import numpy.matlib 
import numpy as np  

i = np.matrix(''1,2;3,4'') 
print i 

It will produce the following output −

[[1  2] 
 [3  4]]

Example

import numpy.matlib 
import numpy as np  

j = np.asarray(i) 
print j 

It will produce the following output −

[[1  2] 
 [3  4]] 

Example

import numpy.matlib 
import numpy as np  

k = np.asmatrix (j) 
print k

It will produce the following output −

[[1  2] 
 [3  4]]

NumPy – Linear Algebra

NumPy package contains numpy.linalg module that provides all the functionality required for linear algebra. Some of the important functions in this module are described in the following table.

NumPy – Matplotlib

Matplotlib is a plotting library for Python. It is used along with NumPy to provide an environment that is an effective open source alternative for MatLab. It can also be used with graphics toolkits like PyQt and wxPython.

Matplotlib module was first written by John D. Hunter. Since 2012, Michael Droettboom is the principal developer. Currently, Matplotlib ver. 1.5.1 is the stable version available. The package is available in binary distribution as well as in the source code form on www.matplotlib.org.

Conventionally, the package is imported into the Python script by adding the following statement −

from matplotlib import pyplot as plt

Here pyplot() is the most important function in matplotlib library, which is used to plot 2D data. The following script plots the equation y = 2x + 5

Example

import numpy as np 
from matplotlib import pyplot as plt 

x = np.arange(1,11) 
y = 2 * x + 5 
plt.title("Matplotlib demo") 
plt.xlabel("x axis caption") 
plt.ylabel("y axis caption") 
plt.plot(x,y) 
plt.show()

An ndarray object x is created from np.arange() function as the values on the x axis. The corresponding values on the y axis are stored in another ndarray object y. These values are plotted using plot() function of pyplot submodule of matplotlib package.

The graphical representation is displayed by show() function.

The above code should produce the following output −

Instead of the linear graph, the values can be displayed discretely by adding a format string to the plot() function. Following formatting characters can be used.

The following color abbreviations are also defined.

To display the circles representing points, instead of the line in the above example, use “ob” as the format string in plot() function.

Example

import numpy as np 
from matplotlib import pyplot as plt 

x = np.arange(1,11) 
y = 2 * x + 5 
plt.title("Matplotlib demo") 
plt.xlabel("x axis caption") 
plt.ylabel("y axis caption") 
plt.plot(x,y,"ob") 
plt.show() 

The above code should produce the following output −

Sine Wave Plot

The following script produces the sine wave plot using matplotlib.

Example

import numpy as np 
import matplotlib.pyplot as plt  

# Compute the x and y coordinates for points on a sine curve 
x = np.arange(0, 3 * np.pi, 0.1) 
y = np.sin(x) 
plt.title("sine wave form") 

# Plot the points using matplotlib 
plt.plot(x, y) 
plt.show() 

subplot()

The subplot() function allows you to plot different things in the same figure. In the following script, sine and cosine values are plotted.

Example

import numpy as np 
import matplotlib.pyplot as plt  
   
# Compute the x and y coordinates for points on sine and cosine curves 
x = np.arange(0, 3 * np.pi, 0.1) 
y_sin = np.sin(x) 
y_cos = np.cos(x)  
   
# Set up a subplot grid that has height 2 and width 1, 
# and set the first such subplot as active. 
plt.subplot(2, 1, 1)
   
# Make the first plot 
plt.plot(x, y_sin) 
plt.title(''Sine'')  
   
# Set the second subplot as active, and make the second plot. 
plt.subplot(2, 1, 2) 
plt.plot(x, y_cos) 
plt.title(''Cosine'')  
   
# Show the figure. 
plt.show()

The above code should produce the following output −

bar()

The pyplot submodule provides bar() function to generate bar graphs. The following example produces the bar graph of two sets of x and y arrays.

Example

from matplotlib import pyplot as plt 
x = [5,8,10] 
y = [12,16,6]  

x2 = [6,9,11] 
y2 = [6,15,7] 
plt.bar(x, y, align = ''center'') 
plt.bar(x2, y2, color = ''g'', align = ''center'') 
plt.title(''Bar graph'') 
plt.ylabel(''Y axis'') 
plt.xlabel(''X axis'')  

plt.show()

This code should produce the following output −

NumPy – Histogram Using Matplotlib

NumPy has a numpy.histogram() function that is a graphical representation of the frequency distribution of data. Rectangles of equal horizontal size corresponding to class interval called bin and variable height corresponding to frequency.

numpy.histogram()

The numpy.histogram() function takes the input array and bins as two parameters. The successive elements in bin array act as the boundary of each bin.

import numpy as np 
   
a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27]) 
np.histogram(a,bins = [0,20,40,60,80,100]) 
hist,bins = np.histogram(a,bins = [0,20,40,60,80,100]) 
print hist 
print bins 

It will produce the following output −

[3 4 5 2 1]
[0 20 40 60 80 100]

plt()

Matplotlib can convert this numeric representation of histogram into a graph. The plt() function of pyplot submodule takes the array containing the data and bin array as parameters and converts into a histogram.

from matplotlib import pyplot as plt 
import numpy as np  
   
a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27]) 
plt.hist(a, bins = [0,20,40,60,80,100]) 
plt.title("histogram") 
plt.show()

It should produce the following output −

I/O with NumPy

The ndarray objects can be saved to and loaded from the disk files. The IO functions available are −

• load() and save() functions handle /numPy binary files (with npy extension)

• loadtxt() and savetxt() functions handle normal text files

NumPy introduces a simple file format for ndarray objects. This .npy file stores data, shape, dtype and other information required to reconstruct the ndarray in a disk file such that the array is correctly retrieved even if the file is on another machine with different architecture.

numpy.save()

The numpy.save() file stores the input array in a disk file with npy extension.

import numpy as np 
a = np.array([1,2,3,4,5]) 
np.save(''outfile'',a)

To reconstruct array from outfile.npy, use load() function.

import numpy as np 
b = np.load(''outfile.npy'') 
print b 

It will produce the following output −

array([1, 2, 3, 4, 5])

The save() and load() functions accept an additional Boolean parameter allow_pickles. A pickle in Python is used to serialize and de-serialize objects before saving to or reading from a disk file.

savetxt()

The storage and retrieval of array data in simple text file format is done with savetxt() and loadtxt() functions.

Example

import numpy as np 

a = np.array([1,2,3,4,5]) 
np.savetxt(''out.txt'',a) 
b = np.loadtxt(''out.txt'') 
print b 

It will produce the following output −

[ 1.  2.  3.  4.  5.] 

The savetxt() and loadtxt() functions accept additional optional parameters such as header, footer, and delimiter.