Plotnine/after_stat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T12:59:03.404989Z",
     "iopub.status.busy": "2021-03-04T12:59:03.402946Z",
     "iopub.status.idle": "2021-03-04T12:59:07.838368Z",
     "shell.execute_reply": "2021-03-04T12:59:07.841198Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from plotnine import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### after_stat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`geom_bar` uses `stat_count` which by default maps the `y` aesthetic to the `count` which is the number of observations at a position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T12:59:07.878808Z",
     "iopub.status.busy": "2021-03-04T12:59:07.857162Z",
     "iopub.status.idle": "2021-03-04T12:59:09.431227Z",
     "shell.execute_reply": "2021-03-04T12:59:09.429323Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (97654321012345679)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'var1': [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5]\n",
    "})\n",
    "\n",
    "(ggplot(df, aes('var1'))\n",
    " + geom_bar()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `after_stat` function, we can instead map to the `prop` which is the ratio of points in the panel at a position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T12:59:09.603539Z",
     "iopub.status.busy": "2021-03-04T12:59:09.521807Z",
     "iopub.status.idle": "2021-03-04T12:59:10.579108Z",
     "shell.execute_reply": "2021-03-04T12:59:10.577573Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (97654321012345679)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df, aes('var1'))\n",
    " + geom_bar(aes(y=after_stat('prop'))) # default is after_stat('count')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With `after_stat` you can used the variables calculated by the stat in expressions. For example we use the `count` to calculated the `prop`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T12:59:10.735910Z",
     "iopub.status.busy": "2021-03-04T12:59:10.647883Z",
     "iopub.status.idle": "2021-03-04T12:59:11.698842Z",
     "shell.execute_reply": "2021-03-04T12:59:11.696995Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (97654321012345679)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df, aes('var1'))\n",
    " + geom_bar(aes(y=after_stat('count / np.sum(count)')))\n",
    " + labs(y='prop')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `geom_bar` uses `stat_count` to compute a frequency table with the `x` aesthetic as the key column. As a result, any mapping (other than the `x` aesthetic is lost) to a continuous variable is lost (if you have a classroom and you compute a frequency table of the gender, you lose any other information like height of students).\n",
    "\n",
    "For example, below `fill='var1'` has no effect, but the `var1` variable has not been lost it has been turned into `x` aesthetic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T12:59:11.829723Z",
     "iopub.status.busy": "2021-03-04T12:59:11.720737Z",
     "iopub.status.idle": "2021-03-04T12:59:12.895555Z",
     "shell.execute_reply": "2021-03-04T12:59:12.893752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (97654321012345679)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df, aes('var1'))\n",
    " + geom_bar(aes(fill='var1'))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `after_stat` to map `fill` to the `x` aesthetic after it has been computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-04T12:59:12.917870Z",
     "iopub.status.busy": "2021-03-04T12:59:12.912538Z",
     "iopub.status.idle": "2021-03-04T12:59:14.308559Z",
     "shell.execute_reply": "2021-03-04T12:59:14.310044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NnvMHAADgiEBRf7WZDToRx3qfqbND7HpBSzKM/IxZ+VkVAABAFkoCfdVu9RRz7JkL7Ok0Mqd9AQAAHBK1jDTu9k3jYc9W/PP+FPeTiUbNP4Q/AADgOaaKFE3zUS8xxy8ULDLiFjOYK2TmDwAAwBGmjj3upTcJ79K1jq2NHk97iWYCU5nVY+YPAADAIaYVSPkNHz3P7B2/eaNjSxqTeR0hMt8Q/gAAgOeYlpH0tK/V5de0Ts9aXa4PzKA2txH+AACA5xxu/yruhg/7Hu7cVfJbP0ybjmC3vAh/y5cv13vvvafW1laVl5drwoQJmjZtmttlAQCAAtV4tFlHzWPhz9KJd/Tm3okw2G7mZ/zLi/BXW1urG264QaFQSAcOHNDdd9+tmpoajRkzxu3SAABAATo5eKoa2g4k2JKb07aJTiUHeMhzckOGDIlbNgxDe/fudakaAABQ6KIpP+olszCYynWD3O3biyeeeEJr1qzRkSNHNHDgQI0bN87tkgAAQIHq6YaPnlkJg2As7HX8JoXbfnsKfwcPHtSZZ56p008/XZs3b06/zCzkTfibOXOmrrvuOu3cuVObN29WOByO297Q0KCGhobYcigUUk1NTdbHNQxDgUBqt4Lni456C61uiX47jX47qxD7XSgS9bVbv9sdLMjjUup3nss8/B1nWTKMTmFPig98KUzr9fR6t1tvvVVnn322IpFI5jVmKG/Cn3Tsi3XGGWfov/7rv7Rq1SrNmTMntq2+vl51dXWx5UWLFmnJkiW2HDcYDNoyjtMqKircLiEj9NtZnfv9d//6gIuVeMsnC25JuL5Qv9/5rrKyMuH6zv02P3eqGu9Lpd/5LpM3fHR73l/HQ54V/2vqAyZe/eabb+ovf/mL5syZo/r6+nRHzVpehb8OpmnGzfJJ0rx581RbWxtbDoVCamxszPpY4XBYLS0tWY/jpEAgoIqKCjU1NSkajbpdTlrot7MKsd+FItGfP/Q7d1Lpd18nC/K4XH6/kwVLu6Uy85f2a9u6PuOvt90TjBeJRDR//nz9+7//u95///2eB8gR18Pf4cOHtWXLFp133nkqLS3Vhx9+qD/96U+66qqr4varrq5WdXV1bPngwYO2/EVsWVbB/YXeIRqNFlzt9NtZhdzvfJeor/Q7d+i3s7zQb9My9NPAPyXd/uDR3yn9ubzub/64ueTHSfdu/OiQ9J1hceuWLl2qiy66SOecc45/w58kvfrqq3rkkUdkmqaqqqo0efJkTZw40e2yAABAgdrVsldje9ie6qvf7LRz5049/vjj2rp1q+PH7sz18HfSSSfZdu0eAACAJA0M1khHkm9vz+ZmkAxt3LhRn3/+ub7+9a9LklpbW9Xa2qrBgwdrx44djl1b7nr4AwAAsFtRUYnubf19T3vELaXw5JaEejrGr8+5NW75qquu0j/+4z/Glp9++mn94Q9/0Nq1a1VeXp5ZARkg/AEAAM+xLENmOrN7Vpd39CYJgyd2SyUtxu9TVlamsrKy2HLfvn1VUlKiwYMHp16nDQh/AADAc6KWoaiVznRel30tqchIdEdw6nr7mVmzZmnWrFkZjJwdwh8AAPAcS4bMtMJfx8+d+NXo8py/tMfK0/e7Ef4AAIDnpPOGjy4nfDPaI7FMY2NuEf4AAIDnHLvmL3H4spRunOu6vcvDnpPV0MuobiH8AQAAz9nb+kXsmr/uISzbGbnEYbDr1qhpZnmc3MjoITcXXnihPvzww4TbduzYoQsvvDCrogAAALLRbhbpaPTYp73rxzRs/hTFfTqO66nTvm+88YaampoSbmtqatJbb72VVVEAAADZGFhaqR3Nnyfe2GUqMNNn/MWGS3J+1zCcf5B0KjI+7Wsk6dQ777yjgQMHZlwQAABAtiyrKPXn/KX4jL8kuytfZ/iSSTn83XfffbrvvvskHQt+48aNU1FRfFOPHDmi9vZ2/dM/JX+RMgAAQK6ZlmSaqYayzvudSHaxEGh1vcUjNVYGj5pxQsrhb9SoUbrllltkWZbuueceTZ8+XaecckrcPsFgUGeddZYmTZpke6EAAACp6ulu3551+pl8vV03SymHv7Fjx2rs2LGSjs383XDDDaqpqclZYQAAAJky0wx/yU/lWvFr0siT+ZodM7rm76677rK7DgAAANtYvZz2Tf3xL11OCVuJt6R4kLyQUfgzTVOPPvqonnvuOX366adqa2uL224Yhj7++GNbCgQAAEjXkWg0bubPnmf9pfZ8vxNbC/yav85uv/12PfDAAxo7dqzGjRunYDBod10AAAAZ+6SpUVHTiDuda9gexpKEweOr2/P0Ic8Zhb//+I//UF1dnX7xi1/YXQ8AAEDWTi8fpL+1fBm3ruvsn91RMDbTd/xAxV56zl9bW5tGjRpldy0AAAC2sCxDVi+PeukcBtO9mePYMXrZnt5wjskokl5zzTV66aWX7K4FAADAHpYky0j50xEW4z5Wl0+X7b2O66Vr/s4//3zdeeed2rdvn8aPH69+/fp12+cHP/hBtrUBAABkrLeZud4HiPslgyiXn3N/GYW/H/7wh5Kk3bt36+mnn+623TAMRaPR7CoDAADIUMdsXXo/lDyuGUYGUa7Q3/DR2a5du+yuAwAAwDaphr9UZwe77tfb+3/zWUbhb+jQoXbXAQAAYJ8UbvjIavj8PKObkozC3549e3rdZ8iQIZkMDQAAkLWdXxxMEtByEQgTJ8GjZn5eApdR+DvttNNk9DLfyTV/AADALf2DFdoV/SL5DrZmwE6DdcqBxUZGMSvnMqrqhRde6LausbFR69ev1+bNm7V06dKsCwMAAMhURTDU8w0XuThta6lLqMzPc8MZhb/LL7884fpZs2bp5ptv1ptvvqmrrroqq8IAAAAyZhlSJtf8pfIjPWW6ztvy9G5f2987cumll2r16tV2DwsAAJA6K8OP2emTaJ2Zxlh5yvaT0e+8845KS0vtHrabYDCoUCiU9TjFxcUqLy+3oSLndFxvGQ6HZRXY7Ub021mF2O9Ckaiv9Dt3Uup3o4MFeZxXvt9pP+cv9oNdlvNzAi9jGYW/BQsWdFsXiUS0fft2bdy4UQsXLsy6sN5EIhFFIpGsxykvL1dzc7MNFTknEAgoGAyqpaWl4G6sod/OKsR+F4pEfaXfuZNKv6ucLMjjcvn9tmPiJiUds3RpMhIlPUuyMpnKy9P5gozCX6L3+paWluqUU07Rww8/rB/96EdZFwYAAJCx2Pt10/yxpFvsm/6bO3eu1q5dq+bmZlVVVWnu3LlatGiRbeP3hjd8AAAAz2lsaz2R5HL6vL+ur/7o/Sduuukm/eY3v1FZWZn+9re/6eKLL9bpp5+uadOm2VRTz/LzATQAAABZONTSJkWduFgv+TEi0cTnnc8+++y45aKiIu3cudPWqnqS8d2+77//vqZOnarq6mqFQiFVV1dr2rRp2rp1q43lAQAApG943yoZpnr/WDZ9EowdLEoes37+858rHA5ryJAhamlp0bXXXutYbzKa+Xv77bc1fvx4DR48WNOnT9egQYO0b98+vfDCCxo5cqReeeUVjRkzxu5aAQAAUpfKNX+Z3tmb0s0cyQe777779Ktf/Urvvfee/vM//1OVlZUpHjh7GYW/O+64Q9/73ve0Zs0aFRefGOLXv/61Jk6cqDvuuEMbN260rUgAAIB0GJb03KjvJd0+ddMbCdfHPdGrc3ZLcmnfs6OTH6P908+kQYOT12gYOvfcc7Vu3TrdddddevDBB5Pua6eMwt/777+v5557Li74ScceibFgwQJNmTLFluIAAAAy0tvMXJLtRuftRqf9cnj5YHt7uz7++OPcHaCLjK75C4fD2r9/f8Jt+/btUzgczqooAACAnErl7R5d3/Jhw1s8Ghsb9eSTT6qpqUmmaWrTpk363e9+p4suuijrf6RUZTTzN2nSJN1+++065ZRT4op99dVX9fOf/1y1tbW2FQgAAJAuw5SmvflG8u1prI1J8Janno7xwoxruh/BMPTYY49pwYIFam9v19e+9jXdcsstmj9/fs/HtlFG4e+BBx7Qtm3bdPHFF6uiokIDBw7U/v371dTUpHPPPVfLli2zu04AAIDUZfiQ555lP16/fv302muv2VBL5jIKf5WVlXr33Xe1Zs0abdy4UY2NjaqqqtKYMWM0ceJEFfVwazMAAECufbT/gIwMXu9mG0M6mqevBM0o/G3YsEF79uzR9ddf3+0U7+OPP66hQ4dq3LhxthQIAACQrtP7VemzL1x817YllRQF3Dt+DzKaorvzzju1b9++hNsOHDigO++8M6uiAAAAslEcCNj3AOdMPm43oAcZhb9t27bpu9/9bsJtI0aM0LZt27IqCgAAIBvJ3rqR2seQYSX5pDFOJncDOyGj076GYejLL79MuK2xsVHRPD3HDQAAfCLDR7HEftiSZBgJ7/AtdBnN/J133nlasWKFrC4NsSxLDz/8sM477zxbigMAAMhIspm73j5mp0+0l1nAXj75KqOZv7q6Oo0bN07f/OY3NWvWLFVXV2vv3r36wx/+oB07duiNN96wuUwAAIA0dDyYOQ1J45oV90t6NeShjMLfyJEjtWHDBt122226/fbbZZqmioqKYuvPP/98u+sEAABIWceNFwllGMqShsMkG/J17i+j8CdJo0eP1qZNm9Ta2qrGxkb169dPffr0sbM2AACAjHx2qCnpzJ/doSzZZYHtUTcfNJhcxuGvQ1lZmcrKyuyoBQAAwBYlOnbNXkI2p79uwx0Pg0VGfs798SoOAADgOYMrypM/g6/rY1myfaZfkvHyNfxlPfMHAACQd3q65i/BvnF6y2x5eiNHqgh/AADAmzINab3dKJLqhF6ehkTCHwAA8Jwe7/ZNRU9BL09DXaoIfwAAwHtMpf2cv846Mp8HX/BB+AMAAN5jqJeZvxRDXY9neHs5/Zuft3sQ/gAAgAcdOdoeH/ByMYPX240ieTpr6Hr4O3r0qFauXKkPPvhAzc3N6t+/v6ZNm6axY8e6XRoAAChQf9v/hQxT7gSw4yHQsw95zlY0GlVVVZUWL16sQYMGafv27brnnns0aNAgnXnmmW6XBwAACtAZg/pr74Gmnney/VUf8YvFRfn5OGXXw19paamuueaa2PLZZ5+ts846S9u3byf8AQCAjKRyt69dN3MY6T4CxmV5F0nb2tq0c+dODR061O1SAABAoTJ7/3R7M0dvb/5Isp+s45+ux8hTrs/8dWaaph566CGdccYZ+va3vx23raGhQQ0NDbHlUCikmpqarI9pGIYCgUDW4zipo95Cq1ui304rxH4XikR9pd+5k1K/2x0syOO88v3O6jl/UtbXC+brRGDehD/LsvTwww/r0KFDqqurk9HlfXj19fWqq6uLLS9atEhLliyx5djBYNCWcZxWUVHhdgkZ6dzvMVcuc7ESb9n4/MKE6wv1+53vKisrE66n37mRSr/Nz52qxvs88f22rPTP61qdf2scD2/HV2aS5LjbNznLsrRy5Urt2rVL9957r8rKyrrtM2/ePNXW1saWQ6GQGhsbsz52OBxWS0tL1uM4KRAIqKKiQk1NTYpGo26Xk5ZC7HehSPT/B/qdO/TbWan0u6+TBXlcLr/fyYKl3TpO0/YqSUAzektu+Tqtl4K8CH/19fX66KOPtHjxYvXp0yfhPtXV1aquro4tHzx40JbgY1lWwQWoDtFotOBqL+R+57tEfaXfuUO/nUW/neWFfie94cOu2bjenvGXx1wPf/v379cf//hHlZSUaPbs2bH1U6ZM0bRp01ysDAAAFKpdn/1PwpsucpXREp1hbm/Pz7s+XA9/AwcO1Isvvuh2GQAAwEMG9T1Jn37+pWPH6xYqLak4kHcPVZGUB+EPAADAbuFQKLVr/jpkOiVow/uDnZafkRQAACAL3Z7R19unp+f7ZbpvgrqOHDmiH/3oRxo2bJjKy8v1jW98Q0899ZSjvWHmDwAAeE7Kd/smEJuwM7quiF+difb2dtXU1GjDhg0aNmyYNm3apIkTJ2rYsGEaOXJkFiOnjvAHAAC8J5Pn/B3X7S7hjE8Jdz9+OBzWPffcE1seM2aMRo8erXfeecex8MdpXwAA4EkZnfI1DRlWl49ppH5KuNMnFS0tLXrvvff093//97ltRifM/AEAAO9J4/268RN7Pae2DCcTEzJNU7NmzdK5556rCRMm2DdwLwh/AADAc75satWKhd9Lun3+v7yR0bhdzwAvvy35MVq/2CupOuE2y7J04403au/evVq/fn2319rmEuEPAAB4TnNTW4/bM70ZxA6WZeknP/mJtm7dqldffVUnnXSSo8cn/AEAAM8ZMrjndwgb5vHzty68lm3+/PnavHmzNmzYoIqKCsePT/gDAACeY1jSzxa/nnx72hu6OJ4dezrG//mXa7qt2717tx5++GGFQiGdeuqpsfWLFi3SokWLUjx4dgh/AADAcwzLkpHJ3Rmdf6TzdXg23ekxdOhQWXbeNZIBwh8AAPCeLB7yHDeIiz+eK4Q/AADgPZbSD1/HZ+Q6n/mNG8LBO3JzifAHAAA8xzCtEzd1pPozvaxz+3StXQh/AADAcwwleMtGltmtx3m/BBvzdZ6Q8AcAADzn4537pC4zf7kMY4kmBaPt0RweMXOEPwAA4Dl/d2p/7W9o6nknG9OgIXWbWQwEAvYdwEaEPwAA4DlFRUbvj3qJe6xLhgfq6RB5eo0g4Q8AAHiOYSmtGz667ZksDHbZMV+v6+sJ4Q8AAHiPZaU189YtxFk69miXPJ29ywbhDwAAeI95/JM2K8HvM5zfy9PcSPgDAACeYyjF17ulFNA8cK63E8IfAADwnpQe8mwpoyRnpfZz3Z4zmCcIfwAAwHMaPvvixIRdjzOAmSa0Hn7u+Gvg2nnOHwAAgDNCgSIZ7Rld9GcDS7KkQCA/zw8T/gAAgOecfPJJ+ovZe/izZNhwCZ8V90vHgHaMnAuEPwAA4DmGlco1f8duDOm+spfQls7Do/MQ4Q8AAHiPZXV7t28aP3zsl44QmOmz/vL0GYEFG/6CwaBCoVDW4xQXF6u8vNyGipxjHP8yhsNhWXn6xUqmEPtdKBL1lX7nDv12Vkr9bnSwII/zxPfbVBbh7zjLOnYKN8ObgvNVwYa/SCSiSCSS9Tjl5eVqbm62oSLnBAIBBYNBtbS0KBrNzzuJkinEfheKRH2l37lDv52VSr+rnCzI43L5/bZj4iYl6c78JXmUX8qvfUthzHxRsOEPAAAgKauXhzyneOYs4WvfYhsLczqQ8AcAADznSGskfuavU9iz6yyudTwJxsbqFgbzc+qP8AcAADxn31//R0bUTDjDZ9d8XdJxjodA82h+XppF+AMAAJ4z9PQB+nz3gV72svu0bafn/RlSUXGRzePbg/AHAAC8x0zlho8kd3mkyuo6hhG/LT/P+hL+AACAB1mWlMIbPnrU9Rq+Xm8SydO01wXhDwAAeI9lZf+Q5QJ7lm6qCH8AAMB7Mpn56ynsZfJYlzwNj4Q/AADgPaaZWvhLOZ9leX1gHsnP21AAAACyYenETR9xHzP+Y2X46TpOomMlCJbLly/Xd7/7XYVCIV199dWOt0Vi5g8AAHjQng8/y/6Gj7R0nxk029u77VVTU6M777xTr776qg4ePOhQbfEIfwAAwHP6D+6rvTs/T7rdyNGr2axO1/kVBbqfYP3BD34gSdq6dSvhDwAAwC5lfYI9zvx1OyObaRjM05s6ekL4AwAA3pPtc/6ShcF0wl6eBkPCHwAA8B7L1JLX5yfdvGjsv/Y+RlGRpOM3biQJcr96c0HSHz+sxt6P4QLCHwAA8J7eXu2WyqygozeMOIfwBwAAPMfq5ZSr5VKwa29vj31M01RbW5sCgYBKSkocq4HwBwAAvMc09fORy3J+mJ6O8dtNi7utW7x4serq6mLLzz77rGbOnKnHH388F+UlRPgDAACec+SrtthpWydvu4i/TaT7ke+++27dfffdDlWTGOEPAAB4zoUz/rcOf9GiqupKlVeGc/Zcv66ORtp1qOELFRcHNPQbpzpyzHQR/gAAgOdcPPN7unjm99wuIy/xbl8AAAAfIfwBAAD4COEPAADARwh/AAAAPkL4AwAA8BHCHwAAgI/kxaNe1qxZo9dee01//etfNXLkSN16661ulwQAAOBJeRH+qqqqNG3aNG3dulXNzc1ulwMAAOBZeRH+Ro0aJUn65JNPCH8AAAA5xDV/AAAAPpIXM3+paGhoUENDQ2w5FAqppqYm63ENw1AgEMh6HCd11FtodUuF2e9Ckaiv9Dt36LezUup3u4MFeRzfb28rmPBXX1+vurq62PKiRYu0ZMkSW8YOBoOx348vmmrLmJBeMZ9NuL5zv2GfysrKhOvpd27Qb2el0m/zc6eq8T6+395WMOFv3rx5qq2tjS2HQiE1NjZmPW44HFZLS0vW46C7RP9+6Hfu0G9n0W9npdLvvk4W5HG5/H4nC5ZwTl6Ev2g0qmg0KtM0ZZqmIpGIioqKVFx8orzq6mpVV1fHlg8ePKhoNJr1sS3LsmUcdJeor/Q7d+i3s+i3s+i3s+i3t+VF+Hv66ae1evXq2PKmTZt04YUX6qabbnKvKAAAAA/Ki/A3Y8YMzZgxw+0yAAAAPI9HvQAAAPgI4Q8AAMBHCH8AAAA+QvgDAADwEcIfAACAjxD+AAAAfITwBwAA4COEPwAAAB8h/AEAAPgI4Q8AAMBHCH8AAAA+QvgDAADwEcIfAACAjxD+AAAAfITwBwAA4COEPwAAAB8h/AEAAPgI4Q8AAMBHCH8AAAA+QvgDAADwEcIfAACAjxD+AAAAfITwBwAA4COEPwAAAB8h/AEAAPgI4Q8AAMBHCH8AAAA+QvgDAADwEcIfAACAjxS7XUCmgsGgQqFQ1uMUFxervLzchorQVaK+0u/cod/Oot/OSqnfjQ4W5HF8v72tYMNfJBJRJBLJepzy8nI1NzfbUBG6StRX+p079NtZ9NtZqfS7ysmCPC6X3287Jm6QHU77AgAA+AjhDwAAwEcIfwAAAD5C+AMAAPARwh8AAICPEP4AAAB8hPAHAADgI4Q/AAAAHyH8AQAA+AjhDwAAwEcIfwAAAD5C+AMAAPARwh8AAICPEP4AAAB8hPAHAADgI4Q/AAAAHyH8AQAA+AjhDwAAwEcIfwAAAD5C+AMAAPARwh8AAICPEP4AAAB8hPAHAADgI4Q/AAAAHyH8AQAA+AjhDwAAwEeK3S5Akg4fPqwVK1bov//7v1VWVqYrrrhCl19+udtlAQAAeE5ehL/6+nodPXpUjz32mPbv369f/OIXOuWUU/Sd73zH7dIAAAA8xfXTvm1tbdq0aZN++MMfqk+fPjrttNM0YcIEvfLKK26XBgAA4Dmuh7/PPvtMlmVp6NChsXXDhg3Tnj17XKwKAADAm1w/7dvW1qY+ffrErQuHw2ptbY1b19DQoIaGhthyKBRSTU1N1sc3DEOBQCDrcdBdor7S79yh386i385Kqd/tDhbkcXy/vc318FdaWtot6H311VcqKyuLW1dfX6+6urrY8qJFi7RkyRJbaggGg7Hfv2I+a8uYSK5zvzc+v9DFSvyhc78/WXCLi5X4Q+d+P/EP/+ZiJf7Qud/SDtfq8JrKJOvj+41C5Xr4+9rXviZJ2rNnj4YMGSJJ2rVrV+z3HebNm6fa2trYcigUUmNjY9bHD4fDamlpyXocJwUCAVVUVKipqUnRaNTtctJCv51Fv51Fv51Fv51lV78rK5NFSzjF9fBXWlqq0aNH68knn9Q///M/68CBA3r55Zf1s5/9LG6/6upqVVdXx5YPHjxoy/9xLMsquP8DdohGowVXO/12Fv12Fv12Fv12ViH3G/FcD3/SsVm95cuXa9asWSorK9OVV17JY14AAAByIC/C30knnaQ77rjD7TIAAAA8z/VHvQAAAMA5hD8AAAAfIfwBAAD4COEPAADARwh/AAAAPkL4AwAA8BHCHwAAgI8Q/gAAAHyE8AcAAOAjhD8AAAAfIfwBAAD4COEPAADARwzLsiy3i0B6GhoaVF9fr3nz5qm6utrtcjyPfjuLfjuLfjuLfiMfMPNXgBoaGlRXV6eGhga3S/EF+u0s+u0s+u0s+o18QPgDAADwEcIfAACAjxD+ClB1dbXuuusurhdxCP12Fv12Fv12Fv1GPuCGDwAAAB9h5g8AAMBHCH8AAAA+Uux2AUjdmjVr9Nprr+mvf/2rRo4cqVtvvdXtkjzt6NGjWrlypT744AM1Nzerf//+mjZtmsaOHet2aZ61fPlyvffee2ptbVV5ebkmTJigadOmuV2W5zU1NenHP/6xqqurtWzZMrfL8aSHHnpIb731loqLT/y1u2LFCg0YMMDFquBXhL8CUlVVpWnTpmnr1q1qbm52uxzPi0ajqqqq0uLFizVo0CBt375d99xzjwYNGqQzzzzT7fI8qba2VjfccINCoZAOHDigu+++WzU1NRozZozbpXnaY489plNPPVXt7e1ul+Jpl19+uWbOnOl2GQCnfQvJqFGjdP7556uiosLtUnyhtLRU11xzjQYPHizDMHT22WfrrLPO0vbt290uzbOGDBmiUCgUWzYMQ3v37nWxIu/785//rL179+qiiy5yuxQADmHmD0hRW1ubdu7cqUmTJrldiqc98cQTWrNmjY4cOaKBAwdq3LhxbpfkWUePHlV9fb1uvvlmffLJJ26X43nr16/X+vXr1b9/f02aNEnjx493uyT4FOEPSIFpmnrooYd0xhln6Nvf/rbb5XjazJkzdd1112nnzp3avHmzwuGw2yV51vPPP69zzjlHw4YNI/zl2KRJkzR79myFw2Ft27ZN999/v8LhsEaNGuV2afAhTvsCvbAsSw8//LAOHTqkW2+9VYZhuF2S5xmGoTPOOEMlJSVatWqV2+V40t69e7VhwwbNmDHD7VJ8Yfjw4aqoqFAgENA3v/lNTZw4UZs2bXK7LPgUM39ADyzL0sqVK7Vr1y7de++9Kisrc7skXzFNUw0NDW6X4Unbt29XY2OjbrzxRklSJBJRJBLRddddp5UrV6pPnz4uV+hthmGIdyzALYS/AhKNRhWNRmWapkzTVCQSUVFRUdyjA2Cv+vp6ffTRR1q8eDF/GebY4cOHtWXLFp133nkqLS3Vhx9+qD/96U+66qqr3C7Nk8aMGaMRI0bElt9++229/vrr+uUvf8l/5OTAxo0bNWLEiNh3e+3atZo7d67bZcGnSA0F5Omnn9bq1atjy5s2bdKFF16om266yb2iPGz//v364x//qJKSEs2ePTu2fsqUKTx7LkdeffVVPfLIIzJNU1VVVZo8ebImTpzodlmeFAqF4u6sDofDCgQCqqysdLEq71qzZo1WrFgh0zTVv39/XXvttbrgggvcLgs+xbt9AQAAfIQbPgAAAHyE8AcAAOAjhD8AAAAfIfwBAAD4COEPAADARwh/AAAAPkL4AwAA8BHCHwAAgI8Q/gAAAHyE8Acg77zyyiuaMWOGhg8fLsMwNH/+fLdLAgDPIPwByDvr1q3TBx98oLFjx6pfv35ulwMAnsK7fQHkjdbWVpWVlck0TRUVHftv09NOO02XXXaZli9f7nJ1AOANzPwBSMvjjz+u4uJi7du3L279oUOHFAwGVV9fr3fffVe1tbWqqalROBzWt771LT355JNx+7/xxhsyDENr167VlClTVFFRoalTp0pSLPgBAOzHn7AA0nLFFVeouLhYzz77bNz6559/XpI0depU7d69W6NHj9ajjz6ql156SVdeeaXmzJmjJ554ott4c+fO1fDhw/XCCy9o4cKFjvwzAICfFbtdAIDC0rdvX1166aVatWpV3I0Yq1at0oQJE1RVVaWrr746tt6yLF1wwQX69NNPVV9fr5kzZ8aNV1tbq/vvv9+x+gHA75j5A5C26dOn691339WePXskSQ0NDXrzzTc1ffp0SVJjY6MWLFigoUOHqqSkRCUlJXrkkUe0Y8eObmNNnDjR0doBwO8IfwDSdtlllykcDmv16tWSpGeeeUalpaWaPHmyJGnWrFlatWqVFi5cqJdffllbtmzR7Nmz1dbW1m2sQYMGOVk6APgep30BpK2srEyTJ0/W6tWrddttt2n16tWaNGmSwuGw2tratGbNGj344IP66U9/GvsZ0zQTjmUYhlNlAwDEzB+ADE2fPl3vv/++1q9fr82bN8dO+R45ckSmaSoYDMb2bW5u1osvvuhWqQCATpj5A5CR8ePH6+STT9bs2bPVr18/XXLJJZKO3RBy7rnnaunSpRowYICKi4u1dOlS9e3bV/v3709p7N27d2vLli2SpK+++koff/yxnnvuOUnSlClTcvMPBAA+QfgDkJGSkhJNmTJF9fX1mjNnTtxM31NPPaV58+Zp5syZOvnkk7VgwQIdPnxYy5YtS2ns119/Xddff31sed26dVq3bp2kY3cPAwAyxxs+AAAAfIRr/gAAAHyE8AcAAOAjhD8AAAAfIfwBAAD4COEPAADARwh/AAAAPkL4AwAA8BHCHwAAgI8Q/gAAAHyE8AcAAOAjhD8AAAAf+f+RfqnfQfrL4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (97654321012345679)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df, aes('var1'))\n",
    " + geom_bar(aes(fill=after_stat('x')))\n",
    " + labs(fill='var1')\n",
    ")"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}