{"id":26433,"date":"2021-03-19T12:07:00","date_gmt":"2021-03-19T06:37:00","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/understanding-box-plot\/"},"modified":"2024-09-03T14:53:34","modified_gmt":"2024-09-03T09:23:34","slug":"understanding-box-plot","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/understanding-box-plot\/","title":{"rendered":"Understanding BoxPlot | What is BoxPlot"},"content":{"rendered":"\n<p>A single box which gives you a visual idea about 5 components in a dataset. It is also known as box and whiskers plot or simply box plot. It is useful for describing measures of central tendencies and <a href=\"https:\/\/www.mygreatlearning.com\/academy\/learn-for-free\/courses\/measures-of-dispersion\" target=\"_blank\" rel=\"noreferrer noopener\">measures of dispersion <\/a>in a dataset.&nbsp;<\/p>\n\n\n\n<p><em>Contributed by: <a href=\"https:\/\/www.linkedin.com\/in\/avantika-shukla-79970a44\" target=\"_blank\" rel=\"nofollow noreferrer noopener\">Avantika Shukla<\/a><\/em><\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"box-plot-represents-the-following-points-in-a-dataset\"><strong>Box Plot represents the following points in a dataset.<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Minimum Value<\/li>\n\n\n\n<li>First Quartile (Q1 or 25<sup>th<\/sup> Percentile)<\/li>\n\n\n\n<li>Second Quartile (Q2 or 50<sup>th<\/sup> Percentile)<\/li>\n\n\n\n<li>Third Quartile (Q3 or 75<sup>th<\/sup> Percentile)<\/li>\n\n\n\n<li>Maximum Value<\/li>\n<\/ul>\n\n\n<figure class=\"wp-block-image size-large zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-2.png\"><img decoding=\"async\" width=\"696\" height=\"155\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-2.png\" alt=\"Box Plot\" class=\"wp-image-26434\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-2.png 696w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-2-300x67.png 300w\" sizes=\"(max-width: 696px) 100vw, 696px\" \/><\/figure>\n\n\n\n<p>Along with the above 5 components. Boxplot also gives us below information:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Outliers: Points lying beyond the minimum and maximum values are outliers<\/li>\n\n\n\n<li>Interquartile range: It is Q3-Q1. It is the spread or range of the middle 50% of the data.<\/li>\n\n\n\n<li>Whiskers: From Minimum Value to Q1 is the first 25% of data<br>From Q3 to Maximum value is the last 25% of the data<\/li>\n<\/ul>\n\n\n\n<p>Let us understand box plot with an example. Suppose you have a dataset of runs scored by a batsman in his 12 matches. You arrange the dataset in descending order. Divide the dataset into 4 equal parts. Now how to find out 3 percentiles? 25<sup>th<\/sup> percentile, 50<sup>th<\/sup> percentile and 75<sup>th<\/sup> percentile of the boxplot. Percentile is the number below which a given percentage falls or you can also apply the below formula to find out the percentile.<\/p>\n\n\n\n<p>Position of the number, for given percentile (Pn) =Percentile(N+1)\/100<\/p>\n\n\n\n<p>N= No. of items in the dataset<\/p>\n\n\n\n<p>If the above result comes in float (in decimals) then, take the mean of 2 numbers of Pn and P(n+1)<\/p>\n\n\n\n<p>If the result comes in integer, then take the value of Pn<\/p>\n\n\n\n<p>In our example N = 12<\/p>\n\n\n\n<p>Position number for 25<sup>th<\/sup> percentile= 25(12+1)\/100= 3.25<\/p>\n\n\n\n<p>The result is a decimal number, we will take mean of 3<sup>rd<\/sup> and 4<sup>th<\/sup> number<\/p>\n\n\n\n<p>Position number for 50<sup>th<\/sup> percentile= 50(12+1)\/100= 6.5<\/p>\n\n\n\n<p>The result is a decimal number, we will take mean of 6<sup>th<\/sup> and 7<sup>th<\/sup> number<\/p>\n\n\n\n<p>Position number for 50<sup>th<\/sup> percentile= 75(12+1)\/100= 9.75<\/p>\n\n\n\n<p>The result is a decimal number, we will take mean of 9<sup>th<\/sup> and 10<sup>th<\/sup> number<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"let-us-visualize-it\"><strong>Let us visualize it:<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Q1 or 25<sup>th<\/sup> percentile = Number below which 25% is falling&nbsp;<\/li>\n\n\n\n<li>Q2 or 50<sup>th<\/sup> percentile = Number below which 50% is falling&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>Q3 or 75<sup>th<\/sup> percentile = Number below which 75% is falling<\/p>\n\n\n<figure class=\"wp-block-image size-large zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-3.png\"><img decoding=\"async\" width=\"772\" height=\"241\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-3.png\" alt=\"Box Plot\" class=\"wp-image-26435\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-3.png 772w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-3-300x94.png 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-3-768x240.png 768w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-3-696x217.png 696w\" sizes=\"(max-width: 772px) 100vw, 772px\" \/><\/figure>\n\n\n\n<p>Q1=17<\/p>\n\n\n\n<p>Q2=19.5<\/p>\n\n\n\n<p>Q3=24<\/p>\n\n\n\n<p>Interquartile range (IQR)=Q3-Q1=24-17=7<\/p>\n\n\n\n<p>Minimum Value= Q1-1.5 * IQR=17-1.5*7=6.5<\/p>\n\n\n\n<p>Maximum Value= Q3 +1.5 * IQR=24+1.5*7=34.5<\/p>\n\n\n\n<p>Outliers of the dataset= 5 &amp; 64<\/p>\n\n\n\n<p>Let us impose all the points of the boxplot on a number line:<\/p>\n\n\n<figure class=\"wp-block-image size-large zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-4.png\"><img decoding=\"async\" width=\"759\" height=\"266\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-4.png\" alt=\"Box Plot\" class=\"wp-image-26436\" srcset=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-4.png 759w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-4-300x105.png 300w, https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-4-696x244.png 696w\" sizes=\"(max-width: 759px) 100vw, 759px\" \/><\/figure>\n\n\n\n<p>Boxplot also tells us about the distribution and symmetry of the data. The above example shows the data is right skewed. Interquartile range shows us that the middle 50% of the data lies between 17 runs to 24 runs. Whiskers of the box plot cover approximately 99.65% of the data.&nbsp;<\/p>\n\n\n\n<p>Also Read: <a href=\"https:\/\/www.mygreatlearning.com\/blog\/python-tutorial-for-beginners-a-complete-guide\/\" target=\"_blank\" rel=\"noreferrer noopener\">Python Tutorial For Beginners \u2013 A Complete Guide | Learn Python Easily<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"graphing-a-boxplot-using-python\">Graphing a boxplot using Python<\/h2>\n\n\n\n<p>Read the data.<\/p>\n\n\n\n<p>The code below will import all the necessary libraries and will read the data:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n#Read the dataset RunScored.xlsx\ndf=pd.read_excel(&#039;RunScored.xlsx&#039;)\n#Read top 5 rows\ndf.head()\n\n<\/pre><\/div>\n\n<figure class=\"wp-block-image size-large zoomable\" data-full=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-5.png\"><img decoding=\"async\" width=\"214\" height=\"165\" src=\"https:\/\/www.mygreatlearning.com\/blog\/wp-content\/uploads\/2021\/03\/image-5.png\" alt=\"Box Plot\" class=\"wp-image-26438\"><\/figure>\n\n\n\n<p>Let us check the dimension of the data.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n#Check the dimensions of the data\ndf.shape\n<\/pre><\/div>\n\n\n<p><\/p>\n\n\n\n<p>The above result tells us that there are 12 rows and 1 column. This dataset contains runs scored in 12 matches.<\/p>\n\n\n\n<p>Now let us see how to make boxplot with seaborn library:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n#Plot boxplot with seaborn\nsns.boxplot(x = &#039;RunsScored&#039;,color = &#039;violet&#039;,data = df);\n\n<\/pre><\/div>\n\n\n<p><\/p>\n\n\n\n<p>The above result shows the player A has a median of 19.5, 2 outliers are also present in data. The middle 50% spread is between 17 to 24.<\/p>\n\n\n\n<p>Now, let us compare runs scored by 2 players (Player A and Player B) through box plot.<\/p>\n\n\n\n<p>Read the data:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n#Reading 2nd database with 2 players to compare their runs scored using boxplots\ndf=pd.read_excel(&#039;RunScored2player.xlsx&#039;)\n#Reading top 5 rows of the dataset \ndf.head()\n\n<\/pre><\/div>\n\n\n<p><\/p>\n\n\n\n<p>Let us see the dimension of the dataset.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n#Check the dimensions of the data\ndf.shape\n\n\n<\/pre><\/div>\n\n\n<p><\/p>\n\n\n\n<p>The above result tells us that there are 12 rows and 2 columns. This dataset contains runs scored in 12 matches.<\/p>\n\n\n\n<p>Let us plot the boxplot of runs scored by 2 players:<\/p>\n\n\n\n<p>By the above plot we can deduce that the median of player B is greater than player A. IQR tells us the spread of Player B for middle 50% is higher. Even the Q3 and maximum value of player B is higher.<\/p>\n\n\n\n<p>If you want to display the median on your boxplot you can make use of function box.annotate().<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nmedians=&#x5B;df.RunsScoredPlayerA.median(),df.RunsScoredPlayerB.median()]\n \nbox=sns.boxplot(data=df);\nfor i in range(2):\n    box.annotate(str(medians&#x5B;i]),xy=(i,medians&#x5B;i]),horizontalalignment=&#039;center&#039;);\n\n<\/pre><\/div>\n\n\n<p><\/p>\n\n\n\n<p>The above result shows the median of player A is 19.5 on the other hand median of player B is 25.<\/p>\n\n\n\n<p>Similarly, we can also display Q1, Q3, Minimum value and Maximum value on the boxplot.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nQ1=&#x5B;df.RunsScoredPlayerA.quantile(.25),df.RunsScoredPlayerB.quantile(.25)]\nQ3=&#x5B;df.RunsScoredPlayerA.quantile(.75),df.RunsScoredPlayerB.quantile(.75)]\nIQR=&#x5B;(Q3&#x5B;0]-Q1&#x5B;0]),(Q3&#x5B;1]-Q1&#x5B;1])]\nMin_value=&#x5B;(Q1&#x5B;0]-1.5*IQR&#x5B;0]),(Q1&#x5B;1]-1.5*IQR&#x5B;1])]\nMax_value=&#x5B;(Q3&#x5B;0]+1.5*IQR&#x5B;0]),(Q3&#x5B;1]+1.5*IQR&#x5B;1])]\n\nbox=sns.boxplot(data=df);\nfor i in range(2):\n    box.annotate(str(medians&#x5B;i]),xy=(i,medians&#x5B;i]),horizontalalignment=&#039;center&#039;);\n    box.annotate(str(Q1&#x5B;i]),xy=(i,Q1&#x5B;i]),horizontalalignment=&#039;center&#039;);\n    box.annotate(str(Q3&#x5B;i]),xy=(i,Q3&#x5B;i]),horizontalalignment=&#039;center&#039;);\n    box.annotate(str(Min_value&#x5B;i]),xy=(i,Min_value&#x5B;i]),horizontalalignment=&#039;center&#039;);\n    box.annotate(str(Max_value&#x5B;i]),xy=(i,Max_value&#x5B;i]),horizontalalignment=&#039;center&#039;);\n\n\n<\/pre><\/div>\n\n\n<p><\/p>\n\n\n\n<p>Displaying values on the boxplot gives a more clear idea.&nbsp;<\/p>\n\n\n\n<p>This brings us to the end of the blog on Understanding boxplot. Hope this helps you gain a better understanding of the same. If you wish to learn more such concepts, head over to <a href=\"https:\/\/www.mygreatlearning.com\/academy\" target=\"_blank\" rel=\"noreferrer noopener\">Great Learning Academy<\/a> and choose from a plethora of free online courses. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>A single box which gives you a visual idea about 5 components in a dataset. It is also known as box and whiskers plot or simply box plot. It is useful for describing measures of central tendencies and measures of dispersion in a dataset.&nbsp; Contributed by: Avantika Shukla Box Plot represents the following points in [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":26446,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[9],"tags":[],"content_type":[],"class_list":["post-26433","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Understanding Box Plot - What does it mean? | What is BoxPlot?<\/title>\n<meta name=\"description\" content=\"What is BoxPlot: A single box which gives you a visual idea about 5 components in a dataset. 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