Matplotlib

Matplotlib is a popular Python library for graphical plotting. It works well with NumPy and Pandas. The gallery on Matplotlib's website shows what kinds of plotting that it can perform. There is also a good reference site on the web.



In [1]:

    
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

To show plots in Jupyter Notebook, we need to execute %matplotlib inline. If we use other editors, we need to run plt.show() at the end to have our plot show up in a window.



In [2]:

    
%matplotlib inline



In [3]:

    
# Generate data
x = np.linspace(0, 5, 11)
y = x ** 2

figure = plt.figure()
axes = figure.add_axes([.1, .1, .8, .8])
axes.plot(x, y)
axes.set_xlabel('X Label')
axes.set_ylabel('Y Label')
axes.set_title('Title')









    Out[3]:





<matplotlib.text.Text at 0x2d7a2b6f710>



In [4]:

    
# Generate data for a sine curve between 0 and 4*pi 
sin_x = np.linspace(0, 4*np.pi, 100)
sin_y = np.sin(sin_x) 

fig = plt.figure(figsize=(8,4))
ax = fig.add_axes([0, 0, 1, 1])
ax.set_xlim(0, 4*np.pi) # 
ax.set_ylim(-1.5, 1.5)
ax.plot(sin_x, sin_y, 'g')









    Out[4]:





[<matplotlib.lines.Line2D at 0x2d7a2c0a518>]

Multiple Axes



In [5]:

    
figure = plt.figure()
axes1 = figure.add_axes([0, 0, 1, 1])
axes1.set_facecolor('#d1ffb8')
axes1.plot(x, y, 'g.--')

axes2 = figure.add_axes([1.1, 0, 1, 1])
axes2.set_facecolor('#a6ddff')
axes2.plot(y, x, 'b*--')









    Out[5]:





[<matplotlib.lines.Line2D at 0x2d7a2dea438>]

Automatic Axis Manager

Instead of creating a figure and calling add_axes(), we can use the subplots() method. It acts like an automatic axis manager.



In [6]:

    
# Create an empty canvas with two subplots
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15,4))
axes[0].plot(x, y, 'g')
axes[1].plot(y, x, 'b')









    Out[6]:





[<matplotlib.lines.Line2D at 0x2d7a2ecc198>]

Overlapping Figures

A common issue with matplolib is overlapping subplots or figures. The method plt.tight_layout() automatically adjusts subplot parameters so that the subplot fits in to the figure canvas. This avoids overlapping content. This is an experimental feature and may not work for some cases. It only checks the extents of ticklabels, axis labels, and titles. For more information read the Tight Layout guide.



In [7]:

    
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)
for ax in [ax1, ax2, ax3, ax4]:
    ax.plot(x, y, 'g')
    ax.set_xlabel('x')
    ax.set_ylabel('y')
    ax.set_title('title')



In [8]:

    
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)
for ax in [ax1, ax2, ax3, ax4]:
    ax.plot(x, y, 'g')
    ax.set_xlabel('x')
    ax.set_ylabel('y')
    ax.set_title('title')
plt.tight_layout()



In [9]:

    
fig, axes = plt.subplots(nrows=2, ncols=2)
i = 0
for outer in axes:
    for ax in outer:
        i += 1
        ax.plot(x, y, 'g')
        ax.set_xlabel('x')
        ax.set_ylabel('y')
        ax.set_title('Axes No.{}'.format(i))
plt.tight_layout()

Colours, Markers and Legends



In [10]:

    
fig, ax = plt.subplots(figsize=(12,6))

ax.plot(x, x**2, label='Squared', color='purple', linewidth=2, linestyle='-.', marker='o')
ax.plot(x, x**3, label='Cubed', color='green', marker='x', markersize=12)

# Use loc=0 to let Matplotlib determine the best location
ax.legend(loc=10)









    Out[10]:





<matplotlib.legend.Legend at 0x2d7a3211668>

Twin axes

Let two or more plots share the same x-axis using twinx() or the same y-axis using twiny().



In [11]:

    
fig = plt.figure()
ax1 = fig.add_axes([0, 0, 1, 1])
ax2 = ax1.twinx() #  Note that ax2 shares the x-axis with ax1
x2 = np.linspace(0., 10., 100)

ax1.set_ylabel('Density (cgs)', color='red')
ax2.set_ylabel('Temperature (K)', color='blue')
ax1.set_xlabel('Time (s)')

ax1.plot(x2, x2 ** 2, 'b-')
ax2.plot(x2, 1000 / (x2 + 1), 'r-')









    Out[11]:





[<matplotlib.lines.Line2D at 0x2d7a460fe10>]

Plot Types



In [12]:

    
plt.scatter(x,y)









    Out[12]:





<matplotlib.collections.PathCollection at 0x2d7a4713f98>



In [13]:

    
from random import sample
data = sample(range(1, 1000), 100)
plt.hist(data)









    Out[13]:





(array([  6.,  10.,  13.,  11.,  10.,  12.,  11.,   8.,  10.,   9.]),
 array([  10. ,  108.1,  206.2,  304.3,  402.4,  500.5,  598.6,  696.7,
         794.8,  892.9,  991. ]),
 <a list of 10 Patch objects>)



In [14]:

    
data = [np.random.normal(0, std, 100) for std in range(1, 4)]
plt.boxplot(data, vert=True, patch_artist=True);



In [ ]: