plot_categories_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5cb62719-134a-4d00-99fb-968e70c1f15d",
   "metadata": {},
   "source": [
    "# Plot some categories from the Steam Hardware Survey dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef6eeabe-107f-4c88-bd8b-176d4a52baae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100   662  100   662    0     0   1886      0 --:--:-- --:--:-- --:--:--     0-:--:-- --:--:-- --:--:--  1891\n",
      "100  683k  100  683k    0     0   863k      0 --:--:-- --:--:-- --:--:--  863k\n"
     ]
    }
   ],
   "source": [
    "!pip install -qq --no-cache-dir seaborn\n",
    "!curl -LO https://github.com/myagues/steam-hss-data/releases/download/2021.12/steam_hw_survey.parquet\n",
    "# !curl -LO https://github.com/myagues/steam-hss-data/releases/download/latest/steam_hw_survey.parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f3bee61-c9f0-49cc-ac87-0b8942aaa02e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from pandas.api.types import CategoricalDtype\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aafd2035-4a25-42c1-924b-9548bf057bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>perc</th>\n",
       "      <th>category</th>\n",
       "      <th>date</th>\n",
       "      <th>platform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unspecified</td>\n",
       "      <td>10.01</td>\n",
       "      <td>Network Speed</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.4 Kbps</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Network Speed</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.8 Kbps</td>\n",
       "      <td>0.18</td>\n",
       "      <td>Network Speed</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.6 Kbps</td>\n",
       "      <td>0.13</td>\n",
       "      <td>Network Speed</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56.0 Kbps</td>\n",
       "      <td>3.38</td>\n",
       "      <td>Network Speed</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262465</th>\n",
       "      <td>AVX512F</td>\n",
       "      <td>3.18</td>\n",
       "      <td>Other Settings</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262466</th>\n",
       "      <td>AVX512ER</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Other Settings</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262467</th>\n",
       "      <td>PrefetchW</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Other Settings</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262468</th>\n",
       "      <td>NTFS</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Other Settings</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262469</th>\n",
       "      <td>AVX512PF</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Other Settings</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>262470 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              index   perc        category       date platform\n",
       "0       Unspecified  10.01   Network Speed 2004-04-01       pc\n",
       "1         14.4 Kbps   0.10   Network Speed 2004-04-01       pc\n",
       "2         28.8 Kbps   0.18   Network Speed 2004-04-01       pc\n",
       "3         33.6 Kbps   0.13   Network Speed 2004-04-01       pc\n",
       "4         56.0 Kbps   3.38   Network Speed 2004-04-01       pc\n",
       "...             ...    ...             ...        ...      ...\n",
       "262465      AVX512F   3.18  Other Settings 2021-12-01    linux\n",
       "262466     AVX512ER   0.00  Other Settings 2021-12-01    linux\n",
       "262467    PrefetchW   0.00  Other Settings 2021-12-01    linux\n",
       "262468         NTFS   0.00  Other Settings 2021-12-01    linux\n",
       "262469     AVX512PF   0.00  Other Settings 2021-12-01    linux\n",
       "\n",
       "[262470 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet(\"steam_hw_survey.parquet\")\n",
    "df.dtypes\n",
    "df[\"category\"] = df[\"category\"].astype(\"category\")\n",
    "df[\"platform\"] = df[\"platform\"].astype(\"category\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91d3b994-28d1-48e9-9524-583fa3e0e6b8",
   "metadata": {},
   "source": [
    "List categories throughout the years, some may be specific to a platform, and others are only available for some months or years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b1fb83c9-f93c-4eb9-b14d-0a2a5ac678fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['16:9 Aspect Primary Displays', '4:3 Aspect Primary Displays',\n",
       "       'AGP GART Settings', 'AMD CPU Speeds', 'ATI Drivers', 'Audio Devices',\n",
       "       'Bus Types', 'CPU', 'Cards which default to DirectX 7 Path on Source',\n",
       "       'Cards which default to DirectX 8 Path on Source',\n",
       "       'Cards which default to DirectX 9 Shader Model 2 Path on Source',\n",
       "       'Cards which default to DirectX 9 Shader Model 3 Path on Source',\n",
       "       'Desktop Bit Depth', 'DirectX 10 GPUs', 'DirectX 10 Systems',\n",
       "       'DirectX 10.x GPUs', 'DirectX 10.x Systems', 'DirectX 11 GPUs',\n",
       "       'DirectX 11 Systems', 'DirectX 12 GPUs', 'DirectX 12 Systems',\n",
       "       'DirectX 9 Shader Model 2.0 GPUs',\n",
       "       'DirectX 9 Shader Model 2b and 3.0 GPUs', 'Drive Type', 'DriveType',\n",
       "       'Free Hard Drive Space', 'Game Renderer', 'Game Screen Depth',\n",
       "       'Game Screen Width', 'Intel CPU Speeds', 'Language', 'Linux Version',\n",
       "       'MSAA - Percent of cards which support each MSAA level',\n",
       "       'MSAA Support Level', 'Mac Hardware Model Summary', 'Microphones',\n",
       "       'Monitor Refresh Rate', 'Multi-GPU Systems',\n",
       "       'Multi-Monitor Desktop Resolution', 'NVIDIA Drivers', 'Network Speed',\n",
       "       'OS Version', 'OS Version (total)', 'OSX Hardware Model Summary',\n",
       "       'OSX Version', 'Other Settings', 'Physical CPUs',\n",
       "       'Primary Display Resolution', 'Processor Vendor', 'System RAM',\n",
       "       'Total Hard Drive Space', 'VR Headsets', 'VRAM',\n",
       "       'Video Card Description', 'Video Card Driver Name', 'Vulkan Systems',\n",
       "       'Windows Version'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"category\"].cat.categories"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97796da7-f675-48e9-8f75-b616007163d2",
   "metadata": {},
   "source": [
    "## Share of CPU manufacturers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "890a4a0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>perc</th>\n",
       "      <th>category</th>\n",
       "      <th>date</th>\n",
       "      <th>platform</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>AuthenticAMD</td>\n",
       "      <td>47.44</td>\n",
       "      <td>Processor Vendor</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>GenuineIntel</td>\n",
       "      <td>52.53</td>\n",
       "      <td>Processor Vendor</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>AuthenticAMD</td>\n",
       "      <td>46.13</td>\n",
       "      <td>Processor Vendor</td>\n",
       "      <td>2004-06-01</td>\n",
       "      <td>pc</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>GenuineIntel</td>\n",
       "      <td>53.83</td>\n",
       "      <td>Processor Vendor</td>\n",
       "      <td>2004-06-01</td>\n",
       "      <td>pc</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>AuthenticAMD</td>\n",
       "      <td>46.13</td>\n",
       "      <td>Processor Vendor</td>\n",
       "      <td>2004-07-01</td>\n",
       "      <td>pc</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261751</th>\n",
       "      <td>AuthenticAMD</td>\n",
       "      <td>39.96</td>\n",
       "      <td>Processor Vendor</td>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>linux</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261965</th>\n",
       "      <td>GenuineIntel</td>\n",
       "      <td>59.29</td>\n",
       "      <td>Processor Vendor</td>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>linux</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261966</th>\n",
       "      <td>AuthenticAMD</td>\n",
       "      <td>40.71</td>\n",
       "      <td>Processor Vendor</td>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>linux</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262183</th>\n",
       "      <td>GenuineIntel</td>\n",
       "      <td>59.99</td>\n",
       "      <td>Processor Vendor</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262184</th>\n",
       "      <td>AuthenticAMD</td>\n",
       "      <td>40.01</td>\n",
       "      <td>Processor Vendor</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>514 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               index   perc          category       date platform  year\n",
       "131     AuthenticAMD  47.44  Processor Vendor 2004-04-01       pc  2004\n",
       "135     GenuineIntel  52.53  Processor Vendor 2004-04-01       pc  2004\n",
       "355     AuthenticAMD  46.13  Processor Vendor 2004-06-01       pc  2004\n",
       "360     GenuineIntel  53.83  Processor Vendor 2004-06-01       pc  2004\n",
       "581     AuthenticAMD  46.13  Processor Vendor 2004-07-01       pc  2004\n",
       "...              ...    ...               ...        ...      ...   ...\n",
       "261751  AuthenticAMD  39.96  Processor Vendor 2021-09-01    linux  2021\n",
       "261965  GenuineIntel  59.29  Processor Vendor 2021-10-01    linux  2021\n",
       "261966  AuthenticAMD  40.71  Processor Vendor 2021-10-01    linux  2021\n",
       "262183  GenuineIntel  59.99  Processor Vendor 2021-12-01    linux  2021\n",
       "262184  AuthenticAMD  40.01  Processor Vendor 2021-12-01    linux  2021\n",
       "\n",
       "[514 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cpu_vendor = df.loc[(df[\"category\"] == \"Processor Vendor\") & (df[\"perc\"] > 1)].copy()\n",
    "df_cpu_vendor[\"year\"] = df_cpu_vendor[\"date\"].dt.year.astype(str)\n",
    "df_cpu_vendor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "13e235da-d846-468d-ab0a-215611265abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>index</th>\n",
       "      <th>perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004</td>\n",
       "      <td>AuthenticAMD</td>\n",
       "      <td>46.572500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>GenuineIntel</td>\n",
       "      <td>53.397500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005</td>\n",
       "      <td>AuthenticAMD</td>\n",
       "      <td>47.441818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>GenuineIntel</td>\n",
       "      <td>52.543636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>AuthenticAMD</td>\n",
       "      <td>51.307500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year         index       perc\n",
       "0  2004  AuthenticAMD  46.572500\n",
       "1  2004  GenuineIntel  53.397500\n",
       "2  2005  AuthenticAMD  47.441818\n",
       "3  2005  GenuineIntel  52.543636\n",
       "4  2006  AuthenticAMD  51.307500"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cpu_vendor_pc = (\n",
    "    df_cpu_vendor.loc[df_cpu_vendor[\"platform\"] == \"pc\"]\n",
    "    .groupby([\"year\", \"index\"])\n",
    "    .mean()\n",
    "    .reset_index()\n",
    ")\n",
    "df_cpu_vendor_pc.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed57663b-a0a0-4a8f-bf5b-85f40671b8e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 368,
       "width": 728
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(nrows=1, figsize=(10, 5), constrained_layout=True)\n",
    "ax.set(xlabel=\"Date\", ylabel=\"Percentage\", ylim=[0, 100])\n",
    "sns.histplot(\n",
    "    df_cpu_vendor_pc,\n",
    "    x=\"year\",\n",
    "    hue=\"index\",\n",
    "    weights=\"perc\",\n",
    "    multiple=\"dodge\",\n",
    "    discrete=True,\n",
    "    hue_order=[\"GenuineIntel\", \"AuthenticAMD\"],\n",
    "    palette=\"muted\",\n",
    "    shrink=0.75,\n",
    "    linewidth=0.0,\n",
    "    ax=ax,\n",
    ")\n",
    "sns.move_legend(\n",
    "    ax, \"lower center\", bbox_to_anchor=(0.5, 1), ncol=2, title=None, frameon=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdbaaa78-43d9-485e-a7da-9ec637ecd313",
   "metadata": {},
   "source": [
    "Same plot with stacked barchart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54f6d6f9-99be-4391-aea7-a30929e35c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 368,
       "width": 728
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(nrows=1, figsize=(10, 5), constrained_layout=True)\n",
    "ax.set(xlabel=\"Date\", ylabel=\"Percentage\", ylim=[0, 100])\n",
    "sns.histplot(\n",
    "    df_cpu_vendor_pc,\n",
    "    x=\"year\",\n",
    "    hue=\"index\",\n",
    "    weights=\"perc\",\n",
    "    multiple=\"stack\",\n",
    "    discrete=True,\n",
    "    hue_order=[\"GenuineIntel\", \"AuthenticAMD\"],\n",
    "    palette=\"muted\",\n",
    "    shrink=0.75,\n",
    "    linewidth=0.0,\n",
    "    ax=ax,\n",
    ")\n",
    "sns.move_legend(\n",
    "    ax, \"lower center\", bbox_to_anchor=(0.5, 1), ncol=2, title=None, frameon=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b45f1b42-8af6-424e-a5cf-933aa159fdb7",
   "metadata": {},
   "source": [
    "Monthly share of CPUs with platform information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb133c03-56a9-471c-b336-9ac24b65da50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 368,
       "width": 728
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(nrows=1, figsize=(10, 5), constrained_layout=True)\n",
    "sns.lineplot(\n",
    "    x=\"date\",\n",
    "    y=\"perc\",\n",
    "    hue=\"index\",\n",
    "    style=\"platform\",\n",
    "    data=df_cpu_vendor,\n",
    "    style_order=[\"pc\", \"mac\", \"linux\"],\n",
    ")\n",
    "ax.set(title=\"CPU Share by Vendor\", xlabel=\"Date\", ylabel=\"Percentage\", ylim=[0, 100])\n",
    "sns.move_legend(ax, \"lower left\", ncol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f07da94-d571-440d-8888-7174a7bb381f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platform</th>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">combined</th>\n",
       "      <th>AuthenticAMD</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenuineIntel</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VirtualApple</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">linux</th>\n",
       "      <th>AuthenticAMD</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.591429</td>\n",
       "      <td>20.853333</td>\n",
       "      <td>18.264444</td>\n",
       "      <td>17.476000</td>\n",
       "      <td>19.083636</td>\n",
       "      <td>22.715833</td>\n",
       "      <td>29.020000</td>\n",
       "      <td>37.69750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenuineIntel</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.408571</td>\n",
       "      <td>79.147778</td>\n",
       "      <td>81.734444</td>\n",
       "      <td>82.523000</td>\n",
       "      <td>80.917273</td>\n",
       "      <td>77.283333</td>\n",
       "      <td>70.977778</td>\n",
       "      <td>62.30125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VirtualApple</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">mac</th>\n",
       "      <th>AuthenticAMD</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenuineIntel</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.92000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VirtualApple</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.05000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">pc</th>\n",
       "      <th>AuthenticAMD</th>\n",
       "      <td>46.5725</td>\n",
       "      <td>47.441818</td>\n",
       "      <td>51.3075</td>\n",
       "      <td>47.230000</td>\n",
       "      <td>41.750</td>\n",
       "      <td>32.826364</td>\n",
       "      <td>30.255</td>\n",
       "      <td>27.256</td>\n",
       "      <td>27.457</td>\n",
       "      <td>26.495833</td>\n",
       "      <td>25.900000</td>\n",
       "      <td>24.983333</td>\n",
       "      <td>22.989167</td>\n",
       "      <td>17.753636</td>\n",
       "      <td>13.811000</td>\n",
       "      <td>18.375455</td>\n",
       "      <td>22.354444</td>\n",
       "      <td>28.42200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenuineIntel</th>\n",
       "      <td>53.3975</td>\n",
       "      <td>52.543636</td>\n",
       "      <td>48.6925</td>\n",
       "      <td>52.763333</td>\n",
       "      <td>58.242</td>\n",
       "      <td>67.163636</td>\n",
       "      <td>69.739</td>\n",
       "      <td>72.742</td>\n",
       "      <td>72.538</td>\n",
       "      <td>73.500833</td>\n",
       "      <td>74.096667</td>\n",
       "      <td>75.015833</td>\n",
       "      <td>77.001667</td>\n",
       "      <td>82.173636</td>\n",
       "      <td>86.151000</td>\n",
       "      <td>81.610909</td>\n",
       "      <td>77.640000</td>\n",
       "      <td>71.56800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VirtualApple</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date                      2004       2005     2006       2007    2008  \\\n",
       "platform index                                                          \n",
       "combined AuthenticAMD      NaN        NaN      NaN        NaN     NaN   \n",
       "         GenuineIntel      NaN        NaN      NaN        NaN     NaN   \n",
       "         VirtualApple      NaN        NaN      NaN        NaN     NaN   \n",
       "linux    AuthenticAMD      NaN        NaN      NaN        NaN     NaN   \n",
       "         GenuineIntel      NaN        NaN      NaN        NaN     NaN   \n",
       "         VirtualApple      NaN        NaN      NaN        NaN     NaN   \n",
       "mac      AuthenticAMD      NaN        NaN      NaN        NaN     NaN   \n",
       "         GenuineIntel      NaN        NaN      NaN        NaN     NaN   \n",
       "         VirtualApple      NaN        NaN      NaN        NaN     NaN   \n",
       "pc       AuthenticAMD  46.5725  47.441818  51.3075  47.230000  41.750   \n",
       "         GenuineIntel  53.3975  52.543636  48.6925  52.763333  58.242   \n",
       "         VirtualApple      NaN        NaN      NaN        NaN     NaN   \n",
       "\n",
       "date                        2009    2010    2011    2012       2013  \\\n",
       "platform index                                                        \n",
       "combined AuthenticAMD        NaN     NaN     NaN     NaN        NaN   \n",
       "         GenuineIntel        NaN     NaN     NaN     NaN        NaN   \n",
       "         VirtualApple        NaN     NaN     NaN     NaN        NaN   \n",
       "linux    AuthenticAMD        NaN     NaN     NaN     NaN        NaN   \n",
       "         GenuineIntel        NaN     NaN     NaN     NaN        NaN   \n",
       "         VirtualApple        NaN     NaN     NaN     NaN        NaN   \n",
       "mac      AuthenticAMD        NaN     NaN     NaN     NaN        NaN   \n",
       "         GenuineIntel        NaN     NaN     NaN     NaN        NaN   \n",
       "         VirtualApple        NaN     NaN     NaN     NaN        NaN   \n",
       "pc       AuthenticAMD  32.826364  30.255  27.256  27.457  26.495833   \n",
       "         GenuineIntel  67.163636  69.739  72.742  72.538  73.500833   \n",
       "         VirtualApple        NaN     NaN     NaN     NaN        NaN   \n",
       "\n",
       "date                        2014       2015       2016       2017       2018  \\\n",
       "platform index                                                                 \n",
       "combined AuthenticAMD        NaN        NaN        NaN        NaN        NaN   \n",
       "         GenuineIntel        NaN        NaN        NaN        NaN        NaN   \n",
       "         VirtualApple        NaN        NaN        NaN        NaN        NaN   \n",
       "linux    AuthenticAMD  22.591429  20.853333  18.264444  17.476000  19.083636   \n",
       "         GenuineIntel  77.408571  79.147778  81.734444  82.523000  80.917273   \n",
       "         VirtualApple        NaN        NaN        NaN        NaN        NaN   \n",
       "mac      AuthenticAMD        NaN        NaN        NaN        NaN        NaN   \n",
       "         GenuineIntel        NaN        NaN        NaN        NaN        NaN   \n",
       "         VirtualApple        NaN        NaN        NaN        NaN        NaN   \n",
       "pc       AuthenticAMD  25.900000  24.983333  22.989167  17.753636  13.811000   \n",
       "         GenuineIntel  74.096667  75.015833  77.001667  82.173636  86.151000   \n",
       "         VirtualApple        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "date                        2019       2020      2021  \n",
       "platform index                                         \n",
       "combined AuthenticAMD        NaN        NaN       NaN  \n",
       "         GenuineIntel        NaN        NaN       NaN  \n",
       "         VirtualApple        NaN        NaN       NaN  \n",
       "linux    AuthenticAMD  22.715833  29.020000  37.69750  \n",
       "         GenuineIntel  77.283333  70.977778  62.30125  \n",
       "         VirtualApple        NaN        NaN       NaN  \n",
       "mac      AuthenticAMD        NaN        NaN       NaN  \n",
       "         GenuineIntel        NaN        NaN  93.92000  \n",
       "         VirtualApple        NaN        NaN   6.05000  \n",
       "pc       AuthenticAMD  18.375455  22.354444  28.42200  \n",
       "         GenuineIntel  81.610909  77.640000  71.56800  \n",
       "         VirtualApple        NaN        NaN       NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cpu_vendor = (\n",
    "    df_cpu_vendor.groupby([\"index\", df_cpu_vendor[\"date\"].dt.year, \"platform\"])\n",
    "    .mean()\n",
    "    .reset_index()\n",
    ")\n",
    "df_cpu_vendor.pivot(index=[\"platform\", \"index\"], columns=\"date\", values=\"perc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b391bcfc-be9e-4066-a33b-8deace39d139",
   "metadata": {},
   "source": [
    "## CPU core count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5b112812-ea84-4cf2-8cc3-b667353a489a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>perc</th>\n",
       "      <th>category</th>\n",
       "      <th>date</th>\n",
       "      <th>platform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1 cpu</td>\n",
       "      <td>99.82</td>\n",
       "      <td>Physical CPUs</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2 cpus</td>\n",
       "      <td>0.18</td>\n",
       "      <td>Physical CPUs</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>4 cpus</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Physical CPUs</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>1 cpu</td>\n",
       "      <td>99.82</td>\n",
       "      <td>Physical CPUs</td>\n",
       "      <td>2004-06-01</td>\n",
       "      <td>pc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>2 cpus</td>\n",
       "      <td>0.18</td>\n",
       "      <td>Physical CPUs</td>\n",
       "      <td>2004-06-01</td>\n",
       "      <td>pc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262221</th>\n",
       "      <td>&gt;16 cpus</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Physical CPUs</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262222</th>\n",
       "      <td>&gt;16 cpus</td>\n",
       "      <td>0.09</td>\n",
       "      <td>Physical CPUs</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262223</th>\n",
       "      <td>&gt;16 cpus</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Physical CPUs</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262224</th>\n",
       "      <td>&gt;16 cpus</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Physical CPUs</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262225</th>\n",
       "      <td>&gt;16 cpus</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Physical CPUs</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6039 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index   perc       category       date platform\n",
       "140        1 cpu  99.82  Physical CPUs 2004-04-01       pc\n",
       "141       2 cpus   0.18  Physical CPUs 2004-04-01       pc\n",
       "142       4 cpus   0.00  Physical CPUs 2004-04-01       pc\n",
       "365        1 cpu  99.82  Physical CPUs 2004-06-01       pc\n",
       "366       2 cpus   0.18  Physical CPUs 2004-06-01       pc\n",
       "...          ...    ...            ...        ...      ...\n",
       "262221  >16 cpus   0.01  Physical CPUs 2021-12-01    linux\n",
       "262222  >16 cpus   0.09  Physical CPUs 2021-12-01    linux\n",
       "262223  >16 cpus   0.01  Physical CPUs 2021-12-01    linux\n",
       "262224  >16 cpus   0.00  Physical CPUs 2021-12-01    linux\n",
       "262225  >16 cpus   0.02  Physical CPUs 2021-12-01    linux\n",
       "\n",
       "[6039 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num_cpu = df.loc[\n",
    "    (df[\"category\"] == \"Physical CPUs\") & (df[\"index\"] != \"Unspecified\")\n",
    "].copy()\n",
    "df_num_cpu[\"num_cpus\"] = df_num_cpu[\"index\"].str.strip(\"cpus\").astype(int)\n",
    "df_num_cpu.loc[df_num_cpu[\"num_cpus\"] > 16, \"index\"] = \">16 cpus\"\n",
    "df_num_cpu.drop(columns=[\"num_cpus\"], inplace=True)\n",
    "df_num_cpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b3368d8-b9c8-42ed-9111-1fa1002c3ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_num_cpu = CategoricalDtype(\n",
    "    [\n",
    "        \"1 cpu\",\n",
    "        \"2 cpus\",\n",
    "        # \"3 cpus\",\n",
    "        \"4 cpus\",\n",
    "        \"6 cpus\",\n",
    "        \"8 cpus\",\n",
    "        \"10 cpus\",\n",
    "        \"12 cpus\",\n",
    "        \"16 cpus\",\n",
    "        \">16 cpus\",\n",
    "    ],\n",
    "    ordered=True,\n",
    ")\n",
    "df_num_cpu[\"index\"] = df_num_cpu[\"index\"].astype(cat_num_cpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b7ca39a0-55d0-49f3-bd27-c15cfd949a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sum all '+16 cpus' percentages for each month\n",
    "df_num_cpu = (\n",
    "    df_num_cpu.groupby([\"index\", \"date\", \"platform\"], as_index=False)\n",
    "    .sum()\n",
    "    .replace(0, np.nan)\n",
    ")\n",
    "# compute mean annual share\n",
    "df_num_cpu = (\n",
    "    df_num_cpu.groupby([\"index\", df_num_cpu[\"date\"].dt.year, \"platform\"])\n",
    "    .mean()\n",
    "    .reset_index()\n",
    ")\n",
    "df_num_cpu[\"date\"] = df_num_cpu[\"date\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c0814e9b-2166-4116-b122-cdbec6eff918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 728,
       "width": 1448
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axes = plt.subplots(\n",
    "    nrows=2,\n",
    "    ncols=2,\n",
    "    figsize=(20, 10),\n",
    "    constrained_layout=True,\n",
    "    sharey=True,\n",
    "    sharex=True,\n",
    ")\n",
    "for ax, subset in zip(axes.flat, [\"pc\", \"combined\", \"mac\", \"linux\"]):\n",
    "    sns.histplot(\n",
    "        df_num_cpu.loc[df_num_cpu[\"platform\"] == subset],\n",
    "        x=\"date\",\n",
    "        hue=\"index\",\n",
    "        weights=\"perc\",\n",
    "        multiple=\"stack\",\n",
    "        discrete=True,\n",
    "        palette=\"muted\",\n",
    "        shrink=0.75,\n",
    "        linewidth=0.0,\n",
    "        ax=ax,\n",
    "        legend=True if subset == \"combined\" else False,\n",
    "    )\n",
    "    ax.set(title=f\"{subset}\", xlabel=\"Date\", ylabel=\"Percentage\", ylim=[0, 100])\n",
    "    if subset == \"combined\":\n",
    "        sns.move_legend(\n",
    "            ax,\n",
    "            \"upper left\",\n",
    "            bbox_to_anchor=(0, 1),\n",
    "            title=None,\n",
    "            frameon=False,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3936d289-e6e7-4806-9d06-46cd8fffde55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platform</th>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">combined</th>\n",
       "      <th>1 cpu</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.978000</td>\n",
       "      <td>6.411111</td>\n",
       "      <td>6.219167</td>\n",
       "      <td>4.152500</td>\n",
       "      <td>3.270833</td>\n",
       "      <td>2.699167</td>\n",
       "      <td>2.144167</td>\n",
       "      <td>1.454167</td>\n",
       "      <td>0.881667</td>\n",
       "      <td>0.800833</td>\n",
       "      <td>0.399167</td>\n",
       "      <td>0.223333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.860000</td>\n",
       "      <td>50.330000</td>\n",
       "      <td>49.110833</td>\n",
       "      <td>47.836667</td>\n",
       "      <td>48.228333</td>\n",
       "      <td>48.675833</td>\n",
       "      <td>47.575833</td>\n",
       "      <td>40.198333</td>\n",
       "      <td>29.143333</td>\n",
       "      <td>26.570833</td>\n",
       "      <td>19.520833</td>\n",
       "      <td>13.772500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.562000</td>\n",
       "      <td>40.516667</td>\n",
       "      <td>40.516667</td>\n",
       "      <td>43.302500</td>\n",
       "      <td>43.798333</td>\n",
       "      <td>43.873333</td>\n",
       "      <td>45.715833</td>\n",
       "      <td>54.397500</td>\n",
       "      <td>62.791667</td>\n",
       "      <td>53.958333</td>\n",
       "      <td>47.677500</td>\n",
       "      <td>39.364167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>1.216667</td>\n",
       "      <td>2.314167</td>\n",
       "      <td>2.279167</td>\n",
       "      <td>1.819167</td>\n",
       "      <td>1.630000</td>\n",
       "      <td>1.530833</td>\n",
       "      <td>1.372500</td>\n",
       "      <td>4.647500</td>\n",
       "      <td>14.005833</td>\n",
       "      <td>23.299167</td>\n",
       "      <td>30.462500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.201111</td>\n",
       "      <td>0.222500</td>\n",
       "      <td>0.290833</td>\n",
       "      <td>0.330833</td>\n",
       "      <td>0.328333</td>\n",
       "      <td>0.286667</td>\n",
       "      <td>0.417500</td>\n",
       "      <td>0.935000</td>\n",
       "      <td>2.920000</td>\n",
       "      <td>7.337500</td>\n",
       "      <td>13.607500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.011667</td>\n",
       "      <td>0.045000</td>\n",
       "      <td>0.070833</td>\n",
       "      <td>0.115833</td>\n",
       "      <td>0.595000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.018333</td>\n",
       "      <td>0.026667</td>\n",
       "      <td>0.067500</td>\n",
       "      <td>0.411667</td>\n",
       "      <td>0.950833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.025833</td>\n",
       "      <td>0.047500</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.247500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;16 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.024167</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">linux</th>\n",
       "      <th>1 cpu</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.693750</td>\n",
       "      <td>2.546667</td>\n",
       "      <td>1.608889</td>\n",
       "      <td>0.978000</td>\n",
       "      <td>0.547273</td>\n",
       "      <td>0.631667</td>\n",
       "      <td>1.391111</td>\n",
       "      <td>0.362500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.576250</td>\n",
       "      <td>47.444444</td>\n",
       "      <td>46.050000</td>\n",
       "      <td>44.182000</td>\n",
       "      <td>38.603636</td>\n",
       "      <td>31.601667</td>\n",
       "      <td>26.153333</td>\n",
       "      <td>20.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.407500</td>\n",
       "      <td>42.063333</td>\n",
       "      <td>44.436667</td>\n",
       "      <td>45.693000</td>\n",
       "      <td>47.511818</td>\n",
       "      <td>47.460833</td>\n",
       "      <td>43.668889</td>\n",
       "      <td>38.651250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>3.856667</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>4.011000</td>\n",
       "      <td>6.330909</td>\n",
       "      <td>10.917500</td>\n",
       "      <td>15.503333</td>\n",
       "      <td>20.671250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.870000</td>\n",
       "      <td>3.347778</td>\n",
       "      <td>3.065556</td>\n",
       "      <td>4.135000</td>\n",
       "      <td>6.103636</td>\n",
       "      <td>8.296667</td>\n",
       "      <td>10.754444</td>\n",
       "      <td>14.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.062727</td>\n",
       "      <td>0.090833</td>\n",
       "      <td>0.104444</td>\n",
       "      <td>0.342500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.037500</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.081111</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>0.170909</td>\n",
       "      <td>0.330833</td>\n",
       "      <td>1.455556</td>\n",
       "      <td>2.826250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018333</td>\n",
       "      <td>0.015714</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.116000</td>\n",
       "      <td>0.299091</td>\n",
       "      <td>0.359167</td>\n",
       "      <td>0.637778</td>\n",
       "      <td>1.231250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;16 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.012857</td>\n",
       "      <td>0.028889</td>\n",
       "      <td>0.051818</td>\n",
       "      <td>0.061667</td>\n",
       "      <td>0.163333</td>\n",
       "      <td>0.283750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">mac</th>\n",
       "      <th>1 cpu</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.252500</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.026667</td>\n",
       "      <td>0.015714</td>\n",
       "      <td>0.018571</td>\n",
       "      <td>0.011429</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.972500</td>\n",
       "      <td>83.924000</td>\n",
       "      <td>72.092000</td>\n",
       "      <td>63.522857</td>\n",
       "      <td>61.815714</td>\n",
       "      <td>62.370000</td>\n",
       "      <td>63.341000</td>\n",
       "      <td>63.421000</td>\n",
       "      <td>63.112000</td>\n",
       "      <td>59.190000</td>\n",
       "      <td>53.678333</td>\n",
       "      <td>38.547000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.487500</td>\n",
       "      <td>13.104000</td>\n",
       "      <td>26.308000</td>\n",
       "      <td>35.540000</td>\n",
       "      <td>37.354286</td>\n",
       "      <td>36.876667</td>\n",
       "      <td>35.959000</td>\n",
       "      <td>35.901000</td>\n",
       "      <td>35.564000</td>\n",
       "      <td>35.263636</td>\n",
       "      <td>35.231667</td>\n",
       "      <td>31.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.092857</td>\n",
       "      <td>0.181429</td>\n",
       "      <td>0.232222</td>\n",
       "      <td>0.243000</td>\n",
       "      <td>0.243000</td>\n",
       "      <td>0.824000</td>\n",
       "      <td>4.476364</td>\n",
       "      <td>7.231667</td>\n",
       "      <td>7.762000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.287500</td>\n",
       "      <td>2.824000</td>\n",
       "      <td>1.426000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.538571</td>\n",
       "      <td>0.421111</td>\n",
       "      <td>0.356000</td>\n",
       "      <td>0.324000</td>\n",
       "      <td>0.331000</td>\n",
       "      <td>0.860909</td>\n",
       "      <td>3.585000</td>\n",
       "      <td>21.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.034000</td>\n",
       "      <td>0.050909</td>\n",
       "      <td>0.073333</td>\n",
       "      <td>0.721000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.086000</td>\n",
       "      <td>0.097500</td>\n",
       "      <td>0.087143</td>\n",
       "      <td>0.087143</td>\n",
       "      <td>0.086667</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.109000</td>\n",
       "      <td>0.126000</td>\n",
       "      <td>0.130909</td>\n",
       "      <td>0.163333</td>\n",
       "      <td>0.157000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>0.021000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;16 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012857</td>\n",
       "      <td>0.015455</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">pc</th>\n",
       "      <th>1 cpu</th>\n",
       "      <td>99.80875</td>\n",
       "      <td>99.735455</td>\n",
       "      <td>95.3225</td>\n",
       "      <td>77.834167</td>\n",
       "      <td>59.521</td>\n",
       "      <td>26.877273</td>\n",
       "      <td>16.075000</td>\n",
       "      <td>6.774000</td>\n",
       "      <td>6.504000</td>\n",
       "      <td>4.311667</td>\n",
       "      <td>3.382500</td>\n",
       "      <td>2.796667</td>\n",
       "      <td>2.226667</td>\n",
       "      <td>1.460909</td>\n",
       "      <td>0.885000</td>\n",
       "      <td>0.824545</td>\n",
       "      <td>0.405556</td>\n",
       "      <td>0.229000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 cpus</th>\n",
       "      <td>0.19125</td>\n",
       "      <td>0.264545</td>\n",
       "      <td>4.6625</td>\n",
       "      <td>21.441667</td>\n",
       "      <td>36.340</td>\n",
       "      <td>55.704545</td>\n",
       "      <td>55.219000</td>\n",
       "      <td>48.192000</td>\n",
       "      <td>48.269000</td>\n",
       "      <td>47.241667</td>\n",
       "      <td>47.739167</td>\n",
       "      <td>48.210000</td>\n",
       "      <td>47.010000</td>\n",
       "      <td>39.033636</td>\n",
       "      <td>27.464000</td>\n",
       "      <td>25.510909</td>\n",
       "      <td>18.444444</td>\n",
       "      <td>12.605000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.722500</td>\n",
       "      <td>4.117</td>\n",
       "      <td>16.676364</td>\n",
       "      <td>27.407000</td>\n",
       "      <td>42.240000</td>\n",
       "      <td>40.930000</td>\n",
       "      <td>43.592500</td>\n",
       "      <td>44.068333</td>\n",
       "      <td>44.138333</td>\n",
       "      <td>46.091667</td>\n",
       "      <td>55.510000</td>\n",
       "      <td>64.470000</td>\n",
       "      <td>54.534545</td>\n",
       "      <td>48.494444</td>\n",
       "      <td>38.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.303333</td>\n",
       "      <td>1.294000</td>\n",
       "      <td>2.411000</td>\n",
       "      <td>2.362500</td>\n",
       "      <td>1.858333</td>\n",
       "      <td>1.653333</td>\n",
       "      <td>1.552500</td>\n",
       "      <td>1.388182</td>\n",
       "      <td>4.678000</td>\n",
       "      <td>14.364545</td>\n",
       "      <td>23.677778</td>\n",
       "      <td>32.256000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.058000</td>\n",
       "      <td>0.076000</td>\n",
       "      <td>0.162000</td>\n",
       "      <td>0.272500</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.290833</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.408182</td>\n",
       "      <td>0.903000</td>\n",
       "      <td>2.962727</td>\n",
       "      <td>7.206667</td>\n",
       "      <td>13.708000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.012727</td>\n",
       "      <td>0.042000</td>\n",
       "      <td>0.070909</td>\n",
       "      <td>0.103333</td>\n",
       "      <td>0.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.014545</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>0.067273</td>\n",
       "      <td>0.391111</td>\n",
       "      <td>0.944000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.101111</td>\n",
       "      <td>0.245000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;16 cpus</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.024444</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date                   2004       2005     2006       2007    2008       2009  \\\n",
       "platform index                                                                  \n",
       "combined 1 cpu          NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         2 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         4 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         6 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         8 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         10 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         12 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         16 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         >16 cpus       NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "linux    1 cpu          NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         2 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         4 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         6 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         8 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         10 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         12 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         16 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         >16 cpus       NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "mac      1 cpu          NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         2 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         4 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         6 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         8 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         10 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         12 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         16 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         >16 cpus       NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "pc       1 cpu     99.80875  99.735455  95.3225  77.834167  59.521  26.877273   \n",
       "         2 cpus     0.19125   0.264545   4.6625  21.441667  36.340  55.704545   \n",
       "         4 cpus         NaN        NaN   0.0300   0.722500   4.117  16.676364   \n",
       "         6 cpus         NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         8 cpus         NaN        NaN      NaN   0.010000   0.016   0.130000   \n",
       "         10 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         12 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         16 cpus        NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "         >16 cpus       NaN        NaN      NaN        NaN     NaN        NaN   \n",
       "\n",
       "date                    2010       2011       2012       2013       2014  \\\n",
       "platform index                                                             \n",
       "combined 1 cpu     13.978000   6.411111   6.219167   4.152500   3.270833   \n",
       "         2 cpus    56.860000  50.330000  49.110833  47.836667  48.228333   \n",
       "         4 cpus    27.562000  40.516667  40.516667  43.302500  43.798333   \n",
       "         6 cpus     0.340000   1.216667   2.314167   2.279167   1.819167   \n",
       "         8 cpus     0.190000   0.201111   0.222500   0.290833   0.330833   \n",
       "         10 cpus         NaN        NaN        NaN        NaN        NaN   \n",
       "         12 cpus    0.010000   0.010000   0.010000   0.010000   0.010000   \n",
       "         16 cpus         NaN        NaN        NaN        NaN        NaN   \n",
       "         >16 cpus        NaN        NaN        NaN        NaN        NaN   \n",
       "linux    1 cpu           NaN        NaN        NaN        NaN   3.693750   \n",
       "         2 cpus          NaN        NaN        NaN        NaN  48.576250   \n",
       "         4 cpus          NaN        NaN        NaN        NaN  40.407500   \n",
       "         6 cpus          NaN        NaN        NaN        NaN   3.520000   \n",
       "         8 cpus          NaN        NaN        NaN        NaN   2.870000   \n",
       "         10 cpus         NaN        NaN        NaN        NaN   0.010000   \n",
       "         12 cpus         NaN        NaN        NaN        NaN   0.037500   \n",
       "         16 cpus         NaN        NaN        NaN        NaN   0.018333   \n",
       "         >16 cpus        NaN        NaN        NaN        NaN   0.010000   \n",
       "mac      1 cpu      0.252500   0.060000   0.026667   0.015714   0.018571   \n",
       "         2 cpus    89.972500  83.924000  72.092000  63.522857  61.815714   \n",
       "         4 cpus     7.487500  13.104000  26.308000  35.540000  37.354286   \n",
       "         6 cpus     0.010000   0.070000   0.080000   0.092857   0.181429   \n",
       "         8 cpus     2.287500   2.824000   1.426000   0.740000   0.538571   \n",
       "         10 cpus         NaN        NaN        NaN        NaN        NaN   \n",
       "         12 cpus         NaN   0.086000   0.097500   0.087143   0.087143   \n",
       "         16 cpus         NaN        NaN        NaN        NaN        NaN   \n",
       "         >16 cpus        NaN        NaN        NaN        NaN        NaN   \n",
       "pc       1 cpu     16.075000   6.774000   6.504000   4.311667   3.382500   \n",
       "         2 cpus    55.219000  48.192000  48.269000  47.241667  47.739167   \n",
       "         4 cpus    27.407000  42.240000  40.930000  43.592500  44.068333   \n",
       "         6 cpus     0.303333   1.294000   2.411000   2.362500   1.858333   \n",
       "         8 cpus     0.058000   0.076000   0.162000   0.272500   0.290000   \n",
       "         10 cpus         NaN        NaN        NaN        NaN        NaN   \n",
       "         12 cpus         NaN   0.010000   0.010000   0.010000   0.010000   \n",
       "         16 cpus         NaN        NaN        NaN        NaN        NaN   \n",
       "         >16 cpus        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "date                    2015       2016       2017       2018       2019  \\\n",
       "platform index                                                             \n",
       "combined 1 cpu      2.699167   2.144167   1.454167   0.881667   0.800833   \n",
       "         2 cpus    48.675833  47.575833  40.198333  29.143333  26.570833   \n",
       "         4 cpus    43.873333  45.715833  54.397500  62.791667  53.958333   \n",
       "         6 cpus     1.630000   1.530833   1.372500   4.647500  14.005833   \n",
       "         8 cpus     0.328333   0.286667   0.417500   0.935000   2.920000   \n",
       "         10 cpus         NaN   0.010000   0.011667   0.045000   0.070833   \n",
       "         12 cpus    0.010000   0.015000   0.018333   0.026667   0.067500   \n",
       "         16 cpus         NaN   0.010000   0.010000   0.025833   0.047500   \n",
       "         >16 cpus        NaN        NaN        NaN   0.010000   0.010000   \n",
       "linux    1 cpu      2.546667   1.608889   0.978000   0.547273   0.631667   \n",
       "         2 cpus    47.444444  46.050000  44.182000  38.603636  31.601667   \n",
       "         4 cpus    42.063333  44.436667  45.693000  47.511818  47.460833   \n",
       "         6 cpus     3.856667   3.866667   4.011000   6.330909  10.917500   \n",
       "         8 cpus     3.347778   3.065556   4.135000   6.103636   8.296667   \n",
       "         10 cpus    0.020000   0.010000   0.030000   0.062727   0.090833   \n",
       "         12 cpus    0.050000   0.081111   0.128000   0.170909   0.330833   \n",
       "         16 cpus    0.015714   0.030000   0.116000   0.299091   0.359167   \n",
       "         >16 cpus   0.015000   0.012857   0.028889   0.051818   0.061667   \n",
       "mac      1 cpu      0.011429   0.010000   0.010000        NaN        NaN   \n",
       "         2 cpus    62.370000  63.341000  63.421000  63.112000  59.190000   \n",
       "         4 cpus    36.876667  35.959000  35.901000  35.564000  35.263636   \n",
       "         6 cpus     0.232222   0.243000   0.243000   0.824000   4.476364   \n",
       "         8 cpus     0.421111   0.356000   0.324000   0.331000   0.860909   \n",
       "         10 cpus         NaN        NaN        NaN   0.034000   0.050909   \n",
       "         12 cpus    0.086667   0.090000   0.109000   0.126000   0.130909   \n",
       "         16 cpus         NaN        NaN        NaN   0.010000   0.010000   \n",
       "         >16 cpus        NaN        NaN        NaN   0.012857   0.015455   \n",
       "pc       1 cpu      2.796667   2.226667   1.460909   0.885000   0.824545   \n",
       "         2 cpus    48.210000  47.010000  39.033636  27.464000  25.510909   \n",
       "         4 cpus    44.138333  46.091667  55.510000  64.470000  54.534545   \n",
       "         6 cpus     1.653333   1.552500   1.388182   4.678000  14.364545   \n",
       "         8 cpus     0.290833   0.255000   0.408182   0.903000   2.962727   \n",
       "         10 cpus         NaN   0.010000   0.012727   0.042000   0.070909   \n",
       "         12 cpus    0.010000   0.010000   0.014545   0.022000   0.067273   \n",
       "         16 cpus         NaN   0.010000   0.010000   0.025000   0.045455   \n",
       "         >16 cpus        NaN        NaN        NaN   0.010000   0.010000   \n",
       "\n",
       "date                    2020       2021  \n",
       "platform index                           \n",
       "combined 1 cpu      0.399167   0.223333  \n",
       "         2 cpus    19.520833  13.772500  \n",
       "         4 cpus    47.677500  39.364167  \n",
       "         6 cpus    23.299167  30.462500  \n",
       "         8 cpus     7.337500  13.607500  \n",
       "         10 cpus    0.115833   0.595000  \n",
       "         12 cpus    0.411667   0.950833  \n",
       "         16 cpus    0.104167   0.247500  \n",
       "         >16 cpus   0.024167   0.041667  \n",
       "linux    1 cpu      1.391111   0.362500  \n",
       "         2 cpus    26.153333  20.870000  \n",
       "         4 cpus    43.668889  38.651250  \n",
       "         6 cpus    15.503333  20.671250  \n",
       "         8 cpus    10.754444  14.575000  \n",
       "         10 cpus    0.104444   0.342500  \n",
       "         12 cpus    1.455556   2.826250  \n",
       "         16 cpus    0.637778   1.231250  \n",
       "         >16 cpus   0.163333   0.283750  \n",
       "mac      1 cpu           NaN        NaN  \n",
       "         2 cpus    53.678333  38.547000  \n",
       "         4 cpus    35.231667  31.250000  \n",
       "         6 cpus     7.231667   7.762000  \n",
       "         8 cpus     3.585000  21.510000  \n",
       "         10 cpus    0.073333   0.721000  \n",
       "         12 cpus    0.163333   0.157000  \n",
       "         16 cpus    0.012000   0.021000  \n",
       "         >16 cpus   0.016667   0.025000  \n",
       "pc       1 cpu      0.405556   0.229000  \n",
       "         2 cpus    18.444444  12.605000  \n",
       "         4 cpus    48.494444  38.600000  \n",
       "         6 cpus    23.677778  32.256000  \n",
       "         8 cpus     7.206667  13.708000  \n",
       "         10 cpus    0.103333   0.610000  \n",
       "         12 cpus    0.391111   0.944000  \n",
       "         16 cpus    0.101111   0.245000  \n",
       "         >16 cpus   0.024444   0.040000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num_cpu.pivot(index=[\"platform\", \"index\"], columns=\"date\", values=\"perc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b33d23c-6d0b-4eb7-a711-138784c78e63",
   "metadata": {},
   "source": [
    "## Linux flavors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d5c1d30d-f9fd-436f-90fb-81633800b3a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>perc</th>\n",
       "      <th>category</th>\n",
       "      <th>date</th>\n",
       "      <th>platform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>241017</th>\n",
       "      <td>Ubuntu 13.10 64 bit</td>\n",
       "      <td>26.33</td>\n",
       "      <td>Linux Version</td>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241018</th>\n",
       "      <td>Ubuntu 12.04.3 LTS 64 bit</td>\n",
       "      <td>7.68</td>\n",
       "      <td>Linux Version</td>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241019</th>\n",
       "      <td>Linux 64 bit</td>\n",
       "      <td>6.76</td>\n",
       "      <td>Linux Version</td>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241020</th>\n",
       "      <td>Linux Mint 16 Petra 64 bit</td>\n",
       "      <td>6.46</td>\n",
       "      <td>Linux Version</td>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241021</th>\n",
       "      <td>Ubuntu 13.10</td>\n",
       "      <td>5.25</td>\n",
       "      <td>Linux Version</td>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262161</th>\n",
       "      <td>Linux Mint 20.2 64 bit</td>\n",
       "      <td>7.04</td>\n",
       "      <td>Linux Version</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262162</th>\n",
       "      <td>Ubuntu 21.10 64 bit</td>\n",
       "      <td>5.42</td>\n",
       "      <td>Linux Version</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262163</th>\n",
       "      <td>Description:Freedesktop.org 21.08.4 (Flatpak r...</td>\n",
       "      <td>4.38</td>\n",
       "      <td>Linux Version</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262164</th>\n",
       "      <td>Linux 5.x 64 bit</td>\n",
       "      <td>4.30</td>\n",
       "      <td>Linux Version</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262165</th>\n",
       "      <td>Other</td>\n",
       "      <td>30.07</td>\n",
       "      <td>Linux Version</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    index   perc  \\\n",
       "241017                                Ubuntu 13.10 64 bit  26.33   \n",
       "241018                          Ubuntu 12.04.3 LTS 64 bit   7.68   \n",
       "241019                                       Linux 64 bit   6.76   \n",
       "241020                         Linux Mint 16 Petra 64 bit   6.46   \n",
       "241021                                       Ubuntu 13.10   5.25   \n",
       "...                                                   ...    ...   \n",
       "262161                             Linux Mint 20.2 64 bit   7.04   \n",
       "262162                                Ubuntu 21.10 64 bit   5.42   \n",
       "262163  Description:Freedesktop.org 21.08.4 (Flatpak r...   4.38   \n",
       "262164                                   Linux 5.x 64 bit   4.30   \n",
       "262165                                              Other  30.07   \n",
       "\n",
       "             category       date platform  \n",
       "241017  Linux Version 2014-02-01    linux  \n",
       "241018  Linux Version 2014-02-01    linux  \n",
       "241019  Linux Version 2014-02-01    linux  \n",
       "241020  Linux Version 2014-02-01    linux  \n",
       "241021  Linux Version 2014-02-01    linux  \n",
       "...               ...        ...      ...  \n",
       "262161  Linux Version 2021-12-01    linux  \n",
       "262162  Linux Version 2021-12-01    linux  \n",
       "262163  Linux Version 2021-12-01    linux  \n",
       "262164  Linux Version 2021-12-01    linux  \n",
       "262165  Linux Version 2021-12-01    linux  \n",
       "\n",
       "[410 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_linux = df.loc[(df[\"category\"] == \"Linux Version\")].copy()\n",
    "df_linux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2b24f5d4-a814-4e1e-893d-99607d3a0c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_linux[\"index\"] = (\n",
    "    df_linux[\"index\"]\n",
    "    .replace(to_replace=r\" \\d.*$\", value=\"\", regex=True)\n",
    "    .astype(\"category\")\n",
    ")\n",
    "# sum the cleaned distro shares\n",
    "df_linux = df_linux.groupby([\"index\", \"date\"], as_index=False).sum()\n",
    "# compute mean annual share\n",
    "df_linux = df_linux.groupby([\"index\", df_linux[\"date\"].dt.year]).mean().reset_index()\n",
    "df_linux[\"date\"] = df_linux[\"date\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d91a2e68-a181-4888-9fea-db7dca970c4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 368,
       "width": 728
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(nrows=1, figsize=(10, 5), constrained_layout=True)\n",
    "sns.histplot(\n",
    "    df_linux,\n",
    "    x=\"date\",\n",
    "    hue=\"index\",\n",
    "    weights=\"perc\",\n",
    "    multiple=\"stack\",\n",
    "    discrete=True,\n",
    "    palette=\"muted\",\n",
    "    shrink=0.75,\n",
    "    linewidth=0.0,\n",
    "    ax=ax,\n",
    ")\n",
    "ax.set(title=\"Linux Distributions\", xlabel=\"Date\", ylabel=\"Percentage\", ylim=[0, 100])\n",
    "sns.move_legend(ax, \"lower left\", bbox_to_anchor=(1, 0.5), title=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "feb3d69c-a812-4941-8dc9-5cb98f640349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>date</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>\"Arch Linux\"</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.688333</td>\n",
       "      <td>10.992222</td>\n",
       "      <td>11.55625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Manjaro Linux\"</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.785833</td>\n",
       "      <td>11.112222</td>\n",
       "      <td>12.01125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description:Freedesktop.org</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.24500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Linux</th>\n",
       "      <td>7.32625</td>\n",
       "      <td>7.554444</td>\n",
       "      <td>9.317778</td>\n",
       "      <td>8.327</td>\n",
       "      <td>7.020909</td>\n",
       "      <td>6.445833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.53750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Linux Mint</th>\n",
       "      <td>8.66875</td>\n",
       "      <td>10.018889</td>\n",
       "      <td>8.320000</td>\n",
       "      <td>4.944</td>\n",
       "      <td>3.080909</td>\n",
       "      <td>3.533333</td>\n",
       "      <td>5.396667</td>\n",
       "      <td>5.51750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>35.82125</td>\n",
       "      <td>37.968889</td>\n",
       "      <td>43.762222</td>\n",
       "      <td>52.562</td>\n",
       "      <td>54.845455</td>\n",
       "      <td>45.117500</td>\n",
       "      <td>40.744444</td>\n",
       "      <td>42.72625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pop!_OS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.635556</td>\n",
       "      <td>3.58250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ubuntu</th>\n",
       "      <td>48.18125</td>\n",
       "      <td>44.457778</td>\n",
       "      <td>38.600000</td>\n",
       "      <td>34.169</td>\n",
       "      <td>35.054545</td>\n",
       "      <td>33.698333</td>\n",
       "      <td>31.118889</td>\n",
       "      <td>22.82625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.730833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date                             2014       2015       2016    2017  \\\n",
       "index                                                                 \n",
       "\"Arch Linux\"                      NaN        NaN        NaN     NaN   \n",
       "\"Manjaro Linux\"                   NaN        NaN        NaN     NaN   \n",
       "Description:Freedesktop.org       NaN        NaN        NaN     NaN   \n",
       "Linux                         7.32625   7.554444   9.317778   8.327   \n",
       "Linux Mint                    8.66875  10.018889   8.320000   4.944   \n",
       "Other                        35.82125  37.968889  43.762222  52.562   \n",
       "Pop!_OS                           NaN        NaN        NaN     NaN   \n",
       "Ubuntu                       48.18125  44.457778  38.600000  34.169   \n",
       "Unknown                           NaN        NaN        NaN     NaN   \n",
       "\n",
       "date                              2018       2019       2020      2021  \n",
       "index                                                                   \n",
       "\"Arch Linux\"                       NaN   2.688333  10.992222  11.55625  \n",
       "\"Manjaro Linux\"                    NaN   7.785833  11.112222  12.01125  \n",
       "Description:Freedesktop.org        NaN        NaN        NaN   1.24500  \n",
       "Linux                         7.020909   6.445833        NaN   0.53750  \n",
       "Linux Mint                    3.080909   3.533333   5.396667   5.51750  \n",
       "Other                        54.845455  45.117500  40.744444  42.72625  \n",
       "Pop!_OS                            NaN        NaN   0.635556   3.58250  \n",
       "Ubuntu                       35.054545  33.698333  31.118889  22.82625  \n",
       "Unknown                            NaN   0.730833        NaN       NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_linux.pivot(index=[\"index\"], columns=\"date\", values=\"perc\").replace(0, np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "406ebc6d-5f2c-4316-94f1-1e7b4d51a48b",
   "metadata": {},
   "source": [
    "The category `Other` shadows many distribution versions, so be careful making assumptions with these values. Another source of distribution usage is [All Roads Lead to Arch: The Evolution of Linux Distros Used for Gaming Over Time](https://boilingsteam.com/all-roads-lead-to-arch-the-evolution-of-linux-distros-used-for-gaming-over-time), although the series begins at the end of 2018."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ee03d4-1c3c-4b99-a7cd-34c82ffc63f6",
   "metadata": {},
   "source": [
    "## Share of GPU manufacturers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "658058f1-ccb2-4dec-9687-96d803957474",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>perc</th>\n",
       "      <th>category</th>\n",
       "      <th>date</th>\n",
       "      <th>platform</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>NVidia GeForce4 MX Series</td>\n",
       "      <td>15.20</td>\n",
       "      <td>Video Card Description</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>NVidia GeForce4 Series</td>\n",
       "      <td>13.41</td>\n",
       "      <td>Video Card Description</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>NVidia GeForce2 MX Series</td>\n",
       "      <td>10.53</td>\n",
       "      <td>Video Card Description</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>NVidia GeForce FX 5200 Series</td>\n",
       "      <td>6.94</td>\n",
       "      <td>Video Card Description</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>ATI Radeon 9600 Series</td>\n",
       "      <td>6.29</td>\n",
       "      <td>Video Card Description</td>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>pc</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262314</th>\n",
       "      <td>NVIDIA GeForce GTX 650</td>\n",
       "      <td>0.17</td>\n",
       "      <td>Video Card Description</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262315</th>\n",
       "      <td>AMD Radeon HD 7700 Series</td>\n",
       "      <td>0.17</td>\n",
       "      <td>Video Card Description</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262316</th>\n",
       "      <td>NVIDIA GeForce MX150</td>\n",
       "      <td>0.16</td>\n",
       "      <td>Video Card Description</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262317</th>\n",
       "      <td>AMD Radeon R4 Graphics</td>\n",
       "      <td>0.16</td>\n",
       "      <td>Video Card Description</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262318</th>\n",
       "      <td>Other</td>\n",
       "      <td>12.93</td>\n",
       "      <td>Video Card Description</td>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>linux</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32301 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                index   perc                category  \\\n",
       "62          NVidia GeForce4 MX Series  15.20  Video Card Description   \n",
       "63             NVidia GeForce4 Series  13.41  Video Card Description   \n",
       "64          NVidia GeForce2 MX Series  10.53  Video Card Description   \n",
       "65      NVidia GeForce FX 5200 Series   6.94  Video Card Description   \n",
       "66             ATI Radeon 9600 Series   6.29  Video Card Description   \n",
       "...                               ...    ...                     ...   \n",
       "262314         NVIDIA GeForce GTX 650   0.17  Video Card Description   \n",
       "262315      AMD Radeon HD 7700 Series   0.17  Video Card Description   \n",
       "262316           NVIDIA GeForce MX150   0.16  Video Card Description   \n",
       "262317         AMD Radeon R4 Graphics   0.16  Video Card Description   \n",
       "262318                          Other  12.93  Video Card Description   \n",
       "\n",
       "             date platform  year  \n",
       "62     2004-04-01       pc  2004  \n",
       "63     2004-04-01       pc  2004  \n",
       "64     2004-04-01       pc  2004  \n",
       "65     2004-04-01       pc  2004  \n",
       "66     2004-04-01       pc  2004  \n",
       "...           ...      ...   ...  \n",
       "262314 2021-12-01    linux  2021  \n",
       "262315 2021-12-01    linux  2021  \n",
       "262316 2021-12-01    linux  2021  \n",
       "262317 2021-12-01    linux  2021  \n",
       "262318 2021-12-01    linux  2021  \n",
       "\n",
       "[32301 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are some errors for data collected in 2015 showing values of 'Other' > 90%\n",
    "# in 2014, AMD enters the series and average values are higher than they should be\n",
    "df_gpu_vendor = df.loc[\n",
    "    (df[\"category\"] == \"Video Card Description\") & (df[\"perc\"] < 90)\n",
    "].copy()\n",
    "df_gpu_vendor[\"year\"] = df_gpu_vendor[\"date\"].dt.year.astype(str)\n",
    "df_gpu_vendor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7c6c931c-68c1-40ba-a0bb-ed113044934e",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_vendor = lambda x: df_gpu_vendor[\"index\"].str.contains(x, case=False)\n",
    "\n",
    "df_gpu_vendor.loc[group_vendor(\"AMD|Radeon\"), \"gpu_vendor\"] = \"AMD\"\n",
    "df_gpu_vendor.loc[group_vendor(\"ATI|ATI Radeon\"), \"gpu_vendor\"] = \"ATI\"\n",
    "df_gpu_vendor.loc[group_vendor(\"Intel\"), \"gpu_vendor\"] = \"Intel\"\n",
    "df_gpu_vendor.loc[group_vendor(\"NVIDIA|GeForce\"), \"gpu_vendor\"] = \"NVIDIA\"\n",
    "df_gpu_vendor.loc[group_vendor(\"Apple\"), \"gpu_vendor\"] = \"Apple\"\n",
    "df_gpu_vendor.loc[group_vendor(\"Other\"), \"gpu_vendor\"] = \"Other\"\n",
    "df_gpu_vendor[\"gpu_vendor\"] = df_gpu_vendor[\"gpu_vendor\"].replace(np.nan, \"Other\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7540da9f-8875-4c2d-8732-3b6f38a6fd0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_gpu_vendor = CategoricalDtype(\n",
    "    [\n",
    "        \"AMD\",\n",
    "        \"Apple\",\n",
    "        \"ATI\",\n",
    "        \"Intel\",\n",
    "        \"NVIDIA\",\n",
    "        \"Other\",\n",
    "    ],\n",
    "    ordered=True,\n",
    ")\n",
    "df_gpu_vendor[\"gpu_vendor\"] = df_gpu_vendor[\"gpu_vendor\"].astype(cat_gpu_vendor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "344f9480-2ca1-403c-a535-a861656a8592",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gpu_vendor = (\n",
    "    df_gpu_vendor.groupby([\"gpu_vendor\", \"date\", \"platform\"], as_index=False)\n",
    "    .sum()\n",
    "    .replace(0, np.nan)\n",
    ")\n",
    "# compute mean annual share\n",
    "df_gpu_vendor = (\n",
    "    df_gpu_vendor.groupby([\"gpu_vendor\", df_gpu_vendor[\"date\"].dt.year, \"platform\"])\n",
    "    .mean()\n",
    "    .reset_index()\n",
    ")\n",
    "df_gpu_vendor[\"date\"] = df_gpu_vendor[\"date\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4bfee9d0-283a-48c8-a75d-fcc8dd43a046",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 728,
       "width": 1448
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axes = plt.subplots(\n",
    "    nrows=2,\n",
    "    ncols=2,\n",
    "    figsize=(20, 10),\n",
    "    constrained_layout=True,\n",
    "    sharey=True,\n",
    "    sharex=True,\n",
    ")\n",
    "for ax, subset in zip(axes.flat, [\"pc\", \"combined\", \"mac\", \"linux\"]):\n",
    "    sns.histplot(\n",
    "        df_gpu_vendor.loc[df_gpu_vendor[\"platform\"] == subset],\n",
    "        x=\"date\",\n",
    "        hue=\"gpu_vendor\",\n",
    "        weights=\"perc\",\n",
    "        multiple=\"stack\",\n",
    "        discrete=True,\n",
    "        palette=\"muted\",\n",
    "        shrink=0.75,\n",
    "        linewidth=0.0,\n",
    "        ax=ax,\n",
    "        legend=True if subset == \"combined\" else False,\n",
    "    )\n",
    "    ax.set(title=f\"{subset}\", xlabel=\"Date\", ylabel=\"Percentage\", ylim=[0, 100])\n",
    "    if subset == \"combined\":\n",
    "        sns.move_legend(\n",
    "            ax,\n",
    "            \"upper left\",\n",
    "            bbox_to_anchor=(0, 1),\n",
    "            title=None,\n",
    "            frameon=False,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "285fc6b2-ac6f-42e4-8d39-60e40b817f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platform</th>\n",
       "      <th>gpu_vendor</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">combined</th>\n",
       "      <th>AMD</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.181429</td>\n",
       "      <td>18.86750</td>\n",
       "      <td>15.977500</td>\n",
       "      <td>9.160000</td>\n",
       "      <td>7.869167</td>\n",
       "      <td>9.463333</td>\n",
       "      <td>11.269167</td>\n",
       "      <td>11.335833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.6620</td>\n",
       "      <td>25.648889</td>\n",
       "      <td>22.473333</td>\n",
       "      <td>17.105000</td>\n",
       "      <td>6.070833</td>\n",
       "      <td>3.07750</td>\n",
       "      <td>2.660000</td>\n",
       "      <td>1.043333</td>\n",
       "      <td>0.502000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intel</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.6960</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>8.057500</td>\n",
       "      <td>12.583333</td>\n",
       "      <td>16.359167</td>\n",
       "      <td>12.40875</td>\n",
       "      <td>17.686250</td>\n",
       "      <td>10.777500</td>\n",
       "      <td>8.878333</td>\n",
       "      <td>10.390833</td>\n",
       "      <td>9.778333</td>\n",
       "      <td>7.681667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NVIDIA</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.1600</td>\n",
       "      <td>46.667778</td>\n",
       "      <td>39.267500</td>\n",
       "      <td>35.126667</td>\n",
       "      <td>37.828333</td>\n",
       "      <td>45.32250</td>\n",
       "      <td>49.960000</td>\n",
       "      <td>57.546000</td>\n",
       "      <td>71.264167</td>\n",
       "      <td>68.046667</td>\n",
       "      <td>68.320000</td>\n",
       "      <td>69.963333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.4820</td>\n",
       "      <td>25.884444</td>\n",
       "      <td>30.200833</td>\n",
       "      <td>35.183333</td>\n",
       "      <td>30.878333</td>\n",
       "      <td>13.04750</td>\n",
       "      <td>13.737500</td>\n",
       "      <td>25.858000</td>\n",
       "      <td>11.565000</td>\n",
       "      <td>11.885000</td>\n",
       "      <td>10.627500</td>\n",
       "      <td>10.994167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">linux</th>\n",
       "      <th>AMD</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.535000</td>\n",
       "      <td>13.23625</td>\n",
       "      <td>10.808889</td>\n",
       "      <td>9.298000</td>\n",
       "      <td>11.147273</td>\n",
       "      <td>14.499167</td>\n",
       "      <td>18.904444</td>\n",
       "      <td>22.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.894000</td>\n",
       "      <td>2.54125</td>\n",
       "      <td>2.011111</td>\n",
       "      <td>1.290000</td>\n",
       "      <td>0.821818</td>\n",
       "      <td>0.444167</td>\n",
       "      <td>0.298750</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intel</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.510000</td>\n",
       "      <td>27.77500</td>\n",
       "      <td>30.360000</td>\n",
       "      <td>30.827000</td>\n",
       "      <td>28.955455</td>\n",
       "      <td>28.169167</td>\n",
       "      <td>25.648889</td>\n",
       "      <td>23.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NVIDIA</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.770000</td>\n",
       "      <td>38.88500</td>\n",
       "      <td>39.955556</td>\n",
       "      <td>40.193000</td>\n",
       "      <td>41.130000</td>\n",
       "      <td>40.410000</td>\n",
       "      <td>37.505556</td>\n",
       "      <td>37.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.606000</td>\n",
       "      <td>17.56125</td>\n",
       "      <td>16.855556</td>\n",
       "      <td>18.379000</td>\n",
       "      <td>17.941818</td>\n",
       "      <td>16.482500</td>\n",
       "      <td>17.681111</td>\n",
       "      <td>15.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">mac</th>\n",
       "      <th>AMD</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.867500</td>\n",
       "      <td>8.48875</td>\n",
       "      <td>8.863000</td>\n",
       "      <td>9.428000</td>\n",
       "      <td>11.522000</td>\n",
       "      <td>15.090909</td>\n",
       "      <td>15.710000</td>\n",
       "      <td>25.479000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.1450</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>35.052000</td>\n",
       "      <td>25.681667</td>\n",
       "      <td>10.240000</td>\n",
       "      <td>5.64500</td>\n",
       "      <td>4.054000</td>\n",
       "      <td>3.079000</td>\n",
       "      <td>2.281000</td>\n",
       "      <td>1.488182</td>\n",
       "      <td>1.098333</td>\n",
       "      <td>0.632000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intel</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.6950</td>\n",
       "      <td>4.186000</td>\n",
       "      <td>21.132000</td>\n",
       "      <td>41.506667</td>\n",
       "      <td>54.736667</td>\n",
       "      <td>62.68375</td>\n",
       "      <td>57.139000</td>\n",
       "      <td>59.318000</td>\n",
       "      <td>68.237000</td>\n",
       "      <td>74.002727</td>\n",
       "      <td>75.688333</td>\n",
       "      <td>61.842000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NVIDIA</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.6550</td>\n",
       "      <td>64.776000</td>\n",
       "      <td>40.442000</td>\n",
       "      <td>24.716667</td>\n",
       "      <td>24.436667</td>\n",
       "      <td>20.88375</td>\n",
       "      <td>10.479000</td>\n",
       "      <td>1.333000</td>\n",
       "      <td>1.393000</td>\n",
       "      <td>1.830909</td>\n",
       "      <td>1.461667</td>\n",
       "      <td>1.117000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.4925</td>\n",
       "      <td>1.084000</td>\n",
       "      <td>3.358000</td>\n",
       "      <td>8.086667</td>\n",
       "      <td>4.676667</td>\n",
       "      <td>2.28875</td>\n",
       "      <td>19.466000</td>\n",
       "      <td>26.846000</td>\n",
       "      <td>16.583000</td>\n",
       "      <td>7.585455</td>\n",
       "      <td>6.043333</td>\n",
       "      <td>4.676000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">pc</th>\n",
       "      <th>AMD</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.772000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.952000</td>\n",
       "      <td>18.87600</td>\n",
       "      <td>16.300000</td>\n",
       "      <td>9.332500</td>\n",
       "      <td>7.810000</td>\n",
       "      <td>9.779091</td>\n",
       "      <td>11.353333</td>\n",
       "      <td>10.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATI</th>\n",
       "      <td>30.10125</td>\n",
       "      <td>39.021818</td>\n",
       "      <td>36.4775</td>\n",
       "      <td>31.225833</td>\n",
       "      <td>23.547</td>\n",
       "      <td>20.176364</td>\n",
       "      <td>23.0900</td>\n",
       "      <td>27.298000</td>\n",
       "      <td>22.240000</td>\n",
       "      <td>17.275000</td>\n",
       "      <td>7.777778</td>\n",
       "      <td>3.15200</td>\n",
       "      <td>2.726250</td>\n",
       "      <td>1.073333</td>\n",
       "      <td>0.458750</td>\n",
       "      <td>0.313750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intel</th>\n",
       "      <td>3.38000</td>\n",
       "      <td>2.493636</td>\n",
       "      <td>1.6200</td>\n",
       "      <td>1.855455</td>\n",
       "      <td>0.743</td>\n",
       "      <td>2.940909</td>\n",
       "      <td>2.6870</td>\n",
       "      <td>1.752000</td>\n",
       "      <td>7.893333</td>\n",
       "      <td>11.640000</td>\n",
       "      <td>14.803000</td>\n",
       "      <td>12.92750</td>\n",
       "      <td>16.201250</td>\n",
       "      <td>9.872500</td>\n",
       "      <td>7.331000</td>\n",
       "      <td>8.550909</td>\n",
       "      <td>7.836667</td>\n",
       "      <td>6.415000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NVIDIA</th>\n",
       "      <td>59.29000</td>\n",
       "      <td>50.067273</td>\n",
       "      <td>49.3600</td>\n",
       "      <td>52.952500</td>\n",
       "      <td>56.810</td>\n",
       "      <td>56.746364</td>\n",
       "      <td>50.1340</td>\n",
       "      <td>45.750000</td>\n",
       "      <td>38.791111</td>\n",
       "      <td>36.006000</td>\n",
       "      <td>37.316000</td>\n",
       "      <td>46.87000</td>\n",
       "      <td>51.545000</td>\n",
       "      <td>58.842000</td>\n",
       "      <td>73.886000</td>\n",
       "      <td>70.643636</td>\n",
       "      <td>71.051111</td>\n",
       "      <td>72.941000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>7.22125</td>\n",
       "      <td>7.610909</td>\n",
       "      <td>12.5425</td>\n",
       "      <td>14.135833</td>\n",
       "      <td>18.900</td>\n",
       "      <td>20.142727</td>\n",
       "      <td>24.1010</td>\n",
       "      <td>25.190000</td>\n",
       "      <td>31.080000</td>\n",
       "      <td>35.075000</td>\n",
       "      <td>34.406000</td>\n",
       "      <td>13.24600</td>\n",
       "      <td>13.212500</td>\n",
       "      <td>25.142000</td>\n",
       "      <td>10.605000</td>\n",
       "      <td>10.798182</td>\n",
       "      <td>9.768889</td>\n",
       "      <td>10.090000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "date                     2004       2005     2006       2007    2008  \\\n",
       "platform gpu_vendor                                                    \n",
       "combined AMD              NaN        NaN      NaN        NaN     NaN   \n",
       "         Apple            NaN        NaN      NaN        NaN     NaN   \n",
       "         ATI              NaN        NaN      NaN        NaN     NaN   \n",
       "         Intel            NaN        NaN      NaN        NaN     NaN   \n",
       "         NVIDIA           NaN        NaN      NaN        NaN     NaN   \n",
       "         Other            NaN        NaN      NaN        NaN     NaN   \n",
       "linux    AMD              NaN        NaN      NaN        NaN     NaN   \n",
       "         Apple            NaN        NaN      NaN        NaN     NaN   \n",
       "         ATI              NaN        NaN      NaN        NaN     NaN   \n",
       "         Intel            NaN        NaN      NaN        NaN     NaN   \n",
       "         NVIDIA           NaN        NaN      NaN        NaN     NaN   \n",
       "         Other            NaN        NaN      NaN        NaN     NaN   \n",
       "mac      AMD              NaN        NaN      NaN        NaN     NaN   \n",
       "         Apple            NaN        NaN      NaN        NaN     NaN   \n",
       "         ATI              NaN        NaN      NaN        NaN     NaN   \n",
       "         Intel            NaN        NaN      NaN        NaN     NaN   \n",
       "         NVIDIA           NaN        NaN      NaN        NaN     NaN   \n",
       "         Other            NaN        NaN      NaN        NaN     NaN   \n",
       "pc       AMD              NaN   1.772000      NaN        NaN     NaN   \n",
       "         Apple            NaN        NaN      NaN        NaN     NaN   \n",
       "         ATI         30.10125  39.021818  36.4775  31.225833  23.547   \n",
       "         Intel        3.38000   2.493636   1.6200   1.855455   0.743   \n",
       "         NVIDIA      59.29000  50.067273  49.3600  52.952500  56.810   \n",
       "         Other        7.22125   7.610909  12.5425  14.135833  18.900   \n",
       "\n",
       "date                      2009     2010       2011       2012       2013  \\\n",
       "platform gpu_vendor                                                        \n",
       "combined AMD               NaN      NaN        NaN        NaN        NaN   \n",
       "         Apple             NaN      NaN        NaN        NaN        NaN   \n",
       "         ATI               NaN  22.6620  25.648889  22.473333  17.105000   \n",
       "         Intel             NaN   3.6960   1.800000   8.057500  12.583333   \n",
       "         NVIDIA            NaN  48.1600  46.667778  39.267500  35.126667   \n",
       "         Other             NaN  25.4820  25.884444  30.200833  35.183333   \n",
       "linux    AMD               NaN      NaN        NaN        NaN        NaN   \n",
       "         Apple             NaN      NaN        NaN        NaN        NaN   \n",
       "         ATI               NaN      NaN        NaN        NaN        NaN   \n",
       "         Intel             NaN      NaN        NaN        NaN        NaN   \n",
       "         NVIDIA            NaN      NaN        NaN        NaN        NaN   \n",
       "         Other             NaN      NaN        NaN        NaN        NaN   \n",
       "mac      AMD               NaN      NaN        NaN        NaN        NaN   \n",
       "         Apple             NaN      NaN        NaN        NaN        NaN   \n",
       "         ATI               NaN  22.1450  29.960000  35.052000  25.681667   \n",
       "         Intel             NaN   7.6950   4.186000  21.132000  41.506667   \n",
       "         NVIDIA            NaN  66.6550  64.776000  40.442000  24.716667   \n",
       "         Other             NaN   3.4925   1.084000   3.358000   8.086667   \n",
       "pc       AMD               NaN      NaN        NaN        NaN        NaN   \n",
       "         Apple             NaN      NaN        NaN        NaN        NaN   \n",
       "         ATI         20.176364  23.0900  27.298000  22.240000  17.275000   \n",
       "         Intel        2.940909   2.6870   1.752000   7.893333  11.640000   \n",
       "         NVIDIA      56.746364  50.1340  45.750000  38.791111  36.006000   \n",
       "         Other       20.142727  24.1010  25.190000  31.080000  35.075000   \n",
       "\n",
       "date                      2014      2015       2016       2017       2018  \\\n",
       "platform gpu_vendor                                                         \n",
       "combined AMD         15.181429  18.86750  15.977500   9.160000   7.869167   \n",
       "         Apple             NaN       NaN        NaN        NaN        NaN   \n",
       "         ATI          6.070833   3.07750   2.660000   1.043333   0.502000   \n",
       "         Intel       16.359167  12.40875  17.686250  10.777500   8.878333   \n",
       "         NVIDIA      37.828333  45.32250  49.960000  57.546000  71.264167   \n",
       "         Other       30.878333  13.04750  13.737500  25.858000  11.565000   \n",
       "linux    AMD         11.535000  13.23625  10.808889   9.298000  11.147273   \n",
       "         Apple             NaN       NaN        NaN        NaN        NaN   \n",
       "         ATI          2.894000   2.54125   2.011111   1.290000   0.821818   \n",
       "         Intel       24.510000  27.77500  30.360000  30.827000  28.955455   \n",
       "         NVIDIA      31.770000  38.88500  39.955556  40.193000  41.130000   \n",
       "         Other       31.606000  17.56125  16.855556  18.379000  17.941818   \n",
       "mac      AMD          8.867500   8.48875   8.863000   9.428000  11.522000   \n",
       "         Apple             NaN       NaN        NaN        NaN        NaN   \n",
       "         ATI         10.240000   5.64500   4.054000   3.079000   2.281000   \n",
       "         Intel       54.736667  62.68375  57.139000  59.318000  68.237000   \n",
       "         NVIDIA      24.436667  20.88375  10.479000   1.333000   1.393000   \n",
       "         Other        4.676667   2.28875  19.466000  26.846000  16.583000   \n",
       "pc       AMD         12.952000  18.87600  16.300000   9.332500   7.810000   \n",
       "         Apple             NaN       NaN        NaN        NaN        NaN   \n",
       "         ATI          7.777778   3.15200   2.726250   1.073333   0.458750   \n",
       "         Intel       14.803000  12.92750  16.201250   9.872500   7.331000   \n",
       "         NVIDIA      37.316000  46.87000  51.545000  58.842000  73.886000   \n",
       "         Other       34.406000  13.24600  13.212500  25.142000  10.605000   \n",
       "\n",
       "date                      2019       2020       2021  \n",
       "platform gpu_vendor                                   \n",
       "combined AMD          9.463333  11.269167  11.335833  \n",
       "         Apple             NaN        NaN   0.170000  \n",
       "         ATI          0.380000        NaN        NaN  \n",
       "         Intel       10.390833   9.778333   7.681667  \n",
       "         NVIDIA      68.046667  68.320000  69.963333  \n",
       "         Other       11.885000  10.627500  10.994167  \n",
       "linux    AMD         14.499167  18.904444  22.870000  \n",
       "         Apple             NaN        NaN        NaN  \n",
       "         ATI          0.444167   0.298750   0.170000  \n",
       "         Intel       28.169167  25.648889  23.975000  \n",
       "         NVIDIA      40.410000  37.505556  37.200000  \n",
       "         Other       16.482500  17.681111  15.760000  \n",
       "mac      AMD         15.090909  15.710000  25.479000  \n",
       "         Apple             NaN        NaN   6.140000  \n",
       "         ATI          1.488182   1.098333   0.632000  \n",
       "         Intel       74.002727  75.688333  61.842000  \n",
       "         NVIDIA       1.830909   1.461667   1.117000  \n",
       "         Other        7.585455   6.043333   4.676000  \n",
       "pc       AMD          9.779091  11.353333  10.525000  \n",
       "         Apple             NaN        NaN        NaN  \n",
       "         ATI          0.313750        NaN        NaN  \n",
       "         Intel        8.550909   7.836667   6.415000  \n",
       "         NVIDIA      70.643636  71.051111  72.941000  \n",
       "         Other       10.798182   9.768889  10.090000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gpu_vendor.pivot(index=[\"platform\", \"gpu_vendor\"], columns=\"date\", values=\"perc\")"
   ]
  }
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