read_multiple_files.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 49.6 s, sys: 28.7 s, total: 1min 18s\n",
      "Wall time: 1min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "files = [\"1.csv\",\"2.csv\",\"3.csv\",\"4.csv\",\"5.csv\"]\n",
    "combined = []\n",
    "for f in files:\n",
    "    combined.append(pd.read_csv(f))\n",
    "combined_df = pd.concat(combined,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.26 s, sys: 12.2 s, total: 15.5 s\n",
      "Wall time: 20.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = dd.read_csv(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 55s, sys: 1min 12s, total: 3min 8s\n",
      "Wall time: 2min 20s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = dt.iread(files)\n",
    "df = dt.rbind(df)\n",
    "df = df.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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('s')\n",
    "ax.set_title('Speed of reading multiple csv files')\n",
    "lib = ['pandas', 'dask', 'datatable']\n",
    "perf = [87,21,140]\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",
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