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krishnaik06/eda_sweetviz

Analysis updated 2026-07-04 · repo last pushed 2020-06-06

25Jupyter NotebookAudience · dataComplexity · 1/5DormantSetup · easy

TLDR

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.

Mindmap

mindmap
  root((repo))
    What it does
      Generates visual data reports
      Highlights key statistics
      Finds missing values
    Use cases
      Summarize new CSV files
      Compare two datasets
      Quick data overview
    Audience
      Data analysts
      Data scientists
      Data learners
    Tech stack
      Jupyter Notebook
      Sweetviz
      Python
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Code map

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What do people build with it?

USE CASE 1

Generate a visual summary report of a CSV file to quickly understand its contents.

USE CASE 2

Compare a training dataset against a testing dataset to spot differences before building a model.

USE CASE 3

Learn the Sweetviz commands needed to create interactive data overview reports for your own projects.

What is it built with?

Jupyter NotebookPythonSweetviz

How does it compare?

krishnaik06/eda_sweetvizkaopanboonyuen/saie2026krishnaik06/autoviz
Stars252219
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Last pushed2020-06-062021-04-25
MaintenanceDormantDormant
Setup difficultyeasymoderateeasy
Complexity1/53/52/5
Audiencedataresearchervibe coder

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

Requires installing the Sweetviz Python package and running Jupyter Notebook, both straightforward steps.

No license information is provided in this repository.

In plain English

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.

Copy-paste prompts

Prompt 1
How do I use Sweetviz in a Jupyter notebook to generate a visual summary report from a CSV file?
Prompt 2
Show me how to compare two datasets using Sweetviz, like a training set and a test set, and interpret the report.
Prompt 3
What Sweetviz commands do I need to automatically detect missing values and data quality issues in my pandas DataFrame?
Prompt 4
Help me install Sweetviz and run my first exploratory data analysis report inside a Jupyter notebook.

Frequently asked questions

What is eda_sweetviz?

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.

What language is eda_sweetviz written in?

Mainly Jupyter Notebook. The stack also includes Jupyter Notebook, Python, Sweetviz.

Is eda_sweetviz actively maintained?

Dormant — no commits in 2+ years (last push 2020-06-06).

What license does eda_sweetviz use?

No license information is provided in this repository.

How hard is eda_sweetviz to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is eda_sweetviz for?

Mainly data.

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