diff --git a/EDA.ipynb b/EDA.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..270b4a5e812066eb44901fddd4e7c4cb35ba0db7
--- /dev/null
+++ b/EDA.ipynb
@@ -0,0 +1,3536 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2ebe003b-d98f-4807-a7a3-d7f885238f28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import csv\n",
+    "import pathlib\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import nltk\n",
+    "import re\n",
+    "import statistics\n",
+    "import string\n",
+    "from collections import Counter\n",
+    "from collections import defaultdict\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import octis\n",
+    "import gensim\n",
+    "import spacy\n",
+    "import sklearn\n",
+    "import torch\n",
+    "import libsvm\n",
+    "import flask\n",
+    "import sentence_transformers\n",
+    "import requests\n",
+    "import tomotopy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f35ef89d-a175-4f70-a146-848d16684017",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib_venn import venn2, venn2_circles, venn2_unweighted\n",
+    "from matplotlib_venn import venn3, venn3_circles\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b77e489a-7ead-4d76-8fc4-4178210a190e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "951784c7-d0eb-4af1-aacb-7d011d5e6045",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.chdir(r\"C:\\Users\\onb1202\\OneDrive - Österreichische Nationalbibliothek\\Praktikum TK\\daten\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "88107346-1c11-43b2-a0bb-2bb7fc38a636",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wz = pd.read_csv('wz_tok.tsv', sep = '\\t', encoding='utf-8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "04fa2f24-e598-4aa5-a0eb-7bb69c0399be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz = pd.read_csv('sz_tok.tsv', sep = '\\t', encoding='utf-8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3017dd13-b047-4689-bbac-e0140816f83f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wz = wz.iloc[: , 1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5c000317-0904-4615-beb6-703d3571ef49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz = sz.iloc[: , 1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3208ad7c-07b1-4319-8f2a-d649c0304b2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz['year'] = sz['manifest_id'].str.findall(r\"(?<=\\D)\\d{4}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "925b6590-38d3-428d-a302-d9d035a251ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz['year'] =sz['year'].str[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ebf31450-78d2-445c-acb2-9b7c1c7e1dbc",
+   "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>ocr</th>\n",
+       "      <th>split</th>\n",
+       "      <th>manifest_id</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>['Salzburger', 'I', 'ntelligenzblatt', '.', 'u...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza1785bl01</td>\n",
+       "      <td>1785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>['-', '\"', '-', 'I', 'Innländische', ',', 'und...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza1785bl02</td>\n",
+       "      <td>1785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>['Sa|', 'e', 'mehr', 'du', 'Mensch', 'bist', '...</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>sza17850105</td>\n",
+       "      <td>1785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>['-----', 'va', 'ar', 'nicht', 'die', 'ehrlich...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza17850112</td>\n",
+       "      <td>1785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>['---', 'T', 'T--', 'Salzburger', '-', 'I.', '...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>sza17850126</td>\n",
+       "      <td>1785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>['769', 'Salzburger', '770', '---', 'Intellige...</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>sza17991207</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>['785-', 'Salzburger', 'Intelligenzblatt', '.'...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza17991214</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>['-', '--', '-', '99', 'E-', 'S', 'urger-', 'S...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza17991221</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>['Intelligenzblatt', '.', 'LII', '.', 'St.', '...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza17991228</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>['–', '-', '–', '-----------------------------...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>sza1799bl01</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>630 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   ocr  split  manifest_id  \\\n",
+       "0    ['Salzburger', 'I', 'ntelligenzblatt', '.', 'u...  train  sza1785bl01   \n",
+       "1    ['-', '\"', '-', 'I', 'Innländische', ',', 'und...  train  sza1785bl02   \n",
+       "2    ['Sa|', 'e', 'mehr', 'du', 'Mensch', 'bist', '...  valid  sza17850105   \n",
+       "3    ['-----', 'va', 'ar', 'nicht', 'die', 'ehrlich...  train  sza17850112   \n",
+       "4    ['---', 'T', 'T--', 'Salzburger', '-', 'I.', '...   test  sza17850126   \n",
+       "..                                                 ...    ...          ...   \n",
+       "625  ['769', 'Salzburger', '770', '---', 'Intellige...  valid  sza17991207   \n",
+       "626  ['785-', 'Salzburger', 'Intelligenzblatt', '.'...  train  sza17991214   \n",
+       "627  ['-', '--', '-', '99', 'E-', 'S', 'urger-', 'S...  train  sza17991221   \n",
+       "628  ['Intelligenzblatt', '.', 'LII', '.', 'St.', '...  train  sza17991228   \n",
+       "629  ['–', '-', '–', '-----------------------------...   test  sza1799bl01   \n",
+       "\n",
+       "     year  \n",
+       "0    1785  \n",
+       "1    1785  \n",
+       "2    1785  \n",
+       "3    1785  \n",
+       "4    1785  \n",
+       "..    ...  \n",
+       "625  1799  \n",
+       "626  1799  \n",
+       "627  1799  \n",
+       "628  1799  \n",
+       "629  1799  \n",
+       "\n",
+       "[630 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "35d08da2-d9ee-4eea-a2f1-a620a3edffbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wz['year'] = wz['manifest_id'].str.findall(r\"(?<=\\D)\\d{4}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bc784bae-0bdf-4537-b0dc-3661967057b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wz['year'] =wz['year'].str[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "960fdcef-58bf-47ff-9f2a-782068bc08d3",
+   "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>ocr</th>\n",
+       "      <th>split</th>\n",
+       "      <th>manifest_id</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>['itz', '.', 'r', 'F-', 'Nro', '.', 'Sonnabmd'...</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>wrz17850101</td>\n",
+       "      <td>1785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>['5i', '.', '2Y', 'F', 'Mittwoch', 'den', '5.'...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>wrz17850105</td>\n",
+       "      <td>1785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>['57', '^', 'Sonnabend', 'den', '8.', 'Janer',...</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>wrz17850108</td>\n",
+       "      <td>1785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>['5k', '8i', 'F', 'Mittwoch', 'den', 'Iäner', ...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>wrz17850112</td>\n",
+       "      <td>1785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>['^', '109', '^', 'Sonnabend', 'den', '15.', '...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>wrz17850115</td>\n",
+       "      <td>1785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1299</th>\n",
+       "      <td>['Sonnabend', ',', 'den', '14', '*', 'December...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>wrz17991214</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1300</th>\n",
+       "      <td>['I', 'm', '4', '-', '77', \"i'\", 'jLiii', '.',...</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>wrz17991218</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>['L', 'Sonnabend', ',', 'den', '21.', 'Decembe...</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>wrz17991221</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1302</th>\n",
+       "      <td>['WVr', '4', 'S73', 'ZMAr', 'if', '&gt;', 'Mittew...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>wrz17991225</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1303</th>\n",
+       "      <td>['Sonnabend', ',', 'den', 'rz', '«', 'December...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>wrz17991228</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1304 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    ocr  split  manifest_id  \\\n",
+       "0     ['itz', '.', 'r', 'F-', 'Nro', '.', 'Sonnabmd'...  valid  wrz17850101   \n",
+       "1     ['5i', '.', '2Y', 'F', 'Mittwoch', 'den', '5.'...  train  wrz17850105   \n",
+       "2     ['57', '^', 'Sonnabend', 'den', '8.', 'Janer',...  valid  wrz17850108   \n",
+       "3     ['5k', '8i', 'F', 'Mittwoch', 'den', 'Iäner', ...  train  wrz17850112   \n",
+       "4     ['^', '109', '^', 'Sonnabend', 'den', '15.', '...  train  wrz17850115   \n",
+       "...                                                 ...    ...          ...   \n",
+       "1299  ['Sonnabend', ',', 'den', '14', '*', 'December...  train  wrz17991214   \n",
+       "1300  ['I', 'm', '4', '-', '77', \"i'\", 'jLiii', '.',...  valid  wrz17991218   \n",
+       "1301  ['L', 'Sonnabend', ',', 'den', '21.', 'Decembe...  valid  wrz17991221   \n",
+       "1302  ['WVr', '4', 'S73', 'ZMAr', 'if', '>', 'Mittew...   test  wrz17991225   \n",
+       "1303  ['Sonnabend', ',', 'den', 'rz', '«', 'December...   test  wrz17991228   \n",
+       "\n",
+       "      year  \n",
+       "0     1785  \n",
+       "1     1785  \n",
+       "2     1785  \n",
+       "3     1785  \n",
+       "4     1785  \n",
+       "...    ...  \n",
+       "1299  1799  \n",
+       "1300  1799  \n",
+       "1301  1799  \n",
+       "1302  1799  \n",
+       "1303  1799  \n",
+       "\n",
+       "[1304 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3e74f5d0-3024-4d6f-9a85-89a345a4a2d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wz['year'] = wz['year'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9da66709-4958-4842-a112-b1365db07d29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wz = wz.loc[wz['year'] > 1788]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8e66a514-bbec-4942-b2b4-f82968a4a03e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz['year'] = sz['year'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "840d6520-6a0e-41a8-ac84-31f995a61310",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz = sz.loc[sz['year'] > 1788]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4401bc5-2cb6-4b0b-89e5-afd85d226969",
+   "metadata": {},
+   "source": [
+    "<h4>count issues</h4>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a8277d76-2cdc-47f9-a80f-e1d53f149f02",
+   "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>ocr</th>\n",
+       "      <th>split</th>\n",
+       "      <th>manifest_id</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1789</th>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1790</th>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1791</th>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1792</th>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1793</th>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1794</th>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1795</th>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1796</th>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1797</th>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1798</th>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1799</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ocr  split  manifest_id\n",
+       "year                         \n",
+       "1789  104    104          104\n",
+       "1790  104    104          104\n",
+       "1791  105    105          105\n",
+       "1792  104    104          104\n",
+       "1793  105    105          105\n",
+       "1794  105    105          105\n",
+       "1795  104    104          104\n",
+       "1796  105    105          105\n",
+       "1797  103    103          103\n",
+       "1798  104    104          104\n",
+       "1799   52     52           52"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wz.groupby('year').count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "cfd267f0-858f-4a92-8d93-126d185942f4",
+   "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>ocr</th>\n",
+       "      <th>split</th>\n",
+       "      <th>manifest_id</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1789</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1790</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1791</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1792</th>\n",
+       "      <td>51</td>\n",
+       "      <td>51</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1793</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1794</th>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1795</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1796</th>\n",
+       "      <td>55</td>\n",
+       "      <td>55</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1797</th>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1798</th>\n",
+       "      <td>53</td>\n",
+       "      <td>53</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1799</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ocr  split  manifest_id\n",
+       "year                         \n",
+       "1789   52     52           52\n",
+       "1790   52     52           52\n",
+       "1791   52     52           52\n",
+       "1792   51     51           51\n",
+       "1793   52     52           52\n",
+       "1794   54     54           54\n",
+       "1795   52     52           52\n",
+       "1796   55     55           55\n",
+       "1797   54     54           54\n",
+       "1798   53     53           53\n",
+       "1799   52     52           52"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sz.groupby('year').count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "7d6baf5d-2921-40d7-9fad-9783a2e85585",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'WZ issues per year')]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data=wz, x='year', color='blue', saturation = 0.1).set(title='WZ issues per year')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "838346f6-9583-4d57-8c4d-2c324edb9c74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1d8537eca90>,\n",
+       " <matplotlib.lines.Line2D at 0x1d8537ecaf0>,\n",
+       " <matplotlib.lines.Line2D at 0x1d8537ecbe0>]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(wz.groupby('year').count())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b6e264b4-5a02-4981-a881-9d41bfb564a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'SZ issues per year')]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data=sz, x='year', color='blue', saturation = 0.1).set(title='SZ issues per year')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "88143896-847a-4602-b8aa-4b316d8cac1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1d8538e2580>,\n",
+       " <matplotlib.lines.Line2D at 0x1d8538e25e0>,\n",
+       " <matplotlib.lines.Line2D at 0x1d8538e26d0>]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(sz.groupby('year').count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "137f62a9-8faf-489b-9c8c-09384297901a",
+   "metadata": {},
+   "source": [
+    "<h4>count number of words</h4>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b3b03dfc-c02c-4ca3-b3aa-437de8540291",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\onb1202\\AppData\\Local\\Temp\\ipykernel_14768\\3999234337.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  wz['ocr'] = wz['ocr'].str.strip('[]').astype(str)\n"
+     ]
+    }
+   ],
+   "source": [
+    "wz['ocr'] = wz['ocr'].str.strip('[]').astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e43cb658-a52c-439e-a263-f30f19d4edfb",
+   "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>ocr</th>\n",
+       "      <th>split</th>\n",
+       "      <th>manifest_id</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>'UM', 'e', 'n', 'e', \"'r-\", 'Ze', 'Sonnabend',...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>wrz17890103</td>\n",
+       "      <td>1789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>'29', '^', 'Mittewoche', 'den', '7.', 'Ianer',...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>wrz17890107</td>\n",
+       "      <td>1789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>'hi', '.', 'L3', \"IZVrenei'\", 'Zeilunn', '.', ...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>wrz17890110</td>\n",
+       "      <td>1789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>'8r', '«', 'V', 'Mittewoche', 'den', '14.', 'I...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>wrz17890114</td>\n",
+       "      <td>1789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>'Sonnabend', 'den', '17.', \"Ia'ner\", '1789', '...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>wrz17890117</td>\n",
+       "      <td>1789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1299</th>\n",
+       "      <td>'Sonnabend', ',', 'den', '14', '*', 'December'...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>wrz17991214</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1300</th>\n",
+       "      <td>'I', 'm', '4', '-', '77', \"i'\", 'jLiii', '.', ...</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>wrz17991218</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>'L', 'Sonnabend', ',', 'den', '21.', 'December...</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>wrz17991221</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1302</th>\n",
+       "      <td>'WVr', '4', 'S73', 'ZMAr', 'if', '&gt;', 'Mittewo...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>wrz17991225</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1303</th>\n",
+       "      <td>'Sonnabend', ',', 'den', 'rz', '«', 'December'...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>wrz17991228</td>\n",
+       "      <td>1799</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1095 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    ocr  split  manifest_id  \\\n",
+       "209   'UM', 'e', 'n', 'e', \"'r-\", 'Ze', 'Sonnabend',...   test  wrz17890103   \n",
+       "210   '29', '^', 'Mittewoche', 'den', '7.', 'Ianer',...  train  wrz17890107   \n",
+       "211   'hi', '.', 'L3', \"IZVrenei'\", 'Zeilunn', '.', ...   test  wrz17890110   \n",
+       "212   '8r', '«', 'V', 'Mittewoche', 'den', '14.', 'I...  train  wrz17890114   \n",
+       "213   'Sonnabend', 'den', '17.', \"Ia'ner\", '1789', '...  train  wrz17890117   \n",
+       "...                                                 ...    ...          ...   \n",
+       "1299  'Sonnabend', ',', 'den', '14', '*', 'December'...  train  wrz17991214   \n",
+       "1300  'I', 'm', '4', '-', '77', \"i'\", 'jLiii', '.', ...  valid  wrz17991218   \n",
+       "1301  'L', 'Sonnabend', ',', 'den', '21.', 'December...  valid  wrz17991221   \n",
+       "1302  'WVr', '4', 'S73', 'ZMAr', 'if', '>', 'Mittewo...   test  wrz17991225   \n",
+       "1303  'Sonnabend', ',', 'den', 'rz', '«', 'December'...   test  wrz17991228   \n",
+       "\n",
+       "      year  \n",
+       "209   1789  \n",
+       "210   1789  \n",
+       "211   1789  \n",
+       "212   1789  \n",
+       "213   1789  \n",
+       "...    ...  \n",
+       "1299  1799  \n",
+       "1300  1799  \n",
+       "1301  1799  \n",
+       "1302  1799  \n",
+       "1303  1799  \n",
+       "\n",
+       "[1095 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e90a3d8e-1801-4de1-bd51-6e62fdf82f33",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\onb1202\\AppData\\Local\\Temp\\ipykernel_14768\\589946026.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  wz['num_words'] = wz['ocr'].str.split().str.len()\n"
+     ]
+    }
+   ],
+   "source": [
+    "wz['num_words'] = wz['ocr'].str.split().str.len()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "801c54e8-b800-48a2-9240-85dd3d11b124",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\onb1202\\AppData\\Local\\Temp\\ipykernel_14768\\3767109603.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  sz['num_words'] = sz['ocr'].str.split().str.len()\n"
+     ]
+    }
+   ],
+   "source": [
+    "sz['num_words'] = sz['ocr'].str.split().str.len()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "770ddc66-07ca-4fd6-b06f-df9b4369eee9",
+   "metadata": {},
+   "source": [
+    "<h4>basic statistics words</h4>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "db757dac-0282-4a2d-9e15-a1ffa60b48b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "611"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(wz['num_words'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "6b59a190-a81f-4a8c-989e-d429ef1ad687",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(sz['num_words'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "2327c485-5118-4a66-afb4-be5df6719bd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "71848"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(wz['num_words'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "d547e54e-b822-42d7-ba8e-d64d86ed9828",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9954"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(sz['num_words'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "68b7483e-48ca-4161-a763-86fe749f1e80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31165.915068493152"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "statistics.mean(wz['num_words'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "ccb7a4bd-7237-48ae-9d8b-0c36359f80ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5236.59585492228"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "statistics.mean(sz['num_words'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "9d73f273-a6b8-4869-a3ab-80b6ab927c45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30422"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "statistics.median(wz['num_words'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3c9bae89-ebe5-4ea7-9d3e-562f2be67277",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5365"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "statistics.median(sz['num_words'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "d2e0d0ff-b32f-4f91-b354-d56a4a6310db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "34126677"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(wz['num_words'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "642c08e4-d4b9-439c-b8d9-0dd423bf5791",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3031989"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sz['num_words'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b032bb6-6973-45b7-a666-9bb093f955c1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e5c04f5-6215-4990-9f09-dc7c46d37918",
+   "metadata": {},
+   "source": [
+    "<h4>word distribution</h4>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a999c572-1747-4d18-b03a-c931545a48ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1d85390e8e0>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(wz, x='num_words').set(title='WZ word distribution')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "e444e4dd-7fb6-4786-b6a8-30cb4179b862",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1d853914af0>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(sz, x='num_words').set(title='SZ word distribution')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "418ebf93-18f8-44cf-a24d-796e3d20fc0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'WZ word distribution')]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=wz['num_words']).set(title='WZ word distribution')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "d0574268-8a09-4efa-9af9-d0dcfabfdb88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'SZ word distribution')]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x=sz['num_words']).set(title='SZ word distribution')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "0f937901-b649-4138-878d-8019c0aeb39d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'WZ word distribution over time')]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(data=wz, x='year', y='num_words', ci=None).set(title=\"WZ word distribution over time\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "b353b160-510a-4126-ada6-7f51aa60088f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1d8fbd1ebb0>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 540x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(data=wz, x='year', y='num_words', kind='kde', fill=True, palette=sns.color_palette('muted')[:2], height=5, aspect=1.5).set(title='WZ word distribution over time')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "1ac8f0c6-a9c3-44c1-ba5f-8a5291b676fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, 'SZ word distribution over time')]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(data=sz, x='year', y='num_words', ci=None).set(title=\"SZ word distribution over time\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "0a026ad1-3f7e-4907-96e7-449cbd29c53f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1d8fb9f9520>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 540x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(data=sz, x='year', y='num_words', kind='kde', fill=True, palette=sns.color_palette('muted')[:2], height=5, aspect=1.5).set(title='SZ word distribution over time')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "310866c4-6151-474a-892b-40766e89e685",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "0fb69906-8841-4426-a305-3552311b60bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts_wz = wz['ocr'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "32906c98-3227-4823-a0d8-3de8ec439d89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts_sz = sz['ocr'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "410565ac-f82e-4e33-936b-234165bebca2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts_wz = texts_wz.split()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "c46898a0-7f79-4ad4-af21-7688581b0be3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts_sz = texts_sz.split()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "590842d0-a026-416c-b5b3-4b5c57838ed0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "34125583"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(texts_wz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "227ad6bc-bd16-402c-aa5c-7a506dbee719",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "aa660149-f947-4c9e-b16e-a90bf29fbd06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3031411"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(texts_sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "0c9d9537-68d2-473b-88bc-981417b1d823",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts_wz = [re.sub(r\"[^\\w\\s]\",\"\",x) for x in texts_wz]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "c08193e6-a3db-4a07-9796-098a96ce0841",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts_sz = [re.sub(r\"[^\\w\\s]\",\"\",x) for x in texts_sz]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "3c82e83a-cc80-4dc7-9f00-298ca4aa3ba8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('', 8232651),\n",
+       " ('und', 592264),\n",
+       " ('der', 570190),\n",
+       " ('zu', 366297),\n",
+       " ('die', 331477),\n",
+       " ('in', 317726),\n",
+       " ('den', 278882),\n",
+       " ('dem', 218667),\n",
+       " ('von', 189926),\n",
+       " ('k', 148324),\n",
+       " ('auf', 136405),\n",
+       " ('mit', 134565),\n",
+       " ('an', 131161),\n",
+       " ('d', 122916),\n",
+       " ('im', 121562),\n",
+       " ('i', 116436),\n",
+       " ('des', 109684),\n",
+       " ('I', 106716),\n",
+       " ('sich', 105422),\n",
+       " ('bey', 102838)]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "freqdist_raw_wz = Counter(texts_wz)\n",
+    "freqdist_raw_wz.most_common(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "2f2aa06b-a9a6-4bff-ae27-41c829fdb168",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2986706"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(freqdist_raw_wz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "04d6b358-5cec-46ce-a944-7e9525788b00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freqdist_raw_wz = list(freqdist_raw_wz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "8d10016f-396e-4330-87d7-973e34d085b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freqdist_raw_sz = Counter(texts_sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "3933cc71-b659-4add-89c3-655e42f3f9ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "178978"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(freqdist_raw_sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "51979a0c-a62f-4a9b-8b6d-e7e82775e356",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freqdist_raw_sz = list(freqdist_raw_sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a327bc02-171e-465a-a708-1dacd0dd50f0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "345583bf-2aa8-474e-ae0c-ee9dcdf4a406",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\onb1202\\AppData\\Local\\Temp\\ipykernel_14768\\3373586664.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  df_news = wz.append(sz)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_news = wz.append(sz)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "101c9569-e12e-4717-93c2-bde83e0dfaa3",
+   "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>ocr</th>\n",
+       "      <th>split</th>\n",
+       "      <th>manifest_id</th>\n",
+       "      <th>year</th>\n",
+       "      <th>num_words</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>['769', 'Salzburger', '770', '---', 'Intellige...</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>sza17991207</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>5434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>['785-', 'Salzburger', 'Intelligenzblatt', '.'...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza17991214</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>5829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>['-', '--', '-', '99', 'E-', 'S', 'urger-', 'S...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza17991221</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>5685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>['Intelligenzblatt', '.', 'LII', '.', 'St.', '...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza17991228</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>4672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>['–', '-', '–', '-----------------------------...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>sza1799bl01</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   ocr  split  manifest_id  \\\n",
+       "625  ['769', 'Salzburger', '770', '---', 'Intellige...  valid  sza17991207   \n",
+       "626  ['785-', 'Salzburger', 'Intelligenzblatt', '.'...  train  sza17991214   \n",
+       "627  ['-', '--', '-', '99', 'E-', 'S', 'urger-', 'S...  train  sza17991221   \n",
+       "628  ['Intelligenzblatt', '.', 'LII', '.', 'St.', '...  train  sza17991228   \n",
+       "629  ['–', '-', '–', '-----------------------------...   test  sza1799bl01   \n",
+       "\n",
+       "     year  num_words  \n",
+       "625  1799       5434  \n",
+       "626  1799       5829  \n",
+       "627  1799       5685  \n",
+       "628  1799       4672  \n",
+       "629  1799         91  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_news.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "2cecc5d7-b559-4f49-9738-988626ee0db7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_news['newspaper'] = df_news['manifest_id'].str.findall(r\"\\D{3}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "dcace628-9a5d-4bda-9fb5-a6bc5d4dded8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_news['newspaper'] = df_news['newspaper'].str[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "f00b9a32-d217-4728-8772-ab709cf74d62",
+   "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>ocr</th>\n",
+       "      <th>split</th>\n",
+       "      <th>manifest_id</th>\n",
+       "      <th>year</th>\n",
+       "      <th>num_words</th>\n",
+       "      <th>newspaper</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>'UM', 'e', 'n', 'e', \"'r-\", 'Ze', 'Sonnabend',...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>wrz17890103</td>\n",
+       "      <td>1789</td>\n",
+       "      <td>25102</td>\n",
+       "      <td>wrz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>210</th>\n",
+       "      <td>'29', '^', 'Mittewoche', 'den', '7.', 'Ianer',...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>wrz17890107</td>\n",
+       "      <td>1789</td>\n",
+       "      <td>20370</td>\n",
+       "      <td>wrz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>211</th>\n",
+       "      <td>'hi', '.', 'L3', \"IZVrenei'\", 'Zeilunn', '.', ...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>wrz17890110</td>\n",
+       "      <td>1789</td>\n",
+       "      <td>24919</td>\n",
+       "      <td>wrz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>'8r', '«', 'V', 'Mittewoche', 'den', '14.', 'I...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>wrz17890114</td>\n",
+       "      <td>1789</td>\n",
+       "      <td>24072</td>\n",
+       "      <td>wrz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>213</th>\n",
+       "      <td>'Sonnabend', 'den', '17.', \"Ia'ner\", '1789', '...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>wrz17890117</td>\n",
+       "      <td>1789</td>\n",
+       "      <td>24759</td>\n",
+       "      <td>wrz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625</th>\n",
+       "      <td>['769', 'Salzburger', '770', '---', 'Intellige...</td>\n",
+       "      <td>valid</td>\n",
+       "      <td>sza17991207</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>5434</td>\n",
+       "      <td>sza</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626</th>\n",
+       "      <td>['785-', 'Salzburger', 'Intelligenzblatt', '.'...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza17991214</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>5829</td>\n",
+       "      <td>sza</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>627</th>\n",
+       "      <td>['-', '--', '-', '99', 'E-', 'S', 'urger-', 'S...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza17991221</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>5685</td>\n",
+       "      <td>sza</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>['Intelligenzblatt', '.', 'LII', '.', 'St.', '...</td>\n",
+       "      <td>train</td>\n",
+       "      <td>sza17991228</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>4672</td>\n",
+       "      <td>sza</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>629</th>\n",
+       "      <td>['–', '-', '–', '-----------------------------...</td>\n",
+       "      <td>test</td>\n",
+       "      <td>sza1799bl01</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>91</td>\n",
+       "      <td>sza</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1674 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   ocr  split  manifest_id  \\\n",
+       "209  'UM', 'e', 'n', 'e', \"'r-\", 'Ze', 'Sonnabend',...   test  wrz17890103   \n",
+       "210  '29', '^', 'Mittewoche', 'den', '7.', 'Ianer',...  train  wrz17890107   \n",
+       "211  'hi', '.', 'L3', \"IZVrenei'\", 'Zeilunn', '.', ...   test  wrz17890110   \n",
+       "212  '8r', '«', 'V', 'Mittewoche', 'den', '14.', 'I...  train  wrz17890114   \n",
+       "213  'Sonnabend', 'den', '17.', \"Ia'ner\", '1789', '...  train  wrz17890117   \n",
+       "..                                                 ...    ...          ...   \n",
+       "625  ['769', 'Salzburger', '770', '---', 'Intellige...  valid  sza17991207   \n",
+       "626  ['785-', 'Salzburger', 'Intelligenzblatt', '.'...  train  sza17991214   \n",
+       "627  ['-', '--', '-', '99', 'E-', 'S', 'urger-', 'S...  train  sza17991221   \n",
+       "628  ['Intelligenzblatt', '.', 'LII', '.', 'St.', '...  train  sza17991228   \n",
+       "629  ['–', '-', '–', '-----------------------------...   test  sza1799bl01   \n",
+       "\n",
+       "     year  num_words newspaper  \n",
+       "209  1789      25102       wrz  \n",
+       "210  1789      20370       wrz  \n",
+       "211  1789      24919       wrz  \n",
+       "212  1789      24072       wrz  \n",
+       "213  1789      24759       wrz  \n",
+       "..    ...        ...       ...  \n",
+       "625  1799       5434       sza  \n",
+       "626  1799       5829       sza  \n",
+       "627  1799       5685       sza  \n",
+       "628  1799       4672       sza  \n",
+       "629  1799         91       sza  \n",
+       "\n",
+       "[1674 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_news"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "e591fef8-207f-48af-b46c-d82f8e86b1a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_news = df_news.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "e90125fe-eb0e-4d5d-9602-ea6fd29a1f99",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "index          integer\n",
+       "ocr             string\n",
+       "split           string\n",
+       "manifest_id     string\n",
+       "year           integer\n",
+       "num_words      integer\n",
+       "newspaper       string\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_news.apply(lambda x: pd.api.types.infer_dtype(x.values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16584de5-8f45-4cba-a1cb-4f076fc3e948",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "f95fb034-5510-49b3-86bd-235c40e54815",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wz_issues = wz.groupby('year').count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "e8e8cb9f-2177-47bb-8515-9c8ca128c27a",
+   "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>ocr</th>\n",
+       "      <th>split</th>\n",
+       "      <th>manifest_id</th>\n",
+       "      <th>num_words</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1789</th>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1790</th>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1791</th>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1792</th>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1793</th>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1794</th>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1795</th>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1796</th>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1797</th>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1798</th>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1799</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ocr  split  manifest_id  num_words\n",
+       "year                                    \n",
+       "1789  104    104          104        104\n",
+       "1790  104    104          104        104\n",
+       "1791  105    105          105        105\n",
+       "1792  104    104          104        104\n",
+       "1793  105    105          105        105\n",
+       "1794  105    105          105        105\n",
+       "1795  104    104          104        104\n",
+       "1796  105    105          105        105\n",
+       "1797  103    103          103        103\n",
+       "1798  104    104          104        104\n",
+       "1799   52     52           52         52"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wz_issues"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "dae87d8e-768f-4c9b-8894-4bad5e1cbf31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wz_issues['newspaper'] = 'wz'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "6caea16b-8241-42fe-aaf9-d7d0fa307153",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz_issues = sz.groupby('year').count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "72fcce1d-5161-4f07-b96c-0acf66a5612a",
+   "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>ocr</th>\n",
+       "      <th>split</th>\n",
+       "      <th>manifest_id</th>\n",
+       "      <th>num_words</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1789</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1790</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1791</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1792</th>\n",
+       "      <td>51</td>\n",
+       "      <td>51</td>\n",
+       "      <td>51</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1793</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1794</th>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1795</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1796</th>\n",
+       "      <td>55</td>\n",
+       "      <td>55</td>\n",
+       "      <td>55</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1797</th>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1798</th>\n",
+       "      <td>53</td>\n",
+       "      <td>53</td>\n",
+       "      <td>53</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1799</th>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ocr  split  manifest_id  num_words\n",
+       "year                                    \n",
+       "1789   52     52           52         52\n",
+       "1790   52     52           52         52\n",
+       "1791   52     52           52         52\n",
+       "1792   51     51           51         51\n",
+       "1793   52     52           52         52\n",
+       "1794   54     54           54         54\n",
+       "1795   52     52           52         52\n",
+       "1796   55     55           55         55\n",
+       "1797   54     54           54         54\n",
+       "1798   53     53           53         53\n",
+       "1799   52     52           52         52"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sz_issues"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "4155e8e3-93b9-4b7f-850f-5f98ebaf9fc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz_issues['newspaper'] = 'sz'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "7bc7994b-2b35-4899-adbb-92d9a3166276",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_issues = pd.concat([wz_issues, sz_issues]).reset_index(drop=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "218dea41-fa21-4b99-8b26-a939284e0564",
+   "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>year</th>\n",
+       "      <th>ocr</th>\n",
+       "      <th>split</th>\n",
+       "      <th>manifest_id</th>\n",
+       "      <th>num_words</th>\n",
+       "      <th>newspaper</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1789</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>wz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1790</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>wz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1791</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>wz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1792</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>wz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1793</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>wz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1794</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>wz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1795</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>wz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1796</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>105</td>\n",
+       "      <td>wz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1797</td>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "      <td>103</td>\n",
+       "      <td>wz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1798</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>wz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1799</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>wz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1789</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>sz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1790</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>sz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1791</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>sz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1792</td>\n",
+       "      <td>51</td>\n",
+       "      <td>51</td>\n",
+       "      <td>51</td>\n",
+       "      <td>51</td>\n",
+       "      <td>sz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1793</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>sz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1794</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>sz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1795</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>sz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1796</td>\n",
+       "      <td>55</td>\n",
+       "      <td>55</td>\n",
+       "      <td>55</td>\n",
+       "      <td>55</td>\n",
+       "      <td>sz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1797</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>54</td>\n",
+       "      <td>sz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1798</td>\n",
+       "      <td>53</td>\n",
+       "      <td>53</td>\n",
+       "      <td>53</td>\n",
+       "      <td>53</td>\n",
+       "      <td>sz</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1799</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>52</td>\n",
+       "      <td>sz</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    year  ocr  split  manifest_id  num_words newspaper\n",
+       "0   1789  104    104          104        104        wz\n",
+       "1   1790  104    104          104        104        wz\n",
+       "2   1791  105    105          105        105        wz\n",
+       "3   1792  104    104          104        104        wz\n",
+       "4   1793  105    105          105        105        wz\n",
+       "5   1794  105    105          105        105        wz\n",
+       "6   1795  104    104          104        104        wz\n",
+       "7   1796  105    105          105        105        wz\n",
+       "8   1797  103    103          103        103        wz\n",
+       "9   1798  104    104          104        104        wz\n",
+       "10  1799   52     52           52         52        wz\n",
+       "11  1789   52     52           52         52        sz\n",
+       "12  1790   52     52           52         52        sz\n",
+       "13  1791   52     52           52         52        sz\n",
+       "14  1792   51     51           51         51        sz\n",
+       "15  1793   52     52           52         52        sz\n",
+       "16  1794   54     54           54         54        sz\n",
+       "17  1795   52     52           52         52        sz\n",
+       "18  1796   55     55           55         55        sz\n",
+       "19  1797   54     54           54         54        sz\n",
+       "20  1798   53     53           53         53        sz\n",
+       "21  1799   52     52           52         52        sz"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_issues"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d167ad9-4e65-4ea0-a850-cffba9957276",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "3b21b0e0-e8e5-41de-b8a7-e06a2015b6c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_palette = {\n",
+    "    'wz': 'tab:orange',\n",
+    "    'sz': 'tab:blue',\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "60d739ea-ca57-4c74-93d2-fee59fde5b89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 110.0)"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 3600x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_style(\"whitegrid\")\n",
+    "g = sns.barplot(data=df_issues, x = 'year', y= 'manifest_id' , hue='newspaper', alpha=0.7, palette= my_palette)\n",
+    "g.axes.set_title(\"Issues per year\",fontsize=45)\n",
+    "g.set_xlabel(\"year\",fontsize=35)\n",
+    "g.set_ylabel(\"issue count\",fontsize=35)\n",
+    "g.tick_params(labelsize=25)\n",
+    "plt.setp(g.get_legend().get_texts(), fontsize='25') # for legend text\n",
+    "plt.setp(g.get_legend().get_title(), fontsize='35') # for legend title\n",
+    "plt.ylim(0, 110)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "350b13d0-fb2d-469f-8a55-80c4bcde3272",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "181a6516-7912-4f73-ad49-ee454c4a93b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 597x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "h = sns.displot(data=df_news, x='num_words', hue='newspaper', kind='kde', fill=True, palette=sns.color_palette('muted')[:2], height=5, aspect=1.5).set(title='word distribution of WZ and SZ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "c498157b-33c4-4af1-82c0-db244236421f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#sns.set_style(\"whitegrid\")sns.displot(data=df_news, x='year', y='num_words', kind='kde', fill=True, hue='newspaper', palette=sns.color_palette('muted')[:2], height=5, aspect=1.5).set(title='WZ and SZ word distribution over time')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08316123-4011-4eb2-b02b-71d910c5a150",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abb6c32b-2017-47fb-93c7-c70f66b6fb4b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16f963e1-d7e8-4814-8f34-a1ca59b177c7",
+   "metadata": {},
+   "source": [
+    "<h4>vocabulary comparison</h4>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "8ac2ce1b-dc0e-40f5-90b1-0aaf9982fbd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intersection_set = set.intersection(set((freqdist_raw_wz)), set((freqdist_raw_sz)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "867d2fb2-d40a-4370-b2f7-745f40081519",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intersection_list = list(intersection_set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "486d831e-d717-4f8c-89ef-d6325d300381",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "91048"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(intersection_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "cde51c86-c8ba-4d5d-884d-6cfaa6e5f9c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAJECAYAAACGtJWBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABmOklEQVR4nO3dd3ib5aH+8a8sy3vv7O3sBUnIABLCCIVCgbLKHu2B82sLPW0phQKlg0IXlNEDHbRAKSmHvTeBJJBJFllkO4ljO97xlK3x++ONnTiesWU/0qv7c126kkiyfCvDvvO8z3D4/X4/IiIiIhI2IkwHEBEREZG+pQIoIiIiEmZUAEVERETCjAqgiIiISJhRARQREREJMyqAIiFIi/dFRKQnVABFQsyiRYu48cYbTccQEZEQFmk6gIh03ebNm7n55psZMGCA6SgiIhLCNAIoEkK8Xq/pCCIiYgMqgCIiIiJhRgVQJET89Kc/5eKLLwYgPz+f0aNHM3/+fMrKypgwYQJjx47lwIEDbX7s7373O0aPHs1f/vKX5tcaPXo0n3/+OW+//TYXXHABkyZN4tRTT+XHP/4xO3fubPN1Ghsb+fe//82ll17K1KlTmTJlChdccAH/+Mc/aGhoaPX8mpoaHnroIS644AJOOOEEpk6dynnnncdDDz1ERUVFl973/v37GT16NN/61rcoKyvjjjvuYNasWUydOpULL7yQ5557Dp/P1+bH7ty5k9tvv51TTz2VCRMmMGfOHH7wgx+wZcuWVs999NFHGT16NC+++CK/+93vmD59OlOnTu3yfMv169fzox/9iLlz5zJp0iQWLFjAPffcQ35+fpu/L//7v//LN77xDSZPntz8Xv72t79RX1/f5vu/+uqr+eyzz1iwYAETJkzg9NNPZ+PGjbz88suMHj2ahx56iE2bNnHDDTdwwgkncNJJJ3H99dezdOnSVp+/6c//ySefbPO9jB49mtGjR1NWVtbi/v/7v//jyiuvZPbs2UycOJH58+dz5513tvv3RUSClwqgSIiYOnUqp556KgBxcXGcd955nHHGGaSlpXHmmWfi8/l49dVXW32cx+Ph9ddfJzIykgsvvLDFY8899xz/8z//Q21tLfPmzSM+Pp433niDSy65hFWrVrV4bl1dHTfccAO//OUv2blzJ5MnT2bWrFkcOHCA3/72t1xzzTVUV1c3P7+hoYGbb76ZJ554gpKSEmbMmMHMmTMpLi7miSee4LLLLqO2trbL77+qqopvfetbvPHGG4wbN47p06ezc+dOfvGLX3DLLbe0KoHvv/8+F1xwAa+++ipJSUmcdtpp5OTk8M4773DJJZfw5ptvtvl5/v73v/PUU08xZcoUxo4dy7BhwzrN9txzz/Gtb32LN998k/T0dObNm4fD4eD555/nwgsvbFGQioqKuOiii3j44YcpKChg9uzZnHTSSezdu5c//OEPXHbZZa2KF1hF8Lvf/S5RUVGceuqpOBwOcnNzmx/fuHEjV1xxBZs2bWL27NkMHz6czz//nG9/+9s89dRTXfxdbt+DDz7I3XffzaZNmxg7diynnXYaLpeLl156iYsvvrjNUi0iQcwvIiFjw4YN/tzcXP9pp53W4v7PP//cn5ub6z/jjDP8Pp+vxWMffPCBPzc313/zzTc333f77bf7c3Nz/bm5uf7777/f7/F4/H6/3+/z+fwPPvhg8+eoq6tr/pi77rrLn5ub67/hhhv8paWlzfdXVVX5v/3tb/tzc3P9d9xxR/P9r732mj83N9d/xRVX+N1ud/P91dXV/osuusifm5vrf/bZZzt9z/v27WvOevLJJ/u3bdvW/NiePXv88+bN8+fm5vqfe+655vvz8vL8kyZN8o8fP97/zjvvtHi9jz76yD9hwgT/xIkT/bt3726+/5FHHmn+PB999FHz/V6vt8N8W7du9Y8fP94/fvz4Fh/n8/n8DzzwgD83N9d/+eWXN99/+eWXN/95VFVVNd9fXl7uv+aaa/y5ubn+G2+8sc33//3vf7/5z7cp10svvdT8+FVXXeWvrKxs/thFixb5x48f7x83bpx/69atzfc3/fn//e9/b/M9Nb1e059zQUGBf/To0f4ZM2b4CwsLW7zHe++915+bm+u/5ZZbOvx9EpHgohFAERuYOXMmQ4cOZe/evaxevbrFYy+99BIAl1xySauPGz9+PLfffjtOpxMAh8PBD37wA8aPH09+fj4ff/wxAMXFxbz88svEx8fz+9//nrS0tObXSEhI4Le//S1xcXG89tprFBcXA9ZIF0BmZiZRUVHNz4+Pj+fnP/85v/rVr5g+ffpxvc97772XUaNGNf96yJAh3H333QD861//ar7/6aefpr6+nuuvv56zzz67xWvMnz+fq6++GrfbzTPPPNPqcwwdOpT58+c3/zoiouMvkwsXLqSxsZFrrrmmxcc5HA5++MMfkpubi9PppLq6mi+++II1a9aQmprKH/7wBxISEpqfn5KSwsMPP0x8fDxLlixpc0Ttuuuuw+FwtJkrOjqahx56iKSkpOb75s2bx1VXXYXH42HhwoUdvo+OHDx4EL/fT0xMDKmpqS3e4/e+9z3uuusuLr/88m6/voj0PRVAERtwOBzN8wNffvnl5vuLi4tZvHgxWVlZzJ07t9XHnXfeec2F4ujXWrBgAQDLli0DYOXKlXg8HsaOHdui/DVJS0tj4sSJeDye5kvHJ510EgDvvPMON954IwsXLmTfvn0ATJo0iUsvvbTFJczOJCYmctppp7W6f+7cucTGxrJz587m0vnZZ58BMHv27DZfq+l1li9f3uqxsWPHdjkTHPk9OvPMM1s95nK5eOONN3j22WdJSEhgxYoVgFVC4+PjWz0/JSWl+TJ/W9nGjRvXbo7Zs2eTkZHR6v5j/yy7Izc3l9TUVAoLC/nmN7/JE088wcaNG/H5fKSnp3P11Vcza9asbr++iPQ97QMoYhNN88reffdd7rrrLuLj43nllVfweDxcdNFFzaN8Rxs6dGibr9WvXz/gyChe0+KS1atXM3r06A5zFBQUAFbJ+/nPf87vfvc7li5d2rwYYfDgwcyfP59LL72UESNGdPn9DR48uM3ROKfTSVZWFnl5eRQVFZGdnd2c4brrrutS1qOlpKR0ORNYo2MA/fv37/JzBw0a1O5zmh5rem6TmJgYYmJi2v249uYqNuVq+rPsjpiYGB577DF+/OMfs23bNrZt28ZDDz3UXFgvuugiFUCREKMCKGIT6enpzJ8/n/fee4/33nuPiy66iFdeeaXF6OCx2ru86T981FxTaWz69dChQ5k4cWKHOQYPHtz88yuuuIJzzz2Xjz76iKVLl7Jy5Ur27t3LU089xbPPPsv999/P+eef36X311aBPTZvZKT1Ja1pQciCBQtaXH4+1rGjn9D5Jd9jeTyeLj+3vdXKbT3n2Nyd5erqn2Vn2ttrctq0abz//vt89tlnfPrpp6xYsYJdu3bx+uuv8/rrr3P55Zfzi1/8okufQ0TMUwEUsZHLLruM9957j3feeYdx48axa9cuZs2a1e6IU3ujQvv37wdoPnEkMzMTsC6P/uEPfziuTMnJyVx00UVcdNFFAGzbto2//OUvvPnmm8dVAAsLC9u83+PxND+Wk5PTnDc/P59bb731uEYZu6PpcxUWFpKdnd3q8UWLFlFbW8uMGTOaH2+6FN6WpsfaupzbkfZ+f5r+LI8eoWwqvm2VvcrKynY/R1RUFKeddlrzJfSDBw/y8ssv8/DDD/Of//yHq666qsUcTREJXpoDKBJC2hqxOtrs2bMZNGgQy5Yt44UXXgDaXvzRpGmRx9H8fj8ffPABAKeccgoA06dPx+FwsGzZsja3bmloaODiiy/m8ssvZ+PGjQD86U9/Yu7cubzxxhstnpubm8vPf/5zAMrKyqirq+vwPTU5ePBg82sfbdGiRTQ0NDBp0qTm+YlN8w8/+uijNl/rxRdf5JxzzuHXv/51lz53R5oWsrT1e+nz+fjVr37FD3/4QwoLC5tzNZXCY1VUVLBkyRKA476k+vnnn+N2u1vd/+677wI0zy0EmucfHnuZGWDNmjWt7vvggw9YsGBB859bk6ysLG6++ebmuZzt7UMpIsFHBVAkhDTNAauurm7zcmLT5d7Gxkaee+45UlJS2lyc0GTRokUtVof6fD7++Mc/smXLFsaOHcucOXMAGDhwIGeffTYVFRXceuutlJSUNH9MQ0MD9957L19++SUFBQXNcwQHDRpEYWEhjz32WKui8dprrwHWhsOxsbFdfv8/+9nPWnzuXbt2NZe4b3/72833X3vttURGRvLnP/+Zt99+u8VrbNiwgd///vfs3LkzIKODV111FRERETz11FMtFlr4fD4eeugh8vPzGT9+PBMnTmTatGlMnTqVsrIybrvtNmpqapqff+jQIX74wx9SW1vLySefzMiRI48rR1lZGffcc0+LDbk//PBDFi5cSFxcHFdccUXz/U0LXd5+++0WI4f79+/n97//favXHjNmDHl5ebz66qutVplv2LCBXbt24XK5OlykIiLBxeFvmiAiIkGvtraWmTNn4na7mTx5MoMHD251Sba4uJh58+bh8Xi49tprufPOO1u9zk9/+lNeeeUV+vXrR0FBAWPHjmXIkCFs2bKFvLw8srOzefLJJ1tczqusrOT6669n06ZNxMXFMWHCBBITE9mwYQPFxcUkJiby1FNPMWHCBMC6vHjTTTexZMkSoqOjOeGEE0hKSmL37t1s27aNmJgYnnzySaZNm9bhe96/fz+nn346LpeLlJQUamtrOemkk2hsbGT58uU0Nja2+T5ffPFFfv7zn+PxeBg2bBgjR46ktLSUtWvX4vf7ueCCC7j//vub5849+uijPPbYY1x55ZXcc889x/Xn8vTTT3P//ffj9/uZOnUqWVlZbN26lby8PFJTU3n22WebC92BAwe47rrryMvLIyUlhRNPPBGfz8fq1aupqqpi7NixPPnkk6Snp7d4/3Fxcaxdu7bV53755Ze54447yMzMpLy8nIyMDCZPnkxRURHr1q0jKiqK3/72t5xzzjnNH1NXV8f555/P3r17iY+P56STTsLtdrNy5UrGjBmD2+1m27ZtLFu2rHlU9fHHH+dPf/oTDoeDSZMmkZOTQ2lpKWvWrMHn8/HTn/6U66+//rh+30TEHM0BFAkhcXFx/P73v+fBBx9k8+bN7Nu3j/Ly8hZ7s2VmZjJ06FB27NjR4eVfgKuvvprExESefvppPv74Y7Kysrjuuuv4zne+02oOWnJyMgsXLmThwoW89dZbbNy4Eb/fT//+/Tn77LO54YYbWswzczqdPPbYYzz11FO89957rF+/nsbGRjIzM/nmN7/Jf/3Xf7W7CrktLpeL559/nj/+8Y8sWbKkuWxde+21nHHGGa2ef/HFFzNu3Dj+8Y9/sHLlSj755BNSUlKYMWMGl19+OWefffZxL/hoz7XXXsv48eP5xz/+wZo1a/jyyy/JyMjgsssu47vf/W6LuYH9+/fnpZde4umnn+a9997js88+IzIykuHDh3Peeedx+eWXd7hwpT3jx4/n29/+Ng8//DCLFy8mPj6er33ta9x0002ttraJjY1l4cKFPProoyxatIglS5aQnZ3NjTfeyM0338y1117b6vX/+7//m4EDB/LCCy+wdetWNm3aRFJSEnPnzuWaa65pd8sdEQlOGgEUsZlt27Zx3nnnceKJJ/Lcc8+1+ZymEcCf/OQnXT7r1pTORsDCXdMI4Lx585rPehYR6YzmAIrYgNvtxu/3U1FRwb333gvQ5iiOiIgI6BKwiC28//773HHHHXi9Xnw+H9OnT+ess84yHUtERIKURgBFbGDkyJFkZGQQExPD2WefzWOPPdbpljEiIhK+NAdQREREJMxoBFBEREQkzHQ4B3DdunVER0f3VRYRERERCRC3282UKVPafKzDAhgdHd1q/ygRERERCX5btmxp9zFdAhYREREJMyqAIiIiImFGBVBEREQkzKgAioiIiIQZFUARERGRMKMCKCIiIhJmVABFREREwowKoIiIiEiYUQEUERERCTMqgCIiIiJhRgVQREREJMyoAIqIiIiEGRVAERERkTCjAigiIiISZlQARURERMKMCqCIiIhImFEBFBEREQkzKoAiIiIiYUYFUERERCTMqACKiIiIhBkVQBEREZEwowIoIiIiEmZUAEVERETCjAqgiIiISJhRARQREREJMyqAIiIiImFGBVBEREQkzKgAioiIiIQZFUARERGRMKMCKCIiIhJmVABFREREwowKoIiIiEiYUQEUERERCTMqgCIiIiJhRgVQREREJMyoAIqIiIiEGRVAERERkTCjAigiIiISZlQARURERMKMCqCIiIhImIk0HUBEgoTPA54qaKwGby143eBrAN/hH73uY37eAL5GwAd+P+C3Xqf55/6W9zsiIcJl3Y7+eYQLHEf93BkDkfHgjLN+bPp5hNPIb4uIiB2pAIrYnd8PjYegodz6sankeapb/txbbzppx5wxEHm4FDrjwZUIUSngSoGoVOvnkXGGQ4qIhAYVQBE78PuhsQLcpdatoQzcZdaPjZXW6F6o89ZbN3dZ+89xRrcuhVFpEJNl/VxERAAVQJHQ46mF+kKoK7R+rC+ySp8dSl5Ped1QV2TdjuWMtopgdJb1Y0wWxGRr1FBEwpIKoEiw8vuty7ZHl726Qusyrhw/rxtq9lm3o7kSDpfCbIgbYN2iUs1kFBHpIyqAIsHC54G6fKjZa91q9wX/vDw7aKy2btW7jtwXGQ+x/SFuIMQPgtiB4Iwyl1FEJMBUAEVM8dRaJa9mL9TuhdoD4PeaTiUAnhqo2m7dABwR1iXjuEEQPwQShlklUUQkRKkAivQVr9saZareCTV54C45vE2KBD2/z7r8XlcIpavA4bAKYcIISBhulcIIl+mUIiJdpgIo0pvqCg+PJO2A2v0a4bMLv//IYpPizyEi0rpcnDDcKoWx/a2SKCISpFQARQLJU2eN8FXtsH5srDKdSPqCzwPVe6wbH0NkLMQPg6QxkJRr7WEoIhJEVABFeqrxEFRutm61+3RZV6z/CDT9nXA4rTmDSWOsmyvBdDoRERVAkW5pqDz8DX6TtXJXpU/a4/daI8JVO+DAW9al4qSxVhmMTjOdTkTClAqgSFc1VBwpfbX5ptNIKPL7j+xFWPC+tZAkeTykTtLegyLSp1QARTriqYWKDVDxpUqfBF79Qet28BOIG2wVweTxmjMoIr1OBVDkWH6fdbmufC0c2qaVu9L7/H5ra6CaPDjwDiSNhpTJkDjS2oNQRCTAVABFmtSXWKWvYoNW74o5Pg9UbLJukfGQMhFSJ0NsP9PJRMRGVAAlvHnd1py+8rWtz4gVMc1TAyXLrVtcf0ibbhXCCH3pFpGe0VcRCU/1JVC6AirWg7fBdBqRztUegNrXoPB9SJ0K6dO1cEREuk0FUMKH329tzlyy3PpRW7dIKPLUWaePlCyDhJGQPuPwXEGdPCIiXacCKPbnbYDydVC60jp/V8QO/P7Dxwxut0YC06dB6gnWKSQiIp1QART7aiiHkpXW/D5vvek0Ir2noRwKPoCDn1olMHM2uJJMpxKRIKYCKPZTVwTFi61Nm3WZV8KJt8Ga4lC6ytpTMPNkiE43nUpEgpAKoNhH7QE4uBiqvlLxk/Dm90LZWmvqQ9JYqwjG9TedSkSCiAqghL6afdalr6odppOIBBe///DxhZshYThknQIJw0ynEpEgoAIooat6j1X8qnebTiIS/Kp3Wbe4gZAz3yqEIhK2VAAl9FTvgqJPoGav6SQioad2P+x6xhoJzDkD4gaYTiQiBqgASuioPQCFH1oFUER6pno37PgbJI2xRgRjskwnEpE+pAIowc9dCoUfwaEtWtwhEmiHtloLp1ImQfZpEJViOpGI9AEVQAlenhrrUm/ZF+D3mU4jYl9+P5Svh4qNkHYiZJ0KrgTTqUSkF6kASvDxNVp7mRUvBa/bdBqR8OH3WifmVKyDzFMgYxZE6NuEiB3pX7YEl/INUPQRNFSaTiISvrwN1rSL8rXQbwEkjTadSEQCTAVQgkP9Qch/C2ryTCcRkSbuMtizEBJHQr+zISbDdCIRCRAVQDHL67bm+ZWu0Dw/kWBVtQOqH4f0GZA9D5zRphOJSA+pAIo5FRuh4D1orDKdREQ64/dCyTKo/BKyT4fUKeBwmE4lIt2kAih9r74YDrytEzxEQlFjNex/zVqdP+A8iM02nUhEukEFUPqOt8E6uq1kuTWaICKhq3Y/7PgLZM6BrLlaLSwSYvQvVvpG9R5r1KCh3HQSEQkUvw8OLoHKzTDg69bxciISElQApXd5G6zj28pW6RQPEbtyl8Kup61NpPudpUUiIiFABVB6j0b9RMJL2RdQvQP6fx2SRplOIyIdUAGUwNOon0j4aqiEPf+G1MnW3oGRsaYTiUgbVAAlsDTqJyJgnS1cswcGXggJQ02nEZFjqABKYPgaoeADjfqJyBENlbD76cMrhU+DCKfpRCJymAqg9Fx9Mex9wTrOTUTkaH4/HFwK1btg0DchOt10IhEBIkwHkBBX9gXs+KvKn4h0rPaAtW9g2RrTSUQEjQBKd3nrIf8NqNhkOomIhApvA+x/Haq2w4DztUBExCAVQDl+tfth70ta6CEi3VO5BWrzYdCF2jxaxBBdApaua5rLs/MfKn8i0jONh2D3v6yTRLRwTKTPaQRQusZTA/tehqqdppOIiF34fVD40ZHRQJ0gItJnNAIonasrsBZ6qPyJSG84tFWLyUT6mAqgdKx8w+FLvpWmk4iInblLYeffoWKj6SQiYUGXgKVtfp+1sXPJMtNJRCRceBtg74vWQrN+Z4FDYxQivUUFUFrz1FpfhKt3mU4iIuGoZDnUHYDBl4IrwXQaEVvSf6+kpboi2PE3lT8RMatmr7VxdF2B6SQitqQCKEdUbLTm4GiLFxEJBo1VsOufcOgr00lEbEcFUCxFn1iXfX2NppOIiBzhbYC8/1iXhUUkYFQAw53fB/tfswqgiEgw8vvhwLuQ/5b1NUtEekyLQMKZtwH2/h9U7TCdRESkc6WroKECBl+sTaNFekgjgOGqaW6Nyp+IhJKq7bBLe5OK9JQKYDiqL4adT2p1nYiEproia8GavoaJdJsKYLipyTv8v+cK00lERLqvsQp2PQXVe0wnEQlJKoDhpGIT7P4XeOpMJxER6TmvG/Y8C4e2mU4iEnJUAMNF2RrY9yL4PKaTiIgEjs8Dec9DxZemk4iEFBXAcFCyAvLfsLZSEBGxG78X9r0MpatNJxEJGSqAdlf8GRx4R+VPROzN74f8N+HgUtNJREKCCqCdFX0CBR+YTiEi0ncKP7RuItIhFUC7KvhAp3uISHg6uPTwqSG68iHSHp0EYjd+v3XJt3Sl6SQiIuaUrgL8MODrppOIBCUVQDvx+63FHmVrTCcRETGvdDU4nND/a6aTiAQdXQK2k/w3Vf5ERI5WsgIOvGc6hUjQUQG0iwPvQdkXplOIiASfkmVaGCJyDBVAOyhaZH2BExGRth1can2tFBFABTD0FX8GRZ+aTiEiEvyKPoWDS0ynEAkKKoChrHSV9vkTETkehR9B8eemU4gYpwIYqsrXw4G3TacQEQk9Be/r2DgJeyqAoahyC+x/TZucioh014G34dBXplOIGKMCGGqqd8HeF8HvM51ERCR0+X3W19La/aaTiBihAhhK6goh73nwe00nEREJfb5G2PMc1JeYTiLS51QAQ0VDJez5N3jdppOIiNiHpxb2PAuN1aaTiPQpFcBQ4K0//AWqynQSERH7aajQf7Al7KgABjufF/L+A/XFppOIiNhXXYE1xcanKTYSHlQAg13+G1C9x3QKERH7q94F+a+ZTiHSJ1QAg9nBxVC+znQKEZHwUb7B+torYnMqgMGqYqPOrRQRMaFokfYIFNtTAQxGtQdg/6va6FlExAS/H/a9rLnXYmsqgMHGUwN7nwefx3QSEZHw5XVD3kLw1JlOItIrVACDSdPO9A2VppOIiIi7DPbp5CWxJxXAYFLwAVTvNp1CRESaVO2EgvdNpxAJOBXAYFGxEUqWmU4hIiLHKlmuHRnEdlQAg0FdEezX3lMiIkEr/02o3W86hUjAqACa5qk7vOij0XQSERFpj88De1+wzg4WsQEVQJOathpwl5lOIiIinWmohH2vaIsusQUVQJMOLoaq7aZTiIhIV1Vth+LPTKcQ6TEVQFNq9sLBT02nEBGR41X0MdTkmU4h0iMqgCZ462HfS9pbSkQkFPl9sPclzQeUkKYCaEL+G9rsWUQklDUe0u4NEtJUAPta2Rqo2GQ6hYiI9NShr6BkhekUIt2iAtiX6kvgwDumU4iISKAUfgB1haZTiBw3FcC+4vNa8/6035+IiH34PIe/tntMJxE5LiqAfaXwQ6grMJ1CREQCrb4YihaZTiFyXFQA+0LVTihdbjqFiIj0lpLPoWaf6RQiXaYC2Nu8bsh/XTvHi4jYmd8P+1/VNB8JGSqAva3wA235IiISDtyl1nQfkRCgAtibqndD2RemU4iISF8pXQnVe0ynEOmUCmBv8TXCfl36FREJK36/tUG0t8F0EpEOqQD2lsKPoKHcdAoREelrDeVQ+L7pFCIdUgHsDTX7oFS7w4uIhK3S1VC9y3QKkXapAAaaz2MN/+vSr4hIeMt/UxtES9BSAQy0g5+Au8R0ChERMc1dBsVLTacQaZMKYCDVFUHx56ZTiIhIsCheam0PIxJkVAAD6cDb4PeZTiEiIsHC57G+N4gEGRXAQCnfADV5plOIiEiwqdoJFRtNpxBpQQUwELxu68QPERGRthS8Z32vEAkSKoCBcPBTaKwynUJERIJVYxUUfWw6hUgzFcCeqi+GEu35JyIinShdBXUFplOIACqAPXfgHfB7TacQEZFg5/dpQYgEDRXAnqjcrJ3eRUSk62r2QcUm0ylEVAC7zddoTeoVERE5HkUfgU9XjsQsFcDuKlkGDZWmU4iISKhxl0HZKtMpJMypAHaHpxaKPzOdQkREQtXBT8FbbzqFhDEVwO44uET7OYmISPd56uDgYtMpJIypAB6vhkoN3YuISM+VroSGCtMpJEypAB6voo+tsx1FRER6wueBwo9Mp5AwpQJ4POqKoGKD6RQiImIXlRuhNt90CglDKoDHo+gj8PtNpxAREbvw+6HwQ9MpJAypAHZVzV44tM10ChERsZvq3VCTZzqFhBkVwK4q/MB0AhERsauiT00nkDCjAtgVVTus43tERER6Q/UufZ+RPqUC2BUHl5hOICIidnfwE9MJJIyoAHamJk9zM0REpPdV7YTa/aZTSJhQAeyMRv9ERKSvFH1iOoGECRXAjtQesOb/iYiI9IWqHdoXUPqECmBHijX6JyIifUyjgNIHVADbU18Mh7aaTiEiIuGmajvUFZhOITanAtie4qU69UNERMwoWWY6gdicCmBbGsqh4kvTKUREJFxVbILGKtMpxMZUANtSvAz8PtMpREQkXPm9ULrSdAqxMRXAY3ndULHOdAoREQl3ZavB12g6hdiUCuCxyteBt8F0ChERCXeeOut7kkgvUAE8mt+vIXcREQkeJcu1IFF6hQrg0ap3grvUdAoRERGLu9TaFkYkwFQAj1aywnQCERGRlkqWm04gNqQC2MRdBtU69k1ERIJM9S6oKzKdQmxGBbBJ6UrNsxARkeBUttp0ArEZFUCwVv1qpZWIiASrii+1JYwElAogQMUG8NabTiEiItI2bz1UbjadQmxEBRCg7AvTCURERDpWtsZ0ArERFcC6QqgrMJ1CRESkYzV52qpMAkYFsHyt6QQiIiJdo+9ZEiDhXQB9XmtirYiISCgoXwd+n+kUYgPhXQCrvgJPrekUIiIiXdNYDYe2mU4hNhDeBVBbv4iISKgp12IQ6bnwLYCeGqjSyR8iIhJiqnZYI4EiPRC+BbBio+ZRiIhI6PH7tCeg9FgYF8D1phOIiIh0T+VG0wkkxIVnAXSXQu0B0ylERES6p3YfNB4ynUJCWHgWQA2di4hIKPP79b1MekQFUEREJBRV6DKwdF/4FcCGCh39JiIioa8uHxoqTaeQEBV+BbByi+kEIiIiPef3Q+Um0ykkRIVfATyky78iImITKoDSTeFVABuroHa/6RQiIiKBUZsPDeWmU0gICq8CWLnFGjIXERGxi16Y2uTzQWMjeDzg9Vq/1rdPe4k0HaBPHdL8PxERsZmqbZA5+7g+pK4OysqgshIOHWr9Y1VV+4XP4bBu0dGQlHTklpIC6enWLS0NXK6evzXpPeFTAD21UJNnOoWIiEhg1ewFTx1Exrb7lIoKyMuDvXutW0lJ90f0/H7rVldn3YqKWj/H4bCK4NChMGyY9WN8fPc+n/SO8CmAVdt19q+IiNiP3wfVOyBlYvNdpaWwa5dV9vLyrFG9Po3kt0pmSQmsXm3dl5VlFcGmW1xc32aSlsKoAO4wnUBERKR3HNpGpWMiGzfCxo1QEITb3R48aN1WrrRGCAcPhqlTYdw4iIoynS78hEcB9PuhepfpFCIiIgHlw0WVZxzleSN45mM/Pr/DdKQu8futkcm8PHjnHRg/Hk44AQYONJ0sfIRHAawvBE+N6RQiIiIB4SGJcvcJVB7Kweu1St+AzHr2HWx/HmCwcrthzRrrlplpjQpOnqw5g70tPApg1U7TCURERHrM7c+mvG4yhw6lc+wajsGZtSFZAI9WXAzvvw8ffwzTpsGpp2quYG8JjwJYrfl/IiISuhr8mZTUzKC6OqlV8WuSmVgDpPdlrF7j8cDy5bB2LcyaBbNna55goNm/AHoboGaf6RQiIiLHzUs8JXUzqazMarf4NYmNqCcmykt9g7NPsvUFtxs++QRWrYJTToHp08Fpn7dnlP0LYM0e8HtNpxAREekyv99JeeMMyiqHNM/x68JHMbx/LZv3JPZqNhNqauDdd61RwXnzrDmCjtBY7xK07H8UXLXm/4mISOio9w8gr+ICisuGHkf5s/RPq+2lVMGhogJefRWeeso6yUS6z/4FUAtAREQkBPiIpLh+LnuLTsXt7t6Et9SY8NjxIi8PHn8cVqzQGcXdZe8C2FgN7hLTKURERDpU7x/A3vJvUFbRv9O5fh2JpJGUhMaA5QpmjY3WHoLPPGOdXSzHx94FsHav6QQiIiIdqmg8kX0Huz/qd6wBmfUBeZ1QsXu3NRq4bZvpJKHF3gVQq39FRCRI+XBRWLuAotJcfAG8jJmZFF4FEKC2Fp57zloo4vOZThMa7L0KuFYFUEREgk+DP50DFfMCNup3tOSY8CuATZYvtxaKfPOb4HKZThPc7DsC6PNAXRCehi0iImGt3j+QfaVn9Er5A4h11hPhCN+VEVu3WvMC6+pMJwlu9i2AdQe0/5+IiASVGt8o9hWfjMfTe99+HfjISXf32uuHgn374MknrdFAaZt9C6Au/4qISBA55JlE/sFp+Hy9v4Nxv7TwvQzcpKTEKoGFhaaTBCf7FkAtABERkSBR6TmBwpLxPdri5XikJ6gAgrU9zD//aa0UlpbsWwA1AigiIkHgkGcSRSWj+6z8ASRGqwA2cbvh3/+GvdoZrgV7FkB3GXjCYzd0EREJXtXesX068tckytFATJTmwTfxeGDhQuuysFjsWQDr8k0nEBGRMFfjG0VByZQ+L38WPwMyw3shyLHq6uDZZ6G62nSS4GDPAlhfZDqBiIiEMbc/h4KSEwO6wfPxyk7RZeBjVVRYG0Y3NJhOYp49C2CdCqCIiJjhJY788lPw9sFq346kxmkjvLYcOAAvvKATQ+xZADUCKCIiBviJ4MChM2lsMH/QVrxLI4Dt2b4d3nrLdAqz7FcAPbXQeMh0ChERCUPFdfOorY0zHQMAJx5SEhpNxwhaX3wBq1aZTmGO/QqgRv9ERMSAKu8EyiuzTcdoYUCmRgE78v77UFZmOoUZKoAiIiI91EgqRWUTTMdoJSVOqx060tgIr7wSnvMBVQBFRER6wI+DwkOn4vWaXfTRloQYFcDO7NsHn31mOkXfUwEUERHpgYrGaUEz7+9YsZGaA9gVn3wSfmcG26sA+n1QX2w6hYiIhIlGUikpH2E6RruinSqAXeH1WpeCvWF0eIq9CmBDBfj0l11ERPpGcc0sfIb3++tIBB4dCddFRUWwaJHpFH3HZgUwTJfyiIhIn6v1jaCqKtl0jE6lJ2tgpKs+/zx8zgu2WQEsN51ARETCgJ8IDlZOMR2jS1K1F2CX+Xzw4YemU/QNFUAREZHjVNE4Dbc7ynSMLknSVjDHZetW2LvXdIrepwIoIiJyHHy4KKscajpGlyVEawTweH3wgekEvc9mBVBzAEVEpHdVNk7F43GajtFlcVEaATxe+/bBzp2mU/QumxVAjQCKiEjv8REZUqN/AFERGgHsjk8+MZ2gd9mnAHpqwKv/5YiISO851DglpEb/AJx4iHaF4VlnPbRvH+zaZTpF77FPAdTon4iI9CI/DsoODTMdo1vSkzVA0h12PiLOPgXQrfl/IiLSe2q8uTQ2RpqO0S2piboM3B27dsGhQ6ZT9A77FMDGStMJRETExiprR5qO0G3J2gqmW/x+WL/edIreYZ8C6Kk2nUBERGzKQxI1NUmmY3SbtoLpvnXrTCfoHSqAIiIinahsmITfdIgeiIrUecDdVVpqz42hbVQAa0wnEBERmzpU3c90hB6JjFAB7Ak7jgLapwA2agRQREQCz+3PoaEhNBd/NIl0qAD2xKZN0Gizq+j2KYBejQCKiEjgVTeMMB2hx5wO7QPYE243bN5sOkVg2aMA+rzgqTOdQkREbKi6Nst0hB6LQCOAPbV9u+kEgWWPAqjRPxER6QWNpFLvjjEdIwD8xESpBPZEXp7pBIFljwKo+X8iItILaj2hefJHW+JidBm4J6qqrBXBdmGPAqgVwCIi0gtqGzJNRwiY2GiNAPaUnUYB7VEAdQlYRER6QX196G7+fCwVwJ5TAQw2XrfpBCIiYjMeEkN++5ejxbh0Cbin9uwxnSBw7FEAfTrjUEREAqvOO9h0hIDSIpCeq6yEigrTKQJDBVBERKQNbk+G6QgBpePgAsMul4FtUgBttj23iIgY19CYYDpCQEVH6hJwIJSUmE4QGDYpgBoBFBGRwGpotMP+f0e4NAIYELoEHExUAEVEJID8RNDgjjIdI6BcESqAgVBZaTpBYNhjeZMKoBhWXF7Hoy9s5NM1ByitqCc5IYpZk7K59bJJDMru/DLSlt3lPPz8l6zechCAccNSuenCccyZ3K/Vc/OLa3jwufV8vqGQqppGBmTFc9Fpw7jx/LFEOlv/n+71JXt45q2v2L6vksQ4FyeMzuR/rpjEsP4db2+xYlMR19z7cafZv3rhW80/r3N7+Msrm3lraR5FZXUMzIrnyrNHccWCUTgcjoBlE+lNZZX1PPT8dj5c8Q7VddUMyBrAgjkLOHvO2UREtD1uUu+u57v3fZeZk2fynW9+p8ufy93g5oX3X2DxF4spqywjOz2bc045h3NOOafNfzNH+8cr/+DVj1/lvlvuY+Koia1e9z/v/Icla5ZQVllGUnwSU8fPIrnfXURHp3Waq7a2iLffPocJE77PmDHXNd//+uvzqanJ7/BjTzrpfoYPvwgAn8/L1q1PsmvXy9TU7MflSiAnZw6TJv2AhIRBneYAqK8v48svHyY//yMaGqpITBzKyJGXMXLk5TgcR/48+iqbXUYAbVIANQdQzCkur+OSO96noLSWOZNyOGf2YHYfqOLNpXksWVvA8785i6H9Etv9+JWbDvKd33yCu9HL/GkDGJAZz5J1Bdx43yfcdf2JXPW13ObnFpXWcukd71NSWc/8aQMY2i+RpesKePC5DWzZXcGffjinxWs/tHADT7y8iaH9ErliwSiKymp5d9k+lm8s4uXfLWBgVvvldEBmPN+7ZEKbj23YUcritQVMG3tkk1yv18etf/yMT9ceYO7U/iyYOYjF6wr45ZNfsP9gDbdfMzVg2UR6S2llPZfe+T77D9aQOzSXU4adws59O3ni/55g045N/Pi6H7cqZl6vlz8+/UeKy4uP63N5fV5+++RvWb15NdPGTWPOlDl8sfkL/vLCXygqLeKGC29o92O37dnG65+83uZjPp+Pex+/l007NjFy8EhmT5lN3oE8Plr2FomJmzjrrBeJimr/a1JjYw1Ll36fxjZO2crNvYbGxqrW78Vbz9at/yAiIor09CNldPny28nLe4OkpOGMGnUVNTX55OW9RWHh5yxY8CLx8QM6+i2ivr6U99+/lJqa/aSnT2bw4KmUl29m9epfcPDgKmbPfrD5z6OvslVVgdcLTmeH0YOeTQqgRgDFnEdf2EhBaS0/vWYq1583pvn+15fs4bZHlvHA02t54qentvmxXq+POx9fQX2Dl4d/OIezZ1nbTtS7PXz7vk944Om1nDylX3OB/OurmymprOeO66Zy3bnW5/rhFZO5/pcf886yvVyxaRQzxlsH12/YUcpfXtnEjHFZ/O3OucREW//czzppL7c++Bl/fnEj9/+/me2+r4FZCXz/0omt7j9U08B5P3qHlIQoHvqfI4Xz7c/38unaA9xw3pjmsnfr5ZP49n2f8M83t3LB3GGMHpISkGwiveX3/1rH/oM1XLZgFlec+9PmcvHPV//JKx+9wgljT+D0mac3P7+qporfP/V71m1dd9yfa+mapazevJoLT7+Q6y+4HoArv34l9/7vvby26DXmnzSfof2Htvq4Rk8jjz73KD5f24s6lm9YzqYdm5g5aSY/vfGnzaOW/3z9OV754D989dXTTJz4vTY/tqYmnyVLvk95+aY2Hz96NPBoq1f/Ar/fxwkn3Ely8igAyso2kpf3Bunpkzj99H/jdFqX1HfseJ5Vq+7hyy8fY+bM+9v9/QFYt+731NTsJzf3ak444WfNfx5r1/6OrVufpF+/U5pH9Poqm98Phw5BamqH0YOe5gCK9NCHK/eRlhTNteeObnH/+acMZXB2AkvXF+Dz+dv82C93lrGvqJqTJ+c0lz+AmOhIfnjlZBq9Pp59Z1uL5wNcfNqI5vtckRFcdNpwANZtP7I87d/vWh/3y5umNxcsgLNnDeayM0YwOLv9EYCO/OafaygsreWn104lKzX2yOd7bzuRTgc3XzS+RbYfXD4Jvx9e/Hhnr2cT6QmP18d7K/aRkhDFf19+QYuRvivPvZLYmFheW/Ra832frv6U/3ff/2Pd1nVMGTPluD/fW4vfwhnh5JKzLmm+L9IZyVVfvwq/388Hyz5o8+NeeO8F8ovzmTx6cpuPb8/bDsDpM09vccn6zNlfA6C0dF2bH7d161O8/fZ5VFRsJTu76/8BKypazvbtz5GVNYORIy9rvr+09EsAhgw5r7lgAQwffhEOR2S7OZr4fB727XuPqKgUJk/+UYs/j0mTbiUyMp6vvnrKSDY7zAO0xwigXxNbxQyv18dNF44nMjKCiIjW83WiXBE0enw0enxER7W+XrD/oHWJZUpu6/3GRg9OAWDN1iOXlVISogFrHmDTaBpAUVkdAGlJ0c33LV5bQO7glDbn0/3yphldeHetbd5dxquLdzN5VDoXzB3WfH9Do5cvd5QyZmgqyQktJ85PGplGbLSTVZsP9mo2kZ4qO+Smtt7DhHFZuFwtpyBEuaIYkDmAnft3UltXS1xsHO999h7RrmjuvuluYqJjjmsUsLGxke152xk2cBgJcS0/16gho4iOimbjjo2tPm53/m5e/OBFLj7rYmrqalj/1fpWz0mMt/4DVVzW8pJ0WWUpQLtzALdte4b4+AFMn/4Lqqr2UFS0vNP34ff7Wbv2tzgcEZx44t0tHouOTgGgpuZAi/vr68vw+z2dzkV0u8vweGrJyppAZGRsi8eczmiSkoZSVraZxsbqVn9evZ3NDvMAbVIA2x5dEeltTmdEq5G/JjvzD7Erv4rB2Qltlj+AKJd1f0Nj6//EVNdac1vzS46cdX3ZmSNYvO4Adz6+gvtunsHgnEQ+31DI31/bQr/0OM6eaY0illbWU3bIzeyJOezMP8RDz61n+cYi/H6YMzmH266a0qXFKcf6w7Pr8fvhf741qcX/xvOLa/B4/Qxu4zWdzghy0uPYU1DVq9lEeioq0hota2j04vVFt3q8pr4Gv99PcXkxQ2KHcNnZlzF2+FiiXFF8uf3L4/pcB8sP4vV56ZfReqGXM8JJRkoGBw62LCden5dH//0o/TL7cclZl/DUa0+1+dqnnngqL7z/Av959z/kZOQwYeQE9hft5y/PP0xEhItRo65s8+OmT/8F2dmziYhwUlW1p0vvIy/vTcrLNzN06DdISclt8Vj//nOJi+vHjh3PkZY2ngEDTqOu7iArV94DOBg9+toOXzsiwvrPpNfb9lW+hoZqwE9NzYFWn7u3s7ltcAKtPQogKoASXHw+P796cjU+v59LzxzR7vPGD7f+l7noiwPcevmkFqt4P1ptrWZrKoIAp08fyKM/Opmf/nkF37jt3eb7xw1L5X9/cgoJcS4ADh4eESwqq+WSO95jSE4i3zxtOLsPVPHe8n2s3nKQF+5fwIDM+C6/p6/yKvhsQyHjh6cxa2JOi8cqqqwv0InxbW+bkRgXxe4DVXi8vl7JJhIIKYnRDMyKZ8ueCvIPVhAfndX82N6CvRSVFAFWEQTavQTbFVU11n+I4mPb/nseHxtP/sF8vF4vzsOrDV796FV27d/FA//zAK5IV7uvnZGawW9u/Q1/eOoP/PKJXzbfnxCXwGmnPUVGRtu5+/U75bjfx9at/wRgzJjWC1YiI+M444x/s2zZbSxb9uPm+yMiojj55IcZNOisDl87OjqF+PiBVFRsobp6X4uVuZWV26mp2QfQ5sKP3s7mtcGFR3vMAVQBlCDi9/u556+rWPZlERNGpHHtOW2PEIK10nbBzEFs31fJ/zz0GbsPHKKqpoE3luzhwefWExvtbDHAvaegij/9ZwPuBi/nzB7MdeeOZsKINDbvLue+f65pHkmsdXsAWLWlmDOmD+TF+8/ijutO4K93zuWuG06ktNLNb/655rje1zNvfwXADee1fj8erzUZvWkE5VhRLut+d4O3V7KJBMoN543B3ejlzj/9ic27NlPnrmPzzs088OQDRLkO/wcnAN9yPF7r30F7RS4y0hqfafBY/7nKP5jPwncW8rVTvsaYYWPa/Jgm9e56Fr69kH2F+5g4aiIXzL+A6ROmU1NXw6pV97S67NldxcWrKS/fRE7OyaSmts7k83nYtOkvlJSsJS1tIqNHX8/gwV8D/HzxxX2Ul2/p9HOMGXMDXq+bxYv/H8XFX9DYWENx8WqWLr0Fp7Npo+7WfyC9na2d9TchxSYjgCLBweP1cfcTK3n5k90Myk7gf39ySvNl3vb8+uYZlFe5eX/Fft5fsR8AlzOC26+dyv99sKP5ErDX6+PmBz5lf1ENT987nxPHWFuw+P1+fvPUGp55ext/+s8GfnL1VCIOX551Rji487oTcB41snjlglE8/dZXfLrmAHVuD7HRnX8ZaGj08vbne0mOj2LBzMGtHm+6xN3oafurYkOjD4cDYqMjA55NJJCuPDuXPQVV/Oud7fz0oZ823z932lwmjJrAu0vfJTqq9eXh4xXtsl6j0dv2NmYejweHw0G0Kxq/38+j/36U5IRkrjn/mk5f+28v/Y3lG5Zz7Teu5ZtnfLP5/qXrVvK7J3/N0qW3sGDBiz1+D7t3WwtiRoy4pM3HN2/+Kzt3Ps+oUVdy4ol3N08bKSlZx0cfXcXixTfz9a9/0GIRxrFyc6+kqmoP27b9iw8/vKL5/iFDziMrawY7dvwHpzO21cf1djY7jADqq6tIgNS5Pc374A3tl8g/7z6N7LS4Tj8uKT6KZ34+n883FLJpdzkJsS7mndCf/pnx/PmFjWQkW//LXbutlN0Hqrhg7tDm8gfgcDi47aopvPrpbl75ZDc/uXoqiYcvBQ/IjCclseU3q4gIB6MHp7CvqJoDJbWMGND5psvLNxZRW+/hwrnDcLUxypd8+NLv0Zerj1ZV20BcTCQREY6AZxMJtJ9dfyJzZ13H52t24sfP+JHjGTV4FA88+QAAKYkpPf4c8XHWpd/auto2H6+pqyEmOoaIiAje/PRNNu/azD0330NsdOuyczSvz8snqz4hKy2Li06/qMVjMyfPoV+/UykoWExl5Q6Sk0d2O7/f7+fAgUU4nbH07z+3zefs3v0KTmc0U6b8pMWc4YyMKQwffjE7diykqGhZux/f5MQTf8aIERdTWLgM8JOZOY309IksXXoLADExLRfR9WW2UKYCKBIAldUNfOc3n7B+eynjhqXy95/NIz256+eIOhwO5kzu1+Lkj/ziGsqr3EwdbX1xKyy1RgJHDEhu9fFRLieDcxLZuLMMd4OXQdkJOCMc7Y7INV2yjW1nccqxPl1jXTI6a2bbu+MPyIrHFRnRvKr5aF6vj8LSWkYMtHIHOptIbxg+aBCZKS03Qt+xdwfxsfGkp6T3+PWz07KJjIykqLSo1WNen5eSihIG5Vj/3j5f9zlAi/l8R/vZIz8D4G/3/g1XpItGTyMDsge0eZJIcvJICgoWU1NzoEcFsLx8E3V1xQwceFarFbpNamsLiI8fSGRk66+FTfvxdfVydErKaFJSWk4/KSvbiMuVSFxcdp9nC/VNoME2BbDj43JEepO7wctND3zK+u2lzBiXxeO3n9q8GKMzjR4f5/zgLUYPSeGx21pOwP5ghTXB+eQpVilsKpS7Cw61eh2P18f+g9UkxbuaL8dOGJHG+u2l7CmoanESicfrY2teBSmJUWSndTya0GTd9lIcDlqc/HG0SGcEk0els2FHKdV1jSTEHnn/G3aUUef2MvXwVjfRUc6AZhMJpB/+6TNWbS5m4YMtFwHs3LeTg2UHmTN1TjsfeXycTie5Q3LZnred2vpa4mKOXC3Ynrcdd4O7ea7f6SedzoRRrU/lWbNlDdv2bGP+jPlkpWcRHxtPlCuKyMjIViuIAfw4qKrKAyA2tu1/y11VUmJtP5OVNa3d58TEZFBXV4THU9+qaDWtMu4sx2ef/ZDi4lWcf/4nREQcaV1lZZupqcln0KCzjWRr50TAkGKDtwAqgGLSg8+tZ+1XJUzNzeBvd87tcvkDa6PknIw4Fq8tIK/gyEq2rXnlPP7SJjKSY7honrXf3oljMslKjeWtpXvZsL20xev8+YWNVFQ1cM7sIc33XXqGtfr4vn9+0WK07R9vbKWwtJYLTh3WYv5dezxeH9v3VjIkJ5Gkdlb5AlwwdxgNjT4eff7IdhiNHh8PP78BgEtOP7IaOlDZRAJt+IAkDpbXsWj50ub7aupqeGzhYwAt5tT11PwZ82n0NLLw7YXN93m8Hv795r8BOGu2VUJPn3k6V5xzRavb6KGjWzyeEJdAlCuKGRNmUFRaxJufvtni863fuoYDBxaRlDSClJSOF5J0prx8MwBpaa1PC2oyePDX8Hhq2bDhTy3ur6j4ip07XyA6OpXs7Fkdfp6kpOHU1R0kL+/Ie2loqGLlyrsAGDeu9bnLfZFNI4DBopMDs0V6S3F5Hf9+z9p1f/iAJP72Wtsrx/7rgnFERzl59P+scnT0EWu3Xz2Vy3/2AZf97AO+fvIQGjw+3lqaR4PHyxO3n9q8ECLK5eT+757Ef/92MVf+/EPOmjGIrLRY1m0rYc1XJYwcmMQPrziyvcM3TxvOotUH+HDVfi647R1OndqfnfsPNc9RPPac37aygbXJtLvRy+Ccjvfmu2jeMF5atIun3vqKbXsrGD88jSXrCtiaV8EN541psXH18WYT6SvXnTuGlxft5oG/P8UpJ+wiOTGZ5RuWU1hSyBXnXsHIwd27bPraoteoqavh/HnnN2/8fPrM0/lw+Ye8tug19hzYw8hBI1mzZQ2783dz4ekXtnkMXFd8+6Jvsy1vG3998a+s+HIFIwaNoKC4gBUbVuB0xjJz5gNtXh4+HtXVewFITBzS7nMmTPguhYWf89VX/6SkZA2ZmdOoqzvI/v3v4/f7OOmk3+ByHdkGZ9eul6mpyWfYsAtJSBgIWMe77d79MitX/ozCws+IiUln//4PqK7ex8SJt5CW1vprRW9kO1Z0z9cBGWeTAmiDKi4haf320uYRrJcW7Wr3edeeO5roKCePvWDt7H90yZowIo3nfnUGDy5cz+uL9+B0OjhpQhbfu2QC44a13I3+5Mn9eP6+M/nfFzexdH0BNXUestNjueG8Mfy/b45vsQ+fw+Hg4R/N4dl3tvHCRzt59t1tpCRE862zRnLr5ZNa7dnXVjaAiiprx9OcTha0OJ0R/P1n83j0+S95Z9levthawuCcBO658US+ddaoFs893mwifSUhzsXCX5/Bb/61n5VfrqfOXceQfkO47hvXMXvK7G6/7hufvMHBsoOcftLpzQXQGeHk3v93LwvfXsjStUvZsmsLORk53HTJTXzt5K91+3NlpGbwxx//kefffZ6VG1eycftGEuITmH3CXJJzbicpaVjnL9IJt7uCiIioDk/McLkSOOOM59i8+S/s3fsu27Y9Q2RkHDk5cxg//r9JT5/U4vm7d7/CwYMrycqa0VwArddYyPr1f6CoaBmNjTWkpOQyZcpP2t2rrzeyHSslpcOHQ4LD72//GI0tW7YwduzYvszTPTv+BrX5plOIiIhNHPJMoqBkfOdPDCFufwwvL2t/VEy67tZbITXVdIrOddTj7DHJJkKjBSIiEjjOiDrTEQJPZyYEhMMBSTbYocomBbDrk+5FREQ643KUm44QcF6/Pb7lm5aYaI9FIPb426ARQBERCSCXo8x2+0t4fPb4lm+aHeb/gQqgiIhIKw58REU3mI4RUI0+GwxbBYHk1nvxhyQVQBERkTZEuepNRwioRo8KYCBoBDCYaA6giIgEWJSr9dGGocztsce3fNMyMjp/Tiiwx98GjQCKiEiARUeWmI4QUA0aAQyIITbZSUcFUEREpA2xzr2mIwRUfYMKYE8lJ+sScHBx2uBMFhERCSqRVBEV5TEdI2DqG+3xLd+koUNNJwgce/xtcLZ/Xp+IiEh3xcQcMh0hYOrcGgHsKbtc/gW7FMBIFUAREQm8uKhi0xECRgWw51QAg40rwXQCERGxobjI3aYjBExtvT2+5ZuSmAjp6aZTBI49/jboErCIiPQCF+XERNthP0CHFoH0kJ1G/8AuBTDCCZGxplOIiIgNJcQdNB2hx3yo/PXUqFGmEwSWPQogaBRQRER6RULUTtMReszrt8+3exOio2HcONMpAss+fyM0D1BERHpBtKMw5LeD8fg1AtgT48eDy2aHjtmnAGolsIiI9JKkhALTEXrE41MB7IkpU0wnCDwbFUCNAIqISO9IjtqAw3SIHtAxcN2Xng6DB5tOEXgqgCIiIp2I5BDx8aG7KXS122bXL/uQHUf/wE4F0JVsOoGIiNhYctwO0xG6rbI2ynSEkORwwOTJplP0DvsUwOg00wlERMTG4p3bcLlCczFIeZVGALtj+HBISjKdonfYpwBGpZpOICIiNubAT1pSaJ4MUlqpEcDumDPHdILeY58CGBkPTv0FFxGR3pPkWkdkpNd0jOPiJRJ3o32+3feVQYOsEUC7stffCI0CiohIL4rAQ1ryHtMxjkuDT5d/u2PePNMJepfNCqDmAYqISO9Kdq0NqVHA2gZdHTtegwbBiBGmU/QumxVAjQCKiEjviqAxpEYBtQXM8TvzTNMJep8KoIiIyHFKca0mOrrBdIwuOaQtYI7LmDH23Pj5WCqAIiIix8mBj6zkdaZjdEl5tUYAuyoiIjxG/8B2BVBzAEVEpG/ERewkMbHSdIxOlVaqAHbV7NnW0W/hwGYFMAUi9BddRET6Rmb8MiIi/KZjtMtHJPUNOge4K7Kz4bTTTKfoO/YqgI4IiMk0nUJERMKEi3IyUneajtEut1eDIl3hdMKFF1o/hgt7FUCAmGzTCUREJIykuFYTF1drOkab6jwqgF0xbx7k5JhO0bdUAEVERHrAgZ+cpMU4nT7TUVqprtcK4M4MGmTvI9/aowIoIiLSQy7KyU7bZDpGKxXaAqZDUVHWpd8I+7WhTtnvLasAioiIAYnOjaQmF5mO0UJ+cYzpCEHtzDMhLUw3ELFfAYyMA1eS6RQiIhKGMmM/CZr5gF4iqdAegO068USYPt10CnPsVwBBo4AiImKEAx/9kz7AFeUxHYWaRo3+tWfUKDj3XNMpzLJnAYxVARQRETOc1DIgdQlOw/sDltfGGv38wap/f7jkkvCc93c0e759jQCKiIhB0Y5C+mV8QYTDXIaiCo0AHis1Fa64wlr8Ee7sWQBjB5hOICIiYS4+Yjv9MtZhpgM6yC+ONvKZg1VsLFx5JSQkmE4SHOxZAKPTIDLedAoREQlzCc4t5GRs6vMS2OCP0hFwR4mMhG99CzIyTCcJHvYsgABxg0wnEBERISlyA9kZX/VpCaxy6/Jvk+hoa+Rv8GDTSYKLfQtgvAqgiIgEh+TINX06ElharQIIkJgI118Pw4aZThJ87FsANQIoIiJBJClyAwOyVvXJ6uCCMhXAjAy48cbwO+O3q+xbAGP7g0PzH0REJHjER+xgYOYSIiN779xgPxEUlob3ApBBg6zyl5JiOknwsm8BjIiE2H6mU4iIiLQQ48hnUPqHxMS4e+X167wx+PwG958xbMwYuOYaa9WvtM++BRB0GVhERIJSlKOUQSlvkJxUFvDXrqwP38u/s2bBpZeCSyfgdcreBVALQUREJEhF0EhO3Htkp28L6IbRxYfCrwDGxVkbPC9YoBM+uirSdIBeFac13yIiEtxSXF8Qk1VIYcVM3O6eH1GRXxxeBXDYMLjoImvFr3SdvQugKwGiM8BdYjqJiIhIu2Ic+QxOfY0y9xzKyvvT3XXCHlxUVIfH9c+oKDj9dJgxAxzhO+Wx2+xdAAESR6gAiohI0IvAQ0b0pyRkD+j2aGB5fXicgjV0KHzjG9bZvtI99i+ACSOgZIXpFCIiIl0S48hnSMqrlDfOoKxyCF5v14e3DpTF9WIy81JSYN48mDxZo349Zf8CGD/U2g/Q7zWdREREpEscDi9pUctIztxASd1JVFZmd+GysINdB+xZAOPj4dRTYdo0cGqL34CwfwF0Rlmrgav3mE4iIiJyXJzUkB37MakxmZTUzKC6OqndIljni6G+wV7tKDoaZs+2tneJ6vn6GDmK/QsgQMJIFUAREQlZUY5i+ie8hTs+m/K6yRw6lN6qCBZX2Wf+X2QkTJ8Op5xibfEigRceBTBxBBR+aDqFiIhIj0Q7isiJe5+MuCTK66dSWdWveY7g3uLQb0pZWTB1KkyaZF32ld4THgUwJgci48FTYzqJiIhIj0VyiMyYT0mPcVHtGUdZzfCQ3f8vOhomTLCK38CBptOEj/AogA4HJAyHii9NJxEREQmYCBpJilxP0hAft/5gMps2wZdfQkGB6WQdczhg8GCr9I0fr6PbTAiPAgiQOFIFUERE7Ckpl+Rka8HE7NlQWgq7d0NeHuzdC5WVZuM5HJCZaZ3aMXQoDBmiuX2mhVEBHAWOCPD7TCcREREJHEeEtdjxKOnp1m3aNOvXFRVWEWwqhCUl4O/ucSNdieSwPn9T4Rs6VHP6gk34FMDIOIgfAtW7TScREREJnPjBEBnb4VNSUqzbpEnWr+vqoKwMDh2yRgeP/bG6GnztjJc4HNYtJgaSkqwzeJOSrNdvKp7p6dZKXgle4fXHkzRWBVBEROwlMfe4PyQ2FgYMsG5t8fmsW1PZg5Y/l9AXYTpAn0oeq7+9IiJiL8ljA/6SERHWCJ7Taf08IkLfPu0mvAqgKxHitMZcRERsIm4ARKWaTiEhKLwKIEDSONMJREREAiN5vOkEEqLCrwD2wlC5iIhIn3M4VACl28KvAEalQGw/0ylERER6JnYARCWbTiEhKvwKIECyLgOLiEiIS5lgOoGEMBVAERGRUONw6HuZ9Eh4FsDodIjrbzqFiIhI98QNAleS6RQSwsKzAAKkTDadQEREpHuSdflXeiaMC+AE6/xEERGRUOKI0OVf6bHwbUCR8ZA4svPniYiIBJPEkeBKMJ1CQlz4FkCA1CmmE4iIiByf1BNMJxAbCO8CmDgaIuNMpxAREekaVwIk5ZpOITYQ3gUwwgkpE02nEBER6ZrUKZq/LgERaTqAcaknQMkK0ylEREQ6lzrVdIJmW7du5cUXX2TZsmUUFRXhdrtJS0tj1KhRzJs3j4svvpiYmJgWH3P11VezcuXK4/o8H330EQMHDgxkdEEFEGKzraPh6gpMJxEREWlf/BBrH9sg8Mgjj/D444/j8/lISEhg8ODBuFwuiouLWbJkCUuWLOHvf/87f/7znxk//sh5xbm5uXg8nk5ff8uWLdTV1QHgcrl67X2EMxVAgLQTIf9N0ylERETalxYciz9eeukl/vznPxMXF8f999/PmWeeidPpbH58586d3Hnnnaxbt44bb7yRt99+m7S0NADuvvvuTl//tdde4yc/+QkA1157LdnZ2b3zRsKcJhIApEwCZ0znzxMRETHBGRM0e/898cQTAPzkJz/h7LPPblH+AEaMGMHjjz9Oeno65eXlPPPMM11+7Q0bNnDXXXcBMHXqVG677bbABZcWVAABnFHaEkZERIJXykSIMH8p9NChQ+zduxeAyZPbP1ErLS2NM844A7BKXVdUVFRwyy230NDQQFpaGg8//LAu//YiXQJukj4DSleA3286iYiISEtp00wnACAy8khtWLRoEePGtT8q+f3vf59rrrmG9PTO5y36/X5uv/12CgoKiIiI4I9//KMu/fYyjQA2iU6DBJ0MIiIiQSZhuLVgMQjExcVxwgnWXMRHH32U22+/nVWrVuH1els9NzMzk5EjR5Kamtrp6/7tb3/jk08+AaziOHv27IDmltYcfn/7Q15btmxh7NixfZnHrKodsPtZ0ylERESOGHpFUG3+vHnzZq688kpqa2ub70tISODEE09k2rRpnHTSSUycOJGIiK6NMa1evZprr70Wj8fDqaeeyl//+lccDkdvxQ8rHfU4FcCj+f2w7TFwl5pOIiIiAtEZkPtdCLJCtGPHDu655x6++OKLNh9PT0/nkksu4eabbyY2Nrbd1ykrK+OCCy6gqKiIAQMG8PLLL5OSktJLqcNPRz1Ol4CP5nBYcwFFRESCQcZJQVf+AEaOHMlzzz3Hq6++yve+9z2mTp3aYsFGaWkpTzzxBOeffz6FhYVtvobP5+O2226jqKgIl8vFn/70J5W/PqQCeKzUKdaqYBEREZMiY4N+h4qxY8fy/e9/n//85z+sWrWKf/zjH9xwww3NCz/27t3Lrbfe2ubHPv744yxduhSAO+64g0mTJvVZblEBbM0ZDSlTTKcQEZFwlzYtKLZ+6arY2FjmzJnD7bffzscff8y5554LwLp169i0aVOL5y5fvpw///nPAHz961/nyiuv7PO84U4FsC2Zs3TYtoiImONwBuWUpHvuuYezzjqLxx9/vMPnxcTE8Mtf/rL5svDu3bubHysuLubHP/4xXq+XkSNH8qtf/apXM0vb1HLaEpVqbbopIiJiQsp4cCWaTtGK2+0mLy+PDz/8sNPnJiQkEB8fD9B8FJzX6+VHP/oRxcXFxMXF8cgjjxAXF9ermaVtKoDtyTw5KCfeiohIGMiYZTpBm84//3wANm7cyMsvv9zhc5cuXUpFRQUpKSnNp4Y8+uijrFixAoBf//rXjBgxoncDS7tUANsTkwlJY0ynEBGRcJM4CmL7mU7Rpjlz5rBgwQIA7rrrLu677z7279/f4jlut5uXXnqJH/zgBwDceuutxMfHs3TpUv7yl78AcOONNzbPERQztA9gR+oKYPtfTKcQEZFwMvI7EDfAdIp2NTQ0cM899/Dqq6/SVCH69+9Peno6brebPXv20NDQgMvl4pZbbuG//uu/ALjmmmuaR/+mTp3a5c2eb775ZubOnds7b8bmOupxOgu4I7H9IHGkdUKIiIhIb0scGdTlDyAqKooHHniAK6+8krfffpsVK1ZQVFTE1q1biY2NZdiwYZx88slcfPHFDB8+vPnjjh5vWrt2bZc/X2mpDmfoDSqAnck6RQVQRET6RvY80wm6bOLEiUyc2PUFk//61796MY0cL80B7Ez8EOsmIiLSmxJHQNxA0ykkTKgAdkXWKaYTiIiI3WXNM51AwogKYFckjoT4waZTiIiIXSUMh/hBplNIGFEB7KqcM0wnEBERu8rWKlfpWyqAXRU/GJJyTacQERG7SRimuebS51QAj0fOGTodREREAsfh0BUmMUIF8HjEZEHKJNMpRETELpInBP2+f2JPKoDHK3s+RGj7RBER6aGISMg53XQKCVMqgMcrKhnSpptOISIioS59BkSlmE4hYUoFsDuyTgFntOkUIiISqiJjIetU0ykkjKkAdkdkHGTOMZ1CRERCVdZccMaYTiFhTAWwuzJmWZeDRUREjkd0mqYSiXEqgN0V4YJ+C0ynEBGRUJN9OkQ4TaeQMKcC2BPJ46zje0RERLoifhCkjDedQkQFsMf6nwMO/U9OREQ64YiA/ueaTiECqAD2XEwGZJxkOoWIiAS79OkQm2M6hQigAhgYWXPBlWg6hYiIBCtXonWQgEiQUAEMBGc05JxpOoWIiASrfgu0f6wEFRXAQEmdBPFDTKcQEZFgkzgCUiaYTiHSggpgIPU/x5rkKyIiAtZ5v/3PMZ1CpBW1lUCKzYbM2aZTiIhIsMg8GaLTTacQaUUFMNCy5kF0hukUIiJiWnSaVQBFgpAKYKBFRMLAb4DDYTqJiIiYNODr1vcEkSCkAtgb4gdBuvYGFBEJW+nTdFKUBDUVwN6SczpEpZpOISIifS0qFXLOMp1CpEMqgL0lwgUDz9elYBGRcOJwWNOAnFGmk4h0SAWwNyUMg7QTTacQEZG+kj4DEoaaTiHSKRXA3pZzJkQlm04hIiK9LTodcs4wnUKkS1QAe5szGgboUrCIiK05HDDwAmv6j0gIUAHsC4kjIH2m6RQiItJbMmZbO0CIhAgVwL6ScwbE9jOdQkREAi0mE7JPM51C5LioAPaVCCcM+qZWhomI2ElEpPW1XRs+S4hRAexLMRnQ72zTKUREJFByzoTYHNMpRI6bCmBfSzsBUsabTiEiIj2VNBoydOqThCYVQBMGnAdRKaZTiIhId7mSrA2fRUKUCqAJzhgYdBE49NsvIhJyHBEw+JsQGWc6iUi3qYGYEj8YsuaaTiEiIscrez7EDzGdQqRHVABNyjoVEkeZTiEiIl2VOAoy55hOIdJjKoAmORzWZYToNNNJRESkM1HJMOhCnewktqACaJozBgZfpv0BRUSCWUQkDL5E8/7ENlQAg0FstnVesIiIBKcBX4e4gaZTiASMCmCwSJkAGbNMpxARkWNlzITUKaZTiASUCmAw6XcmJAwznUJERJokjoB+Z5lOIRJwKoDBxBEBgy+2JhqLiIhZ0Wkw6GLt2Sq2pL/VwSYy3loUooPFRUTMcUbDkG9BZKzpJCK9QgUwGMX1h4EXaKsBERETHA7rtKaYTNNJRHqNCmCwSpkA2aeZTiEiEn6yT4Ok0aZTiPQqFcBglnWqVp6JiPSl1EnW114Rm1MBDHYDzoOEoaZTiIjYX+IIGPAN0ylE+oQKYLCLcMKQyzUXRUSkN8X2g8GXWl9zRcKACmAocMbA0KvAlWg6iYiI/USlwNArrZW/ImFCBTBURCXrC5SISKBFxsGwq8GVYDqJSJ9SAQwlsTkw5DJw6BKFiEiPRbhg6BUQnW46iUifUwEMNQnDrdNCtDO9iEj3NZ28FDfQdBIRI9QiQlHyWBj4DW0ULSLSXf3P0V5/EtZUAENV6mTrC5iIiByffmdB+jTTKUSMUgEMZenTrS9kIiLSNTmnQ+Zs0ylEjFMBDHWZsyF7rukUIiLBL3suZJ1iOoVIUFABtIPs0yBjlukUIiLBK+sUna8uchQVQLvovwDSTjSdQkQk+GTOti79ikgzFUA7GfB1SDvBdAoRkeCRcZLmSou0QQXQThwOGHCe9QVPRCTcpU+D/l8znUIkKKkA2o3DYX3By5xjOomIiDnp06H/uaZTiAStSNMBpJf0O9M65qjoE9NJRET6VtbJkHOG6RQiQU0F0M6y51klsOAD00lERPpGzhlWARSRDqkA2l3mHHC4oOAd8PtNpxER6R0Oh3XJVyd8iHSJCmA4yJgBEZGQ/4ZKoIjYj8MJgy6ElAmmk4iEDBXAcJF2AkREw/5XwOcxnUZEJDAiXDD4EkjKNZ1EJKSoAIaTlPHgSoC8/4CnznQaEZGecUbDkG9BwlDTSURCjraBCTfxQ2D4DRCVYjqJiEj3uRJh+HUqfyLdpAIYjmIyYcSNENvPdBIRkeMXmw0jvq2vYSI9oAIYrlyJMPx6SBxpOomISNcljjp8FSPZdBKRkKYCGM6cUTD0Cp0fLCKhIX0GDP2WNfdPRHpEi0DCnSMCBp4PrmQoWmQ6jYhIaw4H9FsAGTNNJxGxDRVAsWTPheh02P8a+BpNpxERsTijYNA3IWm06SQitqICKEekTIDoTGubmIZy02lEJNy5Eq1pKlrsIRJwmgMoLcVmw8j/goThppOISDiLHwIjb1L5E+klKoDSWmQsDLsKMmebTiIi4ShjJgy/1tq4XkR6hS4BS9scEdDvLIjJsc4Q1rxAEeltzigYcL7O9BXpAyqA0rHUSdbG0Xn/gYZK02lExK6i02HIZRCTZTqJSFjQJWDpXGw/a15g4gjTSUTEjpLGWF9jVP5E+owKoHRNZDwMvQr6nQkOp+k0ImIHjgjIOcMa+dPmziJ9SpeApescDsicY63O2/uStooRke5zJcGgCyFhmOkkImFJI4By/OIGwqibIGW86SQiEoqSx8Ko/1b5EzFII4DSPc4YGHyJtV/ggXe1SlhEOueMgn5n6/xxkSCgAig9k3YixA2GvS9A/UHTaUQkWMX1t450i043nURE0CVgCYSYTBj5HUifYc0TFBFp4nBA1ikw/EaVP5EgohFACYwIFww4B5LHwf7XtEBERCAqGQZeCAlDTScRkWNoBFACK2GoNblbo4Ei4S11yuGFHkNNJxGRNmgEUALPGaXRQJFwFZUMA86DxJGmk4hIBzQCKL1Ho4Ei4cPhsBaFjfp/Kn8iIUAjgNK7NBooYn/R6daony73ioQMFUDpG02jgcWLoXgZ+L2mE4lITzmckDkbsuZChL6diIQS/YuVvuOMss79TJkCB96C6t2mE4lId8UNtEb9YrNNJxGRblABlL4XkwHDr4WKjVDwHjRWmU4kIl3lSjj8H7nJmtsrEsJUAMWclAmQOAoOfgoly8HvM51IRNrjcELGSdblXme06TQi0kMqgGKWMxr6nWXtGXbgbajeYzqRiBwrcaR1hm9MhukkIhIgKoASHGKyYPh1UL4Bij6ChkrTiUQkOg36LYCk0aaTiEiAqQBKcEmdZG0ZU7IMipeC1206kUj4cUZB5imQMUure0VsSv+yJfhERFqHx6edAEWfQNkXmh8o0hccTmsz56xTrcUeImJbKoASvCLjYcC5kDETij6Gys3g95tOJWI/Doe1qjd7HkSlmE4jIn1ABVCCX3Q6DL4Eag9A4YdQvct0IhH7SB4L2fMhJtN0EhHpQyqAEjri+sPwa6wCWPQJ1Ow1nUgkdCUMh5zTIW6A6SQiYoAKoISehOHWrXoPHFysEUGR4xE3EHLmW/+GRCRsqQBK6EoYat1q9llFsGq76UQiwStxBGSeDAnDTCcRkSCgAiihL34QDLvSmiN4cDFUfaXFIiJgLe5IGmsVv7j+ptOISBBRART7iOsPQy+HuiIoXqxVwxK+HE5InQyZc6xFVCIix1ABFPuJzbZWDTdUQOlKKFsD3nrTqUR6nzMKUk+EzFngSjKdRkSCmAqg2FdUinXOcNY8qFgPJSvAXWI6lUjgRaVC+nRInQqRsabTiEgIUAEU+3NGWd8c06ZB9U6rCFbv0OVhCW0OBySOgrTpkDjS+rWISBepAEr4cDisb5SJI6G+xLo8XLEOvA2mk4l0XWScNdKXPs0a+RMR6QYVQAlPMRkw4BzIOQMqN0L5Om0sLcEtboA1kp08wTovW0SkB/RVRMKbMwrSTrBu7lIoXwvl66GxynQyEXAlWIUvdTLE9jOdRkRsRAVQpEl0ujUimD0fqnZYo4KHvgK/13QyCScRkZA0BlImW5s3OyJMJxIRG1IBFDmWIwKScq2bpxYqNkDFl1CbbzqZ2JXDAXGDrZG+5HHgjDGdSERsTgVQpCORcZAx07o1VFibS1duhtr9ppOJHcRmQ9I4q/hFpZhOIyJhRAVQpKuiUiBztnVrqLSK4KHDZVBbykhXOBwQN8i6xJs0BqLTTCcSkTClAijSHVHJ1mkLmbOg8VDLkUG/z3Q6CSYOJyQMg+SxkDjaWtghImKYCqBIT7mSjlwm9tRZm01X7bB+1Gri8BQZCwnDrVG+xFGa0yciQUcFUCSQImMhZYJ1A6grPFwGd0DNPq0otquISOvSbsJw6xbbXydziEhQUwEU6U2xOdYt62TwuqF69+EymGedS6y5g6HJ4YCY7COFL34IRLhMpxIR6TIVQJG+4oyG5DHWDazLxbV7rRNIavdC7QGNEAYrR4RV+OIGQfxga05fZLzpVCIi3aYCKGJKZCwkjbZuAD4P1OVbhbBmL9TuA2+92YzhKjLeOnotdgDED4LYgdapMSIiNqECKBIsIiKtS4nxQ6xf+/3QWGHNI6wvPPJjQ6XRmLbjSoDorMMjfAOsW1Sq6VQiIr1KBVAkWDkcVhGJSrW2EGniqWtZCOsLrXOMfR5zWUOBMxpisg6XvcOFLybL2uxbRCTMqACKhJrIWGsOWsKwI/f5/dBYaRVBdyk0lIK7DBrKrVHEcCmHzmirMLtSrI27o1IhKs0qelHJptOJiAQNFUARO3A4DheeFEgc0fIxv9/arLqh3PrRU23tT+ipPnJrrAr++YbOGGtuXmQcOOPBlXi44KUcLnypVjkWEZFOqQCK2J3DYY1+dTYC5vMcVQZrwddgbV3jawCfG7yHf2zx80bAb938x/wIR/0a60SMCNeRm6Pp55FH/dwFzlir5EXGg/OoHyOcvfZbJCISblQARcQSEXlkFFFERGwtwnQAEREREelbKoAiIiIiYUYFUERERCTMqACKiIiIhBkVQBEREZEwowIoIiIiEmZUAEVERETCjAqgiIiISJhRARQREREJMyqAIiIiImFGBVBEREQkzKgAioiIiIQZFUARERGRMKMCKCIiIhJmIk0HEJHu+81vfsPTTz/NsGHDePfdd9t93o033sjSpUsBuOeee7jyyivbfN6WLVu44IILAHjjjTe4+eabyc/PP65MX3311XE9X0RE+p4KoEgImz17Nk8//TS7d++mrKyMtLS0Vs9xu92sXr26+deLFy9utwCuWrUKgMzMTHJzc5kwYQLZ2dmd5li/fj1erxeXy9XNdyIiIn1JBVAkhE2fPh2Xy0VjYyNr167l9NNPb/WcFStWUF9fz8iRI9mxYwcrV66koaGBqKioVs9tKopz5swB4JFHHuk0w+OPP86aNWsAuO2223rydkREpI9oDqBICIuPj2fixIkAzSXsWE2Xfr/xjW8wePBgamtrW4wIHu3YAtiZDz/8kIcffhiAs88+m2uvvfa48ouIiBkqgCIhbvbs2UD7BXDJkiUAzJo1i1NOOaXFfUfbuXMnpaWlOByOLhXAvXv3cvvtt+P3+xk2bBj33Xdfd9+CiIj0MRVAkRA3a9YsADZu3EhDQ0OLxw4cOMCuXbtITU1l/PjxzWWxrQLYNP9v9OjRpKend/g5GxoauPXWW6muriY2NpZHHnmEhISEQLwdERHpAyqAIiFu8uTJxMXF0dDQwMaNG1s8tnjxYsC6pBsREcHMmTNxuVxs376dwsLCFs9tKoBdGf379a9/zebNmwG49957yc3NDcRbERGRPqICKBLiXC4X06ZNA1pfBm6a/3fyyScDkJCQwOTJk4HWo4BffPFFi+e258033+T5558H4LLLLmveNkZEREKHCqCIDbQ1D9Dj8bBs2TKg5ahe08+PLoD79u2joKCAmJiY5jLZll27dnH33XcDMH78eO66667AvQkREekzKoAiNtA0D/DoArh27Vqqq6sZPXo0WVlZzfc3jfAtX74cn88HHLn8O23atDa3hwGor6/n1ltvpba2luTkZB555JF2nysiIsFNBVDEBpoWbpSXl7N7927gyAjfsZd0J0yYQEpKCpWVlWzduhXo2vy/X/7yl2zbtg2Hw8Hvfvc7Bg4c2BtvRURE+oAKoIgNOBwOZs6cCRwZBTx2/l+TiIiI5hHDFStWAJ3v//fqq6/y0ksvAXDTTTcxb968wL4BERHpUyqAIjbRVOrWr19PeXk5mzdvJjY2ts05fU1Fb/Xq1Rw8eJC9e/eSmZnJ6NGjWz13x44d3HvvvQDMnDmTW265pffehIiI9AkVQBGbaCqAX375JV988QV+v5/p06e3OU+vaUPoLVu2NK/+bWv0r7a2lltuuYW6ujqysrJ48MEHcTqdvfguRESkL6gAitjEwIEDGTRoENu2beOzzz4D2t/SJScnhxEjRpCfn988V7BpJfHR7r33Xnbu3ElkZCQPPfRQpxtEi4hIaFABFLGR2bNn4/F4eO2114CO9/RrGvF755132jz+7YUXXmh+nTvvvLPD7WFERCS0RJoOICKBM3PmTJ5//nlqamro378/I0aMaPe5J598Ms888wy1tbWMGTOGjIyMFo8/8cQTgLXR9Jtvvsmbb77ZpQx3330348aN6/6bEBGRXqcCKGIjs2bNwuFw4Pf7Oz3SbcaMGURFRdHQ0NDmc/1+PwCNjY2tThjpSFVV1fGFFhGRPufwN32Vb8OWLVsYO3ZsX+YRERERkQDoqMdpDqCIiIhImFEBFBEREQkzKoAiIiIiYUYFUERERCTMqACKiIiIhBkVQBEREZEwowIoIiIiEmZUAEVERETCTIcngbjdbrZs2dJXWUREREQkQNxud7uPdXgSiIiIiIjYjy4Bi4iIiIQZFUARERGRMKMCKCIiIhJmVABFREREwowKoIiIiEiY+f/bJXKvIUkBjwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 3600x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "v = venn2(subsets = (2986706, 178978, 91048), set_labels = ('WZ', 'SZ'), set_colors=(\"orange\", \"blue\"),alpha=0.5)\n",
+    "plt.gca().set_facecolor('white')\n",
+    "plt.gca().set_axis_on()\n",
+    "v.get_label_by_id('100').set_text('2.986.706')\n",
+    "v.get_label_by_id('010').set_text('178.978')\n",
+    "v.get_label_by_id('110').set_text('91.048')\n",
+    "plt.rcParams[\"figure.figsize\"] = (50,10)\n",
+    "plt.title(\"types per corpus\", fontsize = 23)\n",
+    "plt.rcParams[\"figure.figsize\"] = (50,10)\n",
+    "for t in v.set_labels: t.set_fontsize(25)\n",
+    "for t in v.subset_labels: t.set_fontsize(20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "137d1e21-0f9f-46d0-8483-7cc8c5ffdf8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5087105677792801"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "91048 / 178978"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "df2e6f98-0bad-4331-8df7-eb2d7ab9f14b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.030484419959647853"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "91048 / 2986706"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b070b610-38c3-4f5e-bb78-1c5ea68db3ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "45812580-7b0f-4094-a191-74eb1a1c99ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install -U sentence-transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "2de85461-de4a-4df7-b588-36562e07af53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import python_utils as utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "2f2f5f71-b3d7-4a85-bd67-79448558a89b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[0.0953]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sentence_transformers import SentenceTransformer, util\n",
+    "\n",
+    "model = SentenceTransformer(\"Sahajtomar/German-semantic\")\n",
+    "\n",
+    "sentences1 = [texts_wz]\n",
+    "\n",
+    "sentences2 = [texts_sz]\n",
+    "\n",
+    "embeddings1 = model.encode(sentences1, convert_to_tensor=True)\n",
+    "embeddings2 = model.encode(sentences2, convert_to_tensor=True)\n",
+    "cosine_scores = util.pytorch_cos_sim(embeddings1, embeddings2)\n",
+    "\n",
+    "print(cosine_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "351f5572-9588-4018-9f7e-aa00c7be0589",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85e7928b-753f-48af-8e0a-043fcc0fe5d6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cca43ffc-4243-43e1-8106-177648a883bb",
+   "metadata": {},
+   "source": [
+    "<h4>compute n-grams</h4>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6f62fb64-0e03-4523-8d73-250fd9dbe962",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.util import ngrams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5e357c83-2592-4eab-95ad-eb7f640962a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "\n",
+    "def get_top_ngram(corpus, n=None):\n",
+    "    vec = CountVectorizer(ngram_range=(n, n)).fit(corpus)\n",
+    "    bag_of_words = vec.transform(corpus)\n",
+    "    sum_words = bag_of_words.sum(axis=0) \n",
+    "    words_freq = [(word, sum_words[0, idx]) \n",
+    "                  for word, idx in vec.vocabulary_.items()]\n",
+    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
+    "    return words_freq[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "0a1876b3-6e1f-4793-8d6b-1a6d9ac37c65",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "KeyboardInterrupt\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "top_n_bigrams=get_top_ngram(wz['ocr'],2)[:10]\n",
+    "x,y=map(list,zip(*top_n_bigrams))\n",
+    "sns.barplot(x=y,y=x).set(title='top bigrams WZ uncleaned')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a258c0bd-efd0-4192-8a09-4afc23b8e6d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_n_bigrams=get_top_ngram(sz['ocr'],2)[:10]\n",
+    "x,y=map(list,zip(*top_n_bigrams))\n",
+    "sns.barplot(x=y,y=x).set(title='top bigrams SZ uncleaned')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8e6fcca-2db1-44eb-a612-c53e240bb30d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f1d5923-9093-4de8-93c4-4b4ef6f37662",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_n_bigrams=get_top_ngram(wz['ocr'],5)[:10]\n",
+    "x,y=map(list,zip(*top_n_bigrams))\n",
+    "sns.barplot(x=y,y=x).set(title='top pentagrams WZ uncleaned')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b556bab-da53-4f35-b376-3974aed46d92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_n_bigrams=get_top_ngram(sz['ocr'],5)[:10]\n",
+    "x,y=map(list,zip(*top_n_bigrams))\n",
+    "sns.barplot(x=y,y=x).set(title='top pentagrams SZ uncleaned')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1754a6f5-5b47-4e2d-aad2-19e474d14c02",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39ece2bf-0002-4a68-bd25-4829720da751",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2724cef-c636-4df3-82b8-2ada4e5d095a",
+   "metadata": {},
+   "source": [
+    "<h4>n-grams of cleaned text</h4>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c8b0cc3-919a-4a46-8378-8ab16fadef14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wz_clean = pd.read_csv('wz_clean.tsv', sep = '\\t', encoding='utf-8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ab042e6-37a5-4f67-bd0e-d6505379de77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz_clean = pd.read_csv('sz_clean.tsv', sep = '\\t', encoding='utf-8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fe485e1-65ee-4a92-84a1-de8d9189adc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts_wz_clean = wz_clean['ocr'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cedeb10-fd5a-440b-b843-1d2e40e42931",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts_wz_clean = texts_wz_clean.split()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2901e04-57cf-4cf6-bd54-025ed572801c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "freqdist_wz_clean = Counter(texts_wz_clean)\n",
+    "freqdist_wz_clean.most_common(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e593f70f-d8da-4fa4-af3b-b4741ff35a5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "texts_sz_clean = sz_clean['ocr'].sum()\n",
+    "texts_sz_clean = texts_sz_clean.split()\n",
+    "freqdist_sz_clean = Counter(texts_sz_clean)\n",
+    "freqdist_sz_clean.most_common(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ae1781d-e080-474e-abd1-b2b2f2b5ea4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_wordfr_wz = pd.DataFrame.from_dict(freqdist_wz_clean, orient='index').reset_index()\n",
+    "df_wordfr_wz = df_wordfr_wz.rename(columns={'index':'token', 0:'count'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0075fa11-ae93-408f-9382-acccf57f1c16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_wordfr_sz = pd.DataFrame.from_dict(freqdist_sz_clean, orient='index').reset_index()\n",
+    "df_wordfr_sz = df_wordfr_sz.rename(columns={'index':'token', 0:'count'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6fc2d0e-1e22-4e20-929b-7d8d80ef3c88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_wordfr_wz['freq'] = (df_wordfr_wz['count'] / len(df_wordfr_wz['token'])*1000000 )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43a5f2b2-f6d6-411a-8903-3f50089abaab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_wordfr_sz['freq'] = (df_wordfr_sz['count'] / len(df_wordfr_sz['token'])*1000000 )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "260ea3cf-eab8-4250-af9c-c4bf7b5af254",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_wordfr_wz = df_wordfr_wz.sort_values(by=['count'], ascending = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9531bc3f-61ee-45ff-bf41-df14cbff0867",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_wordfr_sz = df_wordfr_sz.sort_values(by=['count'], ascending = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a652eeef-0aa4-4817-b3d0-44b10073635e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_wordfr_wz.head(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3942e758-54ad-4ed2-9721-3693924278dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_wordfr_sz.head(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3846e87-8bad-488e-832d-fdffed98937a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ead06da-112e-4463-96b3-da85080dfac7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9df01cb4-cc53-42e7-9d13-6187539946b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_n_bigrams=get_top_ngram(wz_clean['ocr'],2)[:10]\n",
+    "x,y=map(list,zip(*top_n_bigrams))\n",
+    "sns.barplot(x=y,y=x).set(title='top bigrams WZ cleaned')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57c0e9fc-6ebe-4c23-99ef-ca916ffb097e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_n_bigrams=get_top_ngram(sz_clean['ocr'],2)[:10]\n",
+    "x,y=map(list,zip(*top_n_bigrams))\n",
+    "sns.barplot(x=y,y=x).set(title='top bigrams SZ cleaned')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f158d9e-f423-4636-a770-bfc1766b9b36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_n_bigrams=get_top_ngram(wz_clean['ocr'],5)[:10]\n",
+    "x,y=map(list,zip(*top_n_bigrams))\n",
+    "sns.barplot(x=y,y=x).set(title='top pentagrams WZ cleaned')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "430bb276-c900-43b8-bd1a-fb6bd115793f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_n_bigrams=get_top_ngram(sz_clean['ocr'],5)[:10]\n",
+    "x,y=map(list,zip(*top_n_bigrams))\n",
+    "sns.barplot(x=y,y=x).set(title='top pentagrams SZ cleaned')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}