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Jupyter Cookbook

You're reading from   Jupyter Cookbook Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788839440
Length 238 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Toc

Table of Contents (12) Chapters Close

Preface 1. Installation and Setting up the Environment 2. Adding an Engine FREE CHAPTER 3. Accessing and Retrieving Data 4. Visualizing Your Analytics 5. Working with Widgets 6. Jupyter Dashboards 7. Sharing Your Code 8. Multiuser Jupyter 9. Interacting with Big Data 10. Jupyter Security 11. Jupyter Labs

Plotting 3D data using Python


In this example, we display 3D data. We take some automobile miles per gallon data and plot it out according to 3D weight, miles per gallon, and number of cylinders.

How to do it...

We use this script:

%matplotlib inline

# import tools we are using
import pandas as pd
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

# read in the car ‘table’ – not a csv, so we need
# to add in the column names
column_names = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'year', 'origin', 'name']
df = pd.read_table('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data', sep=r"\s+", index_col=0, header=None, names = column_names)
print(df.head())

#start out plotting (uses a subplot as that can be 3d)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')X# pull out the 3 columns that we want
xs = []
ys = []
zs = []
for index, row in df.iterrows():
 xs.append(row['weight'...
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