Choropleth Maps in Python

Choropleth maps are a great way to represent geographical data. I have done a basic implementation of two different data sets. I have used jupyter notebook to show the plots.

World Power Consumption 2014

First do Plotly imports
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode,iplot

Next step is to fetch the dataset, we’ll use Python pandas library to read the read the csv file

import pandas as pd
df = pd.read_csv('2014_World_Power_Consumption')

Next, we need to create data and layout variable which contains a dict

data = dict(type='choropleth',
locations = df['Country'],
locationmode = 'country names', z = df['Power Consumption KWH'],
text = df['Country'], colorbar = {'title':'Power Consumption KWH'},
colorscale = 'Viridis', reversescale = True)

Let’s make a layout

layout = dict(title='2014 World Power Consumption',
geo = dict(showframe=False,projection={'type':'Mercator'}))

Pass the data and layout and plot using iplot

choromap = go.Figure(data = [data],layout = layout)

The output will be be like below:

Check github for full code.

In next post I will try to make a choropleth for a different data set.


Getting and cleaning data using R programming project notes

Brief notes of my learning from course project of getting and cleaning data course from John Hopkins University.

The purpose of this project is to demonstrate the ability to collect, work with, and clean a data set. Final goal here is to prepare tidy data that can be used for later analysis.

One of the most exciting areas in all of the data science right now is wearable computing – see for example companies like Fitbit, Nike, tomtom, Garmin etc are racing to develop the most advanced algorithms to attract new users. In this case study, the data is collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained:

Here is the dataset for the project:

I have created an R script called run_analysis.R which does the following.

  • Merges the training and the test sets to create one data set.
  • Extracts only the measurements on the mean and standard deviation for each measurement.
  • Uses descriptive activity names to name the activities in the data set.
  • Appropriately labels the data set with descriptive variable names.
  • Finally, creates a second, independent tidy data set with the average of each variable for each activity and each subject.References:


For working code and tidy dataset please check my Github repo.


Accessing Github API with OAuth example using R

Modern API provided by Google, Twitter, Facebook, Github etc uses OAuth for authentication and authorization. In this example, I am using GitHub API. We get a JSON response which can be used to fetch specific information. In this code I have used my Github account.Code is written R programming languages.

Here are the steps:
1. Find OAuth settings for Github
2. Create a application in Github
3. Add/Modify secret keys
4. Get OAuth credentials
5. Finally use API and parse json data to show response

## Load required modules

# 1. Find OAuth settings for github:

# 2. To make your own application, register at at
## Use any URL for the homepage URL
# ( is fine) and http://localhost:1410 as the callback url. You will need httpuv

## Add Secret keys
## Secret keys can be get from developer github
myapp <- oauth_app("github",
key = "7cd28c82639b7cf76fcc",
secret = "d1c90e32e12baa81dabec79cd1ea7d8edfd6bf53")

# 3. Get OAuth credentials
github_token <- oauth2.0_token(oauth_endpoints("github"), myapp)
## Authentication will be done automatically

# 4. Use API
gtoken <- config(token = github_token)
req <- GET("", gtoken)
output <- content(req)
## Either of the two can be used to fetch the required info, name and date created of repo ProgrammingAssignment3
out<-list(output[[30]]$name, output[[30]]$created_at)

BROWSE("",authenticate("Access Token","x-oauth-basic","basic"))
# OR:
req <- with_config(gtoken, GET(""))

For updated code please check github