{"id":182544,"date":"2024-03-20T02:29:00","date_gmt":"2024-03-20T01:29:00","guid":{"rendered":"https:\/\/liora.io\/en\/?p=182544"},"modified":"2026-02-06T08:20:18","modified_gmt":"2026-02-06T07:20:18","slug":"understanding-the-pandas-read-sql-function-a-deep-dive","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/understanding-the-pandas-read-sql-function-a-deep-dive","title":{"rendered":"Understanding the Pandas Read SQL Function: A Deep Dive"},"content":{"rendered":"<style>\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><p><strong>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.<\/strong><\/p>\t\t\n\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>&nbsp;<\/p><p>?Related 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:\/\/liora.io\/en\/redis-the-favorite-nosql-database-for-developers\"><a href=\"https:\/\/liora.io\/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:\/\/liora.io\/en\/postgresql-vs-mysql-what-are-the-differences\"><a href=\"https:\/\/liora.io\/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:\/\/liora.io\/en\/microsoft-sql-server-everything-you-need-to-know\"><a href=\"https:\/\/liora.io\/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:\/\/liora.io\/en\/sql-update-update-data-with-this-command\"><a href=\"https:\/\/liora.io\/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:\/\/liora.io\/en\/sql-group-by-everything-you-need-to-know-about-this-query\"><a href=\"https:\/\/liora.io\/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\n\t\t\t<h3>What is Pandas Read_SQL \/ Pandas Read SQL Function?<\/h3>\t\t\n\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:\/\/liora.io\/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\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Training in SQL language<\/a><\/div><\/div>\n\n\t\t\t<h3>How do I use the Pandas Read_SQL query \/ Pandas Read SQL Function?<\/h3>\t\t\n\t\t\t<h4>Prerequisites<\/h4>\t\t\n\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:\/\/liora.io\/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><p>Not to mention access to an SQL database (whether remotely or on a local machine).<\/p>\t\t\n\t\t\t<style>\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>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2023\/10\/Pandas-Read_SQL1.jpg\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t<figcaption><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t<h4>Using Pandas Read SQL Function<\/h4>\t\t\n\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>In addition to reading tables, it&#8217;s also possible to read SQL views. To do this, simply write the syntax below: \n<pre style=\"padding-left: 40px;\"><b>df = pandas.read_sql_query('''SELECT * FROM my_view''', con=cnx))<\/b><\/pre>\n<strong>Good to know<\/strong>: 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.\t\t\n\t\t\t<a href=\"\/en\/courses\/data-ai\/\">\n\t\t\t\t\t\tLearn SQL language\n\t\t\t\t\t<\/a>\n\t\t\t<h4>Controlling data volumes<\/h4>\t\t\n\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:\/\/liora.io\/en\/amazon-dynamodb-the-aws-nosql-database\">very large databases.<\/a><\/p><p>Indeed, if you want to read <a href=\"https:\/\/liora.io\/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:\/\/liora.io\/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\n\t\t\t\t\t\t\t\t\t\t\t\t<figure>\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/liora.io\/app\/uploads\/2023\/10\/Pandas-Read_SQL2.jpg\" title=\"\" alt=\"\" loading=\"lazy\">\t\t\t\t\t\t\t\t\t\t\t<figcaption><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t<h3>What are the limitations of the Pandas Read SQL Function?<\/h3>\t\t\n\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\n\t\t\t<h3>Join Liora to manage your databases<\/h3>\t\t\n\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:\/\/liora.io\/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, Liora 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\n\t\t\t\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex is-content-justification-center\"><div class=\"wp-block-button \"><a class=\"wp-block-button__link wp-element-button \" href=\"\/en\/courses\/data-ai\/\">Discover our SQL training<\/a><\/div><\/div>\n","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":76,"featured_media":182557,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"_acf_changed":false,"editor_notices":[],"footnotes":""},"categories":[2433],"class_list":["post-182544","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/182544","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/users\/76"}],"replies":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/comments?post=182544"}],"version-history":[{"count":1,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/182544\/revisions"}],"predecessor-version":[{"id":205943,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/posts\/182544\/revisions\/205943"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media\/182557"}],"wp:attachment":[{"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/media?parent=182544"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/liora.io\/en\/wp-json\/wp\/v2\/categories?post=182544"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}