Intel_Core_Processors_Analysis_Part1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intel Core Processors - Analysis (Part 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hello and welcome to the 1st part of analysis of Intel Core Processors!**\n",
    "\n",
    "For more than 10 years, most home computer users are pleased to use these kind of Intel processors for general purposes. Surely, lots of generations have been revealed in order to satisfy up-to-date performance, be it the entry-level or high-end. Here, we are going to analyze these processors altogether to gain some insights!\n",
    "\n",
    "So, we're ready now, and let's load the necessary modules and the dataset that holds all Core processors ever produced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "intel_core = pd.read_csv('intel_core_processors_v1_1.csv', na_values='N/A')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, how many different processors do we have in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "885"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm, that's a lot! I think the number is enough to perform analyses. Then, which features (columns) are available?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Product', 'Status', 'Release Date', 'Cores', 'Max. Turbo Freq.(GHz)',\n",
       "       'Base Freq.(GHz)', 'Cache(MB)', 'Cache Info', 'Integrated Graphics'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Total of 9 features.** Well, although some other important features are missing, such as _# of threads, TDP, architecture name, litography,_ etc., the existing ones are still useful for analyses and should not hinder us.\n",
    "\n",
    "OK, let's start something simple: The **status** of these processors that only take specific strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Launched', 'Discontinued', 'Announced'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core['Status'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Launched** should mean that the processor is available for purchase in the technology market and/or inside PCs or notebooks via contracted manufacturers.\n",
    "\n",
    "Likewise, **Discontinued** should indicate that the processor is no longer sold and Intel has stopped producing it.\n",
    "\n",
    "Plus, **Announced** processors are yet to be released in the market in a predefined date.\n",
    "\n",
    "Here, let's create a **bar plot** to get the total count for each:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "status_counts = intel_core['Status'].value_counts()\n",
    "plt.figure(figsize=(13,5))\n",
    "plt.barh(y=status_counts.index, width=status_counts, color='gray')\n",
    "plt.title('Intel Core Processors - Currrent Status', fontsize=16)\n",
    "for i in range(3):\n",
    "    plt.text(status_counts[i]+2.5,i-0.05,status_counts[i], fontsize=15)\n",
    "plt.xlabel('Count', fontsize=15)\n",
    "plt.ylabel('Status', fontsize=15)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are **452** Core processors that are released or available in the market for a long time; that should account for more than half of the total number. Moreover, **more than 400** processors were discontinued and you can barely see them for sale in the second-hand markets.\n",
    "\n",
    "Interestingly, only **9** processors have been marked as Announced. Well, what are they?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Product</th>\n",
       "      <th>Release Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Core i7-1160G7</td>\n",
       "      <td>Q3'20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Core i7-8809G with Radeon RX Vega M GH Graphics</td>\n",
       "      <td>Q1'18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Core i7-8709G with Radeon RX Vega M GH Graphics</td>\n",
       "      <td>Q1'18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Core i7-8705G with Radeon RX Vega M GL Graphics</td>\n",
       "      <td>Q1'18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Core i5-1130G7</td>\n",
       "      <td>Q3'20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>Core i5-8305G with Radeon RX Vega M GL Graphics</td>\n",
       "      <td>Q1'18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Core i3-1125G4</td>\n",
       "      <td>Q3'20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>Core i3-1120G4</td>\n",
       "      <td>Q3'20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>Core i3-1110G4</td>\n",
       "      <td>Q3'20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Product Release Date\n",
       "62                                    Core i7-1160G7        Q3'20\n",
       "98   Core i7-8809G with Radeon RX Vega M GH Graphics        Q1'18\n",
       "100  Core i7-8709G with Radeon RX Vega M GH Graphics        Q1'18\n",
       "103  Core i7-8705G with Radeon RX Vega M GL Graphics        Q1'18\n",
       "215                                   Core i5-1130G7        Q3'20\n",
       "271  Core i5-8305G with Radeon RX Vega M GL Graphics        Q1'18\n",
       "389                                   Core i3-1125G4        Q3'20\n",
       "390                                   Core i3-1120G4        Q3'20\n",
       "394                                   Core i3-1110G4        Q3'20"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core[intel_core['Status'] == 'Announced'][['Product', 'Release Date']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh! Some processors from the newest generations (i.e. 11th gen.) that are planned to be released in the **3rd quarter of 2020**. The remaining ones are from the 8th generation with **Radeon RX Vega M GH/GL** graphics... To be honest, I've never seen such a processor with graphic component from Intel's rival, AMD!\n",
    "\n",
    "Speaking of release dates, you may understand that these for each processor in Intel Ark website are determined as **quarterly periods**. At this point, it's convenient to create another **bar plot** to see how many individual processors have been produced within each quarter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "release_date_counts = intel_core['Release Date'].value_counts()\n",
    "plt.bar(x=release_date_counts.index, height=release_date_counts, color='#07ad0a')\n",
    "plt.text(-1.8, release_date_counts[0]-0.6, release_date_counts[0], fontsize=14, color='#076d0a')\n",
    "plt.title('Intel Core Processors - Counts of Released Processors Quarterly', fontsize=16)\n",
    "plt.xlabel('Release Dates (Quarters)', fontsize=14)\n",
    "plt.ylabel('Counts', fontsize=14)\n",
    "plt.xticks(rotation=80)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.grid(axis='y', color='#b3e3ba')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whoa! There are **46 processors** that were released in the **3rd quarter of 2013** (i.e. 7 years ago), besides the 2nd quarter of 2020, sharing the same number! I wonder which of them are..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Product</th>\n",
       "      <th>Status</th>\n",
       "      <th>Cores</th>\n",
       "      <th>Max. Turbo Freq.(GHz)</th>\n",
       "      <th>Base Freq.(GHz)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Core i7-4960X Extreme Edition</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Core i7-4930K</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Core i7-4820K</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Core i7-4950HQ</td>\n",
       "      <td>Launched</td>\n",
       "      <td>4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Core i7-4850HQ</td>\n",
       "      <td>Launched</td>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Core i7-4750HQ</td>\n",
       "      <td>Launched</td>\n",
       "      <td>4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Core i7-4771</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Core i7-4600U</td>\n",
       "      <td>Launched</td>\n",
       "      <td>2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Core i7-4610Y</td>\n",
       "      <td>Launched</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Core i7-4650U</td>\n",
       "      <td>Launched</td>\n",
       "      <td>2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Core i7-4500U</td>\n",
       "      <td>Launched</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Core i7-4550U</td>\n",
       "      <td>Launched</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Core i7-4558U</td>\n",
       "      <td>Launched</td>\n",
       "      <td>2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>Core i5-4402E</td>\n",
       "      <td>Launched</td>\n",
       "      <td>2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>Core i5-4400E</td>\n",
       "      <td>Launched</td>\n",
       "      <td>2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>Core i5-4440S</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>Core i5-4440</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>Core i5-4300U</td>\n",
       "      <td>Launched</td>\n",
       "      <td>2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>Core i5-4300Y</td>\n",
       "      <td>Launched</td>\n",
       "      <td>2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>Core i5-4302Y</td>\n",
       "      <td>Launched</td>\n",
       "      <td>2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Product        Status  Cores  \\\n",
       "27   Core i7-4960X Extreme Edition  Discontinued      6   \n",
       "29                   Core i7-4930K  Discontinued      6   \n",
       "31                   Core i7-4820K  Discontinued      4   \n",
       "165                 Core i7-4950HQ      Launched      4   \n",
       "170                 Core i7-4850HQ      Launched      4   \n",
       "186                 Core i7-4750HQ      Launched      4   \n",
       "197                   Core i7-4771  Discontinued      4   \n",
       "203                  Core i7-4600U      Launched      2   \n",
       "205                  Core i7-4610Y      Launched      2   \n",
       "206                  Core i7-4650U      Launched      2   \n",
       "207                  Core i7-4500U      Launched      2   \n",
       "209                  Core i7-4550U      Launched      2   \n",
       "210                  Core i7-4558U      Launched      2   \n",
       "354                  Core i5-4402E      Launched      2   \n",
       "355                  Core i5-4400E      Launched      2   \n",
       "359                  Core i5-4440S  Discontinued      4   \n",
       "360                   Core i5-4440  Discontinued      4   \n",
       "364                  Core i5-4300U      Launched      2   \n",
       "365                  Core i5-4300Y      Launched      2   \n",
       "366                  Core i5-4302Y      Launched      2   \n",
       "\n",
       "     Max. Turbo Freq.(GHz)  Base Freq.(GHz)  \n",
       "27                     4.0              3.6  \n",
       "29                     3.9              3.4  \n",
       "31                     3.9              3.7  \n",
       "165                    3.6              2.4  \n",
       "170                    3.5              2.3  \n",
       "186                    3.2              2.0  \n",
       "197                    3.9              3.5  \n",
       "203                    3.3              2.1  \n",
       "205                    2.9              1.7  \n",
       "206                    3.3              1.7  \n",
       "207                    3.0              1.8  \n",
       "209                    3.0              1.5  \n",
       "210                    3.3              2.8  \n",
       "354                    2.7              1.6  \n",
       "355                    3.3              2.7  \n",
       "359                    3.3              2.8  \n",
       "360                    3.3              3.1  \n",
       "364                    2.9              1.9  \n",
       "365                    2.3              1.6  \n",
       "366                    2.3              1.6  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core[intel_core['Release Date'] == \"Q3'13\"] \\\n",
    "[['Product', 'Status', 'Cores', 'Max. Turbo Freq.(GHz)', 'Base Freq.(GHz)']].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm, the year 2013 is for the **4th generation** of Core processors we can imply. Some of them are still available in stocks it seems; some of them, of course, are discontinued.\n",
    "\n",
    "_Note: Due to space limitation, I only listed the first 20 processors above._\n",
    "\n",
    "On the other hand, there are **five** different quarters where Intel had released **just 1** processor. Let's see tham all with the details:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Product</th>\n",
       "      <th>Status</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Cores</th>\n",
       "      <th>Max. Turbo Freq.(GHz)</th>\n",
       "      <th>Base Freq.(GHz)</th>\n",
       "      <th>Cache(MB)</th>\n",
       "      <th>Cache Info</th>\n",
       "      <th>Integrated Graphics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Core i7-3970X Extreme Edition</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q4'12</td>\n",
       "      <td>6</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Core i3-10100F</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q4'20</td>\n",
       "      <td>4</td>\n",
       "      <td>4.30</td>\n",
       "      <td>3.60</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>Core i3-6006U</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q4'16</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel HD 520 Graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>Core2 QX6700 Extreme</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q4'06</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.66</td>\n",
       "      <td>8.0</td>\n",
       "      <td>L2 Cache</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>Core i7-960</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q4'09</td>\n",
       "      <td>4</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.20</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Product        Status Release Date  Cores  \\\n",
       "32   Core i7-3970X Extreme Edition  Discontinued        Q4'12      6   \n",
       "402                 Core i3-10100F      Launched        Q4'20      4   \n",
       "458                  Core i3-6006U      Launched        Q4'16      2   \n",
       "535           Core2 QX6700 Extreme  Discontinued        Q4'06      4   \n",
       "685                    Core i7-960  Discontinued        Q4'09      4   \n",
       "\n",
       "     Max. Turbo Freq.(GHz)  Base Freq.(GHz)  Cache(MB)         Cache Info  \\\n",
       "32                    4.00             3.50       15.0  Intel Smart Cache   \n",
       "402                   4.30             3.60        6.0  Intel Smart Cache   \n",
       "458                    NaN             2.00        3.0  Intel Smart Cache   \n",
       "535                    NaN             2.66        8.0           L2 Cache   \n",
       "685                   3.46             3.20        8.0  Intel Smart Cache   \n",
       "\n",
       "       Integrated Graphics  \n",
       "32                     NaN  \n",
       "402                    NaN  \n",
       "458  Intel HD 520 Graphics  \n",
       "535                    NaN  \n",
       "685                    NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "least_released_dates = [\"Q4'20\", \"Q4'12\", \"Q4'16\", \"Q4'06\", \"Q4'09\"]\n",
    "intel_core[intel_core['Release Date'].isin(least_released_dates)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One remarkable fact above is that Core i3-10100F is the only available processor that has been released in **the last quarter of 2020**!\n",
    "\n",
    "Now, in different aspect, how about taking these processors' release dates to be in **yearly periods** and counting them all again? _This will require a little data wrangling, but it should work..._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_year(date):\n",
    "    if type(date) == float:\n",
    "        return None\n",
    "    return str(20) + date[-2:]\n",
    "intel_core['Release Year'] = intel_core['Release Date'].apply(convert_to_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "release_year_counts = intel_core['Release Year'].value_counts()\n",
    "plt.bar(x=release_year_counts.index, height=release_year_counts, color='#7dbcff')\n",
    "plt.text(-1,release_year_counts[0]-1.2,release_year_counts[0], fontsize=14, color='#3d6c88')\n",
    "plt.text(14.5,release_year_counts[-1]-1.2,release_year_counts[-1], fontsize=14, color='#3d6c88')\n",
    "plt.title('Intel Core Processors - Counts of Processors Release Dates (Yearly)', fontsize=16)\n",
    "plt.xlabel('Release Dates (Years)', fontsize=14)\n",
    "plt.ylabel('Counts', fontsize=14)\n",
    "plt.xticks(rotation=80)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.grid(axis='y', color='#9fdeff')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This must be another **proof** that year **2013** has seen the most launched different Intel Core processors; and that's **more than 100**! Similarly, the year **2006** had the least Core processor launched; that's because it was the 1st year that kind of processor were appeared in the market. Remember that, those were not called as _i-number_ schema; instead, **Core/Core2 Solo/Duo/Quad**.\n",
    "\n",
    "Now, let's move on to the processors' **base** and **maximum turbo frequencies** they can operate. Revealing the average, standard dev., median, min, max values, etc. are a good start:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Max. Turbo Freq.(GHz)</th>\n",
       "      <th>Base Freq.(GHz)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>600.00</td>\n",
       "      <td>876.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.63</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.73</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.20</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.60</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.30</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Max. Turbo Freq.(GHz)  Base Freq.(GHz)\n",
       "count                 600.00           876.00\n",
       "mean                    3.63             2.50\n",
       "std                     0.66             0.72\n",
       "min                     1.73             0.70\n",
       "25%                     3.20             2.00\n",
       "50%                     3.60             2.53\n",
       "75%                     4.00             3.00\n",
       "max                     5.30             4.30"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(intel_core[['Max. Turbo Freq.(GHz)', 'Base Freq.(GHz)']].describe(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that, the count values for both **do not match** the total number of Core processors (which was 885); besides, there are even much more missing values for max. turbo frequencies!\n",
    "\n",
    "At this point, let's show the Core processor(s) whose base frequency is **the highest**, then **the lowest**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Product</th>\n",
       "      <th>Status</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Cores</th>\n",
       "      <th>Max. Turbo Freq.(GHz)</th>\n",
       "      <th>Base Freq.(GHz)</th>\n",
       "      <th>Cache(MB)</th>\n",
       "      <th>Cache Info</th>\n",
       "      <th>Integrated Graphics</th>\n",
       "      <th>Release Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Core i7-7740X X Series</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q2'17</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Product        Status Release Date  Cores  \\\n",
       "18  Core i7-7740X X Series  Discontinued        Q2'17      4   \n",
       "\n",
       "    Max. Turbo Freq.(GHz)  Base Freq.(GHz)  Cache(MB) Cache Info  \\\n",
       "18                    4.5              4.3        8.0        NaN   \n",
       "\n",
       "   Integrated Graphics Release Year  \n",
       "18                 NaN         2017  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core[intel_core['Base Freq.(GHz)'] == intel_core['Base Freq.(GHz)'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Product</th>\n",
       "      <th>Status</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Cores</th>\n",
       "      <th>Max. Turbo Freq.(GHz)</th>\n",
       "      <th>Base Freq.(GHz)</th>\n",
       "      <th>Cache(MB)</th>\n",
       "      <th>Cache Info</th>\n",
       "      <th>Integrated Graphics</th>\n",
       "      <th>Release Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Core i5-1030G4</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q3'19</td>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel Iris Plus Graphics</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Product    Status Release Date  Cores  Max. Turbo Freq.(GHz)  \\\n",
       "235  Core i5-1030G4  Launched        Q3'19      4                    3.5   \n",
       "\n",
       "     Base Freq.(GHz)  Cache(MB)         Cache Info       Integrated Graphics  \\\n",
       "235              0.7        6.0  Intel Smart Cache  Intel Iris Plus Graphics   \n",
       "\n",
       "    Release Year  \n",
       "235         2019  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core[intel_core['Base Freq.(GHz)'] == intel_core['Base Freq.(GHz)'].min()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consequently, **only 1** processor from both situations! I suppose _Core i7-7740X X Series_ should be one of the highest performing CPUs at the time it was released, but not sure about its price/performance ratio for being high or not. In contrast, _Core i5-1030G4_ has the lowest base frequency ever produced, only just **700 MHz**! However, its turbo frequency could go up to **3.5 GHz**, that's immensely high.\n",
    "\n",
    "Now, let's reveal the processor(s) whose maximum turbo freq. is **the highest** and **the lowest**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Product</th>\n",
       "      <th>Status</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Cores</th>\n",
       "      <th>Max. Turbo Freq.(GHz)</th>\n",
       "      <th>Base Freq.(GHz)</th>\n",
       "      <th>Cache(MB)</th>\n",
       "      <th>Cache Info</th>\n",
       "      <th>Integrated Graphics</th>\n",
       "      <th>Release Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Core i9-10885H</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q2'20</td>\n",
       "      <td>8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel UHD Graphics</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Core i9-10980HK</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q2'20</td>\n",
       "      <td>8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel UHD Graphics</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Core i9-10900KF</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q2'20</td>\n",
       "      <td>10</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Core i9-10900K</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q2'20</td>\n",
       "      <td>10</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel UHD 630 Graphics</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Product    Status Release Date  Cores  Max. Turbo Freq.(GHz)  \\\n",
       "40   Core i9-10885H  Launched        Q2'20      8                    5.3   \n",
       "41  Core i9-10980HK  Launched        Q2'20      8                    5.3   \n",
       "44  Core i9-10900KF  Launched        Q2'20     10                    5.3   \n",
       "45   Core i9-10900K  Launched        Q2'20     10                    5.3   \n",
       "\n",
       "    Base Freq.(GHz)  Cache(MB)         Cache Info     Integrated Graphics  \\\n",
       "40              2.4       16.0  Intel Smart Cache      Intel UHD Graphics   \n",
       "41              2.4       16.0  Intel Smart Cache      Intel UHD Graphics   \n",
       "44              3.7       20.0  Intel Smart Cache                     NaN   \n",
       "45              3.7       20.0  Intel Smart Cache  Intel UHD 630 Graphics   \n",
       "\n",
       "   Release Year  \n",
       "40         2020  \n",
       "41         2020  \n",
       "44         2020  \n",
       "45         2020  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core[intel_core['Max. Turbo Freq.(GHz)'] == intel_core['Max. Turbo Freq.(GHz)'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Product</th>\n",
       "      <th>Status</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Cores</th>\n",
       "      <th>Max. Turbo Freq.(GHz)</th>\n",
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       "      <th>Release Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>Core i5-430UM</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q2'10</td>\n",
       "      <td>2</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel HD Graphics for Previous Gen.</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Product        Status Release Date  Cores  Max. Turbo Freq.(GHz)  \\\n",
       "734  Core i5-430UM  Discontinued        Q2'10      2                   1.73   \n",
       "\n",
       "     Base Freq.(GHz)  Cache(MB)         Cache Info  \\\n",
       "734              1.2        3.0  Intel Smart Cache   \n",
       "\n",
       "                     Integrated Graphics Release Year  \n",
       "734  Intel HD Graphics for Previous Gen.         2010  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core[intel_core['Max. Turbo Freq.(GHz)'] == intel_core['Max. Turbo Freq.(GHz)'].min()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! **Four Core i9 processors** from 10th generation have the capabilities to make their frequencies go up to **5.3 GHz**! I believe they should be very expensive to buy, though. Conversely, The maximum frequency of _Core i5-430UM_ could be **1.73 GHz** at most, the least value among all Core processors!\n",
    "\n",
    "Above, we said that the total number of Core processors **did not match** the number of available base frequencies. Let's emerge those that Intel did not explain!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Product</th>\n",
       "      <th>Status</th>\n",
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       "      <th>Cores</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Core i7-1185G7</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q3'20</td>\n",
       "      <td>4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel Iris Xe Graphics</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Core i7-1165G7</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q3'20</td>\n",
       "      <td>4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel Iris Xe Graphics</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Core i7-1160G7</td>\n",
       "      <td>Announced</td>\n",
       "      <td>Q3'20</td>\n",
       "      <td>4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel Iris Xe Graphics</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Core i5-1135G7</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q3'20</td>\n",
       "      <td>4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel Iris Xe Graphics</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Core i5-1130G7</td>\n",
       "      <td>Announced</td>\n",
       "      <td>Q3'20</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel Iris Xe Graphics</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>Core i3-1125G4</td>\n",
       "      <td>Announced</td>\n",
       "      <td>Q3'20</td>\n",
       "      <td>4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel UHD Graphics for 11th Gen Intel Processors</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>Core i3-1120G4</td>\n",
       "      <td>Announced</td>\n",
       "      <td>Q3'20</td>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel UHD Graphics for 11th Gen Intel Processors</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Core i3-1115G4</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q3'20</td>\n",
       "      <td>2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel UHD Graphics for 11th Gen Intel Processors</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>Core i3-1110G4</td>\n",
       "      <td>Announced</td>\n",
       "      <td>Q3'20</td>\n",
       "      <td>2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel UHD Graphics for 11th Gen Intel Processors</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Product     Status Release Date  Cores  Max. Turbo Freq.(GHz)  \\\n",
       "60   Core i7-1185G7   Launched        Q3'20      4                    4.8   \n",
       "61   Core i7-1165G7   Launched        Q3'20      4                    4.7   \n",
       "62   Core i7-1160G7  Announced        Q3'20      4                    4.4   \n",
       "214  Core i5-1135G7   Launched        Q3'20      4                    4.2   \n",
       "215  Core i5-1130G7  Announced        Q3'20      4                    4.0   \n",
       "389  Core i3-1125G4  Announced        Q3'20      4                    3.7   \n",
       "390  Core i3-1120G4  Announced        Q3'20      4                    3.5   \n",
       "393  Core i3-1115G4   Launched        Q3'20      2                    4.1   \n",
       "394  Core i3-1110G4  Announced        Q3'20      2                    3.9   \n",
       "\n",
       "     Base Freq.(GHz)  Cache(MB)         Cache Info  \\\n",
       "60               NaN       12.0  Intel Smart Cache   \n",
       "61               NaN       12.0  Intel Smart Cache   \n",
       "62               NaN       12.0  Intel Smart Cache   \n",
       "214              NaN        8.0  Intel Smart Cache   \n",
       "215              NaN        8.0  Intel Smart Cache   \n",
       "389              NaN        8.0  Intel Smart Cache   \n",
       "390              NaN        8.0  Intel Smart Cache   \n",
       "393              NaN        6.0  Intel Smart Cache   \n",
       "394              NaN        6.0  Intel Smart Cache   \n",
       "\n",
       "                                  Integrated Graphics Release Year  \n",
       "60                             Intel Iris Xe Graphics         2020  \n",
       "61                             Intel Iris Xe Graphics         2020  \n",
       "62                             Intel Iris Xe Graphics         2020  \n",
       "214                            Intel Iris Xe Graphics         2020  \n",
       "215                            Intel Iris Xe Graphics         2020  \n",
       "389  Intel UHD Graphics for 11th Gen Intel Processors         2020  \n",
       "390  Intel UHD Graphics for 11th Gen Intel Processors         2020  \n",
       "393  Intel UHD Graphics for 11th Gen Intel Processors         2020  \n",
       "394  Intel UHD Graphics for 11th Gen Intel Processors         2020  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core[intel_core['Base Freq.(GHz)'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aha! These are the newly launched or announced Core processors that are from the **11th Gen**, the latest one. This is so interesting and confusing why Intel did not put any value to the base frequencies to these processors, although their maximum turbo freq. are already known! Maybe, we should wait a bit more, like until next month, to see their actual values...\n",
    "\n",
    "All right, to go even further, let's get **the differences** of these two values among all processors and get the statistics fast: _(Of course, not all differences can be taken due to missing values, and I didn't prefer filling them in)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    591.00\n",
       "mean       1.08\n",
       "std        0.67\n",
       "min        0.20\n",
       "25%        0.60\n",
       "50%        1.00\n",
       "75%        1.37\n",
       "max        3.80\n",
       "Name: Freq. Diff.(GHz), dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core['Freq. Diff.(GHz)'] = round(intel_core['Max. Turbo Freq.(GHz)'] - intel_core['Base Freq.(GHz)'], 2)\n",
    "round(intel_core['Freq. Diff.(GHz)'].describe(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, there exist processors that can increase its clock frequency by **up to 3.8 GHz**; on the other hand, the lowest possible, only just **200 Mhz**!\n",
    "\n",
    "Let's scrape them all!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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>Product</th>\n",
       "      <th>Status</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Cores</th>\n",
       "      <th>Max. Turbo Freq.(GHz)</th>\n",
       "      <th>Base Freq.(GHz)</th>\n",
       "      <th>Cache(MB)</th>\n",
       "      <th>Cache Info</th>\n",
       "      <th>Integrated Graphics</th>\n",
       "      <th>Release Year</th>\n",
       "      <th>Freq. Diff.(GHz)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Core i7-10810U</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q2'20</td>\n",
       "      <td>6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>Intel UHD Graphics</td>\n",
       "      <td>2020</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Product    Status Release Date  Cores  Max. Turbo Freq.(GHz)  \\\n",
       "66  Core i7-10810U  Launched        Q2'20      6                    4.9   \n",
       "\n",
       "    Base Freq.(GHz)  Cache(MB)         Cache Info Integrated Graphics  \\\n",
       "66              1.1       12.0  Intel Smart Cache  Intel UHD Graphics   \n",
       "\n",
       "   Release Year  Freq. Diff.(GHz)  \n",
       "66         2020               3.8  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core[intel_core['Freq. Diff.(GHz)'] == intel_core['Freq. Diff.(GHz)'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 processors with the least frequency diff.\n"
     ]
    },
    {
     "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>Product</th>\n",
       "      <th>Status</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Cores</th>\n",
       "      <th>Max. Turbo Freq.(GHz)</th>\n",
       "      <th>Base Freq.(GHz)</th>\n",
       "      <th>Cache(MB)</th>\n",
       "      <th>Cache Info</th>\n",
       "      <th>Integrated Graphics</th>\n",
       "      <th>Release Year</th>\n",
       "      <th>Freq. Diff.(GHz)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Core i7-7740X X Series</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q2'17</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Core i5-7640X X Series</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q2'17</td>\n",
       "      <td>4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Core i7-6850K</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q2'16</td>\n",
       "      <td>6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Core i7-6800K</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q2'16</td>\n",
       "      <td>6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Core i7-5930K</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q3'14</td>\n",
       "      <td>6</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Product        Status Release Date  Cores  \\\n",
       "18  Core i7-7740X X Series  Discontinued        Q2'17      4   \n",
       "19  Core i5-7640X X Series  Discontinued        Q2'17      4   \n",
       "22           Core i7-6850K  Discontinued        Q2'16      6   \n",
       "23           Core i7-6800K  Discontinued        Q2'16      6   \n",
       "25           Core i7-5930K  Discontinued        Q3'14      6   \n",
       "\n",
       "    Max. Turbo Freq.(GHz)  Base Freq.(GHz)  Cache(MB)         Cache Info  \\\n",
       "18                    4.5              4.3        8.0                NaN   \n",
       "19                    4.2              4.0        6.0                NaN   \n",
       "22                    3.6              3.4       15.0  Intel Smart Cache   \n",
       "23                    3.6              3.4       15.0  Intel Smart Cache   \n",
       "25                    3.7              3.5       15.0  Intel Smart Cache   \n",
       "\n",
       "   Integrated Graphics Release Year  Freq. Diff.(GHz)  \n",
       "18                 NaN         2017               0.2  \n",
       "19                 NaN         2017               0.2  \n",
       "22                 NaN         2016               0.2  \n",
       "23                 NaN         2016               0.2  \n",
       "25                 NaN         2014               0.2  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_diff_min = intel_core[intel_core['Freq. Diff.(GHz)'] == intel_core['Freq. Diff.(GHz)'].min()]\n",
    "print(freq_diff_min.shape[0], \"processors with the least frequency diff.\")\n",
    "freq_diff_min.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The processor _Core i7-10810U_ has **the biggest difference** between the maximum freq. and the base, this amount is so enormous, right?\n",
    "\n",
    "For the processors with the least difference, there are not just one, but **14 processors**! _(Again, I just showed the first five of them due to space limitations)_\n",
    "\n",
    "OK, taking them all for a bigger picture, let's generate another **bar chart** to count and analyze which difference value is the most appeared one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1044x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14.5,6))\n",
    "freq_diff_top20 = intel_core['Freq. Diff.(GHz)'].value_counts()\n",
    "plt.bar(x=freq_diff_top20.index, height=freq_diff_top20, width=0.08, alpha=0.75, color='#ad8821')\n",
    "plt.text(0.85, freq_diff_top20.max()-1,freq_diff_top20.max(), fontsize=14, color='#5d5520')\n",
    "plt.title('Intel Core Processors - Frequency Diff. (Max Turbo - Base)', fontsize=16)\n",
    "plt.xlabel('Freq. Diff. (GHz)', fontsize=14)\n",
    "plt.ylabel('Counts', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm, from this chart, we can imply that there are **69 Core processors** that can increase their frequency by **1 GHz** at most; the most common value among all of them! Nevertheless, one can combine those values together and realize that the most frequency-increasing changes lie between **0.4 to 1.2 GHz**.\n",
    "\n",
    "Enough of this, let's take another feature now: **The number of cores**. While observing the dataset before the analyses, I assure you that any Core processor could contain a single core, but could increase up to **18 Cores**. Hence, which processors could have such a high number of cores?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Product</th>\n",
       "      <th>Status</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Cores</th>\n",
       "      <th>Max. Turbo Freq.(GHz)</th>\n",
       "      <th>Base Freq.(GHz)</th>\n",
       "      <th>Cache(MB)</th>\n",
       "      <th>Cache Info</th>\n",
       "      <th>Integrated Graphics</th>\n",
       "      <th>Release Year</th>\n",
       "      <th>Freq. Diff.(GHz)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Core i9-10980XE Extreme Edition</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q4'19</td>\n",
       "      <td>18</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.75</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Core i9-9980XE Extreme Edition</td>\n",
       "      <td>Launched</td>\n",
       "      <td>Q4'18</td>\n",
       "      <td>18</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.75</td>\n",
       "      <td>Intel Smart Cache</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Core i9-7980XE Extreme Edition</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q3'17</td>\n",
       "      <td>18</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>24.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Product        Status Release Date  Cores  \\\n",
       "0   Core i9-10980XE Extreme Edition      Launched        Q4'19     18   \n",
       "4    Core i9-9980XE Extreme Edition      Launched        Q4'18     18   \n",
       "11   Core i9-7980XE Extreme Edition  Discontinued        Q3'17     18   \n",
       "\n",
       "    Max. Turbo Freq.(GHz)  Base Freq.(GHz)  Cache(MB)         Cache Info  \\\n",
       "0                     4.6              3.0      24.75  Intel Smart Cache   \n",
       "4                     4.4              3.0      24.75  Intel Smart Cache   \n",
       "11                    4.2              2.6      24.75                NaN   \n",
       "\n",
       "   Integrated Graphics Release Year  Freq. Diff.(GHz)  \n",
       "0                  NaN         2019               1.6  \n",
       "4                  NaN         2018               1.4  \n",
       "11                 NaN         2017               1.6  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core[intel_core['Cores'] == 18]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah, I should've known! The **Extreme Edition Core processors from i9 tier**, the highest-ends in their own generations!\n",
    "\n",
    "In general, how are all these core counts distributed, in descending order?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 cores <-- 427 processors\n",
      "4 cores <-- 325 processors\n",
      "6 cores <-- 66 processors\n",
      "8 cores <-- 29 processors\n",
      "10 cores <-- 13 processors\n",
      "1 cores <-- 12 processors\n",
      "18 cores <-- 3 processors\n",
      "14 cores <-- 3 processors\n",
      "12 cores <-- 3 processors\n",
      "16 cores <-- 2 processors\n",
      "5 cores <-- 2 processors\n"
     ]
    }
   ],
   "source": [
    "core_counts = intel_core['Cores'].value_counts()\n",
    "for x in range(len(core_counts)):\n",
    "    print(\"{} cores <-- {} processors\".format(core_counts.index[x], core_counts.iloc[x]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, as you can see, **2 cores** and **4 cores** are the most common configurations. Such processors with **more than 10 cores** and the odd number, **5 cores** look like very uncommon!\n",
    "\n",
    "OK, I'd like to create a visualization that is a little bit fancy! **A stacked bar chart** that shows the distribution of the number of cores. Check below for what it looks like..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1044x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "core_counts = intel_core['Cores'].astype(str)\n",
    "cc = core_counts.value_counts()\n",
    "plt.figure(figsize=(14.5,6))\n",
    "plt.axis([0,900,-1.25,1.25])\n",
    "plt.barh(y=\" \", width=cc[0], color='#fc0004')\n",
    "plt.barh(y=\" \", width=cc[1], left=cc[0], color='#c20404')\n",
    "plt.barh(y=\" \", width=cc[2], left=sum(cc[0:2]), color='#a10202')\n",
    "plt.barh(y=\" \", width=cc[3], left=sum(cc[0:3]), color='#8c0303')\n",
    "plt.barh(y=\" \", width=cc[4], left=sum(cc[0:4]), color='#610202')\n",
    "plt.barh(y=\" \", width=cc[5], left=sum(cc[0:5]), color='#400101')\n",
    "plt.barh(y=\" \", width=sum(cc[6:]), left=sum(cc[0:6]), color='#200001')\n",
    "plt.annotate(\"2 cores\", xy=(250,0.45), xytext=(80,1), size=14, arrowprops={'width':1, 'color':'#fc0004'})\n",
    "plt.annotate(\"4 cores\", xy=(590,-0.45), xytext=(470,-1.1), size=14, arrowprops={'width':1, 'color':'#c20404'})\n",
    "plt.annotate(\"6 cores\", xy=(790,0.45), xytext=(640,1), size=14, arrowprops={'width':1, 'color':'#a10202'})\n",
    "plt.annotate(\"8 cores\", xy=(830,-0.45), xytext=(730,-0.85), size=14, arrowprops={'width':1, 'color':'#8c0303'})\n",
    "plt.annotate(\"10 cores\", xy=(856,0.45), xytext=(775,0.75), size=14, arrowprops={'width':1, 'color':'#610202'})\n",
    "plt.annotate(\"1 core\", xy=(866,-0.45), xytext=(800,-1.1), size=14, arrowprops={'width':1, 'color':'#400101'})\n",
    "plt.annotate(\"The rest\", xy=(880,0.45), xytext=(820,1.0), size=14, arrowprops={'width':1, 'color':'#200001'})\n",
    "plt.title(\"Intel Core Processors - # of Cores Distribution\", fontsize=16)\n",
    "plt.xlabel(\"Counts\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is so pleasant, right? Very explanatory, and nothing to say more over that.\n",
    "\n",
    "Continuing with another feature, let's obtain the quick statistics for **total cache size (in MB)** of all Core processors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    885.00\n",
       "mean       6.14\n",
       "std        3.96\n",
       "min        1.00\n",
       "25%        3.00\n",
       "50%        6.00\n",
       "75%        8.00\n",
       "max       25.00\n",
       "Name: Cache(MB), dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(intel_core['Cache(MB)'].describe(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This indicates that, around **6 MB** cache size is the most preferable one, as the median value, plus the so-close mean value verify that mutually.\n",
    "\n",
    "As previously above analyses, I'd like to see which processor keeps the highest amount of cache size, i.e. **25 MB**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "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>Product</th>\n",
       "      <th>Status</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Cores</th>\n",
       "      <th>Max. Turbo Freq.(GHz)</th>\n",
       "      <th>Base Freq.(GHz)</th>\n",
       "      <th>Cache(MB)</th>\n",
       "      <th>Cache Info</th>\n",
       "      <th>Integrated Graphics</th>\n",
       "      <th>Release Year</th>\n",
       "      <th>Freq. Diff.(GHz)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Core i7-6950X Extreme Edition</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q2'16</td>\n",
       "      <td>10</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Product        Status Release Date  Cores  \\\n",
       "20  Core i7-6950X Extreme Edition  Discontinued        Q2'16     10   \n",
       "\n",
       "    Max. Turbo Freq.(GHz)  Base Freq.(GHz)  Cache(MB) Cache Info  \\\n",
       "20                    3.5              3.0       25.0        NaN   \n",
       "\n",
       "   Integrated Graphics Release Year  Freq. Diff.(GHz)  \n",
       "20                 NaN         2016               0.5  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core[intel_core['Cache(MB)'] == 25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another Extreme Edition Core processor, _Core i7-6950X,_ got the award in this category!\n",
    "\n",
    "And, the processors with **the least** cache size are..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Product</th>\n",
       "      <th>Status</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Cores</th>\n",
       "      <th>Max. Turbo Freq.(GHz)</th>\n",
       "      <th>Base Freq.(GHz)</th>\n",
       "      <th>Cache(MB)</th>\n",
       "      <th>Cache Info</th>\n",
       "      <th>Integrated Graphics</th>\n",
       "      <th>Release Year</th>\n",
       "      <th>Freq. Diff.(GHz)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>Core2 Solo U2200</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q3'07</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>L2 Cache</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>Core2 Solo U2100</td>\n",
       "      <td>Discontinued</td>\n",
       "      <td>Q3'07</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>L2 Cache</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Product        Status Release Date  Cores  \\\n",
       "875  Core2 Solo U2200  Discontinued        Q3'07      1   \n",
       "876  Core2 Solo U2100  Discontinued        Q3'07      1   \n",
       "\n",
       "     Max. Turbo Freq.(GHz)  Base Freq.(GHz)  Cache(MB) Cache Info  \\\n",
       "875                    NaN             1.20        1.0   L2 Cache   \n",
       "876                    NaN             1.06        1.0   L2 Cache   \n",
       "\n",
       "    Integrated Graphics Release Year  Freq. Diff.(GHz)  \n",
       "875                 NaN         2007               NaN  \n",
       "876                 NaN         2007               NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel_core[intel_core['Cache(MB)'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh, the good ol' **Core2 Solo** processors, back in 2007! I don't understand why Intel had chosen this lowest amount ever...\n",
    "\n",
    "Now, let's carry out another fascinating analysis: This time, instead of visualization, a **cross tabulation** could be great to compare the cache sizes against the **cache description/info**, for a better insight.\n",
    "\n",
    "_Below, I performed some data wrangling beforehand for the corresponding column, to avoid NaN values, converting them to 'No Cache Info'._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Cache Info</th>\n",
       "      <th>Intel Smart Cache</th>\n",
       "      <th>L2 Cache</th>\n",
       "      <th>L3 Cache</th>\n",
       "      <th>No Cache Info</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cache(MB)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.00</th>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.00</th>\n",
       "      <td>151</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.00</th>\n",
       "      <td>106</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.00</th>\n",
       "      <td>147</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.00</th>\n",
       "      <td>106</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.00</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.00</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11.00</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.00</th>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>13.75</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>15.00</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.00</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.50</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.25</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.00</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>24.75</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>25.00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cache Info  Intel Smart Cache  L2 Cache  L3 Cache  No Cache Info\n",
       "Cache(MB)                                                       \n",
       "1.00                        0         2         0              0\n",
       "2.00                        0        51         0              0\n",
       "3.00                      151        21         4             27\n",
       "4.00                      106        28         3             29\n",
       "6.00                      147        32         0             26\n",
       "8.00                      106         5         0             12\n",
       "8.25                        0         0         1              0\n",
       "9.00                       23         0         0              4\n",
       "10.00                       2         0         0              0\n",
       "11.00                       0         0         1              0\n",
       "12.00                      40         9         0              3\n",
       "13.75                       0         0         1              0\n",
       "15.00                       7         0         0              0\n",
       "16.00                      18         0         0              0\n",
       "16.50                       2         0         1              0\n",
       "19.25                       6         0         0              1\n",
       "20.00                       9         0         0              1\n",
       "22.00                       1         0         0              1\n",
       "24.75                       2         0         0              1\n",
       "25.00                       0         0         0              1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def check_cache_info(info):\n",
    "    if type(info) == float:\n",
    "        return \"No Cache Info\"\n",
    "    return info\n",
    "intel_core['Cache Info'] = intel_core['Cache Info'].apply(check_cache_info)\n",
    "pd.crosstab(intel_core['Cache(MB)'], intel_core['Cache Info'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This explains a lot! The most common cache combination is **Intel Smart Cache with 3 MB size**. The runner-up is again **Intel Smart Cache** with higher size, **6 MB**. Next, the same one with **4** and **8 MB** cache sizes, respectively. With human eyes, one can see that _Intel Smart Cache_ is the most appearing cache type.\n",
    "\n",
    "**L2 and L3 Caches** seem to be from the older technology that Intel does not mind using them any more in the latest generations. Likewise, it's hard to say if the processors with undescribed cache info have low performance or not...\n",
    "\n",
    "Lastly, let's inspect the feature **Integrated Graphics**. It is certain that most Intel Core processors come along with the graphical components when it's enough to use without a graphic card installed to motherboards.\n",
    "\n",
    "There are some definite namings for these graphics, such as _Intel UHD, Intel HD and Intel Iris._ Delving into more details, specific numbers could be given ( 3 or 4 digits) to distinguish them from each other and assess how powerful these graphic components are. Here are some examples below:\n",
    "\n",
    "* **Intel UHD Graphics**: 630, 620, 615, ...\n",
    "* **Intel HD Graphics**: 2000, 3000, 4000, 4600, 5000, 6000, 520, 530, 620, ...\n",
    "* **Intel Iris**: _Plus_ or _Pro_ as prefix, then 540, 550, 640, 645, ...\n",
    "\n",
    "For this reason, it is much more suitable to **categorize them all**. However, NOT all Core processors contain any integrated graphics, thus these need to be given as _No Category,_ as I did similarly while takinga look at Cache Info.\n",
    "\n",
    "Enough talking, let's compose another feature that holds the categorical values of Integrated Graphics in Core CPUs, then count them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_graphic_info(info):\n",
    "    if type(info) == float:\n",
    "        return \"No Integrated Graphics\"\n",
    "    return info\n",
    "def get_graphic_cat(info):\n",
    "    if 'Intel UHD' in info or 'UHD Intel' in info:\n",
    "        return 'Intel UHD'\n",
    "    elif 'Intel HD' in info:\n",
    "        return 'Intel HD'\n",
    "    elif 'Intel Iris' in info:\n",
    "        return 'Intel Iris'\n",
    "    else:\n",
    "        return 'No Category'\n",
    "intel_core['Integrated Graphics'] = intel_core['Integrated Graphics'].apply(check_graphic_info)\n",
    "intel_core['Integrated Grap. Cat.'] = intel_core['Integrated Graphics'].apply(get_graphic_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel HD\t416\t47.01 percent\n",
      "No Category\t243\t27.46 percent\n",
      "Intel UHD\t142\t16.05 percent\n",
      "Intel Iris\t84\t9.49 percent\n"
     ]
    }
   ],
   "source": [
    "igc = intel_core['Integrated Grap. Cat.'].value_counts()\n",
    "for i in range(len(igc)):\n",
    "    print(\"{}\\t{}\\t{:.2f} percent\".format(igc.index[i], igc[i], igc[i] / len(intel_core) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a result, **Intel HD** integrated graphics is the most dominant among all Core processors. Remarkably, **243** processors do not come with any graphical component, that's more than a quarter of all.\n",
    "\n",
    "Time to make another visualization for this analysis with **stacked bar chart**, similarly above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1044x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "igc = intel_core['Integrated Grap. Cat.'].value_counts()\n",
    "plt.figure(figsize=(14.5,6))\n",
    "plt.axis([0,900,-1.2,1.2])\n",
    "plt.barh(y=\" \", width=igc[0], color='#c803ff')\n",
    "plt.barh(y=\" \", width=igc[1], left=igc[0], color='#a202cf')\n",
    "plt.barh(y=\" \", width=igc[2], left=igc[0]+igc[1], color='#8402a8')\n",
    "plt.barh(y=\" \", width=igc[3], left=igc[0]+igc[1]+igc[2], color='#6c018a')\n",
    "plt.annotate(\"{} ({})\".format(igc.index[0], igc[0]), xy=(400,0.45), xytext=(250,0.95), size=13, \n",
    "             arrowprops={'width':1, 'color':'#c803ff'})\n",
    "plt.annotate(\"{} ({})\".format(igc.index[1], igc[1]), xy=(640,-0.45), xytext=(450,-0.95), size=13, \n",
    "             arrowprops={'width':1, 'color':'#a202cf'})\n",
    "plt.annotate(\"{} ({})\".format(igc.index[2], igc[2]), xy=(780,0.45), xytext=(630,0.95), size=13, \n",
    "             arrowprops={'width':1, 'color':'#8402a8'})\n",
    "plt.annotate(\"{} ({})\".format(igc.index[3], igc[3]), xy=(870,-0.45), xytext=(740,-0.95), size=13, \n",
    "             arrowprops={'width':1, 'color':'#6c018a'})\n",
    "plt.title(\"Intel Core Processors - Categories of Integrated Graphics\", fontsize=16)\n",
    "plt.xlabel(\"Counts\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And, this concludes the 1st part of analyses of Intel Core processsors...\n",
    "\n",
    "### But, believe me or not, we've made lots of analyses even with a limited number of features; allowing us to gain many insights, right?\n",
    "\n",
    "### OK then, see you in the next part!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
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 },
 "nbformat": 4,
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