Vathsala VathsalaAchar

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

Markdown and reStructuredText

GitHub supports several lightweight markup languages for documentation; the most popular ones (generally, not just at GitHub) are Markdown and reStructuredText. Markdown is sometimes considered easier to use, and is often preferred when the purpose is simply to generate HTML. On the other hand, reStructuredText is more extensible and powerful, with native support (not just embedded HTML) for tables, as well as things like automatic generation of tables of contents.

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

	import dash
	import dash_core_components as dcc
	import dash_html_components as html
	import plotly.graph_objs as go
	import pandas as pd

	app = dash.Dash()

	df = pd.read_csv(
	'https://gist.github.com/chriddyp/'

	# view all environments set up
	$ conda info --envs
	# conda environments:
	#
	dev /home/me/anaconda2/envs/dev
	root * /home/me/anaconda2

	# set up new environment
	$ conda create -n play anaconda

	from keras.models import Sequential
	from keras.layers import Dense

	x, y = ...
	x_val, y_val = ...

	# 1-dimensional MSE linear regression in Keras
	model = Sequential()
	model.add(Dense(1, input_dim=x.shape[1]))
	model.compile(optimizer='rmsprop', loss='mse')

	""" Example using GenSim's LDA and sklearn. """

	import numpy as np

	from gensim import matutils
	from gensim.models.ldamodel import LdaModel
	from sklearn import linear_model
	from sklearn.datasets import fetch_20newsgroups
	from sklearn.feature_extraction.text import CountVectorizer