vespa_classifications.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "amended-cologne",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas\n",
    "import swasputils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ongoing-housing",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pandas.read_csv(\n",
    "    os.path.join(swasputils.DATA_LOCATION, 'vespa-export.csv'),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "mineral-porcelain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['Classification'].value_counts().plot.bar()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}