PhanDuc · May 21, 2018 19:29
diff --git a/working_with_google_ngram.ipynb b/working_with_google_ngram.ipynb
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Try to work with Google Books Ngramm and evaluate the reliability of the data obtained (on your examples).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to track the rise of a word like \"telephone\" and its clipped form \"phone.\" But what if you're only interested in how \"telephone\" and \"phone\" developed as verbs? The graph indicates that \"telephone\" held strong as a verb for much of the 20th century but is now on its way out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.display import IFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"500\"\n",
       "            src=\"https://books.google.com/ngrams/interactive_chart?content=phone_VERB%2C+telephone_VERB&year_start=1880&year_end=2008&corpus=17&smoothing=3&share=&direct_url=t1%3B%2Cphone_VERB%3B%2Cc0%3B.t1%3B%2Ctelephone_VERB%3B%2Cc0\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x20552767470>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(\"https://books.google.com/ngrams/interactive_chart?\" \\\n",
    "       \"content=phone_VERB%2C+telephone_VERB&year_start=1880&year_end=2008\"\\\n",
    "       \"&corpus=17&smoothing=3&share=&direct_url=t1%3B%2Cphone_VERB%3B%2Cc0%3B.t1%3B%2Ctelephone_VERB%3B%2Cc0\",\n",
    "      width  = 1000,\n",
    "      height = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### How \"Star Was\" appearance in the time between 1880 to 2008"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"500\"\n",
       "            src=\"https://books.google.com/ngrams/interactive_chart?content=Star+Wars&year_start=1880&year_end=2008&corpus=15&smoothing=3&share=&direct_url=t1%3B%2CStar%20Wars%3B%2Cc0\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x20557432f28>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(\"https://books.google.com/ngrams/interactive_chart?\"\\\n",
    "       \"content=Star+Wars&year_start=1880&year_end=2008&\"\\\n",
    "       \"corpus=15&smoothing=3&share=&direct_url=t1%3B%2CStar%20Wars%3B%2Cc0\",\n",
    "       width=1000, height=500 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From wikipedia https://en.wikipedia.org/wiki/Star_Wars, series \"Star Wars\" first showed in 1977. From the graph, we can see before that time, there are no information about \"Star Wars\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The standard setting for smoothing is 3 which means that the value for a given year is the average of that year itself as well as the 3 preceeding and following years. The problem with this setting is that it makes rare terms, which may appear 100 times in 1 year and only 5 times in the following to be stable. \n",
    "\n",
    "Later, I changed smoothing from 3 to 0 and see how reliable findings are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"500\"\n",
       "            src=\"https://books.google.com/ngrams/interactive_chart?content=Star+Wars&year_start=1880&year_end=2008&corpus=15&smoothing=0&share=&direct_url=t1%3B%2CStar%20Wars%3B%2Cc0\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x20557432cf8>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(\"https://books.google.com/ngrams/interactive_chart?\"\\\n",
    "       \"content=Star+Wars&year_start=1880&year_end=2008&\"\\\n",
    "       \"corpus=15&smoothing=0&share=&direct_url=t1%3B%2CStar%20Wars%3B%2Cc0\",\n",
    "       width=1000, height=500 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seem showed the right values how people addicted with vodka."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm downloaded \"total_counts\" for English corpus to check the absolute number of that \"Star Wars\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_counts = pd.read_table(\"googlebooks-eng-all-totalcounts-20120701.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1505,32059,231,1</th>\n",
       "      <th>1507,49586,477,1</th>\n",
       "      <th>1515,289011,2197,1</th>\n",
       "      <th>1520,51783,223,1</th>\n",
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       "      <th>1541,5272,59,1</th>\n",
       "      <th>1563,213843,931,1</th>\n",
       "      <th>...</th>\n",
       "      <th>2000,11190986329,54799233,103405</th>\n",
       "      <th>2001,11349375656,55886251,104147</th>\n",
       "      <th>2002,12519922882,62335467,117207</th>\n",
       "      <th>2003,13632028136,68561620,127066</th>\n",
       "      <th>2004,14705541576,73346714,139616</th>\n",
       "      <th>2005,14425183957,72756812,138132</th>\n",
       "      <th>2006,15310495914,77883896,148342</th>\n",
       "      <th>2007,16206118071,82969746,155472</th>\n",
       "      <th>2008,19482936409,108811006,206272</th>\n",
       "      <th>Unnamed: 426</th>\n",
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       "  </tbody>\n",
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       "<p>0 rows × 427 columns</p>\n",
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       "Empty DataFrame\n",
       "Columns: [ , 1505,32059,231,1, 1507,49586,477,1, 1515,289011,2197,1, 1520,51783,223,1, 1524,287177,1275,1, 1525,3559,69,1, 1527,4375,39,1, 1541,5272,59,1, 1563,213843,931,1, 1564,70755,387,1, 1568,153095,1124,2, 1572,177484,797,1, 1574,62235,689,1, 1575,186706,1067,1, 1579,203074,1143,3, 1581,708458,2824,6, 1582,151000,537,1, 1584,151925,393,1, 1587,248361,762,2, 1588,41548,634,2, 1589,36290,238,2, 1590,564921,2260,2, 1592,96955,814,4, 1593,39997,328,2, 1594,11106,67,1, 1595,33664,347,3, 1597,10923,101,1, 1598,85051,768,2, 1600,405205,985,1, 1602,3292,47,1, 1603,69050,561,1, 1605,14493,131,1, 1606,62921,601,3, 1607,381763,1600,2, 1610,6258,75,1, 1611,49641,457,1, 1612,52898,593,1, 1614,8777,57,1, 1618,20166,147,1, 1619,55192,467,1, 1620,229054,2371,3, 1621,64197,679,3, 1623,120443,896,2, 1624,145470,899,3, 1625,69296,551,1, 1626,41890,259,1, 1628,6425,43,1, 1629,288773,1250,2, 1630,152568,1463,3, 1631,474458,1899,1, 1632,43064,299,1, 1634,141378,777,3, 1635,244673,1385,3, 1636,31714,252,2, 1637,681719,2315,3, 1638,243942,876,2, 1640,60550,425,3, 1641,45397,536,2, 1642,137346,769,3, 1643,177489,1238,6, 1644,1018174,4031,5, 1645,252714,1263,3, 1646,55522,253,1, 1647,312270,2015,5, 1648,458975,2306,4, 1649,260987,1796,4, 1650,192820,1161,7, 1651,540758,2221,3, 1652,168692,1023,3, 1653,379618,2677,7, 1654,36496,256,2, 1655,280899,1789,5, 1656,688699,3142,4, 1657,310453,2551,5, 1658,834659,4509,9, 1659,543657,2331,3, 1660,130457,1085,5, 1661,128825,931,3, 1662,239762,1471,3, 1663,208750,2021,5, 1664,290743,2670,6, 1665,269608,2689,11, 1666,81564,843,3, 1667,751217,3449,9, 1668,1065563,3920,6, 1669,342820,2276,4, 1670,734354,3127,5, 1671,149851,1276,4, 1672,425998,2665,5, 1673,935178,5517,11, 1674,126602,643,3, 1675,1644156,8918,14, 1676,1801615,8433,15, 1677,799238,5380,12, 1678,1966870,8516,18, 1679,1112022,6347,13, 1680,1099854,6122,22, 1681,2614565,11444,28, 1682,3667945,15570,30, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 427 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "total_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the reliability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the Absolute Count in 1988**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '1782,21505581,148858,256',\n",
       " '1783,21001833,154110,278',\n",
       " '1784,26735435,196374,310',\n",
       " '1785,26424206,195551,333',\n",
       " '1786,27701969,201168,328',\n",
       " '1787,41147754,274736,406',\n",
       " '1788,43010567,317558,476',\n",
       " '1789,37991018,274486,408',\n",
       " '1790,40363128,290254,448',\n",
       " '1791,44446487,303140,450',\n",
       " '1792,47305037,334531,525',\n",
       " '1793,41628412,306038,536',\n",
       " '1794,48633342,334985,503',\n",
       " '1795,46129522,306795,481',\n",
       " '1796,56007600,402660,600',\n",
       " '1797,47048067,327575,527',\n",
       " '1798,46311447,317118,520',\n",
       " '1799,50259992,358621,543',\n",
       " '1800,70784405,481221,669',\n",
       " '1801,107290136,720762,976',\n",
       " '1802,95731997,593319,843',\n",
       " '1803,104173226,703119,941',\n",
       " '1804,114051906,773467,1079',\n",
       " '1805,115330195,768720,1054',\n",
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       " '1807,128904931,843799,1139',\n",
       " '1808,129988114,825924,1172',\n",
       " '1809,137911980,849578,1188',\n",
       " '1810,150961261,942002,1280',\n",
       " '1811,177318465,1089707,1425',\n",
       " '1812,172538907,966207,1285',\n",
       " '1813,144660671,848854,1148',\n",
       " '1814,168441689,1005881,1325',\n",
       " '1815,156318674,940919,1281',\n",
       " '1816,161561836,993399,1375',\n",
       " '1817,182422107,1112404,1608',\n",
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       " '1820,231277724,1428596,1876',\n",
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       " '1822,271213007,1582135,2049',\n",
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       " '1824,309237910,1818566,2402',\n",
       " '1825,318701311,1931153,2571',\n",
       " '1826,243758959,1459702,2006',\n",
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       " '1832,314184783,1697641,2501',\n",
       " '1833,310441320,1768777,2655',\n",
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       " '1835,355491202,2000520,2946',\n",
       " '1836,365982104,2016239,2951',\n",
       " '1837,337485292,1897476,2642',\n",
       " '1838,358600155,1973223,2813',\n",
       " '1839,413876708,2268357,3195',\n",
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       " '1847,443868440,2210955,3291',\n",
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       " '1855,605758582,3126226,4404',\n",
       " '1856,652385453,3360386,4728',\n",
       " '1857,568489706,2971641,4319',\n",
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       " '1859,588343315,3047548,4572',\n",
       " '1860,607952196,3291751,4921',\n",
       " '1861,463190641,2457516,3664',\n",
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       " '1863,418297294,2280211,3527',\n",
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       " '1866,548257863,2970231,4373',\n",
       " '1867,518622969,2798144,4168',\n",
       " '1868,547590187,3004671,4509',\n",
       " '1869,558291347,3052571,4589',\n",
       " '1870,548870828,3010658,4588',\n",
       " '1871,560339562,3109850,4674',\n",
       " '1872,566620105,3133978,4768',\n",
       " '1873,583981485,3210707,4799',\n",
       " '1874,636667506,3496138,5190',\n",
       " '1875,643873731,3513955,5335',\n",
       " '1876,676820039,3717671,5691',\n",
       " '1877,667722549,3635691,5657',\n",
       " '1878,629401874,3475917,5521',\n",
       " '1879,654448581,3648960,5912',\n",
       " '1880,784223075,4339293,6659',\n",
       " '1881,789254798,4377740,6836',\n",
       " '1882,828502461,4594461,7295',\n",
       " '1883,930196929,5188267,8091',\n",
       " '1884,881638914,4821278,7906',\n",
       " '1885,857166435,4796652,7804',\n",
       " '1886,727723136,3978980,6198',\n",
       " '1887,801865869,4578817,7215',\n",
       " '1888,795886071,4489400,7054',\n",
       " '1889,763170247,4217872,6480',\n",
       " '1890,787152479,4446336,7006',\n",
       " '1891,849750639,4772590,7600',\n",
       " '1892,936056142,5340906,8320',\n",
       " '1893,915629979,5204954,8214',\n",
       " '1894,899615494,5190068,8132',\n",
       " '1895,984856075,5699486,9184',\n",
       " '1896,1050921103,6149427,9663',\n",
       " '1897,1031909734,6036650,9632',\n",
       " '1898,1109257706,6474893,10193',\n",
       " '1899,1232717908,7319283,11421',\n",
       " '1900,1341057959,7880706,12204',\n",
       " '1901,1285712637,7611053,11923',\n",
       " '1902,1311315033,7850395,12325',\n",
       " '1903,1266236889,7672684,12386',\n",
       " '1904,1405505328,8505994,13406',\n",
       " '1905,1351302005,7982387,12833',\n",
       " '1906,1397090480,8324581,13309',\n",
       " '1907,1409945274,8352873,13533',\n",
       " '1908,1417130893,8455420,13826',\n",
       " '1909,1283265090,7678880,12638',\n",
       " '1910,1354824248,8082350,13278',\n",
       " '1911,1350964981,8146435,13659',\n",
       " '1912,1431385638,8498210,14314',\n",
       " '1913,1356693322,8272376,14064',\n",
       " '1914,1324894757,8031654,13964',\n",
       " '1915,1211361619,7359683,13357',\n",
       " '1916,1175413415,7285233,13449',\n",
       " '1917,1183132092,7301665,13535',\n",
       " '1918,1039343103,6427497,12225',\n",
       " '1919,1136614538,6939246,12588',\n",
       " '1920,1388696469,8320305,14671',\n",
       " '1921,1216676110,7129055,12681',\n",
       " '1922,1413237707,8295471,14781',\n",
       " '1923,1151386048,6679296,11962',\n",
       " '1924,1069007206,6285325,11221',\n",
       " '1925,1113107246,6436655,11609',\n",
       " '1926,1053565430,6180969,11513',\n",
       " '1927,1216023821,6992594,12560',\n",
       " '1928,1212716430,6940650,12610',\n",
       " '1929,1153722574,6757530,12430',\n",
       " '1930,1244889331,7172751,13131',\n",
       " '1931,1183806248,6746535,12339',\n",
       " '1932,1057602772,5908248,10940',\n",
       " '1933,915956659,5193167,10129',\n",
       " '1934,1053600093,5813581,10781',\n",
       " '1935,1157109310,6383929,11543',\n",
       " '1936,1199843463,6704700,12168',\n",
       " '1937,1232280287,6867867,12393',\n",
       " '1938,1261812592,7006038,12494',\n",
       " '1939,1249209591,6860069,12255',\n",
       " '1940,1179404138,6458613,11539',\n",
       " '1941,1084154164,5943516,10956',\n",
       " '1942,1045379066,5652409,10561',\n",
       " '1943,890214397,4754157,9221',\n",
       " '1944,812192380,4254836,8696',\n",
       " '1945,926378706,4754610,9542',\n",
       " '1946,1203221497,6293844,12452',\n",
       " '1947,1385834769,7297313,14115',\n",
       " '1948,1486005621,7719563,14721',\n",
       " '1949,1641024100,8474538,15754',\n",
       " '1950,1644401950,8581523,15761',\n",
       " '1951,1603394676,8369856,15418',\n",
       " '1952,1621780754,8271139,15307',\n",
       " '1953,1590464886,8243557,15325',\n",
       " '1954,1662160145,8642537,16201',\n",
       " '1955,1751719755,9009566,16994',\n",
       " '1956,1817491821,9289947,17453',\n",
       " '1957,1952474329,10050283,18977',\n",
       " '1958,1976098333,10184584,19292',\n",
       " '1959,2064236476,10667039,20781',\n",
       " '1960,2341981521,12110214,24048',\n",
       " '1961,2567977722,13168876,25762',\n",
       " '1962,2818694749,14534596,27762',\n",
       " '1963,2955051696,15289261,29569',\n",
       " '1964,2931038992,15327267,30661',\n",
       " '1965,3300623502,16925833,32999',\n",
       " '1966,3466842517,17885635,35243',\n",
       " '1967,3658119990,18856794,37636',\n",
       " '1968,3968752101,20713781,40613',\n",
       " '1969,3942222509,20605052,40154',\n",
       " '1970,4086393350,21493334,42050',\n",
       " '1971,4058576649,21022316,41676',\n",
       " '1972,4174172415,21723303,43701',\n",
       " '1973,4058707895,20934291,42413',\n",
       " '1974,4045487401,20870625,42423',\n",
       " '1975,4104379941,21163884,43866',\n",
       " '1976,4242326406,21741811,44785',\n",
       " '1977,4314577619,22131803,45231',\n",
       " '1978,4365839878,22337808,45652',\n",
       " '1979,4528331460,23121674,47094',\n",
       " '1980,4611609946,23399729,47197',\n",
       " '1981,4627406112,23181513,46107',\n",
       " '1982,4839530894,24286876,48446',\n",
       " '1983,4982167985,24855807,49481',\n",
       " '1984,5309222580,26493896,52068',\n",
       " '1985,5475269397,27311038,53730',\n",
       " '1986,5793946882,28860058,56268',\n",
       " '1987,5936558026,29600208,57856',\n",
       " '1988,6191886939,30977704,60672',\n",
       " '1989,6549339038,32665219,64029',\n",
       " '1990,7075013106,35252588,69220',\n",
       " '1991,6895715366,34521903,68159',\n",
       " '1992,7596808027,37580665,72393',\n",
       " '1993,7492130348,37154768,71658',\n",
       " '1994,8027353540,39575664,76662',\n",
       " '1995,8276258599,40863936,77890',\n",
       " '1996,8745049453,42919779,82091',\n",
       " '1997,8979708108,43952838,84104',\n",
       " '1998,9406708249,45989297,87421',\n",
       " '1999,9997156197,48914071,91983',\n",
       " '2000,11190986329,54799233,103405',\n",
       " '2001,11349375656,55886251,104147',\n",
       " '2002,12519922882,62335467,117207',\n",
       " '2003,13632028136,68561620,127066',\n",
       " '2004,14705541576,73346714,139616',\n",
       " '2005,14425183957,72756812,138132',\n",
       " '2006,15310495914,77883896,148342',\n",
       " '2007,16206118071,82969746,155472',\n",
       " '2008,19482936409,108811006,206272',\n",
       " 'Unnamed: 426']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_counts.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1988,6191886939,30977704,60672',)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'1988,6191886939,30977704,60672',"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 1988, there are 6191886939 total words on 30977704 pages in 60672 books."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this, we can calculate how often \"Star wars\" appeared when it spiked to the highest relative frequeny in 1986: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9894"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(0.0001597920 * 6191886939 / 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In other words, In 1986 there were 473 appearances of \"Star Wars\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_absolute_counts(token, corpus='english', smoothing=0, start_year=1800, end_year=2008, log_scale=True):\n",
    "    '''\n",
    "    Some language can use\n",
    "    'english', 'american english', 'british english', 'english fiction'\n",
    "   'russian'\n",
    "    '''\n",
    "    # Load absolute counts of the totken\n",
    "    absolute_counts = retrieve_absolute_counts(token, corpus, smoothing, start_year, end_year)\n",
    "\n",
    "    years = range(start_year, start_year + len(absolute_counts))\n",
    "\n",
    "    plt.rcParams['figure.figsize'] = (15,8)\n",
    "    plt.rcParams['font.size'] = 10\n",
    "    ax= plt.axes()\n",
    "    if log_scale:\n",
    "        ax.set_yscale('log')\n",
    "    plt.plot(years, absolute_counts, label = '{}'.format(token))\n",
    "    title = 'Absolute Counts of \"{}\" in the \"{}\" corpus with smoothing={}.'.format(token, corpus,smoothing)\n",
    "    if log_scale:\n",
    "        title += ' Log Scale.'\n",
    "    plt.title(title)\n",
    "\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    ax.legend(handles, labels)\n",
    "\n",
    "    legend_title = ax.get_legend().get_title()\n",
    "    legend_title.set_fontsize(15)\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_absolute_counts(token, corpus='english', smoothing=0, start_year=1800, end_year=2000):\n",
    "    '''\n",
    "    Prints out the absolute counts (instead of plotting them)\n",
    "    '''\n",
    "    absolute_counts = retrieve_absolute_counts(token, corpus, smoothing, start_year, end_year)\n",
    "    print ('Absolute Counts for: {}'.format(token))\n",
    "    for i in range(len(absolute_counts)):\n",
    "        print ('{}: {}'.format(start_year + i, int(absolute_counts[i])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_total_counts(corpus_id, start_year, end_year):\n",
    "    '''\n",
    "    This function loads the total counts for a given corpus from Google's source data.\n",
    "    '''\n",
    "\n",
    "    # map from id to url\n",
    "    id_to_url= {\n",
    "    15: 'http://storage.googleapis.com/books/ngrams/books/googlebooks-eng-all-totalcounts-20120701.txt',\n",
    "    17: 'http://storage.googleapis.com/books/ngrams/books/googlebooks-eng-us-all-totalcounts-20120701.txt',\n",
    "    18: 'http://storage.googleapis.com/books/ngrams/books/googlebooks-eng-gb-all-totalcounts-20120701.txt',\n",
    "    16: 'http://storage.googleapis.com/books/ngrams/books/googlebooks-eng-fiction-all-totalcounts-20120701.txt',    \n",
    "    25: 'http://storage.googleapis.com/books/ngrams/books/googlebooks-rus-all-totalcounts-20120701.txt',\n",
    "    }\n",
    "    \n",
    "    response = requests.get(id_to_url[corpus_id]).text\n",
    "    total_counts = []\n",
    "    data = response.split(\"\\t\")\n",
    "    # first and last rows are empty, so remove that elements\n",
    "    data = data[1:len(data)-1]\n",
    "    for row in data:\n",
    "        # try...except to make sure we got no error\n",
    "        try:\n",
    "            year, word_count, _, _ = row.split(',')\n",
    "            if int(year) >= start_year and int(year) <= end_year:\n",
    "                total_counts.append(int(word_count))\n",
    "\n",
    "        except ValueError:\n",
    "            pass\n",
    "\n",
    "    return total_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_absolute_counts(token, corpus, smoothing, start_year, end_year):\n",
    "    # dictionary maps from corpus name to corpus id\n",
    "    corpora = {\n",
    "        'english' : 15,\n",
    "        'american english': 17,\n",
    "        'british english': 18,\n",
    "        'english fiction': 16,        \n",
    "        'russian': 25,        \n",
    "    }\n",
    "    corpus_id = corpora[corpus]\n",
    "    # load the frequency data\n",
    "    token = token.replace(' ', '+')\n",
    "    url = 'https://books.google.com/ngrams/interactive_chart?content={}&year_start={}&year_end={}' \\\n",
    "             '&corpus={}&smoothing={}'.format(token, start_year, end_year, corpus_id, smoothing)\n",
    "    # Load the data from the page\n",
    "    page = requests.get(url).text\n",
    "    start = page.find('var data = ')\n",
    "    end = page.find('];\\n', start)\n",
    "    \n",
    "    data = eval(page[start+12:end])\n",
    "    frequencies = data['timeseries']\n",
    "    \n",
    "    # load total number\n",
    "    total_counts = load_total_counts(corpus_id, start_year, end_year)\n",
    "    \n",
    "    # calculate the absolute number of appearances \n",
    "    # by multiplying the frequencies with the total number of tokens\n",
    "    absolute_counts = [round(frequencies[i] * total_counts[i]) for i in range(len(frequencies))]\n",
    "    return absolute_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_absolute_counts('Star Wars', 'english', smoothing=0, start_year=1800, end_year=2008)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let see in each years, how many times the words \"Star Wars\" had been appearanced**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Absolute Counts for: Star Wars\n",
      "1800: 0\n",
      "1801: 0\n",
      "1802: 0\n",
      "1803: 0\n",
      "1804: 0\n",
      "1805: 0\n",
      "1806: 0\n",
      "1807: 0\n",
      "1808: 0\n",
      "1809: 0\n",
      "1810: 0\n",
      "1811: 0\n",
      "1812: 0\n",
      "1813: 0\n",
      "1814: 0\n",
      "1815: 0\n",
      "1816: 0\n",
      "1817: 0\n",
      "1818: 0\n",
      "1819: 0\n",
      "1820: 0\n",
      "1821: 0\n",
      "1822: 0\n",
      "1823: 0\n",
      "1824: 0\n",
      "1825: 0\n",
      "1826: 0\n",
      "1827: 0\n",
      "1828: 0\n",
      "1829: 0\n",
      "1830: 0\n",
      "1831: 0\n",
      "1832: 0\n",
      "1833: 0\n",
      "1834: 0\n",
      "1835: 0\n",
      "1836: 0\n",
      "1837: 0\n",
      "1838: 0\n",
      "1839: 0\n",
      "1840: 0\n",
      "1841: 0\n",
      "1842: 0\n",
      "1843: 0\n",
      "1844: 0\n",
      "1845: 0\n",
      "1846: 0\n",
      "1847: 0\n",
      "1848: 0\n",
      "1849: 0\n",
      "1850: 0\n",
      "1851: 0\n",
      "1852: 0\n",
      "1853: 1\n",
      "1854: 0\n",
      "1855: 0\n",
      "1856: 0\n",
      "1857: 0\n",
      "1858: 0\n",
      "1859: 0\n",
      "1860: 0\n",
      "1861: 0\n",
      "1862: 0\n",
      "1863: 0\n",
      "1864: 0\n",
      "1865: 0\n",
      "1866: 0\n",
      "1867: 0\n",
      "1868: 0\n",
      "1869: 0\n",
      "1870: 0\n",
      "1871: 2\n",
      "1872: 0\n",
      "1873: 0\n",
      "1874: 0\n",
      "1875: 0\n",
      "1876: 0\n",
      "1877: 0\n",
      "1878: 0\n",
      "1879: 0\n",
      "1880: 1\n",
      "1881: 0\n",
      "1882: 0\n",
      "1883: 0\n",
      "1884: 0\n",
      "1885: 0\n",
      "1886: 0\n",
      "1887: 0\n",
      "1888: 0\n",
      "1889: 0\n",
      "1890: 0\n",
      "1891: 0\n",
      "1892: 0\n",
      "1893: 0\n",
      "1894: 0\n",
      "1895: 0\n",
      "1896: 0\n",
      "1897: 0\n",
      "1898: 2\n",
      "1899: 0\n",
      "1900: 0\n",
      "1901: 0\n",
      "1902: 0\n",
      "1903: 0\n",
      "1904: 0\n",
      "1905: 0\n",
      "1906: 0\n",
      "1907: 0\n",
      "1908: 0\n",
      "1909: 0\n",
      "1910: 0\n",
      "1911: 0\n",
      "1912: 0\n",
      "1913: 0\n",
      "1914: 0\n",
      "1915: 0\n",
      "1916: 0\n",
      "1917: 0\n",
      "1918: 0\n",
      "1919: 0\n",
      "1920: 0\n",
      "1921: 0\n",
      "1922: 0\n",
      "1923: 1\n",
      "1924: 0\n",
      "1925: 8\n",
      "1926: 0\n",
      "1927: 0\n",
      "1928: 0\n",
      "1929: 1\n",
      "1930: 2\n",
      "1931: 1\n",
      "1932: 0\n",
      "1933: 0\n",
      "1934: 0\n",
      "1935: 2\n",
      "1936: 0\n",
      "1937: 0\n",
      "1938: 0\n",
      "1939: 0\n",
      "1940: 0\n",
      "1941: 0\n",
      "1942: 0\n",
      "1943: 0\n",
      "1944: 0\n",
      "1945: 0\n",
      "1946: 3\n",
      "1947: 0\n",
      "1948: 0\n",
      "1949: 0\n",
      "1950: 1\n",
      "1951: 0\n",
      "1952: 0\n",
      "1953: 0\n",
      "1954: 2\n",
      "1955: 0\n",
      "1956: 0\n",
      "1957: 0\n",
      "1958: 0\n",
      "1959: 0\n",
      "1960: 1\n",
      "1961: 1\n",
      "1962: 2\n",
      "1963: 2\n",
      "1964: 1\n",
      "1965: 0\n",
      "1966: 6\n",
      "1967: 2\n",
      "1968: 1\n",
      "1969: 0\n",
      "1970: 13\n",
      "1971: 7\n",
      "1972: 10\n",
      "1973: 6\n",
      "1974: 14\n",
      "1975: 28\n",
      "1976: 37\n",
      "1977: 360\n",
      "1978: 717\n",
      "1979: 1308\n",
      "1980: 1707\n",
      "1981: 1706\n",
      "1982: 2078\n",
      "1983: 4300\n",
      "1984: 3432\n",
      "1985: 5937\n",
      "1986: 9456\n",
      "1987: 10851\n",
      "1988: 9894\n",
      "1989: 7881\n",
      "1990: 7483\n",
      "1991: 5880\n",
      "1992: 6698\n",
      "1993: 4961\n",
      "1994: 4935\n",
      "1995: 5756\n",
      "1996: 6233\n",
      "1997: 8019\n",
      "1998: 8456\n",
      "1999: 14481\n",
      "2000: 13662\n",
      "2001: 11094\n",
      "2002: 14459\n",
      "2003: 14025\n",
      "2004: 15768\n",
      "2005: 14908\n",
      "2006: 17430\n",
      "2007: 15723\n",
      "2008: 15201\n"
     ]
    }
   ],
   "source": [
    "print_absolute_counts('Star Wars', 'english', smoothing=0, start_year=1800, end_year=2008)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** With tested in the both ways, I can see we got the exactly result**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some test with Russian language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_absolute_counts('Война и Мир', 'russian', smoothing=0, start_year=1800, end_year=2008)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Absolute Counts for: Война и Мир\n",
      "1800: 0\n",
      "1801: 0\n",
      "1802: 0\n",
      "1803: 0\n",
      "1804: 0\n",
      "1805: 0\n",
      "1806: 0\n",
      "1807: 0\n",
      "1808: 0\n",
      "1809: 0\n",
      "1810: 0\n",
      "1811: 0\n",
      "1812: 0\n",
      "1813: 0\n",
      "1814: 0\n",
      "1815: 0\n",
      "1816: 0\n",
      "1817: 0\n",
      "1818: 0\n",
      "1819: 0\n",
      "1820: 0\n",
      "1821: 0\n",
      "1822: 0\n",
      "1823: 0\n",
      "1824: 0\n",
      "1825: 0\n",
      "1826: 0\n",
      "1827: 0\n",
      "1828: 0\n",
      "1829: 0\n",
      "1830: 0\n",
      "1831: 0\n",
      "1832: 0\n",
      "1833: 0\n",
      "1834: 0\n",
      "1835: 0\n",
      "1836: 0\n",
      "1837: 0\n",
      "1838: 0\n",
      "1839: 0\n",
      "1840: 0\n",
      "1841: 0\n",
      "1842: 0\n",
      "1843: 0\n",
      "1844: 0\n",
      "1845: 0\n",
      "1846: 0\n",
      "1847: 0\n",
      "1848: 0\n",
      "1849: 0\n",
      "1850: 0\n",
      "1851: 0\n",
      "1852: 0\n",
      "1853: 0\n",
      "1854: 0\n",
      "1855: 0\n",
      "1856: 0\n",
      "1857: 0\n",
      "1858: 0\n",
      "1859: 0\n",
      "1860: 0\n",
      "1861: 0\n",
      "1862: 0\n",
      "1863: 0\n",
      "1864: 0\n",
      "1865: 0\n",
      "1866: 0\n",
      "1867: 0\n",
      "1868: 0\n",
      "1869: 0\n",
      "1870: 0\n",
      "1871: 0\n",
      "1872: 0\n",
      "1873: 0\n",
      "1874: 0\n",
      "1875: 0\n",
      "1876: 0\n",
      "1877: 0\n",
      "1878: 0\n",
      "1879: 0\n",
      "1880: 0\n",
      "1881: 0\n",
      "1882: 0\n",
      "1883: 0\n",
      "1884: 0\n",
      "1885: 0\n",
      "1886: 0\n",
      "1887: 0\n",
      "1888: 0\n",
      "1889: 0\n",
      "1890: 0\n",
      "1891: 0\n",
      "1892: 0\n",
      "1893: 0\n",
      "1894: 0\n",
      "1895: 0\n",
      "1896: 0\n",
      "1897: 0\n",
      "1898: 0\n",
      "1899: 0\n",
      "1900: 0\n",
      "1901: 3\n",
      "1902: 1\n",
      "1903: 0\n",
      "1904: 0\n",
      "1905: 0\n",
      "1906: 1\n",
      "1907: 0\n",
      "1908: 0\n",
      "1909: 0\n",
      "1910: 5\n",
      "1911: 0\n",
      "1912: 0\n",
      "1913: 0\n",
      "1914: 0\n",
      "1915: 0\n",
      "1916: 0\n",
      "1917: 0\n",
      "1918: 0\n",
      "1919: 12\n",
      "1920: 1\n",
      "1921: 5\n",
      "1922: 28\n",
      "1923: 93\n",
      "1924: 87\n",
      "1925: 96\n",
      "1926: 52\n",
      "1927: 72\n",
      "1928: 110\n",
      "1929: 45\n",
      "1930: 18\n",
      "1931: 14\n",
      "1932: 13\n",
      "1933: 9\n",
      "1934: 5\n",
      "1935: 12\n",
      "1936: 13\n",
      "1937: 21\n",
      "1938: 39\n",
      "1939: 0\n",
      "1940: 20\n",
      "1941: 7\n",
      "1942: 0\n",
      "1943: 6\n",
      "1944: 2\n",
      "1945: 7\n",
      "1946: 38\n",
      "1947: 14\n",
      "1948: 14\n",
      "1949: 14\n",
      "1950: 30\n",
      "1951: 10\n",
      "1952: 16\n",
      "1953: 35\n",
      "1954: 13\n",
      "1955: 62\n",
      "1956: 19\n",
      "1957: 14\n",
      "1958: 34\n",
      "1959: 22\n",
      "1960: 30\n",
      "1961: 21\n",
      "1962: 18\n",
      "1963: 21\n",
      "1964: 34\n",
      "1965: 12\n",
      "1966: 70\n",
      "1967: 8\n",
      "1968: 9\n",
      "1969: 30\n",
      "1970: 44\n",
      "1971: 9\n",
      "1972: 11\n",
      "1973: 14\n",
      "1974: 21\n",
      "1975: 17\n",
      "1976: 14\n",
      "1977: 10\n",
      "1978: 26\n",
      "1979: 10\n",
      "1980: 5\n",
      "1981: 10\n",
      "1982: 16\n",
      "1983: 17\n",
      "1984: 6\n",
      "1985: 6\n",
      "1986: 7\n",
      "1987: 11\n",
      "1988: 8\n",
      "1989: 3\n",
      "1990: 27\n",
      "1991: 22\n",
      "1992: 21\n",
      "1993: 21\n",
      "1994: 14\n",
      "1995: 14\n",
      "1996: 35\n",
      "1997: 28\n",
      "1998: 16\n",
      "1999: 51\n",
      "2000: 68\n",
      "2001: 50\n",
      "2002: 46\n",
      "2003: 30\n",
      "2004: 43\n",
      "2005: 34\n",
      "2006: 29\n",
      "2007: 40\n",
      "2008: 18\n"
     ]
    }
   ],
   "source": [
    "print_absolute_counts('Война и Мир', 'russian', smoothing=0, start_year=1800, end_year=2008)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some Problem With Google NGRAM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Overabundance of Scientific Literature**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Google Book’s English language corpus is a mishmash of fiction, nonfiction, reports, proceedings, and, as Dodds’ paper seems to show, a whole lot of scientific literature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"500\"\n",
       "            src=\"https://books.google.com/ngrams/interactive_chart?content=Figure%2C+figure&year_start=1880&year_end=2008&corpus=17&smoothing=3&share=&direct_url=t1%3B%2CFigure%3B%2Cc0%3B.t1%3B%2Cfigure%3B%2Cc0\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
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       "<IPython.lib.display.IFrame at 0x20557e22748>"
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     "execution_count": 185,
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   "source": [
    "IFrame(\"https://books.google.com/ngrams/interactive_chart?content=Figure%2C+figure&year_start=1880&year_end=2008&corpus=17&smoothing=3&share=&direct_url=t1%3B%2CFigure%3B%2Cc0%3B.t1%3B%2Cfigure%3B%2Cc0\", \n",
    "       width=800, \n",
    "       height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Old data, bad OCR**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In older books the medial-s (∫)\n",
    "is often incorrectly recognized as an ‘f’ by the OCR software"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"500\"\n",
       "            src=\"https://books.google.com/ngrams/interactive_chart?content=beft&year_start=1880&year_end=2008&corpus=15&smoothing=3&share=&direct_url=t1%3B%2Cbeft%3B%2Cc0\"\n",
       "            frameborder=\"0\"\n",
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       "        ></iframe>\n",
       "        "
      ],
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       "<IPython.lib.display.IFrame at 0x2055616bdd8>"
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     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "IFrame(\"https://books.google.com/ngrams/interactive_chart?content=beft&year_start=1880&year_end=2008&corpus=15&smoothing=3&share=&direct_url=t1%3B%2Cbeft%3B%2Cc0\", \n",
    "       width=800, \n",
    "       height=500)"
   ]
  }
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