https://github.com/data-describe/data-describe

data⎰describe: Pythonic EDA Accelerator for Data Science
https://github.com/data-describe/data-describe

analysis data-science eda exploratory-data-analysis pypi

Last synced: 1 day ago
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data⎰describe: Pythonic EDA Accelerator for Data Science

• Host: GitHub
• URL: https://github.com/data-describe/data-describe
• Owner: data-describe
• License: other
• Created: 2020-05-04T17:58:14.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-02-22T05:20:46.000Z (over 1 year ago)
• Last Synced: 2024-10-02T22:45:17.654Z (about 1 month ago)
• Topics: analysis, data-science, eda, exploratory-data-analysis, pypi
• Language: Python
• Homepage:
• Size: 126 MB
• Stars: 295
• Watchers: 13
• Forks: 18
• Open Issues: 77
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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# data ⎰ describe

[data-describe](https://data-describe.ai/) is a Python toolkit for Exploratory Data Analysis (EDA). It aims to accelerate data exploration and analysis by providing automated and polished analysis widgets.



For more examples of data-describe in action, see the [Quick Start Tutorial](https://data-describe.ai/docs/master/_notebooks/quick_start.html).

## Main Features

data-describe implements the following basic features:

| Feature      | Description |

| ----------- | ----------- |

| Data Summary      | Curated data summary       |

| Data Heatmap   | Data variation and missingness heatmap        |

| Correlation Matrix   | Correlation heatmaps with categorical support        |

| Distribution Plots   | Generate histograms, violin plots, bar charts        |

| Scatterplots   | Generate scatterplots and evaluate with scatterplot diagnostics        |

| Cluster Analysis   | Automated clustering and plotting        |

| Feature Ranking   | Evaluate feature importance using tree models        |

## Extended Features

data-describe is always looking to elevate the standard for Exploratory Data Analysis. Here are just a few that are implemented:

* Dimensionality Reduction Methods

* Sensitive Data (PII) Redaction

* Text Pre-processing / Topic Modeling

* Big Data Support

## Installation

data-describe can be installed using pip:

```

pip install data-describe

```

## Getting Started

```python

import data_describe as dd

help(dd)

```

See the [User Guide](https://data-describe.ai/docs/master/_notebooks/user_guide.html) for more information.

## Project Status

data-describe is currently in **beta** status.

## Contributing

data-describe welcomes [contributions from the community](./CONTRIBUTING.md).