This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
Latest Version Dependencies status unknown Test status unknown Test coverage unknown
Project Description

Tweet Impact Predictor

Description

A natural language processing pipeline for predicting the impact (reach and popularity) of a tweet. Built as part of the PyCon 2016 Natural Language Processing tutorial and workshop. For more information see the tutorial repository.

Don’t install the latest version from PyPi if you’re working through the tutorial yourself! Tagged version numbers will correspond to sections of the tutorial and handout material so you can maintain pace even if you miss a step along the way. Plus it’ll be easier to set up your API keys if you clone the repository.

GETTING STARTED

Rather than installing this module from the cheese shop, fork the repository on GitHub and then clone it to your laptop (replacing totalgood with your account name:

git clone git@github.com:totalgood/twip.git
cd twip
git checkout v0.1.0

If you don’t already have one, sign up to get a twitter user account (@username): twitter.com/signup

Once you have a user account, sign into it, then set up a twitter App to get an API_KEY: apps.twitter.com/app/new

Copy and paste the Consumer API Key and Consumer API Secret into the indicated places in the file called settings_template.py but don’t save it there. Instead save the file as a new file named settings_secret.py. This file is .gitignored during pushes. Do a git status to make sure you didn’t accidentally save your secret KEYs in the template file or misname your settings_secret.py file. If you see that any tracked/added files have changes then you need to undo them before you do a commit and push to your fork of twip.

To get ready for the first workshop you’ll want to make sure you’ve checked out v0.1.0:

git checkout v0.1.0

If you want to skip the first session and move directly to the second session you can checkout v0.2.0. This with have all the code from the first workshop session completed for you.

Credits

  • Hobson Lane – Data Scientist for Talentpair
  • Rob Ludwick – Co-Instructor, helped craft the proposal and suggested the tweet optimization application
  • Jeremy Robin – Co-Instructor, helped develop the material
  • PyScaffold – Python package setup done right (the one obvious way)
Release History

Release History

0.0.14

This version

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0.0.13

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0.0.11

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0.0.10

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0.0.7

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0.0.7a1

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0.0.7a0

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0.0.6

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Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.3

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Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.2

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Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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0.0.1

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
twip-0.0.14-py2.py3-none-any.whl (50.7 kB) Copy SHA256 Checksum SHA256 2.7 Wheel May 29, 2016
twip-0.0.14.tar.gz (26.6 MB) Copy SHA256 Checksum SHA256 Source May 29, 2016

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