Project file loading.ipynb

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     "text": [
      "Toggle navigation      / / Asset Publisher 18 November 2019 2364-18-11-2019 Mr Makei, Mr Rapota, Colleagues, friends, Welcome to a joint meeting of the collegiums of the foreign ministries of Russia and the Republic of Belarus. This meeting is taking place in the year that marks the 20  anniversary of the Treaty on the Creation of a Union State of Russia and Belarus. Twenty years ago, guided by the fundamental interests of the two peoples, our leaders decided to take Russian-Belarusian relations to a new level. Experience has shown that it was the right decision. Today, Russia and Belarus are close allies and strategic partners. Along with the EAEU, the Union State is at the forefront of integration processes unfolding within our common space. The key goals of the Union State include conducting coordinated foreign policy. Two-year programmes of concerted foreign policy actions of the states parties to the Treaty on the Creation of a Union State have been adopted by the Supreme State Council since 2000. They have proven themselves an effective tool for coordinating actions in the international arena and developing common approaches to global and regional issues. Reviewing the draft Programme for 2020-2021 is one of the main items on our agenda today. Our countries support each other's initiatives at the UN, the OSCE and other international organisations. They co-sponsor draft resolutions and decisions, jointly uphold our common interests, oppose attempts to promote, at multilateral platforms, approaches that infringe on the legitimate rights and interests of Russia and Belarus and violate the fundamental principles of international law, primarily, the UN Charter, and European security commitments. In this context, we have agreed to consider the developments in the Euro-Atlantic space today. We are concerned about NATO's continuing policy of moving its military infrastructure closer to our borders and the intensified NATO military activity near our states. We proceed from the precept that these processes must be considered in the context of the OSCE countries' commitment not to strengthen their security at the expense of the security of other states. One more issue on the current agenda has to do with strategic stability, non-proliferation and arms control. The processes underway in these areas have a direct impact on the national interests of Russia and Belarus. We are worried about the deepening crisis in arms control. I am referring to the US decision to erode the Intermediate-Range Nuclear Forces Treaty (INF Treaty) after withdrawing from the ABM Treaty. As you know, Russia's proposals designed to ensure the viability of this treaty have been rejected. And now the future of the New START Treaty has been put in question as well. In addition, the United States has refused to ratify the Comprehensive Nuclear-Test-Ban Treaty (CTBT), added to its doctrines the possibility of lowering the nuclear threshold and plans to deploy offensive weapons in outer space. It should be said that our Western colleagues' activities are undermining the fundamental principles of the Chemical Weapons Convention (CWC), which is deepening the crisis in the non-proliferation of weapons of mass destruction. In this context, we should strengthen our interaction across the entire range of these problems in the interests of preventing negative trends in the field of strategic stability. We have agreed to discuss all these subjects today as well. Another item on this meeting's agenda concerns the use by a number of our Western colleagues, primarily the US and the EU, of illegitimate tools such as unilateral sanctions in violation of the UN Charter. This practice seriously undermines global trust and understanding. I would like to emphasise that, even as Russia and Belarus discuss at international platforms these sensitive problems and negative phenomena, which cause serious tension in multilateral formats and in bilateral relations with third countries, they always focus on possible constructive solutions rather than on negative scenarios, on solutions that would help us reverse these processes, which have a very negative effect on the state of affairs in our common region and in international affairs in general. I shall note that our colleagues who drafted the decision for today's meeting were relying on this position and have formulated approaches that will help us promote a unifying rather than a disconnecting agenda. This is our common interest in relation to the processes taking place in the European region. I am confident that today's meeting will take place in a friendly atmosphere, and we will discuss our objectives substantively in the field of concerted foreign policy efforts and reach agreements that will strengthen our cooperation and alliance in international affairs.                  Advanced settings         Switcher      6 Photos close\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "with open(\"lavrov list of dicts.json\") as f:\n",
    "    jsonString = f.read()\n",
    "    jsonData = json.loads(jsonString)\n",
    "    print(jsonData[0]['ArticleText'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "                                               Title articleID  \\\n",
       "0  Foreign Minister Sergey Lavrov's remarks at a ...   3904674   \n",
       "1  Foreign Minister Sergey Lavrov's opening remar...   3906668   \n",
       "2  Remarks and answers to media questions by Fore...   3904895   \n",
       "3  Excerpts from Foreign Minister Sergey Lavrov's...   3925748   \n",
       "4  Foreign Minister Sergey Lavrov's remarks and a...   3911585   \n",
       "5  Foreign Minister Sergey Lavrov's answers to me...   3866037   \n",
       "6  Foreign Minister Sergey Lavrov's remarks and a...   3870486   \n",
       "7  Foreign Minister Sergey Lavrov's remarks and a...   3826083   \n",
       "8  Foreign Minister Sergey Lavrov's interview wit...   3852496   \n",
       "9  Press release on Foreign Minister Sergey Lavro...   3810872   \n",
       "\n",
       "                                         article_URL  \\\n",
       "0  https://www.mid.ru/en/press_service/minister_s...   \n",
       "1  https://www.mid.ru/en/press_service/minister_s...   \n",
       "2  https://www.mid.ru/en/press_service/minister_s...   \n",
       "3  https://www.mid.ru/en/press_service/minister_s...   \n",
       "4  https://www.mid.ru/en/press_service/minister_s...   \n",
       "5  https://www.mid.ru/en/press_service/minister_s...   \n",
       "6  https://www.mid.ru/en/press_service/minister_s...   \n",
       "7  https://www.mid.ru/en/press_service/minister_s...   \n",
       "8  https://www.mid.ru/en/press_service/minister_s...   \n",
       "9  https://www.mid.ru/en/press_service/minister_s...   \n",
       "\n",
       "                                         ArticleText  Article_length_words  \\\n",
       "0  Toggle navigation      / / Asset Publisher 18 ...                   838   \n",
       "1  Toggle navigation      / / Asset Publisher 20 ...                   208   \n",
       "2  Toggle navigation      / / Asset Publisher 18 ...                  2170   \n",
       "3  Toggle navigation      / / Asset Publisher 30 ...                   256   \n",
       "4  Toggle navigation      / / Asset Publisher 23 ...                  1465   \n",
       "5  Toggle navigation      / / Asset Publisher 25 ...                    34   \n",
       "6  Toggle navigation      / / Asset Publisher 30 ...                  1229   \n",
       "7  Toggle navigation      / / Asset Publisher 2 O...                 12242   \n",
       "8  Toggle navigation      / / Asset Publisher 14 ...                  1287   \n",
       "9  Toggle navigation      / / Asset Publisher 26 ...                   214   \n",
       "\n",
       "   Title_length_words  \n",
       "0                  35  \n",
       "1                  27  \n",
       "2                  42  \n",
       "3                  36  \n",
       "4                  27  \n",
       "5                  21  \n",
       "6                  41  \n",
       "7                  33  \n",
       "8                  23  \n",
       "9                  16  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "\n",
    "lavrov_df = pd.DataFrame(jsonData)\n",
    "article_words = []\n",
    "article_length_words_list = []\n",
    "title_words = []\n",
    "title_length_words_list = []\n",
    "\n",
    "lavrov_df = pd.DataFrame(lavrov_df)\n",
    "\n",
    "\n",
    "for index, row in lavrov_df.iterrows():\n",
    "    article_words = []\n",
    "    sentences = sent_tokenize(lavrov_df.ArticleText[index])\n",
    "    title_words = []\n",
    "    title_sentences = sent_tokenize(lavrov_df.Title[index])\n",
    "    for sent in sentences:\n",
    "        words_list = word_tokenize(sent)\n",
    "        article_words.extend(words_list)\n",
    "    article_length_words = (len(article_words))\n",
    "    article_length_words_list.append(article_length_words)\n",
    "    for sent in title_sentences:\n",
    "        words_list = word_tokenize(sent)\n",
    "        title_words.extend(words_list)\n",
    "    title_length_words = (len(title_words))\n",
    "    title_length_words_list.append(title_length_words)\n",
    "    \n",
    "lavrov_df['Article_length_words'] = (article_length_words_list)\n",
    "lavrov_df['Title_length_words'] = (title_length_words_list)\n",
    "\n",
    "\n",
    "lavrov_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports list is using pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "import numpy as np\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.parsing.preprocessing import STOPWORDS\n",
    "from nltk.stem.porter import * #nltk to do the stemming\n",
    "np.random.seed(1234)\n",
    "\n",
    "\n",
    "stemmer = PorterStemmer()\n",
    "def preprocess(text):\n",
    "    result = []\n",
    "    #for each word in text, if the word is stopword, remove it, otherwise lower\n",
    "    #for just lowercasing or tokenization\n",
    "    for token in gensim.utils.simple_preprocess(text):\n",
    "        #list of stopwords will give full list, also one in nltk\n",
    "        if token not in gensim.parsing.preprocessing.STOPWORDS:\n",
    "            result.append(stemmer.stem(token))\n",
    "    return result\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 48.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "processed_articles = []\n",
    "for article in range(len(jsonData)):\n",
    "    processed_articles.append(preprocess(jsonData[article]['ArticleText']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This cell is meant for loading all imports required and the pickled data\n",
    "#pickled data includes:\n",
    "#lavrov_df - the lavrov Speeches dataframe\n",
    "#processed_articles - the speech article text processed\n",
    "\n",
    "##RUNNING THE PICKLE\n",
    "#make sure to run the initial code above to produce a pickle-able object\n",
    "#then run the dump to save the pickle to a file on your disc space.\n",
    "#this saves the pickle to the same folder as the jupiter notebook under the name lavrov_dataframe\n",
    "#alternatively, you casn grab the latest pickles from the github. Just save it as a normal file (do not change the extension)\n",
    "\n",
    "##WHEN TO SAVE THE PICKLE\n",
    "#Make sure if you make changes to any of the corresponding file, you save the pickle for others to use. \n",
    "#Also make sure any commits changing the pickle are noted in github desktop for the push\n",
    "\n",
    "#---------uncomment these imports if you are pickling\n",
    "#TO COMMENT ALL LINES AT ONCE, HIGHLIGHT THE LINES YOU WANT UNCOMMENTED AND HIT CNTL + /\n",
    "# import gensim\n",
    "# import numpy as np\n",
    "# from gensim.utils import simple_preprocess\n",
    "# from gensim.parsing.preprocessing import STOPWORDS\n",
    "# from nltk.stem.porter import * #nltk to do the stemming\n",
    "# np.random.seed(1234)\n",
    "# import pandas as pd\n",
    "# import nltk\n",
    "# from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "# import pickle\n",
    "\n",
    "\n",
    "# #----------Saving the Pickle-------\n",
    "# lavrov_df.to_pickle('lavrov_dataframe.pickle')\n",
    "# pickle.dump(processed_articles, open( \"processed_articles.p\", \"wb\" ) )\n",
    "\n",
    "# #---------loading the pickle-------\n",
    "# lavrov_df = pd.read_pickle('lavrov_dataframe.pickle')\n",
    "# processed_articles = pickle.load(open( \"processed_articles.p\", \"rb\" ) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary = gensim.corpora.Dictionary(processed_articles)\n",
    "dictionary.filter_extremes(no_below=15, no_above=0.1, keep_n=100000)\n",
    "bow_corpus = [dictionary.doc2bow(article) for article in processed_articles]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Logging to to help with convergence\n",
    "\n",
    "import logging\n",
    "#logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)\n",
    "logging.basicConfig(level=logging.WARNING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 42.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lda_model = gensim.models.LdaMulticore(bow_corpus, num_topics = 8, id2word = dictionary,\n",
    "                                        workers=3,\n",
    "                                      passes=10, iterations=100, eval_every=10\n",
    "                                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic: 0 \n",
      "Words: 0.010*\"israel\" + 0.009*\"armenia\" + 0.008*\"egypt\" + 0.008*\"azerbaijan\" + 0.008*\"islam\" + 0.007*\"quartet\" + 0.007*\"iraqi\" + 0.007*\"saudi\" + 0.006*\"leagu\" + 0.006*\"palestin\"\n",
      "\n",
      "\n",
      "Topic: 1 \n",
      "Words: 0.005*\"mgimo\" + 0.005*\"kazakhstan\" + 0.004*\"caspian\" + 0.004*\"ngo\" + 0.004*\"unesco\" + 0.004*\"competit\" + 0.003*\"ideolog\" + 0.003*\"commonwealth\" + 0.003*\"student\" + 0.003*\"ga\"\n",
      "\n",
      "\n",
      "Topic: 2 \n",
      "Words: 0.008*\"kiev\" + 0.006*\"african\" + 0.005*\"german\" + 0.005*\"crimea\" + 0.004*\"trump\" + 0.004*\"isi\" + 0.004*\"nusra\" + 0.004*\"ceasefir\" + 0.004*\"donbass\" + 0.004*\"jabhat\"\n",
      "\n",
      "\n",
      "Topic: 3 \n",
      "Words: 0.015*\"chemic\" + 0.007*\"investig\" + 0.006*\"opcw\" + 0.006*\"communiqu\" + 0.006*\"assad\" + 0.006*\"unsc\" + 0.005*\"john\" + 0.005*\"kerri\" + 0.005*\"annan\" + 0.004*\"usa\"\n",
      "\n",
      "\n",
      "Topic: 4 \n",
      "Words: 0.017*\"afghan\" + 0.011*\"chines\" + 0.011*\"india\" + 0.010*\"korean\" + 0.008*\"black\" + 0.007*\"bric\" + 0.007*\"korea\" + 0.007*\"peninsula\" + 0.006*\"vietnam\" + 0.006*\"bsec\"\n",
      "\n",
      "\n",
      "Topic: 5 \n",
      "Words: 0.022*\"ossetia\" + 0.019*\"georgian\" + 0.019*\"abkhazia\" + 0.019*\"georgia\" + 0.018*\"arctic\" + 0.011*\"latin\" + 0.008*\"peacekeep\" + 0.007*\"america\" + 0.007*\"saakashvili\" + 0.006*\"northern\"\n",
      "\n",
      "\n",
      "Topic: 6 \n",
      "Words: 0.011*\"kosovo\" + 0.010*\"defens\" + 0.006*\"serbia\" + 0.006*\"iaea\" + 0.005*\"quartet\" + 0.004*\"balkan\" + 0.003*\"serbian\" + 0.003*\"belgrad\" + 0.003*\"examin\" + 0.003*\"offens\"\n",
      "\n",
      "\n",
      "Topic: 7 \n",
      "Words: 0.017*\"asean\" + 0.011*\"japan\" + 0.009*\"csto\" + 0.009*\"compatriot\" + 0.008*\"japanes\" + 0.008*\"belaru\" + 0.006*\"church\" + 0.006*\"kyrgyzstan\" + 0.005*\"orthodox\" + 0.005*\"eaeu\"\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for idx, topic in lda_model.print_topics(-1):\n",
    "    print(\"Topic: {} \\nWords: {}\".format(idx, topic ))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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\"norwegian\", \"nusra\", \"nusra\", \"nusra\", \"nusra\", \"nusra\", \"nusra\", \"nusra\", \"nusra\", \"obama\", \"obama\", \"obama\", \"obama\", \"obama\", \"obama\", \"obama\", \"obama\", \"odessa\", \"odessa\", \"odessa\", \"offens\", \"offens\", \"offens\", \"offens\", \"offens\", \"offens\", \"offens\", \"offens\", \"oic\", \"oic\", \"oic\", \"oic\", \"oic\", \"oic\", \"oil\", \"oil\", \"oil\", \"oil\", \"oil\", \"oil\", \"oil\", \"oil\", \"opcw\", \"opcw\", \"opcw\", \"opcw\", \"opcw\", \"opcw\", \"opcw\", \"opcw\", \"opium\", \"opium\", \"opium\", \"opium\", \"opium\", \"orthodox\", \"orthodox\", \"orthodox\", \"orthodox\", \"orthodox\", \"orthodox\", \"orthodox\", \"ossetia\", \"ossetia\", \"ossetia\", \"ossetia\", \"ossetia\", \"ossetia\", \"ossetia\", \"ossetia\", \"ossetian\", \"ossetian\", \"ossetian\", \"ossetian\", \"ossetian\", \"ossetian\", \"pakistan\", \"pakistan\", \"pakistan\", \"pakistan\", \"pakistan\", \"pakistan\", \"pakistan\", \"pakistan\", \"pakistani\", \"pakistani\", \"pakistani\", \"pakistani\", \"pakistani\", \"palestin\", \"palestin\", \"palestin\", \"palestin\", \"palestin\", \"palestin\", \"palestin\", \"palestin\", \"patriarch\", \"patriarch\", \"patriarch\", \"patriarch\", \"patriarch\", \"patriarch\", \"peacekeep\", \"peacekeep\", \"peacekeep\", \"peacekeep\", \"peacekeep\", \"peacekeep\", \"peacekeep\", \"peacekeep\", \"peninsula\", \"peninsula\", \"peninsula\", \"peninsula\", \"peninsula\", \"peninsula\", \"peninsula\", \"peninsula\", \"petr\", \"petr\", \"petr\", \"petro\", \"petro\", \"petro\", \"petro\", \"petro\", \"philippin\", \"philippin\", \"philippin\", \"pilgrim\", \"pilgrim\", \"pilgrim\", \"pirat\", \"pirat\", \"pirat\", \"pirat\", \"pirat\", \"plenipotentiari\", \"plenipotentiari\", \"plenipotentiari\", \"plenipotentiari\", \"plo\", \"plo\", \"poison\", \"poison\", \"poison\", \"poison\", \"poison\", \"poison\", \"poland\", \"poland\", \"poland\", \"poland\", \"poland\", \"poland\", \"poland\", \"poland\", \"polish\", \"polish\", \"polish\", \"polish\", \"polish\", \"polish\", \"polish\", \"polish\", \"pollut\", \"pollut\", \"pollut\", \"pollut\", \"pollut\", \"pollut\", \"poroshenko\", \"poroshenko\", \"poroshenko\", \"poroshenko\", \"poroshenko\", \"poroshenko\", \"poroshenko\", \"poroshenko\", \"postwar\", \"postwar\", \"postwar\", \"postwar\", \"postwar\", \"poverti\", \"poverti\", \"poverti\", \"poverti\", \"poverti\", \"poverti\", \"poverti\", \"prc\", \"prc\", \"prc\", \"prc\", \"prc\", \"prc\", \"prc\", \"prc\", \"precis\", \"precis\", \"precis\", \"precis\", \"precis\", \"precis\", \"precis\", \"precis\", \"precondit\", \"precondit\", \"precondit\", \"precondit\", \"precondit\", \"precondit\", \"precondit\", \"precondit\", \"precursor\", \"precursor\", \"precursor\", \"precursor\", \"pretenc\", \"pretenc\", \"pretenc\", \"pretenc\", \"primakov\", \"primakov\", \"primakov\", \"primakov\", \"primakov\", \"primakov\", \"primakov\", \"pristina\", \"pristina\", \"pristina\", \"pristina\", \"pristina\", \"pristina\", \"pristina\", \"pristina\", \"prohibit\", \"prohibit\", \"prohibit\", \"prohibit\", \"prohibit\", \"prohibit\", \"prohibit\", \"prohibit\", \"proposit\", \"proposit\", \"proposit\", \"proposit\", \"proposit\", \"proposit\", \"proposit\", \"proposit\", \"pyongyang\", \"pyongyang\", \"pyongyang\", \"pyongyang\", \"pyongyang\", \"pyongyang\", \"pyongyang\", \"pyongyang\", \"quartet\", \"quartet\", \"quartet\", \"quartet\", \"quartet\", \"quartet\", \"quartet\", \"quartet\", \"rada\", \"rada\", \"rada\", \"rada\", \"rada\", \"rada\", \"ratif\", \"ratif\", \"ratif\", \"ratif\", \"ratif\", \"ratif\", \"ratif\", \"ratif\", \"realis\", \"realis\", \"realis\", \"realis\", \"realis\", \"realis\", \"realis\", \"realis\", \"recogn\", \"recogn\", \"recogn\", \"recogn\", \"recogn\", \"recogn\", \"recogn\", \"recogn\", \"recognis\", \"recognis\", \"recognis\", \"recognis\", \"recognis\", \"recognis\", \"recognis\", \"recognis\", \"referendum\", \"referendum\", \"referendum\", \"referendum\", \"referendum\", \"referendum\", \"referendum\", \"referendum\", \"refuge\", \"refuge\", \"refuge\", \"refuge\", \"refuge\", \"refuge\", \"refuge\", \"refuge\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"remembr\", \"remembr\", \"remembr\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"ric\", \"ric\", \"ric\", \"ric\", \"ric\", \"ric\", \"rice\", \"rice\", \"rice\", \"rice\", \"rice\", \"rice\", \"rice\", \"rice\", \"rich\", \"rich\", \"rich\", \"rich\", \"rich\", \"rich\", \"rich\", \"rich\", \"rio\", \"rio\", \"rio\", \"rnc\", \"rnc\", \"rnc\", \"rnc\", \"rnc\", \"rnc\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"romanian\", \"romanian\", \"romanian\", \"romanian\", \"romanian\", \"ru\", \"ru\", \"ru\", \"ru\", \"ru\", \"ru\", \"ru\", \"ru\", \"russophob\", \"russophob\", \"russophob\", \"russophob\", \"russophob\", \"russophob\", \"saakashvili\", \"saakashvili\", \"saakashvili\", \"saakashvili\", \"saakashvili\", \"saakashvili\", \"saakashvili\", \"sampl\", \"sampl\", \"sampl\", \"sampl\", \"sampl\", \"sar\", \"sar\", \"sar\", \"sar\", \"sar\", \"sar\", \"sar\", \"sargsyan\", \"sargsyan\", \"sarin\", \"sarin\", \"sarkozi\", \"sarkozi\", \"sarkozi\", \"sarkozi\", \"saud\", \"saud\", \"saud\", \"saud\", \"saudi\", \"saudi\", \"saudi\", \"saudi\", \"saudi\", \"saudi\", \"saudi\", \"saudi\", \"school\", \"school\", \"school\", \"school\", \"school\", \"school\", \"school\", \"school\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"scienc\", \"self\", \"self\", \"self\", \"self\", \"self\", \"self\", \"self\", \"self\", \"selfless\", \"selfless\", \"serb\", \"serb\", \"serb\", \"serb\", \"serb\", \"serb\", \"serb\", \"serb\", \"serbia\", \"serbia\", \"serbia\", \"serbia\", \"serbia\", \"serbia\", \"serbia\", \"serbia\", \"serbian\", \"serbian\", \"serbian\", \"serbian\", \"serbian\", \"serbian\", \"serbian\", \"serzh\", \"serzh\", \"shanghai\", \"shanghai\", \"shanghai\", \"shanghai\", \"shanghai\", \"shanghai\", \"shanghai\", \"shanghai\", \"sharm\", \"sharm\", \"sheikh\", \"sheikh\", \"sheikh\", \"sheikh\", \"sheikh\", \"shelf\", \"shelf\", \"shelf\", \"shelf\", \"shelf\", \"shelf\", \"shinzo\", \"shinzo\", \"shinzo\", \"shinzo\", \"sidelin\", \"sidelin\", \"sidelin\", \"sidelin\", \"sidelin\", \"sidelin\", \"sidelin\", \"sidelin\", \"slovenia\", \"slovenia\", \"slovenia\", \"slovenia\", \"slovenian\", \"slovenian\", \"slovenian\", \"slovenian\", \"smm\", \"sochi\", \"sochi\", \"sochi\", \"sochi\", \"sochi\", \"sochi\", \"sochi\", \"sochi\", \"spiritu\", \"spiritu\", \"spiritu\", \"spiritu\", \"spiritu\", \"spiritu\", \"spiritu\", \"spiritu\", \"sri\", \"sri\", \"sri\", \"sri\", \"stanitsa\", \"stereotyp\", \"stereotyp\", \"stereotyp\", \"stereotyp\", \"stereotyp\", \"stereotyp\", \"stereotyp\", \"stereotyp\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"substanc\", \"substanc\", \"substanc\", \"substanc\", \"substanc\", \"substanc\", \"substanc\", \"substanc\", \"sudan\", \"sudan\", \"sudan\", \"sudan\", \"sudan\", \"sudan\", \"sudan\", \"sudan\", \"sudanes\", \"sudanes\", \"sudanes\", \"sudanes\", \"sukhum\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"tajik\", \"tajik\", \"tajik\", \"tajik\", \"tajikistan\", \"tajikistan\", \"tajikistan\", \"tajikistan\", \"tajikistan\", \"tajikistan\", \"tajikistan\", \"tajikistan\", \"taliban\", \"taliban\", \"taliban\", \"taliban\", \"taliban\", \"taliban\", \"taliban\", \"taliban\", \"tbilisi\", \"tbilisi\", \"tbilisi\", \"tbilisi\", \"tbilisi\", \"tbilisi\", \"tbilisi\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"tecton\", \"tecton\", \"tecton\", \"tillerson\", \"tillerson\", \"tillerson\", \"tillerson\", \"tillerson\", \"tillerson\", \"torkunov\", \"torkunov\", \"torkunov\", \"tourism\", \"tourism\", \"tourism\", \"tourism\", \"tourism\", \"tourism\", \"tourism\", \"tourism\", \"trilater\", \"trilater\", \"trilater\", \"trilater\", \"trilater\", \"trilater\", \"trilater\", \"trilater\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"trump\", \"truste\", \"truste\", \"truste\", \"tskhinval\", \"tskhinval\", \"tskhinval\", \"tskhinval\", \"tunisia\", \"tunisia\", \"tunisia\", \"tunisia\", \"tunisia\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turkmenistan\", \"turkmenistan\", \"turkmenistan\", \"turkmenistan\", \"turkmenistan\", \"ufa\", \"ufa\", \"ufa\", \"ufa\", \"ufa\", \"unesco\", \"unesco\", \"unesco\", \"unesco\", \"unesco\", \"unesco\", \"unesco\", \"unesco\", \"uniqu\", \"uniqu\", \"uniqu\", \"uniqu\", \"uniqu\", \"uniqu\", \"uniqu\", \"uniqu\", \"uniti\", \"uniti\", \"uniti\", \"uniti\", \"uniti\", \"uniti\", \"uniti\", \"uniti\", \"unsc\", \"unsc\", \"unsc\", \"unsc\", \"unsc\", \"unsc\", \"unsc\", \"unsc\", \"unscr\", \"unscr\", \"unscr\", \"usa\", \"usa\", \"usa\", \"usa\", \"usa\", \"usa\", \"usa\", \"usa\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuelan\", \"venezuelan\", \"venezuelan\", \"venezuelan\", \"venezuelan\", \"verkhovna\", \"verkhovna\", \"verkhovna\", \"verkhovna\", \"verkhovna\", \"verkhovna\", \"victori\", \"victori\", \"victori\", \"victori\", \"victori\", \"victori\", \"victori\", \"victori\", \"vietnam\", \"vietnam\", \"vietnam\", \"vietnam\", \"vietnam\", \"vietnam\", \"vietnam\", \"vietnam\", \"vietnames\", \"vietnames\", \"vii\", \"vii\", \"vii\", \"vii\", \"violenc\", \"violenc\", \"violenc\", \"violenc\", \"violenc\", \"violenc\", \"violenc\", \"violenc\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vucic\", \"vucic\", \"wang\", \"wang\", \"wang\", \"warhead\", \"warhead\", \"warhead\", \"warhead\", \"warhead\", \"warhead\", \"warhead\", \"will\", \"will\", \"will\", \"will\", \"will\", \"will\", \"will\", \"will\", \"www\", \"www\", \"www\", \"www\", \"www\", \"www\", \"xi\", \"xi\", \"xi\", \"xi\", \"xi\", \"xi\", \"yanukovych\", \"yanukovych\", \"yanukovych\", \"yanukovych\", \"yanukovych\", \"yanukovych\", \"yanukovych\", \"yanukovych\", \"yarosh\", \"yemen\", \"yemen\", \"yemen\", \"yemen\", \"yemen\", \"yemen\", \"yemen\", \"yemen\", \"yemeni\", \"yemeni\", \"yemeni\", \"yemeni\", \"yerevan\", \"yerevan\", \"yerevan\", \"yerevan\", \"yerevan\", \"yevgeni\", \"yevgeni\", \"yevgeni\", \"yevgeni\", \"yevgeni\", \"yevgeni\", \"yevgeni\", \"yi\", \"yi\", \"yi\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"youth\", \"youth\", \"youth\", \"youth\", \"youth\", \"youth\", \"youth\", \"youth\", \"zelenski\", \"zelenski\", \"zor\", \"zor\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 2, 7, 4, 1, 8, 6, 5]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el1203219817062676485288576002\", ldavis_el1203219817062676485288576002_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el1203219817062676485288576002\", ldavis_el1203219817062676485288576002_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el1203219817062676485288576002\", ldavis_el1203219817062676485288576002_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis\n",
    "import pyLDAvis.gensim\n",
    "for_viz = pyLDAvis.gensim.prepare(lda_model, bow_corpus, dictionary)\n",
    "pyLDAvis.display(for_viz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the Average length in words for all articles is 1332.0029019152641\n",
      "The median length in words for all articles is 831.0\n",
      "The maximum length in words over the whole dataset is 21847\n",
      "The minimum length in words over the whole dataset is 13\n",
      "the Average length in words for all articles is 30.719674985490425\n",
      "The median length in words for all articles is 30.0\n",
      "The maximum length in words over the whole dataset is 93\n",
      "The minimum length in words over the whole dataset is 7\n"
     ]
    }
   ],
   "source": [
    "print(\"the Average length in words for all articles is\", lavrov_df.Article_length_words.mean())\n",
    "print(\"The median length in words for all articles is\", lavrov_df.Article_length_words.median())\n",
    "print(\"The maximum length in words over the whole dataset is\", lavrov_df.Article_length_words.max())\n",
    "print(\"The minimum length in words over the whole dataset is\", lavrov_df.Article_length_words.min())\n",
    "print(\"the Average length in words for all articles is\", lavrov_df.Title_length_words.mean())\n",
    "print(\"The median length in words for all articles is\", lavrov_df.Title_length_words.median())\n",
    "print(\"The maximum length in words over the whole dataset is\", lavrov_df.Title_length_words.max())\n",
    "print(\"The minimum length in words over the whole dataset is\", lavrov_df.Title_length_words.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2aef97e3ba8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "lavrov_df.Article_length_words.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "301.0\n"
     ]
    },
    {
     "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": [
    "lavrov_df.Article_length_words.plot.density()\n",
    "print(lavrov_df.Article_length_words.quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD5CAYAAADFqlkBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAVy0lEQVR4nO3df5Dc9X3f8ef79KsCO0Iy+AYLYTmt2px6afhxAQpKKuUSGZw/IFM7Y7kOqntjQWwxTgO11V5niM2IIeDQCTilETnGUgxHqGNsSAhYle5MlAFbh0tA5tKi2lgcEsJGKsXGFnfSu3/s9+SVtNLtSd/T7krPx8zN7r73+/3uezWne+338/18vxuZiSTp9NbW6AYkSY1nGEiSDANJkmEgScIwkCQB0xvdwPE6++yzc+HChY1uQzrCj3/8Y84888xGtyHV9Mwzz/wwM885vN6yYbBw4UKGhoYa3YZ0hMHBQZYuXdroNqSaIuL7teoOE0mSDANJkmEgScIwkCRhGEiSMAyk0vT399PZ2Ul3dzednZ309/c3uiWpbi07tVRqJv39/fT29tLX18f+/fuZNm0aPT09AKxYsaLB3UkTc89AKsHatWvp6+tj2bJlTJ8+nWXLltHX18fatWsb3ZpUF8NAKsHw8DBLliw5pLZkyRKGh4cb1JE0OYaBVIKOjg62bNlySG3Lli10dHQ0qCNpcgwDqQS9vb309PQwMDDA2NgYAwMD9PT00Nvb2+jWpLp4AFkqwfhB4htuuIHh4WE6OjpYu3atB4/VMqJVvwO5q6srvVCdmpEXqlMzi4hnMrPr8LrDRJIkw0CSZBhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJoo4wiIgFETEQEcMR8Z2I+FRRnxcRGyPixeJ2blGPiLgrIrZHxHMRcVHVtlYWy78YESur6hdHxPPFOndFREzFm5Uk1VbPnsEYcGNmdgCXAZ+MiMXAGmBTZi4CNhWPAa4CFhU/q4B7oBIewM3ApcAlwM3jAVIss6pqvStP/K1Jkuo1YRhk5q7M/HZx/01gGJgPXA2sLxZbD1xT3L8a2JAVTwNnRcS5wPuBjZm5JzP3AhuBK4vnfi4zn8rKhZI2VG1LknQSTOqqpRGxELgQ+CbQnpm7oBIYEfHuYrH5wMtVq40UtWPVR2rUa73+Kip7ELS3tzM4ODiZ9qWT4kc/+pG/m2o5dYdBRLwD+Evg9zLz/x1jWL/WE3kc9SOLmeuAdVC5aqlXhlQz8qqlakV1zSaKiBlUguD+zPxKUd5dDPFQ3L5W1EeABVWrnwfsnKB+Xo26JOkkqWc2UQB9wHBm3ln11CPA+IyglcDXqurXFrOKLgPeKIaTngCWR8Tc4sDxcuCJ4rk3I+Ky4rWurdqWJOkkqGeY6Argd4DnI+LZovafgNuAhyKiB9gBfKh47jHgA8B24C3gYwCZuScibgG2Fst9LjP3FPd/F/giMBv4m+JHknSSTBgGmbmF2uP6AN01lk/gk0fZ1n3AfTXqQ0DnRL1IkqaGZyBLkgwDSZJhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIJWmv7+fzs5Ouru76ezspL+/v9EtSXWb3ugGpFNBf38/vb299PX1sX//fqZNm0ZPTw8AK1asaHB30sTcM5BKsHbtWvr6+li2bBnTp09n2bJl9PX1sXbt2ka3JtXFMJBKMDw8zJIlSw6pLVmyhOHh4QZ1JE2OYSCVoKOjgy1bthxS27JlCx0dHQ3qSJocw0AqQW9vLz09PQwMDDA2NsbAwAA9PT309vY2ujWpLh5AlkowfpD4hhtuYHh4mI6ODtauXevBY7WMyMxG93Bcurq6cmhoqNFtSEcYHBxk6dKljW5DqikinsnMrsPrDhNJkgwDSVIdYRAR90XEaxGxrar2BxHxSkQ8W/x8oOq5/xgR2yPif0XE+6vqVxa17RGxpqr+voj4ZkS8GBF/EREzy3yDkqSJ1bNn8EXgyhr1/5KZFxQ/jwFExGLgw8A/L9b5rxExLSKmAX8CXAUsBlYUywL8YbGtRcBeoOdE3pAkafImDIPMfBLYU+f2rgYezMx9mfk9YDtwSfGzPTO/m5lvAw8CV0dEAL8GfLlYfz1wzSTfgyTpBJ3I1NLVEXEtMATcmJl7gfnA01XLjBQ1gJcPq18KvAv4v5k5VmP5I0TEKmAVQHt7O4ODgyfQvlSuTZs28aUvfYkdO3Zw/vnn89GPfpTu7u5GtyXV5XjD4B7gFiCL2z8C/h0QNZZNau+B5DGWrykz1wHroDK11Ol7ahb9/f3cf//93HfffYdcqG7x4sWea6CWcFyziTJzd2buz8wDwL1UhoGg8sl+QdWi5wE7j1H/IXBWREw/rC61FC9Up1Z3XGEQEedWPfwtYHym0SPAhyNiVkS8D1gEfAvYCiwqZg7NpHKQ+ZGsnPE2AHywWH8l8LXj6UlqJC9Up1ZXz9TSfuAp4J9FxEhE9AC3R8TzEfEcsAz49wCZ+R3gIeAF4HHgk8UexBiwGngCGAYeKpYF+Azw+xGxncoxhL5S36F0EnihOrU6L0chleBoX27j9YnUbI52OQovVCeVwAvVqdW5ZyCVzAvVqZl5oTpJ0lEZBlJJ+vv76ezspLu7m87OTvr7+xvdklQ3jxlIJTjaAWTA4wZqCe4ZSCXwpDO1OsNAKoEnnanVGQZSCTzpTK3OMJBK0NvbS09PDwMDA4yNjTEwMEBPTw+9vb2Nbk2qiweQpRJ40planXsGkiT3DKQyOLVUrc7LUUgl6Ozs5JprruGrX/3qwWGi8cfbtm2beAPSSeKF6qQp9MILL/DWW28dsWfw0ksvNbo1qS4eM5BKMHPmTFavXn3ISWerV69m5syZjW5Nqot7BlIJ3n77be6++24uvPBC9u/fz8DAAHfffTdvv/12o1uT6mIYSCVYvHgxs2fPpru7m8wkIrj44os588wzG92aVBeHiaQSzJ8/n6GhISICgIhgaGiI+fPnN7gzqT6GgVSCTZs2ERGcc845h9xu2rSp0a1JdTEMpBLs37+fW2+9lVdffZXNmzfz6quvcuutt7J///5GtybVxTCQJHkAWSpDW1sbvb29zJw5k8WLF3PnnXfS29tLW5uft9QaDAOpBJ/4xCf4whe+wI033nhIffXq1Q3qSJocP7ZIJbj88suZPXv2IbXZs2dz+eWXN6gjaXK8NpFUggULFjA2NsYDDzxw8HIUH/nIR5g+fTovv/xyo9uTDjratYncM5BKMDIywoYNGw65HMWGDRsYGRlpdGtSXQwDqSSbN2+ms7OT7u5uOjs72bx5c6NbkurmAWSpBPPmzeOOO+7g9ttvZ/Hixbzwwgt8+tOfZt68eY1uTaqLYSCV4IwzzuCnP/0pa9asYXR0lBkzZjBr1izOOOOMRrcm1cVhIqkEr7zyCgcOHGB0dBSA0dFRDhw4wCuvvNLgzqT6GAZSCSKCffv20d7eDkB7ezv79u07eOE6qdk5TCSV4MCBA0AlFNra2ogIMpNWnbqt0497BlJJZsyYweuvv86BAwd4/fXXmTFjRqNbkurmnoFUkrGxMT7/+c8fnE100003NbolqW6GgVSi22+/nd27dx88diC1CoeJpJJMnz6d3bt3A7B7926mT/ezllrHhGEQEfdFxGsRsa2qNi8iNkbEi8Xt3KIeEXFXRGyPiOci4qKqdVYWy78YESur6hdHxPPFOneF0y/UgubNm8fo6CjTpk0DYNq0aYyOjnrSmVpGPXsGXwSuPKy2BtiUmYuATcVjgKuARcXPKuAeqIQHcDNwKXAJcPN4gBTLrKpa7/DXklrG+Deb+Q1najUThkFmPgnsOax8NbC+uL8euKaqviErngbOiohzgfcDGzNzT2buBTYCVxbP/VxmPpWVOXgbqrYltYw9eyr/Rar3DKrrUrM73mMG7Zm5C6C4fXdRnw9UX693pKgdqz5Soy61nFmzZrFgwQLa2tpYsGABs2bNanRLUt3KPsJVa7w/j6Nee+MRq6gMKdHe3s7g4OBxtChNjX379rFjxw4OHDhw8Bbw91Qt4XjDYHdEnJuZu4qhnteK+giwoGq584CdRX3pYfXBon5ejeVrysx1wDqofLnN0qVLj7ao1DT8PVUrON5hokeA8RlBK4GvVdWvLWYVXQa8UQwjPQEsj4i5xYHj5cATxXNvRsRlxSyia6u2JbWcOXPm0NbWxpw5cxrdijQpE+4ZREQ/lU/1Z0fECJVZQbcBD0VED7AD+FCx+GPAB4DtwFvAxwAyc09E3AJsLZb7XGaOH1n7XSozlmYDf1P8SC0nIti7dy8Ae/fuPXh9IqkV+B3IUgnGT4+ZO3cue/fuPXgLGAhqKn4HsnQSVO8ZSK3EMJAkGQZSmdra2g65lVqFv7FSia677joeffRRrrvuuka3Ik2KB5ClEkQE73nPe9i1axeZSURw7rnnsnPnTg8gq6l4AFmaYjt37uT666/n0Ucf5frrr2fnzqOePyk1HfcMpBK0tbXV3AOIiIOXpZCagXsG0hQ62oeqVv2wpdOPYSBJMgykMo2fiewX9qnVGAZSicaHhRweUqsxDCRJhoEkyTCQJGEYSJIwDCRJGAaSJAwDSRKGgVQqTzpTqzIMpBJ50plalWEgSTIMJEmGgSQJw0CShGEgScIwkCRhGEiSMAykUs2YMeOQW6lVGAZSiUZHRw+5lVqFYSBJMgwkSYaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJnGAYRMRLEfF8RDwbEUNFbV5EbIyIF4vbuUU9IuKuiNgeEc9FxEVV21lZLP9iRKw8sbckSZqsMvYMlmXmBZnZVTxeA2zKzEXApuIxwFXAouJnFXAPVMIDuBm4FLgEuHk8QCRJJ8dUDBNdDawv7q8Hrqmqb8iKp4GzIuJc4P3Axszck5l7gY3AlVPQlyTpKKaf4PoJfD0iEvjTzFwHtGfmLoDM3BUR7y6WnQ+8XLXuSFE7Wv0IEbGKyl4F7e3tDA4OnmD70tTz91St4ETD4IrM3Fn8wd8YEf9wjGWjRi2PUT+yWAmbdQBdXV25dOnSSbYrnXz+nqoVnNAwUWbuLG5fAx6mMua/uxj+obh9rVh8BFhQtfp5wM5j1CVJJ8lxh0FEnBkR7xy/DywHtgGPAOMzglYCXyvuPwJcW8wqugx4oxhOegJYHhFziwPHy4uaJOkkOZFhonbg4YgY384Dmfl4RGwFHoqIHmAH8KFi+ceADwDbgbeAjwFk5p6IuAXYWiz3uczccwJ9SZImKTJrDs83va6urhwaGmp0GxIAxYeimlr1/5hOTRHxTNWpAAd5BrIkyTCQJBkGkiQMA0kShoEkiRM/A1k65R1rplCZ6zvrSI1kGEgTqOePtFNL1eocJpJKsHr16knVpWbjnoFUgrvvvhuAe++9l3379jFr1iw+/vGPH6xLzc4zkKWSLVzz17x02282ug2pJs9AliQdlWEgSTIMJEmGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnC7zPQaeaXPvt13vjJ6JS/zsI1fz2l258zewZ/f/PyKX0NnV4MA51W3vjJ6JR/18Dg4CBLly6d0teY6rDR6cdhIkmSYSBJMgwkSRgGkiQMA0kShoEkCaeW6jTzzo41/OL6NVP/QuundvPv7ACY2imyOr0YBjqtvDl8m+cZSDU4TCRJMgwkSU00TBQRVwJ/DEwD/iwzb2twSzpFnZQhlsen/tpEUpmaIgwiYhrwJ8BvACPA1oh4JDNfaGxnOtVM9fECqITNyXgdqUzNMkx0CbA9M7+bmW8DDwJXN7gnSTptNMWeATAfeLnq8Qhw6eELRcQqYBVAe3s7g4ODJ6U5nd6WLVs26XXiDyf/OgMDA5NfSSpJs4RB1KjlEYXMdcA6gK6urpzq6XsSQOYRv4rHdDKmlkpla5ZhohFgQdXj84CdDepFkk47zRIGW4FFEfG+iJgJfBh4pME9SdJpoymGiTJzLCJWA09QmVp6X2Z+p8FtSdJpoynCACAzHwMea3QfknQ6apZhIklSAxkGkiTDQJJkGEiSgJjsCTXNIiJ+AHy/0X1INZwN/LDRTUhH8d7MPOfwYsuGgdSsImIoM7sa3Yc0GQ4TSZIMA0mSYSBNhXWNbkCaLI8ZSJLcM5AkGQaSJAwDSRKGgZpIRPxWRGRE/MJRnj8rIj5R9fg9EfHlCbY5GBGTnvN/vOtNsM3D+18aEX9V5mscj4j4UaN7UOMZBmomK4AtVL7c6BARMQ04Czj4xzQzd2bmB09eeyfskP4bISKa5rL1ai6GgZpCRLwDuALooQiD4pPzQEQ8ADwP3Ab844h4NiLuiIiFEbGtWHZaRHw+Ip6PiOci4oYar7E8Ip6KiG9HxH8vXrOe3mquFxEvRcRni/rz43s0EXFORGws6n8aEd+PiLMP77/Y/Dsi4ssR8Q8RcX9E1Po+cCLikoj4SnH/6oj4SUTMjIh/FBHfLeoXRMTTxft/OCLmFvXBiLg1Ir4BfKr4RsGnImJrRNxS9RrnRsSTRX/bIuJX6vn30anBMFCzuAZ4PDP/N7AnIi4q6pcAvZm5GFgD/J/MvCAz/8Nh668C3gdcmJn/Ari/+snij/F/Bn49My8ChoDfn6ipOtb7YVG/B7ipqN0MbC7qDwPnF/Va/V8I/B6wGPh5KoFYy7eLZQF+BdgG/DJwKfDNor4B+Ezx/p8v+hh3Vmb+q8z8I+CPgXsy85eBV6uW+QjwRGZeAPwS8Owx/3F0SjEM1CxWAA8W9x8sHgN8KzO/V8f6vw78t8wcA8jMPYc9fxmVP7h/FxHPAiuB99ax3YnW+0px+wywsLi/ZPy9ZObjwN5jbP9bmTmSmQeo/PFdWGuh4n1tj4gOKgF5J/CrVILhbyNiDpU/+N8oVllfPD/uL6ruXwH0F/f/vKq+FfhYRPwB8IuZ+eYx+tYpxvFDNVxEvAv4NaAzIpLK92Anla9B/XG9mynWOdbzGzNzxTGWOZ719hW3+/nZ/6eaQz0TrH/4Nmr5W+AqYBT4H8AXqfxb3XSMdcYd/u94xL9VZj4ZEb8K/Cbw5xFxR2ZuqGPbOgW4Z6Bm8EFgQ2a+NzMXZuYC4HtUPmFXexN451G28XXg+vEDpBEx77DnnwauiIh/Ujx/RkT80zp6O571tgC/XSy/HJhbR//1eJLKkNJTmfkD4F3ALwDfycw3gL1V4/y/A3yj9mb4O352kP7fjBcj4r3Aa5l5L9AHXFRjXZ2iDAM1gxVUxtar/SWVMeyDMvN1KsM126oOwI77M2AH8FxE/H2NdX8A/FugPyKeo/JHvuYU1hLW+yywPCK+TeWT/C7gzQn6r8c3gXYqoQDwHPBc/uyaMiuBO4o+LwA+d5TtfAr4ZERsBeZU1ZcCz0bE/wT+NZVjCzpNeG0iqWQRMQvYn5ljEfEvqRysvaDRfUnH4jEDqXznAw9FRBvwNvDxBvcjTcg9A53WIuJhKlNSq30mM59oRD/QnD3p1GcYSJI8gCxJMgwkSRgGkiQMA0kS8P8B5aMDANyKFLYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lavrov_df.boxplot(column = \"Article_length_words\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a1363e4e88>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DPyIujoifRcSeiFjb6+NLUs16ep9+RMwB/gP4a2AEeCwiNmXmU73shzSbdfr8yZqlY1zbg8+l+L0/s1+vP5x1EbAnM58GiIh7gRWAoS/NAjV+ydzJ9pgjM0/IjjseLOJK4OLM/FQpfwJ4X2Z+elyb1cDqUnw78LOedXDmOAd4vt+dmIEcl8k5NpOrcWz+IjPf2mlFr6/0o0PdhFedzBwGhnvTnZkpIn6YmYP97sdM47hMzrGZnGMzUa/fyB0BFo4rLwD297gPklStXof+Y8CSiFgcEacAVwObetwHSapWT6d3MnMsIj4NPAzMAe7KzCd72YdZourpraNwXCbn2EzOsRmnp2/kSpL6y0/kSlJFDH1Jqoih30cRsTAitkXEroh4MiJuKPVnRcSWiNhdfp/Z7772S0TMiYjHI+I7pbw4IraXsbmv3BBQnYiYFxEbI+Kn5fx5v+cNRMTny3PpiYi4JyLe6DkzkaHfX2PAmsx8J7AMuD4iLgDWAlszcwmwtZRrdQOwa1z5FuC2MjYvAKv60qv+ux34bma+A3g37TGq+ryJiPnAZ4HBzHwX7ZtFrsZzZgJDv48y80Bm/qgs/4b2E3c+7a+m2FCabQCu6E8P+ysiFgCXAXeWcgAfBjaWJlWOTUS8GfgQsB4gM1/NzBfxvIH2HYmnRcRc4HTgAJ4zExj6M0RELALeA2wHBjLzALRfGIBz+9ezvvoa8AXg/0r5bODFzBwr5RHaL5K1OR94DvhGmfq6MyLOoPLzJjN/CXwFeIZ22L8E7MBzZgJDfwaIiDcB3wY+l5m/7nd/ZoKIuBw4mJk7xld3aFrjPcdzgQuBOzLzPcDLVDaV00l5D2MFsBj4M+AM4JIOTWs8Z/7I0O+ziHgD7cC/OzMfKNXPRsR5Zf15wMF+9a+PPgh8LCL2AvfS/hP9a8C88qc71Ps1HiPASGZuL+WNtF8Eaj9vPgL8IjOfy8zfAw8AH8BzZgJDv4/KHPV6YFdmfnXcqk3AyrK8Eniw133rt8y8KTMXZOYi2m/GPZKZ1wDbgCtLs1rH5lfAvoh4e6laTvvryWs/b54BlkXE6eW5dXhcqj9nxvMTuX0UEX8F/Bewk9fmrb9Ee17/fuDPaZ/IV2Xmob50cgaIiCHgnzLz8og4n/aV/1nA48DfZ+bv+tm/foiIv6T9BvcpwNPAdbQv4qo+byLiX4C/o31n3OPAp2jP4Vd/zhxm6EtSRZzekaSKGPqSVBFDX5IqYuhLUkUMfUmqiKEvSRUx9CWpIv8P6LeCn2W/nUAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "lavrov_df.Title_length_words.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.0\n"
     ]
    },
    {
     "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": [
    "lavrov_df.Title_length_words.plot.density()\n",
    "print(lavrov_df.Title_length_words.quantile(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}