Matplotlib tutorial

Matplotlib Subplot

Matplotlib Subplot

from pylab import *
 
t = arange(0.0, 20.0, 1)
s = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
 
subplot(2,1,1)
xticks([]), yticks([])
title('subplot(2,1,1)')
plot(t,s)
 
subplot(2,1,2)
xticks([]), yticks([])
title('subplot(2,1,2)')
plot(t,s,'r-')
 
show()

from pylab import *
 
t = arange(0.0, 20.0, 1)
s = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
 
subplot(2,2,1)
xticks([]), yticks([])
title('subplot(2,2,1)')
plot(t,s)
 
subplot(2,2,2)
xticks([]), yticks([])
title('subplot(2,2,2)')
plot(t,s,'r-')
 
subplot(2,2,3)
xticks([]), yticks([])
title('subplot(2,2,3)')
plot(t,s,'g-')
 
subplot(2,2,4)
xticks([]), yticks([])
title('subplot(2,2,4)')
plot(t,s,'y-')
 
show()

def plot_images(images, cls_true, cls_pred=None):
    assert len(images) == len(cls_true) == 9
    
    # Create figure with 3x3 sub-plots.
    fig, axes = plt.subplots(3, 3)
    fig.subplots_adjust(hspace=0.3, wspace=0.3)

    for i, ax in enumerate(axes.flat):
        # Plot image.
        ax.imshow(images[i].reshape(img_shape), cmap='binary')

        # Show true and predicted classes.
        if cls_pred is None:
            xlabel = "True: {0}".format(cls_true[i])
        else:
            xlabel = "True: {0}, Pred: {1}".format(cls_true[i], cls_pred[i])

        ax.set_xlabel(xlabel)
        
        # Remove ticks from the plot.
        ax.set_xticks([])
        ax.set_yticks([])
        
    # Ensure the plot is shown correctly with multiple plots
    # in a single Notebook cell.
    plt.show()

import numpy as np
import matplotlib.pyplot as plt
 
# Create data
N = 60
g1 = (0.6 + 0.6 * np.random.rand(N), np.random.rand(N))
g2 = (0.4+0.3 * np.random.rand(N), 0.5*np.random.rand(N))
g3 = (0.3*np.random.rand(N),0.3*np.random.rand(N))
 
data = (g1, g2, g3)
colors = ("red", "green", "blue")
groups = ("coffee", "tea", "water") 
 
# Create plot
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, axisbg="1.0")
 
for data, color, group in zip(data, colors, groups):
    x, y = data
    ax.scatter(x, y, alpha=0.8, c=color, edgecolors='none', s=30, label=group)
 
plt.title('Matplot scatter plot')
plt.legend(loc=2)
plt.show()

histogram

import numpy as np
import matplotlib.pyplot as plt

# Fixing random state for reproducibility
np.random.seed(19680801)

mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)

# the histogram of the data
n, bins, patches = plt.hist(x, 50, density=True, facecolor='g', alpha=0.75)


plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()