{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Make the graphs a bit prettier, and bigger\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (15, 5)\n",
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "\n",
    "# This is necessary to show lots of columns in pandas 0.12. \n",
    "# Not necessary in pandas 0.13.\n",
    "pd.set_option('display.width', 5000) \n",
    "pd.set_option('display.max_columns', 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay! We're going back to our bike path dataset here. I live in Montreal, and I was curious about whether we're more of a commuter city or a biking-for-fun city -- do people bike more on weekends, or on weekdays?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.1 Adding a 'weekday' column to our dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to load up the data. We've done this before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114cd4190>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes = pd.read_csv('../data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')\n",
    "bikes['Berri 1'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next up, we're just going to look at the Berri bike path. Berri is a street in Montreal, with a pretty important bike path. I use it mostly on my way to the library now, but I used to take it to work sometimes when I worked in Old Montreal. \n",
    "\n",
    "So we're going to create a dataframe with just the Berri bikepath in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "berri_bikes = bikes[['Berri 1']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Berri 1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1\n",
       "Date               \n",
       "2012-01-01       35\n",
       "2012-01-02       83\n",
       "2012-01-03      135\n",
       "2012-01-04      144\n",
       "2012-01-05      197"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to add a 'weekday' column. Firstly, we can get the weekday from the index. We haven't talked about indexes yet, but the index is what's on the left on the above dataframe, under 'Date'. It's basically all the days of the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03', '2012-01-04', '2012-01-05', '2012-01-06', '2012-01-07', '2012-01-08', '2012-01-09', '2012-01-10',\n",
       "               ...\n",
       "               '2012-10-27', '2012-10-28', '2012-10-29', '2012-10-30', '2012-10-31', '2012-11-01', '2012-11-02', '2012-11-03', '2012-11-04', '2012-11-05'], dtype='datetime64[ns]', name='Date', length=310, freq=None)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that actually some of the days are missing -- only 310 days of the year are actually there. Who knows why.\n",
    "\n",
    "Pandas has a bunch of really great time series functionality, so if we wanted to get the day of the month for each row, we could do it like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "            ...\n",
       "            27, 28, 29, 30, 31,  1,  2,  3,  4,  5], dtype='int64', name='Date', length=310)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.index.day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We actually want the weekday, though:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "            ...\n",
       "            5, 6, 0, 1, 2, 3, 4, 5, 6, 0], dtype='int64', name='Date', length=310)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.index.weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the days of the week, where 0 is Monday. I found out that 0 was Monday by checking on a calendar.\n",
    "\n",
    "Now that we know how to *get* the weekday, we can add it as a column in our dataframe like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Berri 1</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>144</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>197</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Berri 1  weekday\n",
       "Date                        \n",
       "2012-01-01       35        6\n",
       "2012-01-02       83        0\n",
       "2012-01-03      135        1\n",
       "2012-01-04      144        2\n",
       "2012-01-05      197        3"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.loc[:,'weekday'] = berri_bikes.index.weekday\n",
    "berri_bikes[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.2 Adding up the cyclists by weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This turns out to be really easy!\n",
    "\n",
    "Dataframes have a `.groupby()` method that is similar to SQL groupby, if you're familiar with that. I'm not going to explain more about it right now -- if you want to to know more, [the documentation](http://pandas.pydata.org/pandas-docs/stable/groupby.html) is really good.\n",
    "\n",
    "In this case, `berri_bikes.groupby('weekday').aggregate(sum)` means \"Group the rows by weekday and then add up all the values with the same weekday\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Berri 1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>152972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>101578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>99310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Berri 1\n",
       "weekday         \n",
       "0         134298\n",
       "1         135305\n",
       "2         152972\n",
       "3         160131\n",
       "4         141771\n",
       "5         101578\n",
       "6          99310"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)\n",
    "weekday_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's hard to remember what 0, 1, 2, 3, 4, 5, 6 mean, so we can fix it up and graph it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Berri 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>134298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>135305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>152972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>160131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>141771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>101578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>99310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Berri 1\n",
       "Monday      134298\n",
       "Tuesday     135305\n",
       "Wednesday   152972\n",
       "Thursday    160131\n",
       "Friday      141771\n",
       "Saturday    101578\n",
       "Sunday       99310"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114d5d820>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_counts.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it looks like Montrealers are commuter cyclists -- they bike much more during the week. Neat!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.3 Putting it together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's put all that together, to prove how easy it is. 6 lines of magical pandas!\n",
    "\n",
    "If you want to play around, try changing `sum` to `max`, `numpy.median`, or any other function you like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114e7a430>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes = pd.read_csv('../data/bikes.csv', \n",
    "                    sep=';', encoding='latin1', \n",
    "                    parse_dates=['Date'], dayfirst=True, \n",
    "                    index_col='Date')\n",
    "# Add the weekday column\n",
    "berri_bikes = bikes[['Berri 1']].copy()\n",
    "berri_bikes.loc[:,'weekday'] = berri_bikes.index.weekday\n",
    "\n",
    "# Add up the number of cyclists by weekday, and plot!\n",
    "weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)\n",
    "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "weekday_counts.plot(kind='bar')"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}