2.718281828459045\u2026\u2026<\/p>\nEuler’s number<\/cite><\/blockquote>\n\n\n\n The dots at the end indicate that the number of digits goes on & on which provides the inference that \u2018e\u2019 <\/em>falls into the category of irrational numbers. We shall get things started by first importing the numpy <\/em>library using the following code.<\/p>\n\n\n\n In this article, we will explore the history of Euler’s number and how it is used in the Numpy Exmp1() function.<\/p>\n\n\n Thereafter, we shall explore further the expm1( ) <\/em>function through each of the following sections.<\/p>\n\n\n\n The functionality of the expm1( )<\/em> is similar to that of the exp( )<\/em> function where \u2018e\u2019 <\/em>is raised to the power of a given number followed by subtracting one (1) from it. Following is the syntax of the expm1( ) <\/em>function which contains both the mandatory and the optional inputs required for its functioning.<\/p>\n\n\n where,<\/p>\n\n\n\n You may wonder what is the need to build a specific function for removing one from an exponential, rather than use the already available function exp( )<\/em><\/a> <\/em>& remove one from it! (i.e) exp(x)-1. <\/em><\/p>\n\n\n\n Let\u2019s do just that and find out whether there are any observable differences.<\/p>\n\n\n Following are the results of the above code.<\/p>\n\n\n\n Well, it seems that all is well and both results are synonymous. But, what if we reduce \u2018x\u2019 <\/em>a bit and deploy the same functions? Will those be able to return the same results? Let\u2019s find out!<\/p>\n\n\n The results aren\u2019t the same now, are they? Buha haa haa haa! (Sinister laughter)<\/em> <\/p>\n\n\n\n The main reason behind this is that the exponential of any number close to \u20180\u2019 will be very near to \u20181\u2019. Thus, the exp(x)-1<\/em> seems to be not that accurate in returning the results and a dedicated function expm1( ) <\/em>is made available within the numpy <\/em>library of Python.<\/p>\n\n\n\n You can also find the outcome by assigning a negative number to the expm1( ) <\/em>function. This would in turn return the equivalent of exp(x), <\/em>reciprocate it & then remove \u20181\u2019 from it. For those who would like to jump directly to the results, here they are!<\/p>\n\n\n The expm1( ) <\/em>function also holds good when applied to an N-dimensional array as shown below.<\/p>\n\n\n Now that we have reached the end of this article, hope it has elaborated on how to use the expm1( ) <\/em>function from the numpy <\/em>library. Here\u2019s another article that details the usage of nextafter( ) <\/em>function from the numpy <\/em>library<\/a> in Python. There are numerous other enjoyable and equally informative articles in AskPython<\/a> that might be of great help to those who are looking to level up in Python. Mazel tov<\/em>!<\/p>\n","protected":false},"excerpt":{"rendered":" Euler’s number, also known as ‘e’, is an important mathematical constant that was named after the 18th century mathematician, Leonhard Euler. This number has been estimated to have over a trillion digits of accuracy and is an important tool in mathematical calculations. 2.718281828459045\u2026\u2026 Euler’s number The dots at the end indicate that the number of […]<\/p>\n","protected":false},"author":44,"featured_media":38989,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[93],"tags":[],"class_list":["post-38987","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-numpy"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/38987","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\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/comments?post=38987"}],"version-history":[{"count":0,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/38987\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media\/38989"}],"wp:attachment":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media?parent=38987"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/categories?post=38987"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/tags?post=38987"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\nimport numpy as np\n<\/pre><\/div>\n\n\n
\n
\n\n\n\nSyntax of\u00a0the expm1( )\u00a0function<\/h2>\n\n\n\n
\nnumpy.expm1(x, out=None, *, where=True, dtype=None)\n<\/pre><\/div>\n\n\n
\n
\n\n\n\nWhy use expm1(x) instead of exp(x)-1?<\/h2>\n\n\n\n
\nx = 1.5 \nnp.exp(x)-1 \nnp.expm1(x)\n<\/pre><\/div>\n\n\n
![\"Results](\"https:\/\/www.askpython.com\/wp-content\/uploads\/2022\/12\/Results-when-x1.5.jpg\")
\nx = 0.0001\nnp.exp(x)-1\nnp.expm1(x)\n<\/pre><\/div>\n\n\n
![\"Results](\"https:\/\/www.askpython.com\/wp-content\/uploads\/2022\/12\/Results-when-x0.0001.jpg\")
\n\n\n\nUsing expm1( ) on Scalars<\/h2>\n\n\n\n
\na = -10\nb = 0.000000009\nnp.expm1(a)\nnp.expm1(b)\n<\/pre><\/div>\n\n\n
![\"Using](\"https:\/\/www.askpython.com\/wp-content\/uploads\/2022\/12\/Using-expm1-on-Scalars.jpg\")
\n\n\n\nUsing expm1( ) on N-Dimensional Arrays<\/h2>\n\n\n\n
\nar1 = np.array([[1, 8, 0.09],\n [5.007, -3.303, 2]])\nnp.expm1(ar1)\n<\/pre><\/div>\n\n\n
![\"Using](\"https:\/\/www.askpython.com\/wp-content\/uploads\/2022\/12\/Using-expm1-on-N-Dimensional-Arrays.jpg\")
\n\n\n\nConclusion<\/h2>\n\n\n\n