eBay Product Analysis - tablets in DE.ipynb

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 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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    "from IPython.display import HTML\n",
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    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Intro**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea for this EDA came from the desire to buy a tablet on eBay. Specifically, I wanted to get answers for these questions: \n",
    "+ How much should I be paying for a tablet?\n",
    "+ What is the average price of a tablet on eBay?\n",
    "+ Is there a relationship between tablet price and seller feedback score? (if tablets cost more when sellers have higher feedback scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import pandas as pd\n",
    "import json\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# functions \n",
    "def get_oauth_configuration():\n",
    "    with open('config.json') as config_file:\n",
    "        json_conf = json.load(config_file)\n",
    "    return json_conf\n",
    "\n",
    "def call_limit(total_items):\n",
    "    return int(total_items['total']/200) #200 is the return limit for each call\n",
    "\n",
    "def get_post_code(item_location):\n",
    "    try:\n",
    "        return item_location['postalCode']\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def get_country(item_location):\n",
    "    try:\n",
    "        return item_location['country']\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def get_price(price):\n",
    "    try:\n",
    "        return price['value']\n",
    "    except:\n",
    "        return None\n",
    "    \n",
    "def get_initial_currency(price):\n",
    "    try:\n",
    "        return price['currency']\n",
    "    except:\n",
    "        return None\n",
    "    \n",
    "def get_feedback_score(seller):\n",
    "    try:\n",
    "        return seller['feedbackScore']\n",
    "    except:\n",
    "        return None\n",
    "    \n",
    "def get_seller_username(seller):\n",
    "    try:\n",
    "        return seller['username']\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get access to eBay production servers**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To connnect to the eBay production servers, you will need to become an eBay Developers Program member and get OAuth Application Token. Please follow instructions [here](https://developer.ebay.com/api-docs/static/make-a-call.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# authorization configuration\n",
    "json_conf = get_oauth_configuration()\n",
    "oauth_key = \"Bearer %s\" % (json_conf['OAuth'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Collecting data from eBay production servers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# user inputs, in case the analysis to be used for other products\n",
    "search_item = \"tablet\" \n",
    "category_id = \"171485\"\n",
    "country = \"DE\" # two digit country code\n",
    "buying_option = \"FIXED_PRICE\" # or \"AUCTION\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*user inputs:*\n",
    "+ search_item = \"tablet\" \n",
    "+ category_id = \"171485\"\n",
    "+ country = \"DE\" # two digit country code\n",
    "+ buying_option = \"FIXED_PRICE\" # or \"AUCTION\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For further configuration:\n",
    "+ Detailed list of category IDs can be found [here](https://pages.ebay.com/sellerinformation/growing/categorychanges/computers-all.html)\n",
    "+ Documentation on [URL parameters](https://developer.ebay.com/api-docs/buy/browse/resources/item_summary/methods/search) and [call field filters](https://developer.ebay.com/api-docs/buy/static/ref-buy-browse-filters.html#buyingOptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### initial query \n",
    "url = \"https://api.ebay.com/buy/browse/v1/item_summary/search\"\n",
    "\n",
    "item_location_country = \"itemLocationCountry:%s\" %country\n",
    "buying_option_query_string = \"buyingOptions:{%s}\" %buying_option\n",
    "\n",
    "querystring = {\"q\":search_item,\"category_ids\":category_id,\"filter\":[item_location_country,\n",
    "                                      buying_option_query_string],\n",
    "               \"limit\":\"200\"}\n",
    "\n",
    "headers = {\n",
    "    'Authorization': oauth_key,\n",
    "    'Cache-Control': \"no-cache\",\n",
    "    'Postman-Token': \"ea7eb33b-e3d5-489f-9647-df655f34e5cb\"\n",
    "    }\n",
    "\n",
    "response = requests.request(\"GET\", url, headers=headers, params=querystring)\n",
    "\n",
    "# parsing the response\n",
    "item_text = dict(response.json())\n",
    "item_summaries = item_text['itemSummaries']\n",
    "\n",
    "# dataframe from the first result\n",
    "items_df = pd.DataFrame.from_dict(item_summaries)\n",
    "items_df = items_df[[\n",
    "       'condition', 'conditionId', 'itemId', 'itemLocation',\n",
    "       'itemWebUrl', 'price', 'seller', \n",
    "       'title']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# call function to determine the number of times to call API\n",
    "call_limit_number = call_limit(item_text)\n",
    "\n",
    "# set the initial URL to fetch data from the next page\n",
    "url_for_loop = item_text['next']\n",
    "\n",
    "# for loop to fetch all available data for search item\n",
    "for i in range(call_limit_number):\n",
    "    try:\n",
    "        response_next = requests.request(\"GET\", url_for_loop, headers=headers, params=querystring)\n",
    "\n",
    "\n",
    "        # parsing the response\n",
    "        item_text_iteration = dict(response_next.json())\n",
    "        item_summaries_iteration = item_text_iteration['itemSummaries']\n",
    "\n",
    "        # dataframe from the first result\n",
    "        items_iteration = pd.DataFrame.from_dict(item_summaries_iteration)\n",
    "        items_iteration = items_iteration[[\n",
    "           'condition', 'conditionId', 'itemId', 'itemLocation',\n",
    "           'itemWebUrl', 'price', 'seller',\n",
    "           'title']]\n",
    "        \n",
    "        # appending items_df\n",
    "        items_df = items_df.append(items_iteration)\n",
    "\n",
    "        # set the URL to fetch within the for loop for the next page\n",
    "        url_for_loop = item_text_iteration['next']\n",
    "    \n",
    "    except:\n",
    "        break"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>conditionId</th>\n",
       "      <th>itemId</th>\n",
       "      <th>itemLocation</th>\n",
       "      <th>itemWebUrl</th>\n",
       "      <th>price</th>\n",
       "      <th>seller</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Neu</td>\n",
       "      <td>1000</td>\n",
       "      <td>v1|263656865900|0</td>\n",
       "      <td>{'postalCode': '73525', 'country': 'DE'}</td>\n",
       "      <td>https://www.ebay.com/itm/MICROSOFT-SURFACE-PRO...</td>\n",
       "      <td>{'value': '142.11', 'currency': 'USD', 'conver...</td>\n",
       "      <td>{'username': 'microsoftler83', 'feedbackPercen...</td>\n",
       "      <td>MICROSOFT SURFACE PRO 4 WiFi 128GB TABLET PC I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Used</td>\n",
       "      <td>3000</td>\n",
       "      <td>v1|263692832147|0</td>\n",
       "      <td>{'postalCode': '10178', 'country': 'DE'}</td>\n",
       "      <td>https://www.ebay.com/itm/MINT-Barnes-Noble-Noo...</td>\n",
       "      <td>{'value': '37.43', 'currency': 'USD', 'convert...</td>\n",
       "      <td>{'username': 'truc7de', 'feedbackPercentage': ...</td>\n",
       "      <td>MINT   Barnes  Noble Nook Tablet 7\" (Model Bnt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vom Hersteller generalüberholt</td>\n",
       "      <td>2000</td>\n",
       "      <td>v1|372305712989|0</td>\n",
       "      <td>{'postalCode': '37539', 'country': 'DE'}</td>\n",
       "      <td>https://www.ebay.com/itm/Archos-70C-Xenon-8-GB...</td>\n",
       "      <td>{'value': '70.63', 'currency': 'USD', 'convert...</td>\n",
       "      <td>{'username': 'newborn1977', 'feedbackPercentag...</td>\n",
       "      <td>Archos 70C Xenon 8 GB Tablet Android Dual Core...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        condition conditionId             itemId  \\\n",
       "0                             Neu        1000  v1|263656865900|0   \n",
       "1                            Used        3000  v1|263692832147|0   \n",
       "2  Vom Hersteller generalüberholt        2000  v1|372305712989|0   \n",
       "\n",
       "                               itemLocation  \\\n",
       "0  {'postalCode': '73525', 'country': 'DE'}   \n",
       "1  {'postalCode': '10178', 'country': 'DE'}   \n",
       "2  {'postalCode': '37539', 'country': 'DE'}   \n",
       "\n",
       "                                          itemWebUrl  \\\n",
       "0  https://www.ebay.com/itm/MICROSOFT-SURFACE-PRO...   \n",
       "1  https://www.ebay.com/itm/MINT-Barnes-Noble-Noo...   \n",
       "2  https://www.ebay.com/itm/Archos-70C-Xenon-8-GB...   \n",
       "\n",
       "                                               price  \\\n",
       "0  {'value': '142.11', 'currency': 'USD', 'conver...   \n",
       "1  {'value': '37.43', 'currency': 'USD', 'convert...   \n",
       "2  {'value': '70.63', 'currency': 'USD', 'convert...   \n",
       "\n",
       "                                              seller  \\\n",
       "0  {'username': 'microsoftler83', 'feedbackPercen...   \n",
       "1  {'username': 'truc7de', 'feedbackPercentage': ...   \n",
       "2  {'username': 'newborn1977', 'feedbackPercentag...   \n",
       "\n",
       "                                               title  \n",
       "0  MICROSOFT SURFACE PRO 4 WiFi 128GB TABLET PC I...  \n",
       "1  MINT   Barnes  Noble Nook Tablet 7\" (Model Bnt...  \n",
       "2  Archos 70C Xenon 8 GB Tablet Android Dual Core...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting dataset needs to be cleaned before the analysis. Item location, price, and seller columns have nested structures (dictionaries)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Data Cleaning **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1149 entries, 0 to 148\n",
      "Data columns (total 8 columns):\n",
      "condition       1149 non-null object\n",
      "conditionId     1149 non-null object\n",
      "itemId          1149 non-null object\n",
      "itemLocation    1149 non-null object\n",
      "itemWebUrl      1149 non-null object\n",
      "price           1149 non-null object\n",
      "seller          1149 non-null object\n",
      "title           1149 non-null object\n",
      "dtypes: object(8)\n",
      "memory usage: 80.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# get initial info about dataframe\n",
    "items_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has 1149 rows and 8 columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique items:  987\n"
     ]
    }
   ],
   "source": [
    "# number of unique selling offerings\n",
    "print(\"Unique items: \", items_df.itemId.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of dataset after dropping dulplicates:  (987, 8)\n"
     ]
    }
   ],
   "source": [
    "# droping duplicates\n",
    "items_df.drop_duplicates(subset='itemId',inplace=True)\n",
    "print('The shape of dataset after dropping dulplicates: ', items_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying functions to get rid of dictionaries in rows\n",
    "items_df_clean = items_df.copy()\n",
    "\n",
    "# location info\n",
    "items_df_clean['post_code'] = items_df_clean.itemLocation.apply(get_post_code)\n",
    "items_df_clean['country'] = items_df_clean.itemLocation.apply(get_country)\n",
    "items_df_clean.drop('itemLocation', axis=1, inplace=True)\n",
    "\n",
    "# price info\n",
    "items_df_clean['price_value'] = items_df_clean.price.apply(get_price)\n",
    "items_df_clean['currency'] = items_df_clean.price.apply(get_initial_currency)\n",
    "items_df_clean.drop('price', axis=1, inplace=True)\n",
    "\n",
    "# feedback score and seller info\n",
    "items_df_clean['feedback_score'] = items_df_clean.seller.apply(get_feedback_score)\n",
    "items_df_clean['seller_username'] = items_df_clean.seller.apply(get_seller_username)\n",
    "items_df_clean.drop('seller', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 987 entries, 0 to 148\n",
      "Data columns (total 11 columns):\n",
      "condition          987 non-null object\n",
      "conditionId        987 non-null object\n",
      "itemId             987 non-null object\n",
      "itemWebUrl         987 non-null object\n",
      "title              987 non-null object\n",
      "post_code          890 non-null object\n",
      "country            987 non-null object\n",
      "price_value        987 non-null object\n",
      "currency           987 non-null object\n",
      "feedback_score     987 non-null int64\n",
      "seller_username    987 non-null object\n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 92.5+ KB\n"
     ]
    }
   ],
   "source": [
    "items_df_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that data types of columns _postCode_, _conditionId_, _priceValue_ are non numeric. Therefore, these columns need to be transformed to numeric ones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change the following columns to numeric dtype\n",
    "items_df_clean[['conditionId', 'post_code','price_value', \n",
    "                'feedback_score']] = items_df_clean[['conditionId', 'post_code','price_value', \n",
    "                'feedback_score']].apply(pd.to_numeric, errors='coerse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 987 entries, 0 to 148\n",
      "Data columns (total 11 columns):\n",
      "condition          987 non-null object\n",
      "conditionId        987 non-null int64\n",
      "itemId             987 non-null object\n",
      "itemWebUrl         987 non-null object\n",
      "title              987 non-null object\n",
      "post_code          890 non-null float64\n",
      "country            987 non-null object\n",
      "price_value        987 non-null float64\n",
      "currency           987 non-null object\n",
      "feedback_score     987 non-null int64\n",
      "seller_username    987 non-null object\n",
      "dtypes: float64(2), int64(2), object(7)\n",
      "memory usage: 92.5+ KB\n"
     ]
    }
   ],
   "source": [
    "items_df_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After tranformation, we have all columns in proper data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>conditionId</th>\n",
       "      <th>itemId</th>\n",
       "      <th>itemWebUrl</th>\n",
       "      <th>title</th>\n",
       "      <th>post_code</th>\n",
       "      <th>country</th>\n",
       "      <th>price_value</th>\n",
       "      <th>currency</th>\n",
       "      <th>feedback_score</th>\n",
       "      <th>seller_username</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Neu</td>\n",
       "      <td>1000</td>\n",
       "      <td>v1|263656865900|0</td>\n",
       "      <td>https://www.ebay.com/itm/MICROSOFT-SURFACE-PRO...</td>\n",
       "      <td>MICROSOFT SURFACE PRO 4 WiFi 128GB TABLET PC I...</td>\n",
       "      <td>73525.0</td>\n",
       "      <td>DE</td>\n",
       "      <td>142.11</td>\n",
       "      <td>USD</td>\n",
       "      <td>399</td>\n",
       "      <td>microsoftler83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Used</td>\n",
       "      <td>3000</td>\n",
       "      <td>v1|263692832147|0</td>\n",
       "      <td>https://www.ebay.com/itm/MINT-Barnes-Noble-Noo...</td>\n",
       "      <td>MINT   Barnes  Noble Nook Tablet 7\" (Model Bnt...</td>\n",
       "      <td>10178.0</td>\n",
       "      <td>DE</td>\n",
       "      <td>37.43</td>\n",
       "      <td>USD</td>\n",
       "      <td>14760</td>\n",
       "      <td>truc7de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vom Hersteller generalüberholt</td>\n",
       "      <td>2000</td>\n",
       "      <td>v1|372305712989|0</td>\n",
       "      <td>https://www.ebay.com/itm/Archos-70C-Xenon-8-GB...</td>\n",
       "      <td>Archos 70C Xenon 8 GB Tablet Android Dual Core...</td>\n",
       "      <td>37539.0</td>\n",
       "      <td>DE</td>\n",
       "      <td>70.63</td>\n",
       "      <td>USD</td>\n",
       "      <td>5515</td>\n",
       "      <td>newborn1977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        condition  conditionId             itemId  \\\n",
       "0                             Neu         1000  v1|263656865900|0   \n",
       "1                            Used         3000  v1|263692832147|0   \n",
       "2  Vom Hersteller generalüberholt         2000  v1|372305712989|0   \n",
       "\n",
       "                                          itemWebUrl  \\\n",
       "0  https://www.ebay.com/itm/MICROSOFT-SURFACE-PRO...   \n",
       "1  https://www.ebay.com/itm/MINT-Barnes-Noble-Noo...   \n",
       "2  https://www.ebay.com/itm/Archos-70C-Xenon-8-GB...   \n",
       "\n",
       "                                               title  post_code country  \\\n",
       "0  MICROSOFT SURFACE PRO 4 WiFi 128GB TABLET PC I...    73525.0      DE   \n",
       "1  MINT   Barnes  Noble Nook Tablet 7\" (Model Bnt...    10178.0      DE   \n",
       "2  Archos 70C Xenon 8 GB Tablet Android Dual Core...    37539.0      DE   \n",
       "\n",
       "   price_value currency  feedback_score seller_username  \n",
       "0       142.11      USD             399  microsoftler83  \n",
       "1        37.43      USD           14760         truc7de  \n",
       "2        70.63      USD            5515     newborn1977  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df_clean.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cleaned dataframe looks good. However, lets make sure that some details are matching: \n",
    "+ *country* should be DE (the analysis is only for DE) \n",
    "+ *currency* should be only USD to make it consistent (the initial EDA revealed that there are several currencies in the results)\n",
    "+ *condition* should have no duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available currencies:  ['USD']\n",
      "available countries:  ['DE']\n",
      "available condition types:  ['Neu' 'Used' 'Vom Hersteller generalüberholt' 'Gebraucht' 'New'\n",
      " 'New other (see details)' 'Neu: Sonstige (siehe Artikelbeschreibung)'\n",
      " 'Als Ersatzteil / defekt' 'New: Never Used'\n",
      " 'Vom Verkäufer generalüberholt' 'Manufacturer refurbished' 'Nuovo'] count:  12\n"
     ]
    }
   ],
   "source": [
    "print(\"available currencies: \", items_df_clean.currency.unique())\n",
    "print(\"available countries: \", items_df_clean.country.unique())\n",
    "print(\"available condition types: \", items_df_clean.condition.unique(), \n",
    "      \"count: \", items_df_clean.condition.nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are **12 different condition categores** in the data, however some of them have the same meanings:\n",
    "\n",
    "+ _Manufacturer refurbished_ **=** _Vom Hersteller generalüberholt_\n",
    "+ _New_ **=** _Neu_ **=** _Nuovo_\n",
    "+ _Used_ **=** _Gebraucht_\n",
    "+ _New other (see details)_ **=** _Neu: Sonstige (siehe Artikelbeschreibung)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available conditionID types:  [1000 3000 2000 1500 7000 2500]\n"
     ]
    }
   ],
   "source": [
    "print(\"available conditionID types: \", items_df_clean.conditionId.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matching different categories with category IDs:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>conditionId</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Neu</td>\n",
       "      <td>1000</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>New</td>\n",
       "      <td>1000</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nuovo</td>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neu: Sonstige (siehe Artikelbeschreibung)</td>\n",
       "      <td>1500</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New other (see details)</td>\n",
       "      <td>1500</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>New: Never Used</td>\n",
       "      <td>1500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Manufacturer refurbished</td>\n",
       "      <td>2000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vom Hersteller generalüberholt</td>\n",
       "      <td>2000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Vom Verkäufer generalüberholt</td>\n",
       "      <td>2500</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gebraucht</td>\n",
       "      <td>3000</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Used</td>\n",
       "      <td>3000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Als Ersatzteil / defekt</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    condition  conditionId  count\n",
       "3                                         Neu         1000    391\n",
       "5                                         New         1000     18\n",
       "8                                       Nuovo         1000      2\n",
       "4   Neu: Sonstige (siehe Artikelbeschreibung)         1500    157\n",
       "6                     New other (see details)         1500      7\n",
       "7                             New: Never Used         1500      3\n",
       "2                    Manufacturer refurbished         2000      2\n",
       "10             Vom Hersteller generalüberholt         2000     35\n",
       "11              Vom Verkäufer generalüberholt         2500     30\n",
       "1                                   Gebraucht         3000    317\n",
       "9                                        Used         3000      6\n",
       "0                     Als Ersatzteil / defekt         7000     19"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Matching different categories with category IDs:\\n')\n",
    "\n",
    "items_df_clean.groupby(['condition','conditionId']).size().reset_index().rename(columns={0:'count'}).sort_values(by='conditionId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table above, we can see that there are language differences in _condition_ column, however conditions are still assignable to unique condtion IDs that are languange independent. So, let's clean up the _condition_ column a bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#updating individual cell values\n",
    "items_df_clean.loc[items_df_clean.conditionId==1000, ['condition']] = 'New'\n",
    "items_df_clean.loc[items_df_clean.conditionId==1500, ['condition']] = 'New other (see details)'\n",
    "items_df_clean.loc[items_df_clean.conditionId==2000, ['condition']] = 'Manufacturer refurbished'\n",
    "items_df_clean.loc[items_df_clean.conditionId==2500, ['condition']] = 'Seller refurbished'\n",
    "items_df_clean.loc[items_df_clean.conditionId==3000, ['condition']] = 'Used'\n",
    "items_df_clean.loc[items_df_clean.conditionId==7000, ['condition']] = 'Spare part/defect'\n",
    "\n",
    "#change condition dtype to categorical\n",
    "items_df_clean.condition = pd.Categorical(items_df_clean.condition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>conditionId</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New</td>\n",
       "      <td>1000</td>\n",
       "      <td>411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New other (see details)</td>\n",
       "      <td>1500</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Manufacturer refurbished</td>\n",
       "      <td>2000</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seller refurbished</td>\n",
       "      <td>2500</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Used</td>\n",
       "      <td>3000</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spare part/defect</td>\n",
       "      <td>7000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  condition  conditionId  count\n",
       "1                       New         1000    411\n",
       "2   New other (see details)         1500    167\n",
       "0  Manufacturer refurbished         2000     37\n",
       "3        Seller refurbished         2500     30\n",
       "5                      Used         3000    323\n",
       "4         Spare part/defect         7000     19"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df_clean.groupby(['condition','conditionId']).size().reset_index().rename(columns={0:'count'}).sort_values(by='conditionId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks better now :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to buy a tablet on eEbay and it would be good to know how much the average price is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     987.000000\n",
       "mean      285.810091\n",
       "std       290.248791\n",
       "min         5.890000\n",
       "25%       117.740000\n",
       "50%       176.600000\n",
       "75%       324.330000\n",
       "max      2440.530000\n",
       "Name: price_value, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df_clean.price_value.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average price for a tablet is around 285USD and the maximum price is 2440USD (which is a lot for a tablet :))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the distribution of tablet prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x200429da198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#plot cost distribution\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.swarmplot(x='country', y='price_value', data=items_df_clean, orient='v')\n",
    "plt.title('Distribution of prices for tablets in DE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph, we can see that most of the tablets cost below 500USD. So, paying 500USD or less would be good?\n",
    "+ No fast conclusion, the data needs be explored more, lets look into the distribution of prices per condition category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20041b31198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot cost distribution per condtion\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.violinplot(x='country', y='price_value', data=items_df_clean, hue='condition',orient='v')\n",
    "plt.title('Price breakdown per condition type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph above, we can see that prices of new tablets are distributed in the very long tail with the upper 75% of tablets costing below 500USD and the 50% (median) of tablets costing below 250USD. \n",
    "\n",
    "+ Therefore, I would personally consider it as a reasonable price to pay 250USD for a tablet.\n",
    "\n",
    "little note here: the brand of tablet is not taken into consideration ;)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I want to find out if there is any relationship between feeback score of the seller and the price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x200429a0e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a linear regression between 'price' and 'feedback score'\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.regplot(x='price_value', y='feedback_score', data=items_df_clean, color='green')\n",
    "plt.title('Correlation between price and seller feedback score')\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently, there is no correlation between feedback score and price. The sellers do not charge more as their feedback scores increase, which is good for buyers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us have a look at cumulative distribution of feedback scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ecdf(data):\n",
    "    \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n",
    "\n",
    "    # Number of data points: n\n",
    "    n = len(data)\n",
    "\n",
    "    # x-data for the ECDF: x\n",
    "    x = np.sort(data)\n",
    "\n",
    "    # y-data for the ECDF: y\n",
    "    y = np.arange(1, n+1) / n\n",
    "\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20041e76cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Compute ECDF for versicolor data: x_vers, y_vers# Compu \n",
    "x_vers, y_vers = ecdf(items_df_clean.feedback_score)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(x_vers, y_vers, marker='.', linestyle='none', color='green')\n",
    "plt.margins(0.02)\n",
    "\n",
    "plt.xlabel('Feedback Scores')\n",
    "plt.ylabel('ECDF')\n",
    "plt.title('Cumulative distribution of feedback scores of sellers')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CDF graph shows us that most of the tablet sellers (~97%) have feedback score of less than 100,000. Let us find out what are the seller usernames that have the highest feedback scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feedback_score</th>\n",
       "      <th>seller_username</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>864737</td>\n",
       "      <td>tito-express</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>864378</td>\n",
       "      <td>tito-express</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>863984</td>\n",
       "      <td>tito-express</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>180329</td>\n",
       "      <td>www.pcline24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>180324</td>\n",
       "      <td>www.pcline24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>180322</td>\n",
       "      <td>www.pcline24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>180321</td>\n",
       "      <td>www.pcline24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>180321</td>\n",
       "      <td>www.pcline24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>180321</td>\n",
       "      <td>www.pcline24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>180315</td>\n",
       "      <td>www.pcline24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     feedback_score seller_username\n",
       "113          864737    tito-express\n",
       "109          864378    tito-express\n",
       "112          863984    tito-express\n",
       "108          180329    www.pcline24\n",
       "94           180324    www.pcline24\n",
       "104          180322    www.pcline24\n",
       "119          180321    www.pcline24\n",
       "109          180321    www.pcline24\n",
       "102          180321    www.pcline24\n",
       "93           180315    www.pcline24"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df_clean[['feedback_score', 'seller_username']].sort_values('feedback_score', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking in detail, I see that a seller with the same username can be assigned multiple feedback scores. Is it on purpose or data quality issue?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last but not least, I would want to see where are most of the tablets (postcodes) are located in Germany. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59387.0    105\n",
       "30900.0     69\n",
       "28359.0     49\n",
       "72636.0     40\n",
       "45468.0     21\n",
       "10969.0     21\n",
       "86507.0     18\n",
       "21339.0     15\n",
       "55124.0     14\n",
       "70771.0     14\n",
       "90409.0     10\n",
       "47906.0     10\n",
       "86157.0      9\n",
       "69214.0      8\n",
       "72525.0      8\n",
       "Name: post_code, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df_clean.post_code.value_counts().head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick google maps search shows that the postcode 59387 is Ascheberg, near Münster. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a very simple EDA and a bit of reading of eBay documentations, I could get answers to the initial questions that I asked the data. To sum it up:\n",
    "\n",
    "+ The 50% of new tablets prices are around 250USD or less (which is reasonable price for me to pay for a tablet)\n",
    "+ The average price of a tablet on eBay is 285USD.\n",
    "+ There is no correlation between feedback score and price. The sellers do not charge more as their feedback scores increase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other findings:\n",
    "+ When I queried eBay production servers, the results returned duplicates. Is this the case that the same listing can be found more than once in different pages of eBay?\n",
    "+ Condition types are not unique (language differences), however can be assigned to unique condition ids.\n",
    "+ Seller with the same usernames can be assigned multiple feedback scores. "
   ]
  }
 ],
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