{"id":26331,"date":"2021-12-17T08:43:01","date_gmt":"2021-12-17T03:13:01","guid":{"rendered":"https:\/\/python-programs.com\/?p=26331"},"modified":"2021-12-17T08:43:01","modified_gmt":"2021-12-17T03:13:01","slug":"python-bar-plot-visualization-of-categorical-data","status":"publish","type":"post","link":"https:\/\/python-programs.com\/python-bar-plot-visualization-of-categorical-data\/","title":{"rendered":"Python Bar Plot: Visualization of Categorical Data"},"content":{"rendered":"<p>Data visualization allows us to analyze the data and examine the distribution of data in a pictorial way.<\/p>\n<p>We may use <strong>BarPlot<\/strong> to visualize the distribution of categorical data variables. They depict a discrete value distribution. As a result, it reflects a comparison of category values.<\/p>\n<p>The x-axis shows discrete values, whereas the y axis represents numerical values of comparison and vice versa.<\/p>\n<h4><strong>BarPlot with <\/strong>Matplotlib<\/h4>\n<p>The Python matplotlib package includes a number of functions for plotting data and understanding the distribution of data values.<\/p>\n<p>To construct a Bar plot with the matplotlib module, use the matplotlib.pyplot.bar() function.<\/p>\n<p><strong>Syntax:<\/strong><\/p>\n<pre>matplotlib.pyplot.bar(x, height, width, bottom, align)<\/pre>\n<p><strong>Parameters<\/strong><\/p>\n<p><strong>x:<\/strong> The barplot&#8217;s scalar x-coordinates<\/p>\n<p><strong>height:<\/strong> It is the height of the bars to be plotted.<\/p>\n<p><strong>width:<\/strong> This is optional. It is the width of the bars to be plotted.<\/p>\n<p><strong>bottom:<\/strong> It is the vertical baseline.<\/p>\n<p><strong>align:<\/strong> This is optional. It is the type of bar plot alignment.<\/p>\n<p><strong>Example:<\/strong><\/p>\n<p><strong>Approach:<\/strong><\/p>\n<ul>\n<li>Import matplotlib.pyplot module as plt using the import keyword.<\/li>\n<li>Give some random list(gadgets) as static input and store it in a variable.<\/li>\n<li>Give the other list(cost) as static input and store it in another variable.<\/li>\n<li>Pass the given two lists as the arguments to the plt.bar() function to get the barplot of those lists.<\/li>\n<li>Show the barplot of the given two lists using the show() function.<\/li>\n<li>The Exit of the Program.<\/li>\n<\/ul>\n<p><strong>Below is the implementation:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\"># Import matplotlib.pyplot module using the import keyword.\r\nimport matplotlib.pyplot as plt\r\n# Give some random list(gadgets) as static input and store it in a variable.\r\ngadgets = ['mobile', 'fridge', 'washingmachine', 'tab', 'headphones']\r\n# Give the other list(cost) as static input and store it in another variable.\r\ncosts = [15000, 20000, 18000, 5000, 2500]\r\n# Pass the given two lists as the arguments to the plt.bar() function\r\n# to get the barplot of those lists.\r\nplt.bar(gadgets, costs)\r\n# Show the barplot of the given two lists using the show() function.\r\nplt.show()\r\n<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p><strong>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0<img loading=\"lazy\" class=\"alignnone wp-image-26333\" src=\"https:\/\/python-programs.com\/wp-content\/uploads\/2021\/12\/plot-300x192.png\" alt=\"\" width=\"484\" height=\"310\" srcset=\"https:\/\/python-programs.com\/wp-content\/uploads\/2021\/12\/plot-300x192.png 300w, https:\/\/python-programs.com\/wp-content\/uploads\/2021\/12\/plot.png 387w\" sizes=\"(max-width: 484px) 100vw, 484px\" \/><\/strong><\/p>\n<h4><\/h4>\n<h4><strong>BarPlot with Seaborn Library<\/strong><\/h4>\n<p>Plots are mostly used to depict the relationship between variables. These variables can be entirely numerical or represent a category such as a group, class, or division. This article discusses categorical variables and how to visualize them using Python&#8217;s Seaborn package.<\/p>\n<p>Seaborn, in addition to being a statistical plotting toolkit, includes various default datasets.<\/p>\n<p>The Python <strong>Seaborn<\/strong> module is built on top of the Matplotlib module and provides us with some advanced functionalities for better data visualization.<\/p>\n<p><strong>Syntax:<\/strong><\/p>\n<pre>seaborn.barplot(x, y)<\/pre>\n<p><strong>Example:<\/strong><\/p>\n<p><strong>Approach:<\/strong><\/p>\n<ul>\n<li>\n<div>Import seaborn module using the import keyword.<\/div>\n<\/li>\n<li>\n<div><span style=\"font-size: 16px;\">Import\u00a0matplotlib.pyplot\u00a0module\u00a0as\u00a0plt\u00a0using\u00a0the\u00a0import\u00a0keyword.<\/span><\/div>\n<\/li>\n<li>\n<div>Import dataset using read_csv() function by passing the dataset name as an argument to it.<\/div>\n<\/li>\n<li>\n<div>Store it in a variable.<\/div>\n<\/li>\n<li>\n<div><span style=\"font-size: 16px;\">Pass the id, calories columns, and above dataset as the arguments to the\u00a0<\/span><span style=\"font-size: 16px;\">seaborn.barplot() function to get the barplot of those.<\/span><\/div>\n<\/li>\n<li>\n<div>Display the barplot of the using the show() function.<\/div>\n<\/li>\n<li>\n<div><span style=\"font-size: 16px;\">The Exit of the Program.<\/span><\/div>\n<\/li>\n<\/ul>\n<p><strong>Below is the implementation:<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\"># Import seaborn module using the import keyword.\r\nimport seaborn\r\n# Import matplotlib.pyplot module as plt using the import keyword.\r\nimport matplotlib.pyplot as plt\r\n# Import dataset using read_csv() function by passing the dataset name as\r\n# an argument to it.\r\n# Store it in a variable.\r\ndummy_dataset = pd.read_csv('dummy_data.csv')\r\n# Pass the id, calories columns and above dataset as the arguments to the \r\n# seaborn.barplot() function to get the barplot of those.\r\nseaborn.barplot(x=\"id\", y=\"calories\", data=dummy_dataset)\r\n# Display the barplot of the using the show() function.\r\nplt.show()\r\n<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<p><img loading=\"lazy\" class=\"alignnone wp-image-26336\" src=\"https:\/\/python-programs.com\/wp-content\/uploads\/2021\/12\/bar-300x202.png\" alt=\"\" width=\"510\" height=\"344\" srcset=\"https:\/\/python-programs.com\/wp-content\/uploads\/2021\/12\/bar-300x202.png 300w, https:\/\/python-programs.com\/wp-content\/uploads\/2021\/12\/bar.png 389w\" sizes=\"(max-width: 510px) 100vw, 510px\" \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data visualization allows us to analyze the data and examine the distribution of data in a pictorial way. We may use BarPlot to visualize the distribution of categorical data variables. They depict a discrete value distribution. As a result, it reflects a comparison of category values. The x-axis shows discrete values, whereas the y axis &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/python-programs.com\/python-bar-plot-visualization-of-categorical-data\/\"> <span class=\"screen-reader-text\">Python Bar Plot: Visualization of Categorical Data<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true},"categories":[5],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python Bar Plot: Visualization of Categorical Data - Python Programs<\/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:\/\/python-programs.com\/python-bar-plot-visualization-of-categorical-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python Bar Plot: Visualization of Categorical Data - Python Programs\" \/>\n<meta property=\"og:description\" content=\"Data visualization allows us to analyze the data and examine the distribution of data in a pictorial way. We may use BarPlot to visualize the distribution of categorical data variables. They depict a discrete value distribution. As a result, it reflects a comparison of category values. 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