statistics_litkey_corpus.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spelling Correction of Children's Texts\n",
    "In this notebook, Litkey Corpus will be analysed in order to get an overview of its' characteristics. Furthermore, the corpus will be preprocessed for further analysis (automatic spelling correction). Therefore, several versions of the corpus will be used throughout the notebook. The variants shall be explained in Chapter 1, Section 1.\n",
    "\n",
    "- **Case sensitve is standard for analysis**\n",
    "\n",
    "## Content\n",
    "\n",
    "1. [Chapter 1 - The Data Sets](#1)\n",
    "    * [0 - Data Load](#2)\n",
    "    * [1 - Excerpts from Data Sets](#3)\n",
    "        * [( I. ) Data_raw_token (unprocessed data set)](#17)\n",
    "        * [( II. ) Data_clean_token (preprocessed data set)](#18)\n",
    "        * [( III. ) Data_error_types (errors from preprocessed data set)](#19)\n",
    "    * [2 - Size of Data Sets](#4)\n",
    "2. [Chapter 2 - Data Analysis on Text Base](#5)\n",
    "    * [1 - How long are the texts (histogram)?](#6)\n",
    "    * [2 - Missspellings per Text](#7)\n",
    "        * [a) Are there texts without any missspellings?](#8)\n",
    "        * [b) How many missspellings are there per text (histogram)?](#9)\n",
    "    * [3 - What is the error proportion (histogram)?](#10)\n",
    "3. [Chapter 3 - Data Analysis on Error Base](#11)\n",
    "    * [1 - Levenshtein Distance](#12)\n",
    "        * [a) Cumulative Ratio](#13)\n",
    "    * [2 - What are the most frequent 'original' and 'corrected' type?](#14)\n",
    "    * [3 - Are there origs that are corrected to different targets?](#15)\n",
    "    * [4 -  What are the different variants of missspellings of words?](#16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1'></a><br>\n",
    "# Chapter 1 - The Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import sys \n",
    "sys.path.insert(0, './Lisa')\n",
    "\n",
    "import os\n",
    "import fnmatch\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import numpy as np\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "from nltk.metrics.distance import edit_distance\n",
    "\n",
    "import glob\n",
    "import csv\n",
    "import random\n",
    "import collections\n",
    "\n",
    "import litkey_2\n",
    "import helpers_litkey_2\n",
    "\n",
    "# Do not truncate columns of DataFrame\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "# Silence Silence SettingWithCopyWarning\n",
    "# (occurs in stats on text level->2nd->before preprocessing)\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2'></a><br>\n",
    "## 0 - Data Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load necessary data sets\n",
    "data_raw_token = litkey_2.load_raw(litkey_data_path = \"./litkey-data/\")\n",
    "data_raw_types = litkey_2.load_raw(litkey_data_path = \"./litkey-data/\", toss_duplicates=True)\n",
    "\n",
    "data_error_token = litkey_2.load(litkey_data_path = \"./litkey-data/\", toss_duplicates=False)\n",
    "data_error_types = litkey_2.load(litkey_data_path = \"./litkey-data/\")\n",
    "\n",
    "data_clean_token = litkey_2.load(keep_correct_words=True, toss_duplicates=False)\n",
    "\n",
    "# Further data sets\n",
    "data_clean_types = litkey_2.load(keep_correct_words=True)\n",
    "\n",
    "data_clean_types_lc = litkey_2.load(lower_case=True, keep_correct_words=True)\n",
    "data_error_types_lc = litkey_2.load(lower_case=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8594\n",
      "9484\n"
     ]
    }
   ],
   "source": [
    "#data_clean_token.head(10)\n",
    "#data_error_token.shape[0]\n",
    "print(data_error_types_lc.shape[0])\n",
    "print(data_error_types.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export\n",
    "data_raw_token.to_csv('./Lisa/datasets_litkey_export/data_raw_token_cs.csv', sep='|', index=False)\n",
    "data_raw_types.to_csv('./Lisa/datasets_litkey_export/data_raw_types_cs.csv', sep='|', index=False)\n",
    "\n",
    "data_error_token.to_csv('./Lisa/datasets_litkey_export/data_error_token_cs.csv', sep='|', index=False)\n",
    "data_error_types.to_csv('./Lisa/datasets_litkey_export/data_error_types_cs.csv', sep='|', index=False)\n",
    "\n",
    "data_clean_token.to_csv('./Lisa/datasets_litkey_export/data_clean_token_cs.csv', sep='|', index=False)\n",
    "data_clean_types.to_csv('./Lisa/datasets_litkey_export/data_clean_types_cs.csv', sep='|', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='3'></a><br>\n",
    "## 1 - Excerpts from Data Sets \n",
    "There are mainly three versions of the data set that are frequently used in this notebook. The three are introduced in the following. Also, variants of these three data sets may occur."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='17'></a><br>\n",
    "### ( I. ) Data_raw_token (unprocessed data set)\n",
    "For the analysis in this notebook, a version of the data set without preprocessing is needed. In this case, only NA-cases (line ends(^)) and headline markers (\\h) are removed (as these have been added for annotation purposes). No other preprocessing did take place.\n",
    "\n",
    "The head this version of the dataframe is shown below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>original</th>\n",
       "      <th>corrected</th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lea</td>\n",
       "      <td>Lea</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>und</td>\n",
       "      <td>und</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lars</td>\n",
       "      <td>Lars</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>und</td>\n",
       "      <td>und</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dodo</td>\n",
       "      <td>Dodo</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gehen</td>\n",
       "      <td>gehen</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Eis</td>\n",
       "      <td>Eis</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kaufen</td>\n",
       "      <td>kaufen</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dodo</td>\n",
       "      <td>Dodo</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>belt</td>\n",
       "      <td>bellt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>den</td>\n",
       "      <td>den</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Eismann</td>\n",
       "      <td>Eismann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>an</td>\n",
       "      <td>an</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>der</td>\n",
       "      <td>der</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Eismann</td>\n",
       "      <td>Eismann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>kukt</td>\n",
       "      <td>kuckt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Dodo</td>\n",
       "      <td>Dodo</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>an</td>\n",
       "      <td>an</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   original corrected              filename\n",
       "0       Lea       Lea  01-005-2-III-Eis.csv\n",
       "1       und       und  01-005-2-III-Eis.csv\n",
       "2      Lars      Lars  01-005-2-III-Eis.csv\n",
       "3       und       und  01-005-2-III-Eis.csv\n",
       "4      Dodo      Dodo  01-005-2-III-Eis.csv\n",
       "5     gehen     gehen  01-005-2-III-Eis.csv\n",
       "6       Eis       Eis  01-005-2-III-Eis.csv\n",
       "7    kaufen    kaufen  01-005-2-III-Eis.csv\n",
       "8         .         .  01-005-2-III-Eis.csv\n",
       "9      Dodo      Dodo  01-005-2-III-Eis.csv\n",
       "10     belt     bellt  01-005-2-III-Eis.csv\n",
       "11      den       den  01-005-2-III-Eis.csv\n",
       "12  Eismann   Eismann  01-005-2-III-Eis.csv\n",
       "13       an        an  01-005-2-III-Eis.csv\n",
       "14        .         .  01-005-2-III-Eis.csv\n",
       "15      der       der  01-005-2-III-Eis.csv\n",
       "16  Eismann   Eismann  01-005-2-III-Eis.csv\n",
       "17     kukt     kuckt  01-005-2-III-Eis.csv\n",
       "18     Dodo      Dodo  01-005-2-III-Eis.csv\n",
       "19       an        an  01-005-2-III-Eis.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_raw_token.head(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='18'></a><br>\n",
    "### ( II. ) Data_clean_token (preprocessed data set)\n",
    "Now, the data set from above ( I. ) is preprocessed/cleaned. This includes ...\n",
    "- ... that characters from normalization are removed, namely\n",
    "    - \"|\" and \"_\", i. e. original is one/two-word whereas target is two/one-word\n",
    "    - intended line-break characters '^', '-^', '^-'\n",
    "\n",
    "- ... the exlucsion of some words from further analysis, namely\n",
    "    - words containing illegible character(s), i. e. containing '*' (original)\n",
    "    - non-words ('?' target not identifiable or no standardized spelling | '~' target non existing word form)\n",
    "    - proper names ('Lea', 'Lars', 'Dodo') (corrected)\n",
    "    - common abbreviations/words that contain a dot (corrected), also sentence boundaries\n",
    "    - words less than 2 characters (corrected)\n",
    "    \n",
    "These measures result in corresponding head of data frame shown below (for comparison see ( I. ))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original</th>\n",
       "      <th>corrected</th>\n",
       "      <th>filename</th>\n",
       "      <th>freq_ori</th>\n",
       "      <th>freq_cor</th>\n",
       "      <th>freq_tup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>und</td>\n",
       "      <td>und</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>9718</td>\n",
       "      <td>9827</td>\n",
       "      <td>9713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>und</td>\n",
       "      <td>und</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>9718</td>\n",
       "      <td>9827</td>\n",
       "      <td>9713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gehen</td>\n",
       "      <td>gehen</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>288</td>\n",
       "      <td>377</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eis</td>\n",
       "      <td>Eis</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>395</td>\n",
       "      <td>657</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kaufen</td>\n",
       "      <td>kaufen</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>53</td>\n",
       "      <td>80</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>belt</td>\n",
       "      <td>bellt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>91</td>\n",
       "      <td>256</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>den</td>\n",
       "      <td>den</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>2401</td>\n",
       "      <td>2389</td>\n",
       "      <td>2278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Eismann</td>\n",
       "      <td>Eismann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>an</td>\n",
       "      <td>an</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>1415</td>\n",
       "      <td>1440</td>\n",
       "      <td>1412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>der</td>\n",
       "      <td>der</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>3044</td>\n",
       "      <td>3049</td>\n",
       "      <td>3014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Eismann</td>\n",
       "      <td>Eismann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>kukt</td>\n",
       "      <td>kuckt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>73</td>\n",
       "      <td>215</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>an</td>\n",
       "      <td>an</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>1415</td>\n",
       "      <td>1440</td>\n",
       "      <td>1412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>dan</td>\n",
       "      <td>dann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>627</td>\n",
       "      <td>1379</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>haben</td>\n",
       "      <td>haben</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>462</td>\n",
       "      <td>485</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sie</td>\n",
       "      <td>sie</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>3459</td>\n",
       "      <td>3490</td>\n",
       "      <td>3374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Ihren</td>\n",
       "      <td>ihren</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>7</td>\n",
       "      <td>364</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Eis</td>\n",
       "      <td>Eis</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>395</td>\n",
       "      <td>657</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>gekricht</td>\n",
       "      <td>gekriegt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>und</td>\n",
       "      <td>und</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>9718</td>\n",
       "      <td>9827</td>\n",
       "      <td>9713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    original corrected              filename  freq_ori  freq_cor  freq_tup\n",
       "0        und       und  01-005-2-III-Eis.csv      9718      9827      9713\n",
       "1        und       und  01-005-2-III-Eis.csv      9718      9827      9713\n",
       "2      gehen     gehen  01-005-2-III-Eis.csv       288       377       288\n",
       "3        Eis       Eis  01-005-2-III-Eis.csv       395       657       395\n",
       "4     kaufen    kaufen  01-005-2-III-Eis.csv        53        80        53\n",
       "5       belt     bellt  01-005-2-III-Eis.csv        91       256        91\n",
       "6        den       den  01-005-2-III-Eis.csv      2401      2389      2278\n",
       "7    Eismann   Eismann  01-005-2-III-Eis.csv        10        22        10\n",
       "8         an        an  01-005-2-III-Eis.csv      1415      1440      1412\n",
       "9        der       der  01-005-2-III-Eis.csv      3044      3049      3014\n",
       "10   Eismann   Eismann  01-005-2-III-Eis.csv        10        22        10\n",
       "11      kukt     kuckt  01-005-2-III-Eis.csv        73       215        73\n",
       "12        an        an  01-005-2-III-Eis.csv      1415      1440      1412\n",
       "13       dan      dann  01-005-2-III-Eis.csv       627      1379       621\n",
       "14     haben     haben  01-005-2-III-Eis.csv       462       485       457\n",
       "15       sie       sie  01-005-2-III-Eis.csv      3459      3490      3374\n",
       "16     Ihren     ihren  01-005-2-III-Eis.csv         7       364         7\n",
       "17       Eis       Eis  01-005-2-III-Eis.csv       395       657       395\n",
       "18  gekricht  gekriegt  01-005-2-III-Eis.csv         2        16         2\n",
       "19       und       und  01-005-2-III-Eis.csv      9718      9827      9713"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_clean_token.head(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='19'></a><br>\n",
    "### ( III. ) Data_error_types (errors from preprocessed data set)\n",
    "In addition to preprocessing steps in ( II. ), all correct words are not considered anymore. Only missspellings from litkey data set after preprocessing are kept.\n",
    "\n",
    "Description of column names:\n",
    "- *freq_ori*: how often does original appear in overall data set <br>\n",
    "- *freq_cor*: how often does corrected appear in overall data set <br>\n",
    "- *freq_tup*: how often does this tupel of (original, corrected) appear in overall data set\n",
    "\n",
    "These measures result in corresponding head of data frame shown below (for comparison see ( II. ) and ( I. ))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original</th>\n",
       "      <th>corrected</th>\n",
       "      <th>filename</th>\n",
       "      <th>freq_ori</th>\n",
       "      <th>freq_cor</th>\n",
       "      <th>freq_tup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>belt</td>\n",
       "      <td>bellt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>91</td>\n",
       "      <td>138</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kukt</td>\n",
       "      <td>kuckt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>73</td>\n",
       "      <td>152</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dan</td>\n",
       "      <td>dann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>627</td>\n",
       "      <td>651</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gekricht</td>\n",
       "      <td>gekriegt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>leken</td>\n",
       "      <td>lecken</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>felt</td>\n",
       "      <td>fällt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>93</td>\n",
       "      <td>198</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>wolte</td>\n",
       "      <td>wollte</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>173</td>\n",
       "      <td>201</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lekt</td>\n",
       "      <td>leckt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fom</td>\n",
       "      <td>vom</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dan</td>\n",
       "      <td>dann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>627</td>\n",
       "      <td>651</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>dan</td>\n",
       "      <td>dann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>627</td>\n",
       "      <td>651</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>leken</td>\n",
       "      <td>lecken</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gawen</td>\n",
       "      <td>kaufen</td>\n",
       "      <td>01-006-2-III-Eis.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>din</td>\n",
       "      <td>ein</td>\n",
       "      <td>01-006-2-III-Eis.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Keld</td>\n",
       "      <td>Geld</td>\n",
       "      <td>01-006-2-III-Eis.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>man</td>\n",
       "      <td>Mann</td>\n",
       "      <td>01-006-2-III-Eis.csv</td>\n",
       "      <td>79</td>\n",
       "      <td>132</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>man</td>\n",
       "      <td>Mann</td>\n",
       "      <td>01-006-2-III-Eis.csv</td>\n",
       "      <td>79</td>\n",
       "      <td>132</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gien</td>\n",
       "      <td>gehen</td>\n",
       "      <td>01-006-2-III-Eis.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>nahaise</td>\n",
       "      <td>nachhause</td>\n",
       "      <td>01-006-2-III-Eis.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>esen</td>\n",
       "      <td>essen</td>\n",
       "      <td>01-006-2-III-Eis.csv</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    original  corrected              filename  freq_ori  freq_cor  freq_tup\n",
       "0       belt      bellt  01-005-2-III-Eis.csv        91       138        91\n",
       "1       kukt      kuckt  01-005-2-III-Eis.csv        73       152        73\n",
       "2        dan       dann  01-005-2-III-Eis.csv       627       651       621\n",
       "3   gekricht   gekriegt  01-005-2-III-Eis.csv         2        15         2\n",
       "4      leken     lecken  01-005-2-III-Eis.csv        14        17        14\n",
       "5       felt      fällt  01-005-2-III-Eis.csv        93       198        90\n",
       "6      wolte     wollte  01-005-2-III-Eis.csv       173       201       173\n",
       "7       lekt      leckt  01-005-2-III-Eis.csv        20        42        19\n",
       "8        fom        vom  01-005-2-III-Eis.csv        13        16        13\n",
       "9        dan       dann  01-005-2-III-Eis.csv       627       651       621\n",
       "10       dan       dann  01-005-2-III-Eis.csv       627       651       621\n",
       "11     leken     lecken  01-005-2-III-Eis.csv        14        17        14\n",
       "12     gawen     kaufen  01-006-2-III-Eis.csv         1         6         1\n",
       "13       din        ein  01-006-2-III-Eis.csv         2        26         1\n",
       "14      Keld       Geld  01-006-2-III-Eis.csv         1         5         1\n",
       "15       man       Mann  01-006-2-III-Eis.csv        79       132        79\n",
       "16       man       Mann  01-006-2-III-Eis.csv        79       132        79\n",
       "17      gien      gehen  01-006-2-III-Eis.csv         1        78         1\n",
       "18   nahaise  nachhause  01-006-2-III-Eis.csv         1        10         1\n",
       "19      esen      essen  01-006-2-III-Eis.csv        22        14        12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_error_token.head(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a slight insight in possible missspellings in Litkey Corpus, a random sample is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original</th>\n",
       "      <th>corrected</th>\n",
       "      <th>filename</th>\n",
       "      <th>freq_ori</th>\n",
       "      <th>freq_cor</th>\n",
       "      <th>freq_tup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2952</th>\n",
       "      <td>veticht</td>\n",
       "      <td>fertig</td>\n",
       "      <td>04-135-3-II-Jenga.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6126</th>\n",
       "      <td>kaochen</td>\n",
       "      <td>Knochen</td>\n",
       "      <td>07-197-4-I-Schule.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4954</th>\n",
       "      <td>Aderese</td>\n",
       "      <td>Adresse</td>\n",
       "      <td>06-136-3-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5651</th>\n",
       "      <td>rufft</td>\n",
       "      <td>ruft</td>\n",
       "      <td>06-607-3-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5282</th>\n",
       "      <td>glingelt</td>\n",
       "      <td>klingelt</td>\n",
       "      <td>06-289-3-IV-Weg.csv</td>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9326</th>\n",
       "      <td>Tör</td>\n",
       "      <td>Tür</td>\n",
       "      <td>10-436-4-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5212</th>\n",
       "      <td>geklielt</td>\n",
       "      <td>geklingelt</td>\n",
       "      <td>06-253-3-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9172</th>\n",
       "      <td>pisiert</td>\n",
       "      <td>passiert</td>\n",
       "      <td>10-286-4-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4153</th>\n",
       "      <td>Jeds</td>\n",
       "      <td>Jetzt</td>\n",
       "      <td>05-236-3-III-Staubsauger.csv</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>zwa</td>\n",
       "      <td>zwar</td>\n",
       "      <td>01-307-2-III-Eis.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9130</th>\n",
       "      <td>Adrresse</td>\n",
       "      <td>Adresse</td>\n",
       "      <td>10-249-4-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>mauar</td>\n",
       "      <td>Mauer</td>\n",
       "      <td>03-057-3-I-Frosch.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2543</th>\n",
       "      <td>schbring</td>\n",
       "      <td>spring</td>\n",
       "      <td>03-385-3-I-Frosch.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3991</th>\n",
       "      <td>erger</td>\n",
       "      <td>Ärger</td>\n",
       "      <td>05-168-3-III-Staubsauger.csv</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5304</th>\n",
       "      <td>wenm</td>\n",
       "      <td>wenn</td>\n",
       "      <td>06-303-3-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5286</th>\n",
       "      <td>veck</td>\n",
       "      <td>weg</td>\n",
       "      <td>06-290-3-IV-Weg.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8573</th>\n",
       "      <td>ferlätzt</td>\n",
       "      <td>verletzt</td>\n",
       "      <td>09-417-4-III-Seilbahn.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3687</th>\n",
       "      <td>knalt</td>\n",
       "      <td>knallt</td>\n",
       "      <td>04-682-3-II-Jenga.csv</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9251</th>\n",
       "      <td>Anschlich</td>\n",
       "      <td>Anschließend</td>\n",
       "      <td>10-340-4-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>dies</td>\n",
       "      <td>bis</td>\n",
       "      <td>02-025-2-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       original     corrected                      filename  freq_ori  \\\n",
       "2952    veticht        fertig         04-135-3-II-Jenga.csv         1   \n",
       "6126    kaochen       Knochen         07-197-4-I-Schule.csv         1   \n",
       "4954    Aderese       Adresse           06-136-3-IV-Weg.csv         1   \n",
       "5651      rufft          ruft           06-607-3-IV-Weg.csv         1   \n",
       "5282   glingelt      klingelt           06-289-3-IV-Weg.csv         3   \n",
       "9326        Tör           Tür           10-436-4-IV-Weg.csv         1   \n",
       "5212   geklielt    geklingelt           06-253-3-IV-Weg.csv         1   \n",
       "9172    pisiert      passiert           10-286-4-IV-Weg.csv         1   \n",
       "4153       Jeds         Jetzt  05-236-3-III-Staubsauger.csv         5   \n",
       "593         zwa          zwar          01-307-2-III-Eis.csv         2   \n",
       "9130   Adrresse       Adresse           10-249-4-IV-Weg.csv         1   \n",
       "1977      mauar         Mauer         03-057-3-I-Frosch.csv         1   \n",
       "2543   schbring        spring         03-385-3-I-Frosch.csv         1   \n",
       "3991      erger         Ärger  05-168-3-III-Staubsauger.csv        29   \n",
       "5304       wenm          wenn           06-303-3-IV-Weg.csv         1   \n",
       "5286       veck           weg           06-290-3-IV-Weg.csv         2   \n",
       "8573   ferlätzt      verletzt     09-417-4-III-Seilbahn.csv         1   \n",
       "3687      knalt        knallt         04-682-3-II-Jenga.csv         8   \n",
       "9251  Anschlich  Anschließend           10-340-4-IV-Weg.csv         1   \n",
       "899        dies           bis           02-025-2-IV-Weg.csv         1   \n",
       "\n",
       "      freq_cor  freq_tup  \n",
       "2952        43         1  \n",
       "6126        15         1  \n",
       "4954        11         1  \n",
       "5651        15         1  \n",
       "5282        66         3  \n",
       "9326        15         1  \n",
       "5212        13         1  \n",
       "9172        75         1  \n",
       "4153        27         2  \n",
       "593          3         2  \n",
       "9130        11         1  \n",
       "1977        44         1  \n",
       "2543         8         1  \n",
       "3991        39        29  \n",
       "5304        46         1  \n",
       "5286        94         2  \n",
       "8573         7         1  \n",
       "3687         8         8  \n",
       "9251         4         1  \n",
       "899         21         1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_error_types.sample(n=20, random_state=45) # random_state as random seed to reproduce chance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4'></a><br>\n",
    "## 3 - Size of Data Sets\n",
    "_The statistic is on tupel-base, i. e. one tupel consists of (original, corrected)-pair._\n",
    "- data_raw_token: Contains all token that are contained in litkey data set\n",
    "- data_clean_token: Contains all token, correct and incorrect tupel, that we are interested in for analysis\n",
    "- data_error_token : Contains only the erroneous tupel out of \"data_clean_token\"\n",
    "\n",
    "Vice versa with type base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get frequency of token and types\n",
    "data_raw_token_count = data_raw_token.shape[0]\n",
    "data_clean_token_count = data_clean_token.shape[0]\n",
    "data_error_token_count = data_error_token.shape[0]\n",
    "#data_error_token_count = data_error_types['freq_tup'].sum(axis=0)\n",
    "\n",
    "# Set ascending order for bar chart horizontal\n",
    "label_barh = [\"data_error_token\", \"data_clean_token\", \"data_raw_token\"] # y axis\n",
    "label_barh_types = [\"data_error_types\", \"data_clean_types\", \"data_raw_types\"]\n",
    "\n",
    "data_barh = [data_error_token_count, data_clean_token_count, data_raw_token_count] # x axis\n",
    "data_barh_types = [data_error_types.shape[0], data_clean_types.shape[0], data_raw_types.shape[0]]\n",
    "\n",
    "#y_x_token = zip(label_barh, data_barh)\n",
    "#y_x_types = zip(label_barh_types, data_barh_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12.5,10))\n",
    "\n",
    "def h_bar_chart(y_data, x_data, sub_pos=1):\n",
    "    \n",
    "    axs = fig.add_subplot(2,2,sub_pos)\n",
    "        \n",
    "    axs.barh(y_data, x_data, color=['red', 'gray', 'lightgray'])\n",
    "    \n",
    "    # Workaround\n",
    "    label_format = '{.0f}'\n",
    "    ticks_loc = axs.get_xticks().tolist()\n",
    "    axs.set_xticks(ticks_loc)\n",
    "    axs.set_xticklabels([int(x) for x in ticks_loc], rotation=30, ha='right')\n",
    "\n",
    "    for index, value in enumerate(x_data):\n",
    "        axs.text(value, index, str(value), fontweight='bold')\n",
    "            \n",
    "    return axs\n",
    "\n",
    "axs_token = h_bar_chart(label_barh, data_barh, 1)\n",
    "axs_types = h_bar_chart(label_barh_types, data_barh_types, 2)\n",
    "\n",
    "axs_token.set_title(\"Size of data sets on TOKEN BASE\")\n",
    "axs_token.set_xlabel(\"number of token tupel\")\n",
    "axs_types.set_title(\"Size of data sets on TYPE BASE\")\n",
    "axs_types.set_xlabel(\"number of type tupel\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style='background : yellow' > Key message: </span>\n",
    "\n",
    "<span style='background : yellow' > From 213 000 token in total, only 162 000 are of interest. From these 162 000 tokens, 25 000 tokens are erroneous. </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - What are the number of distinct taget words (corrected types)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for each data set\n",
    "raw_corrected_types = data_raw_types.drop_duplicates(subset='corrected', keep='first')\n",
    "clean_corrected_types = data_clean_types.drop_duplicates(subset='corrected', keep='first')\n",
    "error_corrected_types = data_error_types.drop_duplicates(subset='corrected', keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrected_types = [error_corrected_types, clean_corrected_types, raw_corrected_types]\n",
    "# Convert list elements to string?\n",
    "corrected_types_label = [\"error_corrected_types\", \"clean_corrected_types\", \"raw_corrected_types\"]\n",
    "\n",
    "corrected_types_count = []\n",
    "for e in corrected_types:\n",
    "    corrected_types_count.append(e.shape[0])\n",
    "    #print(e.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for df in corrected_types:\n",
    "#    display(df[df.duplicated(subset='corrected', keep=False)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3154, 5675, 6363]\n"
     ]
    }
   ],
   "source": [
    "print(corrected_types_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.barh(corrected_types_label, corrected_types_count, color='lightblue')\n",
    "#sns.countplot(orient='h')\n",
    "#plt.set_cmap('seismic')\n",
    "\n",
    "for index, value in enumerate(corrected_types_count):\n",
    "        ax.text(value, index, str(value), fontweight='bold')\n",
    "\n",
    "ax.set_title(\"Count of distinct target words ('corrected' on type base)\")\n",
    "ax.set_xlabel(\"number of distinct words\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='5'></a><br>\n",
    "# Chapter 2 - Data Analysis on Text Base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='6'></a><br>\n",
    "## 1 - How long are the texts (histogram)?\n",
    "Length per text is compared before (data_raw_token) and after preprocessing (data_clean_token)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define named tupel\n",
    "Stats = collections.namedtuple('Stats', ['min','max','mean','std'])\n",
    "\n",
    "def groupByFilename(df):\n",
    "    df_grouped = df.groupby(['filename']).size()\n",
    "    return df_grouped\n",
    "\n",
    "def getStats(df_grouped, suppress_grouping=False):\n",
    "    \n",
    "    if suppress_grouping is False:\n",
    "        df_grouped = df_grouped.groupby(['filename']).size()\n",
    "    \n",
    "    df_min = df_grouped.min()\n",
    "    df_max = df_grouped.max()\n",
    "    df_mean = round(df_grouped.mean(), 2)\n",
    "    df_std = round(df_grouped.std(), 2)\n",
    "    \n",
    "    named_tupel = Stats(df_min, df_max, df_mean, df_std)\n",
    "    \n",
    "    return named_tupel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get histogram data\n",
    "grouped_data_raw_token = groupByFilename(data_raw_token)\n",
    "grouped_data_clean_token = groupByFilename(data_clean_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get stats for histogram\n",
    "tupel_data_raw_token = getStats(data_raw_token)\n",
    "tupel_data_clean_token = getStats(data_clean_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.hist(grouped_data_raw_token,\n",
    "         weights = [1/len(grouped_data_raw_token)] * len(grouped_data_raw_token),\n",
    "         bins=range(min(grouped_data_raw_token), max(grouped_data_raw_token) + 10,10),\n",
    "         alpha=0.5,\n",
    "         label = \"data_raw\")\n",
    "\n",
    "plt.hist(grouped_data_clean_token,\n",
    "         weights = [1/len(grouped_data_clean_token)] * len(grouped_data_clean_token),\n",
    "         bins=range(min(grouped_data_clean_token), max(grouped_data_clean_token) + 10,10),\n",
    "         alpha=0.5,\n",
    "         label=\"data_clean\")\n",
    "\n",
    "plt.xlabel(\"text length (TOKEN)\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.title(\"Length per text in data sets\")\n",
    "plt.legend(loc = 'upper right')\n",
    "\n",
    "plt.text(300, 0.08, r'$\\mu$ = '+str(tupel_data_raw_token.mean)+r', $\\sigma$ = '+str(tupel_data_raw_token.std)\n",
    "        +'\\n\\nmax = '+str(tupel_data_raw_token.max)+', min = '+str(tupel_data_raw_token.min), \n",
    "         bbox=dict(facecolor='none', edgecolor='blue', boxstyle='round') )\n",
    "\n",
    "plt.text(300, 0.04, r'$\\mu$ = '+str(tupel_data_clean_token.mean)+r', $\\sigma$ = '+str(tupel_data_clean_token.std)\n",
    "        +'\\n\\nmax = '+str(tupel_data_clean_token.max)+', min = '+str(tupel_data_clean_token.min),\n",
    "        bbox=dict(facecolor='none', edgecolor='orange', boxstyle='round') )\n",
    "\n",
    "plt.gca().yaxis.set_major_formatter(PercentFormatter(1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style='background : yellow' > Key message: </span>\n",
    "\n",
    "<span style='background : yellow' > Distribution of text length is shiftet, texts contain fewer tokens in preprocessed version of data set than in raw version. Average of text length is reduced from 111 tokens in raw data to 85 in cleaned data. </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='7'></a><br>\n",
    "## 2 - Missspellings per Text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='8'></a><br>\n",
    "### a) Are there texts without any missspellings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Texts in total: 1922\n"
     ]
    }
   ],
   "source": [
    "# COUNT TEXTS\n",
    "number_texts = len(fnmatch.filter(os.listdir(\"./litkey-data\"), '*.csv'))\n",
    "print(\"Texts in total: \"+str(number_texts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wanted: Num. of files that are removed in step \"Remove all correct words (ignoring case)\n",
    "# Prefiltering: Check for filenames that are in data_raw, but not in data_error\n",
    "data_raw_not_in_data = data_raw_token[~data_raw_token.filename.isin(data_error_types.filename)]\n",
    "\n",
    "# Perform preprocessing steps on data_raw, extract errors\n",
    "\n",
    "#data_raw_preprocessed_errors = litkey_2.load_raw(remove_characters_words=True, keep_correct_words=False)\n",
    "data_clean_token_errors = litkey_2.load(keep_correct_words=False, toss_duplicates=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Texts without missspelling: 9\n"
     ]
    }
   ],
   "source": [
    "boolean_mask = data_raw_not_in_data.filename.isin(data_clean_token_errors.filename)\n",
    "error_free = data_raw_not_in_data[~boolean_mask]\n",
    "\n",
    "error_free_files = error_free.groupby(['filename']).size()\n",
    "print(\"Texts without missspelling: \"+str(error_free_files.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style='background : yellow' > Key message: </span>\n",
    "\n",
    "<span style='background : yellow' > There are 1922 texts in total from which 9 are without any missspellings. Therefore, words in analysis come from 1913 texts in total. </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='9'></a><br>\n",
    "### b) How many misspellings are there per text (histogram) ?\n",
    "Number of missspellings per text is compared before (data_raw_token_errors) and after preprocessing (data_error_token)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DATA_RAW\n",
    "# TODO: Gibt es diesen df schon?\n",
    "data_raw_token_errors = helpers_litkey_2.removeCorrectWords(data_raw_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get histogram data\n",
    "grouped_data_raw_token_errors = groupByFilename(data_raw_token_errors)\n",
    "grouped_data_error_token = groupByFilename(data_error_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get stats for histogram\n",
    "tupel_data_raw_token_errors = getStats(data_raw_token_errors)\n",
    "tupel_data_error_token = getStats(data_error_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.hist(grouped_data_raw_token_errors,\n",
    "         weights = [1/len(grouped_data_raw_token_errors)] * len(grouped_data_raw_token_errors),\n",
    "         bins=range(min(grouped_data_raw_token_errors), max(grouped_data_raw_token_errors) + 2,2),\n",
    "         alpha=0.5,\n",
    "         label = \"data_raw\")\n",
    "\n",
    "plt.hist(grouped_data_error_token,\n",
    "         weights = [1/len(grouped_data_error_token)] * len(grouped_data_error_token),\n",
    "         bins=range(min(grouped_data_error_token), max(grouped_data_error_token)+ 2,2),\n",
    "         alpha=0.5,\n",
    "         label=\"data_error\")\n",
    "\n",
    "plt.xlabel(\"number of errors (TOKEN)\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.title(\"Errors per text in data sets\")\n",
    "plt.legend(loc = 'upper right')\n",
    "\n",
    "plt.text(60, 0.07, r'$\\mu$ = '+str(tupel_data_raw_token_errors.mean)+r', $\\sigma$ = '+str(tupel_data_raw_token_errors.std)\n",
    "        +'\\n\\nmax = '+str(tupel_data_raw_token_errors.max)+', min = '+str(tupel_data_raw_token_errors.min), \n",
    "         bbox=dict(facecolor='none', edgecolor='blue', boxstyle='round') )\n",
    "\n",
    "plt.text(60, 0.04, r'$\\mu$ = '+str(tupel_data_error_token.mean)+r', $\\sigma$ = '+str(tupel_data_error_token.std)\n",
    "        +'\\n\\nmax = '+str(tupel_data_error_token.max)+', min = '+str(tupel_data_error_token.min),\n",
    "        bbox=dict(facecolor='none', edgecolor='orange', boxstyle='round') )\n",
    "\n",
    "plt.gca().yaxis.set_major_formatter(PercentFormatter(1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style='background : yellow' > Key message: </span>\n",
    "\n",
    "<span style='background : yellow' > Distribution of errors per text is shiftet only slightly, texts contain fewer errors in preprocessed version of data set which contains only errors than in raw version of the data set. Average of errors per text is reduced from 15.25 tokens in data_raw to 12.86 in data_error. </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='10'></a><br>\n",
    "## 3 - What is the error proportion (histogram)?\n",
    "Base: data_clean_token, data_error_token\n",
    "- Ratio between erroneous words/all analyzed words, e. g. in average 24 % of the TOKENS missspelled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get data\n",
    "grouped_data_clean_token = groupByFilename(data_clean_token)\n",
    "grouped_data_error_token = groupByFilename(data_error_token)\n",
    "\n",
    "ratio_errors_all_words = (grouped_data_error_token/grouped_data_clean_token)\n",
    "ratio_errors_all_words.fillna(value=0, inplace=True)\n",
    "\n",
    "# Get stats\n",
    "tupel_ratio = getStats(ratio_errors_all_words, suppress_grouping=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram\n",
    "plt.figure(figsize=(7,5))\n",
    "plt.hist(ratio_errors_all_words, bins=20)\n",
    "plt.title(\"Error proportion (data_error/data_clean) per text\")\n",
    "plt.xlabel(\"percentage (of TOKEN)\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.text(0.45, 150, r'$\\mu$ = '+str(tupel_ratio.mean)+r', $\\sigma$ = '+str(tupel_ratio.std)\n",
    "        +'\\n\\nmax = '+str(round(tupel_ratio.max, 2))+', min = '+str(tupel_ratio.min))\n",
    "\n",
    "# Data between 0 an 1\n",
    "\n",
    "# To PercentFormatter (show proportion as percentage)\n",
    "plt.gca().xaxis.set_major_formatter(PercentFormatter(1))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# TODO: linksschief/rechtsschief?\n",
    "# TODO: Quartile berechnen?\n",
    "# nach Augenmaß"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style='background : yellow' > Key message: </span>\n",
    "\n",
    "<span style='background : yellow' > The ratio between erroneous words to all words analyzed, textwise, is on average 17%. That means that, on average, 17% of the analyzed words in a text is spelled incorrectly. The distribution decreases in direction to higher percentages steadily, maximum is 72%. Distribution decreases also in direction to lower percentages. Minimum is 0%, indicating there are some texts without any errors (see 1.2)). Main part of error proportion is between 5% and 25%. </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='11'></a><br>\n",
    "# Chapter 3 - Data Analysis on Error Base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='12'></a><br>\n",
    "## 2 - Levenshtein Distance\n",
    "- between original and target word\n",
    "- consideration of case sensitive and case insensitive\n",
    "- on type- and tokenbase\n",
    "\n",
    "tuple: (original, corrected)-Pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Levenshtein distance stats\n",
    "data_error_types['distance'] = data_error_types.apply(lambda r: edit_distance(r.original, r.corrected), axis=1)\n",
    "\n",
    "# Same for case insenstive\n",
    "data_error_types_lc['distance'] = data_error_types_lc.apply(lambda r: edit_distance(r.original, r.corrected), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(121) # 1row, 2columns, 1st position\n",
    "ax2 = fig.add_subplot(122) # 1row, 2columns, 2nd position\n",
    "\n",
    "x = np.arange(9) # position of bars on x-axis\n",
    "width = 0.35 # width of the bars\n",
    "\n",
    "# FOR CUMULATIVE RATIO\n",
    "#ax1_cum = ax1.twinx()\n",
    "#ax2_cum = ax2.twinx()\n",
    "\n",
    "#ax1_cum.plot(range(-1, 9), [0] + data.distance.value_counts(True).cumsum().tolist(), 'k.-')\n",
    "#ax1_cum.set_ylabel('Cumulative ratio')\n",
    "#ax2_cum.plot(range(-1, 9), [0] + (a / a.sum()).cumsum().tolist(), 'k.-')\n",
    "#ax2_cum.set_ylabel('Cumulative ratio')\n",
    "\n",
    "#ax3 = fig.add_subplot(223)\n",
    "#ax4 = fig.add_subplot(224)\n",
    "\n",
    "fig.set_figwidth(15)\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "\n",
    "# TYPE - CASE SENSITIVE\n",
    "a = data_error_types.distance.value_counts().max()\n",
    "rects1 = ax1.bar(x-width/2, data_error_types.groupby('distance').size(), width, label='case-sensitive')\n",
    "#ax1.set_ylim(0, 1.1*a)\n",
    "\n",
    "ax1.set_xticks(range(-1, 9))\n",
    "ax1.set_xticklabels([''] + list(range(1, 10)))\n",
    "\n",
    "ax1.set_ylabel('frequency')\n",
    "ax1.set_xlabel('Levenshtein distance')\n",
    "ax1.set_title('\"Type\"-analysis (ignoring duplicate pairs)')\n",
    "\n",
    "# TYPE - CASE INSENSITIVE\n",
    "b = data_error_types_lc.distance.value_counts().max()\n",
    "rects2 = ax1.bar(x+width/2, data_error_types_lc.groupby('distance').size(), width, label='case-insensitive')\n",
    "\n",
    "ax1.set_ylim(0, 1.1*b)\n",
    "\n",
    "ax1.legend()\n",
    "\n",
    "\n",
    "# TOKEN - CASE SENSITIVE\n",
    "c = data_error_types.groupby('distance').freq_ori.sum()\n",
    "rects1 = ax2.bar(x-width/2, c, width, label='case-sensitive')\n",
    "#ax2.set_ylim(0, 1.1*a.max())\n",
    "\n",
    "ax2.set_xticks(range(-1, 9))\n",
    "ax2.set_xticklabels([''] + list(range(1, 10)))\n",
    "\n",
    "ax2.set_ylabel('frequency')\n",
    "ax2.set_xlabel('Levenshtein distance')\n",
    "ax2.set_title('\"Token\"-analysis (counting duplicate pairs)')\n",
    "\n",
    "# TOKEN - CASE INSENSITIVE\n",
    "d = data_error_types_lc.groupby('distance').freq_ori.sum()\n",
    "rects2 = ax2.bar(x+width/2, d, width, label='case-insensitive')\n",
    "\n",
    "ax2.set_ylim(0, 1.1*d.max())\n",
    "\n",
    "ax2.legend()\n",
    "\n",
    "#fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# TODO: Wording überdenken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style='background : yellow' > Key message: </span>\n",
    "\n",
    "<span style='background : yellow' > Comparing between type- and token-analysis, distribution of Levenshtein distance looks similar, more frequency in general in token-analysis. Levenshtein distance 1 is most frequent. Here, case-insensitive has a higher frequency compared to case-sensitive. In all other Levenshtein distances, case-sensitive has a higher frequency than case-insensitive. Looking at type-analysis, with Levenshtein distance 1-3 most cases are catched, with 1-4 there are only few cases left (meaning there are few words with higher Levensthein distance). </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='13'></a><br>\n",
    "### a) Cumulative Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(121) # 1row, 2columns, 1st position\n",
    "ax2 = fig.add_subplot(122) # 1row, 2columns, 2nd position\n",
    "\n",
    "width = 0.35 # width of the bars\n",
    "\n",
    "ax1.plot(range(0, 10), [0] + data_error_types.distance.value_counts(True).cumsum().tolist(), '.-', label='case sensitive')\n",
    "ax1.plot(range(0, 10), [0] + data_error_types_lc.distance.value_counts(True).cumsum().tolist(), '.-', color='orange', label='case insensitive')\n",
    "ax1.set_ylabel('Cumulative ratio')\n",
    "\n",
    "ax2.plot(range(0, 10), [0] + (c / c.sum()).cumsum().tolist(), '.-', label='case sensitive')\n",
    "ax2.plot(range(0, 10), [0] + (d / d.sum()).cumsum().tolist(), '.-', color='orange', label='case insensitive')\n",
    "ax2.set_ylabel('Cumulative ratio')\n",
    "\n",
    "ax1.set_title('\"Type\"-analysis (ignoring duplicate pairs)')\n",
    "ax1.set_xlabel('Levenshtein distance')\n",
    "ax2.set_title('\"Token\"-analysis (counting duplicate pairs)')\n",
    "ax2.set_xlabel('Levenshtein distance')\n",
    "#ax3 = fig.add_subplot(223)\n",
    "#ax4 = fig.add_subplot(224)\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "\n",
    "fig.set_figwidth(15)\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style='background : yellow' > Key message: </span>\n",
    "\n",
    "<span style='background : yellow' > As indicated above, in both analysis nearly 100% types/tokens are catched with Levenshtein distance up to 4. Levenshtein distance up to 2 catches 80% in type-analysis, nearly 90% in token-analysis. Levenshtein distance 1 catches about 50% in type-analysis, 60%+ in token-anylsis. </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='14'></a><br>\n",
    "## 2 - What are the most frequent 'original' and 'corrected' type?\n",
    "Based on data_error_types; Erroneous words (types), case sensitive, are considered.\n",
    "- most frequent original (= missspelling)\n",
    "- most frequent corrected (= target word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Most frequent missspelling (type)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "        }</style><table id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >original</th>        <th class=\"col_heading level0 col1\" >corrected</th>        <th class=\"col_heading level0 col2\" >filename</th>        <th class=\"col_heading level0 col3\" >freq_ori</th>        <th class=\"col_heading level0 col4\" >freq_cor</th>        <th class=\"col_heading level0 col5\" >freq_tup</th>        <th class=\"col_heading level0 col6\" >distance</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725level0_row0\" class=\"row_heading level0 row0\" >88</th>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row0_col0\" class=\"data row0 col0\" >das</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row0_col1\" class=\"data row0 col1\" >dass</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row0_col2\" class=\"data row0 col2\" >01-054-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row0_col3\" class=\"data row0 col3\" >985</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row0_col4\" class=\"data row0 col4\" >1017</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row0_col5\" class=\"data row0 col5\" >981</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row0_col6\" class=\"data row0 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row1_col0\" class=\"data row1 col0\" >dan</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row1_col1\" class=\"data row1 col1\" >dann</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row1_col2\" class=\"data row1 col2\" >01-005-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row1_col3\" class=\"data row1 col3\" >627</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row1_col4\" class=\"data row1 col4\" >651</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row1_col5\" class=\"data row1 col5\" >621</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row1_col6\" class=\"data row1 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725level0_row2\" class=\"row_heading level0 row2\" >38</th>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row2_col0\" class=\"data row2 col0\" >hate</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row2_col1\" class=\"data row2 col1\" >hatte</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row2_col2\" class=\"data row2 col2\" >01-025-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row2_col3\" class=\"data row2 col3\" >400</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row2_col4\" class=\"data row2 col4\" >427</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row2_col5\" class=\"data row2 col5\" >400</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row2_col6\" class=\"data row2 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725level0_row3\" class=\"row_heading level0 row3\" >41</th>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row3_col0\" class=\"data row3 col0\" >in</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row3_col1\" class=\"data row3 col1\" >ihn</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row3_col2\" class=\"data row3 col2\" >01-026-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row3_col3\" class=\"data row3 col3\" >261</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row3_col4\" class=\"data row3 col4\" >270</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row3_col5\" class=\"data row3 col5\" >260</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row3_col6\" class=\"data row3 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725level0_row4\" class=\"row_heading level0 row4\" >42</th>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row4_col0\" class=\"data row4 col0\" >Dan</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row4_col1\" class=\"data row4 col1\" >Dann</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row4_col2\" class=\"data row4 col2\" >01-026-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row4_col3\" class=\"data row4 col3\" >188</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row4_col4\" class=\"data row4 col4\" >197</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row4_col5\" class=\"data row4 col5\" >185</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row4_col6\" class=\"data row4 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725level0_row5\" class=\"row_heading level0 row5\" >130</th>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row5_col0\" class=\"data row5 col0\" >wider</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row5_col1\" class=\"data row5 col1\" >wieder</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row5_col2\" class=\"data row5 col2\" >01-061-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row5_col3\" class=\"data row5 col3\" >178</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row5_col4\" class=\"data row5 col4\" >207</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row5_col5\" class=\"data row5 col5\" >178</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row5_col6\" class=\"data row5 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row6_col0\" class=\"data row6 col0\" >wolte</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row6_col1\" class=\"data row6 col1\" >wollte</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row6_col2\" class=\"data row6 col2\" >01-005-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row6_col3\" class=\"data row6 col3\" >173</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row6_col4\" class=\"data row6 col4\" >201</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row6_col5\" class=\"data row6 col5\" >173</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row6_col6\" class=\"data row6 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725level0_row7\" class=\"row_heading level0 row7\" >232</th>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row7_col0\" class=\"data row7 col0\" >den</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row7_col1\" class=\"data row7 col1\" >denn</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row7_col2\" class=\"data row7 col2\" >01-122-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row7_col3\" class=\"data row7 col3\" >122</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row7_col4\" class=\"data row7 col4\" >119</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row7_col5\" class=\"data row7 col5\" >117</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row7_col6\" class=\"data row7 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725level0_row8\" class=\"row_heading level0 row8\" >867</th>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row8_col0\" class=\"data row8 col0\" >Loli</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row8_col1\" class=\"data row8 col1\" >Lolli</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row8_col2\" class=\"data row8 col2\" >02-005-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row8_col3\" class=\"data row8 col3\" >113</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row8_col4\" class=\"data row8 col4\" >259</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row8_col5\" class=\"data row8 col5\" >113</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row8_col6\" class=\"data row8 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725level0_row9\" class=\"row_heading level0 row9\" >319</th>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row9_col0\" class=\"data row9 col0\" >nich</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row9_col1\" class=\"data row9 col1\" >nicht</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row9_col2\" class=\"data row9 col2\" >01-139-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row9_col3\" class=\"data row9 col3\" >110</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row9_col4\" class=\"data row9 col4\" >135</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row9_col5\" class=\"data row9 col5\" >109</td>\n",
       "                        <td id=\"T_2e876ff0_a403_11eb_9ec3_a45e60e77725row9_col6\" class=\"data row9 col6\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8b588aa970>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Most frequent target (type)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "        }</style><table id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >original</th>        <th class=\"col_heading level0 col1\" >corrected</th>        <th class=\"col_heading level0 col2\" >filename</th>        <th class=\"col_heading level0 col3\" >freq_ori</th>        <th class=\"col_heading level0 col4\" >freq_cor</th>        <th class=\"col_heading level0 col5\" >freq_tup</th>        <th class=\"col_heading level0 col6\" >distance</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725level0_row0\" class=\"row_heading level0 row0\" >88</th>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row0_col0\" class=\"data row0 col0\" >das</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row0_col1\" class=\"data row0 col1\" >dass</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row0_col2\" class=\"data row0 col2\" >01-054-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row0_col3\" class=\"data row0 col3\" >985</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row0_col4\" class=\"data row0 col4\" >1017</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row0_col5\" class=\"data row0 col5\" >981</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row0_col6\" class=\"data row0 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row1_col0\" class=\"data row1 col0\" >dan</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row1_col1\" class=\"data row1 col1\" >dann</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row1_col2\" class=\"data row1 col2\" >01-005-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row1_col3\" class=\"data row1 col3\" >627</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row1_col4\" class=\"data row1 col4\" >651</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row1_col5\" class=\"data row1 col5\" >621</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row1_col6\" class=\"data row1 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725level0_row2\" class=\"row_heading level0 row2\" >38</th>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row2_col0\" class=\"data row2 col0\" >hate</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row2_col1\" class=\"data row2 col1\" >hatte</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row2_col2\" class=\"data row2 col2\" >01-025-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row2_col3\" class=\"data row2 col3\" >400</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row2_col4\" class=\"data row2 col4\" >427</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row2_col5\" class=\"data row2 col5\" >400</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row2_col6\" class=\"data row2 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725level0_row3\" class=\"row_heading level0 row3\" >41</th>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row3_col0\" class=\"data row3 col0\" >in</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row3_col1\" class=\"data row3 col1\" >ihn</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row3_col2\" class=\"data row3 col2\" >01-026-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row3_col3\" class=\"data row3 col3\" >261</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row3_col4\" class=\"data row3 col4\" >270</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row3_col5\" class=\"data row3 col5\" >260</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row3_col6\" class=\"data row3 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725level0_row4\" class=\"row_heading level0 row4\" >2691</th>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row4_col0\" class=\"data row4 col0\" >kartong</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row4_col1\" class=\"data row4 col1\" >Karton</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row4_col2\" class=\"data row4 col2\" >04-005-3-II-Jenga.csv</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row4_col3\" class=\"data row4 col3\" >25</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row4_col4\" class=\"data row4 col4\" >260</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row4_col5\" class=\"data row4 col5\" >25</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row4_col6\" class=\"data row4 col6\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725level0_row5\" class=\"row_heading level0 row5\" >867</th>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row5_col0\" class=\"data row5 col0\" >Loli</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row5_col1\" class=\"data row5 col1\" >Lolli</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row5_col2\" class=\"data row5 col2\" >02-005-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row5_col3\" class=\"data row5 col3\" >113</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row5_col4\" class=\"data row5 col4\" >259</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row5_col5\" class=\"data row5 col5\" >113</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row5_col6\" class=\"data row5 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725level0_row6\" class=\"row_heading level0 row6\" >130</th>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row6_col0\" class=\"data row6 col0\" >wider</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row6_col1\" class=\"data row6 col1\" >wieder</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row6_col2\" class=\"data row6 col2\" >01-061-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row6_col3\" class=\"data row6 col3\" >178</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row6_col4\" class=\"data row6 col4\" >207</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row6_col5\" class=\"data row6 col5\" >178</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row6_col6\" class=\"data row6 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725level0_row7\" class=\"row_heading level0 row7\" >6</th>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row7_col0\" class=\"data row7 col0\" >wolte</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row7_col1\" class=\"data row7 col1\" >wollte</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row7_col2\" class=\"data row7 col2\" >01-005-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row7_col3\" class=\"data row7 col3\" >173</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row7_col4\" class=\"data row7 col4\" >201</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row7_col5\" class=\"data row7 col5\" >173</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row7_col6\" class=\"data row7 col6\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725level0_row8\" class=\"row_heading level0 row8\" >5</th>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row8_col0\" class=\"data row8 col0\" >felt</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row8_col1\" class=\"data row8 col1\" >fällt</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row8_col2\" class=\"data row8 col2\" >01-005-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row8_col3\" class=\"data row8 col3\" >93</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row8_col4\" class=\"data row8 col4\" >198</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row8_col5\" class=\"data row8 col5\" >90</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row8_col6\" class=\"data row8 col6\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725level0_row9\" class=\"row_heading level0 row9\" >42</th>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row9_col0\" class=\"data row9 col0\" >Dan</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row9_col1\" class=\"data row9 col1\" >Dann</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row9_col2\" class=\"data row9 col2\" >01-026-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row9_col3\" class=\"data row9 col3\" >188</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row9_col4\" class=\"data row9 col4\" >197</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row9_col5\" class=\"data row9 col5\" >185</td>\n",
       "                        <td id=\"T_2e8ade92_a403_11eb_9ec3_a45e60e77725row9_col6\" class=\"data row9 col6\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8b595717c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Duplicates of respective column are dropped beforehand so that only types of column (and not tupel) are displayed;\n",
    "# Otherwise different corrections of e. g. \"dan\" are displayed\n",
    "\n",
    "temp = data_error_types.drop_duplicates('original', inplace=False)\n",
    "most_frequent_original_error_types = temp.nlargest(10, 'freq_ori', keep='first')\n",
    "#display('Most frequent missspelling (type)', most_frequent_original_data_error_types)\n",
    "\n",
    "temp_2 = data_error_types.drop_duplicates('corrected', inplace=False)\n",
    "most_frequent_corrected_error_types = temp_2.nlargest(10, 'freq_cor', keep='first')\n",
    "#display(\"Most frequent target (type)\", most_frequent_corrected_data_error_types)\n",
    "\n",
    "def highlight_cols(x, columns):\n",
    "\n",
    "    # copy df to new - original data is not changed \n",
    "    df = x.copy() \n",
    "      \n",
    "    # select all values to grey color \n",
    "    df.loc[:, :] = 'background-color: grey'\n",
    "      \n",
    "    # overwrite values green color \n",
    "    df[columns] = 'background-color: lightgreen'\n",
    "      \n",
    "    # return colour df \n",
    "    return df\n",
    "\n",
    "colour_original = most_frequent_original_error_types.style.apply(highlight_cols, columns=['original', 'freq_ori'], axis=None)\n",
    "colour_corrected = most_frequent_corrected_error_types.style.apply(highlight_cols, columns=['corrected', 'freq_cor'], axis=None)\n",
    "display('Most frequent missspelling (type)', colour_original)\n",
    "display(\"Most frequent target (type)\", colour_corrected)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style='background : yellow' > Key message: </span>\n",
    "\n",
    "<span style='background : yellow' > Most frequent missspelling and most frequen target word are equal in the first 4 places. This indicates that the missspelling of the most frequent target words is wide spread.</span>\n",
    "\n",
    "Top 10 missspellings in general are mainly, but not exclusively, missed double consonants as well as, exclusively, of Levenshtein distance 1.\n",
    "\n",
    "Häufigste corrected: Diese werden am häufigsten falsch geschrieben"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='15'></a><br>\n",
    "## 3 - Are there origs that are corrected to different targets?\n",
    "data_clean_types is considered as base. This data set contains all words (after preprocessing), so that duplicates of two or more are found. E. g. tupel 1 (ach, ach) is not contained in data_error_types (as this one only contains erroneous tupel). Therefore tupel 2 (ach, auch) would be not recognized as duplicate, although two origs are corrected to different targets.\n",
    "\n",
    "Here: Types can be considered, because duplicates are dropped tupelwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GET DUPLICATES\n",
    "\n",
    "def getDuplicates(df, sort_lowercase=False):\n",
    "    df_2 = df.iloc[:,0:2]\n",
    "    \n",
    "    duplicate_originals = df_2[df_2.original.duplicated(keep=False)] # all duplicates are marked as True, only Trues are displayed\n",
    "    \n",
    "    if sort_lowercase is True:\n",
    "        duplicate_originals_argsort = duplicate_originals.iloc[duplicate_originals.original.str.lower().argsort()]\n",
    "        return duplicate_originals_argsort\n",
    "    \n",
    "    else:\n",
    "        duplicate_originals_sort = duplicate_originals.sort_values(by='original', ascending=True)\n",
    "        return duplicate_originals_sort\n",
    "    \n",
    "    \n",
    "def printAnswer(duplicate_df, display_df=True):\n",
    "    display(\"Number of origs that are corrected to different targets: \", duplicate_df.shape[0])\n",
    "    if display_df is True:\n",
    "        display(duplicate_df.head(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Number of origs that are corrected to different targets: '"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "935"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    .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>original</th>\n",
       "      <th>corrected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8289</th>\n",
       "      <td>a</td>\n",
       "      <td>ah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8126</th>\n",
       "      <td>a</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12523</th>\n",
       "      <td>a</td>\n",
       "      <td>ach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9718</th>\n",
       "      <td>a</td>\n",
       "      <td>am</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5763</th>\n",
       "      <td>a</td>\n",
       "      <td>er</td>\n",
       "    </tr>\n",
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       "      <th>702</th>\n",
       "      <td>aas</td>\n",
       "      <td>das</td>\n",
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       "      <th>4571</th>\n",
       "      <td>aas</td>\n",
       "      <td>aus</td>\n",
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       "      <th>31</th>\n",
       "      <td>ab</td>\n",
       "      <td>ab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6666</th>\n",
       "      <td>ab</td>\n",
       "      <td>ob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>abe</td>\n",
       "      <td>habe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9000</th>\n",
       "      <td>abe</td>\n",
       "      <td>aber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743</th>\n",
       "      <td>ach</td>\n",
       "      <td>ach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>ach</td>\n",
       "      <td>auch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2149</th>\n",
       "      <td>ais</td>\n",
       "      <td>heißt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>ais</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6384</th>\n",
       "      <td>allen</td>\n",
       "      <td>allein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2480</th>\n",
       "      <td>allen</td>\n",
       "      <td>allen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>alls</td>\n",
       "      <td>als</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>alls</td>\n",
       "      <td>alles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>als</td>\n",
       "      <td>als</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5822</th>\n",
       "      <td>als</td>\n",
       "      <td>alles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>an</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3012</th>\n",
       "      <td>an</td>\n",
       "      <td>ein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6991</th>\n",
       "      <td>and</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10031</th>\n",
       "      <td>and</td>\n",
       "      <td>end</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8612</th>\n",
       "      <td>and</td>\n",
       "      <td>und</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>anruft</td>\n",
       "      <td>anruft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7249</th>\n",
       "      <td>anruft</td>\n",
       "      <td>anruf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8858</th>\n",
       "      <td>ans</td>\n",
       "      <td>angst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>ans</td>\n",
       "      <td>ans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5033</th>\n",
       "      <td>anz</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6596</th>\n",
       "      <td>anz</td>\n",
       "      <td>ans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4916</th>\n",
       "      <td>as</td>\n",
       "      <td>aß</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7271</th>\n",
       "      <td>as</td>\n",
       "      <td>aus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>as</td>\n",
       "      <td>eis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8204</th>\n",
       "      <td>ast</td>\n",
       "      <td>hast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11685</th>\n",
       "      <td>ast</td>\n",
       "      <td>ast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>au</td>\n",
       "      <td>auch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>au</td>\n",
       "      <td>auf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5277</th>\n",
       "      <td>au</td>\n",
       "      <td>aus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9456</th>\n",
       "      <td>aus</td>\n",
       "      <td>auch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5845</th>\n",
       "      <td>aus</td>\n",
       "      <td>raus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>aus</td>\n",
       "      <td>aus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7115</th>\n",
       "      <td>aus</td>\n",
       "      <td>haus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6521</th>\n",
       "      <td>aus</td>\n",
       "      <td>aufs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12061</th>\n",
       "      <td>baar</td>\n",
       "      <td>paar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3804</th>\n",
       "      <td>baar</td>\n",
       "      <td>aber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3376</th>\n",
       "      <td>baden</td>\n",
       "      <td>baden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7099</th>\n",
       "      <td>baden</td>\n",
       "      <td>boden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2163</th>\n",
       "      <td>bai</td>\n",
       "      <td>weil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1170</th>\n",
       "      <td>bai</td>\n",
       "      <td>bei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12357</th>\n",
       "      <td>bas</td>\n",
       "      <td>dass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9723</th>\n",
       "      <td>bas</td>\n",
       "      <td>bus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3978</th>\n",
       "      <td>bas</td>\n",
       "      <td>pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4126</th>\n",
       "      <td>baum</td>\n",
       "      <td>bauen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>baum</td>\n",
       "      <td>baum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>beim</td>\n",
       "      <td>beim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>beim</td>\n",
       "      <td>bein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>bein</td>\n",
       "      <td>beim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>bein</td>\n",
       "      <td>bein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12611</th>\n",
       "      <td>bein</td>\n",
       "      <td>bei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8505</th>\n",
       "      <td>bekomen</td>\n",
       "      <td>gekommen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>bekomen</td>\n",
       "      <td>bekommen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4960</th>\n",
       "      <td>beld</td>\n",
       "      <td>bellt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7359</th>\n",
       "      <td>beld</td>\n",
       "      <td>bild</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8493</th>\n",
       "      <td>bell</td>\n",
       "      <td>bell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4452</th>\n",
       "      <td>bell</td>\n",
       "      <td>bello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6305</th>\n",
       "      <td>ben</td>\n",
       "      <td>den</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4239</th>\n",
       "      <td>ben</td>\n",
       "      <td>ben</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4929</th>\n",
       "      <td>ber</td>\n",
       "      <td>der</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8860</th>\n",
       "      <td>ber</td>\n",
       "      <td>aber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>bies</td>\n",
       "      <td>bins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>bies</td>\n",
       "      <td>bis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>bis</td>\n",
       "      <td>bins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>bis</td>\n",
       "      <td>bist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>bis</td>\n",
       "      <td>bis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7088</th>\n",
       "      <td>bist</td>\n",
       "      <td>bis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1783</th>\n",
       "      <td>bist</td>\n",
       "      <td>bist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6802</th>\n",
       "      <td>brach</td>\n",
       "      <td>brachte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4005</th>\n",
       "      <td>brach</td>\n",
       "      <td>brach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7898</th>\n",
       "      <td>bu</td>\n",
       "      <td>du</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5936</th>\n",
       "      <td>bu</td>\n",
       "      <td>buh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11370</th>\n",
       "      <td>da</td>\n",
       "      <td>das</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>da</td>\n",
       "      <td>da</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6231</th>\n",
       "      <td>dach</td>\n",
       "      <td>doch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6877</th>\n",
       "      <td>dach</td>\n",
       "      <td>dann</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11012</th>\n",
       "      <td>dach</td>\n",
       "      <td>dach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8269</th>\n",
       "      <td>dach</td>\n",
       "      <td>da</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6456</th>\n",
       "      <td>dach</td>\n",
       "      <td>nach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11952</th>\n",
       "      <td>dan</td>\n",
       "      <td>dran</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2305</th>\n",
       "      <td>dan</td>\n",
       "      <td>dein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>dan</td>\n",
       "      <td>dann</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6857</th>\n",
       "      <td>dang</td>\n",
       "      <td>dong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8488</th>\n",
       "      <td>dang</td>\n",
       "      <td>dank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6424</th>\n",
       "      <td>dank</td>\n",
       "      <td>danke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764</th>\n",
       "      <td>dank</td>\n",
       "      <td>dank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9488</th>\n",
       "      <td>danke</td>\n",
       "      <td>dank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>danke</td>\n",
       "      <td>danke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5282</th>\n",
       "      <td>dar</td>\n",
       "      <td>dass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9721</th>\n",
       "      <td>dar</td>\n",
       "      <td>das</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      original corrected\n",
       "8289         a        ah\n",
       "8126         a        an\n",
       "12523        a       ach\n",
       "9718         a        am\n",
       "5763         a        er\n",
       "702        aas       das\n",
       "4571       aas       aus\n",
       "31          ab        ab\n",
       "6666        ab        ob\n",
       "2207       abe      habe\n",
       "9000       abe      aber\n",
       "1743       ach       ach\n",
       "346        ach      auch\n",
       "2149       ais     heißt\n",
       "933        ais       eis\n",
       "6384     allen    allein\n",
       "2480     allen     allen\n",
       "1501      alls       als\n",
       "1996      alls     alles\n",
       "401        als       als\n",
       "5822       als     alles\n",
       "7           an        an\n",
       "3012        an       ein\n",
       "6991       and        an\n",
       "10031      and       end\n",
       "8612       and       und\n",
       "1277    anruft    anruft\n",
       "7249    anruft     anruf\n",
       "8858       ans     angst\n",
       "1270       ans       ans\n",
       "5033       anz        an\n",
       "6596       anz       ans\n",
       "4916        as        aß\n",
       "7271        as       aus\n",
       "1015        as       eis\n",
       "8204       ast      hast\n",
       "11685      ast       ast\n",
       "970         au      auch\n",
       "152         au       auf\n",
       "5277        au       aus\n",
       "9456       aus      auch\n",
       "5845       aus      raus\n",
       "55         aus       aus\n",
       "7115       aus      haus\n",
       "6521       aus      aufs\n",
       "12061     baar      paar\n",
       "3804      baar      aber\n",
       "3376     baden     baden\n",
       "7099     baden     boden\n",
       "2163       bai      weil\n",
       "1170       bai       bei\n",
       "12357      bas      dass\n",
       "9723       bas       bus\n",
       "3978       bas      pass\n",
       "4126      baum     bauen\n",
       "1756      baum      baum\n",
       "326       beim      beim\n",
       "778       beim      bein\n",
       "1133      bein      beim\n",
       "983       bein      bein\n",
       "12611     bein       bei\n",
       "8505   bekomen  gekommen\n",
       "118    bekomen  bekommen\n",
       "4960      beld     bellt\n",
       "7359      beld      bild\n",
       "8493      bell      bell\n",
       "4452      bell     bello\n",
       "6305       ben       den\n",
       "4239       ben       ben\n",
       "4929       ber       der\n",
       "8860       ber      aber\n",
       "1723      bies      bins\n",
       "560       bies       bis\n",
       "1897       bis      bins\n",
       "1900       bis      bist\n",
       "357        bis       bis\n",
       "7088      bist       bis\n",
       "1783      bist      bist\n",
       "6802     brach   brachte\n",
       "4005     brach     brach\n",
       "7898        bu        du\n",
       "5936        bu       buh\n",
       "11370       da       das\n",
       "215         da        da\n",
       "6231      dach      doch\n",
       "6877      dach      dann\n",
       "11012     dach      dach\n",
       "8269      dach        da\n",
       "6456      dach      nach\n",
       "11952      dan      dran\n",
       "2305       dan      dein\n",
       "10         dan      dann\n",
       "6857      dang      dong\n",
       "8488      dang      dank\n",
       "6424      dank     danke\n",
       "1764      dank      dank\n",
       "9488     danke      dank\n",
       "832      danke     danke\n",
       "5282       dar      dass\n",
       "9721       dar       das"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Base: data_clean_types, case insensitive (lowercase)\n",
    "duplicates_data_clean_types_lc = getDuplicates(data_clean_types_lc)\n",
    "printAnswer(duplicates_data_clean_types_lc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Base: data_clean_types, case sensitive (differentitation)\n",
    "duplicates_data_clean_types = getDuplicates(data_clean_types, sort_lowercase=True)\n",
    "#printAnswer(duplicates_data_clean_types, display_df=False)\n",
    "\n",
    "# Base: data_error_types, case insensitive (lowercase)\n",
    "duplicates_data_error_types_lc = getDuplicates(data_error_types_lc)\n",
    "#printAnswer(duplicates_data_error_types_lc, display_df=False)\n",
    "\n",
    "# Base: data_error_types, case sensitive (differentitation)\n",
    "duplicates_data_error_types = getDuplicates(data_error_types, sort_lowercase=True)\n",
    "#printAnswer(duplicates_data_error_types, display_df=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------------------------+----------------------------------------------------------------------+\n",
      "| Data set                         | Number of origs that are corrected to different targets (tupel base) |\n",
      "+----------------------------------+----------------------------------------------------------------------+\n",
      "| data_clean_types_lc              |                                                                  935 |\n",
      "| data_clean_types, case sensitive |                                                                 1855 |\n",
      "| data_error_types_lc              |                                                                  528 |\n",
      "| data_error_types, case sensitive |                                                                  685 |\n",
      "+----------------------------------+----------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "# Table Overview\n",
    "from prettytable import PrettyTable\n",
    "\n",
    "table = PrettyTable()\n",
    "\n",
    "table.field_names=[\"Data set\", \"Number of origs that are corrected to different targets (tupel base)\"]\n",
    "\n",
    "table.add_row([\"data_clean_types_lc\", duplicates_data_clean_types_lc.shape[0]])\n",
    "table.add_row([\"data_clean_types, case sensitive\", duplicates_data_clean_types.shape[0]])\n",
    "table.add_row([\"data_error_types_lc\", duplicates_data_error_types_lc.shape[0]])\n",
    "table.add_row([\"data_error_types, case sensitive\", duplicates_data_error_types.shape[0]])\n",
    "\n",
    "table.align[\"Data set\"]=\"l\"\n",
    "table.align[\"Number of origs that are corrected to different targets (tupel base)\"]=\"r\"\n",
    "\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style='background : yellow' > Key message: </span>\n",
    "\n",
    "<span style='background : yellow' > In general, there are 935 missspellings that are corrected to different target words (case insensitive). Looking at case sensitive, this number increases resulting in 1855 (difference = 920). Concerning the semantics, case insensitive is more interesting having a look at (see DataFrame). </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='16'></a><br>\n",
    "## 4 - What are the different variants of missspellings of words?\n",
    "Base: data_error_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count how often 'corrected' with same value is in dataframe\n",
    "data_error_types_sorted_corr = data_error_types.copy()\n",
    "data_error_types_sorted_corr['count_variants_corr'] = data_error_types_sorted_corr.groupby('corrected')['corrected'].transform('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col4 {\n",
       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  lightgreen;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row34_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row34_col3 {\n",
       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  lightgreen;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  lightgreen;\n",
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       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row35_col3 {\n",
       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row35_col7 {\n",
       "            background-color:  lightgreen;\n",
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       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col3 {\n",
       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col7 {\n",
       "            background-color:  lightgreen;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row37_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row37_col3 {\n",
       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row37_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col0 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col4 {\n",
       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col7 {\n",
       "            background-color:  lightgreen;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col7 {\n",
       "            background-color:  lightgreen;\n",
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       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col3 {\n",
       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col7 {\n",
       "            background-color:  lightgreen;\n",
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       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col1 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col2 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col3 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col4 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col5 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col6 {\n",
       "            background-color:  grey;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col7 {\n",
       "            background-color:  lightgreen;\n",
       "        }    #T_2ea4553e_a403_11eb_9ec3_a45e60e77725row42_col0 {\n",
       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  grey;\n",
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       "            background-color:  lightgreen;\n",
       "        }</style><table id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >original</th>        <th class=\"col_heading level0 col1\" >corrected</th>        <th class=\"col_heading level0 col2\" >filename</th>        <th class=\"col_heading level0 col3\" >freq_ori</th>        <th class=\"col_heading level0 col4\" >freq_cor</th>        <th class=\"col_heading level0 col5\" >freq_tup</th>        <th class=\"col_heading level0 col6\" >distance</th>        <th class=\"col_heading level0 col7\" >count_variants_corr</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row0\" class=\"row_heading level0 row0\" >6769</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row0_col0\" class=\"data row0 col0\" >Fundbiro</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row0_col1\" class=\"data row0 col1\" >Fundbüro</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row0_col2\" class=\"data row0 col2\" >08-015-4-II-Fundbuero.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row0_col3\" class=\"data row0 col3\" >6</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row0_col4\" class=\"data row0 col4\" >153</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row0_col5\" class=\"data row0 col5\" >6</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row0_col6\" class=\"data row0 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row0_col7\" class=\"data row0 col7\" >68</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row1\" class=\"row_heading level0 row1\" >52</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row1_col0\" class=\"data row1 col0\" >glüklieg</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row1_col1\" class=\"data row1 col1\" >glücklich</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row1_col2\" class=\"data row1 col2\" >01-026-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row1_col3\" class=\"data row1 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row1_col4\" class=\"data row1 col4\" >163</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row1_col5\" class=\"data row1 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row1_col6\" class=\"data row1 col6\" >3</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row1_col7\" class=\"data row1 col7\" >56</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row2\" class=\"row_heading level0 row2\" >2691</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row2_col0\" class=\"data row2 col0\" >kartong</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row2_col1\" class=\"data row2 col1\" >Karton</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row2_col2\" class=\"data row2 col2\" >04-005-3-II-Jenga.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row2_col3\" class=\"data row2 col3\" >25</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row2_col4\" class=\"data row2 col4\" >260</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row2_col5\" class=\"data row2 col5\" >25</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row2_col6\" class=\"data row2 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row2_col7\" class=\"data row2 col7\" >51</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row3\" class=\"row_heading level0 row3\" >80</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row3_col0\" class=\"data row3 col0\" >Spatzirn</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row3_col1\" class=\"data row3 col1\" >spazieren</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row3_col2\" class=\"data row3 col2\" >01-049-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row3_col4\" class=\"data row3 col4\" >116</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row3_col5\" class=\"data row3 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row3_col6\" class=\"data row3 col6\" >4</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row3_col7\" class=\"data row3 col7\" >47</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row4\" class=\"row_heading level0 row4\" >6312</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row4_col0\" class=\"data row4 col0\" >ruKsaK</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row4_col1\" class=\"data row4 col1\" >Rucksack</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row4_col2\" class=\"data row4 col2\" >07-288-4-I-Schule.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row4_col3\" class=\"data row4 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row4_col4\" class=\"data row4 col4\" >161</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row4_col5\" class=\"data row4 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row4_col6\" class=\"data row4 col6\" >5</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row4_col7\" class=\"data row4 col7\" >44</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row5\" class=\"row_heading level0 row5\" >114</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col0\" class=\"data row5 col0\" >traurisch</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col1\" class=\"data row5 col1\" >traurig</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col2\" class=\"data row5 col2\" >01-058-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col3\" class=\"data row5 col3\" >8</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col4\" class=\"data row5 col4\" >119</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col5\" class=\"data row5 col5\" >8</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col6\" class=\"data row5 col6\" >3</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row5_col7\" class=\"data row5 col7\" >42</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row6\" class=\"row_heading level0 row6\" >1893</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col0\" class=\"data row6 col0\" >spingt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col1\" class=\"data row6 col1\" >springt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col2\" class=\"data row6 col2\" >03-005-3-I-Frosch.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col3\" class=\"data row6 col3\" >5</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col4\" class=\"data row6 col4\" >80</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col5\" class=\"data row6 col5\" >5</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col6\" class=\"data row6 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row6_col7\" class=\"data row6 col7\" >40</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row7\" class=\"row_heading level0 row7\" >968</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col0\" class=\"data row7 col0\" >Fernster</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col1\" class=\"data row7 col1\" >Fenster</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col2\" class=\"data row7 col2\" >02-058-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col3\" class=\"data row7 col3\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col4\" class=\"data row7 col4\" >80</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col5\" class=\"data row7 col5\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col6\" class=\"data row7 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row7_col7\" class=\"data row7 col7\" >39</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row8\" class=\"row_heading level0 row8\" >5728</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col0\" class=\"data row8 col0\" >Lerern</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col1\" class=\"data row8 col1\" >Lehrerin</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col2\" class=\"data row8 col2\" >07-023-4-I-Schule.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col3\" class=\"data row8 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col4\" class=\"data row8 col4\" >101</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col5\" class=\"data row8 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col6\" class=\"data row8 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row8_col7\" class=\"data row8 col7\" >37</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row9\" class=\"row_heading level0 row9\" >1057</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col0\" class=\"data row9 col0\" >Plakarte</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col1\" class=\"data row9 col1\" >Plakate</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col2\" class=\"data row9 col2\" >02-105-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col3\" class=\"data row9 col3\" >15</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col4\" class=\"data row9 col4\" >88</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col5\" class=\"data row9 col5\" >15</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col6\" class=\"data row9 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row9_col7\" class=\"data row9 col7\" >35</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row10\" class=\"row_heading level0 row10\" >119</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col0\" class=\"data row10 col0\" >Plözlich</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col1\" class=\"data row10 col1\" >Plötzlich</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col2\" class=\"data row10 col2\" >01-060-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col3\" class=\"data row10 col3\" >47</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col4\" class=\"data row10 col4\" >117</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col5\" class=\"data row10 col5\" >45</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col6\" class=\"data row10 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row10_col7\" class=\"data row10 col7\" >33</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row11\" class=\"row_heading level0 row11\" >272</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col0\" class=\"data row11 col0\" >plözlisch</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col1\" class=\"data row11 col1\" >plötzlich</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col2\" class=\"data row11 col2\" >01-132-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col3\" class=\"data row11 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col4\" class=\"data row11 col4\" >62</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col5\" class=\"data row11 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col6\" class=\"data row11 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row11_col7\" class=\"data row11 col7\" >31</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row12\" class=\"row_heading level0 row12\" >3704</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col0\" class=\"data row12 col0\" >Schubsauger</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col1\" class=\"data row12 col1\" >Staubsauger</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col2\" class=\"data row12 col2\" >05-021-3-III-Staubsauger.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col3\" class=\"data row12 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col4\" class=\"data row12 col4\" >57</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col5\" class=\"data row12 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col6\" class=\"data row12 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row12_col7\" class=\"data row12 col7\" >31</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row13\" class=\"row_heading level0 row13\" >3711</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col0\" class=\"data row13 col0\" >Dultel</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col1\" class=\"data row13 col1\" >Beutel</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col2\" class=\"data row13 col2\" >05-023-3-III-Staubsauger.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col3\" class=\"data row13 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col4\" class=\"data row13 col4\" >49</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col5\" class=\"data row13 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col6\" class=\"data row13 col6\" >3</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row13_col7\" class=\"data row13 col7\" >31</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row14\" class=\"row_heading level0 row14\" >62</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col0\" class=\"data row14 col0\" >on</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col1\" class=\"data row14 col1\" >und</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col2\" class=\"data row14 col2\" >01-029-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col3\" class=\"data row14 col3\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col4\" class=\"data row14 col4\" >106</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col5\" class=\"data row14 col5\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col6\" class=\"data row14 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row14_col7\" class=\"data row14 col7\" >27</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row15\" class=\"row_heading level0 row15\" >71</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col0\" class=\"data row15 col0\" >bischen</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col1\" class=\"data row15 col1\" >bisschen</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col2\" class=\"data row15 col2\" >01-037-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col3\" class=\"data row15 col3\" >20</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col4\" class=\"data row15 col4\" >57</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col5\" class=\"data row15 col5\" >20</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col6\" class=\"data row15 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row15_col7\" class=\"data row15 col7\" >27</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row16\" class=\"row_heading level0 row16\" >1012</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col0\" class=\"data row16 col0\" >kligelt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col1\" class=\"data row16 col1\" >klingelt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col2\" class=\"data row16 col2\" >02-073-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col3\" class=\"data row16 col3\" >13</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col4\" class=\"data row16 col4\" >66</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col5\" class=\"data row16 col5\" >13</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col6\" class=\"data row16 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row16_col7\" class=\"data row16 col7\" >27</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row17\" class=\"row_heading level0 row17\" >1720</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col0\" class=\"data row17 col0\" >fagesen</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col1\" class=\"data row17 col1\" >vergessen</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col2\" class=\"data row17 col2\" >02-397-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col3\" class=\"data row17 col3\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col4\" class=\"data row17 col4\" >139</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col5\" class=\"data row17 col5\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col6\" class=\"data row17 col6\" >4</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row17_col7\" class=\"data row17 col7\" >26</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row18\" class=\"row_heading level0 row18\" >7827</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col0\" class=\"data row18 col0\" >Tedi</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col1\" class=\"data row18 col1\" >Teddy</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col2\" class=\"data row18 col2\" >09-026-4-III-Seilbahn.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col3\" class=\"data row18 col3\" >15</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col4\" class=\"data row18 col4\" >117</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col5\" class=\"data row18 col5\" >15</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col6\" class=\"data row18 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row18_col7\" class=\"data row18 col7\" >26</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row19\" class=\"row_heading level0 row19\" >26</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col0\" class=\"data row19 col0\" >frölich</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col1\" class=\"data row19 col1\" >fröhlich</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col2\" class=\"data row19 col2\" >01-012-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col3\" class=\"data row19 col3\" >51</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col4\" class=\"data row19 col4\" >92</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col5\" class=\"data row19 col5\" >51</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col6\" class=\"data row19 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row19_col7\" class=\"data row19 col7\" >25</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row20\" class=\"row_heading level0 row20\" >919</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col0\" class=\"data row20 col0\" >luch</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col1\" class=\"data row20 col1\" >Lutscher</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col2\" class=\"data row20 col2\" >02-028-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col3\" class=\"data row20 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col4\" class=\"data row20 col4\" >56</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col5\" class=\"data row20 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col6\" class=\"data row20 col6\" >5</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row20_col7\" class=\"data row20 col7\" >25</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row21\" class=\"row_heading level0 row21\" >188</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col0\" class=\"data row21 col0\" >Hart</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col1\" class=\"data row21 col1\" >hat</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col2\" class=\"data row21 col2\" >01-077-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col3\" class=\"data row21 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col4\" class=\"data row21 col4\" >131</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col5\" class=\"data row21 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col6\" class=\"data row21 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row21_col7\" class=\"data row21 col7\" >24</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row22\" class=\"row_heading level0 row22\" >941</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col0\" class=\"data row22 col0\" >umdamd</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col1\" class=\"data row22 col1\" >umarmt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col2\" class=\"data row22 col2\" >02-045-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col3\" class=\"data row22 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col4\" class=\"data row22 col4\" >40</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col5\" class=\"data row22 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col6\" class=\"data row22 col6\" >3</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row22_col7\" class=\"data row22 col7\" >24</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row23\" class=\"row_heading level0 row23\" >980</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col0\" class=\"data row23 col0\" >velore</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col1\" class=\"data row23 col1\" >verloren</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col2\" class=\"data row23 col2\" >02-061-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col3\" class=\"data row23 col3\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col4\" class=\"data row23 col4\" >52</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col5\" class=\"data row23 col5\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col6\" class=\"data row23 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row23_col7\" class=\"data row23 col7\" >24</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row24\" class=\"row_heading level0 row24\" >1131</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col0\" class=\"data row24 col0\" >freudetrenen</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col1\" class=\"data row24 col1\" >Freudentränen</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col2\" class=\"data row24 col2\" >02-132-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col3\" class=\"data row24 col3\" >4</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col4\" class=\"data row24 col4\" >36</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col5\" class=\"data row24 col5\" >4</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col6\" class=\"data row24 col6\" >3</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row24_col7\" class=\"data row24 col7\" >24</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row25\" class=\"row_heading level0 row25\" >7797</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col0\" class=\"data row25 col0\" >TadyBer</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col1\" class=\"data row25 col1\" >Teddybär</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col2\" class=\"data row25 col2\" >09-015-4-III-Seilbahn.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col3\" class=\"data row25 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col4\" class=\"data row25 col4\" >73</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col5\" class=\"data row25 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col6\" class=\"data row25 col6\" >4</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row25_col7\" class=\"data row25 col7\" >24</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row26\" class=\"row_heading level0 row26\" >5</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col0\" class=\"data row26 col0\" >felt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col1\" class=\"data row26 col1\" >fällt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col2\" class=\"data row26 col2\" >01-005-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col3\" class=\"data row26 col3\" >93</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col4\" class=\"data row26 col4\" >198</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col5\" class=\"data row26 col5\" >90</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col6\" class=\"data row26 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row26_col7\" class=\"data row26 col7\" >23</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row27\" class=\"row_heading level0 row27\" >16</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col0\" class=\"data row27 col0\" >aba</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col1\" class=\"data row27 col1\" >aber</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col2\" class=\"data row27 col2\" >01-006-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col3\" class=\"data row27 col3\" >21</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col4\" class=\"data row27 col4\" >60</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col5\" class=\"data row27 col5\" >21</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col6\" class=\"data row27 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row27_col7\" class=\"data row27 col7\" >23</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row28\" class=\"row_heading level0 row28\" >176</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col0\" class=\"data row28 col0\" >gegan</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col1\" class=\"data row28 col1\" >gegangen</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col2\" class=\"data row28 col2\" >01-075-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col3\" class=\"data row28 col3\" >6</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col4\" class=\"data row28 col4\" >57</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col5\" class=\"data row28 col5\" >6</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col6\" class=\"data row28 col6\" >3</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row28_col7\" class=\"data row28 col7\" >23</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row29\" class=\"row_heading level0 row29\" >347</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col0\" class=\"data row29 col0\" >wa</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col1\" class=\"data row29 col1\" >war</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col2\" class=\"data row29 col2\" >01-161-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col3\" class=\"data row29 col3\" >30</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col4\" class=\"data row29 col4\" >109</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col5\" class=\"data row29 col5\" >27</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col6\" class=\"data row29 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row29_col7\" class=\"data row29 col7\" >23</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row30\" class=\"row_heading level0 row30\" >867</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col0\" class=\"data row30 col0\" >Loli</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col1\" class=\"data row30 col1\" >Lolli</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col2\" class=\"data row30 col2\" >02-005-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col3\" class=\"data row30 col3\" >113</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col4\" class=\"data row30 col4\" >259</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col5\" class=\"data row30 col5\" >113</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col6\" class=\"data row30 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row30_col7\" class=\"data row30 col7\" >23</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row31\" class=\"row_heading level0 row31\" >946</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col0\" class=\"data row31 col0\" >Televon</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col1\" class=\"data row31 col1\" >Telefon</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col2\" class=\"data row31 col2\" >02-054-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col3\" class=\"data row31 col3\" >3</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col4\" class=\"data row31 col4\" >39</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col5\" class=\"data row31 col5\" >3</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col6\" class=\"data row31 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row31_col7\" class=\"data row31 col7\" >23</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row32\" class=\"row_heading level0 row32\" >204</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col0\" class=\"data row32 col0\" >fräut</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col1\" class=\"data row32 col1\" >freut</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col2\" class=\"data row32 col2\" >01-102-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col3\" class=\"data row32 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col4\" class=\"data row32 col4\" >42</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col5\" class=\"data row32 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col6\" class=\"data row32 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row32_col7\" class=\"data row32 col7\" >22</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row33\" class=\"row_heading level0 row33\" >554</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col0\" class=\"data row33 col0\" >wok</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col1\" class=\"data row33 col1\" >weg</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col2\" class=\"data row33 col2\" >01-277-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col3\" class=\"data row33 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col4\" class=\"data row33 col4\" >94</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col5\" class=\"data row33 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col6\" class=\"data row33 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row33_col7\" class=\"data row33 col7\" >22</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row34\" class=\"row_heading level0 row34\" >3699</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row34_col0\" class=\"data row34 col0\" >Stabsaugerbeutel</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row34_col1\" class=\"data row34 col1\" >Staubsaugerbeutel</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row34_col2\" class=\"data row34 col2\" >05-012-3-III-Staubsauger.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row34_col3\" class=\"data row34 col3\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row34_col4\" class=\"data row34 col4\" >27</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row34_col5\" class=\"data row34 col5\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row34_col6\" class=\"data row34 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row34_col7\" class=\"data row34 col7\" >22</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row35\" class=\"row_heading level0 row35\" >60</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row35_col0\" class=\"data row35 col0\" >veil</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row35_col1\" class=\"data row35 col1\" >weil</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row35_col2\" class=\"data row35 col2\" >01-029-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row35_col3\" class=\"data row35 col3\" >4</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row35_col4\" class=\"data row35 col4\" >82</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row35_col5\" class=\"data row35 col5\" >4</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row35_col6\" class=\"data row35 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row35_col7\" class=\"data row35 col7\" >21</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row36\" class=\"row_heading level0 row36\" >132</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col0\" class=\"data row36 col0\" >pasirt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col1\" class=\"data row36 col1\" >passiert</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col2\" class=\"data row36 col2\" >01-063-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col3\" class=\"data row36 col3\" >18</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col4\" class=\"data row36 col4\" >75</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col5\" class=\"data row36 col5\" >18</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col6\" class=\"data row36 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row36_col7\" class=\"data row36 col7\" >21</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row37\" class=\"row_heading level0 row37\" >948</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row37_col0\" class=\"data row37 col0\" >gevunden</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row37_col1\" class=\"data row37 col1\" >gefunden</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row37_col2\" class=\"data row37 col2\" >02-054-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row37_col3\" class=\"data row37 col3\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row37_col4\" class=\"data row37 col4\" >40</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row37_col5\" class=\"data row37 col5\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row37_col6\" class=\"data row37 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row37_col7\" class=\"data row37 col7\" >21</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row38\" class=\"row_heading level0 row38\" >994</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col0\" class=\"data row38 col0\" >hemand</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col1\" class=\"data row38 col1\" >jemand</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col2\" class=\"data row38 col2\" >02-063-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col3\" class=\"data row38 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col4\" class=\"data row38 col4\" >46</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col5\" class=\"data row38 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col6\" class=\"data row38 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row38_col7\" class=\"data row38 col7\" >21</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row39\" class=\"row_heading level0 row39\" >1900</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col0\" class=\"data row39 col0\" >Frusch</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col1\" class=\"data row39 col1\" >Frosch</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col2\" class=\"data row39 col2\" >03-006-3-I-Frosch.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col3\" class=\"data row39 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col4\" class=\"data row39 col4\" >77</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col5\" class=\"data row39 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col6\" class=\"data row39 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row39_col7\" class=\"data row39 col7\" >21</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row40\" class=\"row_heading level0 row40\" >95</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col0\" class=\"data row40 col0\" >Hinther</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col1\" class=\"data row40 col1\" >hinterher</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col2\" class=\"data row40 col2\" >01-054-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col3\" class=\"data row40 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col4\" class=\"data row40 col4\" >38</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col5\" class=\"data row40 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col6\" class=\"data row40 col6\" >3</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row40_col7\" class=\"data row40 col7\" >20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row41\" class=\"row_heading level0 row41\" >110</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col0\" class=\"data row41 col0\" >nisct</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col1\" class=\"data row41 col1\" >nicht</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col2\" class=\"data row41 col2\" >01-057-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col3\" class=\"data row41 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col4\" class=\"data row41 col4\" >135</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col5\" class=\"data row41 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col6\" class=\"data row41 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row41_col7\" class=\"data row41 col7\" >20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row42\" class=\"row_heading level0 row42\" >130</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row42_col0\" class=\"data row42 col0\" >wider</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row42_col1\" class=\"data row42 col1\" >wieder</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row42_col2\" class=\"data row42 col2\" >01-061-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row42_col3\" class=\"data row42 col3\" >178</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row42_col4\" class=\"data row42 col4\" >207</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row42_col5\" class=\"data row42 col5\" >178</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row42_col6\" class=\"data row42 col6\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row42_col7\" class=\"data row42 col7\" >20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row43\" class=\"row_heading level0 row43\" >922</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row43_col0\" class=\"data row43 col0\" >geschek</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row43_col1\" class=\"data row43 col1\" >Geschenk</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row43_col2\" class=\"data row43 col2\" >02-029-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row43_col3\" class=\"data row43 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row43_col4\" class=\"data row43 col4\" >21</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row43_col5\" class=\"data row43 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row43_col6\" class=\"data row43 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row43_col7\" class=\"data row43 col7\" >20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row44\" class=\"row_heading level0 row44\" >1732</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row44_col0\" class=\"data row44 col0\" >zurüg</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row44_col1\" class=\"data row44 col1\" >zurück</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row44_col2\" class=\"data row44 col2\" >02-412-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row44_col3\" class=\"data row44 col3\" >4</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row44_col4\" class=\"data row44 col4\" >61</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row44_col5\" class=\"data row44 col5\" >4</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row44_col6\" class=\"data row44 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row44_col7\" class=\"data row44 col7\" >20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row45\" class=\"row_heading level0 row45\" >2701</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row45_col0\" class=\"data row45 col0\" >verschtekt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row45_col1\" class=\"data row45 col1\" >versteckt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row45_col2\" class=\"data row45 col2\" >04-021-3-II-Jenga.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row45_col3\" class=\"data row45 col3\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row45_col4\" class=\"data row45 col4\" >44</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row45_col5\" class=\"data row45 col5\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row45_col6\" class=\"data row45 col6\" >3</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row45_col7\" class=\"data row45 col7\" >20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row46\" class=\"row_heading level0 row46\" >13</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row46_col0\" class=\"data row46 col0\" >gien</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row46_col1\" class=\"data row46 col1\" >gehen</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row46_col2\" class=\"data row46 col2\" >01-006-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row46_col3\" class=\"data row46 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row46_col4\" class=\"data row46 col4\" >78</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row46_col5\" class=\"data row46 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row46_col6\" class=\"data row46 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row46_col7\" class=\"data row46 col7\" >19</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row47\" class=\"row_heading level0 row47\" >145</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row47_col0\" class=\"data row47 col0\" >sackt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row47_col1\" class=\"data row47 col1\" >sagt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row47_col2\" class=\"data row47 col2\" >01-065-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row47_col3\" class=\"data row47 col3\" >6</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row47_col4\" class=\"data row47 col4\" >52</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row47_col5\" class=\"data row47 col5\" >6</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row47_col6\" class=\"data row47 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row47_col7\" class=\"data row47 col7\" >19</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row48\" class=\"row_heading level0 row48\" >153</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row48_col0\" class=\"data row48 col0\" >jerz</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row48_col1\" class=\"data row48 col1\" >jetzt</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row48_col2\" class=\"data row48 col2\" >01-073-2-III-Eis.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row48_col3\" class=\"data row48 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row48_col4\" class=\"data row48 col4\" >75</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row48_col5\" class=\"data row48 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row48_col6\" class=\"data row48 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row48_col7\" class=\"data row48 col7\" >19</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725level0_row49\" class=\"row_heading level0 row49\" >870</th>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row49_col0\" class=\"data row49 col0\" >fatet</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row49_col1\" class=\"data row49 col1\" >wartet</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row49_col2\" class=\"data row49 col2\" >02-006-2-IV-Weg.csv</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row49_col3\" class=\"data row49 col3\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row49_col4\" class=\"data row49 col4\" >67</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row49_col5\" class=\"data row49 col5\" >1</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row49_col6\" class=\"data row49 col6\" >2</td>\n",
       "                        <td id=\"T_2ea4553e_a403_11eb_9ec3_a45e60e77725row49_col7\" class=\"data row49 col7\" >19</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8b704cddc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Which word has the most variants in missspellings? (top 100)\n",
    "\n",
    "data_error_types_sorted_corr_2 = data_error_types_sorted_corr.drop_duplicates('corrected')\n",
    "#data_error_types_sorted_corr_2 = data_error_types_sorted_corr_2.sort_values(by='count_target', ascending=False)\n",
    "data_error_types_sorted_corr_2 = data_error_types_sorted_corr_2.nlargest(100, 'count_variants_corr', keep='first')\n",
    "\n",
    "# Change number in head() to show more or less rows\n",
    "high_data_error_types_sorted_corr_2 = data_error_types_sorted_corr_2.head(50).style.apply(highlight_cols, columns=['corrected', 'count_variants_corr'], axis=None)\n",
    "#display(high_data_error_types_sorted_corr_2.head(50))\n",
    "display(high_data_error_types_sorted_corr_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>original</th>\n",
       "      <th>corrected</th>\n",
       "      <th>filename</th>\n",
       "      <th>freq_ori</th>\n",
       "      <th>freq_cor</th>\n",
       "      <th>freq_tup</th>\n",
       "      <th>distance</th>\n",
       "      <th>count_variants_corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7452</th>\n",
       "      <td>Pfundbero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-340-4-II-Fundbuero.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6871</th>\n",
       "      <td>FundBero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-064-4-II-Fundbuero.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7082</th>\n",
       "      <td>Vundbiro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-167-4-II-Fundbuero.csv</td>\n",
       "      <td>4</td>\n",
       "      <td>153</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7650</th>\n",
       "      <td>fundbero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-468-4-II-Fundbuero.csv</td>\n",
       "      <td>3</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7643</th>\n",
       "      <td>vondbero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-466-4-II-Fundbuero.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6800</th>\n",
       "      <td>Fundbero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-025-4-II-Fundbuero.csv</td>\n",
       "      <td>13</td>\n",
       "      <td>153</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7621</th>\n",
       "      <td>funtbero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-459-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7132</th>\n",
       "      <td>Fuhntbero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-183-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7146</th>\n",
       "      <td>fodBiro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-194-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7613</th>\n",
       "      <td>PfundBerö</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-458-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7068</th>\n",
       "      <td>Pundböro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-161-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7607</th>\n",
       "      <td>Fundberro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-450-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7176</th>\n",
       "      <td>fundpero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-197-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7178</th>\n",
       "      <td>fondbero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-197-4-II-Fundbuero.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7587</th>\n",
       "      <td>Fundburo</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-436-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7577</th>\n",
       "      <td>Fundüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-434-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7222</th>\n",
       "      <td>pfundbüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-208-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7229</th>\n",
       "      <td>funtburo</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-211-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>fontBöro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-472-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7666</th>\n",
       "      <td>fonBöro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-472-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7234</th>\n",
       "      <td>funtberuue</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-218-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6933</th>\n",
       "      <td>fündbero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-088-4-II-Fundbuero.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6996</th>\n",
       "      <td>Fundböra</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-136-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7007</th>\n",
       "      <td>PfundBüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-140-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6961</th>\n",
       "      <td>fundbeo</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-110-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6957</th>\n",
       "      <td>fandbero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-110-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7013</th>\n",
       "      <td>fundbiro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-143-4-II-Fundbuero.csv</td>\n",
       "      <td>5</td>\n",
       "      <td>153</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6769</th>\n",
       "      <td>Fundbiro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-015-4-II-Fundbuero.csv</td>\n",
       "      <td>6</td>\n",
       "      <td>153</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6943</th>\n",
       "      <td>Pfondbüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-097-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6907</th>\n",
       "      <td>Pfundbüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-077-4-II-Fundbuero.csv</td>\n",
       "      <td>14</td>\n",
       "      <td>153</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7061</th>\n",
       "      <td>Fund-büro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-160-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7014</th>\n",
       "      <td>fundbro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-143-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6893</th>\n",
       "      <td>Funbüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-074-4-II-Fundbuero.csv</td>\n",
       "      <td>3</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7745</th>\n",
       "      <td>fundBiro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-622-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7016</th>\n",
       "      <td>Fuhnhbüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-147-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7022</th>\n",
       "      <td>Fuhnbüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-147-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7036</th>\n",
       "      <td>PfundBero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-149-4-II-Fundbuero.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>funtBüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-150-4-II-Fundbuero.csv</td>\n",
       "      <td>3</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7568</th>\n",
       "      <td>funtBiro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-423-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6798</th>\n",
       "      <td>Funkbiro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-024-4-II-Fundbuero.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7379</th>\n",
       "      <td>Vunkbüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-295-4-II-Fundbuero.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7412</th>\n",
       "      <td>Pfandbüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-324-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7416</th>\n",
       "      <td>Funtburo</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-328-4-II-Fundbuero.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7302</th>\n",
       "      <td>vundbero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-262-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7304</th>\n",
       "      <td>vundberp</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-262-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7310</th>\n",
       "      <td>fundberü</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-265-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7311</th>\n",
       "      <td>fundBero</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-265-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7318</th>\n",
       "      <td>Pfundlüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-270-4-II-Fundbuero.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7332</th>\n",
       "      <td>Funktbüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-277-4-II-Fundbuero.csv</td>\n",
       "      <td>4</td>\n",
       "      <td>153</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7489</th>\n",
       "      <td>fuhntbüro</td>\n",
       "      <td>Fundbüro</td>\n",
       "      <td>08-369-4-II-Fundbuero.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        original corrected                   filename  freq_ori  freq_cor  \\\n",
       "7452   Pfundbero  Fundbüro  08-340-4-II-Fundbuero.csv         2       153   \n",
       "6871    FundBero  Fundbüro  08-064-4-II-Fundbuero.csv         2       153   \n",
       "7082    Vundbiro  Fundbüro  08-167-4-II-Fundbuero.csv         4       153   \n",
       "7650    fundbero  Fundbüro  08-468-4-II-Fundbuero.csv         3       153   \n",
       "7643    vondbero  Fundbüro  08-466-4-II-Fundbuero.csv         2       153   \n",
       "6800    Fundbero  Fundbüro  08-025-4-II-Fundbuero.csv        13       153   \n",
       "7621    funtbero  Fundbüro  08-459-4-II-Fundbuero.csv         1       153   \n",
       "7132   Fuhntbero  Fundbüro  08-183-4-II-Fundbuero.csv         1       153   \n",
       "7146     fodBiro  Fundbüro  08-194-4-II-Fundbuero.csv         1       153   \n",
       "7613   PfundBerö  Fundbüro  08-458-4-II-Fundbuero.csv         1       153   \n",
       "7068    Pundböro  Fundbüro  08-161-4-II-Fundbuero.csv         1       153   \n",
       "7607   Fundberro  Fundbüro  08-450-4-II-Fundbuero.csv         1       153   \n",
       "7176    fundpero  Fundbüro  08-197-4-II-Fundbuero.csv         1       153   \n",
       "7178    fondbero  Fundbüro  08-197-4-II-Fundbuero.csv         2       153   \n",
       "7587    Fundburo  Fundbüro  08-436-4-II-Fundbuero.csv         1       153   \n",
       "7577     Fundüro  Fundbüro  08-434-4-II-Fundbuero.csv         1       153   \n",
       "7222   pfundbüro  Fundbüro  08-208-4-II-Fundbuero.csv         1       153   \n",
       "7229    funtburo  Fundbüro  08-211-4-II-Fundbuero.csv         1       153   \n",
       "7664    fontBöro  Fundbüro  08-472-4-II-Fundbuero.csv         1       153   \n",
       "7666     fonBöro  Fundbüro  08-472-4-II-Fundbuero.csv         1       153   \n",
       "7234  funtberuue  Fundbüro  08-218-4-II-Fundbuero.csv         1       153   \n",
       "6933    fündbero  Fundbüro  08-088-4-II-Fundbuero.csv         2       153   \n",
       "6996    Fundböra  Fundbüro  08-136-4-II-Fundbuero.csv         1       153   \n",
       "7007   PfundBüro  Fundbüro  08-140-4-II-Fundbuero.csv         1       153   \n",
       "6961     fundbeo  Fundbüro  08-110-4-II-Fundbuero.csv         1       153   \n",
       "6957    fandbero  Fundbüro  08-110-4-II-Fundbuero.csv         1       153   \n",
       "7013    fundbiro  Fundbüro  08-143-4-II-Fundbuero.csv         5       153   \n",
       "6769    Fundbiro  Fundbüro  08-015-4-II-Fundbuero.csv         6       153   \n",
       "6943   Pfondbüro  Fundbüro  08-097-4-II-Fundbuero.csv         1       153   \n",
       "6907   Pfundbüro  Fundbüro  08-077-4-II-Fundbuero.csv        14       153   \n",
       "7061   Fund-büro  Fundbüro  08-160-4-II-Fundbuero.csv         1       153   \n",
       "7014     fundbro  Fundbüro  08-143-4-II-Fundbuero.csv         1       153   \n",
       "6893     Funbüro  Fundbüro  08-074-4-II-Fundbuero.csv         3       153   \n",
       "7745    fundBiro  Fundbüro  08-622-4-II-Fundbuero.csv         1       153   \n",
       "7016   Fuhnhbüro  Fundbüro  08-147-4-II-Fundbuero.csv         1       153   \n",
       "7022    Fuhnbüro  Fundbüro  08-147-4-II-Fundbuero.csv         1       153   \n",
       "7036   PfundBero  Fundbüro  08-149-4-II-Fundbuero.csv         2       153   \n",
       "7041    funtBüro  Fundbüro  08-150-4-II-Fundbuero.csv         3       153   \n",
       "7568    funtBiro  Fundbüro  08-423-4-II-Fundbuero.csv         1       153   \n",
       "6798    Funkbiro  Fundbüro  08-024-4-II-Fundbuero.csv         2       153   \n",
       "7379    Vunkbüro  Fundbüro  08-295-4-II-Fundbuero.csv         2       153   \n",
       "7412   Pfandbüro  Fundbüro  08-324-4-II-Fundbuero.csv         1       153   \n",
       "7416    Funtburo  Fundbüro  08-328-4-II-Fundbuero.csv         2       153   \n",
       "7302    vundbero  Fundbüro  08-262-4-II-Fundbuero.csv         1       153   \n",
       "7304    vundberp  Fundbüro  08-262-4-II-Fundbuero.csv         1       153   \n",
       "7310    fundberü  Fundbüro  08-265-4-II-Fundbuero.csv         1       153   \n",
       "7311    fundBero  Fundbüro  08-265-4-II-Fundbuero.csv         1       153   \n",
       "7318   Pfundlüro  Fundbüro  08-270-4-II-Fundbuero.csv         1       153   \n",
       "7332   Funktbüro  Fundbüro  08-277-4-II-Fundbuero.csv         4       153   \n",
       "7489   fuhntbüro  Fundbüro  08-369-4-II-Fundbuero.csv         2       153   \n",
       "\n",
       "      freq_tup  distance  count_variants_corr  \n",
       "7452         2         3                   68  \n",
       "6871         2         2                   68  \n",
       "7082         4         2                   68  \n",
       "7650         3         2                   68  \n",
       "7643         2         3                   68  \n",
       "6800        13         1                   68  \n",
       "7621         1         3                   68  \n",
       "7132         1         3                   68  \n",
       "7146         1         5                   68  \n",
       "7613         1         5                   68  \n",
       "7068         1         2                   68  \n",
       "7607         1         2                   68  \n",
       "7176         1         3                   68  \n",
       "7178         2         3                   68  \n",
       "7587         1         1                   68  \n",
       "7577         1         1                   68  \n",
       "7222         1         2                   68  \n",
       "7229         1         3                   68  \n",
       "7664         1         5                   68  \n",
       "7666         1         5                   68  \n",
       "7234         1         6                   68  \n",
       "6933         2         3                   68  \n",
       "6996         1         2                   68  \n",
       "7007         1         3                   68  \n",
       "6961         1         3                   68  \n",
       "6957         1         3                   68  \n",
       "7013         5         2                   68  \n",
       "6769         6         1                   68  \n",
       "6943         1         3                   68  \n",
       "6907        14         2                   68  \n",
       "7061         1         1                   68  \n",
       "7014         1         2                   68  \n",
       "6893         3         1                   68  \n",
       "7745         1         3                   68  \n",
       "7016         1         2                   68  \n",
       "7022         1         2                   68  \n",
       "7036         2         4                   68  \n",
       "7041         3         3                   68  \n",
       "7568         1         4                   68  \n",
       "6798         2         2                   68  \n",
       "7379         2         2                   68  \n",
       "7412         1         3                   68  \n",
       "7416         2         2                   68  \n",
       "7302         1         2                   68  \n",
       "7304         1         3                   68  \n",
       "7310         1         3                   68  \n",
       "7311         1         3                   68  \n",
       "7318         1         3                   68  \n",
       "7332         4         2                   68  \n",
       "7489         2         3                   68  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the variants?\n",
    "data_error_types_sorted_corr.sort_values(by='count_variants_corr', ascending=False).head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <span style='background : yellow' > Key message: </span>\n",
    "\n",
    "<span style='background : yellow' > Having a look at the tables, \"Fundbüro\", \"glücklich\" and \"Karton\" are having the most variants of missspellings. ... </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aftermath (Paper Towards Spellchecking of German Children Writing)\n",
    "- Per testpoint\n",
    "    - durchschnittliche Textlänge (data_clean_token)\n",
    "    - durchschnittliche Fehlerrate (data_error_token)\n",
    "    - absolute Anzahl an Fehlern\n",
    "- Erstmal Tabelle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get testpoints\n",
    "data_error_token['testpoint'] = data_error_token.filename.apply(lambda x: x.split('-')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_clean_token['testpoint'] = data_clean_token.filename.apply(lambda x: x.split('-')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original</th>\n",
       "      <th>corrected</th>\n",
       "      <th>filename</th>\n",
       "      <th>freq_ori</th>\n",
       "      <th>freq_cor</th>\n",
       "      <th>freq_tup</th>\n",
       "      <th>testpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>und</td>\n",
       "      <td>und</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>9718</td>\n",
       "      <td>9827</td>\n",
       "      <td>9713</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>und</td>\n",
       "      <td>und</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>9718</td>\n",
       "      <td>9827</td>\n",
       "      <td>9713</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gehen</td>\n",
       "      <td>gehen</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>288</td>\n",
       "      <td>377</td>\n",
       "      <td>288</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eis</td>\n",
       "      <td>Eis</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>395</td>\n",
       "      <td>657</td>\n",
       "      <td>395</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kaufen</td>\n",
       "      <td>kaufen</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>53</td>\n",
       "      <td>80</td>\n",
       "      <td>53</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>belt</td>\n",
       "      <td>bellt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>91</td>\n",
       "      <td>256</td>\n",
       "      <td>91</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>den</td>\n",
       "      <td>den</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>2401</td>\n",
       "      <td>2389</td>\n",
       "      <td>2278</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Eismann</td>\n",
       "      <td>Eismann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>an</td>\n",
       "      <td>an</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>1415</td>\n",
       "      <td>1440</td>\n",
       "      <td>1412</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>der</td>\n",
       "      <td>der</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>3044</td>\n",
       "      <td>3049</td>\n",
       "      <td>3014</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  original corrected              filename  freq_ori  freq_cor  freq_tup  \\\n",
       "0      und       und  01-005-2-III-Eis.csv      9718      9827      9713   \n",
       "1      und       und  01-005-2-III-Eis.csv      9718      9827      9713   \n",
       "2    gehen     gehen  01-005-2-III-Eis.csv       288       377       288   \n",
       "3      Eis       Eis  01-005-2-III-Eis.csv       395       657       395   \n",
       "4   kaufen    kaufen  01-005-2-III-Eis.csv        53        80        53   \n",
       "5     belt     bellt  01-005-2-III-Eis.csv        91       256        91   \n",
       "6      den       den  01-005-2-III-Eis.csv      2401      2389      2278   \n",
       "7  Eismann   Eismann  01-005-2-III-Eis.csv        10        22        10   \n",
       "8       an        an  01-005-2-III-Eis.csv      1415      1440      1412   \n",
       "9      der       der  01-005-2-III-Eis.csv      3044      3049      3014   \n",
       "\n",
       "  testpoint  \n",
       "0        01  \n",
       "1        01  \n",
       "2        01  \n",
       "3        01  \n",
       "4        01  \n",
       "5        01  \n",
       "6        01  \n",
       "7        01  \n",
       "8        01  \n",
       "9        01  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean_token.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original</th>\n",
       "      <th>corrected</th>\n",
       "      <th>filename</th>\n",
       "      <th>freq_ori</th>\n",
       "      <th>freq_cor</th>\n",
       "      <th>freq_tup</th>\n",
       "      <th>testpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>belt</td>\n",
       "      <td>bellt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>91</td>\n",
       "      <td>138</td>\n",
       "      <td>91</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kukt</td>\n",
       "      <td>kuckt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>73</td>\n",
       "      <td>152</td>\n",
       "      <td>73</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dan</td>\n",
       "      <td>dann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>627</td>\n",
       "      <td>651</td>\n",
       "      <td>621</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gekricht</td>\n",
       "      <td>gekriegt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>leken</td>\n",
       "      <td>lecken</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>felt</td>\n",
       "      <td>fällt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>93</td>\n",
       "      <td>198</td>\n",
       "      <td>90</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>wolte</td>\n",
       "      <td>wollte</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>173</td>\n",
       "      <td>201</td>\n",
       "      <td>173</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lekt</td>\n",
       "      <td>leckt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "      <td>19</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fom</td>\n",
       "      <td>vom</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dan</td>\n",
       "      <td>dann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>627</td>\n",
       "      <td>651</td>\n",
       "      <td>621</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>dan</td>\n",
       "      <td>dann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>627</td>\n",
       "      <td>651</td>\n",
       "      <td>621</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>leken</td>\n",
       "      <td>lecken</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gawen</td>\n",
       "      <td>kaufen</td>\n",
       "      <td>01-006-2-III-Eis.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>din</td>\n",
       "      <td>ein</td>\n",
       "      <td>01-006-2-III-Eis.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Keld</td>\n",
       "      <td>Geld</td>\n",
       "      <td>01-006-2-III-Eis.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    original corrected              filename  freq_ori  freq_cor  freq_tup  \\\n",
       "0       belt     bellt  01-005-2-III-Eis.csv        91       138        91   \n",
       "1       kukt     kuckt  01-005-2-III-Eis.csv        73       152        73   \n",
       "2        dan      dann  01-005-2-III-Eis.csv       627       651       621   \n",
       "3   gekricht  gekriegt  01-005-2-III-Eis.csv         2        15         2   \n",
       "4      leken    lecken  01-005-2-III-Eis.csv        14        17        14   \n",
       "5       felt     fällt  01-005-2-III-Eis.csv        93       198        90   \n",
       "6      wolte    wollte  01-005-2-III-Eis.csv       173       201       173   \n",
       "7       lekt     leckt  01-005-2-III-Eis.csv        20        42        19   \n",
       "8        fom       vom  01-005-2-III-Eis.csv        13        16        13   \n",
       "9        dan      dann  01-005-2-III-Eis.csv       627       651       621   \n",
       "10       dan      dann  01-005-2-III-Eis.csv       627       651       621   \n",
       "11     leken    lecken  01-005-2-III-Eis.csv        14        17        14   \n",
       "12     gawen    kaufen  01-006-2-III-Eis.csv         1         6         1   \n",
       "13       din       ein  01-006-2-III-Eis.csv         2        26         1   \n",
       "14      Keld      Geld  01-006-2-III-Eis.csv         1         5         1   \n",
       "\n",
       "   testpoint  \n",
       "0         01  \n",
       "1         01  \n",
       "2         01  \n",
       "3         01  \n",
       "4         01  \n",
       "5         01  \n",
       "6         01  \n",
       "7         01  \n",
       "8         01  \n",
       "9         01  \n",
       "10        01  \n",
       "11        01  \n",
       "12        01  \n",
       "13        01  \n",
       "14        01  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>original</th>\n",
       "      <th>corrected</th>\n",
       "      <th>filename</th>\n",
       "      <th>freq_ori</th>\n",
       "      <th>freq_cor</th>\n",
       "      <th>freq_tup</th>\n",
       "      <th>testpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24586</th>\n",
       "      <td>weiel</td>\n",
       "      <td>weil</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>8</td>\n",
       "      <td>82</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24587</th>\n",
       "      <td>köntest</td>\n",
       "      <td>könntest</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24588</th>\n",
       "      <td>Ungefär</td>\n",
       "      <td>Ungefähr</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24589</th>\n",
       "      <td>sieh</td>\n",
       "      <td>sich</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>12</td>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24590</th>\n",
       "      <td>nooh</td>\n",
       "      <td>noch</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24591</th>\n",
       "      <td>nieht</td>\n",
       "      <td>nicht</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>135</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24592</th>\n",
       "      <td>vessen</td>\n",
       "      <td>fassen</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24593</th>\n",
       "      <td>entlich</td>\n",
       "      <td>endlich</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24594</th>\n",
       "      <td>Tor</td>\n",
       "      <td>Tür</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24595</th>\n",
       "      <td>umarte</td>\n",
       "      <td>umarmte</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24596</th>\n",
       "      <td>nieht</td>\n",
       "      <td>nicht</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>135</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24597</th>\n",
       "      <td>ass</td>\n",
       "      <td>aß</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24598</th>\n",
       "      <td>mutzte</td>\n",
       "      <td>nutzte</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24599</th>\n",
       "      <td>momen</td>\n",
       "      <td>Moment</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24600</th>\n",
       "      <td>Lutscha</td>\n",
       "      <td>Lutscher</td>\n",
       "      <td>10-693-4-IV-Weg.csv</td>\n",
       "      <td>8</td>\n",
       "      <td>56</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      original corrected             filename  freq_ori  freq_cor  freq_tup  \\\n",
       "24586    weiel      weil  10-693-4-IV-Weg.csv         8        82         8   \n",
       "24587  köntest  könntest  10-693-4-IV-Weg.csv         3         3         3   \n",
       "24588  Ungefär  Ungefähr  10-693-4-IV-Weg.csv         1         1         1   \n",
       "24589     sieh      sich  10-693-4-IV-Weg.csv        12        55         5   \n",
       "24590     nooh      noch  10-693-4-IV-Weg.csv         1        15         1   \n",
       "24591    nieht     nicht  10-693-4-IV-Weg.csv         2       135         2   \n",
       "24592   vessen    fassen  10-693-4-IV-Weg.csv         1         2         1   \n",
       "24593  entlich   endlich  10-693-4-IV-Weg.csv        10        20        10   \n",
       "24594      Tor       Tür  10-693-4-IV-Weg.csv         1        15         1   \n",
       "24595   umarte   umarmte  10-693-4-IV-Weg.csv         2        16         2   \n",
       "24596    nieht     nicht  10-693-4-IV-Weg.csv         2       135         2   \n",
       "24597      ass        aß  10-693-4-IV-Weg.csv         3         7         3   \n",
       "24598   mutzte    nutzte  10-693-4-IV-Weg.csv         1         1         1   \n",
       "24599    momen    Moment  10-693-4-IV-Weg.csv         1         4         1   \n",
       "24600  Lutscha  Lutscher  10-693-4-IV-Weg.csv         8        56         8   \n",
       "\n",
       "      testpoint  \n",
       "24586        10  \n",
       "24587        10  \n",
       "24588        10  \n",
       "24589        10  \n",
       "24590        10  \n",
       "24591        10  \n",
       "24592        10  \n",
       "24593        10  \n",
       "24594        10  \n",
       "24595        10  \n",
       "24596        10  \n",
       "24597        10  \n",
       "24598        10  \n",
       "24599        10  \n",
       "24600        10  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_error_token.head(15))\n",
    "display(data_error_token.tail(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Absolute Anzahl an Fehlern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp_asc = sorted(list(data_error_token['testpoint'].value_counts().index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['01', '02', '03', '04', '05', '06', '07', '08', '09', '10']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tp_asc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2520"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_error_token[data_error_token.testpoint == '04'].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_errors_pt_abs = []\n",
    "for tp in tp_asc:\n",
    "    list_errors_pt_abs.append(data_error_token[data_error_token.testpoint == tp].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1716, 2154, 2028, 2520, 2527, 2924, 2900, 3046, 2549, 2237]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_errors_pt_abs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "testpoint\n",
       "01    1716\n",
       "02    2154\n",
       "03    2028\n",
       "04    2520\n",
       "05    2527\n",
       "06    2924\n",
       "07    2900\n",
       "08    3046\n",
       "09    2549\n",
       "10    2237\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_error_token.groupby(['testpoint']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Absolute Anzahl an Texten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>original</th>\n",
       "      <th>corrected</th>\n",
       "      <th>filename</th>\n",
       "      <th>freq_ori</th>\n",
       "      <th>freq_cor</th>\n",
       "      <th>freq_tup</th>\n",
       "      <th>testpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>belt</td>\n",
       "      <td>bellt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>91</td>\n",
       "      <td>138</td>\n",
       "      <td>91</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kukt</td>\n",
       "      <td>kuckt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>73</td>\n",
       "      <td>152</td>\n",
       "      <td>73</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dan</td>\n",
       "      <td>dann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>627</td>\n",
       "      <td>651</td>\n",
       "      <td>621</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gekricht</td>\n",
       "      <td>gekriegt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>leken</td>\n",
       "      <td>lecken</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>felt</td>\n",
       "      <td>fällt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>93</td>\n",
       "      <td>198</td>\n",
       "      <td>90</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>wolte</td>\n",
       "      <td>wollte</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>173</td>\n",
       "      <td>201</td>\n",
       "      <td>173</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lekt</td>\n",
       "      <td>leckt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "      <td>19</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fom</td>\n",
       "      <td>vom</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dan</td>\n",
       "      <td>dann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>627</td>\n",
       "      <td>651</td>\n",
       "      <td>621</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   original corrected              filename  freq_ori  freq_cor  freq_tup  \\\n",
       "0      belt     bellt  01-005-2-III-Eis.csv        91       138        91   \n",
       "1      kukt     kuckt  01-005-2-III-Eis.csv        73       152        73   \n",
       "2       dan      dann  01-005-2-III-Eis.csv       627       651       621   \n",
       "3  gekricht  gekriegt  01-005-2-III-Eis.csv         2        15         2   \n",
       "4     leken    lecken  01-005-2-III-Eis.csv        14        17        14   \n",
       "5      felt     fällt  01-005-2-III-Eis.csv        93       198        90   \n",
       "6     wolte    wollte  01-005-2-III-Eis.csv       173       201       173   \n",
       "7      lekt     leckt  01-005-2-III-Eis.csv        20        42        19   \n",
       "8       fom       vom  01-005-2-III-Eis.csv        13        16        13   \n",
       "9       dan      dann  01-005-2-III-Eis.csv       627       651       621   \n",
       "\n",
       "  testpoint  \n",
       "0        01  \n",
       "1        01  \n",
       "2        01  \n",
       "3        01  \n",
       "4        01  \n",
       "5        01  \n",
       "6        01  \n",
       "7        01  \n",
       "8        01  \n",
       "9        01  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_error_token.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1922"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean_token['filename'].value_counts().index.shape[0] # 1922 verschiedene Texte; CLEAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1913"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_error_token['filename'].value_counts().index.shape[0] # 1913 verschiedene Texte; ERROR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['01-383-2-III-Eis.csv', '01-307-2-III-Eis.csv', '01-277-2-III-Eis.csv',\n",
       "       '01-139-2-III-Eis.csv', '01-287-2-III-Eis.csv', '01-161-2-III-Eis.csv',\n",
       "       '01-029-2-III-Eis.csv', '01-303-2-III-Eis.csv', '01-399-2-III-Eis.csv',\n",
       "       '01-116-2-III-Eis.csv',\n",
       "       ...\n",
       "       '01-074-2-III-Eis.csv', '01-497-2-III-Eis.csv', '01-090-2-III-Eis.csv',\n",
       "       '01-324-2-III-Eis.csv', '01-172-2-III-Eis.csv', '01-049-2-III-Eis.csv',\n",
       "       '01-422-2-III-Eis.csv', '01-028-2-III-Eis.csv', '01-170-2-III-Eis.csv',\n",
       "       '01-462-2-III-Eis.csv'],\n",
       "      dtype='object', length=141)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data_error_token.groupby(['testpoint', 'filename']).size()\n",
    "data_clean_token[data_clean_token.testpoint == '01']['filename'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['02-025-2-IV-Weg.csv', '02-181-2-IV-Weg.csv', '02-307-2-IV-Weg.csv',\n",
       "       '02-060-2-IV-Weg.csv', '02-197-2-IV-Weg.csv', '02-277-2-IV-Weg.csv',\n",
       "       '02-285-2-IV-Weg.csv', '02-360-2-IV-Weg.csv', '02-291-2-IV-Weg.csv',\n",
       "       '02-265-2-IV-Weg.csv',\n",
       "       ...\n",
       "       '02-317-2-IV-Weg.csv', '02-206-2-IV-Weg.csv', '02-208-2-IV-Weg.csv',\n",
       "       '02-324-2-IV-Weg.csv', '02-075-2-IV-Weg.csv', '02-340-2-IV-Weg.csv',\n",
       "       '02-028-2-IV-Weg.csv', '02-087-2-IV-Weg.csv', '02-226-2-IV-Weg.csv',\n",
       "       '02-170-2-IV-Weg.csv'],\n",
       "      dtype='object', length=165)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean_token[data_clean_token.testpoint == '02']['filename'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get unique filenames\n",
    "list_texts_tp_abs = []\n",
    "\n",
    "for tp in tp_asc:\n",
    "    list_texts_tp_abs.append(data_clean_token[data_clean_token.testpoint == tp]['filename'].value_counts().index.shape[0])\n",
    "    \n",
    "    \n",
    "    #unique_filenames = sorted(list(data_clean_token['filename'].value_counts().index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[141, 165, 162, 173, 173, 231, 223, 215, 217, 222]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_texts_tp_abs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Durchschnittliche Textlänge (data_clean_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get unique filenames\n",
    "unique_filenames = sorted(list(data_clean_token['filename'].value_counts().index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1922"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unique_filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "testpoint\n",
       "01     6840\n",
       "02     9490\n",
       "03     8791\n",
       "04    12523\n",
       "05    13399\n",
       "06    21124\n",
       "07    20411\n",
       "08    23680\n",
       "09    23153\n",
       "10    23015\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean_token.groupby(['testpoint']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#g = data_clean_token.groupby('testpoint')['l1','l2'].apply(lambda x: list(np.unique(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fea891734f0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean_token.groupby(['testpoint'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filenames per TP\n",
    "list_filenames_ct_per_tp = []\n",
    "#for f in unique_filenames:\n",
    "#    list_filenames_ct_per_tp.append(data_clean_token[data_clean_token.testpoint == tp].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12523"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean_token[data_clean_token.testpoint == '04'].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_clean_token.groupby(['testpoint', 'filename']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1) Get length per filename\n",
    "# 2) Average per testpoint\n",
    "#for f in unique_filenames:\n",
    "#    data_clean_token[data_clean_token.filename == f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_clean_token['textlength'] = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "token_count_per_text = data_clean_token.groupby(['filename']).size().reset_index(name='token_count_per_text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_clean_token = token_count_per_text.merge(data_clean_token, how='right', on='filename')\n",
    "data_clean_token = data_clean_token.merge(token_count_per_text, how='left', on='filename')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_clean_token.head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "testpoint\n",
       "01     6840\n",
       "02     9490\n",
       "03     8791\n",
       "04    12523\n",
       "05    13399\n",
       "06    21124\n",
       "07    20411\n",
       "08    23680\n",
       "09    23153\n",
       "10    23015\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Durchschnittliche Textlänge\n",
    "data_clean_token.groupby(['testpoint']).size()\n",
    "#data_clean_token.groupby(['testpoint']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>freq_ori</th>\n",
       "      <th>freq_cor</th>\n",
       "      <th>freq_tup</th>\n",
       "      <th>token_count_per_text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>testpoint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>1798.382310</td>\n",
       "      <td>1933.007602</td>\n",
       "      <td>1740.161550</td>\n",
       "      <td>54.139181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>1458.771760</td>\n",
       "      <td>1573.345205</td>\n",
       "      <td>1415.619600</td>\n",
       "      <td>68.311907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>1499.834376</td>\n",
       "      <td>1601.891594</td>\n",
       "      <td>1470.450006</td>\n",
       "      <td>65.842225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>1443.328995</td>\n",
       "      <td>1517.190929</td>\n",
       "      <td>1408.866965</td>\n",
       "      <td>85.853789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05</th>\n",
       "      <td>1192.803717</td>\n",
       "      <td>1245.663930</td>\n",
       "      <td>1145.747892</td>\n",
       "      <td>91.551235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06</th>\n",
       "      <td>1342.126254</td>\n",
       "      <td>1399.230259</td>\n",
       "      <td>1297.132977</td>\n",
       "      <td>103.936186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07</th>\n",
       "      <td>1272.283328</td>\n",
       "      <td>1319.950958</td>\n",
       "      <td>1230.749988</td>\n",
       "      <td>105.036353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08</th>\n",
       "      <td>1258.852069</td>\n",
       "      <td>1322.632306</td>\n",
       "      <td>1216.200845</td>\n",
       "      <td>124.072551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09</th>\n",
       "      <td>1208.532112</td>\n",
       "      <td>1245.879756</td>\n",
       "      <td>1159.390576</td>\n",
       "      <td>125.899322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1290.042320</td>\n",
       "      <td>1336.581099</td>\n",
       "      <td>1248.251966</td>\n",
       "      <td>119.914273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              freq_ori     freq_cor     freq_tup  token_count_per_text\n",
       "testpoint                                                             \n",
       "01         1798.382310  1933.007602  1740.161550             54.139181\n",
       "02         1458.771760  1573.345205  1415.619600             68.311907\n",
       "03         1499.834376  1601.891594  1470.450006             65.842225\n",
       "04         1443.328995  1517.190929  1408.866965             85.853789\n",
       "05         1192.803717  1245.663930  1145.747892             91.551235\n",
       "06         1342.126254  1399.230259  1297.132977            103.936186\n",
       "07         1272.283328  1319.950958  1230.749988            105.036353\n",
       "08         1258.852069  1322.632306  1216.200845            124.072551\n",
       "09         1208.532112  1245.879756  1159.390576            125.899322\n",
       "10         1290.042320  1336.581099  1248.251966            119.914273"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean_token.groupby(['testpoint']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_text_length = data_clean_token.groupby(['testpoint']).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6840"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean_token[data_clean_token.testpoint == '01'].shape[0] # 6840 Token im ersten Testzeitpunkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_clean_token.groupby(['filename']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_text_length = avg_text_length.token_count_per_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Durschnittliche Fehlerrate (data_error_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get unique filenames\n",
    "unique_filenames_e = sorted(list(data_error_token['filename'].value_counts().index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1913"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(unique_filenames_e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_count_per_text = data_error_token.groupby(['filename']).size().reset_index(name='error_count_per_text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_error_token = data_error_token.merge(error_count_per_text, how='left', on='filename')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>original</th>\n",
       "      <th>corrected</th>\n",
       "      <th>filename</th>\n",
       "      <th>freq_ori</th>\n",
       "      <th>freq_cor</th>\n",
       "      <th>freq_tup</th>\n",
       "      <th>testpoint</th>\n",
       "      <th>error_count_per_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>belt</td>\n",
       "      <td>bellt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>91</td>\n",
       "      <td>138</td>\n",
       "      <td>91</td>\n",
       "      <td>01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kukt</td>\n",
       "      <td>kuckt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>73</td>\n",
       "      <td>152</td>\n",
       "      <td>73</td>\n",
       "      <td>01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dan</td>\n",
       "      <td>dann</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>627</td>\n",
       "      <td>651</td>\n",
       "      <td>621</td>\n",
       "      <td>01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gekricht</td>\n",
       "      <td>gekriegt</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>leken</td>\n",
       "      <td>lecken</td>\n",
       "      <td>01-005-2-III-Eis.csv</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   original corrected              filename  freq_ori  freq_cor  freq_tup  \\\n",
       "0      belt     bellt  01-005-2-III-Eis.csv        91       138        91   \n",
       "1      kukt     kuckt  01-005-2-III-Eis.csv        73       152        73   \n",
       "2       dan      dann  01-005-2-III-Eis.csv       627       651       621   \n",
       "3  gekricht  gekriegt  01-005-2-III-Eis.csv         2        15         2   \n",
       "4     leken    lecken  01-005-2-III-Eis.csv        14        17        14   \n",
       "\n",
       "  testpoint  error_count_per_text  \n",
       "0        01                    12  \n",
       "1        01                    12  \n",
       "2        01                    12  \n",
       "3        01                    12  \n",
       "4        01                    12  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_error_token.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "testpoint\n",
       "01    1716\n",
       "02    2154\n",
       "03    2028\n",
       "04    2520\n",
       "05    2527\n",
       "06    2924\n",
       "07    2900\n",
       "08    3046\n",
       "09    2549\n",
       "10    2237\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Durchschnittliche Fehlerrate\n",
    "data_error_token.groupby(['testpoint']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freq_ori</th>\n",
       "      <th>freq_cor</th>\n",
       "      <th>freq_tup</th>\n",
       "      <th>error_count_per_text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>testpoint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>75.727855</td>\n",
       "      <td>112.290210</td>\n",
       "      <td>74.158508</td>\n",
       "      <td>15.916084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>81.821263</td>\n",
       "      <td>124.192665</td>\n",
       "      <td>80.514856</td>\n",
       "      <td>17.472609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>60.710552</td>\n",
       "      <td>101.953649</td>\n",
       "      <td>59.599112</td>\n",
       "      <td>17.338264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>75.528968</td>\n",
       "      <td>125.979762</td>\n",
       "      <td>74.663095</td>\n",
       "      <td>21.084921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05</th>\n",
       "      <td>84.597151</td>\n",
       "      <td>111.047091</td>\n",
       "      <td>83.463000</td>\n",
       "      <td>20.210131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06</th>\n",
       "      <td>99.523256</td>\n",
       "      <td>137.752052</td>\n",
       "      <td>97.496922</td>\n",
       "      <td>18.517784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07</th>\n",
       "      <td>83.297241</td>\n",
       "      <td>110.110000</td>\n",
       "      <td>82.513793</td>\n",
       "      <td>18.451724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08</th>\n",
       "      <td>90.948457</td>\n",
       "      <td>130.653316</td>\n",
       "      <td>90.234077</td>\n",
       "      <td>21.917269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09</th>\n",
       "      <td>55.587681</td>\n",
       "      <td>83.743429</td>\n",
       "      <td>54.918399</td>\n",
       "      <td>16.801491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>119.527045</td>\n",
       "      <td>155.188646</td>\n",
       "      <td>117.790791</td>\n",
       "      <td>14.851587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             freq_ori    freq_cor    freq_tup  error_count_per_text\n",
       "testpoint                                                          \n",
       "01          75.727855  112.290210   74.158508             15.916084\n",
       "02          81.821263  124.192665   80.514856             17.472609\n",
       "03          60.710552  101.953649   59.599112             17.338264\n",
       "04          75.528968  125.979762   74.663095             21.084921\n",
       "05          84.597151  111.047091   83.463000             20.210131\n",
       "06          99.523256  137.752052   97.496922             18.517784\n",
       "07          83.297241  110.110000   82.513793             18.451724\n",
       "08          90.948457  130.653316   90.234077             21.917269\n",
       "09          55.587681   83.743429   54.918399             16.801491\n",
       "10         119.527045  155.188646  117.790791             14.851587"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_error_token.groupby(['testpoint']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_no_errors = data_error_token.groupby(['testpoint']).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_no_errors = avg_no_errors.error_count_per_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    15.916084\n",
       "1    17.472609\n",
       "2    17.338264\n",
       "3    21.084921\n",
       "4    20.210131\n",
       "5    18.517784\n",
       "6    18.451724\n",
       "7    21.917269\n",
       "8    16.801491\n",
       "9    14.851587\n",
       "Name: error_count_per_text, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_no_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "testpoint\n",
       "01     6840\n",
       "02     9490\n",
       "03     8791\n",
       "04    12523\n",
       "05    13399\n",
       "06    21124\n",
       "07    20411\n",
       "08    23680\n",
       "09    23153\n",
       "10    23015\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean_token.groupby(['testpoint']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anzahl der Fehler pro Text / Textlänge; Durchschnitt (data_error_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.293985\n",
       "1    0.255777\n",
       "2    0.263330\n",
       "3    0.245591\n",
       "4    0.220752\n",
       "5    0.178165\n",
       "6    0.175670\n",
       "7    0.176649\n",
       "8    0.133452\n",
       "9    0.123852\n",
       "dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_no_errors/avg_text_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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