Created on Cognitive Class Labs

resampling_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "resampling time series data with pandas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tracking a self-driving car at 15 minute periods over a year and creating weekly and yearly summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we'll be tracking this self-driving car that travels at an average speed between 0 and 60mph all day long, all year long."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have the average speed over the fifteen minute period in miles per hour,distance in miles and the cumulative distance travelled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#in this case we are using called date_range which takes dates in a particular range and also a frequency.\n",
    "range = pd.date_range('2015-01-01','2015-12-31',freq = '15min')\n",
    "df = pd.DataFrame(index = range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Average speed in miles per hour\n",
    "#in this case we use random function it takes three values low high and size in this case the size is number of \n",
    "#indexes in the above given dataframe\n",
    "\n",
    "df['speed'] = np.random.randint(low = 0,high = 60,size = len(df.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#distance in miles for 0.25 hours\n",
    "df['distance'] = df['speed']*0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cumulative_distance'] = df.distance.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>speed</th>\n",
       "      <th>distance</th>\n",
       "      <th>cumulative_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:15:00</th>\n",
       "      <td>40</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:30:00</th>\n",
       "      <td>46</td>\n",
       "      <td>11.50</td>\n",
       "      <td>21.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:45:00</th>\n",
       "      <td>12</td>\n",
       "      <td>3.00</td>\n",
       "      <td>24.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01 01:00:00</th>\n",
       "      <td>17</td>\n",
       "      <td>4.25</td>\n",
       "      <td>28.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30 23:00:00</th>\n",
       "      <td>31</td>\n",
       "      <td>7.75</td>\n",
       "      <td>257298.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30 23:15:00</th>\n",
       "      <td>53</td>\n",
       "      <td>13.25</td>\n",
       "      <td>257311.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30 23:30:00</th>\n",
       "      <td>49</td>\n",
       "      <td>12.25</td>\n",
       "      <td>257324.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30 23:45:00</th>\n",
       "      <td>47</td>\n",
       "      <td>11.75</td>\n",
       "      <td>257335.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 00:00:00</th>\n",
       "      <td>52</td>\n",
       "      <td>13.00</td>\n",
       "      <td>257348.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34945 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     speed  distance  cumulative_distance\n",
       "2015-01-01 00:00:00      0      0.00                 0.00\n",
       "2015-01-01 00:15:00     40     10.00                10.00\n",
       "2015-01-01 00:30:00     46     11.50                21.50\n",
       "2015-01-01 00:45:00     12      3.00                24.50\n",
       "2015-01-01 01:00:00     17      4.25                28.75\n",
       "...                    ...       ...                  ...\n",
       "2015-12-30 23:00:00     31      7.75            257298.50\n",
       "2015-12-30 23:15:00     53     13.25            257311.75\n",
       "2015-12-30 23:30:00     49     12.25            257324.00\n",
       "2015-12-30 23:45:00     47     11.75            257335.75\n",
       "2015-12-31 00:00:00     52     13.00            257348.75\n",
       "\n",
       "[34945 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "#twins creates a twin axis sharing the x axis\n",
    "ax1.plot(df.index,df['speed'],'g-')\n",
    "ax2.plot(df.index,df['distance'],'b-')\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('Speed',color = 'g')\n",
    "ax2.set_ylabel('distence',color = 'b')\n",
    "\n",
    "plt.show()\n",
    "plt.rcParams['figure.figsize'] = 12,5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above plot we see that there are many datapoints we need to resample hence we perform a weekly summary, in this case we are essentially grouping by certain time span\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets start by resampling the speed of our car"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df.speed.resample(): will be used to resample the speed column of our table\n",
    "\n",
    "'W': this indicates we want to resample by week\n",
    "\n",
    "mean():this indicates we want the mean speed during this period\n",
    "\n",
    "sum():with distance we want the sum of the distances over the week to see how far the car has travelled over a week.\n",
    "\n",
    "last(): with cumulative distance in our hand we just want to take the last value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_summary = pd.DataFrame() #Here we are creating new dataframe\n",
    "weekly_summary['speed'] = df.speed.resample('W').mean()#in this case we are taking speed data for 15mins day and resampling the data f\n",
    "#of one week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_summary['distance'] = df.distance.resample('W').sum()\n",
    "#in this case we are taking distance travelled in previous data and adding the sum for one week\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_summary['cumulative_distance'] = df.cumulative_distance.resample('W').last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>speed</th>\n",
       "      <th>distance</th>\n",
       "      <th>cumulative_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-11</th>\n",
       "      <td>28.331845</td>\n",
       "      <td>4759.75</td>\n",
       "      <td>7739.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-18</th>\n",
       "      <td>30.169643</td>\n",
       "      <td>5068.50</td>\n",
       "      <td>12807.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-25</th>\n",
       "      <td>29.193452</td>\n",
       "      <td>4904.50</td>\n",
       "      <td>17712.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>29.227679</td>\n",
       "      <td>4910.25</td>\n",
       "      <td>22622.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-08</th>\n",
       "      <td>29.523810</td>\n",
       "      <td>4960.00</td>\n",
       "      <td>27582.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                speed  distance  cumulative_distance\n",
       "2015-01-11  28.331845   4759.75              7739.25\n",
       "2015-01-18  30.169643   5068.50             12807.75\n",
       "2015-01-25  29.193452   4904.50             17712.25\n",
       "2015-02-01  29.227679   4910.25             22622.50\n",
       "2015-02-08  29.523810   4960.00             27582.50"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#in this case we are considering only whole week\n",
    "weekly_summary = weekly_summary.truncate(before = '2015-01-05',after = '2015-12-27')\n",
    "weekly_summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(weekly_summary.index,weekly_summary['speed'],'g-')\n",
    "ax2.plot(weekly_summary.index,weekly_summary['distance'],'b-')\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('Speed',color = 'g')\n",
    "ax2.set_ylabel('Distance',color = 'b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}