main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d2f93c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#mudule import\n",
    "import os\n",
    "from PyPDF2 import PdfReader\n",
    "import re\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from tkinter import *\n",
    "import PIL\n",
    "from PIL import ImageTk\n",
    "from PIL import Image\n",
    "\n",
    "#text extraction\n",
    "os.chdir(\"--YOUR PATH--\") #example: C:\\\\Users\\\\Casper\\\\Desktop\\\\kaynaklar\\\\tiy\n",
    "reader = PdfReader(\"İstatistik_Göstergeler_Türkiye_1923-2013.pdf\")\n",
    "page1 = reader.pages[17]\n",
    "text = page1.extractText()\n",
    "patern = r\"\\d{1,2},\\d\"\n",
    "\n",
    "#repository\n",
    "male_year35 = []\n",
    "male_year40 = []\n",
    "male_year45 = []\n",
    "male_year50 = []\n",
    "male_year55 = []\n",
    "male_year60 = []\n",
    "male_year65 = []\n",
    "male_year70 = []\n",
    "male_year75 = []\n",
    "male_year80 = []\n",
    "male_year85 = []\n",
    "male_year90 = []\n",
    "male_year2000 = []\n",
    "male = (male_year35, male_year40, male_year45, male_year50, male_year55, male_year60, male_year65, male_year70, male_year75, male_year80, male_year85, male_year90, male_year2000)\n",
    "\n",
    "female_year35 = []\n",
    "female_year40 = []\n",
    "female_year45 = []\n",
    "female_year50 = []\n",
    "female_year55 = []\n",
    "female_year60 = []\n",
    "female_year65 = []\n",
    "female_year70 = []\n",
    "female_year75 = []\n",
    "female_year80 = []\n",
    "female_year85 = []\n",
    "female_year90 = []\n",
    "female_year2000 = []\n",
    "female = (female_year35, female_year40, female_year45, female_year50, female_year55, female_year60, female_year65, female_year70, female_year75, female_year80, female_year85, female_year90, female_year2000)\n",
    "        \n",
    "#total men-women shares elemination\n",
    "first_half = [*range(260)]\n",
    "first_half_updated = []\n",
    "for i in first_half:\n",
    "    if not i%10 == 0:\n",
    "        first_half_updated.append(i)\n",
    "        \n",
    "#Data extraction\n",
    "center =[*range(0,130,10)]\n",
    "second_half = [*range(520)]\n",
    "gear1 = 0\n",
    "gear2 = 0\n",
    "gear3 = 0\n",
    "\n",
    "for gear2 in center:\n",
    "    try:\n",
    "        for gear1 in first_half_updated:\n",
    "            if gear1 >= gear2 and gear1 < gear2+10:\n",
    "                male[gear3].extend (str(round(float(re.findall(patern, text)[gear1].replace(\",\",\".\")))))\n",
    "                \n",
    "            elif gear1 >= gear2+130 and gear1 < gear2+130+10:\n",
    "                female[gear3].extend (str(round(float(re.findall(patern, text)[gear1].replace(\",\",\".\")))))\n",
    "                \n",
    "                \n",
    "        for gear1 in second_half:\n",
    "            if gear1 >= gear2+260 and gear1 < gear2+260+10:\n",
    "                male[gear3].extend (str(round(float(re.findall(patern, text)[gear1].replace(\",\",\".\")))))\n",
    "                \n",
    "            elif gear1 >= gear2+390 and gear1 < gear2+390+10:\n",
    "                female[gear3].extend (str(round(float(re.findall(patern, text)[gear1].replace(\",\",\".\")))))\n",
    "                \n",
    "    finally:\n",
    "        gear1 += 1\n",
    "        gear2 += 10\n",
    "        if gear3 == 13:\n",
    "            break\n",
    "        else: \n",
    "            gear3 += 1\n",
    "\n",
    "#conversion to integer\n",
    "def converter (maley, femaley):\n",
    "    for i in range(0, len(maley)):\n",
    "        maley[i] = int(maley[i])\n",
    "        \n",
    "    for i in range(0, len(femaley)):\n",
    "        femaley[i] = int(femaley[i])\n",
    "        \n",
    "    return(maley, femaley)\n",
    "\n",
    "#population pyramid display\n",
    "def chart (maley, femaley, year):\n",
    "\n",
    "        plt.style.use(\"seaborn-ticks\")\n",
    "        \n",
    "        age = [\"0-4\", \"5-9\", \"10-14\", \"15-19\", \"20-24\", \"25-29\", \"30-34\", \"35-39\", \"40-44\", \"45-49\", \"50-54\", \"55-59\", \"60-64\", \"65-69\", \"70-74\", \"75-79\", \"80-84\", \"85+\", \"unknown\"]\n",
    "        age_reversed = list(reversed(age))\n",
    "        y = range(0, len(age))\n",
    "        \n",
    "        fig, axes = plt.subplots(ncols=2, sharey=True, figsize=(9, 5))\n",
    "        \n",
    "        axes[0].barh(y, maley, color=\"steelblue\")\n",
    "        axes[0].set(title=\"Males\")\n",
    "        \n",
    "        axes[1].barh(y, femaley, color=\"indianred\")\n",
    "        axes[1].set(title=\"Female\")\n",
    "        \n",
    "        axes[0].set(yticks=y, yticklabels= age)\n",
    "        \n",
    "        x = [*range(10)]\n",
    "        axes[0].set(xticks=x, xticklabels= x)\n",
    "        axes[1].set(xticks=x, xticklabels= x)\n",
    "        \n",
    "        axes[0].invert_xaxis()\n",
    "        \n",
    "        plt.figtext(.495, 0.95, year, fontsize=24, weight=\"bold\")\n",
    "        plt.figtext(.45, -0.02, \"Percentage(%)\", fontsize=20)\n",
    "        \n",
    "        plt.tight_layout()\n",
    "                \n",
    "        plt.show()\n",
    "\n",
    "#combined function for buttons\n",
    "def combined (maley, femaley, year):\n",
    "    converter (maley, femaley)\n",
    "    return chart (maley, femaley, year)\n",
    "\n",
    "#control panel\n",
    "root = Tk()\n",
    "root.geometry(\"100x350\")\n",
    "\n",
    "#buttons    \n",
    "b1 = Button(root, text=\"1935\", command = lambda: combined(male_year35, female_year35, 1935))\n",
    "b1.pack()\n",
    "b2 = Button(root, text=\"1940\", command = lambda: combined(male_year40, female_year40, 1940))\n",
    "b2.pack()\n",
    "b3 = Button(root, text=\"1945\", command = lambda: combined(male_year45, female_year45, 1945))\n",
    "b3.pack()\n",
    "b4 = Button(root, text=\"1950\", command = lambda: combined(male_year50, female_year50, 1950))\n",
    "b4.pack()\n",
    "b5 = Button(root, text=\"1955\", command = lambda: combined(male_year55, female_year55, 1955))\n",
    "b5.pack()\n",
    "b6 = Button(root, text=\"1960\", command = lambda: combined(male_year60, female_year60, 1960))\n",
    "b6.pack()\n",
    "b7 = Button(root, text=\"1965\", command = lambda: combined(male_year65, female_year65, 1965))\n",
    "b7.pack()\n",
    "b8 = Button(root, text=\"1970\", command = lambda: combined(male_year70, female_year70, 1970))\n",
    "b8.pack()\n",
    "b9 = Button(root, text=\"1975\", command = lambda: combined(male_year75, female_year75, 1975))\n",
    "b9.pack()\n",
    "b10 = Button(root, text=\"1980\", command = lambda: combined(male_year80, female_year80, 1980))\n",
    "b10.pack()\n",
    "b11 = Button(root, text=\"1985\", command = lambda: combined(male_year85, female_year85, 1985))\n",
    "b11.pack()\n",
    "b12 = Button(root, text=\"1990\", command = lambda: combined(male_year90, female_year90, 1990))\n",
    "b12.pack()\n",
    "b13 = Button(root, text=\"2000\", command = lambda: combined(male_year2000, female_year2000, 2000))\n",
    "b13.pack()\n",
    "\n",
    "root.mainloop()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}