datascience

A Berkeley library for introductory data science.

written by Professor John DeNero, Professor David Culler, Sam Lau, and Alvin Wan

For an example of usage, see the Berkeley Data 8 class.

Installation

Use pip:

pip install datascience

Changelog

This project adheres to Semantic Versioning.

v0.9.0

• Added "Table.remove"

v0.8.2

• Added proportions_from_distribution method to datascience.util. (993e3d2)
• Table.column now throws a descriptive ValueError instead of a KeyError when the column isn't in the table. (ef8b319)

v0.8.0

Breaking changes

• Change default behavior of table.sample to with_replacement=True instead of False. (3717b67)

Additions

• Added Map.copy.
• Added Map.overlay which overlays a feature(s) on a new copy of Map. (315bb63e)

v0.7.1

• Remove rogue print from table.hist

v0.7.0

• Added predicates for string comparison: containing and contained_in. (#231)

Documentation

API reference is at http://data8.org/datascience/ .

Developing

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 XQuartz upgrade.

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.

Install datascience locally with:

make install

Then, run the tests:

make test

After that, go ahead and start hacking!

The source activate datascience command must be run each time you develop in the package. Alternatively, you can install direnv to auto-load/unload the environment.

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

Using Zenhub

We use Zenhub to organize development on this library. To get started, go ahead and install the Zenhub Chrome Extension.

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.

Example Workflow

1. John creates an issue called "Everything is breaking". It goes into the New Issues pipeline at first.
2. 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.
3. Sam sees the issue, assigns himself to it, and moves it into the In Progress pipeline.
4. After everything is fixed, Sam closes the issue.

Here's another example.

1. Ani creates an issue asking for beautiful histograms. Like before, it goes into the New Issues pipeline.
2. 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.
3. When he has some more time, John assigns himself the issue and moves it into the In Progress pipeline.
4. Once the issue is finished, he closes the issue.

Publishing

python setup.py sdist upload -r pypi