turn a spreadsheet of survey responses into summary statistics and chart ideas before a meeting.

Get pandas code that cleans a sales export full of mixed date formats and duplicates.

Check which statistical test suits your A/B experiment before you report the result.

Who uses Data Analysis AI?

Market researchers

turning survey exports into significance tests and a plain-language read on which differences between groups actually matter

Small business owners

pasting a sales spreadsheet and asking which products, months or regions drive revenue, in language that skips the jargon

Graduate students

getting the right statistical test for their experimental design and the R code to run it, with the assumptions spelled out

How it works

1Paste your code or describe what you need built.
2A code-specialized model writes, fixes, or explains it.
3Copy the code out, or ask follow-ups to iterate on it.

Tips for the best results

Always include your column headers and a handful of example rows, so the generated code references real field names instead of placeholders you have to rename.
State your goal in outcome terms; which factors predict churn beats analyze this, so the analysis targets a question rather than dumping every summary statistic.
Say whether you want Python or R and which plotting library, otherwise you may get code for tools you do not have installed.
Note the type of each variable, whether count, category, date or percentage, because the right test and chart depend on it and the tool cannot always infer it from values alone.
For sensitive figures, anonymize or scale the values before pasting; the analysis logic stays valid on renamed columns and rounded numbers.

FAQ

Frequently asked questions

Should I paste my raw data or just describe it?

Both work, for different jobs. Paste a small sample (column headers plus a couple of dozen rows) when you want direct interpretation. For anything larger, describe the columns, their types and what you want to learn, and you'll get analysis code to run on the full dataset locally.

Does it produce actual charts?

Not rendered ones. You get visualization suggestions and the code to create them, in matplotlib, seaborn, ggplot or whichever library you prefer. That's deliberate: running the plotting code against your full dataset on your own machine gives you accurate charts rather than ones drawn from a sample.

Will it use Python or R?

Whichever you ask for. Python with pandas is the default assumption, but request R and you'll get tidyverse-style code instead. If you're undecided, describe your task and ask which language suits it, the honest answer is usually whichever one your team already knows.

Can it calculate the statistics itself, or only write code?

For a small pasted sample it can compute means, counts and simple summaries directly, but AI arithmetic deserves skepticism, models occasionally miscalculate with confidence. The safer pattern for anything that matters is to have it write the analysis code, run that yourself, then bring the output back for interpretation.

What happens to the data I paste in?

It exists only for the session: nothing is stored once the tab closes, and your data isn't used for training. Prompts are processed by third-party AI providers under their own terms, though, so strip names, emails and other identifiers before pasting, the analysis rarely needs them anyway.

Is this data analysis tool free to use?

Yes. It is free with no account and no sign-up. Paste a dataset or describe one, and you get analysis code, summaries and interpretation at no cost. There is no row limit tied to a paid tier and nothing is gated, though very large pastes are better summarized than dropped in whole.

Which statistical test should I use for my data?

Describe your variables, how many groups you are comparing and whether the data is continuous, categorical or paired, and the tool suggests a suitable test, whether a t-test, ANOVA, chi-square, correlation or regression, and explains why. It also flags the assumptions each test needs so you can check them before trusting the result.

Can it explain the results in plain English for a non-statistician?

Yes. Ask for a plain-language summary and you get what the numbers mean in practice: whether a difference is likely real or down to chance, how strong a relationship is, and what to be cautious about. Request it alongside the code so you get both the working and the interpretation.

What are the usage limits?

Code tools are text tools, so they are unmetered during the current free launch phase, iterate on a bug or generate as many drafts as the work needs. A short per-minute limit guards against scripted abuse only. Paid plans with higher limits and priority model access are coming soon.

Also useful