{"id":182544,"date":"2024-03-20T02:29:00","date_gmt":"2024-03-20T01:29:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=182544"},"modified":"2024-03-10T15:31:23","modified_gmt":"2024-03-10T14:31:23","slug":"understanding-the-pandas-read-sql-function-a-deep-dive","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/understanding-the-pandas-read-sql-function-a-deep-dive","title":{"rendered":"Understanding the Pandas Read SQL Function: A Deep Dive"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"182544\" class=\"elementor elementor-182544\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5f891fb e-flex e-con-boxed e-con\" data-id=\"5f891fb\" 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-1dcc20d elementor-widget elementor-widget-heading\" data-id=\"1dcc20d\" 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\">To manage and analyze functional data, data experts can use a wide variety of tools. These include SQL and Panda and tghe Pandas Read SQL Function.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-99c0102 elementor-widget elementor-widget-text-editor\" data-id=\"99c0102\" 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>Often presented as two alternative options (it&#8217;s either one or the other), they are in fact highly complementary. And for good reason: the Python library is able to read Structured Query Language through its <strong>Pandas Read_SQL functions<\/strong>. Let&#8217;s take a closer look.<\/p><p>\u00a0<\/p><p>\ud83d\udca1Related articles:<\/p><table dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" data-sheets-root=\"1\"><colgroup><col width=\"656\" \/><\/colgroup><tbody><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Redis: The favorite NoSQL database for developers&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/redis-the-favorite-nosql-database-for-developers\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/redis-the-favorite-nosql-database-for-developers\" target=\"_blank\" rel=\"noopener\">Redis: The favorite NoSQL database for developers<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;PostgreSQL vs MySQL: What are the differences?&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/postgresql-vs-mysql-what-are-the-differences\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/postgresql-vs-mysql-what-are-the-differences\" target=\"_blank\" rel=\"noopener\">PostgreSQL vs MySQL: What are the differences?<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Microsoft SQL Server: Everything you need to know&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/microsoft-sql-server-everything-you-need-to-know\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/microsoft-sql-server-everything-you-need-to-know\" target=\"_blank\" rel=\"noopener\">Microsoft SQL Server: Everything you need to know<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;SQL UPDATE: Update data with this command&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/sql-update-update-data-with-this-command\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/sql-update-update-data-with-this-command\" target=\"_blank\" rel=\"noopener\">SQL UPDATE: Update data with this command<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;SQL GROUP BY: Everything you need to know about this query&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/sql-group-by-everything-you-need-to-know-about-this-query\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/sql-group-by-everything-you-need-to-know-about-this-query\" target=\"_blank\" rel=\"noopener\">SQL GROUP BY: Everything you need to know about this query<\/a><\/td><\/tr><\/tbody><\/table>\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-4ed4811 e-flex e-con-boxed e-con\" data-id=\"4ed4811\" 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-414efd0 elementor-widget elementor-widget-heading\" data-id=\"414efd0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is Pandas Read_SQL \/ Pandas Read SQL Function?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-63c6beb elementor-widget elementor-widget-text-editor\" data-id=\"63c6beb\" 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><strong>Pandas Read_SQL<\/strong> is a feature of the <strong>Python library<\/strong> that extracts the results of a SQL query directly into the <a href=\"https:\/\/datascientest.com\/en\/pandas-the-python-library\">Panda dataframe.<\/a><\/p><p>But beware, there are two SQL read methods:<\/p><ul><li><strong>pandas.read_sql_query:<\/strong> this is the original formula for using SQL queries in Pandas.<\/li><li><strong>pandas.read_sql:<\/strong> this simplifies the first option, since it combines read_sql_query and read_sql_table.<\/li><li>The latter allows you to read an entire SQL table in Pandas. With this function, both queries and tables can be read.<\/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-a7a0bd0 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"a7a0bd0\" 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\/en\/data-courses\">\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\">Training in SQL language<\/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-4f23dbc e-flex e-con-boxed e-con\" data-id=\"4f23dbc\" 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-f99066e elementor-widget elementor-widget-heading\" data-id=\"f99066e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How do I use the Pandas Read_SQL query \/ Pandas Read SQL Function?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7299b81 elementor-widget elementor-widget-heading\" data-id=\"7299b81\" 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\">Prerequisites<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bd721c6 elementor-widget elementor-widget-text-editor\" data-id=\"bd721c6\" 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>To use the <strong>Pandas Read_SQL query\/ Pandas Read SQL Function<\/strong> effectively, you&#8217;ll need to install a few Python packages, such as :<\/p><ul><li><strong>SQLAlchemy:<\/strong> this package lets you interact with SQL databases directly in <a href=\"https:\/\/datascientest.com\/en\/python-the-most-popular-language\">Python code.<\/a> It&#8217;s not mandatory, but it makes workflow easier.<\/li><li><strong>An adapter:<\/strong> whether you use PostgreSQL, MySQL, Oracle or any other dialect, you&#8217;ll need an adapter for Python so that Pandas and SQL can complement each other.<\/li><li><strong>A Python package manager:<\/strong> like pip.<\/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-79d4276 elementor-widget elementor-widget-text-editor\" data-id=\"79d4276\" 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>Not to mention access to an SQL database (whether remotely or on a local machine).<\/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-4e622d1 elementor-widget elementor-widget-image\" data-id=\"4e622d1\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Pandas-Read_SQL1.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-591be8d elementor-widget elementor-widget-heading\" data-id=\"591be8d\" 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\">Using Pandas Read SQL Function<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ffda6d elementor-widget elementor-widget-text-editor\" data-id=\"7ffda6d\" 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>Once all the packages have been installed, you need to open a connection to your database source. This is precisely why <strong>SQLAlchemy<\/strong> is useful, as it allows you to create a connection.<\/p><p>Thanks to this connection, you can then extract the results of a<strong> basic SQL query in Pandas<\/strong>. This is where the Pandas read_SQL query comes into play.<\/p><p>This query takes the following form:<\/p><p>df = pandas.read_sql_query(&#8221;&#8217;SELECT * FROM table-name&#8221;&#8217;, con=cnx)<\/p><p>We then need to specify the various parameters of this piece of code:<\/p><ul><li>df: this is the Pandas dataframe where the table data will be stored.<\/li><li>SELECT * FROM table-name: this specifies the data to be selected in the table.<\/li><li>con=cnx: this is the connection between Pandas and SQL.<\/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-044ba04 elementor-widget elementor-widget-text-editor\" data-id=\"044ba04\" 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;\">In addition to reading tables, it&#8217;s also possible to read SQL views. To do this, simply write the syntax below: <\/span>\n<pre style=\"padding-left: 40px;\"><b>df = pandas.read_sql_query('''SELECT * FROM my_view''', con=cnx))<\/b><\/pre>\n<span style=\"font-weight: 400;\"><span style=\"text-decoration: underline;\"><strong>Good to know<\/strong><\/span>: This is a basic model of how to use Pandas Read_SQL. It&#8217;s also possible to create a generalized query string to extract different ranges. And all this while adapting your queries and their variables.<\/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-d810894 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"d810894\" 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\/en\/data-courses\">\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\">Learn SQL language<\/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-64b51f7 elementor-widget elementor-widget-heading\" data-id=\"64b51f7\" 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\">Controlling data volumes<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eebf9ea elementor-widget elementor-widget-text-editor\" data-id=\"eebf9ea\" 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><strong>Although Pandas.Read_SQL<\/strong> can be used to extract several ranges of data, care needs to be taken with the amount of data to be entered. This is particularly true for <a href=\"https:\/\/datascientest.com\/en\/amazon-dynamodb-the-aws-nosql-database\">very large databases.<\/a><\/p><p>Indeed, if you want to read <a href=\"https:\/\/datascientest.com\/en\/sql-learn-all-about-the-programming-language-for-databases\">SQL databases<\/a> with Pandas, remember that the Python library stores not only the data frames, but also the processing of <a href=\"https:\/\/datascientest.com\/en\/insert-into-sql-insert-data-with-this-sql-query\">SQL query results<\/a>. If you don&#8217;t have enough memory, you&#8217;ll run the risk of making a lot of errors.<\/p><p>To overcome this problem, you can use the Chunksize parameter built into Pandas. This controls the volume of imported data. If the limit is reached, it&#8217;s best to extract your SQL data in batches.<\/p><p>To use this function, simply type this query:<\/p><p>df = pandas.read_sql_query(&#8221;&#8217;SELECT * FROM table-name&#8221;&#8217;, con=cnx, chunksize=n)<\/p><p>Here, n refers to the number of rows you wish to include in the dataframe.<\/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-29ea434 elementor-widget elementor-widget-image\" data-id=\"29ea434\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/10\/Pandas-Read_SQL2.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-c91e098 e-flex e-con-boxed e-con\" data-id=\"c91e098\" 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-16d7b34 elementor-widget elementor-widget-heading\" data-id=\"16d7b34\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What are the limitations of the Pandas Read SQL Function?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1e3203b elementor-widget elementor-widget-text-editor\" data-id=\"1e3203b\" 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>Although <strong>Pandas Read_SQL<\/strong> makes it easy to extract SQL databases from the Python library, you should also be aware of its limitations.<\/p><p>Indeed, this query takes up an enormous amount of space, due to the storage of the dataframe and the processing of <strong>SQL query results.<\/strong> Sufficient memory must be available.<\/p><p>In addition to causing potential errors when importing massive amounts of data, this feature is often the cause of <strong>slow loading times<\/strong>. Even when data volumes are modest.<\/p><p>If you want to speed up loading times, Pandas Read-SQL is definitely not the best option for extracting your <strong>SQL databases.<\/strong><\/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-ddb92bf e-flex e-con-boxed e-con\" data-id=\"ddb92bf\" 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-9829b07 elementor-widget elementor-widget-heading\" data-id=\"9829b07\" 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\">Join DataScientest to manage your databases<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-663a02b elementor-widget elementor-widget-text-editor\" data-id=\"663a02b\" 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>Whether it&#8217;s Pandas or SQL queries, these are essential tools for managing and analyzing databases. So, to master<a href=\"https:\/\/datascientest.com\/en\/mariadb-a-solution-for-data-management-and-analysis\"> DBMS<\/a> to perfection, it&#8217;s essential to be trained in both solutions.<\/p><p>Fortunately, DataScientest offers a comprehensive range of data-related training courses. Whether it&#8217;s a bootcamp, ongoing training or a sandwich course, you&#8217;ll quickly learn how to use these tools and be up and running straight away.<\/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-4666c37 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"4666c37\" 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\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our SQL training<\/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-471f9f0 e-flex e-con-boxed e-con\" data-id=\"471f9f0\" 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-d125651 elementor-widget elementor-widget-html\" data-id=\"d125651\" 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\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"What is Pandas Read_SQL?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Pandas Read_SQL is a Python library feature that extracts the results of a SQL query directly into the Panda dataframe.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Data volume control\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"If Pandas.Read_SQL allows you to extract several ranges of data, you need to be careful about the amount of data to be entered. 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Sufficient memory must be available.\"\n    }\n  }]\n}\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>To manage and analyze functional data, data experts can use a wide variety of tools. These include SQL and Panda and tghe Pandas Read SQL Function. Often presented as two alternative options (it&#8217;s either one or the other), they are in fact highly complementary. And for good reason: the Python library is able to read [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":182557,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Understanding the Pandas Read SQL Function: A Deep Dive<\/title>\n<meta name=\"description\" content=\"Discover how the Pandas Read SQL function in Pandas operates to bridge SQL databases and Pandas DataFrames seamlessly by bringing...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/understanding-the-pandas-read-sql-function-a-deep-dive\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Understanding the Pandas Read SQL Function: A Deep Dive\" \/>\n<meta property=\"og:description\" content=\"To manage and analyze functional data, data experts can use a wide variety of tools. 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