{"id":1008,"date":"2020-03-16T10:44:45","date_gmt":"2020-03-16T10:44:45","guid":{"rendered":"https:\/\/statanalytica.com\/blog\/?p=1008"},"modified":"2021-08-14T12:29:35","modified_gmt":"2021-08-14T11:29:35","slug":"python-for-data-science","status":"publish","type":"post","link":"https:\/\/statanalytica.com\/blog\/python-for-data-science\/","title":{"rendered":"The Robust Guide on Python for Data Science"},"content":{"rendered":"\n<p>For the data analytics domain, python is a mandatory learning programming language for all web developers. With the increasing demand for IT professionals, the IT industry is booming up to an extent. Python is one of the preferable coding languages that is used for data-driven development. This blog will help to learn the basics and its library. Now, before proceeding to further details, let\u2019s know some details of data science before dig into the python for data science concept:-<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"what-is-data-science\"><\/span><strong>What is data science?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-light-blue ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69f075651d260\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ff5104;color:#ff5104\" 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: #ff5104;color:#ff5104\" 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><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69f075651d260\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#what-is-data-science\" >What is data science?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#data-science-cycle\" >Data science cycle<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#why-is-there-a-need-for-python-for-data-science\" >Why is there a need for python for data science?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#basic-terminology-of-python-for-data-science\" >Basic terminology of python for data science:<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#variable\" >Variable<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#data-types\" >Data Types<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#operators\" >Operators<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#conditional-statements\" >Conditional Statements<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#loops\" >Loops<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#functions\" >Functions<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#libraries-of-python\" >Libraries of python<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#numpy\" >Numpy<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#pandas\" >Pandas<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#matplotlib\" >Matplotlib<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#seaborn\" >Seaborn<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#scikit-learn\" >Scikit-learn<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#steps-to-learn-python\" >Steps to learn python<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#1st-step-learn-python-fundamentals\" >1st Step: Learn python fundamentals<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#2nd-step-practice-mini-python-projects\" >2nd Step: Practice mini python projects<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#3rd-step-get-familiar-with-the-python-libraries\" >3rd Step: Get familiar with the Python libraries<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#4th-step-apply-advanced-data-techniques\" >4th Step: Apply advanced data techniques<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/statanalytica.com\/blog\/python-for-data-science\/#conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n\n\n\n\n<p>Data science has raised as a career path for several skilled experts. The data science can be used as the problem-solving as it provides insight and outsight solutions that are driven by the data. There is a misconception about data science, but it can be clarified with the help of data science that is described below:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"data-science-cycle\"><\/span><strong>Data science cycle<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>It starts from recognizing the business requirements that are used to prepare the information for model building and expending the insight finally. The process of data science is handled by several experts, such as data engineers, data analysts, and data scientists. The role of them depends upon the company\u2019s size, but sometimes the whole process is done by a single expert.&nbsp;<br><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh3.googleusercontent.com\/LKhruM9Hum5Ts4KAydu-AsDifJDz70yEapaP-Fcmwmu4Mwu6rmLP1aHoomQ3uOglyHRfWXuGk_3hDu4NKuRvQZJ6fjqcSAtnjyFKQCnKE7gpyOSahUC9PbjPrIKG_s__zld2LL6y\" alt=\"\"\/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"why-is-there-a-need-for-python-for-data-science\"><\/span><strong>Why is there a need for python for data science?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Python is the best programming language for data science because of the following reasons that are listed below:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Python is a flexible, free, and powerful open-source coding language.<\/li><li>It reduces developing time in half because of its simplicity and readable syntax.<\/li><li>With the help of python, you can carry out data analysis, manipulation, and visualization.<\/li><li>Python offers libraries for scientific computations and other machine learning applications.<\/li><\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"basic-terminology-of-python-for-data-science\"><\/span><strong>Basic terminology of python for data science:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Now, we will discuss the basic terminologies that are used in python for data science. So that you can easily understand the use of python in web developing:&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"variable\"><\/span><strong>Variable<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>This is referred to as a reserved memory location that is used to save the values. For the python coding language, one does not require to declare the variables before writing them in the programming, or even it does not require to declare their types.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"data-types\"><\/span><strong>Data Types<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>It supports various data types that used to define the various operations which are possible for storage methods and variables. The data types can involve:- List, Tuples, Dictionary, Numeric, Strings, and Sets.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"operators\"><\/span><strong>Operators<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>This helps to manage the value of the operands. The python\u2019s operators involve:- Comparison, Logical, Membership, Assignment, Bitwise, and Identity.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"conditional-statements\"><\/span><strong>Conditional Statements<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>It helps to implement various statements that are based on various conditions. There are basically three conditional statements:- Elif, If, and Else.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"loops\"><\/span><strong>Loops<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>These are used to repeat certain work through a coded program. The loops in python are of three types:- For, While, and nested loop.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"functions\"><\/span><strong>Functions<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>These are used to separate the codes into useable blocks that allow you to order the programs, and make it readable, reusable, and saves time.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"libraries-of-python\"><\/span><strong>Libraries of python <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>This is the section where the actual picture of <strong>python for data science<\/strong> comes into account. Python has several libraries for scientific analysis, visualization, computations, etc. and few of them are described below:<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"numpy\"><\/span><strong>Numpy<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>It is the central library of <strong>python for data science<\/strong> that refers to \u2018numerical python\u2019. This is utilized for scientific or complex computations, that has n power dimensional array objects. Besides this, it offers various tools for integrating C++, C, etc. This also uses for multi-dimensional generic data where one can process several special functions and Numpy operations.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"pandas\"><\/span><strong>Pandas<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>It is an essential library of <strong>python for data science<\/strong> as it can be used for data analysis and data manipulations. It is suitable for various data like unordered and ordered time series, tabular, matrix data, and much more.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"matplotlib\"><\/span><strong>Matplotlib<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>It is a powerful library that is used for visualization in python as python shell, GUI toolkits, scripts, and web application servers. One can use different plots and know-how this plot works.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"seaborn\"><\/span><strong>Seaborn<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>It is a library of statistical plotting. You can use Matplotlib for 2D pictures, and seaborn can add beautiful styles to it and draw a high-level interface graphics.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"scikit-learn\"><\/span><strong>Scikit-learn<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>It is the main attraction as one can implement machine learning by using python. This is a free library that involves efficient and simple tools for mining purposes and data analysis. One can execute algorithms like time series algorithms, logistic regression.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"steps-to-learn-python\"><\/span><strong>Steps to learn python<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>There are certain steps to learn <strong>python for data science<\/strong> that are listed below:-<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1st-step-learn-python-fundamentals\"><\/span><strong>1st Step: Learn python fundamentals<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>When you decide to learn the python programming language, then it is necessary to learn the basics of python language. You can learn it from online as well as from the offline resources.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2nd-step-practice-mini-python-projects\"><\/span><strong>2nd Step: Practice mini python projects<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>If you search for any of the python projects, then you find that there is a list of the project from which you can select mini-projects as per your requirements.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3rd-step-get-familiar-with-the-python-libraries\"><\/span><strong>3rd Step: Get familiar with the Python libraries<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p><strong> <\/strong>We have listed some of the libraries\u2019 terms that help you to get familiar with the libraries of python so that you can easily use those terminologies in your python programmings. So try to get familiar with these before start learning the <strong>python for data science<\/strong>.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4th-step-apply-advanced-data-techniques\"><\/span><strong>4th Step: Apply advanced data techniques<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>Sharpen your skill by learning the advanced data techniques of data science. This will help you to learn new ways to implement the python programming language so that you can easily implement it to your programs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>We have listed all the necessary details on <strong>python for data science,<\/strong> such as libraries and terminologies, and much more. So that you have an idea about the role of python in data science, but if you are not able to understand these terminologies, then you can take our experts\u2019 help for python assignment as they can deliver the high-quality data before the deadlines at a reasonable price. They can provide you the instant help as they are accessible to you for 24*7.  and if you need <a href=\"https:\/\/statanalytica.com\/python-homework-help\">help with python<\/a>, then contact our experts. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>For the data analytics domain, python is a mandatory learning programming language for all web developers. With the increasing demand for IT professionals, the IT industry is booming up to an extent. Python is one of the preferable coding languages that is used for data-driven development. This blog will help to learn the basics and [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1018,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[77],"tags":[],"class_list":["post-1008","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/1008","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/comments?post=1008"}],"version-history":[{"count":0,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/1008\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/media\/1018"}],"wp:attachment":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/media?parent=1008"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/categories?post=1008"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/tags?post=1008"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}