Relative Frequency.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "from matplotlib.pyplot import gca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphabets = [\"অ\",\"আ\",\"ই\",\"ঈ\",\"উ\",\"ঊ\",\"ঋ\",\"এ\",\"ঐ\",\"ও\",\"ঔ\",\"ক\",\"খ\",\"গ\",\"ঘ\",\"ঙ\",\"চ\",\"ছ\",\"জ\",\"ঝ\",\"ঞ\",\"ট\",\"ঠ\",\"ড\",\"ঢ\",\"ণ\",\"ত\",\"থ\",\"দ\",\"ধ\",\"ন\",\"প\",\"ফ\",\"ব\",\n",
    "\"ভ\",\"ম\",\"য\",\"র\",\"ল\",\"শ\",\"ষ\",\"স\",\"হ\",\"ড়\",\"ঢ়\",\"য়\",\"ৎ\",\"ং\",\"ঃ\",\"ঁ\",\"া\",\"ি\",\"ী\",\"ু\",\"ূ\",\"ৃ\",\"ে\",\"ৈ\",\"ো\",\"ৌ\"]\n",
    "\n",
    "freq = {}\n",
    "\n",
    "def read_file(name = 'opohoron_novel.txt'):\n",
    "    cou = 0\n",
    "    with open(name, encoding=\"utf-8\", errors='ignore') as f:\n",
    "        for word in f:\n",
    "            #print(word)\n",
    "            for char in word:\n",
    "                if char in freq:\n",
    "                    freq[char]=freq[char]+1\n",
    "                else:\n",
    "                    freq[char]=int(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "read_file()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frequency(strings, letters):\n",
    "    list_letter = []\n",
    "    list_freq = []\n",
    "    list_letter_freq = [list_letter,list_freq]\n",
    "    for letter in letters:\n",
    "        freq = 0\n",
    "        for i in strings:\n",
    "            if i == letter:\n",
    "                freq += 1   \n",
    "        list_letter.append(letter)\n",
    "        list_freq.append(freq)\n",
    "    return list_letter_freq \n",
    "\n",
    "\n",
    "\n",
    "def get_relative_freq(list_freq):\n",
    "    list_rel_freq = []\n",
    "    for i in list_freq:\n",
    "        list_rel_freq.append((i/sum(list_freq))*100)\n",
    "    return list_rel_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Alphabet</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>া</td>\n",
       "      <td>40068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ে</td>\n",
       "      <td>38783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>র</td>\n",
       "      <td>28100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ক</td>\n",
       "      <td>19452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ি</td>\n",
       "      <td>19080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ন</td>\n",
       "      <td>18304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>ল</td>\n",
       "      <td>14257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>স</td>\n",
       "      <td>10680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ত</td>\n",
       "      <td>10593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ব</td>\n",
       "      <td>10044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>প</td>\n",
       "      <td>8655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ম</td>\n",
       "      <td>8168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ট</td>\n",
       "      <td>8141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>য়</td>\n",
       "      <td>7584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>ু</td>\n",
       "      <td>7221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>ো</td>\n",
       "      <td>5832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>দ</td>\n",
       "      <td>5001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>হ</td>\n",
       "      <td>4785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>জ</td>\n",
       "      <td>4566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>য</td>\n",
       "      <td>4539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ছ</td>\n",
       "      <td>4514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>আ</td>\n",
       "      <td>4437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>এ</td>\n",
       "      <td>4197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>গ</td>\n",
       "      <td>4149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ই</td>\n",
       "      <td>3724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>চ</td>\n",
       "      <td>3557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ড</td>\n",
       "      <td>3321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>থ</td>\n",
       "      <td>3242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>খ</td>\n",
       "      <td>3159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>শ</td>\n",
       "      <td>2914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ও</td>\n",
       "      <td>2676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ভ</td>\n",
       "      <td>2442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ফ</td>\n",
       "      <td>2038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ধ</td>\n",
       "      <td>1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>অ</td>\n",
       "      <td>1862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>উ</td>\n",
       "      <td>1784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ণ</td>\n",
       "      <td>1717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>ঁ</td>\n",
       "      <td>1664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>ী</td>\n",
       "      <td>1396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ষ</td>\n",
       "      <td>1232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ঠ</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ং</td>\n",
       "      <td>767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ঘ</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ঝ</td>\n",
       "      <td>636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>ূ</td>\n",
       "      <td>539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ঙ</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>ৃ</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ঞ</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ঢ</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ৎ</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>ৈ</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>ঃ</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ৌ</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ঈ</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ঊ</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ঐ</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ঋ</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>ড়</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ঔ</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ঢ়</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Alphabet  Count\n",
       "50        া  40068\n",
       "56        ে  38783\n",
       "37        র  28100\n",
       "11        ক  19452\n",
       "51        ি  19080\n",
       "30        ন  18304\n",
       "38        ল  14257\n",
       "41        স  10680\n",
       "26        ত  10593\n",
       "33        ব  10044\n",
       "31        প   8655\n",
       "35        ম   8168\n",
       "21        ট   8141\n",
       "45        য়   7584\n",
       "53        ু   7221\n",
       "58        ো   5832\n",
       "28        দ   5001\n",
       "42        হ   4785\n",
       "18        জ   4566\n",
       "36        য   4539\n",
       "17        ছ   4514\n",
       "1         আ   4437\n",
       "7         এ   4197\n",
       "13        গ   4149\n",
       "2         ই   3724\n",
       "16        চ   3557\n",
       "23        ড   3321\n",
       "27        থ   3242\n",
       "12        খ   3159\n",
       "39        শ   2914\n",
       "9         ও   2676\n",
       "34        ভ   2442\n",
       "32        ফ   2038\n",
       "29        ধ   1934\n",
       "0         অ   1862\n",
       "4         উ   1784\n",
       "25        ণ   1717\n",
       "49        ঁ   1664\n",
       "52        ী   1396\n",
       "40        ষ   1232\n",
       "22        ঠ    956\n",
       "47        ং    767\n",
       "14        ঘ    669\n",
       "19        ঝ    636\n",
       "54        ূ    539\n",
       "15        ঙ    459\n",
       "55        ৃ    334\n",
       "20        ঞ    326\n",
       "24        ঢ    303\n",
       "46        ৎ    205\n",
       "57        ৈ    119\n",
       "48        ঃ    102\n",
       "59        ৌ     90\n",
       "3         ঈ     23\n",
       "5         ঊ      6\n",
       "8         ঐ      4\n",
       "6         ঋ      1\n",
       "43       ড়      0\n",
       "10        ঔ      0\n",
       "44       ঢ়      0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('D:\\Interim Files\\Compressed\\Bangla-Relative-Frequency\\opohoron_novel.txt','r',encoding='utf-8') as f:\n",
    "    string1 = f.read()\n",
    "    \n",
    "\n",
    "\n",
    "result = frequency(string1,alphabets)\n",
    "\n",
    "mean_data = np.array(result)\n",
    "dataset = pd.DataFrame({'Alphabet': mean_data[0,:], 'Count': mean_data[1,:]})\n",
    "\n",
    "\n",
    "dataset['Count'] = dataset['Count'].astype('int')\n",
    "dataset.sort_values(by='Count', ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5552391375007082, 1.3230913281904633, 1.1104782750014164, 0.006858485586743442, 0.5319799255108827, 0.0017891701530635064, 0.00029819502551058444, 1.2515245220679228, 0.0011927801020423377, 0.797969888266324, 0.0, 5.800489636231888, 0.9419980855879362, 1.2372111608434149, 0.199492472066581, 0.13687151670935827, 1.0606797057411488, 1.3460523451547781, 1.3615584864813286, 0.1896520362247317, 0.09721157831645053, 2.4276057026816678, 0.2850744443881187, 0.990305679720651, 0.09035309272970708, 0.5120008588016735, 3.158779905233621, 0.9667482727053147, 1.4912733225784327, 0.5767091793374703, 5.458161746945738, 2.580877945794108, 0.6077214619905711, 2.99507083622831, 0.7281922522968473, 2.4356569683704534, 1.3535072207925427, 8.379280216847421, 4.251366478704402, 0.8689403043378431, 0.36737627142904006, 3.184722872453042, 1.4268631970681465, 0.0, 0.0, 2.2615110734722723, 0.06112998022966981, 0.22871558456661825, 0.030415892602079612, 0.49619652244961254, 11.948078282158097, 5.689561086741951, 0.4162802556127759, 2.1532662792119304, 0.160727118750205, 0.0995971385205352, 11.564897674376997, 0.035485208035759545, 1.7390733887777283, 0.0268375522959526]\n",
      "\n",
      "\n",
      "99.99999999999999\n"
     ]
    }
   ],
   "source": [
    "relative_freq = get_relative_freq(result[1])\n",
    "print(relative_freq)\n",
    "print('\\n')\n",
    "print(sum(relative_freq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.to_csv(\"output.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n",
      "<ipython-input-9-899234a629f8>:7: MatplotlibDeprecationWarning: \n",
      "The createFontList function was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use FontManager.addfont instead.\n",
      "  font_list = fm.createFontList(font_files)\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(40,20))\n",
    "prop = fm.FontProperties(fname='kalpurush.ttf')\n",
    "ax.legend(prop=prop)\n",
    "\n",
    "font_dirs = ['./', ]\n",
    "font_files = fm.findSystemFonts(fontpaths=font_dirs)\n",
    "font_list = fm.createFontList(font_files)\n",
    "fm.fontManager.ttflist.extend(font_list)\n",
    "\n",
    "plt.rcParams['font.family'] = 'kalpurush'\n",
    "#plt.rcParams[\"font.size\"] = \"50\"\n",
    "plt.tick_params(labelsize=36)\n",
    "\n",
    "keys = freq.keys()\n",
    "values = freq.values()\n",
    "\n",
    "#alpha_seq = 'ৎংঃঁািীুূৃেৈোৌ'\n",
    "alpha_seq = 'অআইঈউঊঋএঐওঔকখগঘঙচছজঝঞটঠডঢণতথদধনপফবভমযরলশষসহড়ঢ়য়ৎংঃঁািীুূৃেৈোৌ'\n",
    "alpha_seq =\"\".join(alpha_seq.split()) \n",
    "\n",
    "sorted_values = []\n",
    "total = 0\n",
    "\n",
    "for alpha in alpha_seq:\n",
    "    count = freq[alpha] if alpha in freq else 0\n",
    "    sorted_values.append(count)\n",
    "    total += count\n",
    "    \n",
    "sorted_values = [100*(value/total) for value in sorted_values]\n",
    "\n",
    "plt.bar(range(len(alpha_seq)), sorted_values, align='center')\n",
    "plt.xticks(np.arange(len(alpha_seq)), list(alpha_seq), color='black')\n",
    "\n",
    "\n",
    "plt.xlabel('Letters', fontsize=36)\n",
    "plt.ylabel('Percentage (%)', fontsize=36)\n",
    "\n",
    "plt.savefig(\"figure.png\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}