write.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import dask.dataframe as dd\n",
    "import datatable as dt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data3.csv\")\n",
    "df2 = dd.read_csv(\"data3.csv\")\n",
    "df3 = dt.fread(\"data3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 240 ms, sys: 9.61 ms, total: 249 ms\n",
      "Wall time: 248 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df.to_csv(\"data_output.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 342 ms, sys: 27.9 ms, total: 370 ms\n",
      "Wall time: 371 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['/Users/xiaoxu/Repo/sandbox/source/data_output2.csv']"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df2.to_csv(\"data_output2.csv\",single_file=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 162 ms, sys: 21.8 ms, total: 184 ms\n",
      "Wall time: 183 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df2.to_parquet(path=\"data_output2_parquet\", engine=\"pyarrow\", partition_on=[\"date_updated_local\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 241 ms, sys: 8.6 ms, total: 249 ms\n",
      "Wall time: 28.3 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df3.to_csv(\"data_output3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "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 writing csv file')\n",
    "lib = ['pandas', 'dask-csv', 'dask-parquey','datatable']\n",
    "perf = [249,370,184,249]\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
}