SNCF_found_item_1.ipynb

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   "cell_type": "code",
   "execution_count": 824,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import chart_studio.plotly as py\n",
    "import plotly.colors\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 834,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading train station data\n",
    "df_gares = pd.read_csv('data/referentiel-gares-voyageurs.csv', sep=';')\n",
    "df_items = pd.read_csv('data/objets-trouves-restitution.csv', sep=';')\n",
    "\n",
    "# Merging dataframes\n",
    "df = pd.merge(df_gares, df_items, on='Code UIC', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 835,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conversion to datetime\n",
    "df['Date'] = pd.to_datetime(df.Date, utc=True).dt.tz_convert('Europe/Paris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 836,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>15698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>102793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>111706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>107595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>107662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>116308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>122637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>26143</td>\n",
       "    </tr>\n",
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       "        Date\n",
       "2013   15698\n",
       "2014  102793\n",
       "2015  111706\n",
       "2016  107595\n",
       "2017  107662\n",
       "2018  116308\n",
       "2019  122637\n",
       "2020   26143"
      ]
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     "execution_count": 836,
     "metadata": {},
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   ],
   "source": [
    "pd.DataFrame(df.Date.dt.year.value_counts().sort_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 837,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering by date\n",
    "df = df[df.Date.dt.year >= 2014]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 838,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grouping by train station\n",
    "df_grouped = (df\n",
    "    .groupby(by='Gare')\n",
    "    .count()\n",
    "    .Date\n",
    "    .rename('Count')\n",
    "    .sort_values(ascending=False)\n",
    ")\n",
    "\n",
    "pareto = (df_grouped.cumsum() < df_grouped.sum() * 0.8).sum()\n",
    "\n",
    "# Only keep first 30\n",
    "df_grouped = df_grouped.iloc[:pareto]"
   ]
  },
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         "range": [
          -0.5,
          32.5
         ],
         "tickangle": -90,
         "tickfont": {
          "size": 14
         },
         "type": "category"
        },
        "yaxis": {
         "autorange": true,
         "range": [
          3.618391574483643,
          4.96539485685606
         ],
         "title": {
          "font": {
           "size": 16
          },
          "text": "Found items per station"
         },
         "type": "log"
        }
       }
      },
      "image/png": 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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('ad41125d-84ab-4db3-b76c-7d11def49336');\n",
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Bar(\n",
    "        x=df_grouped.index, \n",
    "        y=df_grouped, \n",
    "        hoverinfo=\"x+y\",\n",
    "        name=\"Found items\",\n",
    "        marker_color=\"#007bff\"\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    margin={'l':20, 'r': 0, 't': 0, 'b': 250},\n",
    "    xaxis=dict(tickangle=-90,\n",
    "               tickfont={'size': 14}),\n",
    "    yaxis=dict(title=\"Found items per station\",\n",
    "               titlefont=dict(size=16),\n",
    "               type=\"log\")\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 840,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://plotly.com/~hugolmn/14/'"
      ]
     },
     "execution_count": 840,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py.plot(fig, filename = 'sncf-found-items', auto_open=True)"
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  },
  {
   "cell_type": "code",
   "execution_count": 841,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouping by year and month\n",
    "df_months = df.groupby(by=[df.Date.dt.year, df.Date.dt.month]).Date.count()\n",
    "df_months.index = df_months.index.map(lambda t: pd.to_datetime('-'.join(map(str, t))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "metadata": {},
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",
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(\n",
    "    go.Bar(\n",
    "        x=df_months.index,\n",
    "        y=df_months, \n",
    "        hoverinfo=\"x+y\",\n",
    "        marker_color=\"#007bff\",  \n",
    "        name='Found items per month in train stations'\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    yaxis=dict(title=\"Found items in train stations\", titlefont=dict(size=16)),\n",
    "    margin={'l': 30, 'r': 30, 't': 50, 'b': 0},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 843,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://plotly.com/~hugolmn/21/'"
      ]
     },
     "execution_count": 843,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py.plot(fig, filename='sncf-monthly-found-items', auto_open=True)"
   ]
  }
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