Matplotlib¶

Python offers sophisticated plotting capabilities. Plotting can be approached in two ways: - Non-Pythonic Approach: This approach relies on external libraries like matplotlib, which provides user-friendly tools for interactive plotting. A common shorthand for importing this module is import matplotlib.pyplot as plt. - Pythonic Approach: In this method, an empty object is created, and plots are constructed programmatically, then assigned to the empty object using code.

In the below, you can see how to run code

Scatter plot¶

The most commonly used plot is the scatter plot, see the following scripts that generate random number and plot

import matplotlib.pyplot as plt
import numpy as np
n = 100
x = 2 * np.random.rand(n)
y=2*x+np.random.rand(n)
plt.scatter(x, y)
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Scatter Plot')
plt.show(block=False)

Indeed, you can customize the appearance of a scatter plot using various arguments:
- Size of point (s): you can adjust the size of the points, for example, s=10 make the point smaller, - colour (c): You can specify the color of pointy. For example, c=red will make the points red. - marker: you can choose different marker styles for points. For example marker=s w ill use squares for presenting points.

plt.scatter(x, y, s=20 /(.2+x) , c='green', marker="s")
plt.show(block=False)

In this example, we've customized the scatter plot to use red square for data points which obviously smaller x get bigger square. You can further explore the links provided for more marker styles and line properties in matplotlib. The following figure displays a more sophisticated plot.

xy=x**2+y**2
select=xy<1
plt.scatter(x, y, alpha=0.3)
plt.scatter(x[select], y[select],facecolor='none',edgecolors='red')
plt.show(block=False)

Fit a linear model to a sample data.

plt.scatter(x, y, alpha=0.5, color='orchid') 
plt.suptitle('Scatter Plot') 
plt.tight_layout(pad=2);
plt.grid(True)
fit = np.polyfit(x, y, deg=1) 
plt.plot(x, fit[0]*x + fit[1], '-',color='red', linewidth=2)
plt.show(block=False)

If you want to save the figure to a file, put the script between

plt.savefig('name.png')

line¶

Using plt.plot can plot the line, to explain let consider timesries data:

import pandas as pd
x=pd.period_range('2019-11-06', periods=12*10,freq='M').to_timestamp()
y = np.random.randn(len(x)).cumsum()
y=abs(min(y))+y
plt.plot(x, y, label='ED')
plt.title('Example Data') 
plt.xlabel('Date') 
plt.ylabel('Y')
plt.grid(True)
plt.figtext(1,0, 'note',ha='right', va='bottom')
plt.legend(loc='best', framealpha=0.5,prop={'size':'small'})
plt.tight_layout(pad=1)
plt.gcf().set_size_inches(10, 5)
plt.show(block=False)
plt.close()

To excercise, let write a function to plot the following function

\[ f(x) = \begin{cases} sin(x), & x\leq \pi/2,\\ cos(x) & x> \pi/2.\\ \end{cases} \]

x=np.arange(0,np.pi,np.pi/100)
y=np.where(x<np.pi/2,np.cos(x),np.sin(x))
plt.plot(x,y)

The other approach is to use two function instead one, it can be done using the following script,

x=np.arange(0,np.pi,np.pi/100)
y=np.where(x<np.pi/2,np.cos(x),np.sin(x))
x0=x[x<np.pi/2]
plt.plot(x0,np.cos(x0), linestyle='--',label='cos(x)')
plt.axis([0,np.pi,0,1])
x1=x[(x>=np.pi/2)]
plt.plot(x1,np.sin(x1), linestyle='--',label='sin(x)')
plt.legend()
# it can be done using
# plt.plot(x0,np.cos(x0), '--',x1,np.sin(x1), '--')

The argument plt.axis() defines axes limits, it can also be done using plt.xlim(,), plt.ylim(,). The style of line is define in '--', other styles are

Type	Description
'-' or 'solid'	solid line
'--' or 'dashed'	dashed line
'-.' or 'dashdot'	dash-dotted line
':' or 'dotted'	dotted line
'None' or ' '	draw nothing

There are more options for axis, for instance plt.axis('equal') and plt.axis('tight'). The labels and title can be added to plot using plt.axes(),

plt.axes(xlim=(0, 10), ylim=(-2, 2),xlabel='x', ylabel='sin(x)', title='A Simple Plot')
plt.plot(x, np.sin(x), '-')
plt.show(block=False)
plt.close()

The following plot lines with different markers

n = 5
linestyles = ['-', '--', '-.', ':']
markers = list('ov^<>')
x = np.linspace(0, 100, 10)
for i in range(n): 
  y = x + x/5*i + i
  st = linestyles[i % len(linestyles)]
  ma = markers[i % len(markers)] 
  plt.plot(x, y,label='Line '+str(i+1)+' '+st+ma, marker=ma,linestyle=st)
plt.grid(True)
plt.axis('tight')
plt.legend(loc='best', prop={'size':'small'}) 
plt.show(block=False)
plt.close()

Pythonic line¶

The following codes shows how pythonic approach can be applied to generate several plots; first generate an empty figure from the global Figure factory, then generate your plot and assign to figure.

fig = plt.figure()
for i in range(1,10):
  x=pd.period_range('2019-11-06', periods=12*10,freq='M').to_timestamp()
  y = np.random.randn(len(x)).cumsum()
  y=abs(min(y))+y
  plt.plot(x, y, label='ED%s'%i)
  plt.title('Example Data') 
  plt.xlabel('Date') 
  plt.ylabel('Y')
  plt.grid(True)
  plt.legend(loc='best', framealpha=0.5,prop={'size':'small'})
  fig = plt.figure(i) # get the figure
plt.show()

You can close figures according the number plt.close(fig.number), all figures plt.close(all), the current one plt.close(). Note, plt.cla clears the current axes, plt.clf() clears the current figure, and the plt.close() closes the entire window.

subplot¶

Figures can be plotted in one figure using .subplot(#row,#col,position),

x = np.linspace(0, 16, 800)
plt.subplot(2, 2, 1)
plt.plot(x, np.sin(x))
plt.title("Fig1")
plt.xlim(0,1.5*np.pi)
plt.xlabel("X-axis")
plt.ylabel("sin(x)")
plt.subplot(2, 2, 2)
plt.plot(x, np.cos(x))
plt.subplot(2, 2, 3)
plt.plot(x, np.sin(x)*np.cos(x))
plt.subplot(2, 2, 4)
plt.plot(x, np.sin(x)+np.cos(x))
plt.show(block=False)

You can not use plt.axes() for subplot.