Set The Color Of A Matplotlib Plot
Import numpy and matplotlib.pyplot
%matplotlib inline
import numpy as np
import matplotlib.pyplot as pltCreate some simulated data.
n = 100
r = 2 * np.random.rand(n)
theta = 2 * np.pi * np.random.rand(n)
area = 200 * r**2 * np.random.rand(n)
colors = thetaCreate a scatterplot using the a colormap.
Full list of colormaps: http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
c = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.RdYlGn)
c1 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.Blues)
c2 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.BrBG)
c3 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.Greens)
c4 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.RdGy)
c5 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.YlOrRd)
c6 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.autumn)
c7 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.binary)
c8 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.gist_earth)
c9 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.gist_heat)
c10 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.hot)
c11 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.spring)
c12 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.summer)
c12 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.winter)
c13 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.bone)
c14 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.cool)
c15 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.YlGn)
c16 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.RdBu)
c17 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.PuOr)
c18 = plt.scatter(theta, r, c=colors, s=area, cmap=plt.cm.Oranges)
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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