createContingencyTable_ObaGWB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1f303393",
   "metadata": {},
   "source": [
    "### A Start at some correspondence analysis like I did for Corpus Linguistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0457e61f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go # for fancy interactive plot\n",
    "import seaborn as sns\n",
    "import requests\n",
    "import re\n",
    "from bs4 import BeautifulSoup\n",
    "import os # to check if directory exists and create it if it doesn't\n",
    "from datetime import datetime # to parse speech date\n",
    "from nrclex import NRCLex\n",
    "import spacy\n",
    "import en_core_web_md\n",
    "import csv\n",
    "import prince # for correspondence analysis\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS\n",
    "from sklearn.feature_extraction import text\n",
    "stopwords = pd.read_table('./Data/word_lists/kaggle_stopwords.txt')\n",
    "import unicodedata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35caeac6",
   "metadata": {},
   "source": [
    "[Skip to load previously saved contingency table](#loadctab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "758c61d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>text</th>\n",
       "      <th>filepath</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>good afternoon  everybody  one year ago this m...</td>\n",
       "      <td>./Data/speeches/2014-07-01-Immigration.txt</td>\n",
       "      <td>oba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-25</th>\n",
       "      <td>good morning  everybody  I want to take just a...</td>\n",
       "      <td>./Data/speeches/2009-12-25-UnderwearBomber.txt</td>\n",
       "      <td>oba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-11-05</th>\n",
       "      <td>hello  Chicago     if there be anyone out ther...</td>\n",
       "      <td>./Data/speeches/2008-11-05-ObamaElected.txt</td>\n",
       "      <td>oba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         text  \\\n",
       "date                                                            \n",
       "2014-07-01  good afternoon  everybody  one year ago this m...   \n",
       "2009-12-25  good morning  everybody  I want to take just a...   \n",
       "2008-11-05  hello  Chicago     if there be anyone out ther...   \n",
       "\n",
       "                                             filepath source  \n",
       "date                                                          \n",
       "2014-07-01      ./Data/speeches/2014-07-01-Immigration.txt    oba  \n",
       "2009-12-25  ./Data/speeches/2009-12-25-UnderwearBomber.txt    oba  \n",
       "2008-11-05     ./Data/speeches/2008-11-05-ObamaElected.txt    oba  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load up the files\n",
    "paths = ['./Data/speeches/']\n",
    "list_of_files = []\n",
    "\n",
    "for path in paths:\n",
    "    for root, dirs, files in os.walk(path):\n",
    "        for file in files:\n",
    "            if file.endswith('.txt'):\n",
    "                list_of_files.append(os.path.join(root,file))\n",
    "\n",
    "speeches = []\n",
    "for file in list_of_files:\n",
    "    with open(file, encoding='utf-8') as f:\n",
    "        #print(file)\n",
    "        text = f.read()\n",
    "    f.close()\n",
    "    speeches.append(text)\n",
    "\n",
    "#clean out goofy unicode  space characters \n",
    "speeches = [unicodedata.normalize(\"NFKD\", speech) for speech in speeches if len(speech)>0 ]\n",
    "\n",
    "# remove [stuff] in between square brackets\n",
    "def remove_bracket(text):\n",
    "    return re.sub('(\\[[^w]*\\]\\s)', '',text)\n",
    "speeches = [remove_bracket(speech) for speech in speeches]\n",
    "\n",
    "# lemmatize text with spacy\n",
    "nlp = spacy.load(\"en_core_web_md\", exclude=[\"parser\", \"ner\"])\n",
    "for i, doc in enumerate(nlp.pipe(speeches)):\n",
    "    words_lemmas_list = [token.lemma_ for token in doc]\n",
    "    speeches[i]= ' '.join(words_lemmas_list)\n",
    "    \n",
    "# Remove punctuation - only care about words here\n",
    "speeches = [re.sub(r'[^\\w\\s]', '', speech) for speech in speeches]\n",
    "\n",
    "# remove numbers\n",
    "speeches = [re.sub(r'\\d', '', speech) for speech in speeches]\n",
    "\n",
    "# Clean up whitespace\n",
    "speeches = [re.sub('[\\s+]', ' ', speech) for speech in speeches]\n",
    "    \n",
    "# Combine speeches and filenames\n",
    "speeches = list(zip(speeches, list_of_files))\n",
    "    \n",
    "def get_source(text):\n",
    "    regex = \"[^./][a-zA-Z]+[^/]\"\n",
    "    string = re.findall(regex, str(text))[0]\n",
    "    if string == 'speeches': string = 'oba'\n",
    "    if string == 'NYTimes': string = 'nyt'\n",
    "    return string.lower()\n",
    "\n",
    "def get_date(text):\n",
    "    regex = \"([0-9]+[\\-][0-9]+[\\-][0-9]+)\"\n",
    "    return re.findall(regex, str(text))[0]\n",
    "\n",
    "def get_filename(text):\n",
    "    regex = \"[-]([a-zA-Z]+)\"\n",
    "    return re.findall(regex, str(text))[0]\n",
    "\n",
    "#dates = pd.read_csv('./Data/genData/speech_and_date.csv') # day-month-year\n",
    "dates = pd.DataFrame(list_of_files, columns = ['filepath'])\n",
    "dates['date'] = [get_date(file) for file in dates.filepath]\n",
    "dates['date'] = pd.to_datetime(dates['date'], format='%Y-%m-%d')\n",
    "\n",
    "cols_speeches = ['text', 'filepath']\n",
    "text_df = pd.DataFrame(speeches, columns=cols_speeches)\n",
    "\n",
    "text_df = pd.merge(text_df, dates, how='left', on='filepath')\n",
    "text_df.sort_values(by='date')\n",
    "text_df['source'] = text_df['filepath'].apply(get_source)\n",
    "text_df.set_index('date', inplace=True)\n",
    "oba = text_df.copy(deep=True)\n",
    "text_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fe67d091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>text</th>\n",
       "      <th>filepath</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-11-27</th>\n",
       "      <td>thank you for come  Prime Minister Olmert  Pre...</td>\n",
       "      <td>./Data/GWB/remarks-the-annapolis-conference-annapol...</td>\n",
       "      <td>gwb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-06-06</th>\n",
       "      <td>good evening  during the next few minute  I wa...</td>\n",
       "      <td>./Data/GWB/address-the-nation-the-proposed-departme...</td>\n",
       "      <td>gwb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-16</th>\n",
       "      <td>our Nation be shock and sadden by the news of ...</td>\n",
       "      <td>./Data/GWB/remarks-the-shootings-virginia-tech-blac...</td>\n",
       "      <td>gwb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         text  \\\n",
       "date                                                            \n",
       "2007-11-27  thank you for come  Prime Minister Olmert  Pre...   \n",
       "2002-06-06  good evening  during the next few minute  I wa...   \n",
       "2007-04-16  our Nation be shock and sadden by the news of ...   \n",
       "\n",
       "                                                     filepath source  \n",
       "date                                                                  \n",
       "2007-11-27  ./Data/GWB/remarks-the-annapolis-conference-annapol...    gwb  \n",
       "2002-06-06  ./Data/GWB/address-the-nation-the-proposed-departme...    gwb  \n",
       "2007-04-16  ./Data/GWB/remarks-the-shootings-virginia-tech-blac...    gwb  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load up the files\n",
    "paths = ['./Data/GWB/']\n",
    "#paths = ['./Data/speeches/']\n",
    "list_of_files = []\n",
    "\n",
    "for path in paths:\n",
    "    for root, dirs, files in os.walk(path):\n",
    "        for file in files:\n",
    "            if file.endswith('.txt'):\n",
    "                list_of_files.append(os.path.join(root,file))\n",
    "\n",
    "speeches = []\n",
    "for file in list_of_files:\n",
    "    with open(file, encoding='utf-8') as f:\n",
    "        #print(file)\n",
    "        text = f.read()\n",
    "    f.close()\n",
    "    speeches.append(text)\n",
    "\n",
    "#clean out goofy unicode  space characters \n",
    "speeches = [unicodedata.normalize(\"NFKD\", speech) for speech in speeches if len(speech)>0 ]\n",
    "\n",
    "# remove [stuff] in between square brackets\n",
    "def remove_bracket(text):\n",
    "    return re.sub('(\\[[^w]*\\]\\s)', '',text)\n",
    "speeches = [remove_bracket(speech) for speech in speeches]\n",
    "\n",
    "# lemmatize text with spacy\n",
    "nlp = spacy.load(\"en_core_web_md\", exclude=[\"parser\", \"ner\"])\n",
    "for i, doc in enumerate(nlp.pipe(speeches)):\n",
    "    words_lemmas_list = [token.lemma_ for token in doc]\n",
    "    speeches[i]= ' '.join(words_lemmas_list)\n",
    "    \n",
    "# Remove punctuation - only care about words here\n",
    "speeches = [re.sub(r'[^\\w\\s]', '', speech) for speech in speeches]\n",
    "\n",
    "# remove numbers\n",
    "speeches = [re.sub(r'\\d', '', speech) for speech in speeches]\n",
    "\n",
    "# Clean up whitespace\n",
    "speeches = [re.sub('[\\s+]', ' ', speech) for speech in speeches]\n",
    "    \n",
    "# Combine speeches and filenames\n",
    "speeches = list(zip(speeches, list_of_files))\n",
    "    \n",
    "def get_source(text):\n",
    "    regex = \"[^./][a-zA-Z]+[^/]\"\n",
    "    string = re.findall(regex, str(text))[0]\n",
    "    if string == 'speeches': string = 'oba'\n",
    "    if string == 'NYTimes': string = 'nyt'\n",
    "    return string.lower()\n",
    "\n",
    "def get_date(text):\n",
    "    regex = \"([0-9]+[\\-][0-9]+[\\-][0-9]+)\"\n",
    "    return re.findall(regex, str(text))[0]\n",
    "\n",
    "def get_filename(text):\n",
    "    regex = \"[-]([a-zA-Z]+)\"\n",
    "    return re.findall(regex, str(text))[0]\n",
    "\n",
    "dates = pd.read_csv('./Data/genData/speech_and_date_gwb.csv') # day-month-year\n",
    "dates['date'] = pd.to_datetime(dates['date'], format='%Y-%m-%d')\n",
    "dates.rename(columns={'file':'filepath'}, inplace=True)\n",
    "dates['filepath'] = [file.replace('GWB/', './Data/GWB/') for file in dates['filepath']]\n",
    "\n",
    "cols_speeches = ['text', 'filepath']\n",
    "text_df = pd.DataFrame(speeches, columns=cols_speeches)\n",
    "\n",
    "text_df = pd.merge(text_df, dates, how='left', on='filepath')\n",
    "text_df.sort_values(by='date')\n",
    "text_df['source'] = text_df['filepath'].apply(get_source)\n",
    "text_df.set_index('date', inplace=True)\n",
    "gwb = text_df.copy(deep=True)\n",
    "text_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b22f17ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_df = pd.concat([oba,gwb])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11dcca37",
   "metadata": {},
   "source": [
    "<A HREF=\"https://regexr.com/\">RegExr</A>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6a9dbc0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load SpaCy English Model\n",
    "nlp = en_core_web_md.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "315d0862",
   "metadata": {},
   "source": [
    "### <A HREF=\"https://www.nltk.org/index.html\">NLTK Documentation</A> <A HREF=\"https://www.tomaarsen.com/nltk/api/nltk.html\">API reference</A>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d3ea8123",
   "metadata": {},
   "outputs": [],
   "source": [
    "# switch for gwb speeches\n",
    "df = text_df.copy()\n",
    "\n",
    "# And shorten GWB speech names\n",
    "remove_text = ['remarks-', 'address-', 'videotaped-', 'press-', \n",
    "              'message-', 'address-', 'statement-', 'proclamation-',\n",
    "              'signing-', 'accepting-', 'fact-sheet-', 'following-',\n",
    "              'announcing-the-', 'setting-the-record-straight-', 'accepting-the-',\n",
    "              'briefing-', 'joint-statement-', 'signing-the-', \n",
    "               'the-', 'statement-', 'press-briefing-', 'before-']\n",
    "\n",
    "for i in range(len(df['filepath'])):\n",
    "    for this in remove_text:\n",
    "        df['filepath'].iloc[i] = df['filepath'].iloc[i].replace(this, '')\n",
    "df['filepath'] = [file[6:-4] for file in df['filepath']]\n",
    "df['filepath'] = [file[0:20] for file in df['filepath']]\n",
    "df['date'] = [str(date)[0:10] for date in df.index]\n",
    "df['filename'] = [str(x) + '-' + str(y) for x, y in zip(df['date'], df['filepath'])]\n",
    "df.drop(['date', 'filepath'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "702ab058",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this if you want to remove stop words before creating contingency table\n",
    "kaggle_file = open(\"./Data/word_lists/kaggle_stopwords.txt\", \"r\")\n",
    "kaggle_data = kaggle_file.read()\n",
    "kaggle_list = [word for word in kaggle_data.split('\\n')]\n",
    "kaggle_file.close()\n",
    "my_list = ['thats', 'just', 'im', 'did', 'thing', 'mr', 'al', 'thank', 'okay', 'thank','thanks', \n",
    "           'question', 'joshua', 'president', 'obama', 'â', u'\\x99s', u'\\x99t', u'\\x99ve', u'\\x99m',u'\\x99re', '\\x99']\n",
    "stop_list = list(set(kaggle_list) | set(my_list))\n",
    "stop_words = ENGLISH_STOP_WORDS.union(stop_list)\n",
    "\n",
    "for i in range(len(df)):\n",
    "        df['text'].iloc[i] = ' '.join([word for word in df['text'].iloc[i].split() if word.lower() not in stop_words])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3b9fd5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Go through word by word and remove this: â\n",
    "for i in range(len(df)):\n",
    "    df['text'].iloc[i] = ' '.join([word.replace(\"â\", \"\") for word in df['text'].iloc[i].split()])\n",
    "# Finally, got rid of that stupid thing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5416ce54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'people': 1664, 'america': 1265, 'work': 1212, 'year': 1080, 'new': 1019, 'world': 992, 'country': 984, 'know': 930, 'time': 867, 'american': 866, ...})"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a frequency list of all words \n",
    "# How to flatten a list\n",
    "#[item for sublist in list_of_lists for item in sublist]\n",
    "\n",
    "freqlist = nltk.FreqDist([word.lower() for text in df['text'] for word in text.split()])\n",
    "freqlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3264f04c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('people', 1664),\n",
       " ('america', 1265),\n",
       " ('work', 1212),\n",
       " ('year', 1080),\n",
       " ('new', 1019),\n",
       " ('world', 992),\n",
       " ('country', 984),\n",
       " ('know', 930),\n",
       " ('time', 867),\n",
       " ('american', 866),\n",
       " ('help', 822),\n",
       " ('nation', 814),\n",
       " ('job', 742),\n",
       " ('come', 741),\n",
       " ('want', 720),\n",
       " ('need', 702),\n",
       " ('americans', 692),\n",
       " ('united', 689),\n",
       " ('today', 654),\n",
       " ('right', 654)]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(dict(freqlist).items(),key=lambda x:x[1], reverse=True)[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c5d191ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of all the words in the freqlist which is from all 100 documents\n",
    "words = list(set(dict(freqlist).keys()))\n",
    "# was getting a weird error below, just making sure all are strings, no numbers\n",
    "words = [str(word) for word in words] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c83f38",
   "metadata": {},
   "source": [
    "### Create Contingency Table - counts of all words in all the documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "656a7ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get counts of all words in 'words' for each speech/article\n",
    "# I'm doing this the slow way so I can be sure the results are correct.\n",
    "# I was getting weird numbers using other methods, \n",
    "#    like using sklearn countvectorizer I was getting 2900 for 'deepwater' when it should be 4\n",
    "# I realize this is ugly and slow\n",
    "counts = []\n",
    "# Switched 'text_oba' to 'text' for GWB speeches\n",
    "for i in range(len(df['text'])):\n",
    "    sums = []\n",
    "    for word in words:\n",
    "        summ = 0\n",
    "        for docword in df['text'].iloc[i].split():\n",
    "            if docword == word:\n",
    "                summ+=1\n",
    "        sums.append(summ)\n",
    "    counts.append(sums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2a4223ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Oh man, this is so ugly, just brute force, no style or class. Oh well, it works!\n",
    "totalcounts = []\n",
    "for i in range(len(words)):\n",
    "    summ = 0\n",
    "    for j in range(len(counts)):\n",
    "        summ += counts[j][i]\n",
    "    totalcounts.append(summ)      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f7ea891d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Contingency table\n",
    "df.dropna(axis=0, inplace=True)\n",
    "df['date'] = df.index\n",
    "filenames = df['date']\n",
    "conttab = pd.DataFrame(counts, columns = words, index = filenames, dtype='int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4fa7fbc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "conttab.loc['sum',:] = conttab.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c9975a29",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>suited</th>\n",
       "      <th>ally</th>\n",
       "      <th>friedman</th>\n",
       "      <th>appalachia</th>\n",
       "      <th>hidden</th>\n",
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       "      <th>clarity</th>\n",
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       "      <th>aires</th>\n",
       "      <th>ceremony</th>\n",
       "      <th>previously</th>\n",
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       "      <th>avian</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2009-12-25 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-11-05 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-16 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-29 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-26 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-17 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09-07 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-09-02 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>1.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185 rows × 9398 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     suited   ally  friedman  appalachia  hidden  steady  \\\n",
       "date                                                                       \n",
       "2014-07-01 00:00:00     0.0    0.0       0.0         0.0     0.0     0.0   \n",
       "2009-12-25 00:00:00     0.0    0.0       0.0         0.0     0.0     0.0   \n",
       "2008-11-05 00:00:00     0.0    0.0       0.0         0.0     0.0     0.0   \n",
       "2011-12-16 00:00:00     0.0    0.0       0.0         0.0     0.0     0.0   \n",
       "2012-04-29 00:00:00     0.0    0.0       0.0         0.0     0.0     0.0   \n",
       "...                     ...    ...       ...         ...     ...     ...   \n",
       "2007-07-26 00:00:00     0.0    0.0       0.0         0.0     0.0     0.0   \n",
       "2007-09-17 00:00:00     0.0    0.0       0.0         0.0     0.0     0.0   \n",
       "2008-09-07 00:00:00     0.0    0.0       0.0         0.0     0.0     0.0   \n",
       "2004-09-02 00:00:00     0.0    4.0       0.0         0.0     0.0     2.0   \n",
       "sum                     1.0  154.0       0.0         0.0     4.0    19.0   \n",
       "\n",
       "                     joke  bore  blurred  colony  ...  pawn  grad  clarity  \\\n",
       "date                                              ...                        \n",
       "2014-07-01 00:00:00   0.0   0.0      0.0     0.0  ...   0.0   0.0      1.0   \n",
       "2009-12-25 00:00:00   0.0   0.0      0.0     0.0  ...   0.0   0.0      0.0   \n",
       "2008-11-05 00:00:00   0.0   0.0      0.0     0.0  ...   0.0   0.0      0.0   \n",
       "2011-12-16 00:00:00   0.0   0.0      0.0     0.0  ...   0.0   0.0      0.0   \n",
       "2012-04-29 00:00:00   0.0   0.0      0.0     0.0  ...   0.0   0.0      0.0   \n",
       "...                   ...   ...      ...     ...  ...   ...   ...      ...   \n",
       "2007-07-26 00:00:00   0.0   0.0      0.0     0.0  ...   0.0   0.0      0.0   \n",
       "2007-09-17 00:00:00   0.0   0.0      0.0     0.0  ...   0.0   0.0      0.0   \n",
       "2008-09-07 00:00:00   0.0   0.0      0.0     0.0  ...   0.0   0.0      0.0   \n",
       "2004-09-02 00:00:00   0.0   0.0      0.0     1.0  ...   0.0   0.0      0.0   \n",
       "sum                   2.0   2.0      1.0     3.0  ...   1.0   1.0      3.0   \n",
       "\n",
       "                     machine  syrian  aires  ceremony  previously  automate  \\\n",
       "date                                                                          \n",
       "2014-07-01 00:00:00      0.0     0.0    0.0       1.0         0.0       0.0   \n",
       "2009-12-25 00:00:00      0.0     0.0    0.0       0.0         0.0       0.0   \n",
       "2008-11-05 00:00:00      0.0     0.0    0.0       0.0         0.0       0.0   \n",
       "2011-12-16 00:00:00      0.0     0.0    0.0       0.0         0.0       0.0   \n",
       "2012-04-29 00:00:00      0.0     0.0    0.0       0.0         0.0       0.0   \n",
       "...                      ...     ...    ...       ...         ...       ...   \n",
       "2007-07-26 00:00:00      0.0     0.0    0.0       0.0         0.0       0.0   \n",
       "2007-09-17 00:00:00      0.0     0.0    0.0       0.0         0.0       0.0   \n",
       "2008-09-07 00:00:00      0.0     0.0    0.0       0.0         0.0       0.0   \n",
       "2004-09-02 00:00:00      0.0     0.0    0.0       0.0         0.0       0.0   \n",
       "sum                      2.0    11.0    0.0       8.0         5.0       1.0   \n",
       "\n",
       "                     avian  \n",
       "date                        \n",
       "2014-07-01 00:00:00    0.0  \n",
       "2009-12-25 00:00:00    0.0  \n",
       "2008-11-05 00:00:00    0.0  \n",
       "2011-12-16 00:00:00    0.0  \n",
       "2012-04-29 00:00:00    0.0  \n",
       "...                    ...  \n",
       "2007-07-26 00:00:00    0.0  \n",
       "2007-09-17 00:00:00    0.0  \n",
       "2008-09-07 00:00:00    0.0  \n",
       "2004-09-02 00:00:00    0.0  \n",
       "sum                    4.0  \n",
       "\n",
       "[185 rows x 9398 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conttab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ce228cf8",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>2001-01-20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-29</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-16</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-03-29</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-28</th>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-17</th>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-05</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-09</th>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>19.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 9398 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            people  work  year  country   new  world  know  time  help  \\\n",
       "date                                                                     \n",
       "2001-01-20     1.0   5.0   2.0      9.0   5.0    3.0   5.0   7.0   0.0   \n",
       "2001-01-22     0.0   2.0   0.0      2.0   0.0    0.0   1.0   0.0   1.0   \n",
       "2001-01-29     5.0   9.0   0.0      2.0   2.0    0.0   2.0   0.0   5.0   \n",
       "2001-02-16     5.0   2.0   0.0      5.0   2.0    0.0   0.0   0.0   2.0   \n",
       "2001-03-29     2.0   2.0   0.0      0.0   0.0    0.0   0.0   0.0   1.0   \n",
       "...            ...   ...   ...      ...   ...    ...   ...   ...   ...   \n",
       "2016-07-28    21.0  22.0  24.0     14.0  10.0    8.0  35.0   7.0   9.0   \n",
       "2016-08-17    13.0   6.0   3.0      3.0   1.0    0.0  11.0   3.0  24.0   \n",
       "2016-09-05     3.0   5.0   8.0      1.0   3.0    2.0   0.0   1.0   3.0   \n",
       "2016-11-09     7.0  10.0   4.0      8.0   0.0    3.0   5.0   3.0   1.0   \n",
       "2017-01-11    19.0  17.0  17.0      9.0   8.0    8.0   5.0   5.0   4.0   \n",
       "\n",
       "            nation  ...  hayden  bosnian  indonesia  rosemary  imf   gm  \\\n",
       "date                ...                                                   \n",
       "2001-01-20    11.0  ...     0.0      0.0        0.0       0.0  0.0  0.0   \n",
       "2001-01-22     1.0  ...     0.0      0.0        0.0       0.0  0.0  0.0   \n",
       "2001-01-29     1.0  ...     0.0      0.0        0.0       0.0  0.0  0.0   \n",
       "2001-02-16     3.0  ...     0.0      0.0        0.0       0.0  0.0  0.0   \n",
       "2001-03-29     1.0  ...     0.0      0.0        0.0       0.0  0.0  0.0   \n",
       "...            ...  ...     ...      ...        ...       ...  ...  ...   \n",
       "2016-07-28     4.0  ...     0.0      0.0        0.0       0.0  0.0  0.0   \n",
       "2016-08-17     1.0  ...     0.0      0.0        0.0       0.0  0.0  0.0   \n",
       "2016-09-05     2.0  ...     0.0      0.0        0.0       0.0  0.0  0.0   \n",
       "2016-11-09     0.0  ...     0.0      0.0        0.0       0.0  0.0  0.0   \n",
       "2017-01-11     9.0  ...     0.0      0.0        0.0       0.0  0.0  0.0   \n",
       "\n",
       "            springfield  secretaries  biden  alcohol  \n",
       "date                                                  \n",
       "2001-01-20          0.0          0.0    0.0      0.0  \n",
       "2001-01-22          0.0          0.0    0.0      0.0  \n",
       "2001-01-29          0.0          0.0    0.0      0.0  \n",
       "2001-02-16          0.0          0.0    0.0      0.0  \n",
       "2001-03-29          0.0          0.0    0.0      0.0  \n",
       "...                 ...          ...    ...      ...  \n",
       "2016-07-28          0.0          0.0    0.0      0.0  \n",
       "2016-08-17          0.0          0.0    0.0      0.0  \n",
       "2016-09-05          0.0          0.0    0.0      0.0  \n",
       "2016-11-09          0.0          0.0    0.0      0.0  \n",
       "2017-01-11          0.0          0.0    0.0      0.0  \n",
       "\n",
       "[184 rows x 9398 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And here is the full Contingency Table, sorted with most frequent words first\n",
    "conttab.sort_values(by='sum', axis=1, ascending=False, inplace=True)\n",
    "conttab.drop('sum', inplace=True)\n",
    "conttab.sort_index(inplace=True)\n",
    "conttab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d63e5c3",
   "metadata": {},
   "source": [
    "## Save contingency table, change destination file name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "34f3e6cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save table so we don't have to do this again\n",
    "#conttab.to_csv('./Data/genData/ContingencyTable_noSW_both.csv')\n",
    "#conttab.to_csv('./Data/genData/contingency_table_noSW_gwb.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67bd74d5",
   "metadata": {},
   "source": [
    "<a id='loadctab'>Load Contingency Table</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1d05dd46",
   "metadata": {},
   "outputs": [],
   "source": [
    "################################ LOAD CONTINGENCY TABLE ###############################\n",
    "# Read in saved contingency table\n",
    "ctab = pd.read_csv('./Data/genData/contingency_table_noSW_gwb.csv',index_col=0)\n",
    "# Shorten row index names to date only\n",
    "ctab = ctab.rename(index = lambda x: x[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1f62bc3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's look at the 150 most common words\n",
    "ctabshort=ctab.iloc[:, : 150]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeb748f8",
   "metadata": {},
   "source": [
    "<A HREF=\"https://github.com/MaxHalford/prince#correspondence-analysis-ca\">Prince Correspondence Analysis</A>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615cf610",
   "metadata": {},
   "source": [
    "<A HREF=\"https://pypi.org/project/prince/#history\">Prince version history</A>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3000193a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check prince version\n",
    "#!pip show prince\n",
    "# ok 0.8.2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "edf969fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "ca = prince.CA(n_components=2,\n",
    "               n_iter=3,copy=True,\n",
    "               check_input=True,\n",
    "               engine='sklearn',\n",
    "               random_state=33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b3d728cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "ctabshort.columns.rename('words', inplace=True)\n",
    "ctabshort.index.rename('speeches', inplace=True)\n",
    "ca = ca.fit(ctabshort)\n",
    "\n",
    "# Something's gone wrong with the Prince correspondency analysis!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
    "# Just stopped working\n",
    "# The prince github repository says he fixed famd transform 4 days ago... perhaps not\n",
    "# https://github.com/MaxHalford/prince\n",
    "# Can try\n",
    "# pip install prince==0.8.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f1ef69ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-01-20</th>\n",
       "      <td>-0.042787</td>\n",
       "      <td>-0.157649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-22</th>\n",
       "      <td>0.134689</td>\n",
       "      <td>-0.066265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-29</th>\n",
       "      <td>-0.036317</td>\n",
       "      <td>-0.008933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-16</th>\n",
       "      <td>0.038862</td>\n",
       "      <td>-0.061580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-03-29</th>\n",
       "      <td>-0.096671</td>\n",
       "      <td>-0.278052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1\n",
       "speeches                      \n",
       "2001-01-20 -0.042787 -0.157649\n",
       "2001-01-22  0.134689 -0.066265\n",
       "2001-01-29 -0.036317 -0.008933\n",
       "2001-02-16  0.038862 -0.061580\n",
       "2001-03-29 -0.096671 -0.278052"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca.row_coordinates(ctabshort).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b6bd9123",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>words</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>people</th>\n",
       "      <td>-0.067714</td>\n",
       "      <td>-0.161022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work</th>\n",
       "      <td>-0.094122</td>\n",
       "      <td>0.112870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>-0.087206</td>\n",
       "      <td>-0.038318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>help</th>\n",
       "      <td>-0.096581</td>\n",
       "      <td>0.194602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>world</th>\n",
       "      <td>0.007555</td>\n",
       "      <td>-0.255559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0         1\n",
       "words                      \n",
       "people  -0.067714 -0.161022\n",
       "work    -0.094122  0.112870\n",
       "country -0.087206 -0.038318\n",
       "help    -0.096581  0.194602\n",
       "world    0.007555 -0.255559"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca.column_coordinates(ctabshort).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e26640b",
   "metadata": {},
   "source": [
    "<A HREF=\"https://matplotlib.org/cheatsheets/cheatsheets.pdf\">Matplotlib basic cheat sheet</A><BR>\n",
    "    <A HREF=\"https://matplotlib.org/cheatsheets/\">other matplotlib cheat sheets</A>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60f8e874",
   "metadata": {},
   "source": [
    "From NRC analysis the speech with particularly high anger value was 2015-01-08\n",
    "and particularly high positive value on 2014-03-07"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "53ac7b2f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#date1 = '2015-01-08'\n",
    "#date2 = '2016-02-16'\n",
    "df1=ca.row_coordinates(ctabshort)\n",
    "df1.columns=['Comp1', 'Comp2']\n",
    "df2=ca.column_coordinates(ctabshort)\n",
    "df2.columns=['Comp1', 'Comp2']\n",
    "\n",
    "# df1 for speeches\n",
    "ax = df1.plot(x='Comp1', y='Comp2', figsize=(10, 10), kind='scatter', color='blue')\n",
    "for i, txt in enumerate(df1.index.values.tolist()):\n",
    "    ax.annotate(txt, (df1['Comp1'][i], df1['Comp2'][i]))\n",
    "\n",
    "# df2 for words\n",
    "df2.plot(ax=ax, x='Comp1', y='Comp2', kind='scatter', color='orange')\n",
    "for i, txt in enumerate(df2.index.values.tolist()):\n",
    "    ax.annotate(txt, (df2['Comp1'][i], df2['Comp2'][i]))\n",
    "\n",
    "#ax.plot(df1.loc[[date1]]['Comp1'], df1.loc[[date1]]['Comp2'], marker='X', color='red', markersize=20)\n",
    "#ax.plot(df1.loc[[date2]]['Comp1'], df1.loc[[date2]]['Comp2'], marker='p', color='green', markersize=20)\n",
    "\n",
    "plt.xlim([-1.7, 1.2])\n",
    "plt.ylim([-1.0, 2.0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea7de0b",
   "metadata": {},
   "source": [
    "### Try correspondence analysis on a pre-selected list of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1463a6f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load words from file\n",
    "# topic words from topic modeling: \"./Data/word_lists/topic-words.txt\"\n",
    "# a custom list: \"./Data/word_lists/customlist.txt\"\n",
    "# common adjective: \"./Data/word_lists/adjectives.txt\"\n",
    "# common adverbs: \"./Data/word_lists/adverbs.txt\"\n",
    "# function words: \"./Data/word_lists/function-words.txt\"\n",
    "wordList = pd.read_csv(\"./Data/word_lists/hope_words.txt\", sep=\" \", header=None, \n",
    "                 names=[\"word\"])\n",
    "wordList = pd.DataFrame([word.lower() for word in list(wordList['word'])], columns=['word'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1fa445e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/muddy/.local/lib/python3.10/site-packages/prince/ca.py:82: RuntimeWarning: divide by zero encountered in power\n",
      "  S = sparse.diags(r**-0.5) @ (X - np.outer(r, c)) @ sparse.diags(c**-0.5)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "array must not contain infs or NaNs",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[62], line 10\u001b[0m\n\u001b[1;32m      8\u001b[0m ctabshort2\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mrename(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwords\u001b[39m\u001b[38;5;124m'\u001b[39m, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      9\u001b[0m ctabshort2\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mrename(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspeeches\u001b[39m\u001b[38;5;124m'\u001b[39m, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 10\u001b[0m ca \u001b[38;5;241m=\u001b[39m \u001b[43mca\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctabshort2\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/prince/ca.py:85\u001b[0m, in \u001b[0;36mCA.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     82\u001b[0m S \u001b[38;5;241m=\u001b[39m sparse\u001b[38;5;241m.\u001b[39mdiags(r\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.5\u001b[39m) \u001b[38;5;241m@\u001b[39m (X \u001b[38;5;241m-\u001b[39m np\u001b[38;5;241m.\u001b[39mouter(r, c)) \u001b[38;5;241m@\u001b[39m sparse\u001b[38;5;241m.\u001b[39mdiags(c\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n\u001b[1;32m     84\u001b[0m \u001b[38;5;66;03m# Compute SVD on the standardised residuals\u001b[39;00m\n\u001b[0;32m---> 85\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msvd_ \u001b[38;5;241m=\u001b[39m \u001b[43msvd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_svd\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     86\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     87\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_components\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mmin\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mmin\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     88\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_iter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_iter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     89\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     90\u001b[0m \u001b[43m    \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;66;03m# Compute total inertia\u001b[39;00m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtotal_inertia_ \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39meinsum(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mij,ji->\u001b[39m\u001b[38;5;124m\"\u001b[39m, S, S\u001b[38;5;241m.\u001b[39mT)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/prince/svd.py:41\u001b[0m, in \u001b[0;36mcompute_svd\u001b[0;34m(X, n_components, n_iter, random_state, engine)\u001b[0m\n\u001b[1;32m     39\u001b[0m     V \u001b[38;5;241m=\u001b[39m V[:n_components, :]\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m engine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msklearn\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 41\u001b[0m     U, s, V \u001b[38;5;241m=\u001b[39m \u001b[43mextmath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandomized_svd\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     42\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_components\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_components\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_iter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_iter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrandom_state\u001b[49m\n\u001b[1;32m     43\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mengine has to be one of (\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfbpca\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscipy\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msklearn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:446\u001b[0m, in \u001b[0;36mrandomized_svd\u001b[0;34m(M, n_components, n_oversamples, n_iter, power_iteration_normalizer, transpose, flip_sign, random_state, svd_lapack_driver)\u001b[0m\n\u001b[1;32m    442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m transpose:\n\u001b[1;32m    443\u001b[0m     \u001b[38;5;66;03m# this implementation is a bit faster with smaller shape[1]\u001b[39;00m\n\u001b[1;32m    444\u001b[0m     M \u001b[38;5;241m=\u001b[39m M\u001b[38;5;241m.\u001b[39mT\n\u001b[0;32m--> 446\u001b[0m Q \u001b[38;5;241m=\u001b[39m \u001b[43mrandomized_range_finder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    447\u001b[0m \u001b[43m    \u001b[49m\u001b[43mM\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    448\u001b[0m \u001b[43m    \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_random\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    449\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_iter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_iter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    450\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpower_iteration_normalizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpower_iteration_normalizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    451\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrandom_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    452\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    454\u001b[0m \u001b[38;5;66;03m# project M to the (k + p) dimensional space using the basis vectors\u001b[39;00m\n\u001b[1;32m    455\u001b[0m B \u001b[38;5;241m=\u001b[39m safe_sparse_dot(Q\u001b[38;5;241m.\u001b[39mT, M)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:274\u001b[0m, in \u001b[0;36mrandomized_range_finder\u001b[0;34m(A, size, n_iter, power_iteration_normalizer, random_state)\u001b[0m\n\u001b[1;32m    272\u001b[0m     Q \u001b[38;5;241m=\u001b[39m safe_sparse_dot(A\u001b[38;5;241m.\u001b[39mT, Q)\n\u001b[1;32m    273\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m power_iteration_normalizer \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLU\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 274\u001b[0m     Q, _ \u001b[38;5;241m=\u001b[39m \u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlu\u001b[49m\u001b[43m(\u001b[49m\u001b[43msafe_sparse_dot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mQ\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpermute_l\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    275\u001b[0m     Q, _ \u001b[38;5;241m=\u001b[39m linalg\u001b[38;5;241m.\u001b[39mlu(safe_sparse_dot(A\u001b[38;5;241m.\u001b[39mT, Q), permute_l\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    276\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m power_iteration_normalizer \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQR\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/scipy/linalg/_decomp_lu.py:213\u001b[0m, in \u001b[0;36mlu\u001b[0;34m(a, permute_l, overwrite_a, check_finite)\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    156\u001b[0m \u001b[38;5;124;03mCompute pivoted LU decomposition of a matrix.\u001b[39;00m\n\u001b[1;32m    157\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    210\u001b[0m \n\u001b[1;32m    211\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_finite:\n\u001b[0;32m--> 213\u001b[0m     a1 \u001b[38;5;241m=\u001b[39m \u001b[43masarray_chkfinite\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    215\u001b[0m     a1 \u001b[38;5;241m=\u001b[39m asarray(a)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/numpy/lib/function_base.py:628\u001b[0m, in \u001b[0;36masarray_chkfinite\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    626\u001b[0m a \u001b[38;5;241m=\u001b[39m asarray(a, dtype\u001b[38;5;241m=\u001b[39mdtype, order\u001b[38;5;241m=\u001b[39morder)\n\u001b[1;32m    627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m a\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mchar \u001b[38;5;129;01min\u001b[39;00m typecodes[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAllFloat\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(a)\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m--> 628\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    629\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray must not contain infs or NaNs\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    630\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n",
      "\u001b[0;31mValueError\u001b[0m: array must not contain infs or NaNs"
     ]
    }
   ],
   "source": [
    "# Make a short contingency list using topic words\n",
    "ctabshort2 = ctab.loc[ : , [col for col in ctab.columns if col in list(wordList['word'])]]\n",
    "\n",
    "# Keep it to the most common 150 words\n",
    "if np.shape(ctabshort2)[1] > 150:\n",
    "    ctabshort2=ctabshort2.iloc[:, : 150]\n",
    "    \n",
    "ctabshort2.columns.rename('words', inplace=True)\n",
    "ctabshort2.index.rename('speeches', inplace=True)\n",
    "ca = ca.fit(ctabshort2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c40c40d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "date1 = '2015-01-08'\n",
    "date2 = '2016-02-16'\n",
    "df1=ca.row_coordinates(ctabshort2)\n",
    "df1.columns=['Comp1', 'Comp2']\n",
    "df2=ca.column_coordinates(ctabshort2)\n",
    "df2.columns=['Comp1', 'Comp2']\n",
    "\n",
    "# df1 for speeches\n",
    "ax = df1.plot(x='Comp1', y='Comp2', figsize=(10, 10), kind='scatter', color='blue')\n",
    "for i, txt in enumerate(df1.index.values.tolist()):\n",
    "    ax.annotate(txt, (df1['Comp1'][i], df1['Comp2'][i]))\n",
    "\n",
    "# df2 for words\n",
    "df2.plot(ax=ax, x='Comp1', y='Comp2', kind='scatter', color='orange')\n",
    "for i, txt in enumerate(df2.index.values.tolist()):\n",
    "    ax.annotate(txt, (df2['Comp1'][i], df2['Comp2'][i]))\n",
    "\n",
    "ax.plot(df1.loc[[date1]]['Comp1'], df1.loc[[date1]]['Comp2'], marker='X', color='red', markersize=20)\n",
    "ax.plot(df1.loc[[date2]]['Comp1'], df1.loc[[date2]]['Comp2'], marker='p', color='green', markersize=20)\n",
    "\n",
    "#plt.xlim([-0.5, 1.5])\n",
    "#plt.ylim([-0.6, 0.4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b0b10b",
   "metadata": {},
   "source": [
    "### Now try using the most frequent 150 words after removing the Kaggle 700 stop words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "237b3bb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load words from file\n",
    "# Kaggle stop words: \"./Data/word_lists/kaggle_stopwords.txt\"\n",
    "wordList = pd.read_csv(\"./Data/word_lists/kaggle_stopwords.txt\", sep=\" \", header=None, \n",
    "                 names=[\"word\"])\n",
    "wordList = pd.DataFrame([word.lower() for word in list(wordList['word'])], columns=['word'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d77bff23",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_words = 400\n",
    "# Make a short contingency list removing the Kaggle stop words\n",
    "ctabshort3 = ctab.loc[ : , [col for col in ctab.columns if col not in list(wordList['word'])]]\n",
    "\n",
    "# Keep it to the most common 150 words\n",
    "if np.shape(ctabshort3)[1] > number_of_words:\n",
    "    ctabshort3=ctabshort3.iloc[:, : number_of_words]\n",
    "    \n",
    "ctabshort3.columns.rename('words', inplace=True)\n",
    "ctabshort3.index.rename('speeches', inplace=True)\n",
    "ca = ca.fit(ctabshort3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06c97e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "date1 = '2015-01-08' # date 1 is red X\n",
    "date2 = '2011-05-02' # date 2 is green pentagon\n",
    "df1=ca.row_coordinates(ctabshort3)\n",
    "df1.columns=['Comp1', 'Comp2']\n",
    "df2=ca.column_coordinates(ctabshort3)\n",
    "df2.columns=['Comp1', 'Comp2']\n",
    "\n",
    "# df1 for speeches - blue\n",
    "ax = df1.plot(x='Comp1', y='Comp2', figsize=(10, 10), kind='scatter', color='blue')\n",
    "for i, txt in enumerate(df1.index.values.tolist()):\n",
    "    ax.annotate(txt, (df1['Comp1'][i], df1['Comp2'][i]))\n",
    "\n",
    "# df2 for words - orange\n",
    "df2.plot(ax=ax, x='Comp1', y='Comp2', kind='scatter', color='orange')\n",
    "for i, txt in enumerate(df2.index.values.tolist()):\n",
    "    ax.annotate(txt, (df2['Comp1'][i], df2['Comp2'][i]))\n",
    "\n",
    "ax.plot(df1.loc[[date1]]['Comp1'], df1.loc[[date1]]['Comp2'], marker='X', color='red', markersize=20)\n",
    "ax.plot(df1.loc[[date2]]['Comp1'], df1.loc[[date2]]['Comp2'], marker='p', color='green', markersize=20)\n",
    "\n",
    "#plt.xlim([0.4, 1.0])\n",
    "#plt.ylim([-0.25, 0.25])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a2fd770",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LETS TRY THIS CODE\n",
    "# https://okomestudio.net/biboroku/2014/05/brand-positioning-by-correspondence-analysis/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bb82a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python2.7\n",
    "# -*- coding: utf-8 -*-\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy.linalg import svd\n",
    "\n",
    "\n",
    "class CA(object):\n",
    "    \"\"\"Simple corresondence analysis.\n",
    "    \n",
    "    Inputs\n",
    "    ------\n",
    "    ct : array_like\n",
    "      Two-way contingency table. If `ct` is a pandas DataFrame object,\n",
    "      the index and column values are used for plotting.\n",
    "    Notes\n",
    "    -----\n",
    "    The implementation follows that presented in 'Correspondence\n",
    "    Analysis in R, with Two- and Three-dimensional Graphics: The ca\n",
    "    Package,' Journal of Statistical Software, May 2007, Volume 20,\n",
    "    Issue 3.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, ct):\n",
    "        self.rows = ct.index.values if hasattr(ct, 'index') else None\n",
    "        self.cols = ct.columns.values if hasattr(ct, 'columns') else None\n",
    "        \n",
    "        # contingency table\n",
    "        N = np.matrix(ct, dtype=float)\n",
    "\n",
    "        # correspondence matrix from contingency table\n",
    "        P = N / N.sum()\n",
    "\n",
    "        # row and column marginal totals of P as vectors\n",
    "        r = P.sum(axis=1)\n",
    "        c = P.sum(axis=0).T\n",
    "\n",
    "        # diagonal matrices of row/column sums\n",
    "        D_r_rsq = np.diag(1. / np.sqrt(r.A1))\n",
    "        D_c_rsq = np.diag(1. / np.sqrt(c.A1))\n",
    "\n",
    "        # the matrix of standarized residuals\n",
    "        S = D_r_rsq * (P - r * c.T) * D_c_rsq\n",
    "\n",
    "        # compute the SVD\n",
    "        U, D_a, V = svd(S, full_matrices=False)\n",
    "        D_a = np.asmatrix(np.diag(D_a))\n",
    "        V = V.T\n",
    "\n",
    "        # principal coordinates of rows\n",
    "        F = D_r_rsq * U * D_a\n",
    "\n",
    "        # principal coordinates of columns\n",
    "        G = D_c_rsq * V * D_a\n",
    "\n",
    "        # standard coordinates of rows\n",
    "        X = D_r_rsq * U\n",
    "\n",
    "        # standard coordinates of columns\n",
    "        Y = D_c_rsq * V\n",
    "\n",
    "        # the total variance of the data matrix\n",
    "        inertia = sum([(P[i,j] - r[i,0] * c[j,0])**2 / (r[i,0] * c[j,0])\n",
    "                       for i in range(N.shape[0])\n",
    "                       for j in range(N.shape[1])])\n",
    "\n",
    "        self.F = F.A\n",
    "        self.G = G.A\n",
    "        self.X = X.A\n",
    "        self.Y = Y.A\n",
    "        self.inertia = inertia\n",
    "        self.eigenvals = np.diag(D_a)**2\n",
    "\n",
    "    def plot(self):\n",
    "        \"\"\"Plot the first and second dimensions.\"\"\"\n",
    "        xmin, xmax = None, None\n",
    "        ymin, ymax = None, None\n",
    "        if self.rows is not None:\n",
    "            for i, t in enumerate(self.rows):\n",
    "                x, y = self.F[i,0], self.F[i,1]\n",
    "                plt.text(x, y, t, va='center', ha='center', color='r')\n",
    "                xmin = min(x, xmin if xmin else x)\n",
    "                xmax = max(x, xmax if xmax else x)\n",
    "                ymin = min(y, ymin if ymin else y)\n",
    "                ymax = max(y, ymax if ymax else y)\n",
    "        else:\n",
    "            plt.plot(self.F[:, 0], self.F[:, 1], 'ro')\n",
    "\n",
    "        if self.cols is not None:\n",
    "            for i, t in enumerate(self.cols):\n",
    "                x, y = self.G[i,0], self.G[i,1]\n",
    "                plt.text(x, y, t, va='center', ha='center', color='b')\n",
    "                xmin = min(x, xmin if xmin else x)\n",
    "                xmax = max(x, xmax if xmax else x)\n",
    "                ymin = min(y, ymin if ymin else y)\n",
    "                ymax = max(y, ymax if ymax else y)\n",
    "        else:\n",
    "            plt.plot(self.G[:, 0], self.G[:, 1], 'bs')\n",
    "\n",
    "        if xmin and xmax:\n",
    "            pad = (xmax - xmin) * 0.1\n",
    "            plt.xlim(xmin - pad, xmax + pad)\n",
    "        if ymin and ymax:\n",
    "            pad = (ymax - ymin) * 0.1\n",
    "            plt.ylim(ymin - pad, ymax + pad)\n",
    "\n",
    "        plt.grid()\n",
    "        plt.xlabel('Dim 1')\n",
    "        plt.ylabel('Dim 2')\n",
    "\n",
    "    def scree_diagram(self, perc=True, *args, **kwargs):\n",
    "        \"\"\"Plot the scree diagram.\"\"\"\n",
    "        eigenvals = self.eigenvals\n",
    "        xs = np.arange(1, eigenvals.size + 1, 1)\n",
    "        ys = 100. * eigenvals / eigenvals.sum() if perc else eigenvals\n",
    "        plt.plot(xs, ys, *args, **kwargs)\n",
    "        plt.xlabel('Dimension')\n",
    "        plt.ylabel('Eigenvalue' + (' [%]' if perc else ''))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01394eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "################################ LOAD CONTINGENCY TABLE ###############################\n",
    "# Read in saved contingency table\n",
    "ctab = pd.read_csv('./Data/genData/obamaContingencyTable_noSW.csv',index_col=0)\n",
    "# Shorten row index names to date only\n",
    "ctab = ctab.rename(index = lambda x: x[0:10])\n",
    "# Let's look at the 150 most common words\n",
    "ctabshort=ctab.iloc[:, : 150]\n",
    "\n",
    "\n",
    "df = pd.io.parsers.read_csv('./Data/genData/obamaContingencyTable_noSW.csv')\n",
    "df = df.set_index('speech')\n",
    "\n",
    "#print(df.describe())\n",
    "#print( df.head())\n",
    "\n",
    "ca = CA(ctabshort)\n",
    "\n",
    "plt.figure(100)\n",
    "ca.plot()\n",
    "\n",
    "plt.figure(101)\n",
    "ca.scree_diagram()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a034a215",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91b5d1e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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