{"id":8801,"date":"2024-01-23T06:00:57","date_gmt":"2024-01-23T14:00:57","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=8801"},"modified":"2025-04-24T17:03:28","modified_gmt":"2025-04-24T17:03:28","slug":"different-plots-used-in-exploratory-data-analysis-eda","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda\/","title":{"rendered":"Different Plots Used in Exploratory Data Analysis\u00a0(EDA)"},"content":{"rendered":"\n<p class=\"graf graf--p\">The importance of EDA in the machine learning world is well known to its users. Making visualizations is one of the finest ways for data scientists to explain data analysis to people outside the business. Exploratory data analysis can help you comprehend your data better, which can aid in future data preprocessing. Additionally, data visualization is essential for streamlining the exploratory data analysis process and facilitating data analysis utilizing beautiful plots and charts.<\/p>\n\n\n\n<p class=\"graf graf--p\">The EDA, the first chance for visualizations, will be the main topic of this article.<\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*zgBaYdd_NCYmkrp0\" alt=\"EDA graphic\"\/><figcaption class=\"wp-element-caption\">Exploratory Data&nbsp;Analysis<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading graf graf--h3\"><strong class=\"markup--strong markup--h3-strong\">What is&nbsp;EDA?&nbsp;<\/strong><\/h3>\n\n\n\n<p class=\"graf graf--p\">Exploratory Data Analysis (EDA) is a method for analyzing and summarizing data, frequently using visual tools. EDA is the first step in every machine learning problem solution. It is most likely the crucial component of a machine learning project.<\/p>\n\n\n\n<h3 class=\"wp-block-heading graf graf--h3\">Why is EDA Important?<\/h3>\n\n\n\n<p class=\"graf graf--p\">Given its exponential growth, business organizations seek to utilize data properly so they may use it to inform their strategic decisions. Most datasets are presented as tables; unless you are superhuman, it is impossible to understand data when there are many records, like a million. The data scientist steps in at this point to help us identify the underlying patterns in the graphs and provide answers to our inquiries.<\/p>\n\n\n\n<h3 class=\"wp-block-heading graf graf--h3\">Different Plots Used in&nbsp;EDA<\/h3>\n\n\n\n<p class=\"graf graf--p\">Let\u2019s now explore data and examine various data visualization charts using different data kinds. Additionally, an integrated dataset of Seaborn tips data, which describes the tips each waiter receives from multiple customers, will be used to demonstrate some of the strategies.<\/p>\n\n\n\n<p class=\"graf graf--p\">Let\u2019s start by importing all libraries:<\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*HuJRrUMCHVagavEx\" alt=\"code screenshot importing libraries\"\/><figcaption class=\"wp-element-caption\">Importing libraries<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading graf graf--h3\"><strong class=\"markup--strong markup--h3-strong\">Different Analysis Used in&nbsp;EDA<\/strong><\/h3>\n\n\n\n<p class=\"graf graf--p\">Exploratory Data Analysis typically uses the following techniques:<\/p>\n\n\n\n<ul class=\"wp-block-list postList\">\n<li><strong class=\"markup--strong markup--li-strong\">Univariate Visualization:<\/strong> It gives information about the fields of raw data.<\/li>\n\n\n\n<li><strong class=\"markup--strong markup--li-strong\">Bivariate Visualization<\/strong>: It is used to determine the link between each variable in the dataset and the target variable of interest<\/li>\n\n\n\n<li><strong class=\"markup--strong markup--li-strong\">Multivariate Visualization<\/strong>: It is used to comprehend relationships between several fields in the dataset<\/li>\n\n\n\n<li><strong class=\"markup--strong markup--li-strong\">Dimensionality Reduction:<\/strong> Although we can visualize in three dimensions, data usually has more dimensions. In these situations, dimension reduction is helpful for visualizations. Ee can compress the dimensions to 2D or 3D using <a class=\"markup--anchor markup--li-anchor\" data-href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.decomposition.PCA.html\" href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.decomposition.PCA.html\" target=\"_blank\" rel=\"noopener\">PCA <\/a>and <a class=\"markup--anchor markup--li-anchor\" data-href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.manifold.TSNE.html?highlight=tsne#sklearn.manifold.TSNE\" href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.manifold.TSNE.html?highlight=tsne#sklearn.manifold.TSNE\" target=\"_blank\" rel=\"noopener\">TSNE<\/a>.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading graf graf--h4\"><strong class=\"markup--strong markup--h4-strong\">Univariate Analysis<\/strong><\/h4>\n\n\n\n<p class=\"graf graf--p\">The simplest type of analysis, known as a univariate analysis, focuses on just one variable and is used to explain the data more accurately. We do the univariate analysis of numerical and categorical variables differently.<\/p>\n\n\n\n<h4 class=\"wp-block-heading graf graf--h4\"><strong class=\"markup--strong markup--h4-strong\">Numerical Data<\/strong><\/h4>\n\n\n\n<p class=\"graf graf--p\">Understanding the distribution of variables allows for more effective data processing, making it crucial to analyze numerical data. Most of the time, you will find that numerical data could be more consistent; therefore, investigate numerical factors.<\/p>\n\n\n\n<ol class=\"wp-block-list postList\">\n<li><strong class=\"markup--strong markup--li-strong\">Histogram: <\/strong>They are used in univariate analysis and provide an approximate indication of the underlying data distribution\u2019s density or, more precisely, its probability distribution. The histogram plotting depends on \u201cbins,\u201d or dividing the entire range into intervals. The bar height for each bin is then calculated based on the number of values inside that bin\u2019s range.<\/li>\n<\/ol>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*ZGfA-LsGr0pRQwY8\" alt=\"code screenshot plotting histogram\"\/><figcaption class=\"wp-element-caption\">Plotting histogram<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Output<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*G72mvWfq0_d4kU3K\" alt=\"histogram\"\/><figcaption class=\"wp-element-caption\">Output of histogram<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Usage of Histogram:<\/strong> It is generally used when dealing with large datasets and to detect any unusual activities or gaps in the data.<\/p>\n\n\n\n<p class=\"graf graf--p\">2. <strong class=\"markup--strong markup--p-strong\">Distplot<\/strong>: A distplot is a somewhat improved version of the histogram. It is often known as the \u201csecond histogram.\u201d It provides a <a class=\"markup--anchor markup--p-anchor\" href=\"https:\/\/scikit-learn.org\/stable\/modules\/density.html?highlight=kde\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/scikit-learn.org\/stable\/modules\/density.html?highlight=kde\">KDE <\/a>(Kernel Density Estimation) over the histogram that describes the PDF (Probability Density Function) or the likelihood that each value in this column will occur.<\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*bQj_Urtwx0lYG9Z2\" alt=\"code screenshot plotting dist. plot\"\/><figcaption class=\"wp-element-caption\">Plotting Distplot<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Output<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*hDRN4qx0nkGJHNAe\" alt=\"bar chart of age vs density\"\/><figcaption class=\"wp-element-caption\">Output of&nbsp;Distplot<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">3. Boxplot: <\/strong>An intriguing boxplot plots a five-number summary. We must define some terms to obtain a five-number summary.<\/p>\n\n\n\n<ul class=\"wp-block-list postList\">\n<li>Median<\/li>\n\n\n\n<li>Percentile\u200a\u2014\u200aProvides any number representing the total number of values below this percentile.<\/li>\n\n\n\n<li>Minimum and Maximum\u200a\u2014\u200aThese are the lower and upper limits of the standard deviation, determined using the interquartile range rather than the minimum and maximum values (IQR).<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*yp3DSi1d6MlK5qBb\" alt=\"code screenshot plotting box plot\"\/><figcaption class=\"wp-element-caption\">Plotting Boxplot<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Output<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*O_JuC2QxPmi0QMDc\" alt=\"box plot output\"\/><figcaption class=\"wp-element-caption\">Output of&nbsp;Boxplot<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Usage of Boxplot<\/strong>: Boxplots are helpful as they show the average score of a data set. It is beneficial to determine whether a distribution is skewed and whether a dataset contains any potential outliers or odd data values.<\/p>\n\n\n\n<h4 class=\"wp-block-heading graf graf--h4\"><strong class=\"markup--strong markup--h4-strong\">Categorical Data<\/strong><\/h4>\n\n\n\n<p class=\"graf graf--p\">Categorical variables are variables that contain information that can be expressed in text. Let\u2019s examine some charts that can be used to display categorical data.<\/p>\n\n\n\n<ol class=\"wp-block-list postList\">\n<li><strong class=\"markup--strong markup--li-strong\">CountPlot: <\/strong>A CountPlot is a frequency count plot presented as a bar graph. Each category\u2019s count is plotted as a separate bar. The value counts function in Pandas takes on the exact visual representation when used in any column.<\/li>\n<\/ol>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*j7z2Zw_GOAixNcx_\" alt=\"code screenshot\"\/><figcaption class=\"wp-element-caption\">Plotting CountPlot<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Output:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*BdgAMYyiSCrNl1Wl\" alt=\"bar chart of survivors\"\/><figcaption class=\"wp-element-caption\">Output of CountPlot<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">2. Pie Chart: <\/strong>The pie chart is identical to the CountPlot, but it additionally provides information about the percentage of each category included in the data, which indicates which category is receiving how much weight in the data.<\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*g0j1hn4i9VO19cmy\" alt=\"code screenshot plotting pie chart\"\/><figcaption class=\"wp-element-caption\">Plotting Pie&nbsp;Chart<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Output<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*iMrZIaTfD074zsfp\" alt=\"pie chart of male and female\"\/><figcaption class=\"wp-element-caption\">Output of Pie&nbsp;Chart<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Usage of Pie Chart:<\/strong> Pie charts are usually used to represent categorical data\u2014for example, comparisons in growth areas within a business, such as profit, market expenses, etc.<\/p>\n\n\n\n<h4 class=\"wp-block-heading graf graf--h4\"><strong class=\"markup--strong markup--h4-strong\">Multivariate Analysis<\/strong><\/h4>\n\n\n\n<p class=\"graf graf--p\">When it\u2019s necessary to investigate the link between two distinct variables, we utilize bivariate analysis. In the end, this is what we need to accomplish to develop a robust model. Multivariate analysis is the process of combining more than two variables for analysis.<\/p>\n\n\n\n<p class=\"graf graf--p\">1. <strong class=\"markup--strong markup--p-strong\">Scatter Plot<\/strong>: Graphs that show the relationship between two variables in a data set are called scatter plots. It displays data points either on a Cartesian system or a two-dimensional plane. The X-axis is used to represent the independent variable or attribute, while the Y-axis is used to plot the dependent variable.<\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*brj1mYivLJUaNXVU\" alt=\"code screenshot to plot scatterplot\"\/><figcaption class=\"wp-element-caption\">Plotting Scatter&nbsp;Plot<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Output:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*41BFtEwzI6XF2QdE\" alt=\"scatter plot output \"\/><figcaption class=\"wp-element-caption\">Output of Scatter&nbsp;Plot<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\">We can also visualize relationships involving three or four variables with a scatter plot.<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Usage of Scatter Plot:<\/strong> It is used to analyze individual points, observe and visualize relationships between variables, or get a general overview of variables.<\/p>\n\n\n\n<p class=\"graf graf--p\">2. <strong class=\"markup--strong markup--p-strong\">Bar Plot: <\/strong>A bar plot is a straightforward diagram that may study the relationship between two variables by plotting a numerical variable on the y-axis and a category variable on the x-axis.<\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*lrZCE1ZXMG0025Dd\" alt=\"code screenshot\"\/><figcaption class=\"wp-element-caption\">Plotting bar&nbsp;plot<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Output<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-image graf graf--figure\"><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*4_HvT2SO4bB7UTkp\" alt=\"bar chart of students and courses\"\/><figcaption class=\"wp-element-caption\">Output of bar&nbsp;plot<\/figcaption><\/figure>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p class=\"graf graf--p\"><strong class=\"markup--strong markup--p-strong\">Usage of Bar Plot:<\/strong> It can be used when comparing variables belonging to the same category or observing how one or more variables change over time. For instance, a bar plot is best for comparing a student\u2019s marks across various topics.<\/p>\n\n\n\n<p class=\"graf graf--p\">EDA is simple to use, which makes it simple for others to grasp our analysis. It is merely a tool to help you better comprehend and describe your data, which enables you to create a robust and more comprehensive model.<\/p>\n\n\n\n<p class=\"graf graf--p\">Approaching the end of the article, we have learned what EDA is, why it is used, why it is essential, and how to plot different plots using Matplotlib or Seaborn.<\/p>\n\n\n\n<p class=\"graf graf--p\">If I missed an important detail or you wish to add anything to this blog post, please feel free to ping me. I look forward to hearing your feedback; that\u2019s how we learn \ud83e\udd17<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The importance of EDA in the machine learning world is well known to its users. Making visualizations is one of the finest ways for data scientists to explain data analysis to people outside the business. Exploratory data analysis can help you comprehend your data better, which can aid in future data preprocessing. Additionally, data visualization [&hellip;]<\/p>\n","protected":false},"author":119,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[6],"tags":[],"coauthors":[216],"class_list":["post-8801","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Different Plots Used in Exploratory Data Analysis\u00a0(EDA) - Comet<\/title>\n<meta name=\"description\" content=\"Learn what EDA is, why it is used, why it is essential, and how to plot different plots using Matplotlib or Seaborn.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Different Plots Used in Exploratory Data Analysis\u00a0(EDA)\" \/>\n<meta property=\"og:description\" content=\"Learn what EDA is, why it is used, why it is essential, and how to plot different plots using Matplotlib or Seaborn.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda\" \/>\n<meta property=\"og:site_name\" content=\"Comet\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cometdotml\" \/>\n<meta property=\"article:published_time\" content=\"2024-01-23T14:00:57+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-24T17:03:28+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*zgBaYdd_NCYmkrp0\" \/>\n<meta name=\"author\" content=\"Sonia Singla\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@Cometml\" \/>\n<meta name=\"twitter:site\" content=\"@Cometml\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Sonia Singla\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"9 minutes\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Different Plots Used in Exploratory Data Analysis\u00a0(EDA) - Comet","description":"Learn what EDA is, why it is used, why it is essential, and how to plot different plots using Matplotlib or Seaborn.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda","og_locale":"en_US","og_type":"article","og_title":"Different Plots Used in Exploratory Data Analysis\u00a0(EDA)","og_description":"Learn what EDA is, why it is used, why it is essential, and how to plot different plots using Matplotlib or Seaborn.","og_url":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda","og_site_name":"Comet","article_publisher":"https:\/\/www.facebook.com\/cometdotml","article_published_time":"2024-01-23T14:00:57+00:00","article_modified_time":"2025-04-24T17:03:28+00:00","og_image":[{"url":"https:\/\/cdn-images-1.medium.com\/max\/800\/0*zgBaYdd_NCYmkrp0","type":"","width":"","height":""}],"author":"Sonia Singla","twitter_card":"summary_large_image","twitter_creator":"@Cometml","twitter_site":"@Cometml","twitter_misc":{"Written by":"Sonia Singla","Est. reading time":"9 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda#article","isPartOf":{"@id":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda\/"},"author":{"name":"Sonia Singla","@id":"https:\/\/www.comet.com\/site\/#\/schema\/person\/63a3bdec26a88b394736d77b9f74aac3"},"headline":"Different Plots Used in Exploratory Data Analysis\u00a0(EDA)","datePublished":"2024-01-23T14:00:57+00:00","dateModified":"2025-04-24T17:03:28+00:00","mainEntityOfPage":{"@id":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda\/"},"wordCount":1187,"publisher":{"@id":"https:\/\/www.comet.com\/site\/#organization"},"image":{"@id":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda#primaryimage"},"thumbnailUrl":"https:\/\/cdn-images-1.medium.com\/max\/800\/0*zgBaYdd_NCYmkrp0","articleSection":["Machine Learning"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda\/","url":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda","name":"Different Plots Used in Exploratory Data Analysis\u00a0(EDA) - Comet","isPartOf":{"@id":"https:\/\/www.comet.com\/site\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda#primaryimage"},"image":{"@id":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda#primaryimage"},"thumbnailUrl":"https:\/\/cdn-images-1.medium.com\/max\/800\/0*zgBaYdd_NCYmkrp0","datePublished":"2024-01-23T14:00:57+00:00","dateModified":"2025-04-24T17:03:28+00:00","description":"Learn what EDA is, why it is used, why it is essential, and how to plot different plots using Matplotlib or Seaborn.","breadcrumb":{"@id":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda#primaryimage","url":"https:\/\/cdn-images-1.medium.com\/max\/800\/0*zgBaYdd_NCYmkrp0","contentUrl":"https:\/\/cdn-images-1.medium.com\/max\/800\/0*zgBaYdd_NCYmkrp0"},{"@type":"BreadcrumbList","@id":"https:\/\/www.comet.com\/site\/blog\/different-plots-used-in-exploratory-data-analysis-eda#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.comet.com\/site\/"},{"@type":"ListItem","position":2,"name":"Different Plots Used in Exploratory Data Analysis\u00a0(EDA)"}]},{"@type":"WebSite","@id":"https:\/\/www.comet.com\/site\/#website","url":"https:\/\/www.comet.com\/site\/","name":"Comet","description":"Build Better Models Faster","publisher":{"@id":"https:\/\/www.comet.com\/site\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.comet.com\/site\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.comet.com\/site\/#organization","name":"Comet ML, Inc.","alternateName":"Comet","url":"https:\/\/www.comet.com\/site\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.comet.com\/site\/#\/schema\/logo\/image\/","url":"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2025\/01\/logo_comet_square.png","contentUrl":"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2025\/01\/logo_comet_square.png","width":310,"height":310,"caption":"Comet ML, Inc."},"image":{"@id":"https:\/\/www.comet.com\/site\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/cometdotml","https:\/\/x.com\/Cometml","https:\/\/www.youtube.com\/channel\/UCmN63HKvfXSCS-UwVwmK8Hw"]},{"@type":"Person","@id":"https:\/\/www.comet.com\/site\/#\/schema\/person\/63a3bdec26a88b394736d77b9f74aac3","name":"Sonia Singla","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.comet.com\/site\/#\/schema\/person\/image\/dda5d9cc592c97ca150365d3021c755b","url":"https:\/\/secure.gravatar.com\/avatar\/297ae94a761fb627f997554ad1b95a278ce72a2970a782656e84d927b7505fa6?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/297ae94a761fb627f997554ad1b95a278ce72a2970a782656e84d927b7505fa6?s=96&d=mm&r=g","caption":"Sonia Singla"},"url":"https:\/\/www.comet.com\/site\/blog\/author\/soniasingla-1812gmail-com\/"}]}},"_links":{"self":[{"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/posts\/8801","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/users\/119"}],"replies":[{"embeddable":true,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/comments?post=8801"}],"version-history":[{"count":1,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/posts\/8801\/revisions"}],"predecessor-version":[{"id":15400,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/posts\/8801\/revisions\/15400"}],"wp:attachment":[{"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/media?parent=8801"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/categories?post=8801"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/tags?post=8801"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.comet.com\/site\/wp-json\/wp\/v2\/coauthors?post=8801"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}