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<\/a>Python NumPy Tutorial – NumPy Array Index<\/p><\/div>\n
1. Integer Indexing in Python NumPy<\/h4>\n
It is possible to create an array from another.<\/p>\n
>>> a=np.array([[1,2],[3,4],[5,6]])\r\n>>> a[[0,1,2],[0,1,0]] #Prints elements at [0,0], [1,1], and [2,0]<\/pre>\nOutput<\/b><\/p>\narray([1, 4, 5])<\/div>\n
Let\u2019s pick elements-<\/p>\n
>>> a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\r\n>>> b = np.array([0, 2, 0, 1])\r\n>>> b<\/pre>\nOutput<\/b><\/p>\narray([0, 2, 0, 1])<\/div>\n
>>> a[np.arange(4), b]<\/pre>\nOutput<\/b><\/p>\narray([ 1, 6, 7, 11])<\/div>\n
>>> a[np.arange(4), b]+=10\r\n>>> a<\/pre>\nOutput<\/b><\/p>\narray([[11, 2, 3],
\n[ 4, 5, 16],
\n[17, 8, 9],
\n[10, 21, 12]])<\/div>\n
2. Boolean Indexing in Python NumPy<\/h4>\n
This will let you pick elements that satisfy a condition.<\/p>\n
>>> a=np.array([[1,2],[3,4],[5,6]])\r\n>>> boolean=(a>3)\r\n>>> boolean<\/pre>\nOutput<\/b><\/p>\narray([[False, False],
\n[False, True],
\n[ True, True]])<\/div>\n
>>> a[boolean]<\/pre>\nOutput<\/b><\/p>\narray([4, 5, 6])<\/div>\n
Mathematical Functions on Arrays in NumPy<\/h3>\n
Let\u2019s now look at some mathematical functions to call on arrays.<\/p>\n
>>> a=np.array([[1,2,3],[4,5,6]])\r\n>>> b=np.array([[7,8,9],[10,11,12]])\r\n>>> np.add(a,b) #a+b does the same<\/pre>\nOutput<\/b><\/p>\narray([[ 8, 10, 12],
\n[14, 16, 18]])<\/div>\n
>>> np.subtract(a,b) #Same as a-b\r\n<\/pre>\nOutput<\/b><\/p>\narray([[-6, -6, -6],
\n[-6, -6, -6]])<\/div>\n
>>> np.multiply(a,b) #a*b works too\r\n<\/pre>\nOutput<\/b><\/p>\narray([[ 7, 16, 27],
\n[40, 55, 72]])<\/div>\n
>>> np.divide(a,b) #Same as a\/b\r\n<\/pre>\nOutput<\/b><\/p>\narray([[0.14285714, 0.25 , 0.33333333],
\n[0.4 , 0.45454545, 0.5 ]])<\/div>\n
>>> np.sqrt(a) #Produces square root\r\n<\/pre>\nOutput<\/b><\/p>\narray([[1. , 1.41421356, 1.73205081],
\n[2. , 2.23606798, 2.44948974]])<\/div>\n
>>> a=np.array([[1,2],[3,4]])\r\n>>> np.sum(a)<\/pre>\nOutput<\/b><\/p>\n10<\/div>\n
>>> np.sum(a,axis=0) #Sum of each column\r\n<\/pre>\nOutput<\/b><\/p>\narray([4, 6])<\/div>\n
>>> np.sum(a,axis=1) #Sum of each row\r\n<\/pre>\nOutput<\/b><\/p>\narray([3, 7])<\/div>\n
To transpose this matrix:<\/p>\n
>>> a.T\r\n<\/pre>\nOutput<\/b><\/p>\narray([[1, 3],
\n[2, 4]])<\/div>\n
>>> np.array([1,3,2]).T #NOP\r\n<\/pre>\nOutput<\/b><\/p>\narray([1, 3, 2])<\/div>\n
Some functions that operate on a larger context-<\/p>\n
>>> x=np.array([[1,2],[3,4]])\r\n>>> y=np.array([[5,6],[7,8]])\r\n>>> v=np.array([9,10])\r\n>>> w=np.array([11,12])\r\n>>> v.dot(w) #Same as np.dot(v,w)<\/pre>\nOutput<\/b><\/p>\n219<\/div>\n
>>> x.dot(v)\r\n<\/pre>\nOutput<\/b><\/p>\narray([29, 67])<\/div>\n
>>> x.dot(y)\r\n<\/pre>\nOutput<\/b><\/p>\narray([[19, 22],
\n[43, 50]])<\/div>\n
So, this was all about Python NumPy Tutorial. Hope you like our explanation.<\/p>\n
Python Interview Questions on NumPy<\/h3>\n\n- What is a Python NumPy array?<\/li>\n
- What is the purpose of NumPy in Python?<\/li>\n
- What is the difference between Pandas and NumPy in Python?<\/li>\n
- How does NumPy work in Python?<\/li>\n
- What functions does NumPy provide in Python?<\/li>\n<\/ol>\n
Conclusion<\/h3>\n
That\u2019s the beauty of NumPy that it has turned Python into a powerhouse for numerics and data. Instead of counting things one by one, you can now handle huge amounts of information at once with incredible speed.<\/p>\n
Hence, in this Python NumPy Tutorial, we studied how to install NumPy and the NumPy ndarray.<\/p>\n
In addition, we discussed NumPy Array with its Functions and data types. This sums it up for NumPy.<\/p>\n","protected":false},"excerpt":{"rendered":"
In our last Python Library tutorial, we studied Python SciPy. Now we are going to study Python NumPy. In this NumPy tutorial, we are going to discuss the features, installation, and NumPy ndarray. Moreover,...<\/p>\n","protected":false},"author":5,"featured_media":22035,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[46],"tags":[3491,4615,4982,6084,6218,7077,9171,9172,10728,15869],"class_list":["post-22004","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-data-types-in-numpy","tag-features-of-numpy","tag-functions-of-numpy-array","tag-how-to-create-numpy-array","tag-how-to-install-numpy","tag-introduction-to-numpy","tag-numpy-array-indexing","tag-numpy-data-types","tag-python-numpy-tutorial","tag-what-is-python-numpy"],"yoast_head":"\n
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<\/a>