Google Datalab and Python Issue
I was loading the data inappropriately. I was using pandas load_csv on my local machine, and BytesIO on in Datalab. The comma in the numberical value was throwing off the import of the data. I had to say that the delimiter is a "," and the thousand separator is also a ","
I have a python script that runs perfectly in my IDE on my local machine, but when I run it on Google Datalab, it throws this error:
ValueError: could not convert string to float: '80,354'
The code is simple, and the graph prints in my Pycharm IDE, but not on GoogleDatalab. 
plt.plot(new_df['Volume'])
plt.show()
The error is related to the last line in the data. I'm using the date as an index. Here's what the data looks like? Is there a slash missing somehwere? What am I doing wrong or missing?
'               Micro Market   Volume\nMonth/Year                          \n2014-01-01  DALLAS-FT WORTH   63,974\n2014-02-01  DALLAS-FT WORTH   68,482\n2014-03-01  DALLAS-FT WORTH   85,866\n2014-04-01  DALLAS-FT WORTH   79,735\n2014-05-01  DALLAS-FT WORTH   75,339\n2014-06-01  DALLAS-FT WORTH   71,739\n2014-07-01  DALLAS-FT WORTH   85,893\n2014-08-01  DALLAS-FT WORTH   83,694\n2014-09-01  DALLAS-FT WORTH   87,567\n2014-10-01  DALLAS-FT WORTH   87,389\n2014-11-01  DALLAS-FT WORTH   68,340\n2014-12-01  DALLAS-FT WORTH   74,805\n2015-01-01  DALLAS-FT WORTH   68,568\n2015-02-01  DALLAS-FT WORTH   61,924\n2015-03-01  DALLAS-FT WORTH   56,885\n2015-04-01  DALLAS-FT WORTH   68,101\n2015-05-01  DALLAS-FT WORTH   52,806\n2015-06-01  DALLAS-FT WORTH   79,918\n2015-07-01  DALLAS-FT WORTH   92,134\n2015-08-01  DALLAS-FT WORTH   88,047\n2015-09-01  DALLAS-FT WORTH   91,377\n2015-10-01  DALLAS-FT WORTH   91,307\n2015-11-01  DALLAS-FT WORTH   65,415\n2015-12-01  DALLAS-FT WORTH   81,456\n2016-01-01  DALLAS-FT WORTH   82,820\n2016-02-01  DALLAS-FT WORTH   91,688\n2016-03-01  DALLAS-FT WORTH   81,495\n2016-04-01  DALLAS-FT WORTH   87,872\n2016-05-01  DALLAS-FT WORTH   82,031\n2016-06-01  DALLAS-FT WORTH  100,783\n2016-07-01  DALLAS-FT WORTH   99,285\n2016-08-01  DALLAS-FT WORTH   99,179\n2016-09-01  DALLAS-FT WORTH   93,939\n2016-10-01  DALLAS-FT WORTH   99,663\n2016-11-01  DALLAS-FT WORTH   86,751\n2016-12-01  DALLAS-FT WORTH   84,551\n2017-01-01  DALLAS-FT WORTH   81,890\n2017-02-01  DALLAS-FT WORTH   90,212\n2017-03-01  DALLAS-FT WORTH   97,798\n2017-04-01  DALLAS-FT WORTH   89,338\n2017-05-01  DALLAS-FT WORTH   96,891\n2017-06-01  DALLAS-FT WORTH   86,613\n2017-07-01  DALLAS-FT WORTH   80,354'