divar task entrance.ipynb

{
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   "cell_type": "markdown",
   "id": "headed-terminology",
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Some-general-information-about-the-dataset\" data-toc-modified-id=\"Some-general-information-about-the-dataset-1\">Some general information about the dataset</a></span></li><li><span><a href=\"#Data-cleaning\" data-toc-modified-id=\"Data-cleaning-2\">Data cleaning</a></span><ul class=\"toc-item\"><li><span><a href=\"#Dropping-duplicates-and-NaNs\" data-toc-modified-id=\"Dropping-duplicates-and-NaNs-2.1\">Dropping duplicates and NaNs</a></span></li><li><span><a href=\"#Timestamp-to-human-date\" data-toc-modified-id=\"Timestamp-to-human-date-2.2\">Timestamp to human date</a></span></li><li><span><a href=\"#Splitting-the-dataset\" data-toc-modified-id=\"Splitting-the-dataset-2.3\">Splitting the dataset</a></span></li></ul></li><li><span><a href=\"#Q2\" data-toc-modified-id=\"Q2-3\">Q2</a></span><ul class=\"toc-item\"><li><span><a href=\"#Dark-query-percent\" data-toc-modified-id=\"Dark-query-percent-3.1\">Dark query percent</a></span></li><li><span><a href=\"#Query-bounce-rate\" data-toc-modified-id=\"Query-bounce-rate-3.2\">Query bounce rate</a></span><ul class=\"toc-item\"><li><span><a href=\"#Venn-Diagram\" data-toc-modified-id=\"Venn-Diagram-3.2.1\">Venn Diagram</a></span></li></ul></li></ul></li><li><span><a href=\"#Q3\" data-toc-modified-id=\"Q3-4\">Q3</a></span><ul class=\"toc-item\"><li><span><a href=\"#Chosen-metric:-number-of-clicks-per-loaded-results\" data-toc-modified-id=\"Chosen-metric:-number-of-clicks-per-loaded-results-4.1\">Chosen metric: number of clicks per loaded results</a></span><ul class=\"toc-item\"><li><span><a href=\"#Scatter-plot\" data-toc-modified-id=\"Scatter-plot-4.1.1\">Scatter plot</a></span></li><li><span><a href=\"#Pie-chart\" data-toc-modified-id=\"Pie-chart-4.1.2\">Pie chart</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sealed-ballet",
   "metadata": {},
   "source": [
    "# Divar Summer Camp Task Entrance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "linear-invitation",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib_venn import venn2\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "adjusted-binding",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('Summer Camp Task Data.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "neural-wallpaper",
   "metadata": {},
   "source": [
    "## Some general information about the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "rocky-scotland",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 111083 entries, 0 to 111082\n",
      "Data columns (total 8 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   action                   111083 non-null  object \n",
      " 1   created_at               111083 non-null  int64  \n",
      " 2   source_event_id          111083 non-null  object \n",
      " 3   device_id                109596 non-null  object \n",
      " 4   post_page_offset         35287 non-null   float64\n",
      " 5   tokens                   35287 non-null   object \n",
      " 6   post_index_in_post_list  75796 non-null   float64\n",
      " 7   post_token               75795 non-null   object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "another-dealing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "action                          2\n",
       "created_at                 109310\n",
       "source_event_id             14296\n",
       "device_id                    5012\n",
       "post_page_offset              162\n",
       "tokens                      29800\n",
       "post_index_in_post_list      1868\n",
       "post_token                  56804\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique values of each column\n",
    "\n",
    "df.apply(lambda column: column.nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "integrated-hardwood",
   "metadata": {},
   "source": [
    "## Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gross-display",
   "metadata": {},
   "source": [
    "### Dropping duplicates and NaNs\n",
    "There are some missing values in device_id column. since we don't wanna use this column for our analysis, we just don't drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "still-input",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distributed-charter",
   "metadata": {},
   "source": [
    "### Timestamp to human date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "swedish-watts",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>action</th>\n",
       "      <th>created_at</th>\n",
       "      <th>source_event_id</th>\n",
       "      <th>device_id</th>\n",
       "      <th>post_page_offset</th>\n",
       "      <th>tokens</th>\n",
       "      <th>post_index_in_post_list</th>\n",
       "      <th>post_token</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>load_post_page</td>\n",
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      "text/plain": [
       "           action       created_at                       source_event_id  \\\n",
       "0  load_post_page  23:50:01.150000  1575558c-a702-46ef-8e18-bc5cef761473   \n",
       "1  load_post_page  23:52:01.641000  0fd004f4-ce9b-4766-ac96-7578dedebacb   \n",
       "2  load_post_page  23:50:09.147000  479905f2-3bb4-4c90-8e88-5ce4b60d0a73   \n",
       "3  load_post_page  00:19:36.979000  b9b609bd-a486-42fb-b1a9-5ca03cf4a215   \n",
       "4  load_post_page  00:26:22.717000  6c390fce-34b6-4e19-837e-6bc295a72514   \n",
       "\n",
       "                device_id  post_page_offset  \\\n",
       "0  Ed9EADRZRXCHcrcEP_Hnkg              26.0   \n",
       "1  5aVIp_PNSAWxqwBETYpmxQ              19.0   \n",
       "2  mt0UAT4AQe2dECdXBvKYBA               0.0   \n",
       "3  huSKUX-mSwyUcGzgSbXX6g               0.0   \n",
       "4  sL5fLDdoR9a7aDGPP8N-jA               1.0   \n",
       "\n",
       "                                              tokens  post_index_in_post_list  \\\n",
       "0  [wXvP3enu, wXvHXzUS, wXvPHXVe, wXvPHRs3, wXvH3...                      NaN   \n",
       "1  [wXujANbk, wXurAJsf, wXufwlh1, wXtr_4tD, wXujg...                      NaN   \n",
       "2  [wXvHbNqU, wXvDrof9, wXv_aXHk, wXvDrSIa, wXv_a...                      NaN   \n",
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       "4  [wXvjJTIc, wXvb5_SX, wXvb54Gs, wXvb5t6J, wXvbJ...                      NaN   \n",
       "\n",
       "  post_token  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's get time of each event. we don't really care about the date part.\n",
    "\n",
    "df['created_at'] = df['created_at'].values.astype(dtype='datetime64[ms]')\n",
    "df['created_at'] = df['created_at'].dt.time\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "divided-smith",
   "metadata": {},
   "source": [
    "### Splitting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "solved-twelve",
   "metadata": {},
   "outputs": [],
   "source": [
    "# seperating actions of load_post_page and click_post; also dropping NaNs and duplicates.\n",
    "\n",
    "load_post = df[df['action'] == 'load_post_page'].reset_index(drop=True)\n",
    "click_post = df[df['action'] == 'click_post'].reset_index(drop=True)\n",
    "\n",
    "click_post.dropna(subset=['post_token'], inplace=True)\n",
    "load_post.dropna(subset=['tokens'], inplace=True)\n",
    "\n",
    "click_post = click_post.drop_duplicates(\n",
    "    subset=['source_event_id', 'post_index_in_post_list', 'post_token'])\n",
    "\n",
    "load_post = load_post.drop_duplicates(\n",
    "    subset=['source_event_id', 'tokens', 'post_page_offset'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "restricted-investing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting unique events for both load_post and click_post\n",
    "\n",
    "load_post_unique_events = pd.Series(\n",
    "    load_post['source_event_id'].unique()).to_frame(name='source_event_id')\n",
    "\n",
    "click_post_unique_events = pd.Series(\n",
    "    click_post['source_event_id'].unique()).to_frame(name='source_event_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perceived-pickup",
   "metadata": {},
   "source": [
    "## Q2\n",
    "### Dark query percent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hispanic-execution",
   "metadata": {},
   "source": [
    "queries with one loaded page could have less than 10 result(in this case, we restrict post_page_offset to 0)\n",
    "and after that we count those pages with less than 10 results. eventually calculating dark_query_percent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cross-despite",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    True\n",
       "12    True\n",
       "34    True\n",
       "35    True\n",
       "36    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choosing, filtering and calculating!\n",
    "\n",
    "load_post_first_load = load_post[load_post['post_page_offset'] ==\n",
    "                                 0].reset_index(drop=True)\n",
    "\n",
    "token_length_filter = load_post_first_load.apply(\n",
    "    lambda x: len(x['tokens'].split(',')) < 10,\n",
    "    axis=1).loc[lambda y: y == True]\n",
    "\n",
    "token_length_filter.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "spare-wrapping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.56"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dark_query_percent = (len(token_length_filter) /\n",
    "                      (load_post.groupby('source_event_id').ngroups)) * 100\n",
    "\n",
    "np.round(dark_query_percent, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "inside-macintosh",
   "metadata": {},
   "source": [
    "### Query bounce rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "romance-enforcement",
   "metadata": {},
   "source": [
    "by merging two dataframes and filtering the result to 'left_only', we'll get the queries that occurred as load_post_page, but not as click_post, so that the results were appeared, but user didn't click on them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "mental-surrey",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_event_id</th>\n",
       "      <th>_merge</th>\n",
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       "      <td>479905f2-3bb4-4c90-8e88-5ce4b60d0a73</td>\n",
       "      <td>left_only</td>\n",
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       "      <td>left_only</td>\n",
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       "      <th>6</th>\n",
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       "      <td>left_only</td>\n",
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       "      <th>10</th>\n",
       "      <td>4f40a5d7-9006-4add-9a14-7d2f53af8a7e</td>\n",
       "      <td>left_only</td>\n",
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       "      <th>16</th>\n",
       "      <td>6c168e02-15ec-4a3e-9a4f-495b91614529</td>\n",
       "      <td>left_only</td>\n",
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      "text/plain": [
       "                         source_event_id     _merge\n",
       "2   479905f2-3bb4-4c90-8e88-5ce4b60d0a73  left_only\n",
       "3   b9b609bd-a486-42fb-b1a9-5ca03cf4a215  left_only\n",
       "6   576278e1-9af0-4ed9-95c8-09a6b6c2e958  left_only\n",
       "10  4f40a5d7-9006-4add-9a14-7d2f53af8a7e  left_only\n",
       "16  6c168e02-15ec-4a3e-9a4f-495b91614529  left_only"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging and choosing\n",
    "\n",
    "not_clicked = load_post_unique_events.merge(click_post_unique_events,\n",
    "                                            on=['source_event_id'],\n",
    "                                            how='left',\n",
    "                                            indicator=True)\n",
    "\n",
    "not_clicked = not_clicked[not_clicked['_merge'] == 'left_only']\n",
    "not_clicked.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "rough-reggae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.05"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating query bounce rate\n",
    "\n",
    "query_bounce_rate = (len(not_clicked)) / (len(load_post_unique_events)) * 100\n",
    "np.round(query_bounce_rate, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "improved-imagination",
   "metadata": {},
   "source": [
    "<b> DATA ERROR </b>\n",
    "<br>\n",
    "There are some queries that are found in click_post, but not in load_page. it means user clicked on the result without entering a query. I suppose they're system's recommendation for each user."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "regulated-complaint",
   "metadata": {},
   "source": [
    "#### Venn Diagram\n",
    "To see how many clicks do we have in general and how many of them are on the recommendation or the loaded results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "featured-benchmark",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c1 = set(load_post_unique_events['source_event_id'])\n",
    "c2 = set(click_post_unique_events['source_event_id'])\n",
    "set_colors = ['#e07a5f', '#81b29a']\n",
    "labels = ['loaded results', 'recommendation clicks', 'loaded results clicks']\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "venn2([c1, c2], ('loaded', 'clicked'), set_colors=set_colors, alpha=0.8)\n",
    "plt.legend(labels, title='status', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accessible-charleston",
   "metadata": {},
   "source": [
    "## Q3\n",
    "\n",
    "### Chosen metric: number of clicks per loaded results\n",
    "next, we count the number of results returned for each query, then count the number of times they were clicked on. Then we merge them, and then we can calculate the average number of clicks per result returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "downtown-monaco",
   "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>source_event_id</th>\n",
       "      <th>token_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00049ef3-c027-4f55-869c-46711f0ea0ad</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00069dda-4175-4bb0-8ef0-c0e1031d4da6</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0017c208-e016-4231-a022-598ba020f1ff</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>002222cf-9e4b-4432-9e36-ad500a753850</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>004dc60d-2288-453f-bcda-1ad5e8d8211f</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        source_event_id  token_length\n",
       "0  00049ef3-c027-4f55-869c-46711f0ea0ad            24\n",
       "1  00069dda-4175-4bb0-8ef0-c0e1031d4da6            24\n",
       "2  0017c208-e016-4231-a022-598ba020f1ff           168\n",
       "3  002222cf-9e4b-4432-9e36-ad500a753850            24\n",
       "4  004dc60d-2288-453f-bcda-1ad5e8d8211f            25"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counting the number of result\n",
    "\n",
    "tokens_length = load_post[['source_event_id']]\n",
    "tokens_length = tokens_length.assign(token_length=load_post.apply(\n",
    "    lambda x: len(x['tokens'].split(',')), axis=1))\n",
    "\n",
    "num_of_tokens_per_query = tokens_length.groupby(\n",
    "    ['source_event_id'])['token_length'].sum().reset_index()\n",
    "\n",
    "num_of_tokens_per_query.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "interesting-thunder",
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouping queries based on source_event_id and calculating sum of clicks\n",
    "\n",
    "num_of_clicks = click_post.groupby(\n",
    "    ['source_event_id'])['post_token'].count().reset_index()\n",
    "\n",
    "num_of_clicks.rename(columns={'post_token': 'num_of_click'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "sharing-supply",
   "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>source_event_id</th>\n",
       "      <th>token_length</th>\n",
       "      <th>num_of_click</th>\n",
       "      <th>click_per_results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0017c208-e016-4231-a022-598ba020f1ff</td>\n",
       "      <td>168</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.023810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>004dc60d-2288-453f-bcda-1ad5e8d8211f</td>\n",
       "      <td>25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0057b345-8ad7-4c41-9e74-415e39a3140c</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>005fd78b-bd9b-4075-bda3-1ea0f855c55c</td>\n",
       "      <td>240</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>006bea27-8a2f-4453-a5f3-55d80a2b88c0</td>\n",
       "      <td>48</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        source_event_id  token_length  num_of_click  \\\n",
       "0  0017c208-e016-4231-a022-598ba020f1ff           168           4.0   \n",
       "1  004dc60d-2288-453f-bcda-1ad5e8d8211f            25           3.0   \n",
       "2  0057b345-8ad7-4c41-9e74-415e39a3140c            24           1.0   \n",
       "3  005fd78b-bd9b-4075-bda3-1ea0f855c55c           240          10.0   \n",
       "4  006bea27-8a2f-4453-a5f3-55d80a2b88c0            48           2.0   \n",
       "\n",
       "   click_per_results  \n",
       "0           0.023810  \n",
       "1           0.120000  \n",
       "2           0.041667  \n",
       "3           0.041667  \n",
       "4           0.041667  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge and find the click_per_results \n",
    "\n",
    "clicked_per_query = num_of_tokens_per_query.merge(num_of_clicks,\n",
    "                                                  on='source_event_id',\n",
    "                                                  how='left',\n",
    "                                                  indicator=True)\n",
    "clicked_per_query = clicked_per_query[\n",
    "    clicked_per_query['_merge'] == 'both'].drop(['_merge'],\n",
    "                                                axis=1).reset_index(drop=True)\n",
    "\n",
    "clicked_per_query['click_per_results'] = clicked_per_query[\n",
    "    'num_of_click'] / clicked_per_query['token_length']\n",
    "clicked_per_query.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "single-agency",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.02062220726426"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(clicked_per_query['click_per_results'].mean())*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "musical-questionnaire",
   "metadata": {},
   "source": [
    "#### Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "headed-intent",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = clicked_per_query['token_length']\n",
    "y = clicked_per_query['num_of_click']\n",
    "size = clicked_per_query['click_per_results']\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x=x, y=y, size=size, color='#1d4e89')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imported-palace",
   "metadata": {},
   "source": [
    "#### Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "radical-contributor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = {\n",
    "    'Very Good':\n",
    "    len(clicked_per_query[clicked_per_query['click_per_results'].between(\n",
    "        0.5, 1)]),\n",
    "    'Fair':\n",
    "    len(clicked_per_query[clicked_per_query['click_per_results'].between(\n",
    "        0.1, 0.5)]),\n",
    "    'Poor':\n",
    "    len(clicked_per_query[clicked_per_query['click_per_results'].between(\n",
    "        0.05, 0.1)]),\n",
    "    'Very Poor':\n",
    "    len(clicked_per_query[clicked_per_query['click_per_results'].lt(0.05)]),\n",
    "    'Excellent/Over Satisfaction':\n",
    "    len(clicked_per_query[clicked_per_query['click_per_results'].between(\n",
    "        1, 3.5)]),\n",
    "}\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.title('User Satisfaction')\n",
    "colors = ['#f2cc8f', '#81b29a', '#3d405b', '#e07a5f', '#f4f1de']\n",
    "plt.pie(x=values.values(),\n",
    "        labels=values.keys(),\n",
    "        autopct=lambda p: '{:.2f}%'.format(p, (p / 100)),\n",
    "        colors=colors)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
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     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
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    }
   },
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 },
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 "nbformat_minor": 5
}