NPM v/s Bower – Intro

This is a nice intro to NPM.


And here’s a nice intro to NPM.


This post captures some difference between npm and bower:




Advertisements to Jekyll

Am trying to understand how to go about exporting my blog from to Jekyll.

My goal is to then host it on GitHub pages  under my user credentials.

Some references:

Jekyll. Ubuntu 16.04

Jekyll has a very short ‘getting started’ guide:

Apparently that’s all that’s needed. However, my experience on Ubuntu 16.04 was more intense than this.

I kept hitting the following [issue](

Was almost about to give up on this thing before finding the links


Intro to Angular 2.0

I attended this meetup and got a basic understanding of Angular 2.0.

The interesting thing I realized is that there has been an immense amount of developments in the world of Web technologies. I would probably take me a week or two to come up-to speed with the developments in this field

  • HTML/CSS/JavaScript/jQuery
  • Bootstrap
  • Node
  • Express
  • Angular
  • React

In any case, the deck from the meetup can be found at


Web requests in Python

Recently tried making web requests in Python.

I used the urllib2 library for making 10 requests to the Azure Machine Learning web service.

Interestingly I found that using urllib2  was incurring a lot of latency.   I replaced urllib2 with the requests libarary and boom, the latency improved tremendously.


  • it seems the requests library  by default uses KeepAlive.  As such, it was not re-initiating the connection each time for the multiple requests. urllib2 on the other hand was re-initiating the connection for each request.
  • Note:  the requests library is still making synchronous calls.

REST Calls in Python. JSON. Pandas.

I recently had to make REST calls in Python for sending data to Azure EventHub.

In this particular case I could not use the Python SDK to talk to EventHub. As I wrote down the code to make the raw REST calls, I came across several gems. Am listing them down below.


  • Use the python ‘requests’ library.
    • i am yet to figure out how to make async calls. can i use this library for async as well or would I have to use something else
  • Sending JSON is way to go.
    • Don’t even try sending anything else
  • Pandas has great functionality to convert  Series/DataFrames to JSON.
    • the ‘to_json’ function has awesome functionality including orient by ‘records’ etc
  • Python has an awesome library called ‘json’ to deal with JSON data.
    • To deserialize ,use json.loads()
    • In particular,  to convert dict to JSON use  json.dumps().
    • Note: If you want to preserve the order, one would have to use ‘collections.OrderedDict’. Check this link

Check this out:

myj = '[{"reward":30,"actionname":"x","age":60,"gender":"M","weight":150,"Scored Labels":30.9928596354},{"reward":20,"actionname":"y","age":60,"gender":"M","weight":150,"Scored Labels":19.0217225957}]'

myj_l = json.loads(myj, object_pairs_hook=collections.OrderedDict)

[OrderedDict([(u'reward', 30), (u'actionname', u'x'), (u'age', 60), (u'gender', u'M'), (u'weight', 150), (u'Scored Labels', 30.9928596354)]),
 OrderedDict([(u'reward', 20), (u'actionname', u'y'), (u'age', 60), (u'gender', u'M'), (u'weight', 150), (u'Scored Labels', 19.0217225957)])]

for item in myj_l:
    print json.dumps(item)

{"reward": 30, "actionname": "x", "age": 60, "gender": "M", "weight": 150, "Scored Labels": 30.9928596354}
{"reward": 20, "actionname": "y", "age": 60, "gender": "M", "weight": 150, "Scored Labels": 19.0217225957}