demand.ipynb

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  {
   "source": [
    "\n",
    "# Demand data analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import os #operating system\n",
    "import pandas as pd #pandas for dataframe management\n",
    "import matplotlib.pyplot as plt #matplotlib for plotting\n",
    "import matplotlib.dates as mdates # alias for date formatting\n",
    "import numpy as np # for generating synthetic data\n",
    "\n",
    "# Handle date time conversions between pandas and matplotlib\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data\n",
    "dataPath = './' # set data path\n",
    "df = pd.read_csv(os.path.join(dataPath, 'data.csv')) # read in data\n",
    "df['date'] = pd.to_datetime(df['date']) # convert column to datetime\n",
    "df.set_index('date', inplace=True) # set date as the index\n",
    "df_avg = df.resample('D').mean() # resample by DAY and take the mean and create a new dataframe called df_avg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "metadata": {},
     "execution_count": 3
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#plot data\n",
    "# You would need to experiment around with some of the plot settings to get the desired result\n",
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "df_avg.plot(ax=ax, kind='bar')\n",
    "\n",
    "# #set ticks every week\n",
    "# ax.xaxis.set_major_locator(mdates.WeekdayLocator())\n",
    "# #set major ticks format\n",
    "# ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))"
   ]
  },
  {
   "source": [
    "# Create synthetic data over time"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This extracts the data as an array, creates a sythetic interval, samples the data based on provided data, and creates a new time series\n",
    "data = df['demand'].values\n",
    "interval = pd.date_range('2018-11-01', '2020-10-30', freq='15min')\n",
    "synthetic_data = np.random.choice(data, len(interval))\n",
    "ts = pd.DataFrame({'demand': synthetic_data}, index=interval)\n",
    "ts.index.name = 'date'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# do analysis\n",
    "ts_daily = ts.resample('D').mean()\n",
    "days = ts_daily.index.strftime(\"%Y-%m-%d\")\n",
    "\n",
    "ts_monthly = ts.resample('M').mean()\n",
    "months = ts_monthly.index.strftime(\"%Y-%m\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "for month in months[2:5]:\n",
    "    fig, ax = plt.subplots(figsize=(15,7))\n",
    "    ts_daily[month].plot(ax=ax, kind='bar')\n",
    "\n",
    "    # display\n",
    "    plt.title(\"Monthly demand for {}\".format(month))\n",
    "    plt.xlabel(\"day of month\")\n",
    "    plt.ylabel('demand')\n",
    "\n",
    "    # #set ticks every week\n",
    "    ax.xaxis.set_major_locator(mdates.WeekdayLocator())\n",
    "    #set major ticks format\n",
    "    ax.xaxis.set_major_formatter(mdates.DateFormatter('%d'))\n",
    "\n",
    "    # plt.show()\n",
    "    plt.savefig('figures/{}'.format(month))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                     demand\n",
       "date                       \n",
       "2020-10-30 00:00:00   43.20\n",
       "2020-10-30 00:15:00   46.08\n",
       "2020-10-30 00:30:00   43.20\n",
       "2020-10-30 00:45:00   43.20\n",
       "2020-10-30 01:00:00   46.08"
      ],
      "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>demand</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-10-30 00:00:00</th>\n      <td>43.20</td>\n    </tr>\n    <tr>\n      <th>2020-10-30 00:15:00</th>\n      <td>46.08</td>\n    </tr>\n    <tr>\n      <th>2020-10-30 00:30:00</th>\n      <td>43.20</td>\n    </tr>\n    <tr>\n      <th>2020-10-30 00:45:00</th>\n      <td>43.20</td>\n    </tr>\n    <tr>\n      <th>2020-10-30 01:00:00</th>\n      <td>46.08</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "## extra\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}