create-controls-df.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "msg_df = pd.read_csv('data/freifahren.tsv', encoding='utf-16', sep='\\t', \n",
    "                     header=None, \n",
    "                     names=['timestamp', 'username', 'forward', 'type', 'text'])[['timestamp', 'type', 'text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "msg_df = msg_df[msg_df.type != '<call>']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>type</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>49291</td>\n",
       "      <td>49291</td>\n",
       "      <td>49291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>48479</td>\n",
       "      <td>2</td>\n",
       "      <td>43688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>26.09.2018 21:30:54</td>\n",
       "      <td>&lt;msg&gt;</td>\n",
       "      <td>[Photo]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>5</td>\n",
       "      <td>45390</td>\n",
       "      <td>3052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  timestamp   type     text\n",
       "count                 49291  49291    49291\n",
       "unique                48479      2    43688\n",
       "top     26.09.2018 21:30:54  <msg>  [Photo]\n",
       "freq                      5  45390     3052"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msg_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "regexp = re.compile(\"(?:^|\\W)([mMsSuU][\\W]{0,1}[1-9][\\d]*)(?:\\W|$)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "msg_df[\"line\"] = msg_df.text.apply(lambda txt: regexp.findall(txt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]           29082\n",
       "[U8]          2273\n",
       "[U7]          1369\n",
       "[U6]          1110\n",
       "[u8]          1068\n",
       "             ...  \n",
       "[u6, u 7]        1\n",
       "[U8, u7]         1\n",
       "[M5, M4]         1\n",
       "[S42, U7]        1\n",
       "[m49]            1\n",
       "Name: line, Length: 423, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msg_df.line.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "msg_df = msg_df[[type(lines) == list and len(lines) > 0 for lines in msg_df.line]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/coding-2019/freifahren-reloaded/myvenv/lib/python3.7/site-packages/pandas/core/generic.py:5208: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "msg_df.line = [re.sub(r'[\\W]', \"\", lines[0]).upper() for lines in msg_df.line]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "U8       3523\n",
       "U7       2134\n",
       "U6       1655\n",
       "U1       1385\n",
       "U2       1151\n",
       "         ... \n",
       "U1351       1\n",
       "M77         1\n",
       "M76         1\n",
       "S19         1\n",
       "S6          1\n",
       "Name: line, Length: 65, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msg_df.line.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/coding-2019/freifahren-reloaded/myvenv/lib/python3.7/site-packages/ipykernel_launcher.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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "msg_df[\"line_type\"] = msg_df.line.apply(lambda line: line[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>type</th>\n",
       "      <th>text</th>\n",
       "      <th>line</th>\n",
       "      <th>line_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>15.02.2018 17:47:39</td>\n",
       "      <td>&lt;msg&gt;</td>\n",
       "      <td>S25 Oranienburgerstr. Richtung Süden</td>\n",
       "      <td>S25</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>15.02.2018 20:25:12</td>\n",
       "      <td>&lt;msg&gt;</td>\n",
       "      <td>Görli u1, richtung Warschauer ausgestiegen</td>\n",
       "      <td>U1</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>15.02.2018 21:20:00</td>\n",
       "      <td>&lt;msg&gt;</td>\n",
       "      <td>U1 Kurfürstenstraße Richtung Uhlandstr</td>\n",
       "      <td>U1</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>16.02.2018 13:12:04</td>\n",
       "      <td>&lt;msg&gt;</td>\n",
       "      <td>S3/S5/S7/S9: Berlin Hbf. Kontrolleure sind wei...</td>\n",
       "      <td>S3</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>16.02.2018 13:18:43</td>\n",
       "      <td>&lt;msg&gt;</td>\n",
       "      <td>Jmd heute schon die westliche u7 langgefahren ...</td>\n",
       "      <td>U7</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              timestamp   type  \\\n",
       "49  15.02.2018 17:47:39  <msg>   \n",
       "62  15.02.2018 20:25:12  <msg>   \n",
       "63  15.02.2018 21:20:00  <msg>   \n",
       "77  16.02.2018 13:12:04  <msg>   \n",
       "78  16.02.2018 13:18:43  <msg>   \n",
       "\n",
       "                                                 text line line_type  \n",
       "49               S25 Oranienburgerstr. Richtung Süden  S25         S  \n",
       "62         Görli u1, richtung Warschauer ausgestiegen   U1         U  \n",
       "63             U1 Kurfürstenstraße Richtung Uhlandstr   U1         U  \n",
       "77  S3/S5/S7/S9: Berlin Hbf. Kontrolleure sind wei...   S3         S  \n",
       "78  Jmd heute schon die westliche u7 langgefahren ...   U7         U  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msg_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "msg_df.to_csv('data/controls.tsv', sep='\\t', encoding='utf-16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import json\n",
    "import datetime\n",
    "from collections import Counter\n",
    "\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['S3', 'S5', 's7', 's9']\n"
     ]
    }
   ],
   "source": [
    "regexp = re.compile(\"(?<!\\w)([MUS]\\W?\\d+)(?:\\W|$)\", re.I)\n",
    "print(regexp.findall(\" S3/S5/s7/s9\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "totalmatches = Counter()\n",
    "\n",
    "bins = {\n",
    "    'S41': 'Ring',\n",
    "    'S42': 'Ring',\n",
    "    'S1': 'S12',\n",
    "    'S12': 'S12',\n",
    "    'S19': 'S12',\n",
    "    'S2': 'S12',\n",
    "    'S21': 'S12',\n",
    "    'S25': 'S12',\n",
    "    'S26': 'S12',\n",
    "    'S3': 'S3579',\n",
    "    'S45': 'Ring',\n",
    "    'S46': 'Ring',\n",
    "    'S47': 'Ring',\n",
    "    'S48': 'Ring',\n",
    "    'S5': 'S3579',\n",
    "    'S7': 'S3579',\n",
    "    'S75': 'S3579',\n",
    "    'S8': 'Ring',\n",
    "    'S85': 'Ring',\n",
    "    'S9': 'S3579',\n",
    "}\n",
    "\n",
    "\n",
    "for row in msg_df.iterrows():\n",
    "    rowbody = row[1][\"text\"]\n",
    "    rowmatches = regexp.findall(rowbody)\n",
    "\n",
    "    #print(rowbody)\n",
    "    #print(rowmatches)\n",
    "    for match in rowmatches:\n",
    "        m = re.sub(r'[\\W]', \"\", match).upper()\n",
    "        totalmatches[bins.get(m, m)] += 1\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_control_count = sum([val for key, val in totalmatches.items()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20209"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_control_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'S12': 1259,\n",
       "         'U1': 1385,\n",
       "         'S3579': 2198,\n",
       "         'U7': 2134,\n",
       "         'Ring': 2626,\n",
       "         'U3': 504,\n",
       "         'U8': 3523,\n",
       "         'U2': 1151,\n",
       "         'M13': 218,\n",
       "         'M10': 884,\n",
       "         'M2': 130,\n",
       "         'U6': 1655,\n",
       "         'M1': 178,\n",
       "         'M4': 139,\n",
       "         'U4': 42,\n",
       "         'U5': 747,\n",
       "         'U9': 1026,\n",
       "         'M45': 5,\n",
       "         'M29': 39,\n",
       "         'M19': 9,\n",
       "         'M85': 8,\n",
       "         'M46': 10,\n",
       "         'M12': 10,\n",
       "         'M17': 36,\n",
       "         'M5': 40,\n",
       "         'M6': 45,\n",
       "         'M48': 10,\n",
       "         'M41': 48,\n",
       "         'M21': 7,\n",
       "         'M27': 11,\n",
       "         'M44': 6,\n",
       "         'M49': 2,\n",
       "         'M8': 91,\n",
       "         'M25': 1,\n",
       "         'M77': 1,\n",
       "         'M76': 1,\n",
       "         'M11': 1,\n",
       "         'M16': 4,\n",
       "         'M50': 11,\n",
       "         'S421': 1,\n",
       "         'S4': 3,\n",
       "         'U10': 2,\n",
       "         'S24': 3,\n",
       "         'M37': 1,\n",
       "         'U25': 1,\n",
       "         'S6': 1,\n",
       "         'U42': 1,\n",
       "         'U1351': 1})"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totalmatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_totalmatches = sorted(list(totalmatches.items()), key=lambda tup: -tup[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "overall_top_10 = sorted_totalmatches[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('U8', 3523),\n",
       " ('Ring', 2626),\n",
       " ('S3579', 2198),\n",
       " ('U7', 2134),\n",
       " ('U6', 1655),\n",
       " ('U1', 1385),\n",
       " ('S12', 1259),\n",
       " ('U2', 1151),\n",
       " ('U9', 1026),\n",
       " ('M10', 884)]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_top_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb514f92630>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lines = [el[0] for el in overall_top_10]\n",
    "count = [el[1] for el in overall_top_10]\n",
    "sns.barplot(x=lines, y=count, color=\"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "sbahncounts = list(filter(lambda tup: tup[0][0] == \"S\" or tup[0] == \"Ring\", sorted_totalmatches))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb5150c39b0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lines = [el[0] for el in sbahncounts]\n",
    "count = [el[1] for el in sbahncounts]\n",
    "sns.barplot(x=lines, y=count, color=\"darkred\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6091"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "tramcounts = list(filter(lambda tup: tup[0][0] == \"M\", sorted_totalmatches))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb5136b90f0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lines = [el[0] for el in tramcounts]\n",
    "count = [el[1] for el in tramcounts]\n",
    "sns.barplot(x=lines, y=count, color=\"yellow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "ubahncounts = list(filter(lambda tup: tup[0][0] == \"U\", sorted_totalmatches))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb514fed438>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lines = [el[0] for el in ubahncounts]\n",
    "count = [el[1] for el in ubahncounts]\n",
    "sns.barplot(x=lines, y=count, color=\"lightblue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12169"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}