Book Image

Jupyter Cookbook

By : Dan Toomey
Book Image

Jupyter Cookbook

By: Dan Toomey

Overview of this book

Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Generating a density map using Python

In this section, we generate a (human) density map of the United States, where each state is color coded based on its relative population density.

How to do it...

We can use the script:

%matplotlib inline

import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
import pandas as pd
import numpy as np
import matplotlib

# create the map
map = Basemap(llcrnrlon=-119,llcrnrlat=22,urcrnrlon=-64,urcrnrlat=49,
 projection='lcc',lat_1=33,lat_2=45,lon_0=-95)# load the shapefile, use the name 'states'
# download from,shx,shp
map.readshapefile('st99_d00', name='states', drawbounds=True)

# collect the state names from the shapefile attributes so we can
# look up the shape obect for a state by it's name
state_names = []
for shape_dict in map.states_info:

ax = plt.gca() # get current axes instance