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## Download
from google.colab import files
df.to_csv('df.csv')
files.download('df.csv')

## Upload
from google.colab import files
df = files.upload()

## Save CSV on remotely in Colab

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from urllib.request import urlopen
from bs4 import BeautifulSoup

import pandas as pd
import random
from urllib.parse import urlparse
import urllib.request 
import lxml.html

# randLink = str(seedFunds.iloc[int(random.uniform(1, len(seedFunds))), 4])

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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt 

data = pd.read_csv('Stages.csv').dropna()

x = "Stage"
y = "Experimental"

plt.figure(figsize=(15,16))

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library(data.table)

DM = data.table(X = 1:5 , B = c("a", letters[6:7], NA, NA), D = c("g", 
				letters[22:25]), Y = c("j", letters[6:9])  )
keycols = names(DM)
setkeyv(DM, keycols)

DT = data.table(X = 1:5, B = c("a", letters[6:9]),
			 D = c("g", letters[2:5]), Y = c("j", letters[16:19]))

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#######  GCP/GCloud VM setup running R/Python using Anaconda  ########

	# Prepared as part of a guest lecture series in parallel programming and GCloud setup
	# at Pepperdine University for their inaugural ML class on 3/16/2018.
	# Rights: MIT Licence

## 10 easy steps: Running R/Python in the cloud

1) Log into GCP 
2) Create a project (if not done already), and log into <your project> from top of screen

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var allDeals = document.getElementsByClassName('u-uncoloredLink js-syndicate_click');

var links = []
for (var i=0; i < allDeals.length; i++) {
  links.push(allDeals[i].getAttribute('data-url'));
};

var csvContent = "data:text/csv;charset=utf-8,";
links.forEach(function(rowArray){
   csvContent += rowArray + "\r\n";

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import pandas as pd
from IPython.display import FileLinks #  FileLink
import os

data = pd.DataFrame([['a', 'b'],['c', 'd'],['e','f']], columns = ['foo', 'bar']) # .values.tolist()
directory = 'testdata/'
filename = 'data2.csv'
pathtofile = directory + filename

if not os.path.exists(directory):

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###### Gcloud: Jupyter Notebook with GPUs from browser

## First Step Install GCP 16.04 LTS with 20GB and Tesla K80 NVIDIA GPU and HTTP/HTTPS

## From Remote shell (*need to run twice after generating keys). Can also use SSH with default user
gcloud compute --project <project name> ssh --zone "us-central1-a" <gce name>

## Install Anaconda and setup path
# Required upfront: Installs nano / curl / bzip2 / etc.
sudo -s

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# Adapted for python 3
# https://github.com/pbharrin/pyconometrics
    
'''
Created on Aug 17, 2010

@author: Peter Harrington
peter.b.harrington@gmail.com
'''
from numpy import *

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