Seaborn-bargraphs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set_context(rc = {'figure.figsize':(11, 9), \"font.size\":15, \"axes.labelsize\":20}, font_scale=2)"
   ]
  },
  {
   "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>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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Seaborn bargraph](https://seaborn.pydata.org/generated/seaborn.barplot.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a set of vertical bar plots grouped by a categorical variable:\n",
    "sns.barplot(x ='day', y = 'total_bill', data = tips);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a set of vertical bars with nested grouping by a two variables:\n",
    "sns.barplot(x = 'day', y = 'total_bill', data = tips, hue = 'sex');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAEcCAYAAACI1cAtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAFc5JREFUeJzt3XuUXWV9//H3JDQXSEQcB7CCBrT9Yl24NELVChZbZCnNr6I2iFJupautIoiKYCrUVlfFC+3vJz+xuBaWSAVBRJEVS7204J0WKq3I5VuQpJpI6TCGTALkNjP9Y5+hw6xJZs7knLOfc+b9Witrn7Ov39kryWeevZ/97L6xsTEkSarbvLoLkCQJDCRJUiEMJElSEQwkSVIRDCRJUhEMJElSEQwkSVIRDCRJUhEMJElSEQwkSVIRDCRJUhH2qruALrAQOBJ4CBipuRZJ6hbzgWcBtwPbZrKBgTS9I4Hv1F2EJHWpo4HvzmRFA2l6DwFs3PgYo6OOjN5O/f1LGBraUncZc4LnujPm8nmeN6+P/fbbBxr/h86EgTS9EYDR0TEDqQM8x53jue4Mz/PMb3XYqUGSVAQDSZJUBC/ZzVB//5K6S5gTBgaW1l3CnOG5fqqt23aweXhr3WXMaQbSDJ1z8Y08svGxusuQ1CbXfOxkNmMg1clLdpKkIhhIkqQiGEiSpCIYSJKkIhhIkqQiGEiSpCIYSJKkIhhIkqQiGEiSpCIYSJKkIhhIkqQiGEiSpCIYSJKkIhhIkqQi1Pb6iYhYDZw2zWrfakx3Zuax7a1IklSnOt+H9CHg8gnfPwXsBM6ZMG+4MV+S1ONqC6TM/Anwk/HvETFM1RK6beJ6EdHp0iRJNeiWN8b2RcQq4G3AAHAncHZm/is8efnvqMx8/vgGEbEMWAuckpmfi4jTqVpk7wQ+CGwDjsnMBzv4c0iSdqFbAukYYG/gHcAC4K+AmyLiOZk50sR+FgDnA2cAzzSMJKkc3RJITwCvy8xHASJiX+AKIIB7mthPH/ChzPz71pcoqdW2bfoZW37+Q8ZGdrT9WKee+jVGRsZaus/58/um3efixYtYufKtLF9+REuP3Y26JZDuGg+jhrWN6dNns68W1COpAx77r7vY+fhQR461YcNwR44zlTVrbjSQ6J5AemzS99HGdDbPUT28h7VI6pB9DjycLT/f0ZEW0oHPXFpbC2nFihNaetxu1S2BNJ0xYP6keUvqKERS6yzc92AW7ntwR4511cdOZnBwc0v3OTCwtOX77GW9MlLDMLB/RCycMO+ouoqRJDWvV1pIa6geqP1MRHwGOBx4D9BMDzxJUo16ooWUmd8AzgOOBm4G3gy8gWrkB0lSF+gbG2vtTbwetAxYe87FN/LIxsl9KyT1imu8h9RS8+b10d+/BOAQYN2MtmlnQZIkzZSBJEkqgoEkSSqCgSRJKoKBJEkqgoEkSSqCgSRJKoKBJEkqgoEkSSqCgSRJKoKBJEkqgoEkSSqCgSRJKoKBJEkqgoEkSSpCr7wxtu0uXXVC3SVIaqOt23bUXcKcZyDN0NDQFkZHfZlhO83ll5l1mudaJfKSnSSpCAaSJKkIBpIkqQgGkiSpCAaSJKkIBpIkqQgGkiSpCAaSJKkIBpIkqQiO1DBD/f1L6i5hThgYWFp3CXOG5xp2bt/Gxk3b6y5DDQbSDN11+QVsHx6quwxJLfTS868ADKRSeMlOklQEA0mSVAQDSZJUBANJklQEA0mSVAQDSZJUBANJklQEA0mSVAQDSZJUBANJklQEA0mSVAQDSZJUBANJklQEA0mSVISufP1ERKwGTtvNKq/JzG9Osd0xwC3A0Zn53fZUJ0maja4MpIb1wMpdLLtnF/N/CLwCuLstFUmSZq2bA2lbZt7WzAaZOQw0tY0kqTO6OZB2KyLWATcAy4GXAlcAN+ElO0kqUlcHUkRMVf9IZo41Pp8DfAL4CLAR2LtTtUlz2b2Dj/P1Bzaybedo3aXs1sIfn8rIyNj0K87S/Pl9e7T/xYsXsXLlW1m+/IgWVlWubg6k5wE7ppj/NuDyxuefAe8dD6hGpwZJbfattZvYMLy97jKm9/iGuiuY1po1NxpIXWA98IYp5v/nhM93T2gtSeqQ3zxkX7aNjJbfQtrvgOJbSCtWnNDCisrWzYG0LTPvmGadhztSiaSneMHA3rxgoPwr5C89/woGBze3bf8DA0vbuv9e44OxkqQiGEiSpCIYSJKkIhhIkqQidGWnhsw8fQbrLJti3q1AX+srkiTtKVtIkqQiGEiSpCIYSJKkIhhIkqQiGEiSpCIYSJKkIhhIkqQiNPUcUkRcC1wD3JyZU736QZKkWWn2wdhXASuBTRFxA1U43eorHiRJe6rZS3bPBo4FrgdeD3wT2BARfx0Rv97q4iRJc0dTLaRGS+gW4JaIeDtwHPBm4BTgnRGxlqrVdHVmZquLlST1rll3asjMkcy8uTGu3CuBLwCHAhcC90TE9yPi9a0pU5LU62Y9uGpE/BrV/aQTgcOAncBXgauBMeCPgS9FxJ9n5odaUKskqYc128vuMKoAOhF4QWP294CzgC9k5i8mrH5dRNwGvBswkCRJu9VsC+mexvQu4E+BazLzZ7tZ/2fAwtkUVprD/+SjdZcgqcV2bt9WdwmaoNlA+ghVh4W7Z7j+SZk50uQxijQ0tIXRUXu3t9PAwFIGBzfXXcac4LlWiZrtZfen060TEQOZOdhYvyfCSJLUfk13aoiIU4A3AUt4ai+9vYClwAuBBS2pTpI0ZzTbqeF84GJgOzAMPBNYD/QDewNPAJe2uEZJ0hzQ7HNIZwD/DuwPvALoA14N7EvV024RcFsrC5QkzQ3NBtIy4KrM3JyZDwIbgaMbD8n+DXAdcG6La5QkzQHNBtIOYGLXnPuBF034fgvwq3talCRp7mk2kO4FfmPC9wSOmPD96fTIc0eSpM5qtpfdlcCnImIh1dBANwHXR8QHqMLqXVT3mCRJakqzzyFdHhEHAe+gunz3JeBa4AONVYaBC1paoSRpTugbG2t+9IGI2Cszd074fjRV1+/vjT8U20OWAWvrLkJSd9m2fTsLFyyYsyNizJvXR3//EoBDgHUz2Wa3gRQR/zSLOsYy87dnsV2plgFrz7v+L3hkyy+mW1eSAFh9xicADKQmAmm6S3aHUr1KYqIDqJ432kjVy24e1X/azwSGqO4lSZLUlN0GUmYum/g9Io4B1gCnA5/LzNEJy94CXAFc1uoiJUm9r9lu35cCV2TmVRPDCCAzP08VRh9sVXGSpLmj2UB6PtVlul1ZDzx79uVIkuaqZgMpgZMiYv7kBRGxCPgD4EetKEySNLfM5gV9nwe+GxFXAg9SdXD4VeBtwHOB32lphZKkOaHZB2Ovi4jFVMF0Of/bA6+PqlvfGzLzGy2tUJI0JzT9gr7MXB0RVwHLqbp7jwEPZuadLa5NkjSHNB1IAI0ednc0/kiStMea7dQgSVJbGEiSpCIYSJKkIhhIkqQizMlAioi+umuQJD3VrHrZdVpEHA68HzgGeAbVqOLfBj6cmTN+Q21EPBv4NNULBte1vFBJ0qwV30KKiBcBPwD2owqS1wDnUb1j47aIeHkTu3s1jiQhSUXqhhbSucDDwPGZOTI+MyK+AtwHXIQhI0ldrxsC6QCqoYnmAU8GUmZuiYhzgX0AGgO+ng+cDDwPGAXuBC7MzFsj4nTgysbmayPis5l5eqd+CEnS7nVDIN0MHA98vzGg6z9l5n0AmfnFCet9HPgj4ALgx1Svwfgz4PqIeC7wVeDPG3/eiKOSS1JRuiGQLgMOBN7T+ExEDAJfAy7NzNsb6/0ysCozn3xjbURsBW4AXpiZt0fETxqL7szMdR2qX1LNnli/mU0/epixHaPTr9wip95yKgAjI2PTrNkeixcvYuXKt7J8+RG1HH82ig+kzBwDLoyIS4DXAsdSdU74feDkiDg7My/LzJMAImIACOBXgP/T2M2CzlcuqRSb7xlkxy+2dvSYGzZv6OjxprJmzY0GUjtk5qPAtY0/RMRLgL8DLomIa6juG30KOBJ4HLgb+Gljc587kuawpb82wOjOzraQDnjaAFBvC2nFihNqOfZsFR1IEXEQ8C/ARZn5mYnLMvPOiLgQ+DJwOPAl4N+AFwL3ZeZoRBwPvKnDZUsqzOKDlrL4oKUdPebqMz4BwODg5o4et5uV/hzSQ1Q9685qvCJ9sgCeoGoB9QP/NzPvabweA+B1jen4zzmCJKlIRbeQMnMkIs6iav3cERGfBO4F9gaOo3pQdhVVy2gYuCgixqiC503AmY1d7dOYPtqYvjEi/n68t54kqX6lt5DIzJuAl1F15X4/8HWq+0gvBk7MzEsycxPwemA+8EXgKuA5wKuAzcDRjd3dAqwBLqbqJi5JKkTRLaRxmfmvwEnTrHMrVYeGyZ42YZ2t/G/PO0lSQYpvIUmS5gYDSZJUBANJklQEA0mSVAQDSZJUBANJklQEA0mSVAQDSZJUBANJklQEA0mSVAQDSZJUBANJklQEA0mSVAQDSZJUBANJklSEvrGxsbprKN0yYG3dRUjqLtu2b2fhggUMDm6uu5RazJvXR3//EoBDgHUz2aYrXtBXgqGhLYyOGt7tNDCwdM7+4+00z3VnDAwsqLuEruIlO0lSEQwkSVIRDCRJUhEMJElSEQwkSVIRDCRJUhEMJElSEQwkSVIRHKlhestwpAZJhdmxdRuPbt5edxm75EgNbXTLu8/jiUeG6i5DkgA4/qoroeBAmg0v2UmSimAgSZKKYCBJkopgIEmSimAgSZKKYCBJkopgIEmSimAgSZKKYCBJkopgIEmSimAgSZKKYCBJkopgIEmSimAgSZKKUOTrJyLicOD9wDHAM4Ah4NvAhzPz32ssTZLUJsW1kCLiRcAPgP2AdwCvAc6jesnTbRHx8hrLkyS1SYktpHOBh4HjM3NkfGZEfAW4D7gI+J2aapMktUmJgXQA0EfVensykDJzS0ScC+wDEBHrgG9m5h+OrxMRpwNXAgdn5vqIWA0cCFwPvA94DnAvcEFmfq0DP4skaYaKu2QH3Ex1ee77EfH2iDhsfEFmfjEzP9vk/l4OvIeqZXUCsBO4ISL2bVXBkqQ9V2IL6TKqVs17Gp+JiEHga8ClmXl7k/vbF3hJZq5t7Osx4FtUHSa+0qKaJekpHtz6BN8bfpQdo2Nt2f+1p57KyEh79j1u8eJFrFz5VpYvP6KtxxlXXCBl5hhwYURcArwWOBZ4NfD7wMkRcXZmXtbELh8aD6OG9Y3pPi0pWJKmcPvmYf57x4627X/jhg1t2/dEa9bcOHcDaVxmPgpc2/hDRLwE+Dvgkoi4poldPT7p+2hjWuLlSkk94silT2N7G1tIex94QEdaSCtWnNDWY0xUVCBFxEHAvwAXZeZnJi7LzDsj4kLgy1T3mMaA+ZN2saQjhUrSNA5dtJhDFy1u2/6Pv+pKBgc3t23/dSitlfAQVc+6syJi0RTLA3gC+AkwDBw8aflR7S1PktQuRbWQMnMkIs4CvgTcERGfpOqmvTdwHNWDsqsyc1NErAFWRcT7gH8Gfhf4rZpKlyTtodJaSGTmTcDLgB9TDR/0dar7SC8GTszMSxqrfhi4AjgfuAl4FnBmxwuWJLVE39hYe2+K9YBlwNpb3n0eTzwyVHctkgSUfw9p3rw++vuXQHXPf92MtmlnQZIkzZSBJEkqgoEkSSqCgSRJKoKBJEkqgoEkSSqCgSRJKoKBJEkqgoEkSSqCgSRJKoKBJEkqgoEkSSqCgSRJKoKBJEkqgoEkSSqC70Oa3jJgbd1FSNJEO7Zu49HN2+suY5dm8z6kol5hXrKhoS2Mjhre7TQwsLToF471Es91Z3iem+MlO0lSEQwkSVIRvGQ3vflQXQ9V+3meO8dz3Rlz9TxP+Lnnz3QbOzVM7yjgO3UXIUld6mjguzNZ0UCa3kLgSOAhYKTmWiSpW8wHngXcDmybyQYGkiSpCHZqkCQVwUCSJBXBQJIkFcFAkiQVwUCSJBXBQJIkFcFAkiQVwaGDdiMi3gJcCBxKNXz6xZl5Va1F9bCIeDHVQ3SHZOb6uuvpNRExD/gj4O1Uf6cfBr4CfCAzHZK6RSKiD3gn1Xk+GPgP4KOZeU2thXUBW0i7EBErgauBrwMnALcCn42I36uzrl4VEQGswV+S2ul84JPAV6n+Tv8VcBpwfZ1F9aBVwCXAZ4EVwDeAqyPixFqr6gKO1LALEfEAcEdmnjRh3nXAizLzBfVV1lsiYi+q39o/AuwAngEcbAuptRq/tQ8Bn8/MsybMfzNwLfCSzPy3uurrFRHxS1Qtz6sz8+wJ828F5mfm0XXV1g1sIU0hIg4FngfcMGnRF4HDIuKQzlfVs44CPkb12/oFNdfSy5YCnwMmXza6rzF9XmfL6VkjwG8CF0+avx1Y1PlyuouXR6Z2WGOak+Y/0JgGvta8Ve4FDs3M/46I0+supldl5jBwzhSLTmhM7+5gOT0rM0eBu+DJVun+wBnAscAf11haVzCQprZvYzo8af74jd+ndbCWnpaZD9ddw1wVES8D3gfcmJn3Tbe+mvZGqqsqUN23+1yNtXQFL9lNbfzNUpNvsI3PH+1gLVLLRcQrgX+gaun/Yc3l9KofUl2+Oxt4JVUoaTdsIU1tU2M6uSW0dNJyqes0OjKspuqO/NrMHKq3ot6UmWupAv/bETFM1Uv3FZn5g5pLK5YtpKmN3zt6/qT5z5+0XOoqEfFu4PPAD4BXZeZDNZfUUyLiGRFxSkT88qRFP2xMn93pmrqJgTSFzHyA6jebyc8cvQm4PzN/2vmqpD0TEWdS9Wb8AlXLyJZ+682jev5ocgeG4xrTuzpbTnfxkt2ufRC4MiI2Uj2w+bvAicBJu91KKlBE7A9cCvwn1cOxy6tnkZ/0QGY+UkdtvSQzH4mITwHvi4jHgTuoHm1YBVyRmV5d2Q0DaRcyc3VELATOo7rp+yBwamZeV29l0qy8FtgbeC7wnSmWn4K9wFrlXcBPgTOBvwDWAx8APl5nUd3AkRokSUXwHpIkqQgGkiSpCAaSJKkIBpIkqQgGkiSpCAaSJKkIBpJUuIjYPyL2aXxeHRE+q6GeZCBJBYuI11GNnTjQmPVpqodYpZ7jSA1S2V4GPH38S2OkaEeLVk+yhSRJKoJDB0mFiojVwGkTZn0LWAeclpl9E9Y5iuoy3v8HXkg1gOr/y8zLO1iutMdsIUnl+jTw5cbndwF/uYv1+qne/no/8F7g58DfRMSqtlcotZCBJBWqcb/oR42vN2bmN3ax6tOBKzPzLZn5SeA1wLeBiyJivw6UKrWEgST1hovHP2TmCPAJYDFwbG0VSU0ykKTu94vMfHjSvPsb02UdrkWaNQNJ6n7bp5g3vzEd6WQh0p4wkKTud0BELJk071ca0/snryyVykCSyjbewtndv9U+4KzxLxGxF3AusAn4x/aVJrWWIzVIZRtsTN8bETfvZr0/i4hlwN3AicBvAGdm5uNtrk9qGVtIUtmuBb4JnAF8dDfrHUf1gOzHgX2AN2bm37a/PKl1HKlB6mLjozmMj9wgdTNbSJKkIhhIkqQiGEiSpCJ4D0mSVARbSJKkIhhIkqQiGEiSpCIYSJKkIhhIkqQiGEiSpCL8D3HzkwEk3PlpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a set of horizontal bars:\n",
    "sns.barplot(x=\"tip\", y=\"day\", data=tips);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Control bar order by passing an explicit order:\n",
    "sns.barplot(x=\"time\", y=\"tip\", data=tips, order=[\"Dinner\", \"Lunch\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Control bar order by passing an explicit order without ci\n",
    "sns.barplot(x=\"time\", y=\"tip\", data=tips, order=[\"Dinner\", \"Lunch\"], ci = None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use median as the estimate of central tendency:\n",
    "sns.barplot(x=\"day\", y=\"tip\", data=tips, estimator=np.median);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}