| Name | dabl JSON |
| Version |
0.3.2
JSON |
| download |
| home_page | None |
| Summary | Data Analysis Baseline Library |
| upload_time | 2024-12-16 23:31:09 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | Copyright (c) 2016, Vighnesh Birodkar All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of project-template nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
data analysis
visualization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# dabl
[](https://github.com/dabl/dabl/actions/workflows/ci.yml)
The data analysis baseline library.
- "Mr Sanchez, are you a data scientist?"
- "I dabl, Mr president."
Find more information on the [website](https://dabl.github.io/).
## Try it out
```
pip install dabl
```
or [](https://mybinder.org/v2/gh/dabl/dabl/main)
## Current scope and upcoming features
This library is very much still under development. Current code focuses mostly on exploratory visualization and preprocessing.
There are also drop-in replacements for GridSearchCV and RandomizedSearchCV using successive halfing.
There are preliminary portfolios in the style of
[POSH
auto-sklearn](https://ml.informatik.uni-freiburg.de/papers/18-AUTOML-AutoChallenge.pdf)
to find strong models quickly. In essence that boils down to a quick search
over different gradient boosting models and other tree ensembles and
potentially kernel methods.
Check out the [the website](https://dabl.github.io/dev/) and [example gallery](https://dabl.github.io/0.1.9/auto_examples/index.html) to get an idea of the visualizations that are available.
Stay Tuned!
## Related packages
## Lux
[Lux](https://github.com/lux-org/lux) is an awesome project for easy interactive visualization of pandas dataframes within notebooks.
### Pandas Profiling
[Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) can
provide a thorough summary of the data in only a single line of code. Using the
```ProfileReport()``` method, you are able to access a HTML report of your data
that can help you find correlations and identify missing data.
`dabl` focuses less on statistical measures of individual columns, and more on
providing a quick overview via visualizations, as well as convienient
preprocessing and model search for machine learning.
Raw data
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