In this article, We will solve one straightforward problem “How to get the base 10 logarithm of an input array, element-wise”. We are going to obtain the logarithmic values of base 10 for elements comprised in an array. You can much better understand by analyzing the example below.<\/p>\n\n\n\n
Let us assume an array, input[2.35,\u00a00,\u00a00.36,\u00a01,\u00a05.69,\u00a01000,\u00a010] – We need to obtain corresponding logarithmic values for each element of this array in the form of a new array as [ 0.37106786 -inf -0.4436975 0. 0.75511227 3. 1. ] Before getting into our code snippet, Let us have quick look below! <\/p>\n\n\n\n Python NumPy library provides some inbuilt methods for different mathematical operations. Among them, Numpy.log()<\/a> is one of the most commonly used functions to perform logarithmic operations on an array. This method returns respective logarithmic values of <\/strong>base e = 2.71 for all elements in our input array(as we are using the NumPy module)<\/em>. <\/p>\n\n\n\n Numpy also provides individual methods to perform logarithmic base 10 ( The Domain Numpy Logarithmic function lies in between the set of positive real numbers and The Range in the set of real numbers. Let us have a quick look below to understand the syntax for our function. <\/strong><\/p>\n\n\n\n The parameters used in the above snippet function as follows:<\/p>\n\n\n\n Now We will move into our code snippet sequentially that lets you explain How to implement our method. We are going with our google collab for our coding today. You guys must know that google collab provides the best platform to work on python as well. Without getting late. Let us get into it.<\/p>\n\n\n\n In this step, We will import the In this step, we will create an array comprising values, for which the logarithmic values are to be obtained. <\/p>\n\n\n In case a user needs to input values into the array manually. He\/she can follow the below code snippet.<\/strong><\/p>\n\n\n Executing the above code snippet will let a user enter the no. of values he wants to join. Moving Further, The user can enter individual values into the array as follows.<\/p>\n\n\n In this step, We will implement our function as below and pass both the arrays created in step 2.<\/p>\n\n\n In this final step, We will print our outputs loaded into variables a and b respectively.<\/p>\n\n\n You can see that The function returns an array comprising respective logarithmic values for all the elements in our input arrays. In this way, The Today, we learned how to use the numpy.log10() method to get the log of a value with the base 10. We tried two arrays as examples to help understand the concept better. If you’re interested in learning more about Numpy, don’t forget to check out our list of numpy tutorials<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" In this article, We will solve one straightforward problem “How to get the base 10 logarithm of an input array, element-wise”. We are going to obtain the logarithmic values of base 10 for elements comprised in an array. You can much better understand by analyzing the example below. Let us assume an array, input[2.35,\u00a00,\u00a00.36,\u00a01,\u00a05.69,\u00a01000,\u00a010] – […]<\/p>\n","protected":false},"author":47,"featured_media":37069,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[93],"tags":[],"class_list":["post-37065","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\/37065","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\/47"}],"replies":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/comments?post=37065"}],"version-history":[{"count":0,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/37065\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media\/37069"}],"wp:attachment":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media?parent=37065"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/categories?post=37065"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/tags?post=37065"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}. <\/code><\/p>\n\n\n\n\n
Understanding Numpy Logarithmic Method<\/h2>\n\n\n\n
NumPy.log10()<\/code>) and logarithmic base 2 (NumPy.log2()<\/code>) operations.<\/code> Today, We will learn how to implement the Numpy.log10()<\/code> method in our code snippet today. <\/strong><\/p>\n\n\n\nThe syntax for Numpy.log10()<\/h2>\n\n\n
\n#syntax for log base 10\nnumpy.log10(x, out=None, where=True, casting='same_kind', order='K', subok : [bool, datatype])\n\n#syntax for log base2\nnumpy.log2(x, out=None, where=True, casting='same_kind', order='K', subok : [bool, datatype])\n<\/pre><\/div>\n\n\n
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Working with Numpy.log10() method<\/h2>\n\n\n\n
Step 1: Import Numpy<\/h3>\n\n\n\n
Numpy<\/code> module to use NumPy.log10()<\/code> function into our computer. We could not implement any NumPy methods without importing their dependency, the “Numpy Module”. <\/p>\n\n\n\nimport numpy\n<\/pre><\/div>\n\n\n
Step 2: Creating arrays to obtain Logarithmic values<\/h3>\n\n\n\n
\ny = numpy.array([2.35, 0, 0.36, 1, 5.69, 1000, 10])\n<\/pre><\/div>\n\n\n
\n# creating x array\nx = []\nn = int(input("Enter the number of values you want to enter : "))\nfor i in range(0, n):\n value = int(input())\n x.append(value) \n<\/pre><\/div>\n\n\n\n Enter the number of values you want to enter : 3\n1\n2\n3\n[1, 2, 3]\n<\/pre><\/div>\n\n\n
Step 3: Implementing Numpy.log10() method<\/h3>\n\n\n\n
\na = numpy.log10(y)\nb = numpy.log10(x)\n<\/pre><\/div>\n\n\n
Step 4: Getting output and Analyzing<\/h3>\n\n\n\n
\nprint(a)\nprint(b)\n\n#output\n [ 0.37106786 -inf -0.4436975 0. 0.75511227 3. 1. ]\n [0. 0.30103 0.47712125]\n<\/pre><\/div>\n\n\n
Numpy.log10()<\/code> method got successfully implemented. <\/p>\n\n\n\nConclusion<\/h2>\n\n\n\n