Awesome

#Mcflyin

###A timeseries transformation API built on Pandas and Flask

This is a small demo of an API to do timeseries transformations built on Flask and Pandas.

Concept

The idea is that you can make a POST request to the API with a simple list/array of timestamps, from any language, and get back some interesting transformations of that data.

Why?

Partly to show how straightforward it is to build such a thing. Python is great because it has very powerful, intuitive, quick-to-learn tools for both building web applications and doing data analysis/statistics.

That puts Python in kind of a unique position: powerful web tools, powerful scientific/numerical/statistical data tools. This API is a very simple example of how you can take advantage of both. Go read the source code- it's short and easy to grok. Bug fixes and pull requests welcome.

Getting Started

First we need to find some data. We're going to use some data that Wes McKinney provided in a recent blog post, with some statistics on Python posts on Stack Overflow. This is something of a contrived example: I'm manipulating the data in Python, sending to a Python backend, and then getting a response to manipulate in Python. Just know that all you need is an array of timestamp strings, no matter your language.

import pandas as pd

data = pd.read_csv('AllPandas.csv')
data = data['CreationDate'].tolist()

A simple array of timestamps:

>>>data[:10]
['2011-04-01 14:50:44',
 '2012-01-18 19:41:27',
 '2012-01-23 03:21:00',
 '2012-01-24 17:59:53',
 '2012-03-04 16:58:45',
 '2012-03-09 22:36:52',
 '2012-03-10 15:35:26',
 '2012-03-18 12:53:06',
 '2012-03-30 13:58:29',
 '2012-04-04 23:17:23']

With the McFlyin application running on localhost, lets make a request to resample the data on an daily basis, to get the number of posts per day:

import requests
import json

freq = {'D': 'Daily'}
sends = {'freq': json.dumps(freq), 'data': json.dumps(data)}
r = requests.post('http://127.0.0.1:5000/resample', data=sends)
response = r.json

The response is simple JSON:

{'Monthly': {'data': [1.0, 2.0, 1.0, 1.0,...
             'time': ['2011-03-31T00:00:00', '2011-04-30T00:00:00', '2011-05-31T00:00:00', '2011-06-30T00:00:00', '2011-07-31T00:00:00',...

Here's the distribution of daily questions on Stack Overflow for Pandas (monthly probably would have been a little more informative):

Daily

Let's call Mcflyin for a rolling sum on a seven-day window. It will resample to the given freq, then apply the window to the result:

freq = {'D': 'Weekly Rolling'}
sends = {'freq': json.dumps(freq), 'data': json.dumps(data), 'window': 7}
r = requests.post('http://127.0.0.1:5000/rolling_sum', data=sends)
response = r.json

Rolling

Let's look at the total questions asked by day:

sends = {'data': json.dumps(data), 'how': json.dumps('sum')}
r = requests.post('http://127.0.0.1:5000/daily', data=sends)
response = r.json

dailysum

and daily means:

sends = {'data': json.dumps(data), 'how': json.dumps('mean')}
r = requests.post('http://127.0.0.1:5000/daily', data=sends)
response = r.json

dailymean

The same for hourly:

sends = {'data': json.dumps(data), 'how': json.dumps('sum')}
r = requests.post('http://127.0.0.1:5000/hourly', data=sends)
response = r.json

dailymean

Finally, we can look at hourly by day-of-week:

sends = {'data': json.dumps(data), 'how': json.dumps('sum')}
r = requests.post('http://127.0.0.1:5000/daily_hours', data=sends)
response = r.json

hourdow

Live demo here

Dependencies

Pandas, Numpy, Requests, Flask

How did you make those colorful graphs?

Vincent and Bearcart

Status

Lots of stuff that could be better- error handling on the requests, probably better handling of weird timestamps, etc. This is just a small demo of how powerful Python can be for building a statistics backend with relatively few lines of code.

If I want to write a front-end in a different language, can I put it in the examples folder?

Yes! PR's welcome.