fuzzy_one_hot_encoding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ast import literal_eval\n",
    "import ast\n",
    "import numpy as np\n",
    "import math\n",
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from difflib import SequenceMatcher\n",
    "from collections import Counter\n",
    "import itertools\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "\n",
    "from fuzzywuzzy import fuzz\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
    "\n",
    "pd.set_option('display.max_columns', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#jaro version\n",
    "def sort_token_alphabetically(word):\n",
    "    token = re.split('[,. ]', word)\n",
    "    sorted_token = sorted(token)\n",
    "    return ' '.join(sorted_token)\n",
    "\n",
    "def get_jaro_distance(first, second, winkler=True, winkler_ajustment=True,\n",
    "                      scaling=0.1, sort_tokens=True):\n",
    "    \"\"\"\n",
    "    :param first: word to calculate distance for\n",
    "    :param second: word to calculate distance with\n",
    "    :param winkler: same as winkler_ajustment\n",
    "    :param winkler_ajustment: add an adjustment factor to the Jaro of the distance\n",
    "    :param scaling: scaling factor for the Winkler adjustment\n",
    "    :return: Jaro distance adjusted (or not)\n",
    "    \"\"\"\n",
    "    if sort_tokens:\n",
    "        first = sort_token_alphabetically(first)\n",
    "        second = sort_token_alphabetically(second)\n",
    "\n",
    "    if not first or not second:\n",
    "        raise JaroDistanceException(\n",
    "            \"Cannot calculate distance from NoneType ({0}, {1})\".format(\n",
    "                first.__class__.__name__,\n",
    "                second.__class__.__name__))\n",
    "\n",
    "    jaro = _score(first, second)\n",
    "    cl = min(len(_get_prefix(first, second)), 4)\n",
    "\n",
    "    if all([winkler, winkler_ajustment]):  # 0.1 as scaling factor\n",
    "        return round((jaro + (scaling * cl * (1.0 - jaro))) * 100.0) / 100.0\n",
    "\n",
    "    return jaro\n",
    "\n",
    "def _score(first, second):\n",
    "    shorter, longer = first.lower(), second.lower()\n",
    "\n",
    "    if len(first) > len(second):\n",
    "        longer, shorter = shorter, longer\n",
    "\n",
    "    m1 = _get_matching_characters(shorter, longer)\n",
    "    m2 = _get_matching_characters(longer, shorter)\n",
    "\n",
    "    if len(m1) == 0 or len(m2) == 0:\n",
    "        return 0.0\n",
    "\n",
    "    return (float(len(m1)) / len(shorter) +\n",
    "            float(len(m2)) / len(longer) +\n",
    "            float(len(m1) - _transpositions(m1, m2)) / len(m1)) / 3.0\n",
    "\n",
    "def _get_diff_index(first, second):\n",
    "    if first == second:\n",
    "        pass\n",
    "\n",
    "    if not first or not second:\n",
    "        return 0\n",
    "\n",
    "    max_len = min(len(first), len(second))\n",
    "    for i in range(0, max_len):\n",
    "        if not first[i] == second[i]:\n",
    "            return i\n",
    "\n",
    "    return max_len\n",
    "\n",
    "def _get_prefix(first, second):\n",
    "    if not first or not second:\n",
    "        return \"\"\n",
    "\n",
    "    index = _get_diff_index(first, second)\n",
    "    if index == -1:\n",
    "        return first\n",
    "\n",
    "    elif index == 0:\n",
    "        return \"\"\n",
    "\n",
    "    else:\n",
    "        return first[0:index]\n",
    "\n",
    "def _get_matching_characters(first, second):\n",
    "    common = []\n",
    "    limit = math.floor(min(len(first), len(second)) / 2)\n",
    "\n",
    "    for i, l in enumerate(first):\n",
    "        left, right = int(max(0, i - limit)), int(\n",
    "            min(i + limit + 1, len(second)))\n",
    "        if l in second[left:right]:\n",
    "            common.append(l)\n",
    "            second = second[0:second.index(l)] + '*' + second[\n",
    "                                                       second.index(l) + 1:]\n",
    "\n",
    "    return ''.join(common)\n",
    "\n",
    "def _transpositions(first, second):\n",
    "    return math.floor(\n",
    "        len([(f, s) for f, s in zip(first, second) if not f == s]) / 2.0)\n",
    "\n",
    "def get_top_matches(reference, value_list, max_results=None):\n",
    "    scores = []\n",
    "    if not max_results:\n",
    "        max_results = len(value_list)\n",
    "    for val in value_list:\n",
    "        score_sorted = get_jaro_distance(reference, val)\n",
    "        score_unsorted = get_jaro_distance(reference, val, sort_tokens=False)\n",
    "        scores.append((val, max(score_sorted, score_unsorted)))\n",
    "    scores.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    return scores[:max_results]\n",
    "\n",
    "class JaroDistanceException(Exception):\n",
    "    def __init__(self, message):\n",
    "        super(Exception, self).__init__(message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>parent_org_name</th>\n",
       "      <th>children_org_name_list</th>\n",
       "      <th>match_confidence_score</th>\n",
       "      <th>match_confidence</th>\n",
       "      <th>children_org_name</th>\n",
       "      <th>jaro_func_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>+MARTINS</td>\n",
       "      <td>[SAN MARINA]</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>SAN MARINA</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>111 SOUTH</td>\n",
       "      <td>[1&amp;1 IONOS]</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>1&amp;1 IONOS</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>247 CUSTOMER PVT. LTD.</td>\n",
       "      <td>[24/7 CUSTOMER PRIVATE LIMITED]</td>\n",
       "      <td>0.843750</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>24/7 CUSTOMER PRIVATE LIMITED</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3M</td>\n",
       "      <td>[3M]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>3M</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>A. K. ENTERPRISE</td>\n",
       "      <td>[ROUSH ENTERPRISE]</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>ROUSH ENTERPRISE</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           parent_org_name           children_org_name_list  \\\n",
       "3                 +MARTINS                     [SAN MARINA]   \n",
       "8                111 SOUTH                      [1&1 IONOS]   \n",
       "13  247 CUSTOMER PVT. LTD.  [24/7 CUSTOMER PRIVATE LIMITED]   \n",
       "18                      3M                             [3M]   \n",
       "57        A. K. ENTERPRISE               [ROUSH ENTERPRISE]   \n",
       "\n",
       "    match_confidence_score match_confidence              children_org_name  \\\n",
       "3                 0.750000           MEDIUM                     SAN MARINA   \n",
       "8                 0.769231           MEDIUM                      1&1 IONOS   \n",
       "13                0.843750           MEDIUM  24/7 CUSTOMER PRIVATE LIMITED   \n",
       "18                1.000000             HIGH                             3M   \n",
       "57                0.812500           MEDIUM               ROUSH ENTERPRISE   \n",
       "\n",
       "    jaro_func_score  \n",
       "3              0.78  \n",
       "8              0.67  \n",
       "13             0.89  \n",
       "18             1.00  \n",
       "57             0.79  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load csv\n",
    "df = pd.read_csv('bo_attr_lookup_new.csv', encoding='latin1')\n",
    "df['children_org_name'] = df['children_org_name_list']\n",
    "df = df.dropna()\n",
    "\n",
    "#conver child col to list\n",
    "df['children_org_name_list'] = df['children_org_name_list'].map(lambda x: [x])\n",
    "\n",
    "#jaro score\n",
    "df['jaro_func_score'] = df[['parent_org_name', 'children_org_name_list']].agg(lambda x: get_top_matches(*x), axis=1)\n",
    "df['jaro_func_score'] = df.jaro_func_score.apply(lambda x: [val[1] for val in x])\n",
    "df['jaro_func_score'] = df.jaro_func_score.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "\n",
    "#create df of just medium and high\n",
    "df1 = df[df['match_confidence'].isin(['MEDIUM','HIGH'])]\n",
    "\n",
    "#create low df\n",
    "df2 = df[df['match_confidence'] == 'LOW']\n",
    "\n",
    "#create sample of 2k rows of low df\n",
    "df2 = df2.sample(2000)\n",
    "\n",
    "#concat dfs\n",
    "frames = [df1,df2]\n",
    "df = pd.concat(frames)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>parent_org_name</th>\n",
       "      <th>children_org_name_list</th>\n",
       "      <th>match_confidence_score</th>\n",
       "      <th>match_confidence</th>\n",
       "      <th>children_org_name</th>\n",
       "      <th>jaro_func_score</th>\n",
       "      <th>total_chars_positions</th>\n",
       "      <th>consecutive_chars</th>\n",
       "      <th>sequence_matcher_ratio</th>\n",
       "      <th>count_same_position</th>\n",
       "      <th>count_total_unique_chars</th>\n",
       "      <th>count_common_chars</th>\n",
       "      <th>percent_matched</th>\n",
       "      <th>percent_same_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>martin</td>\n",
       "      <td>[SAN MARINA]</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>san marina</td>\n",
       "      <td>0.78</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>111 south</td>\n",
       "      <td>[1&amp;1 IONOS]</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>11 iono</td>\n",
       "      <td>0.67</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.44</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>247 custom pvt ltd</td>\n",
       "      <td>[24/7 CUSTOMER PRIVATE LIMITED]</td>\n",
       "      <td>0.843750</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>247 custom privat limit</td>\n",
       "      <td>0.89</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3M</td>\n",
       "      <td>[3M]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>3M</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>A K enterpris</td>\n",
       "      <td>[ROUSH ENTERPRISE]</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>roush enterpris</td>\n",
       "      <td>0.79</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.69</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>ab edward enterpris</td>\n",
       "      <td>[A. G. EDWARDS &amp; SONS]</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>A G edward  son</td>\n",
       "      <td>0.73</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>aap co ltd</td>\n",
       "      <td>[AAP LEHRERFACHVERLAGE]</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>aap lehrerfachverlag</td>\n",
       "      <td>0.73</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>abb</td>\n",
       "      <td>[ABBVIE]</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>abbvi</td>\n",
       "      <td>0.88</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>abbvi</td>\n",
       "      <td>[ABBVIE]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>abbvi</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>abercrombi fitch</td>\n",
       "      <td>[ABERCROMBIE &amp; FITCH]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>abercrombi  fitch</td>\n",
       "      <td>0.98</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         parent_org_name           children_org_name_list  \\\n",
       "3                 martin                     [SAN MARINA]   \n",
       "8              111 south                      [1&1 IONOS]   \n",
       "13    247 custom pvt ltd  [24/7 CUSTOMER PRIVATE LIMITED]   \n",
       "18                    3M                             [3M]   \n",
       "57         A K enterpris               [ROUSH ENTERPRISE]   \n",
       "58   ab edward enterpris           [A. G. EDWARDS & SONS]   \n",
       "70            aap co ltd          [AAP LEHRERFACHVERLAGE]   \n",
       "87                   abb                         [ABBVIE]   \n",
       "94                 abbvi                         [ABBVIE]   \n",
       "111     abercrombi fitch            [ABERCROMBIE & FITCH]   \n",
       "\n",
       "     match_confidence_score match_confidence        children_org_name  \\\n",
       "3                  0.750000           MEDIUM               san marina   \n",
       "8                  0.769231           MEDIUM                  11 iono   \n",
       "13                 0.843750           MEDIUM  247 custom privat limit   \n",
       "18                 1.000000             HIGH                       3M   \n",
       "57                 0.812500           MEDIUM          roush enterpris   \n",
       "58                 0.769231           MEDIUM          A G edward  son   \n",
       "70                 0.900000             HIGH     aap lehrerfachverlag   \n",
       "87                 0.750000           MEDIUM                    abbvi   \n",
       "94                 1.000000             HIGH                    abbvi   \n",
       "111                1.000000             HIGH        abercrombi  fitch   \n",
       "\n",
       "     jaro_func_score  total_chars_positions  consecutive_chars  \\\n",
       "3               0.78                   10.0                3.0   \n",
       "8               0.67                    9.0                2.0   \n",
       "13              0.89                   29.0               12.0   \n",
       "18              1.00                    2.0                2.0   \n",
       "57              0.79                   16.0               11.0   \n",
       "58              0.73                   20.0               10.0   \n",
       "70              0.73                   21.0                4.0   \n",
       "87              0.88                    6.0                3.0   \n",
       "94              1.00                    6.0                6.0   \n",
       "111             0.98                   19.0               12.0   \n",
       "\n",
       "     sequence_matcher_ratio  count_same_position  count_total_unique_chars  \\\n",
       "3                      0.56                  0.0                      15.0   \n",
       "8                      0.44                  4.0                      14.0   \n",
       "13                     0.78                  3.0                      36.0   \n",
       "18                     1.00                  2.0                       4.0   \n",
       "57                     0.69                 11.0                      22.0   \n",
       "58                     0.64                  2.0                      25.0   \n",
       "70                     0.30                  4.0                      20.0   \n",
       "87                     0.67                  3.0                       7.0   \n",
       "94                     1.00                  6.0                      10.0   \n",
       "111                    0.94                 12.0                      25.0   \n",
       "\n",
       "     count_common_chars  percent_matched  percent_same_position  \n",
       "3                   6.0             0.40                   0.00  \n",
       "8                   4.0             0.29                   0.44  \n",
       "13                 16.0             0.44                   0.10  \n",
       "18                  2.0             0.50                   1.00  \n",
       "57                  8.0             0.36                   0.69  \n",
       "58                  9.0             0.36                   0.10  \n",
       "70                  5.0             0.25                   0.19  \n",
       "87                  2.0             0.29                   0.50  \n",
       "94                  5.0             0.50                   1.00  \n",
       "111                12.0             0.48                   0.63  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def total_chars(row):\n",
    "\n",
    "    results0 = []\n",
    "    results1 = []\n",
    "    results2 = []\n",
    "    results3 = []\n",
    "    results4 = []\n",
    "    results5 = []\n",
    "    \n",
    "    p = row['parent_org_name'].lower()\n",
    "    for i in row['children_org_name_list']:\n",
    "        \n",
    "        t = i.lower()\n",
    "        \n",
    "        dict1 = Counter(p)\n",
    "        dict2 = Counter(t)\n",
    "        \n",
    "        commonDict = dict1 & dict2\n",
    "        allChars = len(dict1) + len(dict2)\n",
    "        childCharsPositions = len(t)\n",
    "        \n",
    "        results0.append((i, childCharsPositions)[1]) #totalCharacterPositions\n",
    "        results1.append((i, SequenceMatcher(None, t, p).find_longest_match(0, len(t), 0, len(p))[2])[1]) #consecutiveMatchingChars\n",
    "        results2.append((i, round(SequenceMatcher(None, t, p).ratio(),2))[1]) #sequence_matcher_ratio\n",
    "        results3.append((i, sum(int(k==v) for k,v in zip(t, p)))[1]) #countSamePosition\n",
    "        results4.append((i, allChars)[1])#countTotalUniqueChars\n",
    "        results5.append((i, len(commonDict))[1]) #countCommonChars\n",
    "\n",
    "    return pd.Series([results0, results1, results2, results3, results4, results5])\n",
    "\n",
    "df[['total_chars_positions'\n",
    "    , 'consecutive_chars'\n",
    "    , 'sequence_matcher_ratio'\n",
    "    , 'count_same_position'\n",
    "    , 'count_total_unique_chars'\n",
    "    , 'count_common_chars'\n",
    "   ]]  = df.apply(total_chars, axis=1)\n",
    "\n",
    "df['total_chars_positions'] = df.total_chars_positions.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "df['consecutive_chars'] = df.consecutive_chars.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "df['sequence_matcher_ratio'] = df.sequence_matcher_ratio.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "df['count_same_position'] = df.count_same_position.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "df['count_total_unique_chars'] = df.count_total_unique_chars.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "df['count_common_chars'] = df.count_common_chars.apply(lambda x: ', '.join([str(i) for i in x])).astype(float)\n",
    "\n",
    "df['percent_matched'] = round(df['count_common_chars'] / df['count_total_unique_chars'],2)\n",
    "df['percent_same_position'] = round(df['count_same_position'] / df['total_chars_positions'],2)\n",
    "\n",
    "#remove stop words\n",
    "stop = stopwords.words('english')\n",
    "df['parent_org_name'] = df['parent_org_name'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
    "df['children_org_name'] = df['children_org_name'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
    "\n",
    "#apply stemming\n",
    "porter_stemmer = PorterStemmer()\n",
    "def stem_sentences(sentence):\n",
    "    tokens = sentence.split()\n",
    "    stemmed_tokens = [porter_stemmer.stem(token) for token in tokens]\n",
    "    return ' '.join(stemmed_tokens)\n",
    "\n",
    "df['parent_org_name'] = df['parent_org_name'].apply(stem_sentences)\n",
    "df['children_org_name'] = df['children_org_name'].apply(stem_sentences)\n",
    "\n",
    "#remove special characters\n",
    "spec_chars = [\"!\",'\"',\"#\",\"%\",\"&\",\"'\",\"(\",\")\",\n",
    "              \"*\",\"+\",\",\",\"-\",\".\",\"/\",\":\",\";\",\"<\",\n",
    "              \"=\",\">\",\"?\",\"@\",\"[\",\"\\\\\",\"]\",\"^\",\"_\",\n",
    "              \"`\",\"{\",\"|\",\"}\",\"~\",\"–\"]\n",
    "for char in spec_chars:\n",
    "    df['parent_org_name'] = df['parent_org_name'].str.replace(char, '')\n",
    "    df['children_org_name'] = df['children_org_name'].str.replace(char, '')\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>63</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
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       "      <th>72</th>\n",
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       "      <th>74</th>\n",
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       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>80</th>\n",
       "      <th>81</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>85</th>\n",
       "      <th>86</th>\n",
       "      <th>87</th>\n",
       "      <th>88</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>101</th>\n",
       "      <th>102</th>\n",
       "      <th>103</th>\n",
       "      <th>104</th>\n",
       "      <th>105</th>\n",
       "      <th>106</th>\n",
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       "      <th>110</th>\n",
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       "      <th>112</th>\n",
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       "      <th>118</th>\n",
       "      <th>119</th>\n",
       "      <th>120</th>\n",
       "      <th>121</th>\n",
       "      <th>122</th>\n",
       "      <th>123</th>\n",
       "      <th>124</th>\n",
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       "      <th>126</th>\n",
       "      <th>127</th>\n",
       "      <th>128</th>\n",
       "      <th>129</th>\n",
       "      <th>130</th>\n",
       "      <th>131</th>\n",
       "      <th>132</th>\n",
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       "      <th>134</th>\n",
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       "      <th>136</th>\n",
       "      <th>137</th>\n",
       "      <th>138</th>\n",
       "      <th>139</th>\n",
       "      <th>140</th>\n",
       "      <th>141</th>\n",
       "      <th>142</th>\n",
       "      <th>143</th>\n",
       "      <th>144</th>\n",
       "      <th>145</th>\n",
       "      <th>146</th>\n",
       "      <th>147</th>\n",
       "      <th>148</th>\n",
       "      <th>149</th>\n",
       "      <th>150</th>\n",
       "      <th>151</th>\n",
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       "      <th>153</th>\n",
       "      <th>154</th>\n",
       "      <th>155</th>\n",
       "      <th>156</th>\n",
       "      <th>157</th>\n",
       "      <th>158</th>\n",
       "      <th>159</th>\n",
       "      <th>160</th>\n",
       "      <th>161</th>\n",
       "      <th>162</th>\n",
       "      <th>163</th>\n",
       "      <th>164</th>\n",
       "      <th>165</th>\n",
       "      <th>166</th>\n",
       "      <th>167</th>\n",
       "      <th>168</th>\n",
       "      <th>169</th>\n",
       "      <th>170</th>\n",
       "      <th>171</th>\n",
       "      <th>172</th>\n",
       "      <th>173</th>\n",
       "      <th>174</th>\n",
       "      <th>175</th>\n",
       "      <th>176</th>\n",
       "      <th>177</th>\n",
       "      <th>178</th>\n",
       "      <th>179</th>\n",
       "      <th>180</th>\n",
       "      <th>181</th>\n",
       "      <th>182</th>\n",
       "      <th>183</th>\n",
       "      <th>184</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "      <th>188</th>\n",
       "      <th>189</th>\n",
       "      <th>190</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "      <th>193</th>\n",
       "      <th>194</th>\n",
       "      <th>195</th>\n",
       "      <th>196</th>\n",
       "      <th>197</th>\n",
       "      <th>198</th>\n",
       "      <th>199</th>\n",
       "      <th>200</th>\n",
       "      <th>201</th>\n",
       "      <th>202</th>\n",
       "      <th>203</th>\n",
       "      <th>204</th>\n",
       "      <th>205</th>\n",
       "      <th>206</th>\n",
       "      <th>207</th>\n",
       "      <th>208</th>\n",
       "      <th>209</th>\n",
       "      <th>210</th>\n",
       "      <th>211</th>\n",
       "      <th>212</th>\n",
       "      <th>213</th>\n",
       "      <th>214</th>\n",
       "      <th>215</th>\n",
       "      <th>216</th>\n",
       "      <th>217</th>\n",
       "      <th>218</th>\n",
       "      <th>219</th>\n",
       "      <th>220</th>\n",
       "      <th>221</th>\n",
       "      <th>222</th>\n",
       "      <th>223</th>\n",
       "      <th>224</th>\n",
       "      <th>225</th>\n",
       "      <th>226</th>\n",
       "      <th>227</th>\n",
       "      <th>228</th>\n",
       "      <th>229</th>\n",
       "      <th>230</th>\n",
       "      <th>231</th>\n",
       "      <th>232</th>\n",
       "      <th>233</th>\n",
       "      <th>234</th>\n",
       "      <th>235</th>\n",
       "      <th>236</th>\n",
       "      <th>237</th>\n",
       "      <th>238</th>\n",
       "      <th>239</th>\n",
       "      <th>240</th>\n",
       "      <th>241</th>\n",
       "      <th>242</th>\n",
       "      <th>243</th>\n",
       "      <th>244</th>\n",
       "      <th>245</th>\n",
       "      <th>246</th>\n",
       "      <th>247</th>\n",
       "      <th>248</th>\n",
       "      <th>249</th>\n",
       "      <th>250</th>\n",
       "      <th>251</th>\n",
       "      <th>252</th>\n",
       "      <th>253</th>\n",
       "      <th>254</th>\n",
       "      <th>255</th>\n",
       "      <th>256</th>\n",
       "      <th>257</th>\n",
       "      <th>258</th>\n",
       "      <th>259</th>\n",
       "      <th>260</th>\n",
       "      <th>261</th>\n",
       "      <th>262</th>\n",
       "      <th>263</th>\n",
       "      <th>264</th>\n",
       "      <th>265</th>\n",
       "      <th>266</th>\n",
       "      <th>267</th>\n",
       "      <th>268</th>\n",
       "      <th>269</th>\n",
       "      <th>270</th>\n",
       "      <th>271</th>\n",
       "      <th>272</th>\n",
       "      <th>273</th>\n",
       "      <th>274</th>\n",
       "      <th>275</th>\n",
       "      <th>276</th>\n",
       "      <th>277</th>\n",
       "      <th>278</th>\n",
       "      <th>279</th>\n",
       "      <th>280</th>\n",
       "      <th>281</th>\n",
       "      <th>282</th>\n",
       "      <th>283</th>\n",
       "      <th>284</th>\n",
       "      <th>285</th>\n",
       "      <th>286</th>\n",
       "      <th>287</th>\n",
       "      <th>288</th>\n",
       "      <th>289</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
       "      <th>293</th>\n",
       "      <th>294</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "      <th>300</th>\n",
       "      <th>301</th>\n",
       "      <th>302</th>\n",
       "      <th>303</th>\n",
       "      <th>304</th>\n",
       "      <th>305</th>\n",
       "      <th>306</th>\n",
       "      <th>307</th>\n",
       "      <th>308</th>\n",
       "      <th>309</th>\n",
       "      <th>310</th>\n",
       "      <th>311</th>\n",
       "      <th>312</th>\n",
       "      <th>313</th>\n",
       "      <th>314</th>\n",
       "      <th>315</th>\n",
       "      <th>316</th>\n",
       "      <th>317</th>\n",
       "      <th>318</th>\n",
       "      <th>319</th>\n",
       "      <th>320</th>\n",
       "      <th>321</th>\n",
       "      <th>322</th>\n",
       "      <th>323</th>\n",
       "      <th>324</th>\n",
       "      <th>325</th>\n",
       "      <th>326</th>\n",
       "      <th>327</th>\n",
       "      <th>328</th>\n",
       "      <th>329</th>\n",
       "      <th>330</th>\n",
       "      <th>331</th>\n",
       "      <th>332</th>\n",
       "      <th>333</th>\n",
       "      <th>334</th>\n",
       "      <th>335</th>\n",
       "      <th>336</th>\n",
       "      <th>337</th>\n",
       "      <th>338</th>\n",
       "      <th>339</th>\n",
       "      <th>340</th>\n",
       "      <th>341</th>\n",
       "      <th>342</th>\n",
       "      <th>343</th>\n",
       "      <th>344</th>\n",
       "      <th>345</th>\n",
       "      <th>346</th>\n",
       "      <th>347</th>\n",
       "      <th>348</th>\n",
       "      <th>349</th>\n",
       "      <th>350</th>\n",
       "      <th>351</th>\n",
       "      <th>352</th>\n",
       "      <th>353</th>\n",
       "      <th>354</th>\n",
       "      <th>355</th>\n",
       "      <th>356</th>\n",
       "      <th>357</th>\n",
       "      <th>358</th>\n",
       "      <th>359</th>\n",
       "      <th>360</th>\n",
       "      <th>361</th>\n",
       "      <th>362</th>\n",
       "      <th>363</th>\n",
       "      <th>364</th>\n",
       "      <th>365</th>\n",
       "      <th>366</th>\n",
       "      <th>367</th>\n",
       "      <th>368</th>\n",
       "      <th>369</th>\n",
       "      <th>370</th>\n",
       "      <th>371</th>\n",
       "      <th>372</th>\n",
       "      <th>373</th>\n",
       "      <th>374</th>\n",
       "      <th>375</th>\n",
       "      <th>376</th>\n",
       "      <th>377</th>\n",
       "      <th>378</th>\n",
       "      <th>379</th>\n",
       "      <th>380</th>\n",
       "      <th>381</th>\n",
       "      <th>382</th>\n",
       "      <th>383</th>\n",
       "      <th>384</th>\n",
       "      <th>385</th>\n",
       "      <th>386</th>\n",
       "      <th>387</th>\n",
       "      <th>388</th>\n",
       "      <th>389</th>\n",
       "      <th>390</th>\n",
       "      <th>391</th>\n",
       "      <th>392</th>\n",
       "      <th>393</th>\n",
       "      <th>394</th>\n",
       "      <th>395</th>\n",
       "      <th>396</th>\n",
       "      <th>397</th>\n",
       "      <th>398</th>\n",
       "      <th>399</th>\n",
       "      <th>400</th>\n",
       "      <th>401</th>\n",
       "      <th>402</th>\n",
       "      <th>403</th>\n",
       "      <th>404</th>\n",
       "      <th>405</th>\n",
       "      <th>406</th>\n",
       "      <th>407</th>\n",
       "      <th>408</th>\n",
       "      <th>409</th>\n",
       "      <th>410</th>\n",
       "      <th>411</th>\n",
       "      <th>412</th>\n",
       "      <th>413</th>\n",
       "      <th>414</th>\n",
       "      <th>415</th>\n",
       "      <th>416</th>\n",
       "      <th>417</th>\n",
       "      <th>418</th>\n",
       "      <th>419</th>\n",
       "      <th>420</th>\n",
       "      <th>421</th>\n",
       "      <th>422</th>\n",
       "      <th>423</th>\n",
       "      <th>424</th>\n",
       "      <th>425</th>\n",
       "      <th>426</th>\n",
       "      <th>427</th>\n",
       "      <th>428</th>\n",
       "      <th>429</th>\n",
       "      <th>430</th>\n",
       "      <th>431</th>\n",
       "      <th>432</th>\n",
       "      <th>433</th>\n",
       "      <th>434</th>\n",
       "      <th>435</th>\n",
       "      <th>436</th>\n",
       "      <th>437</th>\n",
       "      <th>438</th>\n",
       "      <th>439</th>\n",
       "      <th>440</th>\n",
       "      <th>441</th>\n",
       "      <th>442</th>\n",
       "      <th>443</th>\n",
       "      <th>444</th>\n",
       "      <th>445</th>\n",
       "      <th>446</th>\n",
       "      <th>447</th>\n",
       "      <th>448</th>\n",
       "      <th>449</th>\n",
       "      <th>450</th>\n",
       "      <th>451</th>\n",
       "      <th>452</th>\n",
       "      <th>453</th>\n",
       "      <th>454</th>\n",
       "      <th>455</th>\n",
       "      <th>456</th>\n",
       "      <th>457</th>\n",
       "      <th>458</th>\n",
       "      <th>459</th>\n",
       "      <th>460</th>\n",
       "      <th>461</th>\n",
       "      <th>462</th>\n",
       "      <th>463</th>\n",
       "      <th>464</th>\n",
       "      <th>465</th>\n",
       "      <th>466</th>\n",
       "      <th>467</th>\n",
       "      <th>468</th>\n",
       "      <th>469</th>\n",
       "      <th>470</th>\n",
       "      <th>471</th>\n",
       "      <th>472</th>\n",
       "      <th>473</th>\n",
       "      <th>474</th>\n",
       "      <th>475</th>\n",
       "      <th>476</th>\n",
       "      <th>477</th>\n",
       "      <th>478</th>\n",
       "      <th>479</th>\n",
       "      <th>480</th>\n",
       "      <th>481</th>\n",
       "      <th>482</th>\n",
       "      <th>483</th>\n",
       "      <th>484</th>\n",
       "      <th>485</th>\n",
       "      <th>486</th>\n",
       "      <th>487</th>\n",
       "      <th>488</th>\n",
       "      <th>489</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "      <th>500</th>\n",
       "      <th>501</th>\n",
       "      <th>...</th>\n",
       "      <th>5642</th>\n",
       "      <th>5643</th>\n",
       "      <th>5644</th>\n",
       "      <th>5645</th>\n",
       "      <th>5646</th>\n",
       "      <th>5647</th>\n",
       "      <th>5648</th>\n",
       "      <th>5649</th>\n",
       "      <th>5650</th>\n",
       "      <th>5651</th>\n",
       "      <th>5652</th>\n",
       "      <th>5653</th>\n",
       "      <th>5654</th>\n",
       "      <th>5655</th>\n",
       "      <th>5656</th>\n",
       "      <th>5657</th>\n",
       "      <th>5658</th>\n",
       "      <th>5659</th>\n",
       "      <th>5660</th>\n",
       "      <th>5661</th>\n",
       "      <th>5662</th>\n",
       "      <th>5663</th>\n",
       "      <th>5664</th>\n",
       "      <th>5665</th>\n",
       "      <th>5666</th>\n",
       "      <th>5667</th>\n",
       "      <th>5668</th>\n",
       "      <th>5669</th>\n",
       "      <th>5670</th>\n",
       "      <th>5671</th>\n",
       "      <th>5672</th>\n",
       "      <th>5673</th>\n",
       "      <th>5674</th>\n",
       "      <th>5675</th>\n",
       "      <th>5676</th>\n",
       "      <th>5677</th>\n",
       "      <th>5678</th>\n",
       "      <th>5679</th>\n",
       "      <th>5680</th>\n",
       "      <th>5681</th>\n",
       "      <th>5682</th>\n",
       "      <th>5683</th>\n",
       "      <th>5684</th>\n",
       "      <th>5685</th>\n",
       "      <th>5686</th>\n",
       "      <th>5687</th>\n",
       "      <th>5688</th>\n",
       "      <th>5689</th>\n",
       "      <th>5690</th>\n",
       "      <th>5691</th>\n",
       "      <th>5692</th>\n",
       "      <th>5693</th>\n",
       "      <th>5694</th>\n",
       "      <th>5695</th>\n",
       "      <th>5696</th>\n",
       "      <th>5697</th>\n",
       "      <th>5698</th>\n",
       "      <th>5699</th>\n",
       "      <th>5700</th>\n",
       "      <th>5701</th>\n",
       "      <th>5702</th>\n",
       "      <th>5703</th>\n",
       "      <th>5704</th>\n",
       "      <th>5705</th>\n",
       "      <th>5706</th>\n",
       "      <th>5707</th>\n",
       "      <th>5708</th>\n",
       "      <th>5709</th>\n",
       "      <th>5710</th>\n",
       "      <th>5711</th>\n",
       "      <th>5712</th>\n",
       "      <th>5713</th>\n",
       "      <th>5714</th>\n",
       "      <th>5715</th>\n",
       "      <th>5716</th>\n",
       "      <th>5717</th>\n",
       "      <th>5718</th>\n",
       "      <th>5719</th>\n",
       "      <th>5720</th>\n",
       "      <th>5721</th>\n",
       "      <th>5722</th>\n",
       "      <th>5723</th>\n",
       "      <th>5724</th>\n",
       "      <th>5725</th>\n",
       "      <th>5726</th>\n",
       "      <th>5727</th>\n",
       "      <th>5728</th>\n",
       "      <th>5729</th>\n",
       "      <th>5730</th>\n",
       "      <th>5731</th>\n",
       "      <th>5732</th>\n",
       "      <th>5733</th>\n",
       "      <th>5734</th>\n",
       "      <th>5735</th>\n",
       "      <th>5736</th>\n",
       "      <th>5737</th>\n",
       "      <th>5738</th>\n",
       "      <th>5739</th>\n",
       "      <th>5740</th>\n",
       "      <th>5741</th>\n",
       "      <th>5742</th>\n",
       "      <th>5743</th>\n",
       "      <th>5744</th>\n",
       "      <th>5745</th>\n",
       "      <th>5746</th>\n",
       "      <th>5747</th>\n",
       "      <th>5748</th>\n",
       "      <th>5749</th>\n",
       "      <th>5750</th>\n",
       "      <th>5751</th>\n",
       "      <th>5752</th>\n",
       "      <th>5753</th>\n",
       "      <th>5754</th>\n",
       "      <th>5755</th>\n",
       "      <th>5756</th>\n",
       "      <th>5757</th>\n",
       "      <th>5758</th>\n",
       "      <th>5759</th>\n",
       "      <th>5760</th>\n",
       "      <th>5761</th>\n",
       "      <th>5762</th>\n",
       "      <th>5763</th>\n",
       "      <th>5764</th>\n",
       "      <th>5765</th>\n",
       "      <th>5766</th>\n",
       "      <th>5767</th>\n",
       "      <th>5768</th>\n",
       "      <th>5769</th>\n",
       "      <th>5770</th>\n",
       "      <th>5771</th>\n",
       "      <th>5772</th>\n",
       "      <th>5773</th>\n",
       "      <th>5774</th>\n",
       "      <th>5775</th>\n",
       "      <th>5776</th>\n",
       "      <th>5777</th>\n",
       "      <th>5778</th>\n",
       "      <th>5779</th>\n",
       "      <th>5780</th>\n",
       "      <th>5781</th>\n",
       "      <th>5782</th>\n",
       "      <th>5783</th>\n",
       "      <th>5784</th>\n",
       "      <th>5785</th>\n",
       "      <th>5786</th>\n",
       "      <th>5787</th>\n",
       "      <th>5788</th>\n",
       "      <th>5789</th>\n",
       "      <th>5790</th>\n",
       "      <th>5791</th>\n",
       "      <th>5792</th>\n",
       "      <th>5793</th>\n",
       "      <th>5794</th>\n",
       "      <th>5795</th>\n",
       "      <th>5796</th>\n",
       "      <th>5797</th>\n",
       "      <th>5798</th>\n",
       "      <th>5799</th>\n",
       "      <th>5800</th>\n",
       "      <th>5801</th>\n",
       "      <th>5802</th>\n",
       "      <th>5803</th>\n",
       "      <th>5804</th>\n",
       "      <th>5805</th>\n",
       "      <th>5806</th>\n",
       "      <th>5807</th>\n",
       "      <th>5808</th>\n",
       "      <th>5809</th>\n",
       "      <th>5810</th>\n",
       "      <th>5811</th>\n",
       "      <th>5812</th>\n",
       "      <th>5813</th>\n",
       "      <th>5814</th>\n",
       "      <th>5815</th>\n",
       "      <th>5816</th>\n",
       "      <th>5817</th>\n",
       "      <th>5818</th>\n",
       "      <th>5819</th>\n",
       "      <th>5820</th>\n",
       "      <th>5821</th>\n",
       "      <th>5822</th>\n",
       "      <th>5823</th>\n",
       "      <th>5824</th>\n",
       "      <th>5825</th>\n",
       "      <th>5826</th>\n",
       "      <th>5827</th>\n",
       "      <th>5828</th>\n",
       "      <th>5829</th>\n",
       "      <th>5830</th>\n",
       "      <th>5831</th>\n",
       "      <th>5832</th>\n",
       "      <th>5833</th>\n",
       "      <th>5834</th>\n",
       "      <th>5835</th>\n",
       "      <th>5836</th>\n",
       "      <th>5837</th>\n",
       "      <th>5838</th>\n",
       "      <th>5839</th>\n",
       "      <th>5840</th>\n",
       "      <th>5841</th>\n",
       "      <th>5842</th>\n",
       "      <th>5843</th>\n",
       "      <th>5844</th>\n",
       "      <th>5845</th>\n",
       "      <th>5846</th>\n",
       "      <th>5847</th>\n",
       "      <th>5848</th>\n",
       "      <th>5849</th>\n",
       "      <th>5850</th>\n",
       "      <th>5851</th>\n",
       "      <th>5852</th>\n",
       "      <th>5853</th>\n",
       "      <th>5854</th>\n",
       "      <th>5855</th>\n",
       "      <th>5856</th>\n",
       "      <th>5857</th>\n",
       "      <th>5858</th>\n",
       "      <th>5859</th>\n",
       "      <th>5860</th>\n",
       "      <th>5861</th>\n",
       "      <th>5862</th>\n",
       "      <th>5863</th>\n",
       "      <th>5864</th>\n",
       "      <th>5865</th>\n",
       "      <th>5866</th>\n",
       "      <th>5867</th>\n",
       "      <th>5868</th>\n",
       "      <th>5869</th>\n",
       "      <th>5870</th>\n",
       "      <th>5871</th>\n",
       "      <th>5872</th>\n",
       "      <th>5873</th>\n",
       "      <th>5874</th>\n",
       "      <th>5875</th>\n",
       "      <th>5876</th>\n",
       "      <th>5877</th>\n",
       "      <th>5878</th>\n",
       "      <th>5879</th>\n",
       "      <th>5880</th>\n",
       "      <th>5881</th>\n",
       "      <th>5882</th>\n",
       "      <th>5883</th>\n",
       "      <th>5884</th>\n",
       "      <th>5885</th>\n",
       "      <th>5886</th>\n",
       "      <th>5887</th>\n",
       "      <th>5888</th>\n",
       "      <th>5889</th>\n",
       "      <th>5890</th>\n",
       "      <th>5891</th>\n",
       "      <th>5892</th>\n",
       "      <th>5893</th>\n",
       "      <th>5894</th>\n",
       "      <th>5895</th>\n",
       "      <th>5896</th>\n",
       "      <th>5897</th>\n",
       "      <th>5898</th>\n",
       "      <th>5899</th>\n",
       "      <th>5900</th>\n",
       "      <th>5901</th>\n",
       "      <th>5902</th>\n",
       "      <th>5903</th>\n",
       "      <th>5904</th>\n",
       "      <th>5905</th>\n",
       "      <th>5906</th>\n",
       "      <th>5907</th>\n",
       "      <th>5908</th>\n",
       "      <th>5909</th>\n",
       "      <th>5910</th>\n",
       "      <th>5911</th>\n",
       "      <th>5912</th>\n",
       "      <th>5913</th>\n",
       "      <th>5914</th>\n",
       "      <th>5915</th>\n",
       "      <th>5916</th>\n",
       "      <th>5917</th>\n",
       "      <th>5918</th>\n",
       "      <th>5919</th>\n",
       "      <th>5920</th>\n",
       "      <th>5921</th>\n",
       "      <th>5922</th>\n",
       "      <th>5923</th>\n",
       "      <th>5924</th>\n",
       "      <th>5925</th>\n",
       "      <th>5926</th>\n",
       "      <th>5927</th>\n",
       "      <th>5928</th>\n",
       "      <th>5929</th>\n",
       "      <th>5930</th>\n",
       "      <th>5931</th>\n",
       "      <th>5932</th>\n",
       "      <th>5933</th>\n",
       "      <th>5934</th>\n",
       "      <th>5935</th>\n",
       "      <th>5936</th>\n",
       "      <th>5937</th>\n",
       "      <th>5938</th>\n",
       "      <th>5939</th>\n",
       "      <th>5940</th>\n",
       "      <th>5941</th>\n",
       "      <th>5942</th>\n",
       "      <th>5943</th>\n",
       "      <th>5944</th>\n",
       "      <th>5945</th>\n",
       "      <th>5946</th>\n",
       "      <th>5947</th>\n",
       "      <th>5948</th>\n",
       "      <th>5949</th>\n",
       "      <th>5950</th>\n",
       "      <th>5951</th>\n",
       "      <th>5952</th>\n",
       "      <th>5953</th>\n",
       "      <th>5954</th>\n",
       "      <th>5955</th>\n",
       "      <th>5956</th>\n",
       "      <th>5957</th>\n",
       "      <th>5958</th>\n",
       "      <th>5959</th>\n",
       "      <th>5960</th>\n",
       "      <th>5961</th>\n",
       "      <th>5962</th>\n",
       "      <th>5963</th>\n",
       "      <th>5964</th>\n",
       "      <th>5965</th>\n",
       "      <th>5966</th>\n",
       "      <th>5967</th>\n",
       "      <th>5968</th>\n",
       "      <th>5969</th>\n",
       "      <th>5970</th>\n",
       "      <th>5971</th>\n",
       "      <th>5972</th>\n",
       "      <th>5973</th>\n",
       "      <th>5974</th>\n",
       "      <th>5975</th>\n",
       "      <th>5976</th>\n",
       "      <th>5977</th>\n",
       "      <th>5978</th>\n",
       "      <th>5979</th>\n",
       "      <th>5980</th>\n",
       "      <th>5981</th>\n",
       "      <th>5982</th>\n",
       "      <th>5983</th>\n",
       "      <th>5984</th>\n",
       "      <th>5985</th>\n",
       "      <th>5986</th>\n",
       "      <th>5987</th>\n",
       "      <th>5988</th>\n",
       "      <th>5989</th>\n",
       "      <th>5990</th>\n",
       "      <th>5991</th>\n",
       "      <th>5992</th>\n",
       "      <th>5993</th>\n",
       "      <th>5994</th>\n",
       "      <th>5995</th>\n",
       "      <th>5996</th>\n",
       "      <th>5997</th>\n",
       "      <th>5998</th>\n",
       "      <th>5999</th>\n",
       "      <th>6000</th>\n",
       "      <th>6001</th>\n",
       "      <th>6002</th>\n",
       "      <th>6003</th>\n",
       "      <th>6004</th>\n",
       "      <th>6005</th>\n",
       "      <th>6006</th>\n",
       "      <th>6007</th>\n",
       "      <th>6008</th>\n",
       "      <th>6009</th>\n",
       "      <th>6010</th>\n",
       "      <th>6011</th>\n",
       "      <th>6012</th>\n",
       "      <th>6013</th>\n",
       "      <th>6014</th>\n",
       "      <th>6015</th>\n",
       "      <th>6016</th>\n",
       "      <th>6017</th>\n",
       "      <th>6018</th>\n",
       "      <th>6019</th>\n",
       "      <th>6020</th>\n",
       "      <th>6021</th>\n",
       "      <th>6022</th>\n",
       "      <th>6023</th>\n",
       "      <th>6024</th>\n",
       "      <th>6025</th>\n",
       "      <th>6026</th>\n",
       "      <th>6027</th>\n",
       "      <th>6028</th>\n",
       "      <th>6029</th>\n",
       "      <th>6030</th>\n",
       "      <th>6031</th>\n",
       "      <th>6032</th>\n",
       "      <th>6033</th>\n",
       "      <th>6034</th>\n",
       "      <th>6035</th>\n",
       "      <th>6036</th>\n",
       "      <th>6037</th>\n",
       "      <th>6038</th>\n",
       "      <th>6039</th>\n",
       "      <th>6040</th>\n",
       "      <th>6041</th>\n",
       "      <th>6042</th>\n",
       "      <th>6043</th>\n",
       "      <th>6044</th>\n",
       "      <th>6045</th>\n",
       "      <th>6046</th>\n",
       "      <th>6047</th>\n",
       "      <th>6048</th>\n",
       "      <th>6049</th>\n",
       "      <th>6050</th>\n",
       "      <th>6051</th>\n",
       "      <th>6052</th>\n",
       "      <th>6053</th>\n",
       "      <th>6054</th>\n",
       "      <th>6055</th>\n",
       "      <th>6056</th>\n",
       "      <th>6057</th>\n",
       "      <th>6058</th>\n",
       "      <th>6059</th>\n",
       "      <th>6060</th>\n",
       "      <th>6061</th>\n",
       "      <th>6062</th>\n",
       "      <th>6063</th>\n",
       "      <th>6064</th>\n",
       "      <th>6065</th>\n",
       "      <th>6066</th>\n",
       "      <th>6067</th>\n",
       "      <th>6068</th>\n",
       "      <th>6069</th>\n",
       "      <th>6070</th>\n",
       "      <th>6071</th>\n",
       "      <th>6072</th>\n",
       "      <th>6073</th>\n",
       "      <th>6074</th>\n",
       "      <th>6075</th>\n",
       "      <th>6076</th>\n",
       "      <th>6077</th>\n",
       "      <th>6078</th>\n",
       "      <th>6079</th>\n",
       "      <th>6080</th>\n",
       "      <th>6081</th>\n",
       "      <th>6082</th>\n",
       "      <th>6083</th>\n",
       "      <th>6084</th>\n",
       "      <th>6085</th>\n",
       "      <th>6086</th>\n",
       "      <th>6087</th>\n",
       "      <th>6088</th>\n",
       "      <th>6089</th>\n",
       "      <th>6090</th>\n",
       "      <th>6091</th>\n",
       "      <th>6092</th>\n",
       "      <th>6093</th>\n",
       "      <th>6094</th>\n",
       "      <th>6095</th>\n",
       "      <th>6096</th>\n",
       "      <th>6097</th>\n",
       "      <th>6098</th>\n",
       "      <th>6099</th>\n",
       "      <th>6100</th>\n",
       "      <th>6101</th>\n",
       "      <th>6102</th>\n",
       "      <th>6103</th>\n",
       "      <th>6104</th>\n",
       "      <th>6105</th>\n",
       "      <th>6106</th>\n",
       "      <th>6107</th>\n",
       "      <th>6108</th>\n",
       "      <th>6109</th>\n",
       "      <th>6110</th>\n",
       "      <th>6111</th>\n",
       "      <th>6112</th>\n",
       "      <th>6113</th>\n",
       "      <th>6114</th>\n",
       "      <th>6115</th>\n",
       "      <th>6116</th>\n",
       "      <th>6117</th>\n",
       "      <th>6118</th>\n",
       "      <th>6119</th>\n",
       "      <th>6120</th>\n",
       "      <th>6121</th>\n",
       "      <th>6122</th>\n",
       "      <th>6123</th>\n",
       "      <th>6124</th>\n",
       "      <th>6125</th>\n",
       "      <th>6126</th>\n",
       "      <th>6127</th>\n",
       "      <th>6128</th>\n",
       "      <th>6129</th>\n",
       "      <th>6130</th>\n",
       "      <th>6131</th>\n",
       "      <th>6132</th>\n",
       "      <th>6133</th>\n",
       "      <th>6134</th>\n",
       "      <th>6135</th>\n",
       "      <th>6136</th>\n",
       "      <th>6137</th>\n",
       "      <th>6138</th>\n",
       "      <th>jaro_func_score</th>\n",
       "      <th>sequence_matcher_ratio</th>\n",
       "      <th>ohe_locations_concat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>martin</td>\n",
       "      <td>[SAN MARINA]</td>\n",
       "      <td>san marina</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.119357e+15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111 south</td>\n",
       "      <td>[1&amp;1 IONOS]</td>\n",
       "      <td>11 iono</td>\n",
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       "      <td>0.67</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2.873584e+23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>247 custom pvt ltd</td>\n",
       "      <td>[24/7 CUSTOMER PRIVATE LIMITED]</td>\n",
       "      <td>247 custom privat limit</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.532323e+57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3M</td>\n",
       "      <td>[3M]</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.240000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A K enterpris</td>\n",
       "      <td>[ROUSH ENTERPRISE]</td>\n",
       "      <td>roush enterpris</td>\n",
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       "<p>5 rows × 6140 columns</p>\n",
       "</div>"
      ],
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       "                    0                                1  \\\n",
       "0              martin                     [SAN MARINA]   \n",
       "1           111 south                      [1&1 IONOS]   \n",
       "2  247 custom pvt ltd  [24/7 CUSTOMER PRIVATE LIMITED]   \n",
       "3                  3M                             [3M]   \n",
       "4       A K enterpris               [ROUSH ENTERPRISE]   \n",
       "\n",
       "                         2  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21  \\\n",
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       "\n",
       "  135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "   153 154 155 156 157 158 159 160 161 162 163 164 165 166 167  168 169 170  \\\n",
       "0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   0   0   \n",
       "1    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   0   0   \n",
       "2  153   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   0   0   \n",
       "3    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   0   0   \n",
       "4    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  168   0   0   \n",
       "\n",
       "  171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  228 229 230 231  232 233 234 235 236 237 238 239 240 241 242 243 244 245  \\\n",
       "0   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0  232   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  265 266 267 268 269 270  271 272 273 274 275 276 277 278 279 280 281 282  \\\n",
       "0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0  271   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  283 284 285 286  287 288 289 290 291 292 293 294 295 296 297 298 299 300  \\\n",
       "0   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0  287   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "   358 359 360 361 362 363 364  365 366  367 368 369 370 371 372 373 374 375  \\\n",
       "0    0   0   0   0   0   0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "1  358   0   0   0   0   0   0  365   0  367   0   0   0   0   0   0   0   0   \n",
       "2    0   0   0   0   0   0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "3    0   0   0   0   0   0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "4    0   0   0   0   0   0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  395 396 397 398 399 400 401 402 403 404  405 406 407 408 409 410 411 412  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0  405   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   \n",
       "\n",
       "  413 414 415 416 417 418 419 420 421 422 423  424 425 426 427 428 429 430  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0  424   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   \n",
       "\n",
       "  431 432 433 434 435 436 437 438 439 440  441 442 443 444 445 446 447 448  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0  441   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   \n",
       "\n",
       "  449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  468 469 470 471  472 473 474 475 476 477 478 479 480 481 482 483 484 485  \\\n",
       "0   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   0   0   0   0  472   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   0   0   0   0    0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "  486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501  ... 5642  \\\n",
       "0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  ...    0   \n",
       "1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  ...    0   \n",
       "2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  ...    0   \n",
       "3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  ...    0   \n",
       "4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  ...    0   \n",
       "\n",
       "  5643 5644 5645 5646 5647 5648 5649 5650  5651 5652 5653 5654 5655 5656 5657  \\\n",
       "0    0    0    0    0    0    0    0    0     0    0    0    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0     0    0    0    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0  5651    0    0    0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0     0    0    0    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0     0    0    0    0    0    0    0   \n",
       "\n",
       "  5658 5659  5660 5661 5662 5663 5664 5665 5666 5667 5668 5669 5670 5671 5672  \\\n",
       "0    0    0     0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "1    0    0  5660    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "2    0    0     0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "3    0    0     0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "4    0    0     0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "\n",
       "  5673 5674 5675 5676 5677 5678 5679 5680 5681 5682 5683 5684 5685 5686 5687  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "\n",
       "  5688 5689 5690 5691 5692 5693 5694 5695 5696 5697 5698 5699 5700 5701 5702  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "\n",
       "  5703 5704 5705 5706 5707 5708 5709 5710 5711 5712 5713 5714 5715 5716 5717  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "\n",
       "  5718 5719 5720 5721 5722 5723 5724 5725 5726 5727 5728 5729 5730 5731 5732  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "\n",
       "  5733 5734 5735 5736 5737 5738 5739 5740 5741 5742 5743 5744 5745 5746 5747  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "\n",
       "  5748 5749 5750 5751 5752 5753 5754 5755 5756 5757 5758 5759 5760 5761 5762  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "\n",
       "  5763 5764 5765 5766 5767 5768 5769 5770 5771 5772 5773 5774  5775 5776 5777  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0    0    0     0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0    0    0     0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0    0    0  5775    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0    0    0     0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0    0    0     0    0    0   \n",
       "\n",
       "  5778 5779 5780 5781 5782 5783 5784 5785 5786 5787 5788 5789 5790 5791 5792  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "\n",
       "  5793 5794 5795 5796 5797 5798 5799 5800 5801 5802 5803 5804 5805 5806 5807  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "\n",
       "  5808 5809 5810 5811 5812 5813 5814 5815 5816 5817 5818 5819 5820 5821 5822  \\\n",
       "0    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "1    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "2    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "3    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "4    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "\n",
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       "4    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "\n",
       "  6138 jaro_func_score sequence_matcher_ratio ohe_locations_concat  \n",
       "0    0            0.78                   0.56         1.119357e+15  \n",
       "1    0            0.67                   0.44         2.873584e+23  \n",
       "2    0            0.89                   0.78         1.532323e+57  \n",
       "3    0            1.00                   1.00         4.240000e+02  \n",
       "4    0            0.79                   0.69         8.116851e+38  \n",
       "\n",
       "[5 rows x 6140 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#trigram function\n",
    "def split_str(s, N):\n",
    "    rmax = np.clip(len(s)-N, a_min=0, a_max=None)\n",
    "    return [s[0+i:N+i] for i in range(0, rmax+1)]\n",
    "\n",
    "#arrange dfs and cols\n",
    "df = df[['parent_org_name'\n",
    "         ,'children_org_name_list'\n",
    "         ,'children_org_name'\n",
    "         ,'jaro_func_score'\n",
    "         ,'sequence_matcher_ratio'\n",
    "        ]]\n",
    "df1 = df[['parent_org_name','jaro_func_score','sequence_matcher_ratio']]\n",
    "df['parent_trigram'] = df['parent_org_name'].apply(lambda x: split_str(x, 3))\n",
    "# del df[['jaro_func_score','sequence_matcher_ratio']]\n",
    "\n",
    "#binarizer option\n",
    "mlb = MultiLabelBinarizer(sparse_output=True)\n",
    "\n",
    "mlb_df = df.join(\n",
    "            pd.DataFrame.sparse.from_spmatrix(\n",
    "                mlb.fit_transform(df.pop('parent_trigram')),\n",
    "                index=df.index,\n",
    "                columns=mlb.classes_))\n",
    "\n",
    "#create numbers as column names\n",
    "mlb_df = mlb_df.rename(columns={x:y for x,y in zip(mlb_df.columns,range(0,len(mlb_df.columns)))})\n",
    "\n",
    "#convert 0,1s to col name\n",
    "values = np.where(mlb_df.eq(1), mlb_df.columns, mlb_df)\n",
    "mlb_df = pd.DataFrame(values, columns=mlb_df.columns)\n",
    "\n",
    "#concat all columns into a list, agg per row, and drop zeros\n",
    "mlb_df['ohe_locations'] = (mlb_df.iloc[:,5:].astype(str)\n",
    " .agg(lambda x: ' '.join(i for i in x if i != '0'), axis=1)\n",
    " .str.split()\n",
    ")\n",
    "\n",
    "#create new db of relevant cols\n",
    "# mlb_df = mlb_df[[0,1,'ohe_locations']]\n",
    "\n",
    "#change column names\n",
    "# mlb_df.columns = [['parent_org_name','children_org_name_list','ohe_locations']]\n",
    "\n",
    "#add back in the jaro score from df1\n",
    "jaro_func_score = list(df1['jaro_func_score'])\n",
    "sequence_matcher_ratio = list(df1['sequence_matcher_ratio'])\n",
    "mlb_df['jaro_func_score'] = jaro_func_score\n",
    "mlb_df['sequence_matcher_ratio'] = sequence_matcher_ratio\n",
    "mlb_df.drop([3, 4], axis=1, inplace=True)\n",
    "\n",
    "#convert ohe_locations column\n",
    "mlb_df['ohe_locations'] = [','.join(map(str, l)) for l in mlb_df['ohe_locations']]\n",
    "mlb_df['ohe_locations'] = mlb_df['ohe_locations'].str.replace(',', '').astype(float)\n",
    "mlb_df['ohe_locations_concat'] = mlb_df['ohe_locations']\n",
    "mlb_df.drop('ohe_locations', axis=1, inplace=True)\n",
    "\n",
    "mlb_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mlb_df['ohe_locations'].apply(lambda x: sum(int(v) for v in ast.literal_eval(x)))\n",
    "# mlb_df.ohe_locations.map(lambda x: sum(map(int, ast.literal_eval(x))))\n",
    "# mlb_df['ohe_locations'] = mlb_df['ohe_locations'].str.replace(',', '').astype(float)\n",
    "cols = mlb_df.iloc[:,3:]\n",
    "cols\n",
    "mlb_df[cols] = mlb_df[cols].astype(float)\n",
    "# mlb_df[cols] = mlb_df[cols].apply(pd.to_numeric, errors='ignore', axis=1).fillna(mlb_df)\n",
    "# mlb_df[cols] = mlb_df[cols].apply(pd.to_numeric, errors='coerce')\n",
    "# mlb_df[\"ohe_locations\"].apply(lambda x: len(ast.literal_eval(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlb_df.to_csv('mlb_df.csv')\n",
    "mlb_df = pd.read_csv('mlb_df.csv')\n",
    "mlb_df.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "mlb_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#classification report function and classifiers\n",
    "def pandas_classification_report(y_true, y_pred):\n",
    "    metrics_summary = precision_recall_fscore_support(\n",
    "        y_true=y_test\n",
    "        , y_pred=y_predictions\n",
    "        , labels=np.unique(y_pred)\n",
    "    )\n",
    "\n",
    "    avg = list(precision_recall_fscore_support(\n",
    "            y_true=y_test, \n",
    "            y_pred=y_predictions,\n",
    "            average='weighted'))\n",
    "\n",
    "    metrics_sum_index = ['precision', 'recall', 'f1-score', 'support']\n",
    "    class_report_df = pd.DataFrame(\n",
    "        list(metrics_summary),\n",
    "        index=metrics_sum_index)\n",
    "\n",
    "    support = class_report_df.loc['support']\n",
    "    total = support.sum() \n",
    "    avg[-1] = total\n",
    "\n",
    "    class_report_df['avg / total'] = avg\n",
    "\n",
    "    return class_report_df.T\n",
    "\n",
    "classifiers = {\n",
    "    \"Naive Bayes\": GaussianNB(),\n",
    "    \"LogisiticRegression\": LogisticRegression(),\n",
    "    \"DecisionTreeClassifier\": DecisionTreeClassifier(),\n",
    "    \"RandomForestClassifier\": RandomForestClassifier(),\n",
    "    \"KNN\": KNeighborsClassifier(),\n",
    "    \"Support Vector Classifier\": SVC(),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report: \n",
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Unknown label type: (array([4.24000000e+002, 5.38000000e+002, 5.44000000e+002, ...,\n       8.12596265e+272, 2.86977121e+281, 1.43151167e+287]),)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-174-65a92f3e5fbc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassifiers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0my_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mdf_class_report\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas_classification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_predictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/naive_bayes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    205\u001b[0m         \"\"\"\n\u001b[1;32m    206\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m         return self._partial_fit(X, y, np.unique(y), _refit=True,\n\u001b[0m\u001b[1;32m    208\u001b[0m                                  sample_weight=sample_weight)\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/naive_bayes.py\u001b[0m in \u001b[0;36m_partial_fit\u001b[0;34m(self, X, y, classes, _refit, sample_weight)\u001b[0m\n\u001b[1;32m    370\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 372\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0m_check_partial_fit_first_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    373\u001b[0m             \u001b[0;31m# This is the first call to partial_fit:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m             \u001b[0;31m# initialize various cumulative counters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36m_check_partial_fit_first_call\u001b[0;34m(clf, classes)\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m             \u001b[0;31m# This is the first call to partial_fit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m             \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    321\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36munique_labels\u001b[0;34m(*ys)\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0m_unique_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_FN_UNIQUE_LABELS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_unique_labels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0mys_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_unique_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unknown label type: (array([4.24000000e+002, 5.38000000e+002, 5.44000000e+002, ...,\n       8.12596265e+272, 2.86977121e+281, 1.43151167e+287]),)"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('always')\n",
    "\n",
    "f, axes = plt.subplots(1, 6, figsize=(20, 5), sharey='row')\n",
    "\n",
    "print('Classification Report: \\n')\n",
    "\n",
    "for i, (key, classifier) in enumerate(classifiers.items()):\n",
    "    y_pred = classifier.fit(X_train, y_train).predict(X_test)\n",
    "    y_predictions = classifier.predict(X_test)\n",
    "    df_class_report = pandas_classification_report(y_true=y_test, y_pred=y_predictions)\n",
    "    print((classifier),'\\n',df_class_report)\n",
    "    cf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    disp = ConfusionMatrixDisplay(cf_matrix)\n",
    "#                                   display_labels=)\n",
    "    disp.plot(ax=axes[i], xticks_rotation=0)\n",
    "    disp.ax_.set_title(key)\n",
    "    disp.im_.colorbar.remove()\n",
    "    disp.ax_.set_xlabel('')\n",
    "    if i!=0:\n",
    "        disp.ax_.set_ylabel('')\n",
    "        \n",
    "f.text(0.4, 0.1, 'Predicted label', ha='left')\n",
    "plt.subplots_adjust(wspace=0.40, hspace=0.1)\n",
    "\n",
    "f.colorbar(disp.im_, ax=axes)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parent df stuff - count unique items in list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>parent_org_name</th>\n",
       "      <th>parent_trigram</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>+MARTINS 111 SOUTH 247 CUSTOMER PVT. LTD. 3M A...</td>\n",
       "      <td>[+MA, MAR, ART, RTI, TIN, INS, NS , S 1,  11, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     parent_org_name  \\\n",
       "0  +MARTINS 111 SOUTH 247 CUSTOMER PVT. LTD. 3M A...   \n",
       "\n",
       "                                      parent_trigram  \n",
       "0  [+MA, MAR, ART, RTI, TIN, INS, NS , S 1,  11, ...  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parent_df = pd.DataFrame(' '.join(df['parent_org_name'].tolist()), columns=['parent_org_name'], index=[0])\n",
    "parent_df['parent_trigram'] = parent_df['parent_org_name'].apply(lambda x: split_str(x, 3))\n",
    "parent_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>parent_trigram</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[+MA, MAR, ART, RTI, TIN, INS, NS , S 1,  11, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      parent_trigram\n",
       "0  [+MA, MAR, ART, RTI, TIN, INS, NS , S 1,  11, ..."
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parent_df = parent_df[['parent_trigram']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110150"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of trigrams\n",
    "parent_df.parent_trigram.str.len().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>parent_trigram</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>+MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ART</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RTI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110150 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   parent_trigram\n",
       "0             +MA\n",
       "0             MAR\n",
       "0             ART\n",
       "0             RTI\n",
       "0             TIN\n",
       "..            ...\n",
       "0             IRA\n",
       "0             RA \n",
       "0             A S\n",
       "0              S \n",
       "0             S A\n",
       "\n",
       "[110150 rows x 1 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of unique trigrams\n",
    "parent_explode_df = parent_df['parent_trigram'].explode()\n",
    "parent_explode_df = pd.DataFrame(parent_explode_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6473"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parent_explode_df.parent_trigram.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'perent_list' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-68eb6967ceb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mparent_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparent_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent_trigram\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mperent_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'perent_list' is not defined"
     ]
    }
   ],
   "source": [
    "parent_list = parent_df.parent_trigram.tolist()\n",
    "perent_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "parent_list = list(df.parent_org_name)\n",
    "\n",
    "def find_ngrams(input_list, n):\n",
    "    return zip(*[input_list[i:] for i in range(n)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#factorize option (fastest)\n",
    "v = df.parent_trigram.values\n",
    "l = [len(x) for x in v.tolist()]\n",
    "f, u = pd.factorize(np.concatenate(v))\n",
    "n, m = len(v), u.size\n",
    "i = np.arange(n).repeat(l)\n",
    "\n",
    "dummies = pd.DataFrame(\n",
    "    np.bincount(i * m + f, minlength=n * m).reshape(n, m),\n",
    "    df.index, u\n",
    ")\n",
    "\n",
    "df.drop('parent_trigram', 1).join(dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get dummies option\n",
    "df.drop('parent_trigram', 1).join(df.parent_trigram.str.join('|').str.get_dummies())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#put all 100k list trigrams on y axis\n",
    "ohe_df = pd.DataFrame(data=df, columns=parent_list)\n",
    "ohe_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#attempts to convert 0,1s to column name\n",
    "mlb_df.loc[:,1:]  * mlb_df.columns[1:].astype(int)\n",
    "for col in mlb_df.columns:\n",
    "    mlb_df.loc[mlb_df[col] == 1, col] = col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create df with all trigrams as columns\n",
    "\n",
    "df1 = df[['parent_org_name']]\n",
    "\n",
    "ohe_df = pd.concat([df1, ohe_df], axis=1, sort=False)\n",
    "ohe_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating instance of one-hot-encoder\n",
    "enc = OneHotEncoder(handle_unknown='ignore')\n",
    "\n",
    "# passing bridge-types-cat column (label encoded values of bridge_types)\n",
    "enc_df = pd.DataFrame(enc.fit_transform(df[['parent_org_name']]).toarray())\n",
    "\n",
    "# merge with main df bridge_df on key values\n",
    "df1 = df.join(enc_df)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "types = df.applymap(type)\n",
    "types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x7fe98ce43a80>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_ngrams(parent_list, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CountVectorizer(analyzer='char', ngram_range=(3, 3),\n",
      "                vocabulary=['allallall', 'allallthis', 'allallhappened',\n",
      "                            'allallmore', 'allallor', 'allallless',\n",
      "                            'allthisall', 'allthisthis', 'allthishappened',\n",
      "                            'allthismore', 'allthisor', 'allthisless',\n",
      "                            'allhappenedall', 'allhappenedthis',\n",
      "                            'allhappenedhappened', 'allhappenedmore',\n",
      "                            'allhappenedor', 'allhappenedless', 'allmoreall',\n",
      "                            'allmorethis', 'allmorehappened', 'allmoremore',\n",
      "                            'allmoreor', 'allmoreless', 'allorall', 'allorthis',\n",
      "                            'allorhappened', 'allormore', 'alloror',\n",
      "                            'allorless', ...])\n"
     ]
    }
   ],
   "source": [
    "input_list = ['all', 'this', 'happened', 'more', 'or', 'less']\n",
    "\n",
    "keywords = [''.join(i) for i in itertools.product(input_list, repeat = 3)]\n",
    "vector = CountVectorizer(analyzer='char', ngram_range=(3,3), vocabulary=keywords)\n",
    "tr_test = vector.transform(['word1'])\n",
    "print(tr_test)\n",
    "print(vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_df['parent_org_name'] = ohe_df.parent_org_name.astype(str)\n",
    "\n",
    "for c in ohe_df['parent_org_name']:\n",
    "    ohe_df[c] = ohe_df['parent_org_name'].apply(lambda x: int(c in x))\n",
    "    print (ohe_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3                           [+MA, MAR, ART, RTI, TIN, INS]\n",
       "8                      [111, 11 , 1 S,  SO, SOU, OUT, UTH]\n",
       "13       [247, 47 , 7 C,  CU, CUS, UST, STO, TOM, OME, ...\n",
       "18                                                    [3M]\n",
       "57       [A. , . K,  K., K. , . E,  EN, ENT, NTE, TER, ...\n",
       "                               ...                        \n",
       "10303    [NAT, ATO, TOM, OMA, MAS, AS , S S,  SC, SCH, ...\n",
       "11965    [RAD, ADI, DIU, IUS, US , S H,  HE, HEA, EAL, ...\n",
       "809      [AME, MER, ERI, RIC, ICA, CAN, AN , N M,  MU, ...\n",
       "840      [AMI, MIX, IXC, XCO, COM, OM , M I,  IN, INC, ...\n",
       "4533                                  [DEV, EVI, VID, IDA]\n",
       "Name: parent_org_name, Length: 5210, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#trigram function\n",
    "def split_str(s, N):\n",
    "    rmax = np.clip(len(s)-N, a_min=0, a_max=None)\n",
    "    return [s[0+i:N+i] for i in range(0, rmax+1)]\n",
    "\n",
    "df['parent_org_name'].apply(lambda x: split_str(x, 3))\n",
    "# input_list.apply(lambda x: split_str(x, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ngrams(input, n):\n",
    "    input = input.split(' ')\n",
    "    output = []\n",
    "    for i in range(len(input)-n+1):\n",
    "        output.append(input[i:i+n])\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'split'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-b04abe1d24ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mngrams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent_org_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-15-d1cb9f32e01b>\u001b[0m in \u001b[0;36mngrams\u001b[0;34m(input, n)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mngrams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5272\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5273\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5274\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'split'"
     ]
    }
   ],
   "source": [
    "ngrams(df.parent_org_name, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_ngrams(input_list, n):\n",
    "    return zip(*[input_list[i:] for i in range(n)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x7f9bd6102b40>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_ngrams(input_list,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = nltk.word_tokenize('this is a string')\n",
    "my_bigrams = nltk.bigrams(words)\n",
    "my_trigrams = nltk.trigrams(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object bigrams at 0x7fe9a7255890>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_bigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "from itertools import islice\n",
    "\n",
    "def ngrams(message, n=1):\n",
    "    it = iter(message.split())\n",
    "    window = deque(islice(it, n), maxlen=n)\n",
    "    yield tuple(window)\n",
    "    for item in it:\n",
    "        window.append(item)\n",
    "        yield tuple(window)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object ngrams at 0x7fe9a7255120>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ngrams(input_list,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk import word_tokenize\n",
    "from nltk.util import ngrams\n",
    "text = \"Hi How are you? i am fine and you\"\n",
    "token=nltk.word_tokenize(text)\n",
    "bigrams=ngrams(token,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object ngrams at 0x7fe9a7278ac0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3                                  +MARTINS\\n8                                 111 SOUTH\\n13                   247 CUSTOMER PVT. LTD.\\n18                                       3M\\n57                         A. K. ENTERPRISE\\n                        ...                \\n10303               NATOMAS SCHOOL DISTRICT\\n11965                     RADIUS HEALTH INC\\n809      AMERICAN MUSEUM OF NATURAL HISTORY\\n840                            AMIXCOM INC.\\n4533                                 DEVIDA\\nName: parent_org_name, Length: 5210, dtype: object'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parent_list = str(df.parent_org_name)\n",
    "parent_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "parent_org_name_string = pd.DataFrame(' '.join(df['parent_org_name'].tolist()), columns=['parent_org_name'], index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>parent_org_name</th>\n",
       "      <th>parent_trigram</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>+MARTINS 111 SOUTH 247 CUSTOMER PVT. LTD. 3M A...</td>\n",
       "      <td>[+MA, MAR, ART, RTI, TIN, INS, NS , S 1,  11, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     parent_org_name  \\\n",
       "0  +MARTINS 111 SOUTH 247 CUSTOMER PVT. LTD. 3M A...   \n",
       "\n",
       "                                      parent_trigram  \n",
       "0  [+MA, MAR, ART, RTI, TIN, INS, NS , S 1,  11, ...  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parent_org_name_string = pd.DataFrame(' '.join(df['parent_org_name'].tolist()), columns=['parent_org_name'], index=[0])\n",
    "parent_org_name_string['parent_trigram'] = parent_org_name_string['parent_org_name'].apply(lambda x: split_str(x, 3))\n",
    "parent_org_name_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('original df shape: \\n', df.shape)\n",
    "print('')\n",
    "\n",
    "print('dtypes: \\n', df.dtypes)\n",
    "print('')\n",
    "\n",
    "print('original match confidence bins values counts: \\n', df.match_confidence.value_counts())\n",
    "print('')\n",
    "\n",
    "#create df of just medium and high\n",
    "df1 = df[df['match_confidence'].isin(['MEDIUM','HIGH'])]\n",
    "print('high low df shape: \\n', df1.shape)\n",
    "print('high and low df value counts: \\n', df1.match_confidence.value_counts())\n",
    "print('')\n",
    "\n",
    "#create low df\n",
    "df2 = df[df['match_confidence'] == 'LOW']\n",
    "print('low df shape: \\n', df2.shape)\n",
    "print('')\n",
    "\n",
    "#create sample of 2k rows of low df\n",
    "df2 = df2.sample(2000)\n",
    "\n",
    "#concat dfs\n",
    "frames = [df1,df2]\n",
    "df = pd.concat(frames)\n",
    "print('new df shape with sample of 2k low rows: \\n', df.shape)\n",
    "print('new df value counts: \\n', df.match_confidence.value_counts())\n",
    "print(df.head())"
   ]
  }
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