Vibhu Jawa VibhuJawa

In your local clone of your forked repository, you can add the original GitHub repository as a "remote". ("Remotes" are like nicknames for the URLs of repositories - origin is one, for example.) Then you can fetch all the branches from that upstream repository, and rebase your work to continue working on the upstream version. In terms of commands that might look like:

Add the remote, call it "upstream":

git remote add upstream https://github.com/whoever/whatever.git

Fetch all the branches of that remote into remote-tracking branches, such as upstream/master:

git fetch upstream

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

	Query 01
	Find top 100 products that are sold together frequently in given stores. Only products in certain categories
	sold in specific stores are considered, and "sold together frequently" means at least 50 customers bought
	these productstogether in a transaction.
	Query 02
	Find the top 30 products that are mostly viewed together with a given product in online store. Note that
	the order of products viewed does not matter, and "viewed together" relates to a web_clickstreams
	click_session of a known user with a session timeout of 60min.If the duration between two click of a user is
	greater then the session timeout, a new session begins. With a session timeout of 60min.
	Query 03

	import datetime
	import getopt
	import os
	import sys
	import time

	import pynvml


	def get_printable_util_mem(dev_count, peak_mem):

	from sklearn import linear_model
	import numpy as np
	import scipy.stats as stat

	class LogisticReg:
	"""
	Wrapper Class for Logistic Regression which has the usual sklearn instance
	in an attribute self.model, and pvalues, z scores and estimated
	errors for each coefficient in

	import os

	from dask.callbacks import Callback
	from dask.dot import dot_graph


	class Track(Callback):
	def __init__(self, path='dasks', save_every=1):
	self.path = path
	self.save_every = save_every