Analysis updated 2026-07-04 · repo last pushed 2020-06-06
Generate a visual summary report of a CSV file to quickly understand its contents.
Compare a training dataset against a testing dataset to spot differences before building a model.
Learn the Sweetviz commands needed to create interactive data overview reports for your own projects.
| krishnaik06/eda_sweetviz | kaopanboonyuen/saie2026 | krishnaik06/autoviz | |
|---|---|---|---|
| Stars | 25 | 22 | 19 |
| Language | Jupyter Notebook | Jupyter Notebook | Jupyter Notebook |
| Last pushed | 2020-06-06 | — | 2021-04-25 |
| Maintenance | Dormant | — | Dormant |
| Setup difficulty | easy | moderate | easy |
| Complexity | 1/5 | 3/5 | 2/5 |
| Audience | data | researcher | vibe coder |
Figures from each repo's GitHub metadata at analysis time.
Requires installing the Sweetviz Python package and running Jupyter Notebook, both straightforward steps.
This repository, eda_sweetviz, is a collection of Jupyter notebooks demonstrating how to use a tool called Sweetviz for exploratory data analysis (EDA). In plain terms, it shows you how to automatically generate a detailed, visual summary of a dataset, which is the first step most people take when trying to understand a new set of data. When you have a large spreadsheet or dataset, figuring out what is inside it can be overwhelming. You might need to know the average values, how many missing entries there are, or whether two columns are related. Instead of writing dozens of lines of code to calculate each of these things manually, Sweetviz scans your data and produces an interactive report. This report highlights key statistics, distributions, and potential data quality issues like missing values, all presented in a visual format that is easy to read and share with others. The project is aimed at data analysts, data scientists, or anyone learning to work with data who wants a faster way to understand their datasets. For example, if you receive a CSV file with thousands of customer records and do not know where to begin, you can use the approaches shown in these notebooks to get an immediate overview. It is also useful for comparing two different datasets, such as looking at the differences between a training set and a testing set before building a predictive model. The README does not go into detail about the specific contents or structure of the notebooks. However, the repository serves as a practical, hands-on guide rather than a standalone application. By working through the notebooks, users can learn the commands needed to generate these reports for their own projects.
A collection of Jupyter notebooks showing how to use Sweetviz to automatically generate visual summaries of datasets, helping you quickly understand data without writing lots of code.
Mainly Jupyter Notebook. The stack also includes Jupyter Notebook, Python, Sweetviz.
Dormant — no commits in 2+ years (last push 2020-06-06).
No license information is provided in this repository.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly data.
This repo across BitVibe Labs
Verify against the repo before relying on details.