jorisvandenbossche

rspeer

jakevdp

rlabbe

# style the notebook
from IPython.core.display import HTML
import urllib.request
# this link is to my Kalman filter book CSS file.
response = urllib.request.urlopen('http://bit.ly/1LC7EI7')
HTML(response.read().decode("utf-8"))

rbnvrw

from igraph import *
import numpy as np

# Create the graph
vertices = [i for i in range(7)]
edges = [(0,2),(0,1),(0,3),(1,0),(1,2),(1,3),(2,0),(2,1),(2,3),(3,0),(3,1),(3,2),(2,4),(4,5),(4,6),(5,4),(5,6),(6,4),(6,5)]

g = Graph(vertex_attrs={"label":vertices}, edges=edges, directed=True)

visual_style = {}

mdml

A dendrogram is a common way to represent hierarchical data. For Python users, Scipy has a hierarchical clustering module that performs hierarchical clustering and outputs the results as dendrogram plots via matplotlib. When it's time to make a prettier, more customized, or web-version of the dendogram, however, it can be tricky to use Scipy's dendrogram to create a suitable visualization. My preferred method of visualizing data -- especially on the web -- is D3. This example includes a script to convert a Scipy dendrogram into JSON format used by D3's cluster method.

In the example, I cluster six genes by their expression values from two experiments. You can easily replace that data with your own, larger data set, to harness the power of both Scipy and D3 for analyzing hierarchical data. The D3 code I used to generate this example is straigh

damianavila

"""
Usage: python remove_output.py notebook.ipynb [ > without_output.ipynb ]
Modified from remove_output by Minrk

"""
import sys
import io
import os
from IPython.nbformat.current import read, write

j4mie

# -*- coding: utf-8 -*-
 
import unicodedata

""" Normalise (normalize) unicode data in Python to remove umlauts, accents etc. """

data = u'naïve café'
normal = unicodedata.normalize('NFKD', data).encode('ASCII', 'ignore')
print normal