jbencook/seaborn-histogram.ipynb Secret

## seaborn-histogram.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import random\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def revenue(region):\n",
    "    \"\"\"\n",
    "    Generate random revenue with a different\n",
    "    mean, variance for each region\n",
    "    \"\"\"\n",
    "    if region == \"AMER\":\n",
    "        return max(0, random.normalvariate(800, 200))\n",
    "    elif region == \"EMEA\":\n",
    "        return max(0, random.normalvariate(200, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>date</th>\n",
       "      <th>region</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>AMER</td>\n",
       "      <td>650.098835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>AMER</td>\n",
       "      <td>519.729759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>AMER</td>\n",
       "      <td>920.096388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>AMER</td>\n",
       "      <td>550.643257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>56.305622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date region     revenue\n",
       "0    1999-01-01   AMER  650.098835\n",
       "150  1999-01-01   AMER  519.729759\n",
       "243  1999-01-01   AMER  920.096388\n",
       "969  1999-01-01   AMER  550.643257\n",
       "146  1999-01-01   EMEA   56.305622"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame with fake sales data for Q1-Q2 1999\n",
    "dates = []\n",
    "regions = []\n",
    "revenues = []\n",
    "\n",
    "days = {1:31, 2:28, 3:31, 4:30, 5:31, 6:30}\n",
    "for i in range(1000):\n",
    "    month = random.choice(range(1, 7))\n",
    "    dates.append(dt.date(1999, month, random.randint(1, days[month])))\n",
    "    region = random.choice([\"AMER\", \"EMEA\"])\n",
    "    regions.append(region)\n",
    "    revenues.append(revenue(region))\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'date': dates,\n",
    "    'region': regions,\n",
    "    'revenue': revenues\n",
    "}).sort_values(['date', 'region'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab43106e10>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df, x=\"revenue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab43398278>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df, x=\"revenue\", bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab43637d30>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFApJREFUeJzt3X/QnWV95/H3B1JEUUsigcb8aMI2qzKdaWWfuvzodFlwVawV20GBYTW1tDit69aytkDZWbf/1V2nte3sIhm1xQ7LBpEuyLpaNkI7rRoNiIAiJUUgMUget/7o6M5q5Lt/nPshh3Alz0nIOfc5PO/XzJlz7uu+nnO+uZPzfHLd1/0jVYUkSfs7qu8CJEnTyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqWlZ3wU8EyeccEKtX7++7zIkaabceeed36iqlYv1m+mAWL9+Pdu3b++7DEmaKUkeGaWfu5gkSU0GhCSpyYCQJDUZEJKkJgNCktQ0toBI8qEke5LcN9S2IsltSR7snpd37Unyx0l2JLknyanjqkuSNJpxjiD+DHjNfm1XAFuraiOwtVsGOBfY2D0uBa4eY12SpBGMLSCq6q+Bf9iv+Tzg2u71tcAbhto/XAOfBY5PsmpctUmSFjfpOYiTquoxgO75xK59NbBzqN+urk2S1JNpmaROo62aHZNLk2xPsn1+fn7MZfVr9dp1JFn0sXrtur5LlfQsNOlLbTyeZFVVPdbtQtrTte8C1g71WwPsbr1BVW0GNgPMzc01Q+TZYveunVxwzacX7bflbWdMoBpJS82kRxC3AJu615uAm4fa39IdzXQa8O2FXVGSpH6MbQSR5HrgLOCEJLuAdwO/D9yQ5BLgUeCNXfePA68FdgDfA946rrokSaMZW0BU1UUHWHVOo28Bbx9XLZKkQzctk9SSpCljQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQZED0a9U5wk9WnSd5QT3ilO0mxwBCFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlq6iUgkvxWki8luS/J9UmOTbIhybYkDybZkuSYPmqTJA1MPCCSrAb+LTBXVT8JHA1cCLwH+MOq2gh8E7hk0rVJkvbpaxfTMuC5SZYBzwMeA84GbuzWXwu8oafaZs9Ry0iy6GP12nV9Vypphiyb9AdW1deSvBd4FPi/wF8CdwLfqqq9XbddwOpJ1zazntjLBdd8etFuW952xgSKkfRs0ccupuXAecAG4MXAccC5ja51gJ+/NMn2JNvn5+fHV6gkLXF97GJ6JfDVqpqvqh8ANwFnAMd3u5wA1gC7Wz9cVZuraq6q5lauXDmZiiVpCeojIB4FTkvyvCQBzgG+DNwOnN/12QTc3ENtkqTOxAOiqrYxmIy+C7i3q2EzcDlwWZIdwIuAD066NknSPhOfpAaoqncD796v+SHgFT2UI0lq8ExqSVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSUy8BkeT4JDcm+UqS+5OcnmRFktuSPNg9L++jNknSQF8jiD8CPlFVLwV+CrgfuALYWlUbga3d8kxZvXYdSRZ9SNIsWDbpD0zyQuDngF8GqKrvA99Pch5wVtftWuAO4PJJ1/dM7N61kwuu+fSi/ba87YwJVCNJz0wfI4iTgXngT5N8IckHkhwHnFRVjwF0zyf2UJskqdNHQCwDTgWurqqXA9/lEHYnJbk0yfYk2+fn58dVoyQteX0ExC5gV1Vt65ZvZBAYjydZBdA972n9cFVtrqq5qppbuXLlRAqWpKVopIBIcuYobaOoqq8DO5O8pGs6B/gycAuwqWvbBNx8OO8vSToyRp2k/hMG/8tfrG1U7wCuS3IM8BDwVgZhdUOSS4BHgTce5ntLko6AgwZEktOBM4CVSS4bWvVC4OjD/dCquhuYa6w653DfU5J0ZC02gjgGeH7X7wVD7d8Bzh9XUZKk/h00IKrqr4C/SvJnVfXIhGqSZt7qtevYvWvnSH1fvGYtX9v56Jgrkg7dqHMQz0myGVg//DNVdfY4ipJm3agnTYInTmp6jRoQHwHeD3wA+OH4ypEkTYtRA2JvVV091kokSVNl1BPlPpbkN5Ks6q66uiLJirFWJknq1agjiIUT2H57qK0YXFdJkvQsNFJAVNWGcRciSZouIwVEkre02qvqw0e2HEnStBh1F9PPDL0+lsEZz3cBBoQkPUuNuovpHcPLSX4U+POxVCRJmgqHe7nv7wEbj2QhkqTpMuocxMcYHLUEg4v0vQy4YVxFSZL6N+ocxHuHXu8FHqmqXWOoR5I0JUbaxdRdtO8rDK7ouhz4/jiLkiT1b9Q7yr0J+ByDm/i8CdiWxMt9z5qjlpFk0cfqtev6rlTSFBh1F9NVwM9U1R6AJCuB/83gftKaFU/sHekKo15dVBKMfhTTUQvh0Pk/h/CzkqQZNOoI4hNJPglc3y1fAHx8PCVJkqbBYvek/gngpKr67SS/BPwsEOAzwHUTqK93h3JnMEl6NllsBPE+4HcBquom4CaAJHPdul8Ya3VTwDuDSVqqFptHWF9V9+zfWFXbGdx+VNKErF67zqPQNFGLjSCOPci65x7JQiQd3KijWUeyOlIWG0F8Psmv7d+Y5BLgzvGUJEmaBouNIN4J/EWSi9kXCHPAMcAvjrMwSVK/DhoQVfU4cEaSfwn8ZNf8P6vqU2OvTJLUq1HvB3E7cPuYa5EkTRHPhpYkNRkQkqQmA0KS1GRASJKaDAhJUlNvAZHk6CRfSHJrt7whybYkDybZkuSYvmqTJPU7gvhN4P6h5fcAf1hVG4FvApf0UpUkCegpIJKsAX4e+EC3HOBs9t2h7lrgDX3UpqXJC+FJTzfqDYOOtPcBvwO8oFt+EfCtqtrbLe8CVvdRmJYmL4QnPd3ERxBJXgfsqarhi/2l0bUO8POXJtmeZPv8/PxYapRm2lHLHA3piOhjBHEm8Pokr2VwOfEXMhhRHJ9kWTeKWAPsbv1wVW0GNgPMzc01Q0Ra0p7Y62hIR8TERxBVdWVVramq9cCFwKeq6mIG13o6v+u2Cbh50rVJkvaZpvMgLgcuS7KDwZzEB3uuR5KWtL4mqQGoqjuAO7rXDwGvmNRnr167jt27dk7q4yRp5vQaEH3yqBVJOrhp2sUkSZoiBoQkqWnJ7mKSDkt3joG0FBgQ0qHwHAMtIe5ikiQ1GRCSpCYDQtJBeaXbpcs5CEkH5TlDS5cBIfWtryOjPCJLizAgpL71dWSUR2RpEc5BSJKaDAg9q406wSrp6dzFpGc1J1ilw+cIQk/nLSsl4QhCLU5eSsIRhCTpAAwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRA6PCNeOc57z4nzaaJ31EuyVrgw8CPAU8Am6vqj5KsALYA64GHgTdV1TcnXZ8OwYh3ngPvPifNoj5GEHuBf1dVLwNOA96e5BTgCmBrVW0EtnbLUtPqtetGGrlIOnwTH0FU1WPAY93rf0xyP7AaOA84q+t2LXAHcPmk69Ns2L1rp/fNlsas1zmIJOuBlwPbgJO68FgIkRMP8DOXJtmeZPv8/PykSpW0mBHnpJyPmh0TH0EsSPJ84KPAO6vqO6PuDqiqzcBmgLm5uRpfhZIOyYhzUo7qZkcvI4gkP8IgHK6rqpu65seTrOrWrwL29FGbpOkx6lyTo5Lx6OMopgAfBO6vqj8YWnULsAn4/e755knXJmm6ONfUrz52MZ0JvBm4N8ndXdvvMgiGG5JcAjwKvLGH2iRJnT6OYvob4EATDudMshZJ0oF5JrUkqcmA0FTxBDhpevR2mKvU4qSkND0cQUiSmgwISVKTASFJajIgJElNTlJLmqzuon6afgaEpMnyRlMzw11MkqQmA0KS1OQuJk2G+52lmWNAaDK8mYw0c9zFJGn2ebvTsXAEIWn2OUIdC0cQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhKSlw/MlDonnQUhaOjxf4pA4gpAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCTtzxPqAE+Uk6Sn84Q6YMpGEElek+SBJDuSXNF3PZK0lE1NQCQ5GvgvwLnAKcBFSU7ptypJWrqmJiCAVwA7quqhqvo+8N+B83quSZImavXadVMz/zFNcxCrgZ1Dy7uAf95TLZLUi927dk7N/EeqauwfMookbwReXVW/2i2/GXhFVb1jv36XApd2iy8BHjjMjzwB+MZh/uykzUqts1InzE6t1nnkzUqt46zzx6tq5WKdpmkEsQtYO7S8Bti9f6eq2gxsfqYflmR7Vc090/eZhFmpdVbqhNmp1TqPvFmpdRrqnKY5iM8DG5NsSHIMcCFwS881SdKSNTUjiKram+TfAJ8EjgY+VFVf6rksSVqypiYgAKrq48DHJ/Rxz3g31QTNSq2zUifMTq3WeeTNSq291zk1k9SSpOkyTXMQkqQpsiQDYpou6ZFkbZLbk9yf5EtJfrNrX5HktiQPds/Lu/Yk+eOu9nuSnDrheo9O8oUkt3bLG5Js6+rc0h1gQJLndMs7uvXrJ1zn8UluTPKVbtuePo3bNMlvdX/v9yW5Psmx07JNk3woyZ4k9w21HfI2TLKp6/9gkk0TqvM/d3/39yT5iyTHD627sqvzgSSvHmof+++FVq1D696VpJKc0C33tk2fVFVL6sFgAvzvgZOBY4AvAqf0WM8q4NTu9QuAv2NwqZH/BFzRtV8BvKd7/VrgfwEBTgO2Tbjey4D/BtzaLd8AXNi9fj/w693r3wDe372+ENgy4TqvBX61e30McPy0bVMGJ4d+FXju0Lb85WnZpsDPAacC9w21HdI2BFYAD3XPy7vXyydQ56uAZd3r9wzVeUr3nX8OsKH7XXD0pH4vtGrt2tcyOEDnEeCEvrfpk3VN4oswTQ/gdOCTQ8tXAlf2XddQPTcD/4rBCYCrurZVwAPd62uAi4b6P9lvArWtAbYCZwO3dv9wvzH0RXxy23b/2E/vXi/r+mVCdb6w+8Wb/dqnapuy7+oBK7ptdCvw6mnapsD6/X7xHtI2BC4Crhlqf0q/cdW537pfBK7rXj/l+76wTSf5e6FVK3Aj8FPAw+wLiF63aVUtyV1MrUt6rO6plqfodhm8HNgGnFRVjwF0zyd23fqs/33A7wBPdMsvAr5VVXsbtTxZZ7f+213/STgZmAf+tNsd9oEkxzFl27Sqvga8F3gUeIzBNrqT6dymCw51G07D9+1XGPxPnIPU01udSV4PfK2qvrjfqt5rXYoBkUZb74dyJXk+8FHgnVX1nYN1bbSNvf4krwP2VNWdI9bS53ZexmAYf3VVvRz4LoPdIQfS1zZdzuCClBuAFwPHMbia8YFqmcp/u50D1dZrzUmuAvYC1y00HaCevv4NPA+4CvgPrdWNtonWuhQDYqRLekxSkh9hEA7XVdVNXfPjSVZ161cBe7r2vuo/E3h9kocZXGn3bAYjiuOTLJxPM1zLk3V2638U+IcJ1Lnw2buqalu3fCODwJi2bfpK4KtVNV9VPwBuAs5gOrfpgkPdhr1937rJ29cBF1e3L2YK6/wnDP6D8MXuu7UGuCvJj01DrUsxIKbqkh5JAnwQuL+q/mBo1S3AwtEJmxjMTSy0v6U7wuE04NsLQ/5xqqorq2pNVa1nsM0+VVUXA7cD5x+gzoX6z+/6T+R/jlX1dWBnkpd0TecAX2bKtimDXUunJXle9+9goc6p26ZDDnUbfhJ4VZLl3YjpVV3bWCV5DXA58Pqq+t5+9V/YHRG2AdgIfI6efi9U1b1VdWJVre++W7sYHLTydaZhm45jYmPaHwyODvg7BkctXNVzLT/LYHh4D3B393gtg33LW4EHu+cVXf8wuLHS3wP3AnM91HwW+45iOpnBF2wH8BHgOV37sd3yjm79yROu8aeB7d12/R8MjvaYum0K/B7wFeA+4M8ZHF0zFdsUuJ7B3MgPGPziuuRwtiGDOYAd3eOtE6pzB4P99AvfqfcP9b+qq/MB4Nyh9rH/XmjVut/6h9k3Sd3bNl14eCa1JKlpKe5ikiSNwICQJDUZEJKkJgNCktRkQEiSmgwISVKTASE1dCcn+f3QkuYXQOokWZ/BvSP+K3AX8OYkn0lyV5KPJHl+knOT3DD0M2cl+Vj3+lX79+/aH07ye137vUle2rX/xyTvGnqv+7oLNpLkXyf5XJK7k1yT5OjJbQlpwICQnuolwIcZXHL9EuCVVXUqg7OyLwNuY3B5jOO6/hcAW7qbvPz7Rv8F3+jarwbexUEkeVn3vmdW1U8DPwQuPkJ/PmlkyxbvIi0pj1TVZ7ur154C/O3gMkkcA3ymqvYm+QTwC0luBH6ewSXQ/0Wr/9D7LlyE8U7glxap4RzgnwGf797ruey7KJ40MQaE9FTf7Z4D3FZVFzX6bAHezuBKqp+vqn/sLrZ3oP4A/697/iH7vnd7eeoo/tihz762qq48zD+DdES4i0lq+yxwZpKfgMF1+5P8027dHQwuH/5rDMJisf4H8nD3PnT3G97QtW8Fzk9yYrduRZIfPxJ/KOlQGBBSQ1XNM7g/9PVJ7mEQAC/t1v2Qwe1Bz+2eD9r/ID4KrEhyN/DrDK4kSlV9mcF8xl9273Ubg1tNShPl1VwlSU2OICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlq+v+22ZeQS86vwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df, x=\"revenue\", bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'$p(x)$')]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(df, x=\"revenue\", bins=30, stat=\"probability\")\n",
    "ax.set(ylabel=\"$p(x)$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'$p(x)$')]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(df, x=\"revenue\", bins=30, stat=\"probability\", hue=\"region\")\n",
    "ax.set(ylabel=\"$p(x)$\")"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}