{"id":4862,"date":"2020-10-27T13:21:07","date_gmt":"2020-10-27T07:51:07","guid":{"rendered":"https:\/\/web.archive.org\/web\/20240926025753\/https:\/\/www.pythonpool.com\/?p=4862"},"modified":"2021-06-14T15:00:19","modified_gmt":"2021-06-14T09:30:19","slug":"numpy-square-root","status":"publish","type":"post","link":"https:\/\/web.archive.org\/web\/20240926025753\/https:\/\/www.pythonpool.com\/numpy-square-root\/","title":{"rendered":"Numpy Square Root | Usecase Evaluation of Math Toolkit"},"content":{"rendered":"\n<p>The Numpy module of python is the toolkit. Because it is a package of functions to perform various operations, these operations are high scientific computations in python. Numpy supports multiple dimensions. The toolkits work on them. An array in numpy can be one dimension and two, three, or higher. Thus we have a quick review. As of now, we will read about&nbsp;<strong>the numpy square root<\/strong>. An easy function to use and understand.<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_65 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title \" >Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #990303;color:#990303\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #990303;color:#990303\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 eztoc-toggle-hide-by-default' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.pythonpool.com\/numpy-square-root\/#About_numpy_square_root\" title=\"About numpy square root\">About numpy square root<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pythonpool.com\/numpy-square-root\/#Syntax_numpy_square_root\" title=\"Syntax numpy square root\">Syntax numpy square root<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pythonpool.com\/numpy-square-root\/#Parameters_Used\" title=\"Parameters Used:\">Parameters Used:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pythonpool.com\/numpy-square-root\/#Examples_to_comprehend\" title=\"Examples to comprehend\">Examples to comprehend<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pythonpool.com\/numpy-square-root\/#Can_I_calculate_the_square_root_of_-1\" title=\"Can I calculate the square root of -1?\">Can I calculate the square root of -1?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pythonpool.com\/numpy-square-root\/#Whats_Next\" title=\"What\u2019s Next?\">What\u2019s Next?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pythonpool.com\/numpy-square-root\/#Conclusion\" title=\"Conclusion\">Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"About_numpy_square_root\"><\/span>About numpy square root<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>np.sqrt() function gets the square root of the matrix elements. To say that the function is to determine the positive square-root of an array, element-wise. Sqrt () is a mathematical tool which does this: <\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/cdn.shortpixel.ai\/spai\/w_64+q_lossy+ret_img+to_webp\/https:\/\/www.sharpsightlabs.com\/wp-content\/ql-cache\/quicklatex.com-a90712a37e7af7cbf3008d4588422cd6_l3.png\" alt=\"\\begin{equation*} \\mbox{\\Huge\\sqrt{x}} \\end{equation*}\" title=\"Rendered by QuickLaTeX.com\"\/><\/figure>\n\n\n\n<p>There are only non-negative outputs from this function.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Syntax_numpy_square_root\"><\/span>Syntax numpy square root<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>We use sqrt in place of the square root.<br>The standard syntax of the Function np.sqrt() is:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>numpy.sqrt(x, \/, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True&#91;, signature, extobj]) = &lt;ufunc 'sqrt'><\/code><\/pre>\n\n\n\n<p>while the usual way of writing the syntax is:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nnumpy.sqrt()\n<\/pre><\/div>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Parameters_Used\"><\/span>Parameters Used:<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table><tbody><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>Parameters<\/strong><\/td><td class=\"has-text-align-center\" data-align=\"center\"><strong>Mandatory or not<\/strong><\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">x<\/td><td class=\"has-text-align-center\" data-align=\"center\">mandatory<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">out<\/td><td class=\"has-text-align-center\" data-align=\"center\">optional<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">where<\/td><td class=\"has-text-align-center\" data-align=\"center\">optional<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">casting<\/td><td class=\"has-text-align-center\" data-align=\"center\">optional<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">keepdims<\/td><td class=\"has-text-align-center\" data-align=\"center\">optional<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">axes<\/td><td class=\"has-text-align-center\" data-align=\"center\">optional<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">order<\/td><td class=\"has-text-align-center\" data-align=\"center\">optional<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">subok<\/td><td class=\"has-text-align-center\" data-align=\"center\">optional<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">dtype<\/td><td class=\"has-text-align-center\" data-align=\"center\">optional<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\">signature<\/td><td class=\"has-text-align-center\" data-align=\"center\">optional<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>x : array_like<\/strong><\/h4>\n\n\n\n<p>This parameter is<em>&nbsp;<\/em>Input values whose square-roots have to be determined. In other words,<a href=\"https:\/\/mathworld.wolfram.com\/Radicand.html\"> it specifies the radicand. <\/a>The radicand is the value under the radical when you compute the square root.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>out<\/strong><\/h4>\n\n\n\n<p>The \u2018out\u2019 keyword argument expects to be a tuple with one entry per output (which can be None for arrays to be allocated by the ufunc). The&nbsp;<code>out<\/code>&nbsp;parameter enables you to specify an array where the output will be stored. This parameter is not used in simpler calculations but at a higher level. This parameter provides a location to store the result. If provided, it must have a shape for the inputs broadcast. A freshly-allocated array returned if not provided or None. A tuple (possible only as a keyword argument) must have a length equal to the number of outputs.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>where<\/strong><\/h4>\n\n\n\n<p>This parameter Accepts a boolean array, which is broadcast together with the operands. Values of True indicate that to calculate the ufunc at that position and False values indicate to leave the value in the output alone. Generalized ufuncs cannot use this argument because those take non-scalar input.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>axes<\/strong><\/h4>\n\n\n\n<p>A list of tuples with indices of axes a generalized ufunc should operate. For instance, for a signature of (i,j),(j,k)-&gt;(i,k) appropriate for matrix multiplication, the base elements are two-dimensional matrices, and these take to store in the two last axes of each argument.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>order<\/strong><\/h4>\n\n\n\n<p>This parameter specifies the calculation iteration order\/memory layout output array. Defaults to \u2018K.\u2019 \u2018C\u2019 means the output should be C-contiguous, \u2018F\u2019 means F-contiguous, \u2018A\u2019 means F-contiguous if the inputs are F-contiguous and not also not C-contiguous<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>axes<\/strong><\/h4>\n\n\n\n<p>A list of tuples with indices of axes a generalized ufunc should operate. For instance, for a signature of (i,j),(j,k)-&gt;(i,k) appropriate for matrix multiplication, the last two axes of each argument take and store the base elements are two-dimensional matrices.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>signature<\/strong><\/h4>\n\n\n\n<p>This argument allows you to provide a specific signature for the 1-d loop to use in the underlying calculation. If the loop specified does not exist for the ufunc, then a TypeError is raised. Usually, a suitable loop is found automatically by comparing the input types with what is available and searching for a loop with data-types. The&nbsp;<strong>types<\/strong>&nbsp;attribute of the ufunc object provide a list of known signatures.<\/p>\n\n\n\n<p>Still, there are more parameters to mention. But you do not need to know all. To know all we need to see the <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/ufuncs.html#ufuncs-kwargs\">unfunc docs<\/a>. So you would get an idea of all the parameters.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Returning value<\/h3>\n\n\n\n<p>As per NumPy, return the non-negative square-root of an array, element-wise.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Examples_to_comprehend\"><\/span>Examples to comprehend<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>now we import the module first<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import numpy <\/code><\/pre>\n\n\n\n<p>Now using numpy in a better way.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\narray_2d = numpy.array(&#x5B;&#x5B;1, 16], &#x5B;25, 49]], dtype=numpy.float)\nprint(array_2d)\n&#x5B;&#x5B; 1.  16.]\n &#x5B; 25. 49.]]\narray_2d_sqrt = numpy.sqrt(array_2d)\nprint(array_2d_sqrt)\n&#x5B;&#x5B;1. 4.]\n &#x5B;5. 7.]]\n<\/pre><\/div>\n\n\n<p>Here we also used the dtype parameter to fix datatype. This example also shows that it retains the dimensionality of the original array.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\n#for complex numbers\narray = numpy.array(&#x5B;4, -1, -5 + 9J], dtype=numpy.complex)\nprint(array)\n&#x5B;4, -1, -5 + 9J]\nnumpy.sqrt(array)\narray(&#x5B;2.00000000+0.j  0.00000000+1.j  1.62721083+2.76546833j])\n<\/pre><\/div>\n\n\n<p>The above example shows the output for complex numbers.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\n#negative numbers\narray = numpy.array(&#x5B;-4, 5, -6])\nnumpy.sqrt(array)\n__main__:1: RuntimeWarning: invalid value encountered in sqrt\narray(&#x5B;nan  2.23606798  nan])\n<\/pre><\/div>\n\n\n<p>The output for negative number is NaN(Not a Number).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Can_I_calculate_the_square_root_of_-1\"><\/span>Can I calculate the square root of -1?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nnumpy.sqrt(-1)\n__main__:1: RuntimeWarning: invalid value encountered in sqrt\nnan\n<\/pre><\/div>\n\n\n<p>Yes, for -1, you can use this function. But it would return NaN as discussed in the last example.<\/p>\n\n\n\n<p>All the examples give the idea of using the function. Now you are ready to go.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Whats_Next\"><\/span><strong>What\u2019s Next?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>NumPy is very powerful and incredibly essential for information science in Python. That being true, if you are interested in <a href=\"https:\/\/www.pythonpool.com\/data-science-internship\/\" >data science<\/a> in Python, you really ought to find out more about Python.<\/p>\n\n\n\n<p>You might like our following tutorials on numpy.<\/p>\n\n\n\n<ul><li><a href=\"https:\/\/www.pythonpool.com\/numpy-mean\/\">Mean: Implementation and Importance<\/a><\/li><li><a href=\"https:\/\/www.pythonpool.com\/numpy-random\/\">Using&nbsp;<\/a><a href=\"https:\/\/www.pythonpool.com\/numpy-random\/\">Random Function to Create Random Data<\/a><\/li><li><a href=\"https:\/\/www.pythonpool.com\/numpy-reshape\/\">Reshape: Reshaping Arrays With Ease<\/a><\/li><li><a href=\"https:\/\/www.pythonpool.com\/numpy-power\/\">In-depth Explanation of np.power() With Examples<\/a><\/li><li><a href=\"https:\/\/www.pythonpool.com\/numpy-clip\/\">Clip Function<\/a><\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In conclusion, this function is useful for all calculations. Those computations can be of broader aspects\u2014both primary and scientific as well.<\/p>\n\n\n\n<p>Still have any doubts or questions do let me know in the comment section below. I will try to help you as soon as possible.<\/p>\n\n\n\n<p><strong><em>Happy Pythoning!<\/em><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Numpy module of python is the toolkit. Because it is a package of functions to perform various operations, these operations are high scientific computations &#8230; <\/p>\n<p class=\"read-more-container\"><a title=\"Numpy Square Root | Usecase Evaluation of Math Toolkit\" class=\"read-more button\" href=\"https:\/\/www.pythonpool.com\/numpy-square-root\/#more-4862\" aria-label=\"More on Numpy Square Root | Usecase Evaluation of Math Toolkit\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":4906,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[1495],"tags":[2399,1876,2396,2395,2398,543,2394,2397],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.1 (Yoast SEO v22.4) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Numpy Square Root | Usecase Evaluation of Math Toolkit - Python Pool<\/title>\n<meta name=\"description\" content=\"numpy square root function gets the square root of the matrix elements. 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