11_Boxplot_Seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boxplot\n",
    "\n",
    "Box plots are used to show distributions of numeric data values, especially when you want to compare them between multiple groups. A boxplot shows the distribution of the data with more detailed information. It shows the outliers more clearly, maximum, minimum, quartile(Q1), third quartile(Q3), interquartile range(IQR), and median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Mahzaib Khalid\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=sns.load_dataset(\"tips\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='total_bill'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"sex\",y=\"total_bill\",data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Boxplot is relationship between two data\n",
    "- Normal bill is between 15 and 25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hue is use to differentiate between classes\n",
    "\n",
    "sns.boxplot(x=\"sex\",y=\"total_bill\",data=data,hue=\"day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"sex\",y=\"total_bill\",data=data,color=\"g\",hue=\"day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boxplot shows mean value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEGCAYAAACNaZVuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdHUlEQVR4nO3de3hV9b3n8fcXCAYURbk4sVRSHyM3QTgEtfYcr0Wt92J7RqZw8NCntBOPlzp4xUvtI099jk6d4yV1aI8HD3rAiljBejoiI+PowUuiqIGgyUxBODAGY4lKCE3gO39kJQbIZSd7rb323uvzep48e++11+WbleSb3/6t3/r+zN0REZFk6Rd3ACIiknlK/iIiCaTkLyKSQEr+IiIJpOQvIpJAA+IOIFXDhw/34uLiuMMQEckplZWVn7r7iIOX50zyLy4upqKiIu4wRERyiplt6Wy5un1ERBJIyV9EJIGU/EVEEihn+vw709zczLZt22hqaoo7lKxTWFjIqFGjKCgoiDsUEclCOZ38t23bxpAhQyguLsbM4g4na7g79fX1bNu2jW984xuxxFBXV8eNN97Igw8+yIgRhww0EJGY5XS3T1NTE8OGDVPiP4iZMWzYsFg/EZWXl1NRUUF5eXlsMYhI13I6+QNK/F2I87zU1dWxYsUK3J1nn32WnTt3xhaLiHQu55O/ZJ/y8nL2798PwP79+9X6F8lCkSd/M9tsZh+Y2XozqwiWHWNmq82sJng8Ouo4JHNWrVpFc3Mz0HpRfuXKlTFHJCIHy1TL/xx3n+zupcHrW4E17l4CrAleS5649NJL20cZFRQUcNlll8UckYgcLK5un8uBJ4LnTwBXxBRHr+zevZuLL76YU045hZNPPpmnn36ayspKzjrrLKZOncoFF1zAjh07aGhoYMyYMXz44YcAzJw5k1//+tcxR585ZWVl9OvX+qvVr18/ysrKYo5IRA6WieTvwEtmVmlm84Jlx7r7DoDgcWQG4kjbH/7wB4477jjee+89qqqquPDCC7n22mtZvnw5lZWVzJ07lwULFnDUUUfxyCOPcPXVV7Ns2TL+9Kc/8aMf/Sju8DNm5MiRzJgxAzPjyiuv1FBPkSyUiXH+33L37WY2ElhtZptS3TD4ZzEP4Pjjj48qvpRNnDiR+fPnc8stt3DJJZdw9NFHU1VVxfTp0wHYt28fRUVFAEyfPp1nnnmGa665hvfeey/OsGNRVlZGbW2tWv0iWSry5O/u24PHOjN7DjgV+MTMitx9h5kVAXVdbLsIWARQWloa+0zzJ510EpWVlbz44ovcdtttTJ8+nQkTJrBu3bpD1t2/fz/V1dUMGjSIzz77jFGjRsUQcXxGjhzJk08+GXcYItKFSLt9zOxwMxvS9hw4H6gCVgJzgtXmAM9HGUdYtm/fzuDBg5k1axbz58/nzTffZOfOne3Jv7m5mQ0bNgDw4IMPMm7cOJYuXcrcuXPbR7+IiGSDqFv+xwLPBTccDQD+xd3/YGZvA781sx8CHwPfjziOUHzwwQfcdNNN9OvXj4KCAn71q18xYMAArrvuOhoaGmhpaeGGG26goKCA3/zmN7z11lsMGTKEM888k3vvvZd77rkn7m8hYzZu3Mjs2bN56qmnGDt2bNzh5DSVypAomHvsvSkpKS0t9YMnc6murmbcuHExRZT94jw/F198MbW1tZSUlPDCCy/EEkO++NnPfsayZcuYOXMmd999d9zhSI4xs8oOw+zb6Q5fCd3GjRupra0FoKamhk2bUr7GLwdRqQyJipK/hO6mm2464PX8+fNjiiT3qVSGREXJX0LX1upvU1NTE1MkuU+lMiQqSv4SuhNPPPGA1yUlJTFFkvtUKkOiouQvobv//vsPeP3AAw/EFEnuU6kMiYqSv4Ru/Pjx7a3/kpISDfVMg0plSFRyehrHg5VdU8auXbtC29/QoUMpf7T7C2xmxqxZs1iyZAkALS0tFBUVcdppp3U7xHHt2rU88MADeTsM8v7772f27Nlq9YdApTIkCnmV/Hft2sXDjz0c2v6u/cm1Pa5z+OGHU1VVxZ49exg0aBCrV6/ma1/7Wmgx5Krx48dTWVkZdxh5QaUyJArq9gnBd77zHX7/+98DsHTpUmbOnNn+3ltvvcUZZ5zBlClTOOOMM9rLPHe0e/du5s6dy7Rp05gyZQrPP58T1S5EJIcp+YfgqquuYtmyZTQ1NfH+++9z2mmntb83duxYXn31Vd59911+/vOfc/vttx+y/cKFCzn33HN5++23eeWVV7jpppvYvXt3Jr8FEUmYvOr2icukSZPYvHkzS5cu5aKLLjrgvYaGBubMmUNNTQ1m1mmBt5deeomVK1e29483NTXx8ccfq3SFAKrtI9FQyz8kl112GfPnzz+gywfgzjvv5JxzzqGqqopVq1bR1NR0yLZtt+6vX7+e9evXK/HLAcrLy6moqNDdvRIqJf+QzJ07l7vuuouJEycesLyhoaH9AvDixYs73faCCy7g4Ycfpq3I3rvvvhtprJI7VNtHopJX3T5Dhw5NaYROb/aXqlGjRnH99dcfsvzmm29mzpw5/PKXv+Tcc8/tdNs777yTG264gUmTJuHuFBcX5+0QUOmdzmr7qLKnhEElnfNY0s9PPvSVT506lS+//LL99RFHHKEhtNIrKuksiZMPfeWq7SNRUfKXvJQvfeWq7SNRUfKXvJQvdfBV20eiouQveSmf6uCXlZVRWlqqVr+ESslf8lI+9ZW31fZRqz+/1NXVMWvWrNi6JJX8JS+pr1yyXdwDEvJqnP/Mq2eyfef20PZ33IjjWLp4abfr9O/f/4Abu373u99RXFwcWgwdFRcXU1FRwfDhwyPZfz5p6ytftmyZ+sol6xw8IKGsrCzjv6N5lfy379zO+ye9H94OP+p5lUGDBrF+/frwjimhUR18yVbZcPOeun0iUFlZyVlnncXUqVO54IIL2LFjBwBnn302P/3pTznzzDMZN24cb7/9NjNmzKCkpIQ77rijffsrrriCqVOnMmHCBBYtWtTpMZ588klOPfVUJk+ezI9//GP27duXke8tl6ivXLJVNgxIUPJP0549e5g8eTKTJ0/mu9/9Ls3NzVx77bUsX76cyspK5s6dy4IFC9rXHzhwIK+++io/+clPuPzyy3n00Uepqqpi8eLF1NfXA/D4449TWVlJRUUFDz30UPvyNtXV1Tz99NO8/vrrrF+/nv79+/PUU09l9PsWkb7LhgEJedXtE4eDu32qqqqoqqpi+vTpAOzbt4+ioqL299t+yBMnTmTChAnt751wwgls3bqVYcOG8dBDD/Hcc88BsHXrVmpqahg2bFj7PtasWUNlZSXTpk0DWv8BjRw5MtLvU0TCU1ZWxooVK4D4BiQo+YfM3ZkwYQLr1q3r9P3DDjsMaP2Btz1ve93S0sLatWt5+eWXWbduHYMHD+bss88+pAy0uzNnzhx+8YtfRPeNiEhksmFAgrp9QjZmzBh27tzZnvybm5vZsGFDyts3NDRw9NFHM3jwYDZt2sQbb7xxyDrnnXcey5cvp66uDoDPPvuMLVu2hPMNiOS5uMfXt4n75r28avkfN+K4lEbo9Gp/vTRw4ECWL1/OddddR0NDAy0tLdxwww1MmDAhpe0vvPBCHnvsMSZNmsSYMWM4/fTTD1ln/Pjx3HvvvZx//vns37+fgoICHn30UUaPHt3reEWSpuP4+jjLY7cNSIiLSjrnMZ0fkQPV1dXx7W9/m71793LYYYexZs2avB8NppLOIpJ4+VLwLwxK/iKSGNkwvj5b5Hzyz5Vuq0zTeRE5VDaMr88WGbnga2b9gQrg3939EjM7BngaKAY2A3/t7n/q7X4LCwupr69n2LBhmFmYIec0d6e+vp7CwsK4Q5EULFy4kE2bNnX5fttIrp4u6I8dO/aAGwrlUNkwvj5bZGq0z/VANXBk8PpWYI2732dmtwavb+ntTkeNGsW2bdtSGrLV0NDQ/nGvMy0tLQAMGND1KSkoKOCoo47qbZixKCwsZNSoUXGHISFobGyMO4S8kQ3j67NF5KN9zGwU8ASwELgxaPl/CJzt7jvMrAhY6+5juttPZ6N9eqOn1lV1dTVAt6Nj1LKSOMyePRuAJUuWxBxJfqirq+PGG2/kwQcfTETy72q0TyZa/v8NuBkY0mHZse6+AyD4B9BpbQIzmwfMAzj++OPTCqKnpK0/MJFkiHt8fbaINPmb2SVAnbtXmtnZvd3e3RcBi6C15R9udNJXPX2KgtT6qfVJSiQ+Ubf8vwVcZmYXAYXAkWb2JPCJmRV16PapizgOyTD1U4tkt0iTv7vfBtwGELT857v7LDO7H5gD3Bc8Ph9lHBKuVFrr6kYTyW5xjfO/D5huZjXA9OC1iIhkSMYKu7n7WmBt8LweOC9TxxYRkQPl/B2+IiLSe0r+IiIJpOQvIpJASv4iIgmk5C8ikkBK/iIiCaTkLyKSQEr+IiIJpOQvIpJASv4iIgmUsfIOImELY/pDlZWWpFLLX/JWY2OjSktL1qqrq2PWrFkpTUMbBbX8JWdpdjbJZeXl5VRUVFBeXs7dd9+d8eMr+YsIoG60TKqrq2PFihW4O88++yxlZWUZn09Y3T4ikhJ1o4WnvLyc/fv3A7B//37Ky8szHoNa/iICqBstk1atWkVzczMAzc3NrFy5MuNdP2r5i4hk2KWXXkpBQQEABQUFXHbZZRmPQS1/EckruXDtoqysjBUrVgDQr18/ysrKIjtWV9TyF5FEyYZrFyNHjmTGjBmYGVdeeWXGL/aCWv4ikmdy5dpFWVkZtbW1sbT6QclfRCQWI0eO5Mknn4zt+Or2ERFJICV/EZEEUvIXEUkgJX8RkQRS8hcRSSAlfxGRBFLyFxFJICV/EZEEUvIXEUmgbu/wNbNVgHf1vrtnvhSdiIikrafyDg9kJAoREcmobrt93P1/dffV087NrNDM3jKz98xsg5ndEyw/xsxWm1lN8Hh0WN+QiEguiHsC926Tv5l9YGbvd/WVwv73Aue6+ynAZOBCMzsduBVY4+4lwJrgtYhIYnScwD0OPXX7XJLOzt3dgS+DlwXBlwOXA2cHy58A1gK3pHMsEZFckfUTuLv7lu6+UjmAmfU3s/VAHbDa3d8EjnX3HcExdgAju9h2nplVmFlFXB+NRETClg0TuPfU7fNa8PiFmX1+8GMqB3D3fe4+GRgFnGpmJ6canLsvcvdSdy+NY6YbEZEodDaBe6b11PL/y+BxiLsfefBjbw7k7rto7d65EPjEzIoAgse6vgQvIpKLsmEC95Rv8jKzvzCz68zsWjObkuI2I8xsaPB8EPBtYBOwEpgTrDYHeL5XUYuI5LCysjL69WtNv1k9gbuZ3UXrhdlhwHBgsZndkcKmRcArwcigt2nt838BuA+YbmY1wPTgtYhIIuTSBO4zgSnu3gRgZvcB7wD3dreRu78PHPIpwd3rgfN6F6qISP7IlQncNwOFQFPw+jDg/0QRkIhIEsQ9gXtPtX0epnVc/l5gg5mtDl5PB16LPjwREYlCTy3/iuCxEniuw/K1kUQjIiIZ0W3yd/cnUtmJmT3r7leGE5KIiEQtrHr+J4S0HxERyYCwkn+XNf9FRCT7pDraR0REUrRw4UI2bdrU7TpbtrSWRxs9enSX64wdO5YFCxaEGlubsJK/hbQfEZFEaGxsjPX4YSV/lWMW6UIqrcDuVFdXAzB79uy04oiyFSkHSuU8t/08lyxZEnU4neppnP8HdN6fb7SW659E65OXIohNJC9s2rSJjRs3UnJSSZ+2LywsBKC5pbnPMdR8VNPnbSU/RTqZSyZlQ+tKLSvpSslJJTy66NHYjn/NvGtiO7Zkp57G+ac0YUs2iLt1pZaViOSSlPr8g3l3HwbGAQOB/sDu3tb0j1qcrSu1rMKXDZ/mQJ/oJD+lesH3EeAq4BmgFPgb4MSoghKB1k9zb7zzBi1HtvRp+/5/7g/Aa7V9L0M14HONhpb8lPJvtrvXmll/d98H/JOZ/VuEcYkA0HJkC7tO3xXb8Ye+MTS2Y4tEKdXk32hmA4H1Zvb3wA7g8OjCEpEwbdu6jaamprS6wDQoIr+kmvxn01oK4u+AnwJfB2ZEFZSIhGvPnj183vR5Wl1g6XajqQstu6T607jC3f+B1slc7gEws+uBf4gqMBEJl7rQpKNUk/8cDk30V3eyTPJAuqNsIJwugrbaJyISvp7u8J0J/CfgG2a2ssNbRwL1UQYm8Un3ngkI574JM4OCPocgIt3oqeX/b7Re3B0O/NcOy78A3o8qKIlfNtyRWltTG9vxRfJdKnf4bgG+aWbHAtOCt6rdvW+Dr0VEJHYpTeZiZt8H3gK+D/w18KaZfS/KwEREJDqpXvC9A5jm7nUAZjYCeBlYHlVg+ainC6mpTO4AGistIulLNfn3a0v8gXrCmwJSAnFP7iAiyZFq8v9XM/sfwNLg9X8EXowmpPzVU2s97skdRHJBNhT8y4dP36kmfwf+O/CXtE7ksgg4PaqgRES6ovLt4Ug1+U9391uAFW0LzOweNH2jiMRA5dvT19NNXv8ZKANOMLOO4/qHAK9HGZiIiESnp5b/vwD/CvwCuLXD8i/c/bPIohIRkUj1dJNXA9AAzMxMOCIikgkarikikkAqsC0SsS1btrB79+5YLxQ2NjbSv7l/bMeX7BNpy9/Mvm5mr5hZtZltCOYAwMyOMbPVZlYTPB4dZRwiInKgqFv+LcB/cfd3zGwIUGlmq2mdC2CNu99nZrfSejFZw0YlL40ePZrmluZYq6Sef/b5fDZQYzTkK5G2/N19h7u/Ezz/AqgGvgZcDjwRrPYEcEWUcYiIyIEydsHXzIqBKcCbwLHuvgNa/0EAI7vYZp6ZVZhZxc6dOzMVqohI3stI8jezI4BngRvc/fNUt3P3Re5e6u6lI0aMiC5AEZGEiTz5m1kBrYn/KXdvKw/xiZkVBe8XAXVdbS8iIuGLerSPAf9I68xfv+zw1kpaJ4UneHw+yjhERORAUY/2+RYwG/jAzNYHy24H7gN+a2Y/BD6mdYYwEZGcsGXLlrRKQkP8paUjTf7u/hqtJaA7c16UxxYRiUpjYyNvvPMGLUf2fSrz/n9uvenutdrX+rT9gM/TS9+6w1cOkQ13pNZ8VENjYyMD+g1g6BtDY4tjwOcD2qfXFOmo5cgWdp2+K7bjp/t3odo+IiIJpJa/HCIb7ki9Zt411NbUUl9QH3vravTo0bEdXyQqavmLiCRQ3rT84+6nrvmohsMPPzyWY4v0ZO/evQxo0vUT+UreJH8RSYZsaOjt3bsXCmI5fGjyJvnH3U99zbxrKBiQ478NkrcOO+wwdg/cresn0i5vkr+IJEM2NPRqa2rZze5Yjh8WJf8QpXvXXxh3/EF6d/2JSDIo+Yco3bv+0r3jD9K/609EkkGZImS5ftefiCSDxvmLiCSQWv4hampqYsAejaUWkeyn5C+SATUf1fR5XPq2rdsAGPX1UX0+fmNjY9o3efXf3XpNat/h+/q0va5HZRf9NEJUWFjI7gKNpZYDjR07Nq3tm5qaANK6j6RtGtR0fjfaRqONO3Fcn/eR7rmQ8Cj5i0Qs3WG3bUN/lyxZEkY4OR+HhEMXfEVEEkgtf8lqAz7vez91un3UbccXyUf6zZZOpXOBEtK/SNlWJXXcuL73L4fRRw3qp5b8pOQvhwgj2aV7kXL8+PFpl6lQH3X+inP0VFtVz1wf1q3kL4cIoy6QEq9EJe7RU+PHj6eqqoq9f96bVhxxU/IXkZySDaOnZs+ezWu1r+X0sG6N9hERSSAlfxGRBFLyFxFJoLzq8497BICZ9WlbEck96dyDAvHXSsqb5J8NIwC2bNlCfXN9WnGISPYLYzh03LWS8ib5Z8sIgK21W9OKQ0SyXz4Mh1afv4hIAuVNyz9bqBaNiOQCZYoQpdsPqFo0IpIpSv4hyobrDiIiqYi0z9/MHjezOjOr6rDsGDNbbWY1wePRUcYgIiKHivqC72LgwoOW3QqscfcSYE3wWkREMijS5O/urwKfHbT4cuCJ4PkTwBVRxiAiIoeKY6jnse6+AyB4HNnVimY2z8wqzKxi586dGQtQRCTfZfUFX3dfBCwCKC0t9ZjDEclrCxcuZNOmTV2+3zYarW1gQmfSnYBHMieO5P+JmRW5+w4zKwLqYohBRHpp8ODBcYcgIYoj+a8E5gD3BY/PxxCDiBxELfZkiXqo51JgHTDGzLaZ2Q9pTfrTzawGmB68FhGRDIq05e/uM7t467woj9sZ9WeKiHwlqy/4ZpL6M0UkLD01NiH+Bmdikr9a7CKSTeJucCYm+YuIZEouNDaV/KXXcuEjrYh0T8lfIhH3R1pJLg3uSI2Sv/Ravv9RZFoYyQqSkbDCoIZJKyV/kSynZNU7+geYGiV/kZgpWUkcNIG7iEgCKfmLiCSQkr+ISAKpzz+DNKpDRLKFkn8W0agOEckUJf8MUmtdRLKF+vxFRBJIyV9EJIGU/EVEEkjJX0QkgZT8RUQSSMlfRCSBlPxFRBJIyV9EJIF0k5fkLM3YJNJ3Sv6St1QuQ6RrSv6Ss9RiF+k79fmLiCSQkr+ISAIp+YuIJJCSv4hIAin5i4gkkJK/iEgCKfmLiCSQkr+ISAKZu8cdQ0rMbCewJe44UjAc+DTuIPKEzmW4dD7DlSvnc7S7jzh4Yc4k/1xhZhXuXhp3HPlA5zJcOp/hyvXzqW4fEZEEUvIXEUkgJf/wLYo7gDyicxkunc9w5fT5VJ+/iEgCqeUvIpJASv4iIgmk5J8GM9tnZus7fBV3ss6LZjY089HlDjNbYGYbzOz94Dye1s26V5vZcZmML1f05jzKocxsWIe/5f9nZv8ePN9lZhvjji9smskrPXvcfXJnb5iZ0XpN5aLMhpRbzOybwCXAX7j7XjMbDgzsZpOrgSpgewbCyxl9OI9yEHevByYDmNnPgC/d/YGgUfdCX/drZgPcvSWMGMOkln+IzKzYzKrNrBx4B/i6mW0O/hClc0XAp+6+F8DdP3X37WZ2l5m9bWZVZrbIWn0PKAWeClpkg2KNPLt0dR7bf//MrNTM1gbPf2Zmj5vZWjP7v2Z2XXyh54T+Zvbr4JPVS22/e8H5Kw2eDzezzcHzq83sGTNbBbwUX9hdU/JPz6AOHxOfC5aNAf7Z3ae4ey6Uo4jbS7T+k/zIzMrN7Kxg+SPuPs3dTwYGAZe4+3KgAviBu0929z1xBZ2FujqP3RkLXACcCtxtZgWRRpjbSoBH3X0CsAu4MoVtvgnMcfdzowysr9Ttk54Dun2Cj4db3P2N2CLKMe7+pZlNBf4KOAd42sxuBb4ws5uBwcAxwAZgVXyRZrduzmN3fh98UthrZnXAscC2iEPNVX909/XB80qgOIVtVrv7Z5FFlCYl//DtjjuAXOPu+4C1wFoz+wD4MTAJKHX3rUH/a2F8EeaGTs7jHKCFrz7hH3wO93Z4vg/lg+4cfK7auhy7O79ZnQvU7SOxMrMxZlbSYdFk4MPg+admdgTwvQ7vfwEMyVB4OaOL87gF2AxMDZal0lUhvbOZr87v97pZL+voP73E7Qjg4WA4bAtQC8yjtV/1A1r/uN7usP5i4DEz2wN8U/3+7bo6j+OAfzSz24E34wsvbz0A/NbMZgP/M+5gekPlHUREEkjdPiIiCaTkLyKSQEr+IiIJpOQvIpJASv4iIgmk5C/SS0FdnPlxxyGSDiV/EZEEUvIXSUFQK/9DM3uZ1uJ9mNmPgsqj75nZs2Y22MyGmNkf24qkmdmRQWVNFU2TrKLkL9KDoGDaVcAUYAYwLXhrRVB59BSgGvihu39Ba32di4N1rgKedffmzEYt0j0lf5Ge/RXwnLs3uvvnwMpg+clm9r+DImo/ACYEy38D/G3w/G+Bf8potCIpUPIXSU1ndVAWA3/n7hOBewiqOrr760BxUFO/v7tXZSxKkRQp+Yv07FXgu2Y2yMyGAJcGy4cAO4L+/B8ctM0/A0tRq1+ylAq7iaTAzBYAf0NrmeRtwEZa67XfHCz7ABji7lcH6/8H4I9AkbvviiFkkW4p+YtEIJhv+HJ3nx13LCKdUT1/kZCZ2cPAd4CL4o5FpCtq+YuIJJAu+IqIJJCSv4hIAin5i4gkkJK/iEgCKfmLiCTQ/wfj/CXvXFvMEgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Order of day or sex\n",
    "sns.boxplot(x=\"day\",y=\"total_bill\",data=data,color=\"g\",hue=\"sex\",order=[\"Fri\",\"Sat\",\"Sun\",\"Thur\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "8de2fd29472d8520918489d2f7698684b73d156be72bb6a0af57d9dbbf4a217c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}