{"id":216593,"date":"2023-10-30T05:30:00","date_gmt":"2023-10-30T05:30:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=216593"},"modified":"2023-11-09T13:40:56","modified_gmt":"2023-11-09T13:40:56","slug":"pandas-read-sql-tout-savoir","status":"publish","type":"post","link":"https:\/\/datascientest.com\/pandas-read-sql-tout-savoir","title":{"rendered":"Fonction Read_SQL sur Pandas : Comment \u00e7a fonctionne ?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"216593\" class=\"elementor elementor-216593\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c282337 e-flex e-con-boxed e-con\" data-id=\"c282337\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b72d5e6 elementor-widget elementor-widget-heading\" data-id=\"b72d5e6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Pour g\u00e9rer et analyser les donn\u00e9es fonctionnelles, les experts data peuvent utiliser une grande vari\u00e9t\u00e9 d\u2019outils. Et notamment SQL ou Panda.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-332a30b elementor-widget elementor-widget-text-editor\" data-id=\"332a30b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">Souvent pr\u00e9sent\u00e9es comme deux options alternatives (c\u2019est soit l\u2019une, soit l\u2019autre), elles sont en r\u00e9alit\u00e9 tr\u00e8s compl\u00e9mentaires. Et pour cause, la biblioth\u00e8que de Python est capable de lire le Structured Query Language \u00e0 travers ses <b>fonctions Pandas Read_SQL<\/b>. D\u00e9couvrons-les plus en d\u00e9tail.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4dff512 e-flex e-con-boxed e-con\" data-id=\"4dff512\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b5a0871 elementor-widget elementor-widget-heading\" data-id=\"b5a0871\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">C\u2019est quoi Pandas Read_SQL ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fb61fa9 elementor-widget elementor-widget-text-editor\" data-id=\"fb61fa9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Pandas Read_SQL est une fonctionnalit\u00e9 de la biblioth\u00e8que de Python qui permet <\/span><b>d\u2019extraire les r\u00e9sultats d&rsquo;une requ\u00eate SQL directement dans le dataframe Panda.\u00a0<\/b><\/p><p><span style=\"font-weight: 400;\">Mais attention, il existe deux m\u00e9thodes de lecture <a href=\"https:\/\/datascientest.com\/sql-tout-savoir\">SQL<\/a> :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>pandas.read_sql_query<\/b><span style=\"font-weight: 400;\"> : c\u2019est la formule originale pour utiliser les <a href=\"https:\/\/datascientest.com\/cinq-requetes-sql-a-connaitre\">requ\u00eates SQL<\/a> dans Pandas. <\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>pandas.read_sql <\/b>: il s\u2019agit de simplifier la premi\u00e8re option, puisqu\u2019elle combine read_sql_query et de read_sql_table. Or, cette derni\u00e8re permet de lire une table SQL enti\u00e8re dans <a href=\"https:\/\/datascientest.com\/pandas-python-data-science\">Pandas<\/a>. Avec cette fonction, il est donc possible de lire aussi bien des requ\u00eates que des tables.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9ad7985 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"9ad7985\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"\/formation-sql\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Se former au langage SQL<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6e550fd e-flex e-con-boxed e-con\" data-id=\"6e550fd\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5c60904 elementor-widget elementor-widget-heading\" data-id=\"5c60904\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Comment utiliser la requ\u00eate Pandas Read_SQL ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5772b3d elementor-widget elementor-widget-heading\" data-id=\"5772b3d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Les pr\u00e9requis<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f233270 elementor-widget elementor-widget-text-editor\" data-id=\"f233270\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Afin d\u2019utiliser efficacement la requ\u00eate Pandas Read_SQL, il convient d\u2019installer quelques paquets Python, tels que :\u00a0<\/span>\n<ul>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>SQLAlchemy <\/b><span style=\"font-weight: 400;\">: ce paquet permet d&rsquo;interagir avec des <a href=\"https:\/\/datascientest.com\/base-de-donnees-definition\">bases de donn\u00e9es<\/a> SQL directement dans le code Python. Il n\u2019est pas obligatoire, mais cela facilite le workflow.\u00a0<\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Un adaptateur <\/b><span style=\"font-weight: 400;\">: que vous utilisiez <a href=\"https:\/\/datascientest.com\/postgresql-tout-savoir\">PostgreSQL<\/a>, MySQL, Oracle ou tout autre dialecte, vous aurez besoin d\u2019un adaptateur pour Python afin que Pandas et SQL puissent \u00eatre compl\u00e9mentaires. <\/span><\/li>\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Un gestionnaire de paquets Python<\/b><span> : comme via pip.<\/span><\/li>\n<\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-84cf78f elementor-widget elementor-widget-text-editor\" data-id=\"84cf78f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Sans oublier un acc\u00e8s \u00e0 une base de donn\u00e9es SQL (qu\u2019il soit \u00e0 distance ou sur une machine locale). <\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0a55d9a elementor-widget elementor-widget-image\" data-id=\"0a55d9a\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"900\" height=\"500\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Pandas-Read_SQL1.jpg\" class=\"attachment-full size-full wp-image-216619\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Pandas-Read_SQL1.jpg 900w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Pandas-Read_SQL1-300x167.jpg 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Pandas-Read_SQL1-768x427.jpg 768w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0e73d9e elementor-widget elementor-widget-heading\" data-id=\"0e73d9e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">L\u2019utilisation de Pandas Read_SQL<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2c8c444 elementor-widget elementor-widget-text-editor\" data-id=\"2c8c444\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">D\u00e8s lors que tous les paquets sont install\u00e9s, vous devez<\/span><b> ouvrir une connexion \u00e0 votre source de base de donn\u00e9es.<\/b><span style=\"font-weight: 400;\"> C\u2019est justement pour cette raison que SQLAlchemy est utile, puisqu\u2019il permet de cr\u00e9er une connexion.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Gr\u00e2ce \u00e0 cette connexion, vous pourrez ensuite <\/span><b>extraire les r\u00e9sultats d&rsquo;une requ\u00eate SQL de base dans Pandas. <\/b><span style=\"font-weight: 400;\">Et c\u2019est \u00e0 cet instant qu\u2019intervient la requ\u00eate Pandas read_SQL.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Cette requ\u00eate se pr\u00e9sente sous cette forme :\u00a0<\/span><\/p><pre style=\"padding-left: 40px;\"><b>df = pandas.read_sql_query('''SELECT * FROM nom-de-table''', con=cnx)<\/b><\/pre><p><span style=\"font-weight: 400;\">Il convient alors de pr\u00e9ciser les diff\u00e9rents param\u00e8tres de ce bout de code :\u00a0<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">df : c\u2019est le dataframe Pandas o\u00f9 seront stock\u00e9es les donn\u00e9es de la table.\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SELECT * FROM nom-de-table : il s\u2019agit de pr\u00e9ciser les donn\u00e9es \u00e0 s\u00e9lection dans la table.\u00a0<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">con=cnx : c\u2019est la connexion entre Pandas et SQL. <\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-30876f7 elementor-widget elementor-widget-text-editor\" data-id=\"30876f7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">En plus de lire les tables, il est aussi possible de lire des vues SQL. Pour cela, il suffit d\u2019\u00e9crire la syntaxe ci-dessous :\u00a0<\/span><\/p><pre style=\"padding-left: 40px;\"><b>df = pandas.read_sql_query(\u2018\u2019\u2019SELECT * FROM my_view\u2019\u2019\u2019, con=cnx))<\/b><\/pre><p><span style=\"font-weight: 400;\"><span style=\"text-decoration: underline;\"><strong>Bon \u00e0 savoir<\/strong><\/span> : Il s\u2019agit ici d\u2019un mod\u00e8le basique de l\u2019utilisation de Pandas Read_SQL. Il est aussi possible de\u00a0 cr\u00e9er une cha\u00eene de requ\u00eate g\u00e9n\u00e9ralis\u00e9e pour extraire diff\u00e9rentes plages. Et ce, tout en adaptant vos requ\u00eates et leurs variables.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d6650bd elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"d6650bd\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"\/formation-sql\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Apprendre le langage SQL<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66419d5 elementor-widget elementor-widget-heading\" data-id=\"66419d5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Le contr\u00f4le des volumes de donn\u00e9es<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3b9e09a elementor-widget elementor-widget-text-editor\" data-id=\"3b9e09a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Si Pandas.Read_SQL permet d\u2019extraire plusieurs plages de donn\u00e9es, il convient de faire attention aux quantit\u00e9s de donn\u00e9es \u00e0 introduire. En particulier pour les bases de donn\u00e9es tr\u00e8s volumineuses. En effet, si vous souhaitez lire les databases SQL avec Pandas, sachez que la biblioth\u00e8que de Python stocke les dataframes, mais aussi les <\/span><span style=\"font-weight: 400;\">traitements des r\u00e9sultats des requ\u00eates SQL. Il convient donc de disposer d\u2019une <\/span><b>m\u00e9moire suffisante au risque de provoquer de nombreuses erreurs.\u00a0<\/b><\/p><p><span style=\"font-weight: 400;\">Pour y pallier, il est possible d\u2019utiliser le param\u00e8tre Chunksize int\u00e9gr\u00e9 \u00e0 Pandas. Celui-ci contr\u00f4le alors le volume de donn\u00e9es import\u00e9es. Si la limite est proche, mieux vaut extraire vos donn\u00e9es SQL en plusieurs fois.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Pour utiliser cette fonction, il suffit de taper cette requ\u00eate :\u00a0<\/span><\/p><pre style=\"padding-left: 40px;\"><b>df = pandas.read_sql_query('''SELECT * FROM nom-de-table''', con=cnx, chunksize=n)<\/b><\/pre><p><span style=\"font-weight: 400;\">Ici, n renvoie au nombre de lignes que vous souhaitez int\u00e9grer dans le dataframe.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0b1dcd8 elementor-widget elementor-widget-image\" data-id=\"0b1dcd8\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"900\" height=\"500\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Pandas-Read_SQL2.jpg\" class=\"attachment-full size-full wp-image-216626\" alt=\"\" loading=\"lazy\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Pandas-Read_SQL2.jpg 900w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Pandas-Read_SQL2-300x167.jpg 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Pandas-Read_SQL2-768x427.jpg 768w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/>\t\t\t\t\t\t\t\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f40c243 e-flex e-con-boxed e-con\" data-id=\"f40c243\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8a55e46 elementor-widget elementor-widget-heading\" data-id=\"8a55e46\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Quelles sont les limites de Pandas Read-SQL ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7e05b8c elementor-widget elementor-widget-text-editor\" data-id=\"7e05b8c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Bien que Pandas Read_SQL permettent d\u2019extraire facilement les bases de donn\u00e9es SQL dans la biblioth\u00e8que de Python, il convient aussi de conna\u00eetre ses limites.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">En effet<\/span><b>, cette requ\u00eate prend \u00e9norm\u00e9ment d\u2019espace, <\/b><span style=\"font-weight: 400;\">du fait du stockage du dataframe et du traitement des r\u00e9sultats de requ\u00eate SQL. Il convient de disposer d\u2019une m\u00e9moire suffisante.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">En plus de provoquer de potentielles erreurs en cas d\u2019importations de donn\u00e9es massives,\u00a0 cette fonctionnalit\u00e9 est souvent \u00e0 l\u2019origine des lenteurs en termes de temps de chargement. Et ce, m\u00eame lorsque les volumes de donn\u00e9es sont modestes.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Si vous souhaitez acc\u00e9l\u00e9rer le temps de chargement, Pandas Read-SQL n\u2019est d\u00e9finitivement pas la meilleure option pour extraire vos bases de donn\u00e9es SQL. <\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d694a1c e-flex e-con-boxed e-con\" data-id=\"d694a1c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e2fa403 elementor-widget elementor-widget-heading\" data-id=\"e2fa403\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Rejoignez DataScientest pour g\u00e9rer vos databases<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2a1508 elementor-widget elementor-widget-text-editor\" data-id=\"a2a1508\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<span style=\"font-weight: 400;\">Qu\u2019il s\u2019agisse de Pandas ou des requ\u00eates SQL, ce sont des outils incontournables pour g\u00e9rer et analyser les bases de donn\u00e9es. Alors pour ma\u00eetriser \u00e0 la perfection les SGBD, il est primordial de se former \u00e0 ces deux solutions. Heureusement, DataScientest vous propose des <a href=\"https:\/\/datascientest.com\/formations-data\">formations compl\u00e8tes autour de la donn\u00e9e<\/a>. Que ce soit \u00e0 travers des bootcamp, de la formation en continu ou de la formation en alternance, vous saurez rapidement utiliser ces outils et deviendrez op\u00e9rationnel d\u00e8s l\u2019issue du parcours. <\/span>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-399a55e elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"399a55e\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-sql\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">D\u00e9couvrir notre formation SQL<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ba0e602 e-flex e-con-boxed e-con\" data-id=\"ba0e602\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2148a09 elementor-widget elementor-widget-html\" data-id=\"2148a09\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"C\u2019est quoi Pandas Read_SQL ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Pandas Read_SQL est une fonctionnalit\u00e9 de la biblioth\u00e8que de Python qui permet d\u2019extraire les r\u00e9sultats d'une requ\u00eate SQL directement dans le dataframe Panda.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Le contr\u00f4le des volumes de donn\u00e9es\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Si Pandas.Read_SQL permet d\u2019extraire plusieurs plages de donn\u00e9es, il convient de faire attention aux quantit\u00e9s de donn\u00e9es \u00e0 introduire. En particulier pour les bases de donn\u00e9es tr\u00e8s volumineuses. En effet, si vous souhaitez lire les databases SQL avec Pandas, sachez que la biblioth\u00e8que de Python stocke les dataframes, mais aussi les traitements des r\u00e9sultats des requ\u00eates SQL. Il convient donc de disposer d\u2019une m\u00e9moire suffisante au risque de provoquer de nombreuses erreurs.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Quelles sont les limites de Pandas Read-SQL ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Bien que Pandas Read_SQL permettent d\u2019extraire facilement les bases de donn\u00e9es SQL dans la biblioth\u00e8que de Python, il convient aussi de conna\u00eetre ses limites.  En effet, cette requ\u00eate prend \u00e9norm\u00e9ment d\u2019espace, du fait du stockage du dataframe et du traitement des r\u00e9sultats de requ\u00eate SQL. Il convient de disposer d\u2019une m\u00e9moire suffisante.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Pour g\u00e9rer et analyser les donn\u00e9es fonctionnelles, les experts data peuvent utiliser une grande vari\u00e9t\u00e9 d\u2019outils. Et notamment SQL ou Panda. Souvent pr\u00e9sent\u00e9es comme deux options alternatives (c\u2019est soit l\u2019une, soit l\u2019autre), elles sont en r\u00e9alit\u00e9 tr\u00e8s compl\u00e9mentaires. Et pour cause, la biblioth\u00e8que de Python est capable de lire le Structured Query Language \u00e0 travers [&hellip;]<\/p>\n","protected":false},"author":77,"featured_media":216599,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Fonction Read_SQL sur Pandas : Comment \u00e7a fonctionne ?<\/title>\n<meta name=\"description\" content=\"Pandas, la biblioth\u00e8que de Python est capable de lire le langage SQL \u00e0 travers ses fonctions Pandas Read_SQL. D\u00e9cryptage.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/pandas-read-sql-tout-savoir\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fonction Read_SQL sur Pandas : Comment \u00e7a fonctionne ?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/pandas-read-sql-tout-savoir\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-30T05:30:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-09T13:40:56+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Illu_BLOG__Pandas-Read_SQL.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"563\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Raphael Kassel\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Raphael Kassel\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/pandas-read-sql-tout-savoir\",\"url\":\"https:\/\/datascientest.com\/pandas-read-sql-tout-savoir\",\"name\":\"Fonction Read_SQL sur Pandas : Comment \u00e7a fonctionne ?\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2023-10-30T05:30:00+00:00\",\"dateModified\":\"2023-11-09T13:40:56+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\"},\"description\":\"Pandas, la biblioth\u00e8que de Python est capable de lire le langage SQL \u00e0 travers ses fonctions Pandas Read_SQL. 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