Benchmarking_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2731415f-ec5f-4abf-9f66-88248866c211",
   "metadata": {},
   "source": [
    "# Pandas Vs. Polars Vs. Vaex - A Benchmarking Analysis.     \n",
    "@Author: Zoumana Keita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2902225c-7559-4f77-aed6-b80e0bb1670f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4dcd7a08-dadf-48d7-82ea-e8a7f0df44ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%bash \n",
    "#pip3 install polars\n",
    "#pip3 install pandas\n",
    "#!pip3 install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b159d84-d27b-45f7-9ef6-77a922ada9a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import vaex as vx \n",
    "import polars as pl \n",
    "import time\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "160dc84b-ea73-4c5b-8c99-c5ed13173248",
   "metadata": {},
   "source": [
    "## Create the benchmarking data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7c48593-751a-4ce1-955a-36064fab7915",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./data/benchmark_data.csv\")\n",
    "\n",
    "#benchmarking_df = df.loc[df.index.repeat(100000)]\n",
    "benchmarking_df = df.loc[df.index.repeat(50000)]\n",
    "\n",
    "# Store the data for experimentation\n",
    "benchmarking_df.to_csv(\"./data/diabetes_benchmark_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cda89a6-8427-4fc5-85ab-a5cafa6804ec",
   "metadata": {},
   "source": [
    "## 1. Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c8162320-1f63-4ad9-9074-92fc19fc529b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the benchmarking dictionary\n",
    "loading_times = {}\n",
    "\n",
    "# Benchmarking data path\n",
    "benchmarking_data_path = \"./data/diabetes_benchmark_data.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b96e3503-0ce9-4aed-88e5-a2459c96692d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to perform the benchmarking analysis\n",
    "\n",
    "def data_loading(library, data_path):\n",
    "    \n",
    "    lib = library.lower()\n",
    "    \n",
    "    match lib:\n",
    "        \n",
    "        case \"pandas\":\n",
    "            t_start = time.time()\n",
    "            pd_df = pd.read_csv(data_path)\n",
    "            t_end = time.time() - t_start\n",
    "  \n",
    "            \n",
    "        case \"polars\":\n",
    "            t_start = time.time()\n",
    "            pl_df = pl.read_csv(data_path)\n",
    "            t_end = time.time() - t_start\n",
    "\n",
    "    loading_times[lib] = t_end\n",
    "            \n",
    "        \n",
    "# Helper function to plot the times\n",
    "\n",
    "def plot_time(benchmark_time, plot_title):\n",
    "    \n",
    "    libraries = list(benchmark_time.keys())\n",
    "    times = [float(t) for t in list(benchmark_time.values())]\n",
    "    loading_plot = sns.barplot(x=libraries, y=times)\n",
    "\n",
    "    loading_plot.set(xlabel =\"Libraries\", ylabel = \"Time\", title =plot_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6098e966-5572-42dd-a30b-a61ee8e5024a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loading_times"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f4f4e4-a984-4fb4-bbad-e363b6293c32",
   "metadata": {},
   "source": [
    "### Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6c674043-3d00-4575-985e-01eb65296a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loading(\"pandas\", benchmarking_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df1ddb9-9422-480e-b738-768a500bc724",
   "metadata": {},
   "source": [
    "### Polars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "abef952c-a44b-4c29-b008-b73a2ae71140",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loading(\"polars\", benchmarking_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1bc731da-626e-4e5f-9ee9-67c4d22844d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_time(loading_times, \"Loading Time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "153a27f5-0690-4426-a834-df23b9f98b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pandas': 28.517988204956055, 'polars': 10.196640968322754}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loading_times"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e8b162-0ad9-4e5c-91eb-635c431fd7aa",
   "metadata": {},
   "source": [
    "## 2. Data Offloading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e40102ed-19f5-4d88-9002-d5ab71f770e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "offloading_times = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "abdef336-e8a2-4941-8189-8396e9a735e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Offloading function\n",
    "def offdata_loading(library, df):\n",
    "    \n",
    "    lib = library.lower()\n",
    "    \n",
    "    if(lib == \"pandas\" or lib == \"polars\"):\n",
    "        t_start = time.time()\n",
    "        df.to_numpy()\n",
    "        t_end = time.time() - t_start  \n",
    "    \n",
    "    offloading_times[lib] = t_end    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fab01bc9-d7a3-4af2-8dc6-b59a7599cd4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_df = pd.read_csv(benchmarking_data_path)\n",
    "pl_df = pl.read_csv(benchmarking_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5bcfbb32-e896-447b-8c12-1ce77afa0f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "offdata_loading(\"pandas\", pd_df)\n",
    "offdata_loading(\"polars\", pl_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "70443aa2-efa9-47e1-8700-51475a48b4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pandas': 2.8378379344940186, 'polars': 2.2786760330200195}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offloading_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e51dbf66-e305-4393-bbbc-62bc0f6a05d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_time(offloading_times, \"Offloading Time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991f92db-4608-4e56-b605-bc9bbdb879db",
   "metadata": {},
   "source": [
    "## 3. Data Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8e048f46-be10-4baf-bea3-a357794b482c",
   "metadata": {},
   "outputs": [],
   "source": [
    "aggregation_time = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "bbaf56a0-39d0-41de-9719-78cd6b9f6430",
   "metadata": {},
   "outputs": [],
   "source": [
    "def grouping_data(library, df):\n",
    "    \n",
    "    lib = library.lower()\n",
    "    \n",
    "    if(library == \"pandas\"):\n",
    "        \n",
    "        t_start = time.time()  \n",
    "        df.groupby([\"Outcome\"], \n",
    "                      as_index = False)[['Age']].mean()\n",
    "        t_end = time.time() - t_start\n",
    "        \n",
    "    elif(library == \"polars\"):\n",
    "        t_start = time.time()  \n",
    "        df.groupby(['Outcome']).agg(pl.col(\"Age\").mean())\n",
    "        t_end = time.time() - t_start        \n",
    "        \n",
    "    aggregation_time[lib] = t_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b05f693b-394a-46de-8686-1d4126f27d43",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouping_data(\"pandas\", pd_df)\n",
    "grouping_data(\"polars\", pl_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "cc04f5ee-7dd1-426e-9fa5-3fba20f223cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_time(aggregation_time, \"Aggregation Time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "14889b74-9afe-432a-8bb9-5d0ee36563ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pandas': 1.1558399200439453, 'polars': 0.22764992713928223}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregation_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8d208d-a8b8-4526-a2c0-82c344af7f60",
   "metadata": {},
   "source": [
    "## 4. Data Sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "581dd832-657d-45d4-8790-44bc71b4f469",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorting_time = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c88eb3fe-760e-4617-901b-fe3eab809690",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sorting_data(library, df):\n",
    "    \n",
    "    lib = library.lower()\n",
    "    \n",
    "    if(library == \"pandas\"):\n",
    "        \n",
    "        t_start = time.time()  \n",
    "        df.sort_values(by='Pregnancies')\n",
    "        t_end = time.time() - t_start\n",
    "        \n",
    "    elif(library == \"polars\"):\n",
    "        t_start = time.time()  \n",
    "        df.sort(['Pregnancies'])\n",
    "        t_end = time.time() - t_start        \n",
    "        \n",
    "    sorting_time[lib] = t_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1799d51f-7599-4ec6-992e-94a4c8acd777",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorting_data(\"pandas\", pd_df)\n",
    "sorting_data(\"polars\", pl_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "9be902d0-7f24-4c2f-88a3-23d4b174c181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_time(sorting_time, \"Sorting Time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "64e4c83c-1044-44ae-be3b-0f0c953311c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pandas': 6.49990701675415, 'polars': 2.197705030441284}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorting_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f35720-e1a1-49d9-9284-502ad82ac321",
   "metadata": {},
   "source": [
    "## 5. Data Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5398016f-ab2f-4dfe-9e10-ca1769084ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtering_time = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9c410eef-1e8a-4e44-bb13-b7445da048e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filtering_data(library, df, column_name, value):\n",
    "    \n",
    "    lib = library.lower()\n",
    "    \n",
    "    if(library == \"pandas\"):\n",
    "        \n",
    "        t_start = time.time()  \n",
    "        pd_df[pd_df[str(column_name)] > value]\n",
    "        t_end = time.time() - t_start\n",
    "        \n",
    "    elif(library == \"polars\"):\n",
    "        t_start = time.time()  \n",
    "        df.filter(pl.col(str(column_name)) > value)\n",
    "        t_end = time.time() - t_start        \n",
    "        \n",
    "    filtering_time[lib] = t_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1bce9833-3385-4a67-900e-9bea5c61ec68",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtering_data(\"pandas\", pd_df, \"BloodPressure\", 74)\n",
    "filtering_data(\"polars\", pl_df, \"BloodPressure\", 74)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "9ba365d2-5529-4b06-9e03-806d9e70d408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_time(filtering_time, \"Filtering Time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d597ddc0-d065-4aa4-8209-77c43c68f2fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pandas': 1.1616041660308838, 'polars': 0.24312996864318848}"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtering_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c07bf5cc-5951-40ca-a3d6-a5aa186db5de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}