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Easy NumPy Tutorial Part 3

In this last part of easy numpy tutorial we will learn about Sorting , File I/O and other topics.

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Outline:

15. Sorting
16. Some set operations
17. File Input/Output with numpy arrays
18. Linear Algebra with NumPy

Introduction:

The Part 3 of easy numpy tutorial series comprises of Sorting , Set Operations , File I/O and Linear Algebra.

Sorting

ndarrays can be sorted with sort method:

arr=np.random.randn(10)
arr
---------Out:
array([ 0.15010146, -0.90865459, -1.32660676, -1.2586473 ,  0.33456535,
       -0.52628284,  1.08191402,  0.01194585,  1.16277344, -0.85564764])

arr.sort()
arr
---------Out:
array([-1.32660676, -1.2586473 , -0.90865459, -0.85564764, -0.52628284,0.01194585,  0.15010146,  0.33456535,  1.08191402,  1.16277344])

For multidimensional arrays you can pass the axis which you want to be sorted:

arr=np.random.randn(3,4)
arr
---------Out:
array([[-0.9994893 , -1.59630403, -0.9099843 ,  0.02968692],
       [ 0.11655207,  0.96398831, -0.28124549,  0.33964778],
       [ 1.45948445,  1.30783811, -0.71988526,  0.2722295 ]])

arr.sort(axis=1)
arr
---------Out:
array([[-1.59630403, -0.9994893 , -0.9099843 ,  0.02968692],
       [-0.28124549,  0.11655207,  0.33964778,  0.96398831],
       [-0.71988526,  0.2722295 ,  1.30783811,  1.45948445]])

The module level np.sort returns a new sorted copy of the array instead of modifying it.

Some set operations

NumPy provides some basic set operations for one dimensional arrays, the most common is unique which returns sorted unique values from the array:

arr=np.array(['apple','mango','apple','banana','avocado','mango'])
np.unique(arr)
---------Out:
array(['apple', 'avocado', 'banana', 'mango'], dtype='<U7')

File Input/Output with numpy arrays

NumPy provides an easy to use API for loading and saving data from and to disks in text or binary format. np.save function saves the data in raw binary with .npy extension:

arr=np.arange(10)
np.save('array',arr)

The .npy extension is already added if not specified. The file on the disk can be loaded with np.load:

np.load('array.npy')
---------Out:
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

Linear Algebra with NumPy

Common linear algebra like matrix multiplication , determinant can easily be done with NumPY. You can perform array dot product with dot present both at top level function and as an array instance method:

x=np.array([[1,2,3,4],[5,6,7,8]])
y=np.array([[9,10,],[11,12],[13,14,],[15,16]])
x
---------Out:
array([[1, 2, 3, 4],
       [5, 6, 7, 8]])

y
---------Out:
array([[ 9, 10],
       [11, 12],
       [13, 14],
       [15, 16]])

x.dot(y)
---------Out:
array([[130, 140],
       [322, 348]])

As of Python 3.5 , you can also use @ for matrix multiplication:

x@y
---------Out:
array([[130, 140],
       [322, 348]])

numpy.linalg provides standard functions like matrix decomposition, inverse, determinant etc:

from numpy.linalg import inv
arr=np.random.randn(2,2)
arr
---------Out:
array([[-1.97007582,  2.3830042 ],
       [-0.48148139, -1.41080847]])

inv(arr)
---------Out:
array([[-0.35927946, -0.60686088],
       [ 0.12261507, -0.50170367]])

Some other common functions it provides are : diag ,dot,trace etc.

Conclusion

After completing this tutorial , you must be familiar with important concepts of NumPy , which you will find useful in various numerical computing and data analysis problems. If you enjoyed this series , consider donating to my buy me a coffee which will help me creating more content. Thank You . Go to Part 1 or Part 2 .

References

Some examples and topics are referenced from Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython Book by Wes McKinney . It is a great for beginners who want to learn data analysis with python , you can buy it here: