PandasBasics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Version 1.0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas basics "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi! In this programming assignment you need to refresh your `pandas` knowledge. You will need to do several [`groupby`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html)s and [`join`]()`s to solve the task. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "from grader import Grader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "DATA_FOLDER = '../readonly/final_project_data/'\n",
    "\n",
    "transactions    = pd.read_csv(os.path.join(DATA_FOLDER, 'sales_train.csv.gz'))\n",
    "items           = pd.read_csv(os.path.join(DATA_FOLDER, 'items.csv'))\n",
    "item_categories = pd.read_csv(os.path.join(DATA_FOLDER, 'item_categories.csv'))\n",
    "shops           = pd.read_csv(os.path.join(DATA_FOLDER, 'shops.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset we are going to use is taken from the competition, that serves as the final project for this course. You can find complete data description at the [competition web page](https://www.kaggle.com/c/competitive-data-science-final-project/data). To join the competition use [this link](https://www.kaggle.com/t/1ea93815dca248e99221df42ebde3540)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create a grader instace below and use it to collect your answers. When function `submit_tag` is called, grader will store your answer *locally*. The answers will *not* be submited to the platform immediately so you can call `submit_tag` function as many times as you need. \n",
    "\n",
    "When you are ready to push your answers to the platform you should fill your credentials and run `submit` function in the <a href=\"#Authorization-&-Submission\">last paragraph</a>  of the assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grader = Grader()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with a simple task. \n",
    "\n",
    "<ol start=\"0\">\n",
    "  <li><b>Print the shape of the loaded dataframes and use [`df.head`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.head.html) function to print several rows. Examine the features you are given.</b></li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transactions shpae :  (2935849, 6)\n",
      "items shpae :  (22170, 3)\n",
      "item_categories shape :  (84, 2)\n",
      "shops shape :  (60, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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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",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_price</th>\n",
       "      <th>item_cnt_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>22154</td>\n",
       "      <td>999.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>899.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>899.00</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2554</td>\n",
       "      <td>1709.05</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2555</td>\n",
       "      <td>1099.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  date_block_num  shop_id  item_id  item_price  item_cnt_day\n",
       "0  02.01.2013               0       59    22154      999.00           1.0\n",
       "1  03.01.2013               0       25     2552      899.00           1.0\n",
       "2  05.01.2013               0       25     2552      899.00          -1.0\n",
       "3  06.01.2013               0       25     2554     1709.05           1.0\n",
       "4  15.01.2013               0       25     2555     1099.00           1.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YOUR CODE GOES HERE\n",
    "print(\"transactions shpae : \", transactions.shape)\n",
    "print(\"items shpae : \", items.shape)\n",
    "print(\"item_categories shape : \", item_categories.shape)\n",
    "print(\"shops shape : \", shops.shape)\n",
    "\n",
    "transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>!ABBYY FineReader 12 Professional Edition Full...</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>***В ЛУЧАХ СЛАВЫ   (UNV)                    D</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>***ГОЛУБАЯ ВОЛНА  (Univ)                      D</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>***КОРОБКА (СТЕКЛО)                       D</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                           item_name  item_id  \\\n",
       "0          ! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D        0   \n",
       "1  !ABBYY FineReader 12 Professional Edition Full...        1   \n",
       "2      ***В ЛУЧАХ СЛАВЫ   (UNV)                    D        2   \n",
       "3    ***ГОЛУБАЯ ВОЛНА  (Univ)                      D        3   \n",
       "4        ***КОРОБКА (СТЕКЛО)                       D        4   \n",
       "\n",
       "   item_category_id  \n",
       "0                40  \n",
       "1                76  \n",
       "2                40  \n",
       "3                40  \n",
       "4                40  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_category_name</th>\n",
       "      <th>item_category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PC - Гарнитуры/Наушники</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Аксессуары - PS2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Аксессуары - PS3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Аксессуары - PS4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Аксессуары - PSP</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_category_name  item_category_id\n",
       "0  PC - Гарнитуры/Наушники                 0\n",
       "1         Аксессуары - PS2                 1\n",
       "2         Аксессуары - PS3                 2\n",
       "3         Аксессуары - PS4                 3\n",
       "4         Аксессуары - PSP                 4"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_categories.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shop_name</th>\n",
       "      <th>shop_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!Якутск Орджоникидзе, 56 фран</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>!Якутск ТЦ \"Центральный\" фран</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Адыгея ТЦ \"Мега\"</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Балашиха ТРК \"Октябрь-Киномир\"</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Волжский ТЦ \"Волга Молл\"</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        shop_name  shop_id\n",
       "0   !Якутск Орджоникидзе, 56 фран        0\n",
       "1   !Якутск ТЦ \"Центральный\" фран        1\n",
       "2                Адыгея ТЦ \"Мега\"        2\n",
       "3  Балашиха ТРК \"Октябрь-Киномир\"        3\n",
       "4        Волжский ТЦ \"Волга Молл\"        4"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shops.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use your `pandas` skills to get answers for the following questions. \n",
    "The first question is:\n",
    "\n",
    "1. ** What was the maximum total revenue among all the shops in September, 2014?** \n",
    "\n",
    "\n",
    "* Hereinafter *revenue* refers to total sales minus value of goods returned.\n",
    "\n",
    "*Hints:*\n",
    "\n",
    "* Sometimes items are returned, find such examples in the dataset. \n",
    "* It is handy to split `date` field into [`day`, `month`, `year`] components and use `df.year == 14` and `df.month == 9` in order to select target subset of dates.\n",
    "* You may work with `date` feature as with strings, or you may first convert it to `pd.datetime` type with `pd.to_datetime` function, but do not forget to set correct `format` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current answer for task max_revenue is: 7982852.2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# YOUR CODE GOES HERE\n",
    "transactions[\"date\"] = pd.to_datetime(transactions.date, format=\"%d.%m.%Y\")\n",
    "\n",
    "df = transactions[transactions[\"date\"].dt.year==2014]\n",
    "sep_df = df[df['date'].dt.month==9]\n",
    "sep_df[\"revenue\"] = sep_df.item_price*sep_df.item_cnt_day\n",
    "\n",
    "shops_revenue = sep_df['revenue'].groupby(sep_df['shop_id']).sum()\n",
    "\n",
    "max_revenue = shops_revenue.max() # PUT YOUR ANSWER IN THIS VARIABLE\n",
    "grader.submit_tag('max_revenue', max_revenue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Let's move on and answer another question:\n",
    "\n",
    "<ol start=\"2\">\n",
    "  <li><b>What item category generated the highest revenue in summer 2014?</b></li>\n",
    "</ol>\n",
    "\n",
    "* Submit `id` of the category found.\n",
    "    \n",
    "* Here we call \"summer\" the period from June to August.\n",
    "\n",
    "*Hints:*\n",
    "\n",
    "* Note, that for an object `x` of type `pd.Series`: `x.argmax()` returns **index** of the maximum element. `pd.Series` can have non-trivial index (not `[1, 2, 3, ... ]`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current answer for task category_id_with_max_revenue is: 20\n"
     ]
    }
   ],
   "source": [
    "# YOUR CODE GOES HERE\n",
    "date_range = pd.date_range(start=\"2014-06-01\", end=\"2014.08.31\")\n",
    "summer_df = transactions[transactions['date'].isin(date_range)]\n",
    "\n",
    "summer_item = pd.merge(summer_df, items, how='inner', on='item_id')\n",
    "summer_item['revenue'] = summer_item.item_price*summer_item.item_cnt_day\n",
    "\n",
    "category_revenue = summer_item['revenue'].groupby(summer_item['item_category_id']).sum()\n",
    "\n",
    "category_id_with_max_revenue = category_revenue.argmax() # PUT YOUR ANSWER IN THIS VARIABLE\n",
    "grader.submit_tag('category_id_with_max_revenue', category_id_with_max_revenue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ol start=\"3\">\n",
    "  <li><b>How many items are there, such that their price stays constant (to the best of our knowledge) during the whole period of time?</b></li>\n",
    "</ol>\n",
    "\n",
    "* Let's assume, that the items are returned for the same price as they had been sold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current answer for task num_items_constant_price is: 5926\n"
     ]
    }
   ],
   "source": [
    "# YOUR CODE GOES HERE\n",
    "grouped = transactions['item_price'].groupby(transactions['item_id'])\n",
    "\n",
    "count = 0\n",
    "for g in grouped:\n",
    "    if len(g[1].unique())==1:\n",
    "        count+=1\n",
    "\n",
    "num_items_constant_price = count # PUT YOUR ANSWER IN THIS VARIABLE\n",
    "grader.submit_tag('num_items_constant_price', num_items_constant_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember, the data can sometimes be noisy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ol start=\"4\">\n",
    "  <li><b>What was the variance of the number of sold items per day sequence for the shop with `shop_id = 25` in December, 2014? Do not count the items, that were sold but returned back later.</b></li>\n",
    "</ol>\n",
    "\n",
    "* Fill `total_num_items_sold` and `days` arrays, and plot the sequence with the code below.\n",
    "* Then compute variance. Remember, there can be differences in how you normalize variance (biased or unbiased estimate, see [link](https://math.stackexchange.com/questions/496627/the-difference-between-unbiased-biased-estimator-variance)). Compute ***unbiased*** estimate (use the right value for `ddof` argument in `pd.var` or `np.var`). \n",
    "* If there were no sales at a given day, ***do not*** impute missing value with zero, just ignore that day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HRSRVRH4SkTODHbMxxjSU577lwRim6xHMMw9fypP4lar+BIwCEJFIYAfwHs4w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\nGGNMg/1/iA2138vet+QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b2aadb828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current answer for task total_num_items_sold_var is: 117167.702299\n"
     ]
    }
   ],
   "source": [
    "shop_id = 25\n",
    "\n",
    "total_num_items_sold = [] # YOUR CODE GOES HERE\n",
    "days = [] # YOUR CODE GOES HERE\n",
    "\n",
    "transactions[\"date\"] = pd.to_datetime(transactions.date, format=\"%d.%m.%Y\")\n",
    "\n",
    "shops = transactions[transactions[\"date\"].dt.year==2014]\n",
    "shops = shops[shops['date'].dt.month==12]\n",
    "shops = shops[shops['shop_id']==25]\n",
    "\n",
    "grouped = shops['item_cnt_day'].groupby(shops['date'])\n",
    "\n",
    "for day, sold in grouped:\n",
    "    days.append(day)\n",
    "    total_num_items_sold.append(sold.sum())\n",
    "\n",
    "\n",
    "# Plot it\n",
    "plt.plot(days, total_num_items_sold)\n",
    "plt.ylabel('Num items')\n",
    "plt.xlabel('Day')\n",
    "plt.title(\"Daily revenue for shop_id = 25\")\n",
    "plt.show()\n",
    "\n",
    "total_num_items_sold_var = grouped.sum().var()# PUT YOUR ANSWER IN THIS VARIABLE\n",
    "grader.submit_tag('total_num_items_sold_var', total_num_items_sold_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authorization & Submission\n",
    "To submit assignment to Cousera platform, please, enter your e-mail and token into the variables below. You can generate token on the programming assignment page. *Note:* Token expires 30 minutes after generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You want to submit these numbers:\n",
      "Task max_revenue: 7982852.2\n",
      "Task category_id_with_max_revenue: 20\n",
      "Task num_items_constant_price: 5926\n",
      "Task total_num_items_sold_var: 117167.702299\n"
     ]
    }
   ],
   "source": [
    "STUDENT_EMAIL = \"kjha2142@hanyang.ac.kr\" # EMAIL HERE\n",
    "STUDENT_TOKEN = \"Wpbm9cRVi4qMeMT6\" # TOKEN HERE\n",
    "grader.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitted to Coursera platform. See results on assignment page!\n"
     ]
    }
   ],
   "source": [
    "grader.submit(STUDENT_EMAIL, STUDENT_TOKEN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well done! :)"
   ]
  }
 ],
 "metadata": {
  "hw_version": "1.0.0",
  "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}