{"id":167262,"date":"2023-03-13T16:36:13","date_gmt":"2023-03-13T15:36:13","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=167262"},"modified":"2023-10-30T18:50:01","modified_gmt":"2023-10-30T17:50:01","slug":"numpy-the-python-library-in-data-science","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/numpy-the-python-library-in-data-science","title":{"rendered":"NumPy : the most used Python library in Data Science"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"167262\" class=\"elementor elementor-167262\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cd96d71 e-flex e-con-boxed e-con\" data-id=\"cd96d71\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-560f561 elementor-widget elementor-widget-text-editor\" data-id=\"560f561\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">NumPy is a very popular Python library that is mainly used to perform mathematical and scientific calculations. It offers <\/span><b>many features and tools<\/b><span style=\"font-weight: 400;\"> that can be useful for Data Science projects. Becoming familiar with NumPy is an essential step in a <\/span><b>Data Science training project<\/b><span style=\"font-weight: 400;\">. Find out everything you need to know to master Numpy.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c7e7b65 e-flex e-con-boxed e-con\" data-id=\"c7e7b65\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ecf81af elementor-widget elementor-widget-heading\" data-id=\"ecf81af\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">What is NumPy?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-889a761 elementor-widget elementor-widget-text-editor\" data-id=\"889a761\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The term NumPy is an abbreviation for &#8220;<\/span><b>Numerical Python<\/b><span style=\"font-weight: 400;\">&#8220;. It is an open-source library in the <\/span><a href=\"https:\/\/datascientest.com\/en\/python-the-most-popular-programming-language\"><b>Python language<\/b><\/a><span style=\"font-weight: 400;\">. It is used for scientific programming in Python, and in particular for programming in Data Science, engineering, mathematics, or science.<\/span><\/p><p><span style=\"font-weight: 400;\">Data Science is based on highly<\/span><b> complex scientific calculations<\/b><span style=\"font-weight: 400;\">. To perform these calculations, <\/span><a href=\"https:\/\/datascientest.com\/en\/average-data-scientist-salary-in-canada-in-2023\"><b>Data Scientists<\/b><\/a><span style=\"font-weight: 400;\"> need powerful tools. This library is very useful to perform mathematical and statistical operations in Python. It works great for <\/span><b>multiplying matrices <\/b><span style=\"font-weight: 400;\">or multidimensional arrays. Integration with C\/C++ and Fortran is very easy.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66e00d2 elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"66e00d2\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=xECXZ3tyONo&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-video .elementor-widget-container{overflow:hidden;transform:translateZ(0)}.elementor-widget-video .elementor-wrapper{aspect-ratio:var(--video-aspect-ratio)}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{height:100%;width:100%;display:flex;border:none;background-color:#000}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-wrapper{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .elementor-open-inline .elementor-custom-embed-image-overlay{position:absolute;top:0;right:0;bottom:0;left:0;background-size:cover;background-position:50%}.elementor-widget-video .elementor-custom-embed-image-overlay{cursor:pointer;text-align:center}.elementor-widget-video .elementor-custom-embed-image-overlay:hover .elementor-custom-embed-play i{opacity:1}.elementor-widget-video .elementor-custom-embed-image-overlay img{display:block;width:100%;aspect-ratio:var(--video-aspect-ratio);-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-custom-embed-image-overlay{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-custom-embed-image-overlay img{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .e-hosted-video .elementor-video{-o-object-fit:cover;object-fit:cover}.e-con-inner>.elementor-widget-video,.e-con>.elementor-widget-video{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-448ceec e-flex e-con-boxed e-con\" data-id=\"448ceec\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1f5a8cd elementor-widget elementor-widget-heading\" data-id=\"1f5a8cd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How does NumPy work?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-02e664b elementor-widget elementor-widget-text-editor\" data-id=\"02e664b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">This platform includes multidimensional objects in arrays and a package with integration tools for Python implementation. Simply put, NumPy is a mix between C and Python that is used as an alternative to traditional MATLAB programming.<\/span><\/p><p><span style=\"font-weight: 400;\">The data, in the form of numbers, are treated as arrays for multidimensional functions and rearrangement operations. It is a <\/span><b>widely used tool<\/b><span style=\"font-weight: 400;\"> in the field of Data Science.<\/span><\/p><p><span style=\"font-weight: 400;\">Among the many libraries in Python, <\/span><b>NumPy is one of the most used<\/b><span style=\"font-weight: 400;\">. This is because many data science techniques require large tables and matrices and complex calculations to extract valuable information from data. NumPy simplifies this process with a <\/span><b>variety of mathematical functions<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Although basic, it is<\/span><b> one of the most important Python libraries<\/b><span style=\"font-weight: 400;\"> for scientific computing. In addition, other libraries rely heavily on the NumPy arrays they use as inputs and outputs. For example, <\/span><a href=\"https:\/\/datascientest.com\/en\/tensor-flow-all-about-googles-machine-learning-framework\"><b>TensorFlow<\/b><\/a><span style=\"font-weight: 400;\"> and Scikit learn to use NumPy arrays to calculate matrix multiplications.<\/span><\/p><p><span style=\"font-weight: 400;\">Beyond that, NumPy also<\/span><b> provides functions<\/b><span style=\"font-weight: 400;\"> that allow developers to perform basic or advanced mathematical and statistical functions on arrays and multidimensional matrices with few lines of code.<\/span><\/p><p><span style=\"font-weight: 400;\">The ndarray or n-dimensional array data structure is the <\/span><b>main feature <\/b><span style=\"font-weight: 400;\">of NumPy. These arrays have the particularity of being homogeneous, so all elements must be of the same type.<\/span><\/p><p><span style=\"font-weight: 400;\">In general, NumPy arrays are faster than Python lists. However, since it is only possible to <\/span><b>store data<\/b><span style=\"font-weight: 400;\"> of the same type in each column, Python lists are more flexible.<\/span><\/p><p><span style=\"font-weight: 400;\">To use NumPy you must first import the library, most often it is used under its alias &#8220;np&#8221; which makes it easier to use.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d7fed35 elementor-widget__width-initial elementor-widget elementor-widget-html\" data-id=\"d7fed35\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script src=\"https:\/\/gist.github.com\/Thomas-AH-Heller\/fc8f990b640e161ca691dee4c4e3d496.js\"><\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-31d65a1 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"31d65a1\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-analyst-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Start using NumPy<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a60e70 elementor-widget elementor-widget-text-editor\" data-id=\"5a60e70\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Here we can see an example of an array. The square brackets are used to delimit the <\/span><b>lists of elements<\/b><span style=\"font-weight: 400;\"> in the array, as we can see here where we have first [0,1,2,3] which represents our first dimension.<\/span><\/p><p><span style=\"font-weight: 400;\">It is also possible to create an array using the np.array() function of Numpy.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fbdc50a elementor-widget__width-initial elementor-widget elementor-widget-html\" data-id=\"fbdc50a\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script src=\"https:\/\/gist.github.com\/Thomas-AH-Heller\/50b83e41c4d432cfde90982628eb387e.js\"><\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2038fa elementor-widget elementor-widget-text-editor\" data-id=\"a2038fa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">From our list L we created, we can transform it into a numpy array. Don&#8217;t forget that NumPy arrays can only take <\/span><b>one type of data at a time<\/b><span style=\"font-weight: 400;\">, unlike lists which can mix numerical values and characters.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e7143f elementor-widget__width-initial elementor-widget elementor-widget-html\" data-id=\"3e7143f\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script src=\"https:\/\/gist.github.com\/Thomas-AH-Heller\/4dab94d097d6fd7244d405c21b17818b.js\"><\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2937dff elementor-widget elementor-widget-text-editor\" data-id=\"2937dff\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">This time our array is <\/span><b>multidimensional<\/b><span style=\"font-weight: 400;\">, that is, it will be composed of several comma-delimited lists. Using the shape attribute of our array, we see that we have a <\/span><b>4\u00d74 array<\/b><span style=\"font-weight: 400;\">. Arrays are comparable to matrices (2d), and a one-dimensional array to a vector (1d). It is also possible to make lists of matrices (3d), this format is notably used for <\/span><b>image processing<\/b><span style=\"font-weight: 400;\">, the third dimension being the color (<\/span><b>RGB<\/b><span style=\"font-weight: 400;\"> for Red, Green, and Blue).<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9cc866e elementor-widget__width-initial elementor-widget elementor-widget-html\" data-id=\"9cc866e\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script src=\"https:\/\/gist.github.com\/Thomas-AH-Heller\/32b95abb41689f2be54ce38d91fdc555.js\"><\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-def76c0 elementor-widget elementor-widget-text-editor\" data-id=\"def76c0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">You can use the <\/span><b>Dot attribute<\/b><span style=\"font-weight: 400;\"> of Numpy arrays to do matrix multiplication, however, the @ operator is the way recommended by numPy to do it even though the Dot attribute gives the same result.<\/span><\/p><p><span style=\"font-weight: 400;\">In general, NumPy arrays are <\/span><b>faster than Python lists<\/b><span style=\"font-weight: 400;\">. However, since it is only possible to store data of the same type in each column, Python lists are more flexible.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cbb1bf3 elementor-widget__width-initial elementor-widget elementor-widget-html\" data-id=\"cbb1bf3\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script src=\"https:\/\/gist.github.com\/Thomas-AH-Heller\/6bc52da3e2dbc295bf34c0948407513e.js\"><\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f7fd5ee elementor-widget elementor-widget-text-editor\" data-id=\"f7fd5ee\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Here we can see that NumPy is approximately <\/span><b>35 times faster<\/b><span style=\"font-weight: 400;\"> on average than python lists for sum operations.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5871e8e e-flex e-con-boxed e-con\" data-id=\"5871e8e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2b7689a elementor-widget elementor-widget-heading\" data-id=\"2b7689a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is Numpy used for?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-126ae0c elementor-widget elementor-widget-text-editor\" data-id=\"126ae0c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">To summarize NumPy, here are its main features. It is a combination of C and Python, based on multidimensional and homogeneous data arrays: Ndarrays (ndimensional arrays).<\/span><\/p><p><span style=\"font-weight: 400;\">As in MATLAB, the basic type is a multidimensional array, which allows to speed up the computation speed on arrays. Even if there are differences in syntax, the <\/span><b>behavior is similar <\/b><span style=\"font-weight: 400;\">between NumPy and MATLAB. With the help of other Python libraries, notably <\/span><b>Scikit-Learn<\/b><span style=\"font-weight: 400;\">, Numpy allows Python to be the language of choice in Data Science.<\/span><\/p><p><span style=\"font-weight: 400;\">This tool is compatible with many other popular Python packages, including <\/span><b>pandas and Matplotlib<\/b><span style=\"font-weight: 400;\">. Its popularity is due to the fact that it is faster than regular Python arrays, thanks to the pre-compiled and optimized C code.<\/span><\/p><p><span style=\"font-weight: 400;\">In addition, <\/span><b>arrays and operations are vectorized<\/b><span style=\"font-weight: 400;\"> which means that there is no explicit looping or indexing in the code. Thus, the code is more readable and similar to standard mathematical notation.<\/span><\/p><p><span style=\"font-weight: 400;\">One can use NumPy to create an identity matrix using the NumPy Identity function. This tool allows you to <\/span><b>create arrays of any dimensions<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Within a NumPy array, the first axis is axis 0. It is possible to add elements to the arrays, allowing assembly vectors and matrices.<\/span><\/p><p><span style=\"font-weight: 400;\">A wide variety of data types are supported by NumPy arrays, and all kinds of numerical calculations can be performed.<\/span><\/p><p><span style=\"font-weight: 400;\">In addition, it is possible to <\/span><b>convert a NumPy array into a list of strings<\/b><span style=\"font-weight: 400;\">, a list of tuples, or a list of lists. Conversely, lists can be converted to array, matrix, ndarray, string, or CSV.<\/span><\/p><p><span style=\"font-weight: 400;\">In general, NumPy allows you to easily perform many mathematical operations used in <\/span><b>scientific computing<\/b><span style=\"font-weight: 400;\"> such as vector-vector, matrix-matrix, or matrix-vector multiplication.<\/span><\/p><p><span style=\"font-weight: 400;\">This package also allows operations on vectors and matrices like addition, subtraction, multiplication, or division by a number. You can also perform comparisons, apply functions to vectors and matrices, and perform reduction and statistical operations.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-90e5a4e e-flex e-con-boxed e-con\" data-id=\"90e5a4e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a2c7371 elementor-widget elementor-widget-heading\" data-id=\"a2c7371\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What are the advantages of NumPy?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8c49fb7 elementor-widget elementor-widget-text-editor\" data-id=\"8c49fb7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">NumPy is very useful for performing logical and mathematical calculations on arrays and matrices. This tool performs these operations <\/span><b>much faster and more efficiently<\/b><span style=\"font-weight: 400;\"> than Python lists.<\/span><\/p><p><span style=\"font-weight: 400;\">Numpy uses less memory and storage space, which is the main advantage. <\/span><span style=\"font-weight: 400;\">In addition, NumPy offers <\/span><b>better performance<\/b><span style=\"font-weight: 400;\"> in terms of execution speed. However, it is easier and more convenient to use.<\/span><\/p><p><span style=\"font-weight: 400;\">Moreover, it is an <\/span><b>open-source tool<\/b><span style=\"font-weight: 400;\"> that can be used completely free of charge. It is based on Python which is an extremely popular programming language with many high-quality libraries for any task. Finally, it is very easy to connect existing C code to the Python interpreter<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-05d3a53 e-flex e-con-boxed e-con\" data-id=\"05d3a53\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ca0097b elementor-widget elementor-widget-heading\" data-id=\"ca0097b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Which training to learn how to use NumPy?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c5e372 elementor-widget elementor-widget-text-editor\" data-id=\"5c5e372\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Currently, Python is the most popular programming language in the computer industry. Mastering this language offers many career opportunities all over the world.<\/span><\/p><p><span style=\"font-weight: 400;\">Thi<\/span><b>s high-level programming language <\/b><span style=\"font-weight: 400;\">has many advantages, including its concise syntax. It is one of the best tools for dynamic scripting, web development, application development, and data science. In this favorable context, learning to handle Python and NumPy can open many doors for you. To acquire these skills, you can opt for <\/span><a href=\"https:\/\/datascientest.com\/en\/data-courses\"><b>DataScientest training<\/b><\/a><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Indeed, NumPy is at the heart of the Programming module of our <\/span><a href=\"https:\/\/datascientest.com\/en\/data-analyst-course\"><b>Data Analyst<\/b><\/a><span style=\"font-weight: 400;\"> and <\/span><a href=\"https:\/\/datascientest.com\/en\/data-scientist-course\"><b>Data Scientist training course<\/b><\/a><span style=\"font-weight: 400;\">. It is also part of our <\/span><a href=\"https:\/\/datascientest.com\/en\/data-management-course\"><b>Data Management training program<\/b><\/a><span style=\"font-weight: 400;\">, in the Introduction to Python module. These three courses allow you to access the Data Science profession.<\/span><\/p><p><span style=\"font-weight: 400;\">All our courses can be done either in <\/span><b>Continuing Education or in BootCamp.<\/b><span style=\"font-weight: 400;\"> They adopt an innovative &#8220;<\/span><b>blended learning<\/b><span style=\"font-weight: 400;\">&#8221; approach combining face-to-face and distance learning.<\/span><\/p><p><span style=\"font-weight: 400;\">Among the alumni, <\/span><b>93% found a job immediately<\/b><span style=\"font-weight: 400;\"> after the course. Don&#8217;t waste another second, and learn how to use Python and NumPy through our various Data Science courses!<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aca9b6a elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"aca9b6a\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover the Data Scientist training program<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-782d6bf elementor-widget elementor-widget-text-editor\" data-id=\"782d6bf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">You know everything about NumPy. Discover our <\/span><a href=\"https:\/\/datascientest.com\/en\/python-the-most-popular-programming-language\"><b>complete file on the Python language<\/b><\/a><span style=\"font-weight: 400;\">, and our <\/span><a href=\"https:\/\/datascientest.com\/en\/data-science-definition-issues-and-use-cases\"><b>introduction to Data Science<\/b><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e0bca4e e-flex e-con-boxed e-con\" data-id=\"e0bca4e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7124d53 elementor-widget elementor-widget-html\" data-id=\"7124d53\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What is NumPy?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"The term NumPy is an abbreviation for \\\"Numerical Python\\\". 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[&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":167264,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>NumPy : the most used Python library in Data Science<\/title>\n<meta name=\"description\" content=\"NumPy is a very popular Python library that is mainly used to perform mathematical and scientific calculations.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/numpy-the-python-library-in-data-science\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NumPy : the most used Python library in Data Science\" \/>\n<meta property=\"og:description\" content=\"NumPy is a very popular Python library that is mainly used to perform mathematical and scientific calculations. 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