{"id":3924,"date":"2024-11-23T12:43:04","date_gmt":"2024-11-23T12:43:04","guid":{"rendered":"https:\/\/ebotsolutions.com\/web\/?p=3924"},"modified":"2024-11-23T13:49:06","modified_gmt":"2024-11-23T13:49:06","slug":"woocommerce-product-variation-using-python-script","status":"publish","type":"post","link":"https:\/\/ebotsolutions.com\/web\/woocommerce-product-variation-using-python-script\/","title":{"rendered":"Effortlessly Create WooCommerce Product Variations with Python: A Step-by-Step Guide"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3924\" class=\"elementor elementor-3924\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-30100b5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"30100b5\" data-element_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-b1ded2f\" data-id=\"b1ded2f\" data-element_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-2ea631d elementor-widget elementor-widget-heading\" data-id=\"2ea631d\" data-element_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\">WooCommerce Product variation Using Python Script<\/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-061246f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"061246f\" data-element_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-fffca2f\" data-id=\"fffca2f\" data-element_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-e65a99e elementor-widget elementor-widget-text-editor\" data-id=\"e65a99e\" 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\t\t<p>Managing product variations effectively is critical for e-commerce platforms. This Python script demonstrates how to process a CSV file of products, generate variations based on size, and save the enriched data back to a new file. Below is a breakdown of the script, step by step, to help you understand how it works.<\/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-cfb6732 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cfb6732\" data-element_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-429e373\" data-id=\"429e373\" data-element_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-260853c elementor-widget elementor-widget-video\" data-id=\"260853c\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=kvGO1nsUgUQ&amp;t=7s&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\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-fc55b79 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fc55b79\" data-element_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-f4f3140\" data-id=\"f4f3140\" data-element_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-e35212a elementor-widget elementor-widget-text-editor\" data-id=\"e35212a\" 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\t\t<h2><strong>1. Importing Required Libraries<\/strong><\/h2><p>The script begins by importing essential Python libraries:<\/p><div class=\"contain-inline-size rounded-md border-[0.5px] border-token-border-medium relative bg-token-sidebar-surface-primary dark:bg-gray-950\"><div class=\"sticky top-9 md:top-[5.75rem]\"><div class=\"absolute bottom-0 right-2 flex h-9 items-center\"><div class=\"flex items-center rounded bg-token-sidebar-surface-primary px-2 font-sans text-xs text-token-text-secondary dark:bg-token-main-surface-secondary\">\u00a0<\/div><\/div><\/div><div class=\"overflow-y-auto p-4\" dir=\"ltr\"><code class=\"!whitespace-pre hljs language-python\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd<br \/>\n<span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np<br \/>\n<span class=\"hljs-keyword\">import<\/span> os<br \/>\n<\/code><\/div><\/div><ul><li><strong>pandas (<code>pd<\/code>)<\/strong>: For data manipulation and handling CSV files.<\/li><li><strong>numpy (<code>np<\/code>)<\/strong>: Provides numerical operations, though it&#8217;s not directly used here.<\/li><li><strong>os<\/strong>: Used to interact with the operating system, specifically for path management.<\/li><\/ul><h2><strong>2. Setting Up File Paths<\/strong><\/h2><div class=\"contain-inline-size rounded-md border-[0.5px] border-token-border-medium relative bg-token-sidebar-surface-primary dark:bg-gray-950\"><div class=\"sticky top-9 md:top-[5.75rem]\"><div class=\"absolute bottom-0 right-2 flex h-9 items-center\"><div class=\"flex items-center rounded bg-token-sidebar-surface-primary px-2 font-sans text-xs text-token-text-secondary dark:bg-token-main-surface-secondary\">\u00a0<\/div><\/div><\/div><div class=\"overflow-y-auto p-4\" dir=\"ltr\"><p><code class=\"!whitespace-pre hljs language-python\"><span class=\"hljs-built_in\">print<\/span>(os.path.dirname(__file__)) <\/code><\/p><code class=\"!whitespace-pre hljs language-python\">\n<\/code><p><code class=\"!whitespace-pre hljs language-python\">filepath = <span class=\"hljs-string\">\"&lt;input file Path&gt;\"<\/span><br \/>\ntgt = <span class=\"hljs-string\">\"&lt;output file Path&gt;\"<\/span><br \/>\n<\/code><\/p><\/div><\/div><ul><li><code>os.path.dirname(__file__)<\/code> prints the directory where the script is located.<\/li><li><code>filepath<\/code> is the path to the input CSV file containing product data.<\/li><li><code>tgt<\/code> is the path to the output CSV file where processed data will be saved.<\/li><\/ul><h2><strong>3. Defining Size Variations and Prices<\/strong><\/h2><div class=\"contain-inline-size rounded-md border-[0.5px] border-token-border-medium relative bg-token-sidebar-surface-primary dark:bg-gray-950\"><div class=\"overflow-y-auto p-4\" dir=\"ltr\"><p><code class=\"!whitespace-pre hljs language-python\">\u00a0<\/code><\/p><p><code class=\"!whitespace-pre hljs language-python\">size = {<span class=\"hljs-string\">'variation_values'<\/span>: [<span class=\"hljs-string\">\"small\"<\/span>, <span class=\"hljs-string\">\"large\"<\/span>, <span class=\"hljs-string\">\"medium\"<\/span>]}<\/code><\/p><code class=\"!whitespace-pre hljs language-python\"><code class=\"!whitespace-pre hljs language-python\"><\/code><\/code><p>standard_price = <span class=\"hljs-number\">100<\/span><\/p><code class=\"!whitespace-pre hljs language-python\">\n<\/code><p><code class=\"!whitespace-pre hljs language-python\">small = standard_price<br \/>\nmedium = standard_price + <span class=\"hljs-number\">20<\/span><br \/>\nlarge = standard_price + <span class=\"hljs-number\">40<\/span><br \/>\n<\/code><\/p><\/div><\/div><ul><li>The <code>size<\/code> dictionary contains three size categories: <strong>small<\/strong>, <strong>medium<\/strong>, and <strong>large<\/strong>.<\/li><li>A base price (<code>standard_price<\/code>) is defined, with price adjustments for larger sizes.<\/li><\/ul><h2><strong>4. Reading the Input CSV<\/strong><\/h2><div class=\"contain-inline-size rounded-md border-[0.5px] border-token-border-medium relative bg-token-sidebar-surface-primary dark:bg-gray-950\"><div class=\"flex items-center text-token-text-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md h-9 bg-token-sidebar-surface-primary dark:bg-token-main-surface-secondary select-none\">\u00a0<\/div><div class=\"sticky top-9 md:top-[5.75rem]\"><div class=\"absolute bottom-0 right-2 flex h-9 items-center\">\u00a0<\/div><\/div><div class=\"overflow-y-auto p-4\" dir=\"ltr\"><code class=\"!whitespace-pre hljs language-python\">df_prod_var = pd.read_csv(filepath)<br \/>\ndfhrdconfig = df_prod_var.copy(deep=<span class=\"hljs-literal\">True<\/span>)<br \/>\n<\/code><\/div><\/div><ul><li><code>df_prod_var<\/code> loads the input product data from the CSV.<\/li><li><code>dfhrdconfig<\/code> creates a deep copy of this DataFrame for later use.<\/li><\/ul><h2><strong>5. Creating a DataFrame for Size Variations<\/strong><\/h2><div class=\"contain-inline-size rounded-md border-[0.5px] border-token-border-medium relative bg-token-sidebar-surface-primary dark:bg-gray-950\"><div class=\"flex items-center text-token-text-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md h-9 bg-token-sidebar-surface-primary dark:bg-token-main-surface-secondary select-none\">\u00a0<\/div><div class=\"sticky top-9 md:top-[5.75rem]\"><div class=\"absolute bottom-0 right-2 flex h-9 items-center\">\u00a0<\/div><\/div><div class=\"overflow-y-auto p-4\" dir=\"ltr\"><code class=\"!whitespace-pre hljs language-python\">df_size = pd.DataFrame(size)<br \/>\n<\/code><\/div><\/div><ul><li>Converts the <code>size<\/code> dictionary into a DataFrame for easy merging.<\/li><\/ul><h2><strong>6. Merging Product Data with Size Variations<\/strong><\/h2><div class=\"contain-inline-size rounded-md border-[0.5px] border-token-border-medium relative bg-token-sidebar-surface-primary dark:bg-gray-950\"><div class=\"flex items-center text-token-text-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md h-9 bg-token-sidebar-surface-primary dark:bg-token-main-surface-secondary select-none\">\u00a0<\/div><div class=\"sticky top-9 md:top-[5.75rem]\"><div class=\"absolute bottom-0 right-2 flex h-9 items-center\">\u00a0<\/div><\/div><div class=\"overflow-y-auto p-4\" dir=\"ltr\"><code class=\"!whitespace-pre hljs language-python\"><code class=\"!whitespace-pre hljs language-python\">df_prod_var[<span class=\"hljs-string\">'key'<\/span>] = <span class=\"hljs-number\">1<\/span><br \/>\ndf_size[<span class=\"hljs-string\">'key'<\/span>] = <span class=\"hljs-number\">1<\/span><\/code><\/code><p>\u00a0<\/p><code class=\"!whitespace-pre hljs language-python\">\n<\/code><p><code class=\"!whitespace-pre hljs language-python\">dfMerge = pd.merge(df_prod_var, df_size, on=<span class=\"hljs-string\">'key'<\/span>)<br \/>\n<\/code><\/p><\/div><\/div><ul><li>A dummy column (<code>key<\/code>) is added to both DataFrames to facilitate a Cartesian join.<\/li><li><code>dfMerge<\/code> combines each product with all size variations.<\/li><\/ul><h2><strong>7. Preparing the Variations DataFrame<\/strong><\/h2><div class=\"contain-inline-size rounded-md border-[0.5px] border-token-border-medium relative bg-token-sidebar-surface-primary dark:bg-gray-950\"><div class=\"flex items-center text-token-text-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md h-9 bg-token-sidebar-surface-primary dark:bg-token-main-surface-secondary select-none\">\u00a0<\/div><div class=\"sticky top-9 md:top-[5.75rem]\"><div class=\"absolute bottom-0 right-2 flex h-9 items-center\">\u00a0<\/div><\/div><div class=\"overflow-y-auto p-4\" dir=\"ltr\"><code class=\"!whitespace-pre hljs language-python\">dfhrdconfig[<span class=\"hljs-string\">'Type'<\/span>] = <span class=\"hljs-string\">'variable'<\/span><br \/>\ndfhrdconfig[<span class=\"hljs-string\">'Parent'<\/span>] = <span class=\"hljs-string\">''<\/span><br \/>\ndfunion = pd.concat([dfhrdconfig, dfMerge], ignore_index=<span class=\"hljs-literal\">True<\/span>)<br \/>\n<\/code><\/div><\/div><ul><li><code>dfhrdconfig<\/code> is updated with:<ul><li><code>Type<\/code>: Indicates this is a <strong>variable<\/strong> product.<\/li><li><code>Parent<\/code>: Empty column to store parent product information.<\/li><\/ul><\/li><li><code>pd.concat<\/code> merges the original and size-variation DataFrames.<\/li><\/ul><h2><strong>8. Adding SKU and Position Information<\/strong><\/h2><div class=\"contain-inline-size rounded-md border-[0.5px] border-token-border-medium relative bg-token-sidebar-surface-primary dark:bg-gray-950\"><div class=\"flex items-center text-token-text-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md h-9 bg-token-sidebar-surface-primary dark:bg-token-main-surface-secondary select-none\">\u00a0<\/div><div class=\"sticky top-9 md:top-[5.75rem]\"><div class=\"absolute bottom-0 right-2 flex h-9 items-center\">\u00a0<\/div><\/div><div class=\"overflow-y-auto p-4\" dir=\"ltr\"><code class=\"!whitespace-pre hljs language-python\">dfunion.loc[dfunion.index &gt; <span class=\"hljs-number\">0<\/span> , <span class=\"hljs-string\">'SKU'<\/span>] = dfunion[<span class=\"hljs-string\">'SKU'<\/span>] + <span class=\"hljs-string\">'-VAR-'<\/span> + dfunion.index.astype(<span class=\"hljs-built_in\">str<\/span>)<br \/>\ndfunion[<span class=\"hljs-string\">'Position'<\/span>] =  dfunion.index.astype(<span class=\"hljs-built_in\">str<\/span>)<br \/>\n<\/code><\/div><\/div><ul><li>For size variations (<code>index &gt; 0<\/code>), a unique SKU is generated by appending the variation and index.<\/li><li><code>Position<\/code> assigns a sequential index to each entry.<\/li><\/ul><h2><strong>9. Assigning Attributes and Prices<\/strong><\/h2><div class=\"contain-inline-size rounded-md border-[0.5px] border-token-border-medium relative bg-token-sidebar-surface-primary dark:bg-gray-950\"><div class=\"flex items-center text-token-text-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md h-9 bg-token-sidebar-surface-primary dark:bg-token-main-surface-secondary select-none\">\u00a0<\/div><div class=\"sticky top-9 md:top-[5.75rem]\"><div class=\"absolute bottom-0 right-2 flex h-9 items-center\">\u00a0<\/div><\/div><div class=\"overflow-y-auto p-4\" dir=\"ltr\"><p><code class=\"!whitespace-pre hljs language-python\">dfunion.loc[dfunion.index &gt; <span class=\"hljs-number\">0<\/span> , <span class=\"hljs-string\">'Attribute 1 value(s)'<\/span>]  = dfunion[<span class=\"hljs-string\">'variation_values'<\/span>] <\/code><\/p><code class=\"!whitespace-pre hljs language-python\">\n<\/code><p><code class=\"!whitespace-pre hljs language-python\">dfunion.loc[(dfunion.index &gt; <span class=\"hljs-number\">0<\/span> ) &amp; (dfunion[<span class=\"hljs-string\">'variation_values'<\/span>] == <span class=\"hljs-string\">'small'<\/span>), <span class=\"hljs-string\">'Regular price'<\/span>] = small<br \/>\ndfunion.loc[(dfunion.index &gt; <span class=\"hljs-number\">0<\/span> ) &amp; (dfunion[<span class=\"hljs-string\">'variation_values'<\/span>] == <span class=\"hljs-string\">'medium'<\/span>), <span class=\"hljs-string\">'Regular price'<\/span>] = medium<br \/>\ndfunion.loc[(dfunion.index &gt; <span class=\"hljs-number\">0<\/span> ) &amp; (dfunion[<span class=\"hljs-string\">'variation_values'<\/span>] == <span class=\"hljs-string\">'large'<\/span>), <span class=\"hljs-string\">'Regular price'<\/span>] = large<br \/>\n<\/code><\/p><\/div><\/div><ul><li><code>Attribute 1 value(s)<\/code>: Specifies size for each variation.<\/li><li><code>Regular price<\/code>: Assigns prices based on the size category.<\/li><\/ul><h2><strong>10. Adding Tax Class and Final Adjustments<\/strong><\/h2><div class=\"contain-inline-size rounded-md border-[0.5px] border-token-border-medium relative bg-token-sidebar-surface-primary dark:bg-gray-950\"><div class=\"flex items-center text-token-text-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md h-9 bg-token-sidebar-surface-primary dark:bg-token-main-surface-secondary select-none\">\u00a0<\/div><div class=\"sticky top-9 md:top-[5.75rem]\"><div class=\"absolute bottom-0 right-2 flex h-9 items-center\">\u00a0<\/div><\/div><div class=\"overflow-y-auto p-4\" dir=\"ltr\"><code class=\"!whitespace-pre hljs language-python\"><code class=\"!whitespace-pre hljs language-python\">dfunion.loc[(dfunion.index &gt; <span class=\"hljs-number\">0<\/span> ), <span class=\"hljs-string\">'Tax class'<\/span>] = <span class=\"hljs-string\">'parent'<\/span><br \/>\ndfunion.loc[(dfunion.index &gt; <span class=\"hljs-number\">0<\/span> ), <span class=\"hljs-string\">'Type'<\/span>] = <span class=\"hljs-string\">'variation'<\/span><\/code><\/code><p>\u00a0<\/p><code class=\"!whitespace-pre hljs language-python\">\n<\/code><p><code class=\"!whitespace-pre hljs language-python\">dfunion = dfunion.drop(<span class=\"hljs-string\">'key'<\/span>, axis=<span class=\"hljs-number\">1<\/span>)<br \/>\ndfunion = dfunion.drop(<span class=\"hljs-string\">'variation_values'<\/span>, axis=<span class=\"hljs-number\">1<\/span>)<br \/>\n<\/code><\/p><\/div><\/div><ul><li><code>Tax class<\/code>: Indicates the parent tax category for variations.<\/li><li><code>Type<\/code>: Updated to <strong>variation<\/strong> for size-specific entries.<\/li><li>Drops intermediate columns (<code>key<\/code> and <code>variation_values<\/code>) used for processing.<\/li><\/ul><h2><strong>11. Exporting the Final DataFrame<\/strong><\/h2><div class=\"contain-inline-size rounded-md border-[0.5px] border-token-border-medium relative bg-token-sidebar-surface-primary dark:bg-gray-950\"><div class=\"flex items-center text-token-text-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md h-9 bg-token-sidebar-surface-primary dark:bg-token-main-surface-secondary select-none\">\u00a0<\/div><div class=\"sticky top-9 md:top-[5.75rem]\"><div class=\"absolute bottom-0 right-2 flex h-9 items-center\">\u00a0<\/div><\/div><div class=\"overflow-y-auto p-4\" dir=\"ltr\"><code class=\"!whitespace-pre hljs language-python\">dfunion.to_csv(tgt)<br \/>\n<\/code><\/div><\/div><p>The processed data is saved to the output file specified by <code>tgt<\/code>.<\/p><hr \/><h2><strong>Key Outputs<\/strong><\/h2><p>The final CSV will include:<\/p><ul><li>Original product entries (Type = &#8220;variable&#8221;).<\/li><li>Variations for each size with attributes like SKU, size, price, and tax class.<\/li><\/ul>\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>\n\t\t","protected":false},"excerpt":{"rendered":"<p>WooCommerce Product variation Using Python Script Managing product variations effectively is critical for e-commerce platforms. This Python script demonstrates how to process a CSV file of products, generate variations based on size, and save the enriched data back to a &hellip; <a href=\"https:\/\/ebotsolutions.com\/web\/woocommerce-product-variation-using-python-script\/\">Read More<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[28,29,27,26,25,24],"class_list":["post-3924","post","type-post","status-publish","format-standard","hentry","category-python-automation","tag-automation","tag-csv","tag-python","tag-variation","tag-woocommerce","tag-wordpress"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Effortlessly Create WooCommerce Product Variations with Python: A Step-by-Step Guide - Ebot Solutions : Website &amp; Software Development Company in Chennai India<\/title>\n<meta name=\"description\" content=\"Learn how to automate the creation of WooCommerce product variations using Python. 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