Deep profiling
Column-level quality scores, null analysis, outlier detection, cardinality, and correlation heatmaps — all in one pass.
with AI-powered EDA — no code required
No-code data analysis.
CSV to clarity.
Explore CSV and Excel files with automated exploratory data analysis (EDA). Profile missing values, visualize relationships, review cleaning suggestions, and engineer features without writing code.
✓ No credit card required◇ Open source & self-hostable⟨⟩ Open data formats
Interactive sample · illustrative data
customer_churn.csvExploratory analysis
Rows profiled7,043
Quality score91
Missing cells8.4%
Feature ideas18
Column health
Select a column to exploreDistribution
age
18–2474–80
Age is broadly distributed; review missing entries before choosing a fill strategy.
Built for analysis work
Profile, clean, and prepare your data.
Guided cleaning
Review suggested fixes before changing data, then export a cleaned file, notebook, or report.
AI narrative
A plain-English summary of what your data contains, what's wrong with it, and which features to engineer first.
Explore your next data task
CSV data analysis
Analyze CSV files without writing code. Explore column statistics, missing values, distributions, outliers, and correlations with edaverse.
Explore the workflow →Excel data profiling
Profile Excel data without code. Inspect column types, missing values, distributions, and correlations before cleaning or analyzing your dataset.
Explore the workflow →Automated data cleaning
Find data quality issues in CSV and Excel files. Review cleaning suggestions, prepare features, and export a cleaned dataset with edaverse.
Explore the workflow →From a raw dataset to a useful next step
- Upload and inspect. Add a CSV or Excel file, then check the parsed columns, detected types, and warnings.
- Explore and decide. Review missing values, distributions, outliers, and correlations before choosing cleaning operations.
- Prepare and export. Apply suitable fixes, engineer features, and export a cleaned file, notebook, or report.
Use edaverse for a course project, a first pass on a business export, or preparation for a modeling workflow. Review suggested changes using your knowledge of the data.
Questions before you start
What is exploratory data analysis (EDA)?
EDA is the first investigation of a dataset: checking its structure, missing values, distributions, and relationships. It helps you decide which questions to ask and which preparation steps to take before deeper analysis.
What can I do with edaverse?
Upload CSV or Excel data, inspect column profiles and visualizations, review cleaning suggestions, engineer features, and export a cleaned file, notebook, or report.
Do I need a dataset to try it?
No. Choose View demo to explore a sample dataset before uploading your own. Sign in when you are ready to use your own data.
Are uploaded files processed only in my browser?
No. The hosted application sends uploads to its backend for processing. Use the demo to explore the product before deciding which data to upload. The open-source repository also includes self-hosting instructions.
Ready when your next dataset is.
Start now