raw_data_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "from tqdm import tqdm\n",
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# import plotly.express as px\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(82076, 6)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/raw_data/combined.csv',delimiter=\",\")\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                0\n",
       "country           0\n",
       "name              0\n",
       "NACE           5997\n",
       "description    1822\n",
       "text              4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Na-values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n",
      "['SE' 'DK' 'FI' 'NO' 'US' 'JP' 'CN' 'AU' 'GB' 'BM' 'CA' 'AT' 'LU' 'IN'\n",
      " 'FR' 'IT' 'SG' 'JE' 'ID' 'TR' 'PL' 'TH' 'BR' 'DE' 'IL' 'HK' 'CH' 'NZ'\n",
      " 'MX' 'BE' 'RU' 'IM' 'PT' 'KR' 'MY' 'NL' 'ES' 'KY' 'EE' 'PA' 'IE' 'ZA'\n",
      " 'PH' 'VG' 'GR' 'HU' 'LT' 'GG']\n"
     ]
    }
   ],
   "source": [
    "# Different countries\n",
    "print(len(df['country'].unique()))\n",
    "print(df['country'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1130"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['id']) - len(df['id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9460"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df) - len(df['text'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11737"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df) - len(df['description'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pivot = df['country'].astype(str).value_counts()\n",
    "# print(sum(pivot.values))\n",
    "# print(sum(pivot.values[:4]/sum(pivot.values)))\n",
    "# print(pivot)\n",
    "\n",
    "a = sorted(zip(pivot.values,pivot.index),reverse=True)\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(figsize=(18,5))\n",
    "plt.bar(list(zip(*a))[1],list(zip(*a))[0])\n",
    "plt.title(\"Absolute number of documents per country\")\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)\n",
    "\n",
    "ax.yaxis.grid()\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "plt.savefig('graphs/country-pivot.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outside / in europe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9891563916370193\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "f = open('util/continent_codes.json')\n",
    "continent_codes = json.load(f)\n",
    "\n",
    "continents = [continent_codes[c] for c in df['country']]\n",
    "df['continent'] = continents\n",
    "\n",
    "pivot = df['continent'].astype(str).value_counts()\n",
    "\n",
    "print(pivot.values[0]/sum(pivot.values))\n",
    "\n",
    "a = sorted(zip(pivot.values,pivot.index),reverse=True)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(18,5))\n",
    "plt.bar(list(zip(*a))[1],list(zip(*a))[0])\n",
    "plt.title(\"Absolute number of documents per continent\")\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)\n",
    "\n",
    "ax.yaxis.grid()\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "plt.savefig('graphs/continent-pivot.svg')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize word and character count of the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wordCount(text, max_words = np.inf):\n",
    "    num = len(str(text).split())\n",
    "    return max_words if num > max_words else num\n",
    "\n",
    "def charCount(text, max_chars = np.inf):\n",
    "    num = len(str(text))\n",
    "    return max_chars if num > max_chars else num\n",
    "\n",
    "def countWordsAndCharacters(dataf, col_name, max_words = np.inf, max_chars = np.inf):\n",
    "    wc = []\n",
    "    cc = []\n",
    "    for _, row in dataf.iterrows():\n",
    "        wc.append(wordCount(row[col_name], max_words))\n",
    "        cc.append(charCount(row[col_name], max_chars))\n",
    "    return (wc,cc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num words has mean: 22.94 and median: 19 with std 34.06\n",
      "Num chars has mean: 159.38 and median: 129 with std 232.72\n",
      "Words - min: 1, max:3170\n",
      "Chars - min: 1, max:24232\n"
     ]
    }
   ],
   "source": [
    "# Get median and mean of the number of words and characters\n",
    "wc,cc = countWordsAndCharacters(df, 'description')\n",
    "\n",
    "print(\"DESCRIPTION\")\n",
    "print(\"Num words has mean: {:.2f} and median: {:.0f} with std {:.2f}\".format(np.mean(wc),np.median(wc),np.std(wc)))\n",
    "print(\"Num chars has mean: {:.2f} and median: {:.0f} with std {:.2f}\".format(np.mean(cc),np.median(cc),np.std(cc)))\n",
    "print(f\"Words - min: {min(wc)}, max:{max(wc)}\")\n",
    "print(f\"Chars - min: {min(cc)}, max:{max(cc)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEXT\n",
      "Num words has mean: 575.76 and median: 22 with std 1662.01\n",
      "Num chars has mean: 3786.99 and median: 150 with std 10696.98\n",
      "Words - min: 1, max:174567\n",
      "Chars - min: 1, max:1027535\n"
     ]
    }
   ],
   "source": [
    "# Get median and mean of the number of words and characters\n",
    "wc,cc = countWordsAndCharacters(df, 'text')\n",
    "\n",
    "print(\"TEXT\")\n",
    "print(\"Num words has mean: {:.2f} and median: {:.0f} with std {:.2f}\".format(np.mean(wc),np.median(wc),np.std(wc)))\n",
    "print(\"Num chars has mean: {:.2f} and median: {:.0f} with std {:.2f}\".format(np.mean(cc),np.median(cc),np.std(cc)))\n",
    "print(f\"Words - min: {min(wc)}, max:{max(wc)}\")\n",
    "print(f\"Chars - min: {min(cc)}, max:{max(cc)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# With no max cap\n",
    "wc,cc = countWordsAndCharacters(df, 'description')\n",
    "\n",
    "fig, axs = plt.subplots(1,2,figsize=(9,4))\n",
    "axs[0].hist(wc, bins=50)\n",
    "axs[1].hist(cc, bins=50)\n",
    "\n",
    "fig.suptitle(\"Histogram over length of descriptions\",fontweight='semibold')\n",
    "axs[0].set_title('Word count')\n",
    "axs[1].set_title('Character count')\n",
    "\n",
    "axs[0].yaxis.grid()\n",
    "axs[0].set_axisbelow(True)\n",
    "axs[1].yaxis.grid()\n",
    "axs[1].set_axisbelow(True)\n",
    "\n",
    "plt.savefig('graphs/description-length-bar.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# With a max word cap of 100 and max chars at 1000\n",
    "wc,cc = countWordsAndCharacters(dataf=df, col_name='description', max_words=22.94+2*34, max_chars=159.38+2*232.72)\n",
    "\n",
    "fig, axs = plt.subplots(1,2,figsize=(7,4))\n",
    "axs[0].hist(wc, bins=50)\n",
    "axs[1].hist(cc, bins=50)\n",
    "\n",
    "fig.suptitle(\"Histogram over length of capped descriptions\",fontweight='semibold')\n",
    "axs[0].set_title('Word count')\n",
    "axs[1].set_title('Character count')\n",
    "\n",
    "axs[0].yaxis.grid()\n",
    "axs[0].set_axisbelow(True)\n",
    "axs[1].yaxis.grid()\n",
    "axs[1].set_axisbelow(True)\n",
    "\n",
    "plt.savefig('graphs/description-length-capped-bar.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many words will be removed because they have more than 100 words\n",
    "wc = np.array(wc)\n",
    "len(wc[(22.94+2*34) < wc])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NACE codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pj6d9+/YFZcuXL6dbt24kJydzySWXsG3btiLbfPrpp9SrV48HHngAgF27dnHRRRfRpk0bkpKSuO222wrq7tmzh6uuuopWrVrRtWtXsrKyKqVdIlK9lJvMzay5mS0yszVmtsrMhoflDc1sgZmtD38eX2ib281sg5mtNbNehco7m9nKcN2jZmZh+VFm9vew/D0zSzwMbRXZz4ABA5g3b16RsiFDhjBhwgRWrlxJ3759uf/++4usHzlyJBdccEGRstGjR/Pxxx/z4YcfsnjxYl5//XUApk2bxvHHH8+GDRsYOXIkv/vd7w5vg0SkWqrImXkeMMrd2wJnALeYWTvgNuBNd28NvBk+J1zXH0gCegOPmVnNcF9TgBuB1uHSOywfDHzt7q2Ah4D7DkHbRMrVvXt3GjZsWKRs7dq1dO/eHYDzzjuPF154oWDdyy+/TMuWLUlKSiooq1OnDueeey4AtWvXplOnTuTk5AAwe/ZsbrjhBgD69evHm2++ibsf1jaJSPVTbjJ39y3u/kH4eDuwBmgGXAo8FVZ7CvhF+PhSYKa773H3jcAGoIuZNQEauPs7HrybPV1sm/x9PQ/0zD9rF6ls7du3Z86cOQDMmjWL7OxsAHbu3Ml9993H3XffXeq233zzDa+88go9e/YEYNOmTTRv3hyAuLg4jj32WL788svD3AIRqW4OaMw87P4+HXgPaOzuWyBI+EB8WK0ZkF1os5ywrFn4uHh5kW3cPQ/4FjjhQGITOVSmT5/O5MmT6dy5M9u3b6d27doA3H333YwcOZJ69eqVuF1eXh5paWkMGzaMli1bApR4Fq7PqSJyqMVVtKKZ1QNeAEa4+7Yy3pBKWuFllJe1TfEYbiTopqdx48ZkZGSUE7VI+T777DN27txZ5O/p97//PQDZ2dnEx8eTkZHB/PnzeeaZZxg2bBg7duygRo0aZGdn07dvXwDuu+8+jjnmGFJSUgr2VadOHWbPnk1SUhL79u1j69atrFixQgn9MND7gVRnFUrmZlaLIJE/6+4vhsWfm1kTd98SdqHnhuU5QPNCmycAm8PyhBLKC2+TY2ZxwLHAV8XjcPepwFSA1NRU79GjR0XCFylTVlYWdevWJf/vKTc3l/j4eL7//nsGDBjArbfeSo8ePVixYkXBNmPGjKFevXqMHj0agDvvvJM6deowa9YsatT4ocNrwIABrFy5kltuuYWZM2fSq1evgvH1I8a812IdAQB6P5DqrCKz2Q2YBqxx94mFVs0Bbggf3wDMLlTeP5yhfjLBRLclYVf8djM7I9zn9cW2yd9XP2Cha5aQVIK0tDS6devG2rVrSUhIYNq0aaSnp3PqqafSpk0bmjZtysCBA8vcR05ODuPGjWP16tV06tSJlJQU/vrXvwIwePBgvvzyS1q1asXEiROZMGFCZTRLRKoZKy9nmtnZwL+AlcD3YfHvCcbNnwNOAj4FrnD3r8Jt7gAGEcyEH+Hur4flqcCTwDHA68Cv3d3N7GhgBsF4/FdAf3f/pKy4UlNTfenSpQfaXhE5xBJvqxpn5lkTLop1CCKHlZktc/fUktaV283u7m9T8pg2QM9SthkHjCuhfCnQvoTy3cAV5cUicihVhSSkBCQih4KuACciIhJxSuYiIiIRp2QuIiIScUrmIiIiEadkLiIiEnFK5iIiIhGnZC4iIhJxSuYiIiIRp2QuIiIScUrmIiIiEadkLiIiEnFK5iIiIhGnZC4iIhJxSuYiIiIRp2QuIiIScUrmIiIiEadkLiIiEnFK5iIiIhGnZC4iIhJxSuYiIiIRp2QuIiIScUrmIiIiEadkLiIiEnFK5iIiIhGnZC4iIhJxSuYiIiIRp2QuIiIScUrmIiIiEadkLiIiEnFK5iIiIhGnZC4iIhJxSuYiIiIRp2QuIiISceUmczObbma5ZvZRobIxZrbJzDLD5cJC6243sw1mttbMehUq72xmK8N1j5qZheVHmdnfw/L3zCzxELdRRETkiFaRM/Mngd4llD/k7inhMhfAzNoB/YGkcJvHzKxmWH8KcCPQOlzy9zkY+NrdWwEPAfcdZFtERESqpXKTubu/BXxVwf1dCsx09z3uvhHYAHQxsyZAA3d/x90deBr4RaFtngofPw/0zD9rFxERkfLF/Yhth5rZ9cBSYJS7fw00A94tVCcnLPsufFy8nPBnNoC755nZt8AJwNbiL2hmNxKc3dO4cWMyMjJ+RPgisae/4UNHx1Kqs4NN5lOAsYCHPx8EBgElnVF7GeWUs65ooftUYCpAamqq9+jR44CCFili3muxjoAj4m+4ChxHOEKOpchBOqjZ7O7+ubvvc/fvgSeALuGqHKB5oaoJwOawPKGE8iLbmFkccCwV79YXERGp9g4qmYdj4Pn6Avkz3ecA/cMZ6icTTHRb4u5bgO1mdkY4Hn49MLvQNjeEj/sBC8NxdREREamAcrvZzSwd6AE0MrMc4G6gh5mlEHSHZwG/AnD3VWb2HLAayANucfd94a5uJpgZfwzwergATANmmNkGgjPy/oegXSIiItVGRWazp7l7E3ev5e4J7j7N3a9z92R37+DufcIz7/z649z9FHc/zd1fL1S+1N3bh+uG5p99u/tud7/C3Vu5exd3/+TwNFVEBAYNGkR8fDzt27cvKLvrrrvo0KEDKSkpnH/++WzeHIwCLliwgM6dO5OcnEznzp1ZuHDhfvvr06dPkX3t2bOHq666ilatWtG1a1eysrIOe5tEdAU4EalWBgwYwLx584qU3XrrraxYsYLMzEwuvvhi7rnnHgAaNWrEK6+8wsqVK3nqqae47rrrimz34osvUq9evSJl06ZN4/jjj2fDhg2MHDmS3/3ud4e3QSIomYtINdO9e3caNmxYpKxBgwYFj3fu3En+pS5OP/10mjZtCkBSUhK7d+9mz549AOzYsYOJEydy5513FtnX7NmzueGGYBpQv379ePPNN9E0IDncfsz3zEVEjhh33HEHTz/9NMceeyyLFi3ab/0LL7zA6aefzlFHHQUEXfOjRo2iTp06Rept2rSJ5s2DL/XExcVx7LHH8uWXX9KoUaPD3wiptnRmLiICjBs3juzsbK655homTZpUZN2qVav43e9+x+OPPw5AZmYmGzZsoG/fvvvtp6SzcF3UUg43JXMRkUKuvvpqXnjhhYLnOTk59O3bl6effppTTjkFgHfeeYdly5aRmJjI2Wefzbp16wouWpOQkEB2djYAeXl5fPvtt/t164scakrmIlLtrV+/vuDxnDlzaNOmDQDffPMNF110EePHj+ess84qqHPzzTezefNmsrKyePvttzn11FMLLifbp08fnnoquN3E888/z89+9jOdmcthpzFzEalW0tLSyMjIYOvWrSQkJPCHP/yBuXPnsnbtWmrUqEGLFi34y1/+AsCkSZPYsGEDY8eOZezYsQDMnz+f+Pj4Uvc/ePBgrrvuOlq1akXDhg2ZOXNmpbRLqjeL6izL1NRUX7p0aazDkAhLvC321xTPmnBRrEP40arCcYQj41iKlMXMlrl7aknrdGYuItVCVfjQoQ8ccrhozFxERCTilMxFREQiTslcREQk4pTMRUREIk7JXEREJOKUzEVERCJOyVxERCTilMxFREQiTslcREQk4pTMRUREIk7JXEREJOKUzEVERCJOyVxERCTilMxFREQiTslcREQk4pTMRUREIk7JXEREJOKUzEVERCJOyVxERCTilMxFREQiTslcREQk4pTMRUREIk7JXEREJOLKTeZmNt3Mcs3so0JlDc1sgZmtD38eX2jd7Wa2wczWmlmvQuWdzWxluO5RM7Ow/Cgz+3tY/p6ZJR7iNoqIiBzRKnJm/iTQu1jZbcCb7t4aeDN8jpm1A/oDSeE2j5lZzXCbKcCNQOtwyd/nYOBrd28FPATcd7CNERERqY7KTebu/hbwVbHiS4GnwsdPAb8oVD7T3fe4+0ZgA9DFzJoADdz9HXd34Oli2+Tv63mgZ/5Zu4iIiJQv7iC3a+zuWwDcfYuZxYflzYB3C9XLCcu+Cx8XL8/fJjvcV56ZfQucAGwt/qJmdiPB2T2NGzcmIyPjIMMXqRr0N3zoROFYRiFGiaaDTealKemM2ssoL2ub/QvdpwJTAVJTU71Hjx4HEaJIaN5rsY6AI+JvuAocR6jAsawCcR4Rv2+pkg52NvvnYdc54c/csDwHaF6oXgKwOSxPKKG8yDZmFgccy/7d+iIiIlKKg03mc4Abwsc3ALMLlfcPZ6ifTDDRbUnYJb/dzM4Ix8OvL7ZN/r76AQvDcXURERGpgHK72c0sHegBNDKzHOBuYALwnJkNBj4FrgBw91Vm9hywGsgDbnH3feGubiaYGX8M8Hq4AEwDZpjZBoIz8v6HpGUiIiLVRLnJ3N3TSlnVs5T644BxJZQvBdqXUL6b8MOAiIiIHDhdAU5ERCTilMxFREQiTslcREQk4pTMRUREIk7JXEREJOKUzEVERCJOyVxERCTilMxFREQiTslcREQk4pTMRUREIk7JXEREJOKUzEVERCJOyVxERCTilMxFREQiTslcREQk4pTMRUREIk7JXEREJOKUzEVERCJOyVxERKqNxMREkpOTSUlJITU1FYDly5fTrVs3kpOTueSSS9i2bRsAe/fuZeDAgSQnJ9OxY0cyMjIA2L59OykpKQVLo0aNGDFiRIxaFIiL6auLiIhUskWLFtGoUaOC50OGDOGBBx7gpz/9KdOnT+f+++9n7NixPPHEEwCsXLmS3NxcLrjgAt5//33q169PZmZmwfadO3fmsssuq+xmFKEzcxERqdbWrl1L9+7dATjvvPN44YUXAFi9ejU9e/YEID4+nuOOO46lS5cW2Xb9+vXk5uZyzjnnVG7QxSiZi4hItWFmnH/++XTu3JmpU6cC0L59e+bMmQPArFmzyM7OBqBjx47Mnj2bvLw8Nm7cyLJlywrW5UtPT+eqq67CzCq3IcWom11ERKqNxYsX07RpU3JzcznvvPNo06YN06dPZ9iwYdxzzz306dOH2rVrAzBo0CDWrFlDamoqLVq04MwzzyQurmjanDlzJjNmzIhFU4pQMhcRkWqjadOmQNBt3rdvX5YsWcLo0aOZP38+AOvWreO1114DIC4ujoceeqhg2zPPPJPWrVsXPF++fDl5eXl07ty5EltQMnWzi4hItbBz5062b99e8Hj+/Pm0b9+e3NxcAL7//nvuvfdebrrpJgB27drFzp07AViwYAFxcXG0a9euYH/p6emkpaVVcitKpjNzERGpFj7//HP69u0LQF5eHldffTW9e/fmkUceYfLkyQBcdtllDBw4EIDc3Fx69epFjRo1aNas2X7d6c899xxz586t3EaUQslcRESqhZYtW7J8+fL9yocPH87w4cP3K09MTGTt2rWl7u+TTz45pPH9GErmIiJV3L59+0hNTaVZs2a8+uqrLF++nJtuuokdO3aQmJjIs88+S4MGDViyZAk33ngjAO7OmDFjCs5Ee/fuzZYtW8jLy+Occ85h8uTJ1KxZM5bNOqwSb3st1iGQNeGiSnstjZmLiFRxjzzyCG3bti14PmTIECZMmMDKlSvp27cv999/PxB8xWrp0qVkZmYyb948fvWrX5GXlwcEXcLLly/no48+4osvvmDWrFkxaYscHkrmIiJVWE5ODq+99hpDhgwpKCvtIid16tQp+OrU7t27i3z3uUGDBkAwVrx3796Yfy9aDi0lc5EI2rdvH6effjoXX3xxQdmf//xnTjvtNJKSkvjtb38LwJdffsm5555LvXr1GDp0aIn76tOnD+3bt6+UuOXAjRgxgj/96U/UqPHD23VpFzkBeO+990hKSiI5OZm//OUvRb4X3atXL+Lj46lfvz79+vWrvEbIYadkLhJBxbtdFy1axOzZs1mxYgWrVq1i9OjRABx99NGMHTuWBx54oMT9vPjii9SrV69SYpYD9+qrrxIfH7/f95inT5/O5MmT6dy5M9u3by+4yAlA165dWbVqFe+//z7jx49n9+7dBev+8Y9/sGXLFvbs2cPChQsrrR1y+P2oZG5mWWa20swyzWxpWNbQzBaY2frw5/GF6t9uZhvMbK2Z9SpU3jnczwYze9TU/yNSqpK6XadMmcJtt93GUUcdBQQXxACoW7cuZ599NkcfffR++9mxYwcTJ07kzjvvrJzA5YAtXryYOXPmkJiYSP/+/Vm4cCHXXnstbdq0Yf78+Sxbtoy0tDROOeWU/bZt27YtdevW5aOPPipSfvTRR9OnTx9mz55dWc2QSnAozszPdfcUd08Nn98GvOnurYE3w+eYWTugP5AE9AYeM7P8qZRTgBuB1uHS+xDEJXJEKqnbdd26dfzrX/+ia9eu/PSnP+X9998vdz933XUXo0aNok6dOoczXPkRxo8fT05ODllZWcycOZOf/exnPPPMM6Ve5GTjxo0FE97+85//sHbtWhITE9mxYwdbtmwBgjHzuXPn0qZNm9g0Sg6Lw9HNfinwVPj4KeAXhcpnuvsed98IbAC6mFkToIG7v+PuDjxdaBsRKaS0bte8vDy+/vpr3n33Xe6//36uvPJKgn+nkmVmZrJhw4aCry1JtKSnp3PqqafSpk0bmjZtWnCRk7fffpuOHTuSkpJC3759eeyxx2jUqBE7d+6kT58+dOjQgY4dOxIfH1/wAUCODD/2e+YOzDczBx5396lAY3ffAuDuW8wsPqzbDHi30LY5Ydl34ePi5SJSTH6369y5c9m9ezfbtm3j2muvJSEhgcsuuwwzo0uXLtSoUYOtW7dy4oknlrifd955h2XLlpGYmEheXh65ubn06NGDjIyMym2QVFiPHj3o0aMHUPpFTq677jquu+66/cobN25cod4aia4fm8zPcvfNYcJeYGYfl1G3pHFwL6N8/x2Y3UjQHU/jxo31xiORd6B/w7169aJXr2C6SWZmJn//+98ZMmQIc+bMYfr06QBkZ2ezfft2Pvroo4KvH3388cds2rSp4PXatm3L3/72NwA+++wzbr/9dsaMGRPp/6koxF5ejAPm7aycQMrwZO+6sQ7hiFGZf5M/Kpm7++bwZ66ZvQR0AT43sybhWXkTIDesngM0L7R5ArA5LE8oobyk15sKTAVITU31/E+pIgdlXuyvEPVj/4bfeOMNevTowZlnnsmgQYMYOnQotWvXZubMmZx77rlAcEnKbdu2sXfvXt5//33mz59f5GYRWVlZ1K1b9+BjqQLHESpwLKtAnEdEjFFRzY7lQSdzM6sL1HD37eHj84F7gDnADcCE8Gf+lMk5wN/MbCLQlGCi2xJ332dm283sDOA94Hrgzwcbl0h1UbjbtXbt2jzzzDMl1svKyipzP4mJifvNeBaRaPkxZ+aNgZfCbrw44G/uPs/M3geeM7PBwKfAFQDuvsrMngNWA3nALe6+L9zXzcCTwDHA6+EiUu1Vt+tLi8jBOehk7u6fAB1LKP8S6FnKNuOAcSWULwV0CSoREZGDoCvAiYiIRJySuYiISMQpmYuIiEScknnEZWdnc+6559K2bVuSkpJ45JFHgOA7yGeccQYpKSmkpqayZMkSoPS7aO3atYuLLrqINm3akJSUxG233RaT9oiIyIFTMo+4uLg4HnzwQdasWcO7777L5MmTWb16Nb/97W+5++67yczM5J577im4JWZZd9EaPXo0H3/8MR9++CGLFy/m9df1pQIRkShQMo+4Jk2a0KlTJwDq169P27Zt2bRpE2bGtm3bAPj2229p2rQpUPpdtOrUqVNwkZHatWvTqVMncnJyEBGRqk/J/AiSlZXFhx9+SNeuXXn44Ye59dZbad68OaNHj2b8+PEV3s8333zDK6+8Qs+eJX7DUESkiNKG+/I98MADmBlbt24FYO/evQwcOJDk5GQ6duxY5LKn6enpJCcn06FDB3r37l2wjZRNyfwIsWPHDi6//HIefvhhGjRowJQpU3jooYfIzs7moYceYvDgwRXaT15eHmlpaQwbNoyWLVse5qhF5EhQ2nAfBIl+wYIFnHTSSQX1n3jiCQBWrlzJggULGDVqFN9//z15eXkMHz6cRYsWsWLFCjp06MCkSZNi0qaoUTI/Anz33XdcfvnlXHPNNVx22WUAPPXUUwWPr7jiioIJcOW58cYbad26NSNGjDhc4YrIEaa04T6AkSNH8qc//angpj8Aq1evLuj5i4+P57jjjmPp0qW4O+7Ozp07cXe2bdtWMEQoZVMyjzh3Z/DgwbRt25bf/OY3BeVNmzbln//8JwALFy6kdevW5e7rzjvv5Ntvv+Xhhx8+XOGKyBGu8HDfnDlzaNasGR07Fr1YaMeOHZk9ezZ5eXls3LiRZcuWkZ2dTa1atZgyZQrJyck0bdqU1atXV7hXsbr7sbdAlRhbvHgxM2bMIDk5mZSUFAD++Mc/8sQTTzB8+HDy8vI4+uijmTp1asE2he+i9fLLLzN//nwaNGjAuHHjaNOmTcEn7KFDhzJkyJBYNEtEIqjwcF9cXBzjxo1j/vz5+9UbNGgQa9asITU1lRYtWnDmmWcSFxfHd999x5QpU/jwww9p2bIlv/71rxk/fjx33nlnDFoTLUrmZRg0aBCvvvoq8fHxBXeVWr58OTfddBM7duwgMTGRZ599lgYNGgAwfvx4pk2bRs2aNXn00UcL7ju9d+9ehg4dSkZGBjVq1GDcuHFcfvnlhyTGs88+G/cSb//OsmXLSiwv7S5ape1HRKQ8xYf7Vq5cycaNGwvOynNycujUqRNLlizhJz/5CQ899FDBtmeeeSatW7cmMzMTgFNOOQWAK6+8kgkTJlR6W6JI3exlGDBgAPPmzStSNmTIECZMmMDKlSvp27cv999/PxCMAc2cOZNVq1Yxb948/ud//od9+4Kbwo0bN474+HjWrVvH6tWr+elPf1rpbREROVxKGu5LTk4mNzeXrKwssrKySEhI4IMPPuAnP/kJu3btYufOnQAsWLCAuLg42rVrR7NmzVi9ejVffPFFwbq2bdvGrF1RojPzMnTv3n2/s9i1a9fSvXt3AM477zx69erF2LFjmT17Nv379+eoo47i5JNPplWrVixZsoRu3boxffp0Pv74YwBq1KhBo0aNDjom3RJTRKqa0ob7LrzwwhLr5+bm0qtXL2rUqEGzZs2YMWMGEMz1ufvuu+nevTu1atWiRYsWPPnkk5XUimjTmfkBat++PXPmzAFg1qxZZGdnA7Bp0yaaN29eUC8hIYFNmzbxzTffAHDXXXfRqVMnrrjiCj7//PNKjzvWBg0aRHx8PO3b/3Cn2zFjxtCsWTNSUlJISUlh7ty5QNBdd8MNN5CcnEzbtm2LfEd+2bJlJCcn06pVK4YNG6ahAZEqIH+4b8WKFWRmZpKZmblfIs/Kyio4kUlMTGTt2rWsWbOGN954gxYtWhTUu+mmm1izZg0rVqzglVde4YQTTqjUtkSVzswP0PTp0xk2bBj33HMPffr0oXbt2kDJ481mRl5eHjk5OZx11llMnDiRiRMnMnr06IJPotXFgAEDGDp0KNdff32R8pEjRzJ69OgiZbNmzWLPnj2sXLmSXbt20a5dO9LS0khMTOTmm29m6tSpnHHGGVx44YXMmzePCy64oDKbIlLtqYew6tGZ+QFq06YN8+fPZ9myZaSlpRVM1EhISCg4S4dgskfTpk054YQTqFOnDn379gWC73x/8MEHMYk9lrp3707Dhg0rVNfM2LlzJ3l5efz3v/+ldu3aNGjQgC1btrBt2za6deuGmXH99dfz8ssvH97ARUQiQMn8AOXm5gLw/fffc++993LTTTcB0KdPH2bOnMmePXvYuHEj69evp0uXLpgZl1xyScHlCt98803atWsXq/CrnEmTJtGhQwcGDRrE119/DUC/fv2oW7cuTZo04aSTTmL06NE0bNiQTZs2kZCQULBt/lCGiEh1p2RehrS0NLp168batWtJSEhg2rRppKenc+qpp9KmTRuaNm3KwIEDAUhKSuLKK6+kXbt29O7dm8mTJ1OzZk0A7rvvPsaMGUOHDh2YMWMGDz74YCybVWXcfPPN/Pvf/yYzM5MmTZowatQoAJYsWULNmjXZvHkzGzdu5MEHH+STTz4pdShDRKS605h5GdLT00ssHz58eInld9xxB3fcccd+5S1atOCtt946pLEdCRo3blzw+Je//CUXX3wxAH/729/o3bs3tWrVIj4+nrPOOoulS5dyzjnnFLmTW/5QhohIdadkHtKEjsq3ZcsWmjRpAsBLL71UMNP9pJNOYuHChVx77bXs2rWLd999lxEjRtCkSRPq16/Pu+++S9euXXn66af59a9/HcsmiIhUCUrmUinS0tLIyMhg69atJCQk8Ic//IGMjAwyMzMxMxITE3n88ccBuOWWWxg4cCDt27fH3Rk4cCAdOnQAYMqUKQwYMID//ve/XHDBBZrJLiKCkrlUkpKGLEq7gUK9evWYNWtWietSU1MLLq0rIiIBJXM55DRkISJSuTSbXUREJOKUzEVERCJOyVxERCTilMxFREQiTslcREQk4pTMRUREIk7JXEREJOKUzEVERCJOyVxERCTiqkwyN7PeZrbWzDaY2W2xjkdERCQqqkQyN7OawGTgAqAdkGZm7WIblYiISDRUiWQOdAE2uPsn7r4XmAlcGuOYREREIsHcPdYxYGb9gN7uPiR8fh3Q1d2HFqt3I3Bj+PQ0YG2lBlq+RsDWWAdRjijECNGIMwoxQjTiVIyHThTijEKMUPXibOHuJ5a0oqrcNc1KKNvvU4a7TwWmHv5wDo6ZLXX31FjHUZYoxAjRiDMKMUI04lSMh04U4oxCjBCdOKHqdLPnAM0LPU8ANscoFhERkUipKsn8faC1mZ1sZrWB/sCcGMckIiISCVWim93d88xsKPAPoCYw3d1XxTisg1FlhwAKiUKMEI04oxAjRCNOxXjoRCHOKMQI0YmzakyAExERkYNXVbrZRURE5CApmYuIiESckvkhYGbTzSzXzD6KdSylMbPmZrbIzNaY2SozGx7rmIozs6PNbImZLQ9j/EOsYyqNmdU0sw/N7NVYx1IaM8sys5VmlmlmS2MdT2nM7Dgze97MPg7/PrvFOqbCzOy08BjmL9vMbESs4yrOzEaG/zcfmVm6mR0d65hKYmbDwxhXxfo4mpmb2YOFno82szGFnjcxs/mFno80s91mdmwlh1ouJfND40mgd6yDKEceMMrd2wJnALdUwUvm7gF+5u4dgRSgt5mdEduQSjUcWBPrICrgXHdPqeLflX0EmOfubYCOVLHj6u5rw2OYAnQGdgEvxTaqosysGTAMSHX39gQTifvHNqr9mVl74JcEV/3sCFxsZq1jGNIe4DIza1TK+t4EE7PzpRF8+6rv4Q7sQCmZHwLu/hbwVazjKIu7b3H3D8LH2wneMJvFNqqiPLAjfForXKrcDE0zSwAuAv4a61iizswaAN2BaQDuvtfdv4lpUGXrCfzb3f8T60BKEAccY2ZxQB2q5rU62gLvuvsud88D/klsE2MewYz1kaWs7w28DmBmpwD1gDsJknqVomReDZlZInA68F6MQ9lP2H2dCeQCC9y9ysUIPAz8Fvg+xnGUx4H5ZrYsvBRyVdQS+AL4v3DY4q9mVjfWQZWhP5Ae6yCKc/dNwAPAp8AW4Ft3n1/2VjHxEdDdzE4wszrAhRS9YFgsTAauKd51Ht4A7DR3Xx0WpRH87v8FnGZm8ZUbZtmUzKsZM6sHvACMcPdtsY6nOHffF3ZnJgBdwm65KsPMLgZy3X1ZrGOpgLPcvRPB3QhvMbPusQ6oBHFAJ2CKu58O7ASq5C2Qwwta9QFmxTqW4szseIKbU50MNAXqmtm1sY1qf+6+BrgPWADMA5YTnB3HMqZtwNMEwxSFdaXoCU9/YKa7fw+8CFxRORFWjJJ5NWJmtQgS+bPu/mKs4ylL2NWaQdWbi3AW0MfMsgju7vczM3smtiGVzN03hz9zCcZ4u8Q2ohLlADmFemCeJ0juVdEFwAfu/nmsAynBz4GN7v6Fu39HkGzOjHFMJXL3ae7eyd27EwxPro91TAS9bYOBwr1CFxB84MDMOgCtgQXh/35/qlhXu5J5NWFmRjAuucbdJ8Y6npKY2Ylmdlz4+BiCN6iPYxpUMe5+u7snuHsiwT/0QnevcmdAZlbXzOrnPwbOJ+jirFLc/TMg28xOC4t6AqvL2CSW8rtZq6JPgTPMrE74v96TKjaRMF9+97SZnQRcRhU4pu7+FfAcQULP1xN4M3ycBoxx98RwaQo0M7MWlRxqqZTMDwEzSwfeIRhHyTGzweVtEwNnAdcRnEnmf8XmwlgHVUwTYJGZrSCYMbrA3avsV7+quMbA22a2HFgCvObu82IcU2l+DTwb/t5TgD/GNpz9heO75xGc8VY5Yc/G88AHwEqC9/aqeinSF8xsNfAKcIu7fx3rgEIPEtzyFDM7EdhdaCiyP/t/g+ElqtA3BnQ5VxERkULC+QYJ7j4h1rFUlJK5iIhIxKmbXUREJOKUzEVERCJOyVxERCTilMxFREQiTslcREQk4pTMRUREIu7/BwrH1YDOw6XKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get how many companies have the same first number of NACE code, n=NaN\n",
    "pivot = df['NACE'].astype(str).str[0].value_counts()\n",
    "#print(pivot)\n",
    "a = sorted(zip(pivot.index,pivot.values))\n",
    "a[-1] = ('N/A',a[-1][1])\n",
    "\n",
    "f, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "\n",
    "ax.bar(list(zip(*a))[0],list(zip(*a))[1])\n",
    "ax.yaxis.grid()\n",
    "ax.set_axisbelow(True)\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)\n",
    "\n",
    "plt.title(\"Counts of first NACE code digit\")\n",
    "plt.savefig('graphs/nace-pivot.svg')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get how many companies have the same first TWO numbers of NACE code, n=NaN\n",
    "pivot = df['NACE'].astype(str).str[:2].value_counts()\n",
    "#print(pivot)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(20,5))\n",
    "a = sorted(zip(pivot.index,pivot.values))\n",
    "plt.bar(list(zip(*a))[0],list(zip(*a))[1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{3: 5997, 5: 1355, 6: 74724}\n"
     ]
    }
   ],
   "source": [
    "# Get how many companies have the same number of digits in their NACE code\n",
    "pivot = list(df['NACE'].astype(str).str.len())\n",
    "\n",
    "keys = set(pivot)\n",
    "unique = dict(zip(keys, [0]*len(keys)))\n",
    "\n",
    "for i in pivot:\n",
    "    unique[i] += 1\n",
    "\n",
    "# Nan values have length 3, others are on the format: '7022.0', '210.0'\n",
    "print(unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.unique(df['NACE'].tolist())\n",
    "x = x[~np.isnan(x)]\n",
    "x = np.unique(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "635"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(69204, 7)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/processed_data/cleaned.csv',delimiter=\",\")\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "98e209c0c079eda3a7c6693f6d47ddaebb956ef4ec9d4998363437f41a2e508b"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}