word_frequency_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\jinwoo\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import re\n",
    "import codecs\n",
    "import pathlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize\n",
    "from nltk.tokenize import word_tokenize\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import datetime\n",
    "from scipy.spatial.distance import cosine\n",
    "\n",
    "nltk.download('punkt')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import data\n",
    "\n",
    "### Please change the directories based on your local machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Please change directories based on your local machine\n",
    "base_dir = os.path.join(os.getcwd(),'data')\n",
    "preprocessed_dir = os.path.join(base_dir,'preprocessed','lemmatized')\n",
    "\n",
    "lexicon_dir = os.path.join(base_dir,'lexicon')\n",
    "conflict = pd.read_excel(os.path.join(lexicon_dir,'enh_conflict_lexicon.xlsx'))\n",
    "peace = pd.read_excel(os.path.join(lexicon_dir,'enh_peace_lexicon.xlsx'))\n",
    "resilence = pd.read_excel(os.path.join(lexicon_dir,'enh_resilience_lexicon.xlsx'))\n",
    "\n",
    "\n",
    "#Please designate the save directory of csv and image file\n",
    "output_csv_dir = os.path.join(base_dir,'preprocessed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jinwoo\\0.JupyterNotebook\\1. Captsone Project\\2. Matt Github\\data\\preprocessed\\lemmatized\\HK\n"
     ]
    }
   ],
   "source": [
    "country_dir = os.path.join(preprocessed_dir,'HK')\n",
    "print(country_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read Files from local machine\n",
    "start_time = datetime.datetime.now()\n",
    "df_dict_country = {\"id\": [], \"title\": [], \"publisher\": [], \"article_text\":[],\"url\":[], \"path\":[]}\n",
    "for entry_publisher in os.scandir(country_dir):\n",
    "    for entry_year in os.scandir(entry_publisher.path):\n",
    "        for entry_final in os.scandir(entry_year.path):\n",
    "            try:\n",
    "                with open(entry_final, \"r\") as f:\n",
    "                    article_id = f.readline().strip()\n",
    "                    article_title = f.readline().strip()\n",
    "                    publisher = f.readline().strip()\n",
    "                    url = f.readline()\n",
    "                    f.readline()\n",
    "                    f.readline()\n",
    "                    article_text = f.readline().strip()\n",
    "                    df_dict_country['url'].append(url)\n",
    "                    df_dict_country['id'].append(article_id)\n",
    "                    df_dict_country['title'].append(article_title)\n",
    "                    df_dict_country['publisher'].append(publisher)\n",
    "                    df_dict_country['article_text'].append(article_text)\n",
    "                    df_dict_country['path'].append(entry_final.name)\n",
    "            except:\n",
    "                pass\n",
    "end_time = datetime.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(microseconds=357073)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#time taken for reading one country\n",
    "end_time - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dict_country = pd.DataFrame.from_dict(df_dict_country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>publisher</th>\n",
       "      <th>article_text</th>\n",
       "      <th>url</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13517304</td>\n",
       "      <td>Toyota Motor Thailand Revises Thailand Car Sal...</td>\n",
       "      <td>ACN Newswire (press release)</td>\n",
       "      <td>bangkok july jcn newswire toyota motor thailan...</td>\n",
       "      <td>http://en.acnnewswire.com/press-release/englis...</td>\n",
       "      <td>13517304_HK_26-07-16.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13642498</td>\n",
       "      <td>Fujitsu Creates One of Europe's Biggest Servic...</td>\n",
       "      <td>ACN Newswire (press release)</td>\n",
       "      <td>public cloud platform continue new growth area...</td>\n",
       "      <td>http://en.acnnewswire.com/press-release/englis...</td>\n",
       "      <td>13642498_HK_02-08-16.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14204012</td>\n",
       "      <td>Mars, Incorporated announced as Spikes Asia Ad...</td>\n",
       "      <td>ACN Newswire (press release)</td>\n",
       "      <td>singapore sept acn newswire spike asia today a...</td>\n",
       "      <td>http://en.acnnewswire.com/press-release/englis...</td>\n",
       "      <td>14204012_HK_05-09-16.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14270310</td>\n",
       "      <td>IBDExpo 2016 to Accelerate Indonesia's Busines...</td>\n",
       "      <td>ACN Newswire (press release)</td>\n",
       "      <td>ibdexpo international trade exhibition design ...</td>\n",
       "      <td>http://en.acnnewswire.com/press-release/englis...</td>\n",
       "      <td>14270310_HK_08-09-16.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14354286</td>\n",
       "      <td>NEC Demonstrates Transport Technology and Solu...</td>\n",
       "      <td>ACN Newswire (press release)</td>\n",
       "      <td>tokyo sept jcn newswire nec corporation tse to...</td>\n",
       "      <td>http://en.acnnewswire.com/press-release/englis...</td>\n",
       "      <td>14354286_HK_14-09-16.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5065</th>\n",
       "      <td>10305109</td>\n",
       "      <td>Step Aside, IKEA: China's Stock-Trading Halls ...</td>\n",
       "      <td>Wall Street Journal</td>\n",
       "      <td>nvestors playng card shangha stock tradng hall...</td>\n",
       "      <td>http://blogs.wsj.com/chinarealtime/2016/02/16/...</td>\n",
       "      <td>10305109_HK_15-02-16.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5066</th>\n",
       "      <td>7837939</td>\n",
       "      <td>Once Bitten, Twice Shy: Zhou Xiaochuan Didn't ...</td>\n",
       "      <td>Wall Street Journal</td>\n",
       "      <td>zhou xaochuan governor people 's bank chna lst...</td>\n",
       "      <td>http://blogs.wsj.com/chinarealtime/2016/03/22/...</td>\n",
       "      <td>7837939_HK_22-03-16.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5067</th>\n",
       "      <td>8212433</td>\n",
       "      <td>Field of Dreams: China Aims to Become 'Top-Qua...</td>\n",
       "      <td>Wall Street Journal</td>\n",
       "      <td>n th march fle photo student take part n socce...</td>\n",
       "      <td>http://blogs.wsj.com/chinarealtime/2016/04/12/...</td>\n",
       "      <td>8212433_HK_12-04-16.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5068</th>\n",
       "      <td>8709245</td>\n",
       "      <td>Maoist Overtones in Beijing Concert Raise Red ...</td>\n",
       "      <td>Wall Street Journal</td>\n",
       "      <td>recent concert featurng maost red song stag n ...</td>\n",
       "      <td>http://blogs.wsj.com/chinarealtime/2016/05/08/...</td>\n",
       "      <td>8709245_HK_08-05-16.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5069</th>\n",
       "      <td>8730828</td>\n",
       "      <td>Window Plummets from 76th Floor of Shanghai To...</td>\n",
       "      <td>Wall Street Journal</td>\n",
       "      <td>shangha tower n cty 's fnancal dstrct pudong f...</td>\n",
       "      <td>http://blogs.wsj.com/chinarealtime/2016/05/09/...</td>\n",
       "      <td>8730828_HK_09-05-16.txt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5070 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                                              title  \\\n",
       "0     13517304  Toyota Motor Thailand Revises Thailand Car Sal...   \n",
       "1     13642498  Fujitsu Creates One of Europe's Biggest Servic...   \n",
       "2     14204012  Mars, Incorporated announced as Spikes Asia Ad...   \n",
       "3     14270310  IBDExpo 2016 to Accelerate Indonesia's Busines...   \n",
       "4     14354286  NEC Demonstrates Transport Technology and Solu...   \n",
       "...        ...                                                ...   \n",
       "5065  10305109  Step Aside, IKEA: China's Stock-Trading Halls ...   \n",
       "5066   7837939  Once Bitten, Twice Shy: Zhou Xiaochuan Didn't ...   \n",
       "5067   8212433  Field of Dreams: China Aims to Become 'Top-Qua...   \n",
       "5068   8709245  Maoist Overtones in Beijing Concert Raise Red ...   \n",
       "5069   8730828  Window Plummets from 76th Floor of Shanghai To...   \n",
       "\n",
       "                         publisher  \\\n",
       "0     ACN Newswire (press release)   \n",
       "1     ACN Newswire (press release)   \n",
       "2     ACN Newswire (press release)   \n",
       "3     ACN Newswire (press release)   \n",
       "4     ACN Newswire (press release)   \n",
       "...                            ...   \n",
       "5065           Wall Street Journal   \n",
       "5066           Wall Street Journal   \n",
       "5067           Wall Street Journal   \n",
       "5068           Wall Street Journal   \n",
       "5069           Wall Street Journal   \n",
       "\n",
       "                                           article_text  \\\n",
       "0     bangkok july jcn newswire toyota motor thailan...   \n",
       "1     public cloud platform continue new growth area...   \n",
       "2     singapore sept acn newswire spike asia today a...   \n",
       "3     ibdexpo international trade exhibition design ...   \n",
       "4     tokyo sept jcn newswire nec corporation tse to...   \n",
       "...                                                 ...   \n",
       "5065  nvestors playng card shangha stock tradng hall...   \n",
       "5066  zhou xaochuan governor people 's bank chna lst...   \n",
       "5067  n th march fle photo student take part n socce...   \n",
       "5068  recent concert featurng maost red song stag n ...   \n",
       "5069  shangha tower n cty 's fnancal dstrct pudong f...   \n",
       "\n",
       "                                                    url  \\\n",
       "0     http://en.acnnewswire.com/press-release/englis...   \n",
       "1     http://en.acnnewswire.com/press-release/englis...   \n",
       "2     http://en.acnnewswire.com/press-release/englis...   \n",
       "3     http://en.acnnewswire.com/press-release/englis...   \n",
       "4     http://en.acnnewswire.com/press-release/englis...   \n",
       "...                                                 ...   \n",
       "5065  http://blogs.wsj.com/chinarealtime/2016/02/16/...   \n",
       "5066  http://blogs.wsj.com/chinarealtime/2016/03/22/...   \n",
       "5067  http://blogs.wsj.com/chinarealtime/2016/04/12/...   \n",
       "5068  http://blogs.wsj.com/chinarealtime/2016/05/08/...   \n",
       "5069  http://blogs.wsj.com/chinarealtime/2016/05/09/...   \n",
       "\n",
       "                          path  \n",
       "0     13517304_HK_26-07-16.txt  \n",
       "1     13642498_HK_02-08-16.txt  \n",
       "2     14204012_HK_05-09-16.txt  \n",
       "3     14270310_HK_08-09-16.txt  \n",
       "4     14354286_HK_14-09-16.txt  \n",
       "...                        ...  \n",
       "5065  10305109_HK_15-02-16.txt  \n",
       "5066   7837939_HK_22-03-16.txt  \n",
       "5067   8212433_HK_12-04-16.txt  \n",
       "5068   8709245_HK_08-05-16.txt  \n",
       "5069   8730828_HK_09-05-16.txt  \n",
       "\n",
       "[5070 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dict_country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Frequency Analysis with visualization\n",
    "\n",
    "create_vocab_dict: creates the vocab dictionary based on the lexicons from *peace*, *conflict*, *resilence*; have only counted lexicons less than 5 words (4-gram max)\n",
    "\n",
    "word_freq: uses Countvectorizer to creates the dictionary with word counts {str(word):int(count)}\n",
    "\n",
    "plot_n_most_common_words: using the dictionary with word counts, output the graph with given integer n."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "conflict['label'] = 'conflict'\n",
    "peace['label'] = 'peace'\n",
    "resilence['label'] = 'resilence'\n",
    "conflict = conflict.loc[:,['Term', 'label']]\n",
    "peace['Term'] = peace['academic freedom']\n",
    "peace = peace.loc[:,['Term', 'label']]\n",
    "resilence = resilence.loc[:,['Term', 'label']]\n",
    "\n",
    "vocab_df = conflict.copy()\n",
    "vocab_df = vocab_df.append(peace)\n",
    "vocab_df = vocab_df.append(resilence)\n",
    "vocab_df.index = np.arange(0, len(vocab_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dictionary for the vocab in CountVectorizer\n",
    "def create_vocab_dict(vocab_df):\n",
    "    vocabs = vocab_df.Term.str.lower()\n",
    "    vocabs = list(set(vocabs))\n",
    "    vocab_dictionary = {}\n",
    "    idx = 0\n",
    "    for vocab in vocabs:\n",
    "        voc_len = len(word_tokenize(vocab))\n",
    "        if voc_len < 5:\n",
    "            vocab_dictionary[vocab] = idx\n",
    "            idx += 1\n",
    "    return vocab_dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using Countvectorizer to count all the n-grams\n",
    "def word_freq(df, vocab_dictionary):\n",
    "    count_vectorizer = CountVectorizer(stop_words='english', ngram_range=(1,4), vocabulary=vocab_dictionary)\n",
    "    count_data = count_vectorizer.fit_transform(df.article_text)\n",
    "    words = count_vectorizer.get_feature_names()\n",
    "    total_counts = np.zeros(len(words))\n",
    "    for t in count_data:\n",
    "        total_counts += t.toarray()[0]\n",
    "  \n",
    "    count_dict = (zip(words, total_counts))\n",
    "    count_dict = sorted(count_dict, key = lambda x:x[1], reverse = True)\n",
    "\n",
    "    return count_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_n_most_common_words(count_dict, n):\n",
    "    count_dict = sorted(count_dict, key=lambda x:x[1], reverse=True)[0:n]\n",
    "    words = [w[0] for w in count_dict]\n",
    "    counts = [w[1] for w in count_dict]\n",
    "    x_pos = np.arange(len(words)) \n",
    "    plt.figure(2, figsize=(15, 15/1.6180))\n",
    "    plt.subplot(title= str(n) + ' most common words')\n",
    "    sns.set_context(\"notebook\", font_scale=1.25, rc={\"lines.linewidth\": 2.5})\n",
    "    sns.barplot(x_pos, counts, palette='husl')\n",
    "    plt.xticks(x_pos, words, rotation=90) \n",
    "    plt.xlabel('words')\n",
    "    plt.ylabel('counts')\n",
    "    plt.show()\n",
    "    plt.savefig('elbow_method.jpg', bbox_inches='tight', dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Word Freq\n",
    "\n",
    "If you have used different columns for cleaned_text, please add another column or change the column name to text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_dictionary = create_vocab_dict(vocab_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dict_country['text'] = df_dict_country['article_text']\n",
    "start_time = datetime.datetime.now()\n",
    "count_dict= word_freq(df_dict_country, vocab_dictionary)\n",
    "end_time = datetime.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(seconds=4, microseconds=26849)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Time taken for creating count_dict\n",
    "end_time - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jinwoo\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x667.491 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_n_most_common_words(count_dict, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export\n",
    "\n",
    "\n",
    "#### The country_directory should be in the form of .../COUNTRY_CODE_IN_TWO_ALPHABET.\n",
    "One can change the country_name part (hard-code) in the below funciton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#expects the country_directory to be written as .../COUNTRY_CODE\n",
    "country_name =  country_dir[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_countdict_df(count_dict, vocab_df, save_dir, country_name):\n",
    "    words = [w[0] for w in count_dict]\n",
    "    counts = [w[1] for w in count_dict]\n",
    "    \n",
    "    df_count_dict = pd.DataFrame(words,columns = ['Term'])\n",
    "    df_count_dict['count'] = counts\n",
    "    df_count_dict['nation'] = country_name\n",
    "    \n",
    "    vocab_df['Term_lower'] = vocab_df.Term.str.lower()\n",
    "    label_dictionary_lower = dict(zip(vocab_df['Term_lower'], vocab_df['label']))\n",
    "    og_term_dictionary = dict(zip(vocab_df['Term_lower'], vocab_df['Term']))\n",
    "    \n",
    "    df_count_dict['label'] = ''\n",
    "    df_count_dict['Original_Term'] = ''\n",
    "    for i in range(len(df_count_dict)):\n",
    "        df_count_dict['label'][i] = label_dictionary_lower[df_count_dict['Term'][i]]\n",
    "        df_count_dict['Original_Term'][i] = og_term_dictionary[df_count_dict['Term'][i]]\n",
    "    \n",
    "    return df_count_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_plot(count_dict, n, save_dir, country_name):\n",
    "    \n",
    "    img_dir = os.path.join(save_dir, country_name + '.jpg')\n",
    "    \n",
    "    count_dict = sorted(count_dict, key=lambda x:x[1], reverse=True)[0:n]\n",
    "    words = [w[0] for w in count_dict]\n",
    "    counts = [w[1] for w in count_dict]\n",
    "    x_pos = np.arange(len(words)) \n",
    "    plt.figure(2, figsize=(15, 15/1.6180))\n",
    "    plt.subplot(title= str(n) + ' most common words')\n",
    "    sns.set_context(\"notebook\", font_scale=1.25, rc={\"lines.linewidth\": 2.5})\n",
    "    sns.barplot(x_pos, counts, palette='husl')\n",
    "    plt.xticks(x_pos, words, rotation=90) \n",
    "    plt.xlabel('words')\n",
    "    plt.ylabel('counts')\n",
    "    \n",
    "    plt.savefig(img_dir, bbox_inches='tight', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-84b463629c14>:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_count_dict['label'][i] = label_dictionary_lower[df_count_dict['Term'][i]]\n",
      "<ipython-input-17-84b463629c14>:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_count_dict['Original_Term'][i] = og_term_dictionary[df_count_dict['Term'][i]]\n",
      "C:\\Users\\jinwoo\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x667.491 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test = create_countdict_df(count_dict = count_dict, vocab_df = vocab_df,save_dir =  output_csv_dir, country_name = country_name)\n",
    "df_test.to_csv(os.path.join(output_csv_dir, country_name + '.csv'))\n",
    "save_plot(count_dict, 10,  output_csv_dir, country_name)"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}