A Berkeley library for introductory data science.
For an example of usage, see the Berkeley Data 8 class.
pip install datascience
This project adheres to Semantic Versioning.
- Fixes a bug in HTML table generation (https://github.com/data-8/datascience/pull/315)
CurrencyFormatterto handle commas.
Table.histto keep histograms in the order of the columns.
joinso that it keeps all rows in the inner join of two tables.
group_barto plot counts by a grouping category, a common use case.
- Added options to
histto produce a histogram for each group on a column.
- Deprecated Table method
pivot_hist. Added an option to
- DistributionFormatter added.
- Fix bug for relabeled columns that had a format already.
- Circles bound to values determine the circle area, not radius.
- Scatter diagrams can take data-driven size and color parameters.
- Changed signature of
binto accept multiple columns without a list
countsin favor of
- Rename various positional args (technically could break some code, but won't)
with_columns(not a breaking change)
groups(not a breaking change)
- Added "Table.remove"
Table.columnnow throws a descriptive
ValueErrorinstead of a
KeyErrorwhen the column isn't in the table. (ef8b319)
- Change default behavior of
Map.overlaywhich overlays a feature(s) on a new copy of Map. (315bb63e)
- Remove rogue print from
- Added predicates for string comparison:
API reference is at http://data8.org/datascience/ .
The required environment for installation and tests is the Anaconda Python3 distribution
If you encounter an
Image not found error on Mac OSX, you may need an
Start by cloning this repository:
git clone https://github.com/data-8/datascience
Install the dependencies into a Conda environment with:
conda env create -f osx_environment.yml -n datascience # For Linux, use conda env create -f linux_environment.yml -n datascience
Source the environment to use the correct packages while developing:
source activate datascience # `source deactivate` will unload the environment
The above command must be run each time you develop in the package. You can also install direnv to auto-load/unload the environment.
datascience locally with:
Then, run the tests:
After that, go ahead and start hacking!
source activate datascience command must be run each time you develop in
the package. Alternatively, you can install direnv to auto-load/unload
Documentation is generated from the docstrings in the methods and is pushed online at http://data8.org/datascience/ automatically. If you want to preview the docs locally, use these commands:
make docs # Generates docs inside doc/ folder make serve_docs # Starts a local server to view docs
Then navigate to the issue board or press
b. You'll see a screen
that looks something like this:
- New Issues are issues that are just created and haven't been prioritized.
- Backlogged issues are issues that are not high priority, like nice-to-have features.
- To Do issues are high priority and should get done ASAP, such as breaking bugs or functionality that we need to lecture on soon.
- Once someone has been assigned to an issue, that issue should be moved into the In Progress column.
- When the task is complete, we close the related issue.
- John creates an issue called "Everything is breaking". It goes into the New Issues pipeline at first.
- This issue is important, so John immediately moves it into the To Do pipeline. Since he has to go lecture for 61A, he doesn't assign it to himself right away.
- Sam sees the issue, assigns himself to it, and moves it into the In Progress pipeline.
- After everything is fixed, Sam closes the issue.
Here's another example.
- Ani creates an issue asking for beautiful histograms. Like before, it goes into the New Issues pipeline.
- John decides that the issue is not as high priority right now because other things are breaking, so he moves it into the Backlog pipeline.
- When he has some more time, John assigns himself the issue and moves it into the In Progress pipeline.
- Once the issue is finished, he closes the issue.
python setup.py sdist upload -r pypi