Once again, similar to the Python standard library, NumPy also provides us with the slice operation on numpy arrays, using which we can access the array slice of elements to give us a corresponding subarray.<\/p>\n\n\n
\n>>> b[:]\narray([[1, 2, 3],\n [4, 5, 6]])\n>>> b[:1]\narray([1, 2, 3])\n<\/pre><\/div>\n\n\nIn fact, this is the widely recommended way to use NumPy arrays, due to the highly optimized nature of the numpy operations. Since native python methods are quite slow in comparison, we should only use numpy methods to manipulate numpy arrays. Pure Python iterative loops and other list comprehensions are not used with numpy as a result.<\/p>\n\n\n\n
\n\n\n\nOther ways to generate numpy arrays<\/h2>\n\n\n\n
We can use numpy built-in arange(n)<\/code> method to construct a 1-Dimensional array consisting of the numbers 0<\/code> to n-1<\/code>.<\/p>\n\n\n\n>>> c = np.arange(12)\n>>> print(c)\n[0 1 2 3 4 5 6 7 8 9 10 11]\n>>> c.shape\n(12,)\n<\/pre><\/div>\n\n\nUsing random.randint(limit, size=N)<\/code> generates a random integer array with all elements between 0 and limit<\/code>, and with a size of N<\/code>, specified as a keyword argument.<\/p>\n\n\n\n>>> d = np.random.randint(10, size=6)\n>>> d\narray([7, 7, 8, 8, 3, 3])\n>>> e = np.random.randint(10, size=(3,4))\n>>> e\narray([[2, 2, 0, 5],\n [8, 9, 7, 3],\n [5, 7, 7, 0]])\n<\/pre><\/div>\n\n\n
\n\n\n\nManipulating NumPy Arrays<\/h2>\n\n\n\n
NumPy provides a method reshape()<\/code>, which can be used to change the dimensions of the numpy array and modify the original array in place. Here, we show an illustration of using reshape()<\/code> to change the shape of c<\/code> to (4, 3)<\/code><\/p>\n\n\n\n>>> c\narray([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\n>>> c.shape\n(12,)\n>>> c.reshape(4, 3)\narray([[ 0, 1, 2],\n [ 3, 4, 5],\n [ 6, 7, 8],\n [ 9, 10, 11]])\n<\/pre><\/div>\n\n\nSince numpy operations are designed to be highly optimized, any subarray that is created from an array is still holding the reference to the original array. This means that if the subarray is modified in place, the original array is also modified.<\/p>\n\n\n
\n>>> f = e[:3, :2]\n>>> f\narray([[2, 2],\n [8, 9],\n [5, 7]])\n>>> f[0,0] *= 3\n>>> f\narray([[6, 2],\n [8, 9],\n [5, 7]])\n>>> e\narray([[6, 2, 0, 5],\n [8, 9, 7, 3],\n [5, 7, 7, 0]])\n<\/pre><\/div>\n\n\nHere, the original array e<\/code> is also modified with any change in the subarray slice f<\/code>. This is because numpy slices only return a view<\/strong> of the original array.<\/p>\n\n\n\nTo ensure that the original array is not modified with any change in the subarray slice, we use numpy copy()<\/code> method to create a copy of the array and modify the cloned object, instead of dealing with a reference of the original object. <\/p>\n\n\n\nThe below snippet shows how copy<\/code> deals with this issue.<\/p>\n\n\n\n>>> e\narray([[6, 2, 0, 5],\n [8, 9, 7, 3],\n [5, 7, 7, 0]])\n>>> f = e[:3, :2].copy()\n>>> f\narray([[6, 2],\n [8, 9],\n [5, 7]])\n>>> f[0,0] = 100\n>>> f\narray([[100, 2],\n [ 8, 9],\n [ 5, 7]])\n>>> e\n# No change is reflected in the original array\n# We are safe!\narray([[6, 2, 0, 5],\n [8, 9, 7, 3],\n [5, 7, 7, 0]])\n<\/pre><\/div>\n\n\n
\n\n\n\nConclusion<\/h2>\n\n\n\n
In this article, we learned about numpy arrays and some elementary operations and manipulations involving them, including their attributes, array slicing, reshaping, and copying.<\/p>\n\n\n\n
References<\/h2>\n\n\n\n
NumPy Docs<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"NumPy is the most commonly used scientific computing Python library. It provides a fast Pythonic interface, while still using the much faster C++ under the hood for computation. This ensures that the high-level readability and Pythonic features are still present while making the actual computation much faster than what pure Python code could. Here, we […]<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[93],"tags":[],"class_list":["post-1070","post","type-post","status-publish","format-standard","hentry","category-numpy"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/1070","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/comments?post=1070"}],"version-history":[{"count":0,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/1070\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media?parent=1070"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/categories?post=1070"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/tags?post=1070"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}