{"id":145657,"date":"2025-06-25T12:40:03","date_gmt":"2025-06-25T07:10:03","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=145657"},"modified":"2025-06-25T12:40:03","modified_gmt":"2025-06-25T07:10:03","slug":"e-commerce-sales-visualization-using-seaborn","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/e-commerce-sales-visualization-using-seaborn\/","title":{"rendered":"Seaborn Project &#8211; E-Commerce Sales Visualization"},"content":{"rendered":"<h3>Program 1<\/h3>\n<p><a href=\"https:\/\/drive.google.com\/file\/d\/1_StmZ1jmHXa7Hci0-PT6Aq3M22LPVgYd\/view?usp=sharing\" target=\"_blank\" rel=\"noopener\"><strong>E-Commerce Sales Dataset<\/strong><\/a><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\"># E-Commerce Sales Visualization using Seaborn\r\n\r\n#This project analyzes e-commerce sales data using Seaborn and Matplotlib. \r\n# It visualizes trends over time, category-wise performance, regional patterns, \r\n# and customer behavior through various charts like line plots, bar plots, and box plots.\r\n\r\n\r\nimport pandas as pd\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n\r\n# Load the dataset\r\ndf = pd.read_csv(\"ecommerce_sales.csv\")\r\n#print(df.isnull().sum())\r\n# Convert 'Date' to datetime format\r\ndf['Date'] = pd.to_datetime(df['Date'])\r\n\r\n# Plot 1: Lineplot of total sales over time\r\n\r\n# plt.figure(figsize=(10, 5))\r\n# daily_sales = df.groupby('Date')['Sales'].sum().reset_index()\r\n# print(daily_sales)\r\n# sns.lineplot(data=daily_sales, x='Date', y='Sales', marker='o')\r\n# plt.title(\"Total Sales Over Time\")\r\n# plt.ylabel(\"Sales ($)\")\r\n# plt.xlabel(\"Date\")\r\n# plt.xticks(rotation=45)\r\n# #plt.tight_layout()\r\n# plt.show()\r\n\r\n# Plot 2: Barplot of sales by category\r\n# plt.figure(figsize=(8, 5))\r\n# category_sales = df.groupby('Category')['Sales'].sum().reset_index()\r\n# print(category_sales)\r\n# sns.barplot(data=category_sales, x='Category', y='Sales', palette='pastel')\r\n# plt.title(\"Sales by Product Category\")\r\n# plt.ylabel(\"Total Sales ($)\")\r\n# plt.xlabel(\"Category\")\r\n# plt.tight_layout()\r\n# plt.show()\r\n\r\n# Plot 3: Sales by Region and Customer Type\r\n# Calculate cumulative sales\r\ndf_sorted = df.sort_values('Date')\r\ndf_sorted['Cumulative_Sales'] = df_sorted['Sales'].cumsum()\r\nprint(df_sorted )\r\n#Plot cumulative sales\r\n# plt.figure(figsize=(10, 5))\r\n# sns.lineplot(data=df_sorted, x='Date', y='Cumulative_Sales', marker='o', color='green')\r\n# plt.title(\"Cumulative Sales Over Time\")\r\n# plt.xlabel(\"Date\")\r\n# plt.ylabel(\"Cumulative Sales ($)\")\r\n# plt.xticks(rotation=45)\r\n# plt.tight_layout()\r\n# plt.show()\r\n\r\n#Box Plot of Sales Distribution by Category\r\nplt.figure(figsize=(10, 6))\r\nsns.boxplot(data=df, x='Category', y='Sales', palette='Set2')\r\nplt.title(\"Sales Distribution by Product Category\")\r\nplt.xlabel(\"Product Category\")\r\nplt.ylabel(\"Sales ($)\")\r\nplt.tight_layout()\r\nplt.show()<\/pre>\n<p>&nbsp;<span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:47,&quot;href&quot;:&quot;https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1_StmZ1jmHXa7Hci0-PT6Aq3M22LPVgYd\\\/view?usp=sharing&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251205112004\\\/https:\\\/\\\/drive.google.com\\\/file\\\/d\\\/1_StmZ1jmHXa7Hci0-PT6Aq3M22LPVgYd\\\/view?usp=sharing&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2026-01-18 09:33:11&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-01-27 09:22:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-02-10 21:47:50&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-10 14:01:57&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-22 06:15:06&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-21 05:07:59&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-29 02:17:41&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-18 00:30:27&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-18 00:30:27&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 E-Commerce Sales Dataset # E-Commerce Sales Visualization using Seaborn #This project analyzes e-commerce sales data using Seaborn and Matplotlib. # It visualizes trends over time, category-wise performance, regional patterns, # and customer&#46;&#46;&#46;<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[34737,34736,34735,8431,20697,34739,34738,33219,33220,34734],"class_list":["post-145657","post","type-post","status-publish","format-standard","hentry","category-machine-learning","tag-e-commerce-sales-visualization-project","tag-e-commerce-sales-visualization-using-seaborn","tag-e-commerce-sales-visualization","tag-machine-learning","tag-machine-learning-project","tag-seaborn-e-commerce-sales-visualization","tag-seaborn-e-commerce-sales-visualization-project","tag-seaborn-practical","tag-seaborn-program","tag-seaborn-project"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Seaborn Project - E-Commerce Sales Visualization - DataFlair<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/e-commerce-sales-visualization-using-seaborn\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Seaborn Project - E-Commerce Sales Visualization - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Program 1 E-Commerce Sales Dataset # 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