box-plots-finding-all-outliers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "source": [
    "* # Box-Plots: Finding Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook presents a quick way of finding the values of all outliers appearing in box-plots."
   ]
  },
  {
   "attachments": {
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xR8fPH/i39ojx+7Xupx2Nr4U0eZ1IQiGGMXEqA9CdseSvBeWfpyK6b4xwyN/wWO+BUgRjGPBlzlsHH+q1jv8AiPzFfflFAH54f8FGNFuvC/7SHwS+KXirwre+MvhD4eEkWsWdtAbmO1mLlvNkiPy4IMTfNw3kbSemfN/2/f2vfDv7R37NOo6D8KfC+u614V024tb7WPFEmnPZ6dp8azLHHCu8AvI0kiDaAMDJGQDt/VeigD46/am/aT1H4D/sy+BNA8IRS3nxQ8a6faaP4cs7dd0qSNDEsk4Hqm9Qv+26cEBq+dvit+xPr/7G3wj+Hfxo+Hc8l78S/ArfbvFzJI8i6pFKc3HHUxx7mjOACYmZjgrX6nUUAfD37a/xa0b4+f8ABMHxV468Nln03WLXTJxE3LwOuqWqyxPj+JHV1Pb5SelaPxEjc/8ABJW1UKxYfDDS8rjkYsoM19oUUAeB/sFAr+xz8JsjH/EjiP6tXz9/wTljeP8Aam/bP3Iy58ZqRuGP+X3VT/Ij86+/aKAPgb9uiGSX9uL9kUIjOf7YnPyjPAntiT+A5rY/4Kl/D3xZqnhD4a/Ebwlo03iG4+H3iBdVu9Nt0Z5GhJjfzNqgkqrQoGwCQHLYwpI+4aKAPz9+I3/BQ60/aG+EviHwp8FfAHiXxN4p1bSri11B9Sshb2WiQvCwme4l3lSyqW2qDhiByeFb0X/glGrL+xJ4N3KRm71EjI7fbJa+vaKAPgL/AIIsRvD+y34qV0ZGHjO7BDDB4srEfzqby3H/AAWMBKsAfA+4HHbZjP58V970UAfDP/BY1Gf9ka3CqWP/AAktl0Gf+Wc9fafhdSnhrSVYFWFpCCCMEfIK06KACiiigAooooAKKKKACiiigAooooAKKKKAPKv2af8AknWr/wDY5+LP/Uh1GvVa8q/Zp/5J1q//AGOfiz/1IdRr1WgAryr9rH/k1n4yf9iZrP8A6QzV6rXlX7WP/JrPxk/7EzWf/SGagD1WiiigAooooAKKKKACiiigAoqK6uYrO3luJ5FhgiQySSOcKqgZJJ9AK+ef2M/2jPE37UGj+OPGF7penaX4Hi1yTTfC32eKVbu5t4s7552ZyrbtyABFXaVkB3YBoA+i6K5j4hfE7wl8JvD7654y8Rad4a0lW2fatSuFiVm7KuTlm/2VBPtXh+j/APBST9m3XNYGmW3xSsI7ksU8y8sby1gyDj/XSwrHj33Yxz0oA+l6KrabqdnrWn21/p93BfWNzGssF1bSLJFKhGQyspIYEdCKi1zXNO8M6Pearq9/baXpdnE01ze3kqxQwxgZLO7EBQPUmgC9RXnXwX/aF+H37Q2l6pqPw98RR+I7LTLr7Hdyx208Ajl2hgAJUQsCDkMuVPrXotABRRRQAUUVk+K/Fui+BfD19r3iLVbTRdGsYzLc319MsUUS+pYnHsB3JAFAGtRXBfBn47eBv2hPCtx4k+H+ur4h0W3vHsJblbaa32ToiOyFZURvuyIc4wdw5rvaACisHx1440P4aeD9X8U+Jb4aZoOk27XV7eNG8nlRr1O1AWb6KCT2FfOf/D0b9mL/AKKZ/wCUDVP/AJGoA+qqK4n4QfGjwZ8evBqeKvAmtLr2gvPJbC6W3lgIkQjcpSVFcEZHUcggivH/AI5ftpeGPCPhf4t6Z4H1zSdS+J/gOwN7LoWqRS7HCqjyFQGQyqqFt3lt8hHzY7gH0tRXh/wt/aq8H+LtX8DeBtX8QaYvxU1vw7BrF7oenK5S3lNvHNLFzu8s4Z2WN237EJPYnr/it8fPAXwRufDdv4219dEm8RXn9n6WjW0832ifKjb+6Rtoy6/M2FGRzQB6DRRXg3xK/bs+Avwj1qbSPE/xJ0y21OB/KmtbGKe/khfuri3jk2Edw2MUAe80V5j8H/2mfhd8fFk/4QHxtpniKeJPNks4naK6ROPmaCQLIF5AyVxniu38XeLNJ8CeFtX8R69epp2i6TayXt7dyBmEUMalnbCgk4APABJ6AE0Aa9FfK8f/AAVD/ZjmkRF+JqhmIUbtC1NRz6k22B9TXv8A8Ofil4R+L3h1Nd8F+I9O8S6SzbDc6fOsgRu6OByjdPlYA8jigDqaK+cPGH7SviH4cftm+Evhb4i07TE8C+NNJeTQtYhjkS5TUI874JmMhRlIUY2oDmaIZPNfR9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZXirWT4d8L6xqwTzDYWc10EIzu2IWx1Hp61q1W1Gwh1XT7qyuV329zE0Mi+qsCCPyNAHwD/wRt8Nre/B3x78QtRZb3xN4k8TTRXeoSYaWRI4o5PmPYmWeZiO+Qa/QWSNJo2jkVXRgVZWGQQeoIr8uv2CPjFo/7EfxC+I/7P8A8W9Qi8J7dYbUtJ1rUj5Vpc7o1jLNIflRZIooXVmO37ykhsA/aPxc/bd+DHwf8H3uu3/j7QtXmihaS20rRdRhvLu7fHyokcbMRk4G5sKM5JAFAHy5/wAEz7U/Dv8Aac/ae+G9gPL8PabrZmsbaM/u4FS6uI1AHYmMxg/9cx6V9neOvhz4Y8P6H8TfFmn6HZWviXXtHmGp6skQ+0XQitPLjVnPO1UjUBRgcZxkk18qf8EofAer6h4b+JXxs8QRrDqPxK1yS8hhR9wWFJpnd/bdNNKMHnEanoefrT4xeNvDuk+A/F2n32v6XZ3/APZF0PstxeRxy/NA235S2ecjH1oA+Wf+COH/ACaLc/8AYy3v/oqCvuivgH/gj/4v0LSf2V5NPvtb06zv5PEt3stbi7jjlfdHAFwhOTk8Djmvv6gAooooAKKKKACiiigAooooAK4/RfjJ4A8S+KrnwxpHjjw3qviW2aRJ9GstXt5ryJoyRIrQq5dSpByCOMc12FfgR4b8P6x4J1r4g/Hzw2skmq/Df4iwPdQqxCSWc886sG9AXRIz7Tt6CgD996wvGXjzwz8OtIGq+K/EWk+GNLMqwi+1m+itIDIwJVN8jBdxAOBnPBqXwd4s0zx54T0bxJo1wLrSdXs4b60mGPnikQOp+uCK/NT9v28m/ae+OXi7wNZyu/g/4P8AgbV/EurvGxCvqZsneBMjglW+z4B9Jx60Afpj4d8S6R4w0W01nQdVstb0i7Xfb6hp1wlxbzKCRlJEJVhkEZB7GtKvlX/glx/yYn8Mv+4n/wCnS7rT+KX/AAUC+Gfw38dXvgvTrDxR8RfFVhkX+l+B9JOoSWZBAIkYui5BOCFYlTwcHigD6Xorxn9nz9rb4eftKNqdp4WvL2y8QaUM6j4d1u1NpqFoN23LxkkEA8EqzAEgHBIpv7R/7XXw5/Zb0/T5fGmoXMmpaln7BoulwfaL26AIBKpkALk43OygngEnigDof2gvjtoH7Nvwt1Px74mtNSvtH0+SGKWDSYo5LhjLKsa7VkdF4LAnLDgHr0rYk+K3hmx+F9r8QtV1KPQ/Cs2nQ6o17qTCIQwyorJv5IB+dRgE8nAzX53/ALcX7b/w4/aD/ZJ8ceF9MXWvC3i+KfT5x4d8VWP2K9kiF3ES6AMyuMfNgNuxzjANfWWu3fgrT/2CdOuviNpFzr3gi38FafNqmm2ZImniW3hO1CHjIbcFIO9cEdRQB7p4P8ZaJ8QPDOn+IfDeqW2taJqEfm2t9ZuHilXJBIPsQQfQgitmvGfgb42+Gnhf9lvw54s8L2k3g74X2ejNqNvBqO5pLK0G53MnzyMzA7ifmcknqa8Qvv8AgrJ8IoVkvLHwx8QdZ8PRttfxFY6Cv2BPmwSXkmVgBxn5c8jvxQB9q0Vxvwj+L3hT45eBbDxh4L1VNX0K93BJlRkZHU4aN0YAqynggj35BBrwK9/4KY/BnS4vFKahPrVnqmha5N4fXRzZJLe6lcRMVZrWKORi0eRjfJs5IBwSBQB9X0V84fs+/t7/AAx/aJ8aXPg7So9d8MeMIUeRdC8UWAtLmVUUMxTa7qSFO7aWDYBOMAkfR9ABRRRQAUUUUAFFFFABRRRQB49+1/8AFi7+CH7M/wAQfGmnOItT07TSllK2MR3EzrBC/PXa8qnHfGK8R/4Jf/s+6F4B/Z70Lx/eWMd9478YLJql7rV0nmXIhkdvLiSRvmClAHb+8zknOBj079vzwJe/Ej9jv4n6Jp8Mlxef2at9HDEMu5tp47kqB3JEJGO/Suc/4Jp/FLS/iZ+yD4Ihs7pJdR8PW50TULbcN8EkJIjBHo0XlsD6NjqDQB1n7Z/7PXhv9oP4EeKtN1XS7efW7HTri70bUjEDPaXUaF02OBuCsyhWUcMCe+Meef8ABLH4rah8U/2Q9CXVbp72/wDDd7PoDXEjbnaOIJJCCf8AZimjQeyCvZf2pvi3pXwR+APjXxXqt3Ha/Z9Nmhs0dgGuLuRGWCJB3LOR9ACTwDXhv/BJv4c3vw//AGPdIub+3a2m8Salc64sbghvKcJDEx9mSBWH+ywoA9M8VftVfs/fs5NfeEb/AMW+HvCUuhKBL4f023Ktb7lDhEghQ8kMpwo7182/8E7fAOq+LP2jvjB8ebDw9deEfh14q8220Owuo1gN5vnSU3AiA4A8snI4zMwBbBNfY/jL9m34T/ELVL7U/Evw08Ja5qt9t+06lfaLbyXcu1Qqlpim8kKqgHdwABXwb8C9KH7Mf/BUTUPhD8OL66/4Vxrtg93qOgyXDTRadJ9ia4UjcSdwdUAY/NsnCkng0AfpzRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFfOH7RH7SniHwD8cvhH8JvAlhpepeKfGN60+oNqsckkdjpceTLKFjdDvISUqSSv7lgRyMfR9ABRXz349/wCCgP7Pnw11qbSdd+J2mC/hcxyxadBcagI2HVWa2jkAI6EE8HivQfhH+0J8OPjxZzXPgHxhpniUQKGngtZSs8Kk4BeFwJEBPdlFAHodFZfibxTo3gvQ7vWvEGq2WiaPaJvuL/UJ1ggiXOMs7EAc4HJ718+w/wDBSL9m2bXTpC/FLTxdB9nmPZXa2+f+u5h8rHvuxQB9LUVW03UrTWNPtr6wuob6xuY1mgubaQSRyowyrKwJDKQQQRwas0AFFFFABRRRQAUV5ZpP7UPws1/4wSfC3S/GVlqXjqOOSR9LtEllC7FLOpmVDEHUA5TfuGDkV6nQAUUV85eOv+Ch/wCz58NfGGr+FvEfxBXT9d0m4a1vbVdH1CbypV+8u+OBlOPYmgD6Norw74N/tsfBb4/+Lm8L+AvGi65rq2z3f2Q6ZeWxMSFQzBpoUUkbhwDnqccGus+Ln7Qnw/8AgTceGofHXiK38Pf8JDeNZWEtyreWXVdzM7AERoMqC7YALrkgZIAPRaK+R/CX7c1j4avPjRf/ABb1PQfDfhjwl4obRNBm0+GY3OoxFTInyF3M0hjaJsxKBgliAuDX05q3jbQ9E8E3ni+81GOLw1Z6c+rTagoZ0W1SIytKAoJI2AtwCT2FAG5RXIfCn4s+FPjd4JsvF/grVhrfh28aRILwW8sG9kco42SqrjDKRyB0rP8Aiz8fPh38C9PgvPHvi/TPDMdxnyI7yXM02OpjiUF3A7lVOMj1oA7+ivnjwL/wUI/Z5+I2tRaTovxP037dMypHHqVvc6esjE4Cq9zFGpJJAABzX0MrBlBByDyCKAFor55+I3/BQL4BfCXxpqnhLxV4/TTfEGlyCK8tE0m+uBE5UNt3xQMhOGGcMcdDzXSfCD9r/wCDnx51Iad4G8fabrOpsrMunyLJaXThc7isM6I7AYycA8c0AexUV4h+2R8XPGvwH+Bep+PfBGlaXrd1olzBPqNjqkcrCSxL7JjGY3Uq6lkbcdwCq+VNenfD3xxpnxM8CeH/ABboshl0rW7GG/tmb72yRAwDejDOCOxBoA6GiiigAooooAKKKKACiiigDyr9mn/knWr/APY5+LP/AFIdRr1WvKv2af8AknWr/wDY5+LP/Uh1GvVaACvKv2sf+TWfjJ/2Jms/+kM1eq15V+1j/wAms/GT/sTNZ/8ASGagD1WiiigAooooAKKKKACiiigD5x/4KI/EKX4b/scfEnULd2S7vbFdJhKnB/0qRIHIPYhJHP4V0H7EvgWH4c/sm/CzRooVhdtCt76dVwf31yv2iXJHU75W5rx7/grlDJL+xjrTIhZY9WsGcgfdHm4yfxIH419PfBm8g1D4P+Bbq2GLafQrGWIYAwrW6EcD2IoA/PX4QeCbL/gob+2h8TfGHxBEur/Dz4eXQ0rQ/Ds0jC2kYySIjMoIyG8hpXX+JnRSSq7a+2vFv7H/AMFPGnheXQNR+F/haPT3iMSNY6VDazQDGMxSxKroR6qRXyF/wSj1CPwT8Tf2hPhjqbrb67p2vfakglG2SaNJZoZGAzkhSIvb96Oea/R6gD87P+Ccutaz8E/2iPjD+zRqWr3Gr6L4cZ9V0GS6bLRQ+YgYDsN6XEDlRwGEhA5Ne/8Ax7/ZBuP2mPitp11458Zag/wo0y1iaLwPpztAl9eh3Ly3Mi4JTbsAAy3Bwyc7vnr9lGeL4tf8FRPj98QtKZbrw/pWnDRxdw/6t5wbW3XB6MCLOc5HXANfozQB+dn/AARrt4rPwX8X7eFBHDF4nEaIvRVEZAH5V+idfnh/wR1/5FT4yf8AY1f+0zX6H0AFFePftF6T8cNU0/RF+Ces+EdHvUllOpt4sSZkePC+WIvLjfkHdnIHUVz37Peh/tL6X4wvpfjN4i8Bav4ZawdbaHwtHcLcrd+ZGVZjJCg8vYJQeScleOtAH0FXyn8fv2OX+P3xK1LxP8RvF1/q3w80e0E2h+BbR2gthcLB889yy4LnfuwBzg43YJU/VlZfir/kWNY/685v/QDQB8Kf8EVP+TWfFP8A2Od1/wCkNjX3/XwB/wAEVP8Ak1nxT/2Od1/6Q2Nff9AHgX7e3/JnPxZ/7Akv/oS18+fssfFj9k+1+Anw20nxPqHw2h8VR6Jaw6gNXsrVZVuBEN/mySJjdnOSzda+7vEfhvSvGGhX2i65p1tq2kX0TQXVjeRCWGaM9VZTwRXkXib9iP4C+K9Bn0i6+EvhK0t5YzH5+maTDZXK8Yys0Kq4Pvu60Aeq+D9J8O6P4ftYvCtlpdjocg8+3TR4o47Zg3O9BGApz1yOtfnP/wAFE/hH49+LS/EfxHrPgrwr4K8D+B9Pa5svFqxrc61rzlFKQo6spji3NtYOPly23fk46X/gkfrGqaRL8cvhudVuNX8LeDdfjh0eeZtw2ySXaSbCOAG+zxvtXjLsf4ufvvxF4b0jxhot3o2vaVZa3pF2uy40/UbdLi3mUEHDxuCrDIBwR2FAHw1+wL8I/HvwR1zQbOPwX4V1/wCHninRU12L4i2iLb6vGJYVlW2uuWaZt0igbcLt3OGJygx/+Cs3/I1/s4f9jU//AKMta/QXS9LstD0uz03TbO30/TrOFLe2s7WJYoYIkUKkaIoAVVUAAAYAAAr8+v8AgrN/yNf7OH/Y1P8A+jLWgD0b/gqX8eNd+Df7PNvpPhW4ns/EfjDUF0aG6tmKSwwFGaZkbszALGCMECQkEECuz/Zx/YI+FHwN+HulaZeeDdE8TeJvs6nU9b1ixju5Z5yB5mwyBvLjB4CrgYAJyck+Df8ABYyzu9H8G/CLxtFA09p4e8THzlCbhl0WRM+x+zkfjX6AaDrlh4m0PT9Y0u6jvdNv7eO6trmFgySxOoZGBHUEEGgD85P+CjX7Neg/s+6DoX7QHwfsbfwB4r8N6rALyPRYxb2txFK2xXMK4UEOVRgAA6SuGzgV9NftCeOIPiZ/wT+8Z+L7ZPKt9e8Byaokec7BNaeZt+o3Y/CvPf8Agrh44sPDX7Hur6FPNGNQ8S6jZWVpB1dzHOly7AegEOCf9oDuK6L4h+Drr4e/8ExdV8NX8bRahpfw2+y3UbHO2ZbECQfg+6gDnf8AgnD4B8F65+wf8P7zxH4c0HUI5U1MXVxqljBIHUaldD94zqcjAA57CvI/+Ce+m6TYft3/AB/X4YOH+D6WmyNrF91ibzz4TEIyOCo/00JjjYeOCK4r9lH/AIJp/DP9pD9jXw14vurrVtF8daxFf7dUt7nfBHJFe3EMRaBhgptjQMAQTzggnNe+/wDBLX4lsngzxj8GNf0fS9C8bfDzUns7yPTbaO3F7FuaPznCAeZIrxsrSYywMRJJYkgEH/BXDT7rQPhL8PfiXpH7vXfBPiy1u7aboUV1Ynn/AK6w2/5V9vaDrEHiHQ9O1W2Oba+to7mL/ddQw/QivkT/AIK2XNvB+xb4gSZA0k2p6fHCTj5X88MSP+Aqw49a+kfgTay2XwR+HtvOnlzQ+HdOjdT2YW0YI/OgDuaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA8/+LXwA+HXx2sbe08e+D9M8TJb58ia7ixPAD1EcykOgOBkKwBwM9K8z8H/APBO/wDZ08D6g17pnws0qadlK41We41FACMcJcyyKD7gZr6MooA+ItP/AOCfvin4G/FjTfE/wC+Jd34Q8LXGpwT634M1WR5rKW28xTMITtf5jGGVQ6FgTxIo6ev/ABU/YN+BXxr8daj4x8Z+BRrHiTUBELq+GrX1v5nlxrEmUinVBhEUcDtzXv1FAHzDov8AwTR/Zt8O6zYarp/w3EF/Y3Ed1bynXNScJIjBlO1rgqcEDggg9xX09RRQAUUUUAFFFFABRRRQAUUUUAFfmP8A8E7fh7p3xY0H9rbwbqy50/XdZewlbAJTzDdqHX/aUkMD2Kiv04r44/4J7/s8+PPgT4g+Nlz410ZNJh8ReIhd6Wy3kM/2iBXnPmYjdtgIkXAfDeoFAHl37Cf7TC/BH9mT4r+D/H7+Vr3wYuLqOS1kfBmgaRxDErN1JuBJEMcYeL1FN/Z3+GOpaH/wTx+PPxO8TqZPGfxO8Pa74hvrh1wxgazuDAP9075JRz0mHpWd+2Z/wT08cfF79q6x8R+Comg8DeMo7SDxlcxX0UH2cQyx+YxiZw0oZIoXUKrfvIyTjg19xfGv4fz69+zh498D+FdPjFzeeE7/AEXSbBHWJN72ckMEQZiFUZKrkkAdzQB85fsJ+ILvwn/wS/0bW7Bgl9pui+ILy3YjIEkd7fOp/NRXzv8A8E8fHnxz8E/AmW9+HfwEs/iBaa1qlzd3vii48W2dncXk4bYVkjlbzBsxgbuu4sPvV9s/sN/B7X/hP+yF4N8AePNJSw1q1i1CLUNNaeO4ULNe3EgUvGzI2Y5Vzgnrivnn4f8AwN/aZ/Yf1bXdA+EOiaF8WvhfqN9Jf2WmanqK2d7YMwxt3yyIo4C7sFg23cAhYigB/wAPvhh8fvGn7e/g/wCMniX4P2vwx0aPS59L15rbxHZXqXieROI5HET+Yz7mgX7pH7lMkAE1X+Gun23xM/4LAfE278QiO8k8G+HYzottMu7yCI7NPMXI7faZm9mmBHSvoH4E+JP2nPF3xAF58UfCPhDwJ4JjtZP+JbYXhvNRlnO3ZmRJHj2j5s42/j28z/aV/Zl+K3hn9pWw/aE+Ay6XqnieSyGn674a1SZYE1GNUCAh2KqcokakM6lTFGwJ5AAG/wDBXXwF4f139k7U/E19pVvPr+h3tn/Z2olcTQCW4SORQw5KsrHKnIyAcZAI6P47f8owdR/7J/Y/+k8NeMftLfBX9rP9rr4Q61H4m0bRvBUFiIJtL8AaNqUM0+q3QnQM91dNJ5QRIjK6qHwWVcjIBr6Y+LXwj8U+Jv2E7/4c6Zpy3XjB/B9tpSWH2iNA1ykEatGJGYJ95SNxbHvQBzP7Nvi7wj4D/wCCc3gzXvHotX8H2fhYNqUN5As8U0RLL5RjYESFywQIR8xYDvXJeFv2vfjR8WfC8OpfCv8AZZuNT8AXERisrnXfENnpn2qDlVK28gACEDHyl1PY4rqdP/ZV1/x5/wAE69N+CPiFl8NeJ30K3t5N8iTJb3cM63Eau0ZZSm+NAxQngnGTXAfDKT9s7wf8L9E+E9p8OPCOky6PYx6NafEG71uKW2itokEcUgtEZpGkVAuCy4JXLJzggGd/wRokmj+CPxEtZIvsiQ+MJ8WavuSBjbQBlB6cYAz3wKx/+CaHgHw/qf7Qn7Sniy80m3uvEWm+LJ7Oxv5k3PbRyXF0ZBHnhS21QWHOBjOCQfYP+CcP7O3jf9m3wL8QdB8b2oiubzxTPd2N4J4pPt9t5UaLcbY3fy95QnYxDDuKP2FPgJ45+C/j34+aj4w0VdKsvFHihtR0iUXcE/2m3Mtw2/EbsU4kThwp56UAea/trabbaH/wUC/ZV12whS11bUL97K6uolAeaFZolCse42zyjnsxr9Ba+Q/2sPgL45+Jn7VH7OfjDw3og1Hw74T1OW41q8+1wRfZIzNAwOx3V3yEbhAx4+lfXlABRRRQAUUUUAFFFFABRRRQAjKGUgjIPBBr4m8cf8EwdGh8d33i/wCDvxJ8SfBPV79i9zBoZaWzJLbiFjWSJlUkk7C5QZwFUDFfbVFAH58+MP8AglDqfxS0W/f4ifH/AMYeOvEccDLpN1fR7bSyk4wTA8spKnGCEdM5J612X7PuuftEaH4sj+CHxq8C2uu+D7rTLixt/H3h/Mdv9nSAqqSeWoVSwGxdywsCQcNX2pRQB8TR/wDBP/4leELZtK+H/wC1P458N+GuVi03VLYak8CYwFjk86LywOfuKOTnrXqH7Lv7EvhD9mTUtZ8RQ6pqfjLx3rQK6h4o12QSXMilgzIn90MwDMSWZiBljgAfRNFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfAn7Pc4+Lv/BUL45+LrhhPbeC9Mi8O2CEgiB9yRuR3zuhuen/AD0Na/8AwVU+L/iLwz8O/Bfwu8I3b2GvfErVDpbXEbFG+yq0SyR7hyu954VPqm8dzXPf8E7HWz/a7/bIs54mS6l8V+fHuXny/t2onP0IkQ+9ZP8AwVXdvA/xe/Zn+JFyp/sTQfERN7MU3LEUuLSdQfXcsUxx/sGgD6c+Cn7DXwc+C3guw0W28DaHruoRwqt5rWs6fFd3V3Jgb3LSK20EjOxcKOMCvkH/AIKAfBDSv2N/EXgf9oT4N2EPg6/tNZjsNV0jSwILK5R0Zx+6X5URlieN1UYO9TgEEn9OLa5hvLeK4t5UnglQSRyxsGV1IyGBHBBHevg7/gsV4vgtv2efD3gu223XiDxN4ht0tLFPmlkjiV2ZlXr99oU/7aCgD658ZeCfBHx/+HGnxeLNJt9d8LXa22rpa3jssR2gSxs+1hkDOSpypHUEV8wftwTfs2eHP2XfF2lXFr4Hh1BtLmh8OWGiw2ou0v8AYRbGBIfmVVk27iMKF3BuDg/VnhfwDaWfwi0jwRq0Au7GLQ4dGu4S5xLGLcQuu5cHkZGRjrXy/wCP/wDglT8E7rQLifwFY6l8N/Flqpn03XtN1m7kNtOoyjMJpX+UHGdu1sdGB5oA7X/gm74O8VeBf2PfA+l+L4Lmy1LFxcQWV4pWa3tpJ3eFGB5HytuAOCAwGBivpuvjD/glj+0Z4u/aC+BeqjxrdyatrHh3Uv7PTV5v9bdQtGrp5h/ikXLAt1I25yck/ZdxG8tvKkcphkZSFkABKEjg4PBx70AeQftgfFTXPgn+zb458beGzbjXNItI5bU3cXmRBmmjjJK5GeHPfriup+A/jDUPiH8D/h34q1YxHVdc8O6dqd2YU2R+dNbRyPtXsNzHA7V8BftjfsvfGrwN+zT4513xN+0zq/jfQrO1ie60C48Ppbx3imeNQpkFwxXDEN90/drt/wBmv9lj436n8JPhX4hsv2odZ0vw/caHpV/D4dj8OxulvbNbxOtqJDcchUIj3FecZ29qAP0AryT9pj4T+L/jV4BsvCvhPxzcfD+O61KM6zqdihN1LpwjlEsEJGCru5i+bIwFbOQSret0UAfll8J/gf4U/Z3/AOCrXhHwV4NtZrXRrTwvLJuuZjNNNK9pMXkdj1Zj6AAdAAOK/U2vzw1r/lM/oH/Yqt/6Rz1+h9ABX5d/Anx58FfBP7XX7Vn/AAuC48L2/wBq8Swf2X/wklpHPnbLeed5W9Gx96LdjGfl9K/USvMta/Zh+D/iXWdS1fWPhb4P1fVdSkM13fX+h20800h6sXdCcn1z70AQfBeb4L+KI5fEPwri8F3ZVTBNfeGILUSxgkExyGIBlzgHa2OgOK4/9szQvH/jPwRpHhTwL8OfC/jxtcu2tb658WlXs9Ij27hcNEeX6N8ynKkLhWLAV8i/Eb4Z6H+yX/wUn+DC/CqM+HdO8aIsGr+H7ORjb+W8rxO2wk4QjDBcYVoSRjt+n1AH4yfs+/sw+Mvh/wDEL4ieIPAOjeGPjHrXw78Sf2Le+GfEVmqLOuxd1xZF5CsUqujqN3RY1I3EbK/Tv9pyS6l/ZE+LD3ttHZ3jeBtWM9vDJ5iRSHT5dyK2BuAOQDgZx0FeieHvA3hvwjfave6F4f0rRbzWLg3epXGn2UUEl7OSSZZmRQZHJZjubJ+Y+tcN+1j/AMms/GT/ALEzWf8A0hmoA+f/APgmj4mtfBf/AAT30nxDfBjZaSusX8+3r5cVxO7Y98Ka8j/YE+A+lfteal4v/aH+NGnQeM9U1XV5bPSNJ1RfPsrWGMAn903yuq7xEisCFEbHBLZHb/sPeGbnxt/wSt1bw7ZKzXmr6P4j0+BVGSZJWuY1AHc5YVr/APBH3xpY+IP2TRoUUyf2j4e1m7t7q36OiysJkcjrg72APqjDtQB7B8cv2F/g/wDGzwPqGiy+CdD8P6pJCwsdb0fT4rW5tJcHY26NV3qCclGyp54zyPIP+CT/AMXvEHjP4N+JfAfiu7e+1v4f6r/ZaTSyb3Fqwbyoyx5bY8cyg/3Qg/hr7X1fVrPQdJvdT1G5jstPsoHubm5mbakUSKWd2PYAAkn2r8/f+CRdjP4j/wCF7/Ev7O8Fh4s8TqLbcNoPlmeZsD2+2Adeox2oAzf2ZfDuleI/+Cnv7SEOraZZ6pDHaF0jvbdJlVvNthkBgcHFc1/wUa8IeCdD+PvwPj+FNjpul/GGbXV8228NxxxTGMSR+VJcJGOGD7trNglRJnIXjnvCX7M3gf8Aag/4KP8A7QOheOra8udP09TewLZXTQMJd8CZJHUYY8Vu+APhvpn/AATb/bm8LaA0Vrq/w7+I0Rs9K13VraFtQ0ycsEMQuAoIHmNCHxtUpMpIyhoA/Sf4ieDrX4ieAPEvha9VWs9b02506Xd02yxNGT/49XyJ/wAEifHV54m/ZUk8P6iz/a/CeuXekhJCSyxMEnUZ9A00igdgnpivtyvz9/4JENHeeG/jVqFpEItOufGMhtwuMBdm4KMeiuvtzQB+gVFFFABRRRQAUUUUAFFFFAHlX7NP/JOtX/7HPxZ/6kOo16rXlX7NP/JOtX/7HPxZ/wCpDqNeq0AFeVftY/8AJrPxk/7EzWf/AEhmr1WvKv2sf+TWfjJ/2Jms/wDpDNQB6rRRRQAUUUUAFFFFABRRRQB4v+2V8K5vjR+y/wDEXwnawfadQutLe4soRwXuYGWeFQexLxKv41x//BOD4lRfEz9jn4eziVXu9GtDoVzGGyY2tT5can6wiFvowr6Yryj4H/s2+GP2fdW8c3fhS81VbPxbqh1i40m7mje0srht2/7MqxqyK25QQzNxGgGMcgHiv7Sn7BN58QvipF8XfhN44uPhh8UUULcXUSFrW/2rtBkC8qxUKrHDqyqAUzk1xt/+zn+2t8RrI+HfGfx88L6J4buF8m8vPDFiRfzRnhvu20BUkZ4SRRyR0r7zooA/Lr4S+KPiF/wSti1Xwr42+GLeMPhlfag12vjzwrGTOxICr9oViQDgKBG7R4O7az8k/X3x/wBB/ad1PxtBL8HfEfw/0nwoLKNZYPE8Vw10bre+9gY4XGzaY8cjkHjufoeigD81/gD+xz+1/wDs12PiC08GeMvhXFDrl79vvPtz3sxMuMZX/RBgY7V+klv5v2eLzyhn2jzDGCF3Y5xntmpKKACiiigAr5l+P3hP9qjxB401FPhd4m+HWmeCLi1SKK28QxXP20OUxKWKQOuCxOMHpjivpqigD85v2Y/2RP2t/wBl3w63hjwv4v8Ahavhu61b+1L2O5a9uJmZkijk2k2q/wAEK4GRz35r6b/bO+EfxW+M3w10zRvhH48/4QHXYNTjurm8+3XFl59uEdTH50Cs4+ZlbbjDbea9+ooA8k+Mvwt8c/ED4QafoPhX4i3ngjxpYva3C+IbWLzFnliXDpLHkbo5CSSDxnGQQMH571b4N/tveLdPk8P6n8ZvAei6RMvlTa1othKNSZDwSF+zqqMRnlHUjsRX2/RQB45+yx+y/wCGf2UfhmvhPw7NNqE885u9S1a6AE17cEAFyBwqgABVHQDqSST7HRRQAV8s/tufsp+KP2ltZ+E154b1PSNPTwlrh1G+XVJJUMkJaEkRbI3y48o8NtByORX1NRQBxfxi+Efhv46/DnWvBHiy0a70TVIvLk8tgssTAhkljYg7XVgGBwRkcgjIPxh4X/Y9/av/AGf7VvDnwh+Oegah4KiZvsWn+LrRt9ohJIVP3E+APRWVSSTtFfoHRQB+YHxc/YS/aR1DVfDvxc13x1ovxn+IHhu9S7j8I31sYdOMSEOFg5iQncoJTZGGwOSRg+9eCfjdq/7d3wJ+Mfw3n8F6h8M/iHZaR/Zd/Y+IA628c13HOsTBtgk25hYtmPKgjG/rX2LRQB4t+xr8E9a/Z1/Zt8H/AA98Q3dhf6zo4u/tFxpju9uxmvJ512F0RjhZVByo5Brzi6/ZL8V+G/29LX45eDdU0ez8L6vppsvFGl3csqXNw3llN0KrGyEZjtn+Zl+ZG65r6wooA+Bv+CpJm+KmrfBH4F6XIzaj4v8AEq3l0sJG6G1hXyjIw/ugTyP/ANsG9K+9LW2is7aK3gQRwxII0ReiqBgAfhXl7fs4eF7j9opfjRe3Op6j4rg0n+xrG3upY2s9PhySzQIIwyu2+QElyMSvwM16rQAUUUUAFFFFABRRRQAUUUUAFFFFAHln7Unxab4F/s9+O/HEW37ZpWmubLcMqbqQiG3BHcebJHkelYtlpfxV+G3wD8C6D4Ugt/HHjxYba01PVvGWrSiKBjC73F3M+GllAlAQRr82JF5whrg/+Clkgt/2X7i6uD/xK7bxBo02ochf9HF9Fu+Y/d+YrzkfWvePi18WvDHwR8C6h4t8W6gun6TZgDgbpZ5D9yGJOryOeAo/QAkAHifwf/aF+JNv+0de/BT4u6L4aTxDLoI8R6XrXg95xZ3Ft5xiKSRTlnR9yvznHyHjkE5en/FX9on4n/Ez4naX4Ab4Y6f4e8I68dEj/wCEkstRe7lIgil3s0M4Q/63HAHTp3rb/Zp+FfiTxJ8RNf8Aj78R7JtK8YeJLFNM0bw7IPm0DR1ffHBJ6zyNiST+6SQMZYB/jr9lX4M+LofGHjuHWdU8N6jqE097f+K/D/i28tVtbiNfLkm2pP5CtH5eDuTgqQelAGp8ev2nP+GWfgvouv8Ajt/D+p+NLlra3fR7HUfsEV5IZYo7mW1EweRo4hKHIwxAxuIzmsX9pL9o7+zvh74B8RfCzxnpOq2mqePdI0C91DR5rbUYJIJZCJ4N43qrFduSMMMjBGa+Y/iB42174yf8EqfC3jL4gQR33iT+07KL+1LyBRLJCurJEJckcb40TcRgNjPQ17j/AMFEvA2hXHwb+HvhyLTYLDR9S+ImiW9xb6egtgyyO6OQY8bWKn7w56UAfTvh34oeDfGGtaho2g+LdC1vV9POLzT9O1KG4uLYg4IkjRiyc8fMBW3rN6+m6PfXcaqz28Ekqq3QlVJAP5V8e/Gj4T+C/gr+0d+zHqvgfwtpfhW6l1u80Kb+x7ZLUXFpJZv8kuwDzMEbgWycknua+uvFX/Isax/15zf+gGgD4h8H/tQftOar+zja/HWXQPhhrfhCOxm1a90KwXULPUhaQu4mZJJJZItyrGzYIPAOMnCn640341eD5vhb4b+IGra7p/hfw3run2uo211rt3FaIqTwrKis0jBQ21umexr4e/ZG/Zr8f/G79jvwPpOs/Gi80z4XatZSpceFNG0C3gumt/tMgeA37OzlWIbPyDIbHI67vxI8JeIrj9uzQPBvhjwz4J1jTPCvw7gbwzonja6nisbaP7R5Us1uscMu6YLGkZyvCJnPFAH3HpPjjw5r/hn/AISTTNf0vUfDvlNP/a9pexy2nlqCWfzlYptABJOcDFUtF+KfgvxHrVto+k+L9B1TV7mxj1ODT7LU4JriW0dQ0dwsasWaJlZSHA2kEEHmvlLwt8EfHHwr8LftNa14kh8GaDpHijw293B4Y8F3dxLbWlyljcRzTlJYIhGZQEJ2j5ihz0rnvh78NdF+GP8AwTSfxx4L8O2lv8QJvAEl+fEUcCvqavLajzWW4IMihEHyqDhVjUAAAUAfZGn/ABg8B6t4sl8LWPjbw7e+J4SVk0W31aCS9QjqDCH3jHuKf4y+LXgf4c3VpbeLPGfh/wAMXF3/AMe8Os6pBaPNzj5BI6luQRx6V4p+zp8CPglY/A/4Ta3pvhjwzLJFZ6df2HiBraJbyW+ZUbzPP++ZWlzlNx5+XGBivnz4J+GfiL8S/ip+0RqkPgn4X+L9VHji+0e8k8eXdz9thsolRbWCNEtZQLbyx8rZG8hsj5QaAP0E1bxVonh/T7W/1TWLDTbG6mit7e6u7pIoppZWCxIjMQGZ2ICgcsSAM1maB8U/BfivxDf6Boni/QdY13T8/bNL0/U4J7m2wcHzIkYsnPHIFfBvxk+DfiL4V/sE+GvAfi7VLC9nh8d2MEJ8P3080NnayaidlvFNIiSAxbmQZGV2gDpXov7QHwf8C/A34sfsyav4F8J6X4Wvk8ZR6CZdKt1t2ltJ7aVXSVlGZegOXJOS3PzGgD1HTP22Ph3d/tAeJPhnd+I/DWnRaTaWzw61P4htwl5eSSNHJZLGcYljKgFQ5bJwVHffk8ceL7fxL8Z4rjxF4Pu9L0DTobjR9N0eVpNX012tXkJ1GNiVTeyho+PmUE15P4F8H+H5v+CiXxVhm0PTJFXwjpN3FHJaRkCQzSbpVBH3icZYc5rK8E/8nPftz/8AYH8Of+mWegD2z9ln4n6j42/Zb8CeOfGusQPqN7o6XupapcCK2iLZO6RtoVEGB2AFegeCfid4O+JUNzN4R8WaH4qitWCTyaJqUN4sTHoHMbNtPB61+cPiB9a1L9kH9jXw5aW2j32ia1q9tFe2PiK6kt9MvJkSR7aC6aNGJjZwx24OWRflPb6H8GfAH4n2f7TngD4hapo3ww8CabpljqGmata+C726NzrNvLDmGJke1iVhFMiOOcgZ9AKAPon4z+FNV8cfCfxboWhavfaDrt7ps0en6lpty9vPb3O0mF1kQhgN4XOCMjI71z/7LPxcPx0/Z78C+N5WVr3U9NT7dtGALuMmK4AHYebHJgemK9Ur5Z/4Jsss37MyXVv/AMgq68R61Np46gW5vpQvPfkN3P1oA+pqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArw74YfHzV/H83xm12PRf7S8H+D9Un0jRbfRoGk1LVJrODdebQ0gSQtMRHEF2glTljnI9o1K6ax0+6uUiM7wxNIsS9XIBO0cd8V8z/8Ez4xJ+xb4Cv3uFvLzUpNRvru5XrLM+oXBct7jhT/ALtAFfxZ+37ZeBfDt9r3iH4GfGTRtFsU8y5vr3w/axwwrkDLMbvA5IH412XxG/a60b4eXHgOyTwJ458Vav4x0uTV7HSfDumQz3cEKLEzidHnTaw85QQu4Ag89M8F+1PJ/wAL4+O3w1+AFqRPo3mr4x8Zopyv9nWrj7PbSDGCs0+0EcEbVPQ16F8a/gH4t+IHxW8F+PfBvjyz8Fat4c0+/sP9N0L+1EnW5MJPy+fFtwIvU9aAOy+D/wAWpfi1pmoXk3gXxh4ENpMsItvGGnx2ks+VzvjCSyBlHQkkc1e8E/Fvwz8TPAU3jDwlqH9t6IhuUWdYZIN8kDuki7ZFVhh0YZxg4yMjBrxD4RfErxj8TPGnxT+BXxOu4bfxP4dtbWdPE3gqafTjd2dyu5XX5y8Ey/KDtbHzHHTJ8y/4J0/C610v9my48XL4g8TXFzK2uWZ0u51qeXTFVbqVfMW1LeWJDsBLgZJZj/EaAPqX9nH4xj9oH4J+FfiCNI/sH+3IHn/s77T9o8jbK8ePM2Ju+5nO0da9Jr8vPgn8I/HNv/wTwtfijp3xl8YaBq3hzQL7WdC0bRblLbS7eK1eeVobiAIftTSFJCWkJA3qAMLg/op8H/GUvxF+EvgnxXMqrNruh2OqOqLtUNPbpKQB2GXoA8q+IH7Zmk+B/ip4g+H+n/Db4ieOdc0G2trq/fwnpEF5DEk6b48lrhGyRn+HqDjNeg/BH47+Ev2gvCc2v+E7m4aO1upLC+sb+3a3u7C6TG+CeJuUcZHqOetfK3/CdePvBH7e3x1fwJ8MZviVPc6PoC3MceuWulraYt32MzT/AHgxLfcBI29ORWH49n+JX7JvwN8YeMdVnh0v4k/Fzx7awyR+G7U6iNDjuAVCwrt/fzLFE4BxhpHUDJ5IB+hFFfBHgnxd4r8I/Gb4cDwGnx61/R9U1Iad4qtfiTo2oSWK2siNi8SWeIC3eOTaSEKoQcYAHOt4Z8GeNfj9+1F8fNAvvi3408KeDfC+q6e1lp/hjUzbT+fNYoRiVg4SFdjN5Krtd5Cz5xggH3DRXxf4O0HxR+2F8UPi9qOpfE/xp4H8MeDfE1x4R0bR/BepjTSJbZU866uHClpizONquNoAIwe1L9pj4neKPBPxA+EnwbuPE3ju9sbnR7jUvEOu+CdJebXdUEOI40TyEJhDOGeR4xkfLyueQD7dor5A/ZT8X+MYfjx4k8KQWvxRv/hVN4fTVLLVPifpV3DdWWpLcCOS0jubhA8qPG6yAOzEbGxgA58Q+Bvgv4n/ABa/YZT4v6n8ePHtp4n07StTvtFtrHUjHar9jlnGL0EM12ZHgbLSNhVZVVfl5AP0F8a/E7wz8O77w1Z+IdTGnXPiTU49H0pDBJJ9ou3VmWPKKQuQjfM2F468iqvjH4jT+EvGngzw/H4T8Qa5F4innhk1bSrPzbPSvLQMHu3yPLV84U85KnvgH4e/aAsT8evDX7HnjzVNe8Q6fqfivV9Etr2HSNWns7eGSS2lllngjRgIp97somX59oUZwK9o+K76v8K/jf8AsteD9G8U+I5tHu9U1iC//tHV57mXUUWzMiC6dmzPtZsrvzjAx0FAHrXww+OC/Ej4rfFXwWNFOnnwLe2Vmb77V5n237Rb+du2bB5e37uNzZ65HSvUq+GPD/xIuPg/4+/bl8a2lst3eaG+nXtvBICUeVNMJjDY527sZ9s15SfiN4/tPhLo3jrwnqP7Qnib4vtBaam8F74bv5PDeotI0bz24tVi8hYCjOEeMBsBSG5oA/T2vIPgF8YtX+ImsfEjwx4otbGx8VeC/EMumzxaeHWOazkUTWVwFdmZTJC4zzjcjEYGAPWLG4e6s7eaSFreSSNXaFuqEjJU+46V8w+A2Ok/8FGPipZ2uTb6v4I0nUbwr90TxTyQxhvfyycdOO3egD6looooAKKKKACiiigAooooAKKKKACiiigAooooA+AfCls3wH/4KzeJLK4It9E+Knh77XY7uEa6jVWcZ6bt1tccH/nqvqM/XPx++A/hb9pH4Y6l4H8XQSPpt2VliuLdgs9pOudk0TEEB1yeoIILKQQSKzfjV+zb4X+OfiLwJr+sXeqaTrvgvU11TStR0eaOKZWyjNE5eNw0TGNNy4BO3qOc+r0Afn94Z/ZT/bE+COlxeGPhr8dfDOr+EbRTFYxeKrNhNbRj7irm3nIAHAXftAAwAOB5/wDEj9iT9orwb458MfHa88UaX8e/H/h+5FxP4b1C2aK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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Slide1.jpg](attachment:Slide1.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips = sns.load_dataset(\"tips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=\"day\", y=\"total_bill\", data=tips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def box_outliers(series):\n",
    "    Q1 = series.quantile(0.25)\n",
    "    Q3 = series.quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    mask = (series < Q1 - 1.5 * IQR) | (series > Q3 + 1.5 * IQR)\n",
    "    series = series[mask]\n",
    "    return series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_outliers = pd.DataFrame([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in tips.copy().day.unique():\n",
    "    df_outlier = tips.copy().sort_values(['day'])\n",
    "    df_outlier = df_outlier.loc[df_outlier['day'] == i, :].reset_index(drop=True)\n",
    "    df_outlier = df_outlier[['day', 'total_bill']].set_index('day').apply(box_outliers)\n",
    "    df_outlier = df_outlier.reset_index()\n",
    "    data_outliers = data_outliers.append(df_outlier) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>total_bill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sun</td>\n",
       "      <td>45.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sun</td>\n",
       "      <td>48.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sat</td>\n",
       "      <td>50.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat</td>\n",
       "      <td>48.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat</td>\n",
       "      <td>48.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sat</td>\n",
       "      <td>44.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thur</td>\n",
       "      <td>34.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thur</td>\n",
       "      <td>32.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thur</td>\n",
       "      <td>43.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Thur</td>\n",
       "      <td>41.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Thur</td>\n",
       "      <td>34.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Fri</td>\n",
       "      <td>40.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     day  total_bill\n",
       "0    Sun       45.35\n",
       "1    Sun       48.17\n",
       "2    Sat       50.81\n",
       "3    Sat       48.33\n",
       "4    Sat       48.27\n",
       "5    Sat       44.30\n",
       "6   Thur       34.83\n",
       "7   Thur       32.68\n",
       "8   Thur       43.11\n",
       "9   Thur       41.19\n",
       "10  Thur       34.30\n",
       "11   Fri       40.17"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_outliers.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the above box-plot, these are actually the outlier values!"
   ]
  }
 ],
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