benfordslaw-and-Turkish-elections.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "47c3ab22",
   "metadata": {},
   "source": [
    "# Turkish 2019 Elections in a perspective of Benford's Law with using Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59289733",
   "metadata": {},
   "source": [
    "Let's see how Benford's Law can be used in Python and how it looks in Turkey 2019 local elections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa50c16",
   "metadata": {},
   "source": [
    "### What is Benford’s Law?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9644099a",
   "metadata": {},
   "source": [
    "Before starting lets explain a little bit Benford's Law. ref: <a href=\"https://en.wikipedia.org/wiki/Benford%27s_law\" target=\"_blank\">Wikipedia</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ac5f07c",
   "metadata": {},
   "source": [
    "A better description at: <a href=\"https://brilliant.org/wiki/benfords-law/\">Brilliant</a> thanks to, Patrick Corn, Hua Zhi Vee, Christopher Williams, E. Ross and J.Khim <br><b>Benford's law</b> is an observation about the leading digits of the numbers found in real-world data sets. Intuitively, one might expect that the leading digits of these numbers would be uniformly distributed so that each of the digits from 1 to 9 is equally likely to appear. In fact, it is often the case that 1 occurs more frequently than 2, 2 more frequently than 3, and so on. This observation is a simplified version of Benford's law. More precisely, the law gives a prediction of the frequency of leading digits using base-10 logarithms that predicts specific frequencies which decrease as the digits increase from 1 to 9."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "041df533",
   "metadata": {},
   "source": [
    "<img src=\"rozklad_benforda.png\" width=500 style=\"float:left;padding-left:20px\">\n",
    "<p>The distribution of first digits, according to Benford's law. Each bar represents a digit, and the height of the bar is the percentage of numbers that start with that digit.</p>\n",
    "<p style=\"padding-right:20px\">This phenomenon occurs generally in many different instances of real-world data. It becomes more pronounced and more likely when more data is combined together from different sources. Not every data set satisfies Benford's law, and it is surprisingly difficult to explain the law's occurrence in the data sets it does describe, but nevertheless it does occur consistently in well-understood circumstances. Scientists have even begun to use versions of the law to detect potential fraud in published data (tax returns, election results) that are expected to satisfy the law. </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a17bedd8",
   "metadata": {},
   "source": [
    "In this study, we will look at how Benford's Law can be integrated into Python.\n",
    "After making sure that the algorithm works correctly, we will analyze the results of the 2019 Local Elections in Turkey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "30334512",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing Matplotlib a creating a dictionary for numbers with zero values.\n",
    "#The dictionary will hold the numbers 1-9 and we will increment each number to analyze the ratios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a876bb4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from tabulate import tabulate\n",
    "from functools import reduce\n",
    "import random\n",
    "\n",
    "letters = {\n",
    "    '1': 0,\n",
    "    '2': 0,\n",
    "    '3': 0,\n",
    "    '4': 0,\n",
    "    '5': 0,\n",
    "    '6': 0,\n",
    "    '7': 0,\n",
    "    '8': 0,\n",
    "    '9': 0\n",
    "}\n",
    "\n",
    "colours = ['#b0dfe5','#95C8D8','#73C2FB','#89cff0','#7285A5','#588BAE','#468284','#1c6e78','#4C516D']\n",
    "#fibonacci series for testing\n",
    "def iterativeFibIntoList(n):\n",
    "    a, b = 0, 1\n",
    "    l = []\n",
    "    for i in range(n):\n",
    "        a, b = b, a + b\n",
    "        l.append(a)\n",
    "    return l\n",
    "\n",
    "list_of_nums = iterativeFibIntoList(1000)\n",
    "\n",
    "for n in list_of_nums:\n",
    "    first_digit = str(n)[0]\n",
    "    letters[first_digit] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "216aae89",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_fib = list(letters.keys())\n",
    "y_fib = [round(val / 1000, 2) for val in letters.values()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99420345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bars = plt.bar(x_fib, y_fib, color=colours)\n",
    "\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "\n",
    "    plt.annotate(f'{height}',\n",
    "        xy = (bar.get_x() + bar.get_width() / 2, height),\n",
    "        xytext = (0, 3),\n",
    "        textcoords = 'offset points',\n",
    "        ha = 'center',\n",
    "        va = 'bottom'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5eb2806f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets try an another example by creating an another series \n",
    "letters = {\n",
    "    '1': 0,\n",
    "    '2': 0,\n",
    "    '3': 0,\n",
    "    '4': 0,\n",
    "    '5': 0,\n",
    "    '6': 0,\n",
    "    '7': 0,\n",
    "    '8': 0,\n",
    "    '9': 0\n",
    "}\n",
    "\n",
    "list_of_nums = map(lambda x: x+random.randint(1,100)*x, range(1,500))\n",
    "list_of_nums = list(list_of_nums)\n",
    "#print(list_of_nums)\n",
    "\n",
    "for n in list_of_nums:\n",
    "    first_digit = str(n)[0]\n",
    "    letters[first_digit] += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b51c751",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_rand = list(letters.keys())\n",
    "y_rand = [round(val / 499, 2) for val in letters.values()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "547df31c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bars = plt.bar(x_rand, y_rand, color=colours)\n",
    "\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "\n",
    "    plt.annotate(f'{height}',\n",
    "        xy = (bar.get_x() + bar.get_width() / 2, height),\n",
    "        xytext = (0, 3),\n",
    "        textcoords = 'offset points',\n",
    "        ha = 'center',\n",
    "        va = 'bottom'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6a1cda4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------------+--------------------+-----------------+\n",
      "|   Number |   Expected Ratios |   Fibonacci Series |   Random Series |\n",
      "+==========+===================+====================+=================+\n",
      "|        1 |                31 |               0.30 |            0.28 |\n",
      "+----------+-------------------+--------------------+-----------------+\n",
      "|        2 |                18 |               0.18 |            0.21 |\n",
      "+----------+-------------------+--------------------+-----------------+\n",
      "|        3 |                13 |               0.12 |            0.15 |\n",
      "+----------+-------------------+--------------------+-----------------+\n",
      "|        4 |                10 |               0.10 |            0.10 |\n",
      "+----------+-------------------+--------------------+-----------------+\n",
      "|        5 |                 8 |               0.08 |            0.05 |\n",
      "+----------+-------------------+--------------------+-----------------+\n",
      "|        6 |                 7 |               0.07 |            0.05 |\n",
      "+----------+-------------------+--------------------+-----------------+\n",
      "|        7 |                 6 |               0.06 |            0.04 |\n",
      "+----------+-------------------+--------------------+-----------------+\n",
      "|        8 |                 5 |               0.05 |            0.06 |\n",
      "+----------+-------------------+--------------------+-----------------+\n",
      "|        9 |                 5 |               0.04 |            0.06 |\n",
      "+----------+-------------------+--------------------+-----------------+\n"
     ]
    }
   ],
   "source": [
    "expected = [31,18,13,10,8,7,6,5,5]\n",
    "data = zip(x_rand,expected,y_fib,y_rand)\n",
    "\n",
    "head = [\"Number\",\"Expected Ratios\",\"Fibonacci Series\", 'Random Series']\n",
    "\n",
    "print(tabulate(data, headers=head, tablefmt=\"grid\", floatfmt = \".2f\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4678eb9",
   "metadata": {},
   "source": [
    "<b>Let's see how Benford's Law can be used in Python and how it looks in Turkey 2019 local elections</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f25e1c",
   "metadata": {},
   "source": [
    "Dataset is 2019_Istanbul.xlsx. You can download from github or from original site <a href=\"https://www.ysk.gov.tr/tr/31-mart-2019-mahalli-i%CC%87dareler-secimi/77916\" target=\"_blank\">YSK</a>\n",
    "\n",
    "Dataset includes all cities and counties and their votes. There are two parties AK Parti and CHP.\n",
    "In this study we are examining each parties votes count and their summaries with the Benford's Law\n",
    "\n",
    "\n",
    "<img style=\"float:left;padding-right:20px\" src=\"https://upload.wikimedia.org/wikipedia/tr/thumb/d/d5/Adalet_ve_Kalk%C4%B1nma_Partisi_logo.png/124px-Adalet_ve_Kalk%C4%B1nma_Partisi_logo.png\" width=\"150\"><p style=\"margin-top:100px;margin-left:30px\">First we are looking to <b>AK Parti's</b> votes.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddd790c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    CityName CountyName PartyName  PartyVotes\n",
      "799    DÜZCE   CUMAYERI  AK Parti        2657\n",
      "800    DÜZCE    GÖLYAKA  AK Parti        3330\n",
      "801    DÜZCE    ÇILIMLI  AK Parti        2288\n",
      "802    DÜZCE   GÜMÜSOVA  AK Parti        2336\n",
      "803    DÜZCE   KAYNASLI  AK Parti        2333\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import ExcelWriter\n",
    "from pandas import ExcelFile\n",
    "\n",
    "df = pd.read_excel('2019_Turkey.xlsx', sheet_name='akp')#akp\n",
    "\n",
    "\n",
    "\n",
    "votesNormal = {\n",
    "    '1': 0,\n",
    "    '2': 0,\n",
    "    '3': 0,\n",
    "    '4': 0,\n",
    "    '5': 0,\n",
    "    '6': 0,\n",
    "    '7': 0,\n",
    "    '8': 0,\n",
    "    '9': 0\n",
    "}\n",
    "\n",
    "PartyVotes = df['PartyVotes'] \n",
    "\n",
    "for n in PartyVotes:\n",
    "    first_digit = str(n)[0]\n",
    "    votesNormal[first_digit] += 1\n",
    "    \n",
    "#looking to first 5 lines\n",
    "print(df.tail(n=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e1928a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_akp = list(votesNormal.keys())\n",
    "y_akp = [round(val / len(PartyVotes), 2) for val in letters.values()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c183f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bars = plt.bar(x_akp, y_akp, color=colours)\n",
    "\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "\n",
    "    plt.annotate(f'{height}',\n",
    "        xy = (bar.get_x() + bar.get_width() / 2, height),\n",
    "        xytext = (0, 3),\n",
    "        textcoords = 'offset points',\n",
    "        ha = 'center',\n",
    "        va = 'bottom'\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8b4dcf6",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<img style=\"float:left;padding-right:20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/e/ef/Cumhuriyet_Halk_Partisi_Logo.svg/126px-Cumhuriyet_Halk_Partisi_Logo.svg.png\" width=\"150\"><p style=\"margin-top:50px\">Now we are looking to <b>CHP's</b> votes.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "05983f8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     CityName  CountyName PartyName  PartyVotes\n",
      "509  OSMANIYE      DÜZIÇI       CHP       17133\n",
      "510  OSMANIYE  TOPRAKKALE       CHP        1483\n",
      "511     DÜZCE    AKÇAKOCA       CHP        3930\n",
      "512     DÜZCE     YIGILCA       CHP         148\n",
      "513     DÜZCE     GÖLYAKA       CHP        1045\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import ExcelWriter\n",
    "from pandas import ExcelFile\n",
    "\n",
    "df = pd.read_excel('2019_Turkey.xlsx', sheet_name='chp')#chp\n",
    "\n",
    "\n",
    "\n",
    "votesNormal = {\n",
    "    '1': 0,\n",
    "    '2': 0,\n",
    "    '3': 0,\n",
    "    '4': 0,\n",
    "    '5': 0,\n",
    "    '6': 0,\n",
    "    '7': 0,\n",
    "    '8': 0,\n",
    "    '9': 0\n",
    "}\n",
    "\n",
    "PartyVotes = df['PartyVotes'] \n",
    "\n",
    "for n in PartyVotes:\n",
    "    first_digit = str(n)[0]\n",
    "    votesNormal[first_digit] += 1\n",
    "    \n",
    "#looking to first 5 lines\n",
    "print(df.tail(n=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a1ad975c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_chp = list(votesNormal.keys())\n",
    "y_chp = [round(val / len(PartyVotes), 2) for val in letters.values()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd5987e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bars = plt.bar(x_chp, y_chp, color=colours)\n",
    "\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "\n",
    "    plt.annotate(f'{height}',\n",
    "        xy = (bar.get_x() + bar.get_width() / 2, height),\n",
    "        xytext = (0, 3),\n",
    "        textcoords = 'offset points',\n",
    "        ha = 'center',\n",
    "        va = 'bottom'\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc55651",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "<hr>\n",
    "\n",
    "<b>As a result</b>, we can compare ratios and detect abnormalities in a perspective of Benford's Law.\n",
    "Which looks normal in total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9947ee9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------------+--------------------+-----------------+-------+------------+\n",
      "|   Number |   Expected Ratios |   Fibonacci Series |   Random Series |   CHP |   AK Parti |\n",
      "+==========+===================+====================+=================+=======+============+\n",
      "|        1 |                31 |               0.30 |            0.28 |  0.28 |       0.18 |\n",
      "+----------+-------------------+--------------------+-----------------+-------+------------+\n",
      "|        2 |                18 |               0.18 |            0.21 |  0.20 |       0.13 |\n",
      "+----------+-------------------+--------------------+-----------------+-------+------------+\n",
      "|        3 |                13 |               0.12 |            0.15 |  0.15 |       0.09 |\n",
      "+----------+-------------------+--------------------+-----------------+-------+------------+\n",
      "|        4 |                10 |               0.10 |            0.10 |  0.10 |       0.06 |\n",
      "+----------+-------------------+--------------------+-----------------+-------+------------+\n",
      "|        5 |                 8 |               0.08 |            0.05 |  0.05 |       0.03 |\n",
      "+----------+-------------------+--------------------+-----------------+-------+------------+\n",
      "|        6 |                 7 |               0.07 |            0.05 |  0.05 |       0.03 |\n",
      "+----------+-------------------+--------------------+-----------------+-------+------------+\n",
      "|        7 |                 6 |               0.06 |            0.04 |  0.04 |       0.02 |\n",
      "+----------+-------------------+--------------------+-----------------+-------+------------+\n",
      "|        8 |                 5 |               0.05 |            0.06 |  0.06 |       0.04 |\n",
      "+----------+-------------------+--------------------+-----------------+-------+------------+\n",
      "|        9 |                 5 |               0.04 |            0.06 |  0.05 |       0.03 |\n",
      "+----------+-------------------+--------------------+-----------------+-------+------------+\n"
     ]
    }
   ],
   "source": [
    "expected = [31,18,13,10,8,7,6,5,5]\n",
    "data = zip(x_akp,expected,y_fib,y_rand,y_chp,y_akp)\n",
    "\n",
    "head = [\"Number\",\"Expected Ratios\",\"Fibonacci Series\", 'Random Series', 'CHP', 'AK Parti']\n",
    "\n",
    "print(tabulate(data, headers=head, tablefmt=\"grid\", floatfmt = \".2f\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b875ada",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/denizumutdereli/\" target=\"_blank\">@Deniz Umut Dereli</a>"
   ]
  }
 ],
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