DAT_Projekt/notebooks/hourly_bikes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d6fa6fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2f9a292",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4598/1257658190.py:2: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df = df.replace([\"na\", \"NA\", \"Na\"], np.nan)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operator_name                  0\n",
      "domain_name                    0\n",
      "domain_id                      0\n",
      "counter_site                   0\n",
      "counter_site_id                0\n",
      "counter_serial                 0\n",
      "longitude                      0\n",
      "latitude                       0\n",
      "timezone                       0\n",
      "iso_timestamp                  0\n",
      "channels_in                    0\n",
      "channels_out                   0\n",
      "channels_unknown          698715\n",
      "channels_all                   0\n",
      "site_temperature            8846\n",
      "site_rain_accumulation      8846\n",
      "site_snow_accumulation    698715\n",
      "year                           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/processed/hourly_bikes_mannheim.csv\", low_memory=False)\n",
    "df = df.replace([\"na\", \"NA\", \"Na\"], np.nan)\n",
    "\n",
    "print(df.isna().sum())\n",
    "df[\"site_temperature\"] = df[\"site_temperature\"].astype(float)\n",
    "df[\"site_rain_accumulation\"] = df[\"site_rain_accumulation\"].astype(float)\n",
    "df[\"site_snow_accumulation\"] = df[\"site_snow_accumulation\"].astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "623979f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4598/2721099372.py:17: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  agg_df = df_ma.groupby('time_of_day')['channels_all'].mean().reset_index()\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ensure the 'iso_timestamp' is properly parsed with utc=True\n",
    "df['timestamp'] = pd.to_datetime(df['iso_timestamp'], utc=True)  # Convert to datetime with UTC handling\n",
    "\n",
    "# Extract the hour from the timestamp\n",
    "df['hour'] = df['timestamp'].dt.hour\n",
    "\n",
    "bins = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17,18,19,20,21,22,23,24]  # These are the edges of your 3-hour bins\n",
    "\n",
    "\n",
    "# Add a new column with the time bin for each row\n",
    "df['time_of_day'] = pd.cut(df['hour'], bins=bins, right=False)\n",
    "\n",
    "# Aggregate the data by time_of_day\n",
    "#agg_df = df.groupby('time_of_day')['channels_all'].sum().reset_index()\n",
    "\n",
    "df_ma = df[df['counter_site'] == \"Renzstraße\"]\n",
    "agg_df = df_ma.groupby('time_of_day')['channels_all'].mean().reset_index()\n",
    "\n",
    "\n",
    "\n",
    "# Create a Seaborn bar plot to visualize the aggregated data\n",
    "plt.figure(figsize=(20, 10))\n",
    "ax = sns.barplot( data=agg_df,x=\"time_of_day\", y=\"channels_all\")\n",
    "\n",
    "\n",
    "n = len(agg_df)  # bei dir 24\n",
    "\n",
    "# Ticks auf die Grenzen legen: -0.5, 0.5, 1.5, ..., 23.5\n",
    "ax.set_xlim(-0.5, n - 0.5)\n",
    "ax.set_xticks(np.arange(-0.5, n + 0.5, 1))\n",
    "\n",
    "# Tick-Labels: 0..24 (Grenzwerte)\n",
    "ax.set_xticklabels([str(i) for i in range(n + 1)], rotation=0)\n",
    "ax.ticklabel_format(axis='y', style='plain', useOffset=False)\n",
    "\n",
    "# Add some labels and title to the plot\n",
    "ax.set_title('Fahrradpassagen pro Stunde als arithmetisches Mittel über den Beobachtungszeitraum (Mannheim, Renzstraße, ab 2014)', fontsize=16)\n",
    "plt.xlabel('Uhrzeit', fontsize=16)\n",
    "\n",
    "from matplotlib.ticker import EngFormatter, FuncFormatter\n",
    "\n",
    "#ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f\"{x/1e6:.1f}\"))\n",
    "plt.ylabel('Fahrradpassagen', fontsize=16)\n",
    "\n",
    "# Optionally, rotate the x-axis labels for better readability\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0364d24a",
   "metadata": {
    "vscode": {
     "languageId": "ruby"
    }
   },
   "outputs": [],
   "source": [
    "df['hour'] = df['timestamp'].dt.hour\n",
    "df['day_of_week'] = df['timestamp'].dt.dayofweek  # 0=Monday, 6=Sunday\n",
    "df['month'] = df['timestamp'].dt.month\n",
    "df['is_weekend'] = df['day_of_week'].isin([5, 6]).astype(int)  # 1 if weekend, 0 if weekday\n",
    "# You can manually add holidays or use a library like `holidays` to check if the day is a public holiday.\n",
    "de_holidays = holidays.Germany(years=2023)  # For Germany, for example\n",
    "df['is_holiday'] = df['timestamp'].dt.date.isin(de_holidays.keys()).astype(int)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dat (3.12.3)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}