dabl/dabl
Data Analysis Baseline Library
| repo name | dabl/dabl |
| repo link | https://github.com/dabl/dabl |
| homepage | https://dabl.github.io/ |
| language | Jupyter Notebook |
| size (curr.) | 6957 kB |
| stars (curr.) | 431 |
| created | 2018-09-14 |
| license | BSD 3-Clause “New” or “Revised” License |
dabl
The data analysis baseline library.
- “Mr Sanchez, are you a data scientist?”
- “I dabl, Mr president.”
Find more information on the website.
State of the library
Right now, this library is still a prototype. API might change, and you shouldn’t rely on it in any critical settings.
Try it out
pip install dabl
or 
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 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.
Stay Tuned!
Pandas Profiling package
The Pandas Profiling package is useful for initial data analysis. Using 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.
Try it out
pip install pandas-profiling
or [https://github.com/pandas-profiling/pandas-profiling]
