group_by.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import dask.dataframe as dd\n",
    "import datatable as dt\n",
    "import matplotlib.pyplot as plt\n",
    "from datatable import f, by, sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 63.7 ms, sys: 16.5 ms, total: 80.2 ms\n",
      "Wall time: 81.8 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = pd.read_csv(\"data3.csv\")\n",
    "df_agg = df.groupby(\"date_updated_local\").agg({\"amount_cost_eur\": \"sum\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 71.6 ms, sys: 13.2 ms, total: 84.8 ms\n",
      "Wall time: 84.2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = dd.read_csv(\"data3.csv\")\n",
    "df_agg = df.groupby(\"date_updated_local\").agg({\"amount_cost_eur\": \"sum\"}).compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 879 ms, sys: 32 ms, total: 911 ms\n",
      "Wall time: 98.4 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = dt.fread(\"data3.csv\")\n",
    "df_agg = df[:,sum(f.amount_cost_eur), by(f.date_updated_local)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.set_ylabel('ms')\n",
    "ax.set_title('Speed of aggregating csv file')\n",
    "lib = ['pandas', 'dask', 'datatable']\n",
    "perf = [81.8,84.2,98.4]\n",
    "ax.bar(lib,perf)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.0 64-bit ('3.9.0')",
   "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": "3.9.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "285b4027c56aef32f9cffa7b798ac9ff266d7923f973d093da0977b0f49ab1ea"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}