{"id":15780,"date":"2022-04-23T19:21:18","date_gmt":"2022-04-23T18:21:18","guid":{"rendered":"https:\/\/complex-systems-ai.com\/?page_id=15780"},"modified":"2022-04-23T20:20:01","modified_gmt":"2022-04-23T19:20:01","slug":"analyse-semi-automatique-des-donnees","status":"publish","type":"page","link":"https:\/\/complex-systems-ai.com\/en\/descriptive-analysis\/semi-automatic-data-analysis\/","title":{"rendered":"Semi-automatic data analysis"},"content":{"rendered":"<div data-elementor-type=\"wp-page\" data-elementor-id=\"15780\" class=\"elementor elementor-15780\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c4e1240 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c4e1240\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-7a7a636\" data-id=\"7a7a636\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-084af97 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"084af97\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/en\/descriptive-analysis\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Descriptive analysis<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-0841556\" data-id=\"0841556\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ff76242 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"ff76242\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/en\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Home page<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-65db182\" data-id=\"65db182\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1e8a0ad elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"1e8a0ad\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/en.wikipedia.org\/wiki\/Descriptive_statistics\" target=\"_blank\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Wiki<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4db8991 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4db8991\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9e32d71\" data-id=\"9e32d71\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6a548b7 elementor-widget elementor-widget-text-editor\" data-id=\"6a548b7\" data-element_type=\"widget\" data-e-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\t\t<p>Exploratory data analysis, also known as EDA, has become an increasingly hot topic in data science. As the name suggests, it is a process of trial and error in an uncertain space, with the goal of finding information. This usually happens early in the data science life cycle. In this page, I present a semi-automated EDA (semi-automated data analysis) process.<\/p><p><img decoding=\"async\" class=\"aligncenter wp-image-11096 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/09\/cropped-Capture.png\" alt=\"semi-automatic data analysis\" width=\"97\" height=\"97\" title=\"\"><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6c0bbf9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6c0bbf9\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-79728b1\" data-id=\"79728b1\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-da18ab9 elementor-widget elementor-widget-heading\" data-id=\"da18ab9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><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><\/span><\/a><\/span><\/div>\n<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:\/\/complex-systems-ai.com\/en\/descriptive-analysis\/semi-automatic-data-analysis\/#Lanalyse-semi-automatique-des-donnees-connaitre-ses-donnees\" >Semi-automatic data analysis: knowing your data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/complex-systems-ai.com\/en\/descriptive-analysis\/semi-automatic-data-analysis\/#Valeur-manquante-et-preprocessing\" >Missing value and preprocessing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/complex-systems-ai.com\/en\/descriptive-analysis\/semi-automatic-data-analysis\/#Analyse-univariee\" >Univariate analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/complex-systems-ai.com\/en\/descriptive-analysis\/semi-automatic-data-analysis\/#Analyse-multivariee-quantitative-vs-quantitative\" >Multivariate analysis: quantitative vs. quantitative<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/complex-systems-ai.com\/en\/descriptive-analysis\/semi-automatic-data-analysis\/#Categorie-vs-Categorie\" >Category vs Category<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/complex-systems-ai.com\/en\/descriptive-analysis\/semi-automatic-data-analysis\/#Categorie-vs-Quantitatif\" >Category vs Quantitative<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Lanalyse-semi-automatique-des-donnees-connaitre-ses-donnees\"><\/span>Semi-automatic data analysis: knowing your data<span class=\"ez-toc-section-end\"><\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-56b9451 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"56b9451\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-87c6c3a\" data-id=\"87c6c3a\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7538514 elementor-widget elementor-widget-text-editor\" data-id=\"7538514\" data-element_type=\"widget\" data-e-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\t\t<p id=\"8b0c\" class=\"pw-post-body-paragraph li lj jd lk b ll wm ke ln lo wn kh lq lr wo lt lu lv wp lx ly lz wq mb mc md iw gc\" data-selectable-paragraph=\"\">I will use four main libraries: Numpy \u2014 for working with arrays; Pandas \u2013 to manipulate data in a spreadsheet format we know; Seaborn and matplotlib \u2014 to create a data visualization.<\/p><pre class=\"kt ku kv kw gz wy bt wz\"><span id=\"2842\" class=\"gc wr wa jd xa b do xb xc l xd\" data-selectable-paragraph=\"\"><mark class=\"xe xf ms\">import pandas as pd <br \/>import seaborn as sns <br \/>import matplotlib.pyplot as plt <br \/>import numpy as np from pandas.api.types <br \/>import is_string_dtype, is_numeric_dtype<\/mark><\/span><\/pre><p id=\"6591\" class=\"pw-post-body-paragraph li lj jd lk b ll wm ke ln lo wn kh lq lr wo lt lu lv wp lx ly lz wq mb mc md iw gc\" data-selectable-paragraph=\"\">Create a dataframe from the imported dataset by copying the dataset path and use df.head(5) to take a look at the first 5 rows of data.<\/p><p data-selectable-paragraph=\"\"><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter wp-image-15786 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_0ISNSmP6HX-Yqi-yTB0SeQ.png\" alt=\"\" width=\"700\" height=\"303\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_0ISNSmP6HX-Yqi-yTB0SeQ.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_0ISNSmP6HX-Yqi-yTB0SeQ-300x130.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_0ISNSmP6HX-Yqi-yTB0SeQ-18x8.png 18w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_0ISNSmP6HX-Yqi-yTB0SeQ-600x260.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p><p data-selectable-paragraph=\"\">Before we zoom in on each field, let&#039;s first look at the general characteristics of the dataset. info() gives the number of non-null values for each column and its data type.<\/p><p data-selectable-paragraph=\"\"><img decoding=\"async\" class=\"aligncenter wp-image-15787 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_YArwzEh-AzuSE5nvt4641A.png\" alt=\"\" width=\"700\" height=\"252\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_YArwzEh-AzuSE5nvt4641A.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_YArwzEh-AzuSE5nvt4641A-300x108.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_YArwzEh-AzuSE5nvt4641A-18x6.png 18w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_YArwzEh-AzuSE5nvt4641A-600x216.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p><p data-selectable-paragraph=\"\">describe() provides basic statistics about each column. By passing the &#039;include=&#039;all&#039; parameter, it outputs the count of values, unique count, upper frequency value of categorical variables and count, mean, standard deviation, min, max and percentile of numeric variables.<\/p><p data-selectable-paragraph=\"\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15788 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_9d94tfqa73cce4zxdZq1tQ.png\" alt=\"\" width=\"700\" height=\"240\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_9d94tfqa73cce4zxdZq1tQ.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_9d94tfqa73cce4zxdZq1tQ-300x103.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_9d94tfqa73cce4zxdZq1tQ-18x6.png 18w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_9d94tfqa73cce4zxdZq1tQ-600x206.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9411ca7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9411ca7\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-49657b9\" data-id=\"49657b9\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c892282 elementor-widget elementor-widget-heading\" data-id=\"c892282\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Valeur-manquante-et-preprocessing\"><\/span>Missing value and preprocessing<span class=\"ez-toc-section-end\"><\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-026eb84 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"026eb84\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-22758b9\" data-id=\"22758b9\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-11e5606 elementor-widget elementor-widget-text-editor\" data-id=\"11e5606\" data-element_type=\"widget\" data-e-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\t\t<p>Regarding the subject of missing values, I invite you to choose the tab <a href=\"https:\/\/complex-systems-ai.com\/en\/correlation-and-regressions\/\">Correlation<\/a> and Regressions in the rubric <a href=\"https:\/\/complex-systems-ai.com\/en\/data-analysis\/\">Data Analysis<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ff6c31f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ff6c31f\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-119a31f\" data-id=\"119a31f\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c848084 elementor-widget elementor-widget-heading\" data-id=\"c848084\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Analyse-univariee\"><\/span>Univariate analysis<span class=\"ez-toc-section-end\"><\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-98c4e36 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"98c4e36\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8332ddf\" data-id=\"8332ddf\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-34dc865 elementor-widget elementor-widget-text-editor\" data-id=\"34dc865\" data-element_type=\"widget\" data-e-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\t\t<p>The describe() function mentioned in the first section has already provided univariate analysis in a non-graphical way. In this section, we will generate more insights by visualizing the data and spot hidden patterns through graphical analysis.<\/p><p id=\"9cf4\" class=\"pw-post-body-paragraph li lj jd lk b ll lm ke ln lo lp kh lq lr ls lt lu lv lw lx ly lz ma mb mc md iw gc\" data-selectable-paragraph=\"\"><strong class=\"lk je\">Categorical variables \u2192 Histograms<\/strong><\/p><p id=\"77a3\" class=\"pw-post-body-paragraph li lj jd lk b ll lm ke ln lo lp kh lq lr ls lt lu lv lw lx ly lz ma mb mc md iw gc\" data-selectable-paragraph=\"\">The easiest and most intuitive way to visualize the property of a categorical variable is to use a bar chart to plot the frequency of each categorical value.<\/p><p id=\"8178\" class=\"pw-post-body-paragraph li lj jd lk b ll lm ke ln lo lp kh lq lr ls lt lu lv lw lx ly lz ma mb mc md iw gc\" data-selectable-paragraph=\"\"><strong class=\"lk je\">Quantitative variables \u2192 Histograms<\/strong><\/p><p id=\"ec21\" class=\"pw-post-body-paragraph li lj jd lk b ll lm ke ln lo lp kh lq lr ls lt lu lv lw lx ly lz ma mb mc md iw gc\" data-selectable-paragraph=\"\">To graphically represent the distribution of numerical variables, we can use a histogram which is very similar to a bar chart. It divides continuous numbers into groups of equal size and plots the frequency of records between the interval.<\/p><figure class=\"kt ku kv kw gz kx gn go paragraph-image\"><div class=\"ky kz dq la cf lb\" tabindex=\"0\" role=\"button\"><div class=\"gn go yp\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15789 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_2ankWTl6eg3-clhdYbXKuA.png\" alt=\"\" width=\"700\" height=\"154\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_2ankWTl6eg3-clhdYbXKuA.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_2ankWTl6eg3-clhdYbXKuA-300x66.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_2ankWTl6eg3-clhdYbXKuA-18x4.png 18w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_2ankWTl6eg3-clhdYbXKuA-600x132.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/div><\/div><\/figure><p id=\"0e93\" class=\"pw-post-body-paragraph li lj jd lk b ll lm ke ln lo lp kh lq lr ls lt lu lv lw lx ly lz ma mb mc md iw gc\" data-selectable-paragraph=\"\">I use this for loop to loop through the columns of the data frame and create a plot for each column. Then use a histogram if they are numeric variables and a bar chart if they are categorical variables.<\/p><p data-selectable-paragraph=\"\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15790 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_JR_gURrwF8RQ3NMR_VjSUQ.png\" alt=\"\" width=\"700\" height=\"906\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_JR_gURrwF8RQ3NMR_VjSUQ.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_JR_gURrwF8RQ3NMR_VjSUQ-232x300.png 232w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_JR_gURrwF8RQ3NMR_VjSUQ-9x12.png 9w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_JR_gURrwF8RQ3NMR_VjSUQ-600x777.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5fc50ff elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5fc50ff\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0fbae32\" data-id=\"0fbae32\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a1561bd elementor-widget elementor-widget-heading\" data-id=\"a1561bd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Analyse-multivariee-quantitative-vs-quantitative\"><\/span>Multivariate analysis: quantitative vs. quantitative<span class=\"ez-toc-section-end\"><\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e34817c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e34817c\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2d26d8d\" data-id=\"2d26d8d\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c3af581 elementor-widget elementor-widget-text-editor\" data-id=\"c3af581\" data-element_type=\"widget\" data-e-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\t\t<p>A very important part of semi-automatic data analysis is multivariate analysis, how the columns influence each other.<\/p><p>First, let&#039;s use the correlation matrix to find the correlation of all numeric data type columns. Then use a heat map to visualize the result. The annotation inside each cell indicates the correlation coefficient of the relationship.<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15791 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_fHGkXi8qgcrC5x_YulTB1g.png\" alt=\"\" width=\"700\" height=\"606\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_fHGkXi8qgcrC5x_YulTB1g.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_fHGkXi8qgcrC5x_YulTB1g-300x260.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_fHGkXi8qgcrC5x_YulTB1g-14x12.png 14w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_fHGkXi8qgcrC5x_YulTB1g-600x519.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p><p>Second, since the correlation matrix only indicates the strength of the linear relationship, it is best to plot the numeric variables using the seaborn sns.pairplot() function. Note that the sns.heatmap() and sns.pairplot() functions ignore non-numeric data types.<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15792 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_n7QI1ILvWVanVfcSQ7q1KA.png\" alt=\"\" width=\"700\" height=\"630\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_n7QI1ILvWVanVfcSQ7q1KA.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_n7QI1ILvWVanVfcSQ7q1KA-300x270.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_n7QI1ILvWVanVfcSQ7q1KA-13x12.png 13w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_n7QI1ILvWVanVfcSQ7q1KA-600x540.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p><p>Here is an example with another dataset:<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15793 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_cBYNRfCfFXU5McA7AfhzKg.png\" alt=\"\" width=\"700\" height=\"659\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_cBYNRfCfFXU5McA7AfhzKg.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_cBYNRfCfFXU5McA7AfhzKg-300x282.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_cBYNRfCfFXU5McA7AfhzKg-13x12.png 13w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_cBYNRfCfFXU5McA7AfhzKg-600x565.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p><p>The pair plot or scatterplot is a good complement to the correlation matrix, especially where non-linear relationships (e.g., exponential, inverse relationship) may exist. For example, the inverse relationship between \u201cRank\u201d and \u201cSales\u201d seen in the restaurant dataset can be mistaken for a strong linear relationship if we just look at the number \u201c-0.92\u201d in the correlation matrix.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c29cdc5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c29cdc5\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-072b3e1\" data-id=\"072b3e1\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3b50bbf elementor-widget elementor-widget-heading\" data-id=\"3b50bbf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Categorie-vs-Categorie\"><\/span>Category vs Category<span class=\"ez-toc-section-end\"><\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ef85b99 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ef85b99\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cfdeee7\" data-id=\"cfdeee7\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3c0e183 elementor-widget elementor-widget-text-editor\" data-id=\"3c0e183\" data-element_type=\"widget\" data-e-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\t\t<p>The relationship between two categorical variables can be visualized using clustered histograms. The frequency of primary categorical variables is broken down by secondary category. This can be achieved using sns.countplot().<\/p><p>I&#039;m using a nested for loop, where the outer loop loops through all the categorical variables and assigns them as the primary category, then the inner loop loops through the list again to associate the primary category with another secondary category.<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15794 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_oWPHjS6TM939gADpA6CArA.png\" alt=\"\" width=\"700\" height=\"203\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_oWPHjS6TM939gADpA6CArA.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_oWPHjS6TM939gADpA6CArA-300x87.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_oWPHjS6TM939gADpA6CArA-18x5.png 18w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_oWPHjS6TM939gADpA6CArA-600x174.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p><p>In a clustered bar chart, if the frequency distribution always follows the same pattern in different clusters, it suggests that there is no dependence between the primary category and the secondary category. However, if the distribution is different, it indicates that there is likely a dependency between two variables.<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15795 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-210851.png\" alt=\"\" width=\"706\" height=\"329\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-210851.png 706w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-210851-300x140.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-210851-18x8.png 18w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-210851-600x280.png 600w\" sizes=\"(max-width: 706px) 100vw, 706px\" \/><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ba0169f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ba0169f\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-fc8313b\" data-id=\"fc8313b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8242ff8 elementor-widget elementor-widget-heading\" data-id=\"8242ff8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Categorie-vs-Quantitatif\"><\/span>Category vs Quantitative<span class=\"ez-toc-section-end\"><\/span><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0289c03 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0289c03\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3a6f8b4\" data-id=\"3a6f8b4\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f517119 elementor-widget elementor-widget-text-editor\" data-id=\"f517119\" data-element_type=\"widget\" data-e-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\t\t<p>The boxplot is usually adopted when we need to compare the variation of numerical data between groups. This is an intuitive way to graphically represent whether the variation in categorical characteristics contributes to the difference in values, which can further be quantified using ANOVA analysis.<\/p><p>In this process, I associate each column of the categorical list with all the columns of the numeric list and plot the boxplot accordingly.<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15796 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_3U_X2UXX0GDDCyzcqhwZzA.png\" alt=\"\" width=\"700\" height=\"120\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_3U_X2UXX0GDDCyzcqhwZzA.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_3U_X2UXX0GDDCyzcqhwZzA-300x51.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_3U_X2UXX0GDDCyzcqhwZzA-18x3.png 18w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_3U_X2UXX0GDDCyzcqhwZzA-600x103.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p><p>In the \u201creddit_wsb\u201d dataset, no significant differences are observed between the different categories.<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15797 size-large\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211124-1024x326.png\" alt=\"\" width=\"1024\" height=\"326\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211124-1024x326.png 1024w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211124-300x95.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211124-768x244.png 768w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211124-18x6.png 18w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211124-600x191.png 600w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211124.png 1063w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p><p>Let&#039;s see the differences that may exist using another dataset.<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15798 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211237.png\" alt=\"\" width=\"700\" height=\"670\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211237.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211237-300x287.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211237-13x12.png 13w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/2022-04-23-211237-600x574.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p><p>Another approach is based on the pairplot we did earlier for numeric versus numeric. To introduce the categorical variable, we can use different hues to represent. Just like what we did for countplot. To do this, we can simply iterate over the categorical list and add each element as a tint of the pairplot.<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15799 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_6S66Kyneit2k1wQ1pDWFoA.png\" alt=\"\" width=\"700\" height=\"79\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_6S66Kyneit2k1wQ1pDWFoA.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_6S66Kyneit2k1wQ1pDWFoA-300x34.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_6S66Kyneit2k1wQ1pDWFoA-18x2.png 18w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_6S66Kyneit2k1wQ1pDWFoA-600x68.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p><p>Here are the results on the second dataset:<\/p><p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-15800 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_kP-jKWZpg1wN6Utb80F9wg.png\" alt=\"\" width=\"700\" height=\"609\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_kP-jKWZpg1wN6Utb80F9wg.png 700w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_kP-jKWZpg1wN6Utb80F9wg-300x261.png 300w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_kP-jKWZpg1wN6Utb80F9wg-14x12.png 14w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2022\/04\/1_kP-jKWZpg1wN6Utb80F9wg-600x522.png 600w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-965fe77 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"965fe77\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-16c92f9\" data-id=\"16c92f9\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c109468 elementor-widget elementor-widget-text-editor\" data-id=\"c109468\" data-element_type=\"widget\" data-e-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\t\t<p>This marks the end of our semi-automatic data analysis that you can use for all your datasets.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Descriptive Analysis Wiki Home Page Exploratory data analysis, also known as EDA, has become an increasingly hot topic in \u2026 <\/p>","protected":false},"author":1,"featured_media":0,"parent":15506,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-15780","page","type-page","status-publish","hentry"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/15780","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/comments?post=15780"}],"version-history":[{"count":3,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/15780\/revisions"}],"predecessor-version":[{"id":15803,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/15780\/revisions\/15803"}],"up":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/15506"}],"wp:attachment":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/media?parent=15780"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}