{"id":25521172,"date":"2022-04-17T07:45:28","date_gmt":"2022-04-17T02:15:28","guid":{"rendered":"https:\/\/entri.app\/blog\/?p=25521172"},"modified":"2023-05-03T12:51:49","modified_gmt":"2023-05-03T07:21:49","slug":"data-analysis-with-python-all-you-need-to-know","status":"publish","type":"post","link":"https:\/\/entri.app\/blog\/data-analysis-with-python-all-you-need-to-know\/","title":{"rendered":"Data Analysis With Python: All You Need To Know"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-custom 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-69e6a56026d15\" 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: #999;color:#999\" 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: #999;color:#999\" 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-69e6a56026d15\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/entri.app\/blog\/data-analysis-with-python-all-you-need-to-know\/#Data_Analysis_using_Python\" >Data Analysis using Python<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/entri.app\/blog\/data-analysis-with-python-all-you-need-to-know\/#The_Role_of_a_Data_Analyst\" >The Role of a Data Analyst<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/entri.app\/blog\/data-analysis-with-python-all-you-need-to-know\/#Data_Analysis_process_with_Python\" >Data Analysis process with Python<\/a><\/li><\/ul><\/nav><\/div>\n<p class=\" b_entityTitle\"><span style=\"color: #333333; font-size: 15px;\">Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today&#8217;s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.<\/span><\/p>\n<p><a href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-25520910 size-full\" src=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square.png\" alt=\"Python and Machine Learning Square\" width=\"345\" height=\"345\" srcset=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square.png 345w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-300x300.png 300w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-150x150.png 150w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-24x24.png 24w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-48x48.png 48w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-96x96.png 96w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-75x75.png 75w\" sizes=\"auto, (max-width: 345px) 100vw, 345px\" \/><\/a><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Data_Analysis_using_Python\"><\/span><strong>Data Analysis using Python<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis.<\/p>\n<p><strong>Key Features<\/strong><\/p>\n<ol>\n<li>Bridge your data analysis with the power of programming, complex algorithms, and AI<\/li>\n<\/ol>\n<p>2.\u00a0 Use Python and its extensive libraries to power your way to new levels of data insight<\/p>\n<p>3. Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series<\/p>\n<p>4. Explore this modern approach across with key industry case studies and hands-on projects<\/p>\n<p>Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You&#8217;ll be working with complex algorithms, and cutting-edge AI in your data analysis.<\/p>\n<div class=\"b_lBottom b_snippet\">\n<div>Python provides libraries for graphics and\u00a0<strong>data visualization to build plots<\/strong>. It has broad community support to help solve many kinds of queries. One of the main reasons why Data Analytics using Python has become the most preferred and popular mode of data analysis is that it provides a range of libraries.<\/div>\n<\/div>\n<p style=\"text-align: center;\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\">&#8220;Ready to take your python skills to the next level? Sign up for a free demo today!&#8221;<\/a><\/strong><\/p>\n<div><\/div>\n<div>\n<div class=\"b_module_expansion\">\n<div class=\"b_expansion_wrapper b_collapse b_onpage_expansion\" role=\"button\" data-bm=\"99\">\n<div class=\"b_expansion_text b_1linetrunc\" aria-label=\"Why data analytics using Python is popular?\"><strong>Why data analytics using Python is popular?<\/strong><\/div>\n<\/div>\n<\/div>\n<div id=\"wire5\" class=\"b_expandable_inline_container\" data-rinterval=\"-1\" data-errormessage=\"We can't find any more info about this page right now\">\n<div class=\"df_alsocon b_primtxt\" data-tag=\"RelatedQnA.ItemDetails\">\n<div>\n<div class=\"rwrl rwrl_small rwrl_padref\">Here are some of the reasons why Data Analytics using Python has become popular: Python is easy to learn and understand and has a simple syntax. The programming language is scalable and flexible. It has a vast collection of libraries for numerical computation and data manipulation.<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"iw ix iy iz ja\">\n<figure class=\"kt ku kv kw gz kx gn go paragraph-image\"><\/figure>\n<h2 id=\"ed10\" class=\"li lj jd bn lk ll lm ln lo lp lq lr ls kj lt kk lu km lv kn lw kp lx kq ly lz gc\"><span class=\"ez-toc-section\" id=\"The_Role_of_a_Data_Analyst\"><\/span><strong>The Role of a Data Analyst<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p id=\"3afb\" class=\"pw-post-body-paragraph ma mb jd mc b md me ke mf mg mh kh mi mj mk ml mm mn mo mp mq mr ms mt mu mv iw gc\" data-selectable-paragraph=\"\">A data analyst uses programming tools to mine large amounts of complex data, and find relevant information from this data.<\/p>\n<p id=\"190c\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">In short, an analyst is someone who derives meaning from messy data. A data analyst needs to have skills in the following areas, in order to be useful in the workplace:<\/p>\n<ul class=\"\">\n<li id=\"edfb\" class=\"uy uz jd mc b md ut mg uu mj va mn vb mr vc mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Domain Expertise<\/strong>\u00a0\u2014 In order to mine data and come up with insights that are relevant to their workplace, an analyst needs to have domain expertise.<\/li>\n<li id=\"de78\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Programming Skills\u00a0<\/strong>\u2014As a data analyst, you will need to know the right libraries to use in order to clean data, mine, and gain insights from it.<\/li>\n<li id=\"af2c\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Statistics<\/strong>\u00a0\u2014 An analyst might need to use some statistical tools to derive meaning from data.<\/li>\n<li id=\"10b3\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Visualization Skills<\/strong>\u00a0\u2014 A data analyst needs to have great data visualization skills, in order to summarize and present data to a third party.<\/li>\n<li id=\"24ea\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Storytelling \u2014\u00a0<\/strong>Finally, an analyst needs to communicate their findings to a stakeholder or client. This means that they will need to create a data story, and have the ability to narrate it.<\/li>\n<\/ul>\n<p id=\"3fd3\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">In this article, I am going to take you through the end-to-end data analysis process with Python.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Data_Analysis_process_with_Python\"><\/span><strong>Data Analysis process with Python<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div class=\"_1yuv87zj m-b-1\">\n<ul>\n<li class=\"_y1d9czk m-b-1s\">\n<div class=\"_wmgtrl9\">\n<div id=\"\" class=\"rc-CML show-soft-breaks\" dir=\"auto\">\n<h3><strong>Describe Python data acquisition and analysis techniques.<\/strong><\/h3>\n<p id=\"9da4\" class=\"pw-post-body-paragraph ln lo jd lp b lq lr kn ls lt lu kq lv lw lx ly lz ma mb mc md me mf mg mh mi iw gc\" data-selectable-paragraph=\"\">Every data analyst should have a good understanding of the below techniques. So the goal of this article is to take the readers through these techniques and to explain these on a basic level.<\/p>\n<p id=\"f5e0\" class=\"pw-post-body-paragraph ln lo jd lp b lq lr kn ls lt lu kq lv lw lx ly lz ma mb mc md me mf mg mh mi iw gc\" data-selectable-paragraph=\"\">These are the topics we will go through and discuss:<\/p>\n<ol class=\"\">\n<li id=\"7961\" class=\"th ti jd lp b lq lr lt lu lw tj ma tk me tl mi tm tn to tp gc\" data-selectable-paragraph=\"\">Basic filtering-When you want to get a subset of your data based on the values in a column, we are talking about\u00a0<em class=\"vt\">filtering<\/em>\u00a0data.\n<p id=\"beb0\" class=\"pw-post-body-paragraph ln lo jd lp b lq lr kn ls lt lu kq lv lw lx ly lz ma mb mc md me mf mg mh mi iw gc\" data-selectable-paragraph=\"\">In pandas we have multiple ways to do that, for now we look at the most common ones:<\/p>\n<ol class=\"\">\n<li id=\"0f4a\" class=\"th ti jd lp b lq lr lt lu lw tj ma tk me tl mi tm tn to tp gc\" data-selectable-paragraph=\"\">Using boolean indexing with square brackets <code class=\"fr tv tw tx ty b\"><strong class=\"lp jn\">[]<\/strong><\/code><\/li>\n<li id=\"7985\" class=\"th ti jd lp b lq tq lt tr lw ts ma tt me tu mi tm tn to tp gc\" data-selectable-paragraph=\"\">Using boolean indexing with<code class=\"fr tv tw tx ty b\"><strong class=\"lp jn\">.loc<\/strong><\/code><\/li>\n<\/ol>\n<\/li>\n<li id=\"c73b\" class=\"th ti jd lp b lq tq lt tr lw ts ma tt me tu mi tm tn to tp gc\" data-selectable-paragraph=\"\">Filtering with multiple conditions-\n<p id=\"3f5d\" class=\"pw-post-body-paragraph ln lo jd lp b lq uy kn ls lt uz kq lv lw va ly lz ma vb mc md me vc mg mh mi iw gc\" data-selectable-paragraph=\"\">We applied our first filter, which was pretty straight forward. But let\u2019s say you want to apply a filter with multiple conditions. How would we do that in pandas? For that we have look at Python operators.<\/p>\n<p id=\"2d7a\" class=\"pw-post-body-paragraph ln lo jd lp b lq lr kn ls lt lu kq lv lw lx ly lz ma mb mc md me mf mg mh mi iw gc\" data-selectable-paragraph=\"\"><strong class=\"lp jn\">The <em class=\"vt\">&amp;<\/em> operator, The | operator<\/strong><\/p>\n<\/li>\n<li id=\"439d\" class=\"th ti jd lp b lq tq lt tr lw ts ma tt me tu mi tm tn to tp gc\" data-selectable-paragraph=\"\">Aggregation-\u00a0Sometimes there\u2019s the need to aggregate data so you can create certain overviews or to do some\u00a0 calculation. In pandas we use <code class=\"fr tv tw tx ty b\"><strong class=\"lp jn\">groupby ( we are referring to a process involving splitting , applying or combining )<\/strong><\/code><strong class=\"lp jn\">\u00a0<\/strong>for this.\n<p id=\"f3a5\" class=\"pw-post-body-paragraph ln lo jd lp b lq lr kn ls lt lu kq lv lw lx ly lz ma mb mc md me mf mg mh mi iw gc\" data-selectable-paragraph=\"\">\n<\/li>\n<li id=\"edb7\" class=\"th ti jd lp b lq tq lt tr lw ts ma tt me tu mi tm tn to tp gc\" data-selectable-paragraph=\"\">Joins-\u00a0 Joins are combining two dataframes on a side by side manner based on a common column. Most of the time these columns are referred to askey columns .<br \/>\nThe term joinis originated from the database language SQL, and was needed because the data modelling of SQL databases is mostly done by using relational modelling.<br \/>\nThere are many types of joins, and your output will be based on which type of join your perform. Because this is an introductionary tutorial, we will look at the most common one: inner join.The\u00a0<em class=\"vt\">inner join<\/em>\u00a0is derived from\u00a0<code class=\"fr tv tw tx ty b\"><strong class=\"lp jn\">venn diagrams<\/strong><\/code>\u00a0which represents\u00a0<em class=\"vt\">inner<\/em>\u00a0(intersection) part of both sets.<\/li>\n<\/ol>\n<p style=\"text-align: center;\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\">&#8220;Experience the power of our web development course with a free demo &#8211; enroll now!&#8221;<\/a><\/strong><\/p>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<div class=\"_1yuv87zj m-b-1\">\n<ul>\n<li class=\"_y1d9czk m-b-1s\">\n<div class=\"_wmgtrl9\">\n<div id=\"\" class=\"rc-CML show-soft-breaks\" dir=\"auto\">\n<h3><strong>Analyze Python data using a dataset.<\/strong><\/h3>\n<p id=\"d2a6\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">For our analysis, we will make use of the pandas library in Python. After downloading the dataset, you will need to read the <em class=\"tr\">.csv<\/em>\u00a0file as a data frame in Python. You can do this using the Pandas library.<\/p>\n<p id=\"8bb5\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">If you do not have it installed, you can do so with a simple \u201c<em class=\"tr\">pip install pandas\u201d\u00a0<\/em>in your terminal.<\/p>\n<h6 class=\"o dz sj wd ii we\" role=\"separator\"><strong><span style=\"color: #1d1f20; font-size: 1.953em;\">Pandas Profiling<\/span><\/strong><\/h6>\n<div class=\"iw ix iy iz ja\">\n<p id=\"ab73\" class=\"pw-post-body-paragraph ma mb jd mc b md me ke mf mg mh kh mi mj mk ml mm mn mo mp mq mr ms mt mu mv iw gc\" data-selectable-paragraph=\"\">This is a very useful tool that can be used by analysts. It generates an analysis report on the data frame, and helps you better understand the correlation between variables.<\/p>\n<p id=\"0cf0\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">To generate a Pandas Profiling report, run the following lines of code:<\/p>\n<pre class=\"kt ku kv kw gz vu bt vv\"><span id=\"cb52\" class=\"gc vm lj jd vw b do vx vy l vz\" data-selectable-paragraph=\"\">import pandas_profiling as pp\r\npp.ProfileReport(df)<\/span><\/pre>\n<p id=\"7a81\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">This report will give you some overall statistical information on the dataset, which looks like this:<\/p>\n<figure class=\"kt ku kv kw gz kx gn go paragraph-image\">\n<div class=\"ky kz dq la cf lb\" role=\"button\">\n<div class=\"gn go wi\"><img loading=\"lazy\" decoding=\"async\" class=\"cf lc ld\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/max\/700\/0*HKeT57wYUVLWkTRn.png\" alt=\"\" width=\"700\" height=\"382\" \/><\/div>\n<\/div>\n<\/figure>\n<p id=\"a828\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">By just glancing at the dataset statistics, we can see that there are no missing or duplicate cells in our data frame.<\/p>\n<p id=\"66f9\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">The information provided above usually requires us to run a few lines of codes to find, but is generated a lot more easily with Pandas Profiling.<\/p>\n<p id=\"d866\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">Pandas Profiling also provides more information on each variable. I will show you an example:<\/p>\n<figure class=\"kt ku kv kw gz kx gn go paragraph-image\">\n<div class=\"ky kz dq la cf lb\" role=\"button\">\n<div class=\"gn go wj\"><img loading=\"lazy\" decoding=\"async\" class=\"cf lc ld\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/max\/700\/0*nncGqrWOTh86ph1X.png\" alt=\"\" width=\"700\" height=\"286\" \/><\/div>\n<\/div>\n<\/figure>\n<p id=\"8b5e\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">This is information generated for the variable called \u201c<em class=\"tr\">Pregnancies.\u201d<\/em><\/p>\n<p id=\"3dda\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">As an analyst,\u00a0<strong class=\"mc je\">this report saves a lot of time<\/strong>, as we don\u2019t have to go through each individual variable and run too many lines of code.<\/p>\n<p id=\"2a17\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">From here, we can see that:<\/p>\n<ul class=\"\">\n<li id=\"bf7b\" class=\"uy uz jd mc b md ut mg uu mj va mn vb mr vc mv vd ve vf vg gc\" data-selectable-paragraph=\"\">The variable \u201c<em class=\"tr\">Pregnancies\u201d\u00a0<\/em>has 17 distinct values.<\/li>\n<li id=\"d053\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\">The minimum number of pregnancies a person has is 0, and the maximum is 17.<\/li>\n<li id=\"66d6\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\">The number of zero values in this column is pretty low (only 14.5%). This means that\u00a0<strong class=\"mc je\">above 80% of the patients in the dataset are pregnant<\/strong>.<\/li>\n<\/ul>\n<p id=\"c112\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">In the report, there is information like this provided for each variable. This helps us a lot in our understanding of the dataset and all the columns in it.<\/p>\n<figure class=\"kt ku kv kw gz kx gn go paragraph-image\">\n<div class=\"ky kz dq la cf lb\" role=\"button\">\n<div class=\"gn go wk\"><img loading=\"lazy\" decoding=\"async\" class=\"cf lc ld\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/max\/700\/0*9lcVt7ZxDiHgm1LW.png\" alt=\"\" width=\"700\" height=\"477\" \/><\/div>\n<\/div><figcaption class=\"le bm gp gn go lf lg bn b bo bp co\" data-selectable-paragraph=\"\"><\/figcaption><\/figure>\n<p id=\"7eab\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">The plot above is a correlation matrix. It helps us gain a\u00a0<strong class=\"mc je\">better understanding of the correlation between the variables in the dataset<\/strong>.<\/p>\n<p id=\"d27b\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">There is a slight positive correlation between the variables \u201c<em class=\"tr\">Age<\/em>\u201d and \u201c<em class=\"tr\">Skin Thickness<\/em>\u201d, which can be looked into further in the visualization section of the analysis.<\/p>\n<p id=\"659b\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">Since there are\u00a0<strong class=\"mc je\">no missing or duplicate rows<\/strong>\u00a0in the data frame as seen above, we don\u2019t need to do any additional data cleaning.<\/p>\n<p style=\"text-align: center;\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\">&#8220;Get hands-on with our python course &#8211; sign up for a free demo!&#8221;<\/a><\/strong><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<div class=\"_1yuv87zj m-b-1\">\n<ul>\n<li class=\"_y1d9czk m-b-1s\">\n<div class=\"_wmgtrl9\">\n<div id=\"\" class=\"rc-CML show-soft-breaks\" dir=\"auto\">\n<div>\n<h3><strong>Identify three Python libraries and describe their uses.<\/strong><\/h3>\n<h4 id=\"c77e\" class=\"ko kp ii bn kq kr ks kt ku kv kw kx ky kz la lb lc ld le lf lg lh li lj lk ll gj\"><strong>Matplotlib:<\/strong><\/h4>\n<p id=\"2169\" class=\"pw-post-body-paragraph jq jr ii js b jt lm jv jw jx ln jz ka kb lo kd ke kf lp kh ki kj lq kl km kn ib gj\" data-selectable-paragraph=\"\">Matplotlib is the standard graphing library in python, and is typically the first graphing library a data scientist will learn when using python. It is functionally integrated with pandas and numpy for easy and efficient plotting. Furthermore, Matplotlib gives the user full control over fonts, graph styling and axes properties, though this control comes at the potential cost of lengthy blocks of code. Matplotlib is especially good for performing exploratory analysis because of the integration with pandas, allowing for quick transformations from dataframe to graph. Matplotlib is particularly good for creating basic plots like scatter plots, bargraphs and lineplots, but looks a little rough when creating more complex plots like polar scatterplots.<\/p>\n<h4 id=\"472e\" class=\"ko kp ii bn kq kr ks kt ku kv kw kx ky kz la lb lc ld le lf lg lh li lj lk ll gj\"><strong>Seaborn:<\/strong><\/h4>\n<p id=\"b8b1\" class=\"pw-post-body-paragraph jq jr ii js b jt lm jv jw jx ln jz ka kb lo kd ke kf lp kh ki kj lq kl km kn ib gj\" data-selectable-paragraph=\"\">Seaborn is a library built on top of the pyplot module in Matplotlib. It provides a high level interface to create a more intuitive feel. This entails using a simpler syntax and more intuitive parameter settings. Additionally, Seaborn includes a more aesthetically pleasing collection of colors, themes and styles. This produces a smoother and more professional looking plot than those created from the pyplot module. This library is especially useful when creating more complex plots where more refined graphics<\/p>\n<h4 id=\"80a3\" class=\"ko kp ii bn kq kr ks kt ku kv kw kx ky kz la lb lc ld le lf lg lh li lj lk ll gj\"><strong>Plotly:<\/strong><\/h4>\n<p id=\"e0e9\" class=\"pw-post-body-paragraph jq jr ii js b jt lm jv jw jx ln jz ka kb lo kd ke kf lp kh ki kj lq kl km kn ib gj\" data-selectable-paragraph=\"\">Unlike Matplotlib and Seaborn, Plotly is used to make interactive charts. While the plots look very similar to those produced by Seaborn in terms of graphics, they have the added utility of displaying information when a user hovers their mouse over the chart. This effect is accomplished by utilizing JavaScript behind the scenes and is a particularly useful feature when looking at busy or complex charts as you are immediately able to select the information that you are interested in. The drawback to using charts in Plotly, is that the code can get a bit complex and quite long depending on the method being used.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<div class=\"_1yuv87zj m-b-1\">\n<ul>\n<li class=\"_y1d9czk m-b-1s\">\n<div class=\"_wmgtrl9\">\n<div id=\"\" class=\"rc-CML show-soft-breaks\" dir=\"auto\">\n<h3><strong>Read data using Python&#8217;s Pandas package.<\/strong><\/h3>\n<p id=\"3c95\" class=\"pw-post-body-paragraph ma mb jd mc b md me ke mf mg mh kh mi mj mk ml mm mn mo mp mq mr ms mt mu mv iw gc\" data-selectable-paragraph=\"\">To read the data frame into Python, you will need to import Pandas first. Then, you can read the file and create a data frame with the following lines of code:<\/p>\n<pre class=\"kt ku kv kw gz vu bt vv\"><span id=\"ff7b\" class=\"gc vm lj jd vw b do vx vy l vz\" data-selectable-paragraph=\"\"><mark class=\"wa wb nk\">import pandas as pd\r\ndf = pd.read_csv('diabetes.csv')<\/mark><\/span><\/pre>\n<p id=\"3315\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">To check the head of the data frame, run:<\/p>\n<pre class=\"kt ku kv kw gz vu bt vv\"><span id=\"bb7f\" class=\"gc vm lj jd vw b do vx vy l vz\" data-selectable-paragraph=\"\">df.head()<\/span><\/pre>\n<figure class=\"kt ku kv kw gz kx gn go paragraph-image\">\n<div class=\"ky kz dq la cf lb\" role=\"button\">\n<div class=\"gn go wc\"><img loading=\"lazy\" decoding=\"async\" class=\"cf lc ld\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/max\/700\/0*RJPj5uZJUyfsBCQq.png\" alt=\"\" width=\"700\" height=\"170\" \/><\/div>\n<\/div>\n<\/figure>\n<p id=\"5c3c\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">From the screenshot above, you can see 9 different variables related to a patient\u2019s health.<\/p>\n<p id=\"2467\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">As an analyst, you will need to have a basic understanding of these variables:<\/p>\n<ul class=\"\">\n<li id=\"56e9\" class=\"uy uz jd mc b md ut mg uu mj va mn vb mr vc mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Pregnancies<\/strong>: The number of pregnancies the patient had<\/li>\n<li id=\"8c6d\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Glucose<\/strong>: The patient\u2019s glucose level<\/li>\n<li id=\"c3e8\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Blood Pressure<\/strong><\/li>\n<li id=\"1859\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Skin Thickness<\/strong>: The thickness of the patient\u2019s skin in mm<\/li>\n<li id=\"4d28\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Insulin<\/strong>: Insulin level of the patient<\/li>\n<li id=\"cfba\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">BMI<\/strong>: Body Mass Index of patient<\/li>\n<li id=\"d25c\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">DiabetesPedigreeFunction<\/strong>: History of diabetes mellitus in relatives<\/li>\n<li id=\"b045\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Age<\/strong><\/li>\n<li id=\"0a11\" class=\"uy uz jd mc b md vh mg vi mj vj mn vk mr vl mv vd ve vf vg gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Outcome<\/strong>: Whether or not a patient has diabetes<\/li>\n<\/ul>\n<p id=\"5d1b\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">As an analyst, you will need to know the difference between these variable types \u2014 Numeric and Categorical.<\/p>\n<p id=\"b86d\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Numeric variables\u00a0<\/strong>are variables that are a measure, and have some kind of numeric meaning. All the variables in this dataset except for \u201c<em class=\"tr\">outcome<\/em>\u201d are numeric.<\/p>\n<p id=\"62d5\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\"><strong class=\"mc je\">Categorical variables<\/strong>\u00a0are also called nominal variables, and have two or more categories that can be classified.<\/p>\n<p id=\"ac80\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">The variable \u201c<em class=\"tr\">outcome<\/em>\u201d is categorical \u2014 0 represents the absence of diabetes, and 1 represents the presence of diabetes.<\/p>\n<\/div>\n<\/div>\n<div class=\"b_module_expansion\">\n<div class=\"b_expansion_wrapper b_collapse b_onpage_expansion\" role=\"button\" data-bm=\"98\">\n<div class=\"b_expansion_text b_1linetrunc\" aria-label=\"What are the parts of Python data analysis?\">What are the parts of Python data analysis?<\/div>\n<\/div>\n<\/div>\n<div id=\"wire3\" class=\"b_expandable_inline_container\" data-rinterval=\"-1\" data-errormessage=\"We can't find any more info about this page right now\">\n<div class=\"df_alsocon b_primtxt\" data-tag=\"RelatedQnA.ItemDetails\">\n<div>\n<div class=\"rwrl rwrl_small rwrl_padref\">It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets.<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<p id=\"bc0d\" class=\"pw-post-body-paragraph ma mb jd mc b md me ke mf mg mh kh mi mj mk ml mm mn mo mp mq mr ms mt mu mv iw gc\" data-selectable-paragraph=\"\">We will start with downloading and cleaning the dataset, and then move on to the analysis and visualization.<\/p>\n<\/div>\n<div role=\"separator\"><\/div>\n<div class=\"o dz sj wd ii we\" role=\"separator\"><strong style=\"color: #1d1f20; font-size: 1.953em;\">Data Visualization<\/strong><\/div>\n<div class=\"iw ix iy iz ja\">\n<p id=\"a36b\" class=\"pw-post-body-paragraph ma mb jd mc b md me ke mf mg mh kh mi mj mk ml mm mn mo mp mq mr ms mt mu mv iw gc\" data-selectable-paragraph=\"\">Now that we have a basic understanding of each variable, we can try to find the relationship between them.<\/p>\n<p id=\"ad96\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">The simplest and fastest way to do this is by generating visualizations.<\/p>\n<p id=\"655d\" class=\"pw-post-body-paragraph ma mb jd mc b md ut ke mf mg uu kh mi mj uv ml mm mn uw mp mq mr ux mt mu mv iw gc\" data-selectable-paragraph=\"\">Visualizations can be done using the three libraries that have been already discussed above in the article \u2014 Matplotlib, Seaborn, and Plotly.<\/p>\n<h3><a href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-25494072 size-full\" src=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2021\/10\/Web-Development-Square.png\" alt=\"\" width=\"345\" height=\"345\" srcset=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2021\/10\/Web-Development-Square.png 345w, 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Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today&#8217;s business world, data [&hellip;]<\/p>\n","protected":false},"author":111,"featured_media":25521236,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[802,1888],"tags":[],"class_list":["post-25521172","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","category-python-programming"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Data Analysis With Python: All You Need To Know - Entri Blog<\/title>\n<meta name=\"description\" content=\"Learn a modern approach to data analysis with Python to harness the power of programming and AI across your data. 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