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.
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'...