data_prep-exercises_with_solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation with Python: Practice Exercises with Solutions\n",
    "\n",
    "The objective of this practice session is to help you learn applying commonly used data preparation steps on the raw dataset in Python.\n",
    "\n",
    "Suppose it is November 2009 and you wish to sell your copy of MarioKart for the Nintendo Wii on eBay. You are curious how much people is willing to pay for this particular game and therefore you scrape all the eBay auctions from the previous month.\n",
    "\n",
    "### Data Overview - Mario Kart auctions from eBay\n",
    "\n",
    "The data, collected in October 2009, has the following variables:\n",
    "\n",
    "- **`id`**: Numeric, Auction ID assigned by eBay.\n",
    "\n",
    "- **`duration`**: Numeric, Auction length, in days.\n",
    "\n",
    "- **`n_bids`**: Numeric, Number of bids.\n",
    "\n",
    "- **`cond`**: Binary, Game condition, either new or used.\n",
    "\n",
    "- **`start_pr`**: Numeric, Start price of the auction.\n",
    "\n",
    "- **`ship_pr`**: Numeric, Shipping price.\n",
    "\n",
    "- **`total_pr`**: Numeric, Total price, which equals the auction price plus the shipping price.\n",
    "\n",
    "- **`ship_sp`**: Categorical, Shipping speed or method.\n",
    "\n",
    "- **`seller_rate`**: Numeric, The seller's rating on eBay. This is the number of positive ratings minus the number of negative ratings for the seller.\n",
    "\n",
    "- **`stock_photo`**: Binary, Whether the auction feature photo was a stock photo or not. If the picture was used in many auctions, then it was called a stock photo.\n",
    "\n",
    "- **`wheels`**: Numeric, Number of Wii wheels included in the auction. These are steering wheel attachments to make it seem as though you are actually driving in the game. When used with the controller, turning the wheel actually causes the character on screen to turn.\n",
    "\n",
    "- **`title`**: String, The title of the auctions.\n",
    "\n",
    "The modified dataset is available as a CSV file named `mariokart.csv` and can be downloaded from [here](https://raw.githubusercontent.com/imranture/practice_stats/main/datasets/mariokart.csv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 0**: Read in the dataset as `df` and display the first 10 rows. How many rows and columns are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 145 rows and 12 columns\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('datasets/mariokart.csv')\n",
    "print(f'There are {df.shape[0]} rows and {df.shape[1]} columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1**:  Display 5 randomly sampled rows and make sure that the variable types match the data descriptions outlined in the Data Overview above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>n_bids</th>\n",
       "      <th>cond</th>\n",
       "      <th>start_pr</th>\n",
       "      <th>ship_pr</th>\n",
       "      <th>total_pr</th>\n",
       "      <th>ship_sp</th>\n",
       "      <th>seller_rate</th>\n",
       "      <th>stock_photo</th>\n",
       "      <th>wheels</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>4.000000e+11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>new</td>\n",
       "      <td>54.99</td>\n",
       "      <td>11.45</td>\n",
       "      <td>66.44</td>\n",
       "      <td>upsGround</td>\n",
       "      <td>118345</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>MARIO KART WITH WII WHEEL+1 GT PRO PINK WII WH...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2.300000e+11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>used</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>priority</td>\n",
       "      <td>15</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Mario Kart Wii WITH  BONUS WHEEL!!!!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.000000e+11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>used</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>upsGround</td>\n",
       "      <td>15</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Mario Kart Wii (Wii)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2.300000e+11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>used</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.00</td>\n",
       "      <td>40.06</td>\n",
       "      <td>firstClass</td>\n",
       "      <td>4473</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>Mario Kart Wii (Wii) - Comes with Wheel - Used</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1.800000e+11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>used</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>standard</td>\n",
       "      <td>2046</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>Mario Kart Wii Nintendo Wii Game &amp;amp; Wheel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  duration  n_bids  cond  start_pr  ship_pr  total_pr  \\\n",
       "103  4.000000e+11       1.0     1.0   new     54.99    11.45     66.44   \n",
       "94   2.300000e+11       7.0    15.0  used     12.00     0.00     46.00   \n",
       "27   3.000000e+11       3.0     5.0  used      0.99     0.00     36.00   \n",
       "56   2.300000e+11       7.0    10.0  used      0.99     4.00     40.06   \n",
       "52   1.800000e+11       3.0    16.0  used      0.99     0.00     36.00   \n",
       "\n",
       "        ship_sp  seller_rate stock_photo  wheels  \\\n",
       "103   upsGround       118345         yes       2   \n",
       "94     priority           15         yes       1   \n",
       "27    upsGround           15         yes       0   \n",
       "56   firstClass         4473          no       1   \n",
       "52     standard         2046          no       1   \n",
       "\n",
       "                                                 title  \n",
       "103  MARIO KART WITH WII WHEEL+1 GT PRO PINK WII WH...  \n",
       "94               Mario Kart Wii WITH  BONUS WHEEL!!!!!  \n",
       "27                                Mario Kart Wii (Wii)  \n",
       "56      Mario Kart Wii (Wii) - Comes with Wheel - Used  \n",
       "52        Mario Kart Wii Nintendo Wii Game &amp; Wheel  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id             float64\n",
       "duration       float64\n",
       "n_bids         float64\n",
       "cond            object\n",
       "start_pr       float64\n",
       "ship_pr        float64\n",
       "total_pr       float64\n",
       "ship_sp         object\n",
       "seller_rate      int64\n",
       "stock_photo     object\n",
       "wheels           int64\n",
       "title           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 2**: Generate summary statistics of `df`. \n",
    "\n",
    "**Hint**: Use the `describe()` method with `include = np.number` and `include = np.object`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>n_bids</th>\n",
       "      <th>start_pr</th>\n",
       "      <th>ship_pr</th>\n",
       "      <th>total_pr</th>\n",
       "      <th>seller_rate</th>\n",
       "      <th>wheels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>144.00</td>\n",
       "      <td>144.00</td>\n",
       "      <td>145.00</td>\n",
       "      <td>145.00</td>\n",
       "      <td>143.00</td>\n",
       "      <td>145.00</td>\n",
       "      <td>145.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.224828e+11</td>\n",
       "      <td>3.78</td>\n",
       "      <td>13.53</td>\n",
       "      <td>8.67</td>\n",
       "      <td>3.10</td>\n",
       "      <td>49.88</td>\n",
       "      <td>15988.64</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.785990e+10</td>\n",
       "      <td>2.58</td>\n",
       "      <td>5.86</td>\n",
       "      <td>14.99</td>\n",
       "      <td>3.21</td>\n",
       "      <td>25.69</td>\n",
       "      <td>51549.24</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000e+11</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.400000e+11</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.18</td>\n",
       "      <td>115.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.110000e+11</td>\n",
       "      <td>3.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.99</td>\n",
       "      <td>46.50</td>\n",
       "      <td>820.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.900000e+11</td>\n",
       "      <td>7.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>53.99</td>\n",
       "      <td>4858.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000e+11</td>\n",
       "      <td>10.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>69.95</td>\n",
       "      <td>25.51</td>\n",
       "      <td>326.51</td>\n",
       "      <td>270144.00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  duration  n_bids  start_pr  ship_pr  total_pr  \\\n",
       "count  1.450000e+02    144.00  144.00    145.00   145.00    143.00   \n",
       "mean   2.224828e+11      3.78   13.53      8.67     3.10     49.88   \n",
       "std    8.785990e+10      2.58    5.86     14.99     3.21     25.69   \n",
       "min    1.100000e+11      1.00    1.00      0.01     0.00     28.98   \n",
       "25%    1.400000e+11      1.00   10.00      0.99     0.00     41.18   \n",
       "50%    2.110000e+11      3.00   14.00      1.00     2.99     46.50   \n",
       "75%    2.900000e+11      7.00   17.00     10.00     4.00     53.99   \n",
       "max    4.000000e+11     10.00   29.00     69.95    25.51    326.51   \n",
       "\n",
       "       seller_rate  wheels  \n",
       "count       145.00  145.00  \n",
       "mean      15988.64    1.13  \n",
       "std       51549.24    0.85  \n",
       "min           0.00    0.00  \n",
       "25%         115.00    0.00  \n",
       "50%         820.00    1.00  \n",
       "75%        4858.00    2.00  \n",
       "max      270144.00    4.00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "df.describe(include = np.number).round(2)  # numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cond</th>\n",
       "      <th>ship_sp</th>\n",
       "      <th>stock_photo</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>145</td>\n",
       "      <td>144</td>\n",
       "      <td>145</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>used</td>\n",
       "      <td>standard</td>\n",
       "      <td>yes</td>\n",
       "      <td>BRAND NEW NINTENDO MARIO KART WITH 2 WHEELS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>85</td>\n",
       "      <td>34</td>\n",
       "      <td>105</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        cond   ship_sp stock_photo  \\\n",
       "count    145       144         145   \n",
       "unique     2         8           2   \n",
       "top     used  standard         yes   \n",
       "freq      85        34         105   \n",
       "\n",
       "                                              title  \n",
       "count                                           144  \n",
       "unique                                           82  \n",
       "top     BRAND NEW NINTENDO MARIO KART WITH 2 WHEELS  \n",
       "freq                                             23  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = object)  # categorical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 3**: Plot a histogram of `total_pr` with 30 bins to see the distribution. What do you notice?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(df['total_pr'], bins = 30)\n",
    "plt.xlabel('total_pr')\n",
    "plt.title('Histogram of total_pr')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be 2 possible outliers. Dealing with outliers can be tricky and the solutions depend on the problem you will be working on. Before we decide what to do, let's explore those possible outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>duration</th>\n",
       "      <th>n_bids</th>\n",
       "      <th>cond</th>\n",
       "      <th>start_pr</th>\n",
       "      <th>ship_pr</th>\n",
       "      <th>total_pr</th>\n",
       "      <th>ship_sp</th>\n",
       "      <th>seller_rate</th>\n",
       "      <th>stock_photo</th>\n",
       "      <th>wheels</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.100000e+11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>used</td>\n",
       "      <td>1.00</td>\n",
       "      <td>25.51</td>\n",
       "      <td>326.51</td>\n",
       "      <td>parcel</td>\n",
       "      <td>115</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>Nintedo Wii Console Bundle Guitar Hero 5 Mario...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1.300000e+11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>used</td>\n",
       "      <td>6.95</td>\n",
       "      <td>4.00</td>\n",
       "      <td>118.50</td>\n",
       "      <td>parcel</td>\n",
       "      <td>41</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>10 Nintendo Wii Games - MarioKart Wii, SpiderM...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  duration  n_bids  cond  start_pr  ship_pr  total_pr ship_sp  \\\n",
       "19  1.100000e+11       7.0    22.0  used      1.00    25.51    326.51  parcel   \n",
       "64  1.300000e+11       3.0    27.0  used      6.95     4.00    118.50  parcel   \n",
       "\n",
       "    seller_rate stock_photo  wheels  \\\n",
       "19          115          no       2   \n",
       "64           41          no       0   \n",
       "\n",
       "                                                title  \n",
       "19  Nintedo Wii Console Bundle Guitar Hero 5 Mario...  \n",
       "64  10 Nintendo Wii Games - MarioKart Wii, SpiderM...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['total_pr'] > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the `title` for Row 19 is \"Nintedio Wii Console...\". This means this auction included the game console and other game in a bundle. That might explain why this auction sold at a higher price. Meanwhile, Row 64 apparently was a sale which contains 9 other Nintendo Wii games besides MarioKart. It is also reasonable why it had a higher selling price.\n",
    "\n",
    "Based on these, should we remove or keep these rows? It depends on the objective of your statistical modelling! Say, we aim to develop an analytic model to predict the price of MarioKart only (and maybe bundled with the wheels). In this case, removing Row 19 and Row 64 would be better since these observations include other products. \n",
    "\n",
    "In conclusion, let's remove Row 19 and Row 64. In general, if we do not have additional information, it is safer to remove outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['total_pr'].notna() < 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 4**: Are there any ID-like column(s)? If there are, remove them. Why should we remove ID-like columns?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has two ID-like columns: `id` and `title`. Such variables are irrelevant in statistical modeling and \n",
    "they must be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>998</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>new</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>standard</td>\n",
       "      <td>7</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>new</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>71.00</td>\n",
       "      <td>media</td>\n",
       "      <td>820</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  n_bids  cond  start_pr  ship_pr  total_pr     ship_sp  \\\n",
       "0       3.0    20.0   new      0.99     4.00     51.55    standard   \n",
       "1       7.0    13.0  used      0.99     3.99     37.04  firstClass   \n",
       "2       3.0    16.0   new      0.99     3.50     45.50  firstClass   \n",
       "3       3.0    18.0   new      0.99     0.00     44.00    standard   \n",
       "4       1.0    20.0   new      0.01     0.00     71.00       media   \n",
       "\n",
       "   seller_rate stock_photo  wheels  \n",
       "0         1580         yes       1  \n",
       "1          365         yes       1  \n",
       "2          998          no       1  \n",
       "3            7         yes       1  \n",
       "4          820         yes       2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['id', 'title'], axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 5**: Are there any missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration       1\n",
       "n_bids         1\n",
       "cond           0\n",
       "start_pr       0\n",
       "ship_pr        0\n",
       "total_pr       2\n",
       "ship_sp        1\n",
       "seller_rate    0\n",
       "stock_photo    0\n",
       "wheels         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 6**: Identify the row(s) with missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>91</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>no</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44312</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "     duration  n_bids  cond  start_pr  ship_pr  total_pr   ship_sp  \\\n",
       "91        5.0    12.0   new      0.99      0.0       NaN  standard   \n",
       "128       NaN     NaN  used      0.99      0.0       NaN       NaN   \n",
       "\n",
       "     seller_rate stock_photo  wheels  \n",
       "91           567          no       0  \n",
       "128        44312          no       0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isna().any(axis = 1)] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 7**: Remove the row(s) with missing values and make sure there are no missing values left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration       0\n",
       "n_bids         0\n",
       "cond           0\n",
       "start_pr       0\n",
       "ship_pr        0\n",
       "total_pr       0\n",
       "ship_sp        0\n",
       "seller_rate    0\n",
       "stock_photo    0\n",
       "wheels         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 8**: `seller_rate` is the number of positive reviews minus the number of negative reviews. Discretize the numerical variable, `seller_rate` and make it a categorical variable with 5 categories: *very low*, *low*, *medium*, *high* and *very high*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>yes</td>\n",
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       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>new</td>\n",
       "      <td>0.99</td>\n",
       "      <td>3.50</td>\n",
       "      <td>45.50</td>\n",
       "      <td>firstClass</td>\n",
       "      <td>medium</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>new</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>standard</td>\n",
       "      <td>very low</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>new</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>71.00</td>\n",
       "      <td>media</td>\n",
       "      <td>medium</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  n_bids  cond  start_pr  ship_pr  total_pr     ship_sp  \\\n",
       "0       3.0    20.0   new      0.99     4.00     51.55    standard   \n",
       "1       7.0    13.0  used      0.99     3.99     37.04  firstClass   \n",
       "2       3.0    16.0   new      0.99     3.50     45.50  firstClass   \n",
       "3       3.0    18.0   new      0.99     0.00     44.00    standard   \n",
       "4       1.0    20.0   new      0.01     0.00     71.00       media   \n",
       "\n",
       "  seller_rate stock_photo  wheels  \n",
       "0        high         yes       1  \n",
       "1      medium         yes       1  \n",
       "2      medium          no       1  \n",
       "3    very low         yes       1  \n",
       "4      medium         yes       2  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seller_rate'] = pd.qcut(df['seller_rate'], q = 5, labels=['very low', 'low', 'medium', 'high', 'very high'])\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make sure we performed the dicretization correctly using the value_counts method in Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_rate\n",
       "high         32\n",
       "very low     30\n",
       "medium       29\n",
       "low          27\n",
       "very high    25\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['seller_rate'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 9**: Perform integer encoding for `seller_rate` such that *very low* is 0, *low* is 1, *medium* is 2, *high* is 3 and *very high* is 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>wheels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>new</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.00</td>\n",
       "      <td>51.55</td>\n",
       "      <td>standard</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>used</td>\n",
       "      <td>0.99</td>\n",
       "      <td>3.99</td>\n",
       "      <td>37.04</td>\n",
       "      <td>firstClass</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>new</td>\n",
       "      <td>0.99</td>\n",
       "      <td>3.50</td>\n",
       "      <td>45.50</td>\n",
       "      <td>firstClass</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>new</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>standard</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>new</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>71.00</td>\n",
       "      <td>media</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  n_bids  cond  start_pr  ship_pr  total_pr     ship_sp  \\\n",
       "0       3.0    20.0   new      0.99     4.00     51.55    standard   \n",
       "1       7.0    13.0  used      0.99     3.99     37.04  firstClass   \n",
       "2       3.0    16.0   new      0.99     3.50     45.50  firstClass   \n",
       "3       3.0    18.0   new      0.99     0.00     44.00    standard   \n",
       "4       1.0    20.0   new      0.01     0.00     71.00       media   \n",
       "\n",
       "  seller_rate stock_photo  wheels  \n",
       "0           3         yes       1  \n",
       "1           2         yes       1  \n",
       "2           2          no       1  \n",
       "3           0         yes       1  \n",
       "4           2         yes       2  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "level_mapping = {'very low': 0, 'low': 1, 'medium': 2, 'high': 3, 'very high': 4}\n",
    "df['seller_rate'] = df['seller_rate'].replace(level_mapping)\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 10**: Use one hot encoding for the remaining categorical features. How many columns do we have now? Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>duration</th>\n",
       "      <th>n_bids</th>\n",
       "      <th>cond</th>\n",
       "      <th>start_pr</th>\n",
       "      <th>ship_pr</th>\n",
       "      <th>total_pr</th>\n",
       "      <th>stock_photo</th>\n",
       "      <th>wheels</th>\n",
       "      <th>ship_sp_firstClass</th>\n",
       "      <th>ship_sp_media</th>\n",
       "      <th>...</th>\n",
       "      <th>ship_sp_parcel</th>\n",
       "      <th>ship_sp_priority</th>\n",
       "      <th>ship_sp_standard</th>\n",
       "      <th>ship_sp_ups3Day</th>\n",
       "      <th>ship_sp_upsGround</th>\n",
       "      <th>seller_rate_0</th>\n",
       "      <th>seller_rate_1</th>\n",
       "      <th>seller_rate_2</th>\n",
       "      <th>seller_rate_3</th>\n",
       "      <th>seller_rate_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.00</td>\n",
       "      <td>51.55</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.99</td>\n",
       "      <td>3.99</td>\n",
       "      <td>37.04</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.99</td>\n",
       "      <td>3.50</td>\n",
       "      <td>45.50</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>71.00</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  n_bids   cond  start_pr  ship_pr  total_pr  stock_photo  wheels  \\\n",
       "0       3.0    20.0  False      0.99     4.00     51.55         True       1   \n",
       "1       7.0    13.0   True      0.99     3.99     37.04         True       1   \n",
       "2       3.0    16.0  False      0.99     3.50     45.50        False       1   \n",
       "3       3.0    18.0  False      0.99     0.00     44.00         True       1   \n",
       "4       1.0    20.0  False      0.01     0.00     71.00         True       2   \n",
       "\n",
       "   ship_sp_firstClass  ship_sp_media  ...  ship_sp_parcel  ship_sp_priority  \\\n",
       "0               False          False  ...           False             False   \n",
       "1                True          False  ...           False             False   \n",
       "2                True          False  ...           False             False   \n",
       "3               False          False  ...           False             False   \n",
       "4               False           True  ...           False             False   \n",
       "\n",
       "   ship_sp_standard  ship_sp_ups3Day  ship_sp_upsGround  seller_rate_0  \\\n",
       "0              True            False              False          False   \n",
       "1             False            False              False          False   \n",
       "2             False            False              False          False   \n",
       "3              True            False              False           True   \n",
       "4             False            False              False          False   \n",
       "\n",
       "   seller_rate_1  seller_rate_2  seller_rate_3  seller_rate_4  \n",
       "0          False          False           True          False  \n",
       "1          False           True          False          False  \n",
       "2          False           True          False          False  \n",
       "3          False          False          False          False  \n",
       "4          False           True          False          False  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the list of categorical descriptive features\n",
    "categorical_cols = df.columns[df.dtypes == object].tolist()\n",
    "\n",
    "# if a categorical descriptive feature has only 2 levels,\n",
    "# define only one binary variable\n",
    "for col in categorical_cols:\n",
    "    n = len(df[col].unique())\n",
    "    if (n == 2):\n",
    "        df[col] = pd.get_dummies(df[col], drop_first=True)\n",
    "    \n",
    "# for other categorical features (with > 2 levels), \n",
    "# use regular one-hot-encoding \n",
    "# if a feature is numeric, it will be untouched\n",
    "df = pd.get_dummies(df)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 143 rows and 21 columns.\n",
      "One hot encoding creates a column for each level of the categorical feature, however when there is a binary class we can encode it as 0 or 1.\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {df.shape[0]} rows and {df.shape[1]} columns.')\n",
    "print('One hot encoding creates a column for each level of the categorical feature, however when there is a binary class we can encode it as 0 or 1.')"
   ]
  }
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