99_99-kmong(interview) (3).ipynb

{
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  {
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    "### 실전 데이터분석반 최종 과제 - 크몽\n",
    "\n",
    "안녕하세요. **DS School** X **크몽**입니다.\n",
    "\n",
    "실전 데이터분석반의 최종 과제를 전달합니다. 이번 과제는 프리랜서 마켓 플랫폼 크몽의 데이터를 활용하여, 특정 사용자의 액티비티를 살펴보거나 전체 통계량을 살펴보는 일을 할 것입니다.\n",
    "\n",
    "<Img src = 'https://t1.daumcdn.net/thumb/R1280x0/?fname=http://t1.daumcdn.net/brunch/service/user/11Yx/image/jNo1MfbPhIsud4DhAFh5fej9nQA.jpg' width=240>\n",
    "\n",
    "크몽에서는 사용자가 서비스에 방문하면서 일어나는 모든 행동을 로그로 기록하고 있습니다. 로그 데이터는 서비스 자체를 상징하기에, 로그 데이터를 분석할 수 있다면 서비스를 다양한 관점에서 살펴보는 것이 가능합니다. 로그 데이터를 활용해서 할 수 있는 주요 업무는 다음과 같습니다.\n",
    "\n",
    "  * 회사의 주요 핵심 지표 분석. 가령 전환율(Conversion Rate), 이탈률(Bounce rate) 등\n",
    "  * 이를 더 세분화하여, 각 페이지별 전환율과 이탈률을 분석.\n",
    "  * 사용자의 인구통계학정보(나이, 성별 등), 지역(서울, 부산 등), 사용 기기(아이폰, 갤럭시) 마다의 핵심 지표 분석.\n",
    "  * 특정 상황에서 서비스상의 버그로 인해 사용자가 이탈하는 현상 감지.\n",
    "\n",
    "<small>(참고 자료: <a href=\"http://woowabros.github.io/woowabros/2017/07/30/logdata.html\">우아한형제들 기술블로그. 로그 데이터로 유저 이해하기</a>)</small>\n",
    "\n",
    "그러므로 이번 최종 과제에서는 크몽의 로그 데이터를 활용하여, 사용자의 액티비티를 실시간으로 확인하거나, 서비스상에 문제(버그 등)가 없는지를 데이터 분석을 통해 알아볼 것입니다.\n",
    "\n",
    "과제를 푸는데 주어진 시간은 없습니다. 과제를 받은 뒤, 시간에 구애받지 않고 천천히 문제를 풀어주세요. 이번 과제는 빠르게 푸는 것 보다 정확하게 푸는 것이 더 중요하다고 강조하고 싶습니다. 과제를 정확하게 풀었다는 기준은 다음과 같습니다.\n",
    "\n",
    "  * 예시와 동일한 결과가 출력되는 코드를 작성해야 합니다. 결과가 예시와 다를 경우 무언가 코드에 오류가 있을 가능성이 있습니다.\n",
    "  * 코드가 깔끔하게 정리되어 있고, 모듈화(=함수)가 잘 되어있으며, 코멘트가 자세하게 적혀 있어서 비 개발 직군(ex: 운영팀, 마케팅팀)이 코드와 코멘트를 보고 직접 수정할 수 있어야 합니다.\n",
    "  * 장기적인 관점에서 확장 가능한 코드를 작성해야 합니다. 가령 사용자의 액티비티를 확인하는 코드를 작성할 때, 1) 로그 데이터에 새로운 컬럼이 추가되거나 2) 홈페이지나 어플리케이션에 새로운 기능이 생겼을 때 이를 자동으로 반영할 수 있어야 합니다.\n",
    "  \n",
    "과제를 모두 푼 분들은 datascience@dsschool.co.kr 에 1) 과제를 푼 쥬피터 노트북(.ipynb 파일)과, 2) 본인의 이력서를 첨부파일로 제출해주세요. 제출 결과를 확인한 뒤 크몽 담당 팀에게 전달하도록 하겠습니다. (단, 면접용 과제이기 때문에 이전 수업처럼 솔루션을 제공해드릴 수 없는 점 양해 부탁드립니다)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt  # 파이플롯 사용\n",
    "from IPython.display import set_matplotlib_formats\n",
    "\n",
    "set_matplotlib_formats('retina')  # 한글코드를 더 선명하게 해주는 조치, 레티나 설정\n",
    "matplotlib.rc('font', family='AppleGothic') # 폰트 설정\n",
    "matplotlib.rc('axes', unicode_minus=False) # 마이너스 폰트가 깨지는 경우가 있으므로 조치\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "먼저 크몽의 로그 데이터를 하나씩 가져오겠습니다. 먼저 가져올 데이터는 크몽 어플리케이션의 이벤트와 전환을 나타내는 ````kmong-conversion-v2-.csv``` 입니다. 컬럼 정보는 다음과 같습니다.\n",
    "\n",
    "* ```eventcategory```: 앱의 이벤트에 대한 카테고리입니다. 크게 다음의 종류가 있습니다.\n",
    "  - ```install```: 앱설치\n",
    "  - ```launch```: 앱실행 \n",
    "  - ```deeplinkLaunch```: 딥링크 통한 앱실행\n",
    "  - ```goal```: 앱 내에서의 활동 여부\n",
    "  - ```exit```: 앱 종료\n",
    "  - ```foreground```, background```: 앱을 foreground, background로 전환\n",
    "  - ```launchlnSession```: Session의 시작.\n",
    "* ```isfirstactivity```: 해당 이벤트가 해당 유저에 한하여 첫 번째인지 혹은 두 번째 이상인지를 알려주는 값입니다. \n",
    "  - boolean 형식으로, 해당 이벤트가 해당 유저에 한하여 첫 번째 이벤트이면 True, 아니면 False를 갖습니다.\n",
    "* ```apppackagename```: 앱의 패키지의 고유한 이름입니다. 안드로이드의 경우 applicationId을, iOS의 경우 Bundle ID를 가져오게 됩니다.\n",
    "* ```appversion```: kmong 어플리케이션의 버전입니다.\n",
    "* ```devicetype```: 사용자가 kmong app을 실행하고 있는 기기의 제품명입니다.\n",
    "* ```devicemanufacturer```: 사용자가 kmong app을 실행하고 있는 기기의 제조회사입니다. \n",
    "* ```osversion```: 사용자가 kmong app을 실행하고 있는 기기의 os 버전입니다.\n",
    "* ```canonicaldeviceuuid```: 사용자가 kmong app을 실행하고 있는 기기의 고유 식별자입니다. (사용자를 구분하는 식별자로 이용할 수 있습니다)\n",
    "* ```sourcetype```: 해당 로그를 만든 고객이 유입된 경로입니다. 유료 광고 채널, 바이럴 채널, 앱 마켓 등으로 구분합니다.\n",
    "* ```channel```: sourcetype과 동일하지만 조금 더 세분화되어 있습니다.\n",
    "  - unattributed, WEB, google-play, m_naver, google, (not set), google.adwords, m_naverpowercontents, pc_naver, apple.searchads, facebook, m_daum\n",
    "* ```params_campaign```: 마케터가 입력한 캠페인 파라미터 중 캠페인명입니다. \n",
    "* ```params_medium```: 마케터가 입력한 캠페인 파라미터 중 매체입니다.\n",
    "* ```params_term```: 마케터가 입력한 캠페인 파라미터 중 키워드입니다.\n",
    "* ```inappeventcategory```: 인앱이벤트 식별자값으로 인앱이벤트를 지칭하는 3개의 값(category > action > label) 중 가장 상위에 속하는 구분값입니다.\n",
    "  - ```eventcategory```가 goal인 경우에만 제공됩니다.\n",
    "  - 차후에 읽어올 funnel 데이터셋과 연결할 수 있습니다.\n",
    "  - ex) seller_selling_history.view, gig_detail.view\n",
    "* ```inappeventlabel```: categoryid와 동일합니다.\n",
    "  - 차후에 읽어올 category 데이터셋과 연결할 수 있습니다.\n",
    "  - 인앱이벤트를 지칭하는 3개의 값(category > action > label) 중 마지막에 속하는 구분값입니다.\n",
    "* ```eventdatetime```: 모바일 클라이언트 상에서의 실제 이벤트 발생시간입니다. ISO 8601 표준에 따라서 제공됩니다. \n",
    "* ```isfirstgoalactivity```: goal 이벤트의 경우, 해당 Goal Label, Description, Key, Category를 가진 이벤트가 해당 유저에 한하여 첫 번째인지 혹은 두 번째 이상 인지를 알려주는 boolean 값입니다. 예를 들어 동일한 유저로부터 두 번 이상의 같은 Goal 이벤트가 발생한 경우(e.g. 동일한 물건 재구매 등) 이 값은 false가 됩니다.\n",
    "\n",
    "* ```event_rank```: 데이터를 정렬하는데 필요한 컬럼입니다. log엔 기록되지 않으며, DS_School에서 제공하는 전처리된 컬럼입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48 개 파일들 - 24시간의 데이터를 28일, 29일 양일간 뽑았음\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['kmong-conversion-v2-20180928000000-20180928005959.csv',\n",
       " 'kmong-conversion-v2-20180928010000-20180928015959.csv',\n",
       " 'kmong-conversion-v2-20180928020000-20180928025959.csv',\n",
       " 'kmong-conversion-v2-20180928030000-20180928035959.csv',\n",
       " 'kmong-conversion-v2-20180928040000-20180928045959.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kmong-conversion- 관련 csv파일 경로를 전부 가져옵니다.\n",
    "file_list = glob.glob('kmong-conversion-*.csv')\n",
    "\n",
    "# 리스트의 메소드 중 하나인 .sort()를 이용하여 시간 순서대로 리스트를 정렬합니다.\n",
    "file_list.sort()\n",
    "\n",
    "# 상위 5개를 출력합니다.\n",
    "print(len(file_list),\"개 파일들 -\", \"24시간의 데이터를 28일, 29일 양일간 뽑았음\")\n",
    "file_list[:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "HBox(children=(IntProgress(value=0, max=48), HTML(value='')))"
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     "text": [
      "\n",
      "(434244, 19)\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eventcategory</th>\n",
       "      <th>isfirstactivity</th>\n",
       "      <th>apppackagename</th>\n",
       "      <th>appversion</th>\n",
       "      <th>devicetype</th>\n",
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       "      <th>channel</th>\n",
       "      <th>params_campaign</th>\n",
       "      <th>params_medium</th>\n",
       "      <th>params_term</th>\n",
       "      <th>inappeventcategory</th>\n",
       "      <th>inappeventlabel</th>\n",
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       "      <th>rowuuid</th>\n",
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       "      <th>event_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>goal</td>\n",
       "      <td>False</td>\n",
       "      <td>com.kmong.iOS</td>\n",
       "      <td>4.0.4</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>Apple</td>\n",
       "      <td>iOS11.4.1</td>\n",
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       "      <td>unattributed</td>\n",
       "      <td>unattributed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>home.view</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-28T00:00:00+09:00</td>\n",
       "      <td>fd2a188c-bc9b-4702-9c47-b546b2614817</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>goal</td>\n",
       "      <td>False</td>\n",
       "      <td>com.kmong.kmong</td>\n",
       "      <td>3.3.5</td>\n",
       "      <td>SM-N935S</td>\n",
       "      <td>samsung</td>\n",
       "      <td>Android7.0</td>\n",
       "      <td>8a871e50-0717-4aed-9bad-04ac3c3793be</td>\n",
       "      <td>unattributed</td>\n",
       "      <td>unattributed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gig_detail.view</td>\n",
       "      <td>41201.0</td>\n",
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       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>goal</td>\n",
       "      <td>False</td>\n",
       "      <td>com.kmong.iOS</td>\n",
       "      <td>4.0.4</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>Apple</td>\n",
       "      <td>iOS12.0</td>\n",
       "      <td>A9E5778A-8F3D-4597-9718-74BF953A9F64</td>\n",
       "      <td>unattributed</td>\n",
       "      <td>unattributed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>inbox_detail.view</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-09-28T00:00:00+09:00</td>\n",
       "      <td>14eb3197-db83-493a-b7be-83582960c40b</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>foreground</td>\n",
       "      <td>NaN</td>\n",
       "      <td>com.kmong.iOS</td>\n",
       "      <td>4.0.4</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>Apple</td>\n",
       "      <td>iOS11.4.1</td>\n",
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       "      <th>4</th>\n",
       "      <td>goal</td>\n",
       "      <td>False</td>\n",
       "      <td>com.kmong.iOS</td>\n",
       "      <td>4.0.4</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>Apple</td>\n",
       "      <td>iOS11.4.1</td>\n",
       "      <td>ACABB7C0-4C76-413A-B314-E5D6DA0D0E5D</td>\n",
       "      <td>viral</td>\n",
       "      <td>WEB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>buyer_order_track.view</td>\n",
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       "      <td>2018-09-28T00:00:02+09:00</td>\n",
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      ],
      "text/plain": [
       "  eventcategory isfirstactivity   apppackagename appversion devicetype  \\\n",
       "0          goal           False    com.kmong.iOS      4.0.4     iPhone   \n",
       "1          goal           False  com.kmong.kmong      3.3.5   SM-N935S   \n",
       "2          goal           False    com.kmong.iOS      4.0.4     iPhone   \n",
       "3    foreground             NaN    com.kmong.iOS      4.0.4     iPhone   \n",
       "4          goal           False    com.kmong.iOS      4.0.4     iPhone   \n",
       "\n",
       "  devicemanufacturer   osversion                   canonicaldeviceuuid  \\\n",
       "0              Apple   iOS11.4.1  F36FAA62-ADAC-4AA5-9B00-1FD6CB7EE957   \n",
       "1            samsung  Android7.0  8a871e50-0717-4aed-9bad-04ac3c3793be   \n",
       "2              Apple     iOS12.0  A9E5778A-8F3D-4597-9718-74BF953A9F64   \n",
       "3              Apple   iOS11.4.1  168761CB-CB67-4592-867D-52780D651297   \n",
       "4              Apple   iOS11.4.1  ACABB7C0-4C76-413A-B314-E5D6DA0D0E5D   \n",
       "\n",
       "     sourcetype       channel params_campaign params_medium params_term  \\\n",
       "0  unattributed  unattributed             NaN           NaN         NaN   \n",
       "1  unattributed  unattributed             NaN           NaN         NaN   \n",
       "2  unattributed  unattributed             NaN           NaN         NaN   \n",
       "3           NaN           NaN             NaN           NaN         NaN   \n",
       "4         viral           WEB             NaN           NaN         NaN   \n",
       "\n",
       "       inappeventcategory  inappeventlabel              eventdatetime  \\\n",
       "0               home.view              NaN  2018-09-28T00:00:00+09:00   \n",
       "1         gig_detail.view          41201.0  2018-09-28T00:00:00+09:00   \n",
       "2       inbox_detail.view              NaN  2018-09-28T00:00:00+09:00   \n",
       "3                     NaN              NaN  2018-09-28T00:00:01+09:00   \n",
       "4  buyer_order_track.view              NaN  2018-09-28T00:00:02+09:00   \n",
       "\n",
       "                                rowuuid isfirstgoalactivity  event_rank  \n",
       "0  fd2a188c-bc9b-4702-9c47-b546b2614817               False        True  \n",
       "1  e62dccef-dd70-4415-8a33-c8324ddaed38               False        True  \n",
       "2  14eb3197-db83-493a-b7be-83582960c40b               False        True  \n",
       "3  f9bb91af-248b-44dc-9f5c-1c00b37ea97b                 NaN        True  \n",
       "4  236e9946-7801-4898-b609-06c8ab1139dc               False        True  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tqdm import tqdm_notebook\n",
    "\n",
    "# read_csv로 모든 kmong-conversion 데이터를 읽어온 뒤\n",
    "# 이를 판다스의 concatenate로 하나로 합칩니다.\n",
    "log = pd.concat([pd.read_csv(csv, low_memory=False) for csv in tqdm_notebook(file_list)])\n",
    "\n",
    "# log 변수에 할당된 데이터의 행렬 사이즈를 출력합니다.\n",
    "# 출력은 (row, column) 으로 표시됩니다.\n",
    "print(log.shape)\n",
    "\n",
    "# head()로 log 데이터의 상위 5개를 띄웁니다.\n",
    "log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Funnel_ID_app.p**\n",
    "\n",
    "그 다음 가져올 데이터셋은 퍼널(Funnel) 정보입니다.\n",
    "\n",
    "퍼널은은 한국어로 해석하면 깔대기로, 사용자가 서비스 접속 후 상품을 구매하기까지의 경로 구조를 의미합니다. 결제를 하기 위해서는 무조건 상품 설명을 통해서 방문해야하고, 상품 설명을 보기 위해서는 해당 상품을 찾을 수 있는 카테고리탭을 통해서만 방문할 수 있기 때문에 가장 마지막 단계 페이지의 방문자가 가장 적을 수 밖에 없습니다. 이러한 구조가 깔대기같다 하여 퍼널이라 불립니다.\n",
    "\n",
    "퍼널을 이용해 전환과 이탈률을 측정함으로써 집중 개선 대상 페이지를 발견할 수 있으며 개선된 페이지의 성과까지 간단히 추적해 나갈 수 있습니다. 크몽 또한 앱에 다양한 퍼널이 구성되어 있고, 그 정보가 'Funnel_ID_app.p'에 저장되어 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(53, 6)\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>Lv2</th>\n",
       "      <th>viewid</th>\n",
       "      <th>viewid desc</th>\n",
       "      <th>Lv1</th>\n",
       "      <th>funnel name</th>\n",
       "      <th>funnel desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1100</td>\n",
       "      <td>home</td>\n",
       "      <td>홈 (탭)</td>\n",
       "      <td>11</td>\n",
       "      <td>home</td>\n",
       "      <td>홈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1210</td>\n",
       "      <td>category_list</td>\n",
       "      <td>카테고리 목록 (탭)</td>\n",
       "      <td>12</td>\n",
       "      <td>category</td>\n",
       "      <td>카테고리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1200</td>\n",
       "      <td>category_gig</td>\n",
       "      <td>카테고리-상품목록</td>\n",
       "      <td>12</td>\n",
       "      <td>category</td>\n",
       "      <td>카테고리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1300</td>\n",
       "      <td>search</td>\n",
       "      <td>검색</td>\n",
       "      <td>13</td>\n",
       "      <td>search</td>\n",
       "      <td>검색</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1301</td>\n",
       "      <td>search_gig</td>\n",
       "      <td>검색-상품목록</td>\n",
       "      <td>13</td>\n",
       "      <td>search</td>\n",
       "      <td>검색</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1302</td>\n",
       "      <td>search_seller</td>\n",
       "      <td>검색-전문가</td>\n",
       "      <td>13</td>\n",
       "      <td>search</td>\n",
       "      <td>검색</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1400</td>\n",
       "      <td>gig_detail</td>\n",
       "      <td>상품상세</td>\n",
       "      <td>14</td>\n",
       "      <td>gig</td>\n",
       "      <td>상품</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1401</td>\n",
       "      <td>gig_detail_option</td>\n",
       "      <td>상품상세-상품선택</td>\n",
       "      <td>14</td>\n",
       "      <td>gig</td>\n",
       "      <td>상품</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1420</td>\n",
       "      <td>profile</td>\n",
       "      <td>전문가프로필</td>\n",
       "      <td>14</td>\n",
       "      <td>gig</td>\n",
       "      <td>상품</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1520</td>\n",
       "      <td>login_sns</td>\n",
       "      <td>간편로그인</td>\n",
       "      <td>15</td>\n",
       "      <td>login</td>\n",
       "      <td>로그인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1500</td>\n",
       "      <td>login_email</td>\n",
       "      <td>간편로그인-이메일로그인</td>\n",
       "      <td>15</td>\n",
       "      <td>login</td>\n",
       "      <td>로그인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1510</td>\n",
       "      <td>find_account</td>\n",
       "      <td>간편로그인-이메일로그인-아이디/비밀번호-찾기</td>\n",
       "      <td>15</td>\n",
       "      <td>login</td>\n",
       "      <td>로그인</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1600</td>\n",
       "      <td>inbox</td>\n",
       "      <td>메시지목록 (탭)</td>\n",
       "      <td>16</td>\n",
       "      <td>inbox</td>\n",
       "      <td>메시지</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1610</td>\n",
       "      <td>inbox_detail</td>\n",
       "      <td>메시지목록-상세</td>\n",
       "      <td>16</td>\n",
       "      <td>inbox</td>\n",
       "      <td>메시지</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1611</td>\n",
       "      <td>inbox_detail_orderRequest</td>\n",
       "      <td>메시지목록-상세-결제요청</td>\n",
       "      <td>16</td>\n",
       "      <td>inbox</td>\n",
       "      <td>메시지</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1700</td>\n",
       "      <td>bookmarks_gig</td>\n",
       "      <td>메뉴목록-찜목록-서비스</td>\n",
       "      <td>17</td>\n",
       "      <td>bookmarks</td>\n",
       "      <td>찜하기</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1701</td>\n",
       "      <td>bookmarks_portfolio</td>\n",
       "      <td>메뉴목록-찜목록-포트폴리오</td>\n",
       "      <td>17</td>\n",
       "      <td>bookmarks</td>\n",
       "      <td>찜하기</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1801</td>\n",
       "      <td>order_addOption</td>\n",
       "      <td>상품상세-주문하기</td>\n",
       "      <td>18</td>\n",
       "      <td>order</td>\n",
       "      <td>결제</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1800</td>\n",
       "      <td>order_payment</td>\n",
       "      <td>상품상세-주문하기-결제하기</td>\n",
       "      <td>18</td>\n",
       "      <td>order</td>\n",
       "      <td>결제</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1810</td>\n",
       "      <td>kmongCash_charge</td>\n",
       "      <td>메뉴목록-크몽캐시-캐시충전하기</td>\n",
       "      <td>18</td>\n",
       "      <td>order</td>\n",
       "      <td>결제</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1900</td>\n",
       "      <td>thankyou</td>\n",
       "      <td>결제완료</td>\n",
       "      <td>19</td>\n",
       "      <td>thankyou</td>\n",
       "      <td>결제완료</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2000</td>\n",
       "      <td>signup</td>\n",
       "      <td>개인/기업-회원가입</td>\n",
       "      <td>20</td>\n",
       "      <td>new</td>\n",
       "      <td>신규등록</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2001</td>\n",
       "      <td>mobileAuth</td>\n",
       "      <td>휴대폰인증</td>\n",
       "      <td>20</td>\n",
       "      <td>new</td>\n",
       "      <td>신규등록</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020</td>\n",
       "      <td>seller_gig_register</td>\n",
       "      <td>메뉴목록-서비스등록</td>\n",
       "      <td>20</td>\n",
       "      <td>new</td>\n",
       "      <td>신규등록</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2200</td>\n",
       "      <td>account_setting</td>\n",
       "      <td>메뉴목록-계정설정</td>\n",
       "      <td>22</td>\n",
       "      <td>account</td>\n",
       "      <td>계정관리 - 회원공통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2210</td>\n",
       "      <td>account_setting_notification</td>\n",
       "      <td>메뉴목록-계정설정-알림설정</td>\n",
       "      <td>22</td>\n",
       "      <td>account</td>\n",
       "      <td>계정관리 - 회원공통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2220</td>\n",
       "      <td>account_setting_AuthStatus</td>\n",
       "      <td>메뉴목록-계정설정-인증정보</td>\n",
       "      <td>22</td>\n",
       "      <td>account</td>\n",
       "      <td>계정관리 - 회원공통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2230</td>\n",
       "      <td>account_setting_pwd</td>\n",
       "      <td>메뉴목록-계정설정-비밀번호 변경</td>\n",
       "      <td>22</td>\n",
       "      <td>account</td>\n",
       "      <td>계정관리 - 회원공통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2240</td>\n",
       "      <td>account_setting_profile</td>\n",
       "      <td>메뉴목록-계정설정-프로필</td>\n",
       "      <td>22</td>\n",
       "      <td>account</td>\n",
       "      <td>계정관리 - 회원공통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2320</td>\n",
       "      <td>seller_gig</td>\n",
       "      <td>메뉴목록-나의서비스</td>\n",
       "      <td>23</td>\n",
       "      <td>account_seller</td>\n",
       "      <td>계정관리 - 판매자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2400</td>\n",
       "      <td>menu</td>\n",
       "      <td>메뉴목록 (탭)</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2410</td>\n",
       "      <td>buyer_order_track</td>\n",
       "      <td>메뉴목록-구매관리-거래메시지</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2411</td>\n",
       "      <td>buyer_order_track_cancel</td>\n",
       "      <td>메뉴목록-구매관리-거래메시지-취소/문제해결</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2412</td>\n",
       "      <td>buyer_order_track_cancel_submit</td>\n",
       "      <td>메뉴목록-구매관리-거래메시지-취소/문제해결-제출</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2420</td>\n",
       "      <td>buyer_order_history</td>\n",
       "      <td>메뉴목록-구매관리</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2421</td>\n",
       "      <td>buyer_coupon_history</td>\n",
       "      <td>메뉴목록-쿠폰내역</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2430</td>\n",
       "      <td>seller_selling_history</td>\n",
       "      <td>메뉴목록-판매관리</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2431</td>\n",
       "      <td>seller_mileage_history</td>\n",
       "      <td>메뉴목록-마일리지 내역</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2432</td>\n",
       "      <td>seller_profits_history</td>\n",
       "      <td>메뉴목록-수익관리</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2450</td>\n",
       "      <td>buyer_payment_history</td>\n",
       "      <td>메뉴목록-결제내역</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2451</td>\n",
       "      <td>kmongCash_history</td>\n",
       "      <td>메뉴목록-크몽캐시</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2452</td>\n",
       "      <td>kmongCash_history_refundRequest</td>\n",
       "      <td>메뉴목록-크몽캐시-캐시환불</td>\n",
       "      <td>24</td>\n",
       "      <td>transaction_history</td>\n",
       "      <td>거래관리</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2510</td>\n",
       "      <td>about_howToUse</td>\n",
       "      <td>메뉴목록-크몽이용방법</td>\n",
       "      <td>25</td>\n",
       "      <td>info</td>\n",
       "      <td>소개 및 안내</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2520</td>\n",
       "      <td>termsOfService</td>\n",
       "      <td>메인하단-이용약관</td>\n",
       "      <td>25</td>\n",
       "      <td>info</td>\n",
       "      <td>소개 및 안내</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2521</td>\n",
       "      <td>privacyPolicy</td>\n",
       "      <td>메인하단-개인정보처리방침</td>\n",
       "      <td>25</td>\n",
       "      <td>info</td>\n",
       "      <td>소개 및 안내</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2530</td>\n",
       "      <td>tutorial</td>\n",
       "      <td>튜토리얼</td>\n",
       "      <td>25</td>\n",
       "      <td>info</td>\n",
       "      <td>소개 및 안내</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2700</td>\n",
       "      <td>referrerProgram</td>\n",
       "      <td>메뉴목록-친구초대</td>\n",
       "      <td>27</td>\n",
       "      <td>referral_program</td>\n",
       "      <td>친구초대프로그램</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2800</td>\n",
       "      <td>event</td>\n",
       "      <td>이벤트</td>\n",
       "      <td>28</td>\n",
       "      <td>event</td>\n",
       "      <td>이벤트프로모션</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>3010</td>\n",
       "      <td>kmongNotification</td>\n",
       "      <td>메뉴목록-크몽안내</td>\n",
       "      <td>30</td>\n",
       "      <td>notification</td>\n",
       "      <td>알림</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>3011</td>\n",
       "      <td>kmongNotification_detail</td>\n",
       "      <td>메뉴목록-크몽안내-상세</td>\n",
       "      <td>30</td>\n",
       "      <td>notification</td>\n",
       "      <td>알림</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>3000</td>\n",
       "      <td>notification</td>\n",
       "      <td>알림-알림</td>\n",
       "      <td>30</td>\n",
       "      <td>notification</td>\n",
       "      <td>알림</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>3001</td>\n",
       "      <td>notification_detail</td>\n",
       "      <td>알림-알림-상세</td>\n",
       "      <td>30</td>\n",
       "      <td>notification</td>\n",
       "      <td>알림</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>9910</td>\n",
       "      <td>NPS</td>\n",
       "      <td>NPS</td>\n",
       "      <td>99</td>\n",
       "      <td>others</td>\n",
       "      <td>기타</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Lv2                           viewid                 viewid desc  Lv1  \\\n",
       "0   1100                             home                       홈 (탭)   11   \n",
       "1   1210                    category_list                 카테고리 목록 (탭)   12   \n",
       "2   1200                     category_gig                   카테고리-상품목록   12   \n",
       "3   1300                           search                          검색   13   \n",
       "4   1301                       search_gig                     검색-상품목록   13   \n",
       "5   1302                    search_seller                      검색-전문가   13   \n",
       "6   1400                       gig_detail                        상품상세   14   \n",
       "7   1401                gig_detail_option                   상품상세-상품선택   14   \n",
       "8   1420                          profile                      전문가프로필   14   \n",
       "9   1520                        login_sns                       간편로그인   15   \n",
       "10  1500                      login_email                간편로그인-이메일로그인   15   \n",
       "11  1510                     find_account    간편로그인-이메일로그인-아이디/비밀번호-찾기   15   \n",
       "12  1600                            inbox                   메시지목록 (탭)   16   \n",
       "13  1610                     inbox_detail                    메시지목록-상세   16   \n",
       "14  1611        inbox_detail_orderRequest               메시지목록-상세-결제요청   16   \n",
       "15  1700                    bookmarks_gig                메뉴목록-찜목록-서비스   17   \n",
       "16  1701              bookmarks_portfolio              메뉴목록-찜목록-포트폴리오   17   \n",
       "17  1801                  order_addOption                   상품상세-주문하기   18   \n",
       "18  1800                    order_payment              상품상세-주문하기-결제하기   18   \n",
       "19  1810                 kmongCash_charge            메뉴목록-크몽캐시-캐시충전하기   18   \n",
       "20  1900                         thankyou                        결제완료   19   \n",
       "21  2000                           signup                  개인/기업-회원가입   20   \n",
       "22  2001                       mobileAuth                       휴대폰인증   20   \n",
       "23  2020              seller_gig_register                  메뉴목록-서비스등록   20   \n",
       "24  2200                  account_setting                   메뉴목록-계정설정   22   \n",
       "25  2210     account_setting_notification              메뉴목록-계정설정-알림설정   22   \n",
       "26  2220       account_setting_AuthStatus              메뉴목록-계정설정-인증정보   22   \n",
       "27  2230              account_setting_pwd           메뉴목록-계정설정-비밀번호 변경   22   \n",
       "28  2240          account_setting_profile               메뉴목록-계정설정-프로필   22   \n",
       "29  2320                       seller_gig                  메뉴목록-나의서비스   23   \n",
       "30  2400                             menu                    메뉴목록 (탭)   24   \n",
       "31  2410                buyer_order_track             메뉴목록-구매관리-거래메시지   24   \n",
       "32  2411         buyer_order_track_cancel     메뉴목록-구매관리-거래메시지-취소/문제해결   24   \n",
       "33  2412  buyer_order_track_cancel_submit  메뉴목록-구매관리-거래메시지-취소/문제해결-제출   24   \n",
       "34  2420              buyer_order_history                   메뉴목록-구매관리   24   \n",
       "35  2421             buyer_coupon_history                   메뉴목록-쿠폰내역   24   \n",
       "36  2430           seller_selling_history                   메뉴목록-판매관리   24   \n",
       "37  2431           seller_mileage_history                메뉴목록-마일리지 내역   24   \n",
       "38  2432           seller_profits_history                   메뉴목록-수익관리   24   \n",
       "39  2450            buyer_payment_history                   메뉴목록-결제내역   24   \n",
       "40  2451                kmongCash_history                   메뉴목록-크몽캐시   24   \n",
       "41  2452  kmongCash_history_refundRequest              메뉴목록-크몽캐시-캐시환불   24   \n",
       "42  2510                   about_howToUse                 메뉴목록-크몽이용방법   25   \n",
       "43  2520                   termsOfService                   메인하단-이용약관   25   \n",
       "44  2521                    privacyPolicy               메인하단-개인정보처리방침   25   \n",
       "45  2530                         tutorial                        튜토리얼   25   \n",
       "46  2700                  referrerProgram                   메뉴목록-친구초대   27   \n",
       "47  2800                            event                         이벤트   28   \n",
       "48  3010                kmongNotification                   메뉴목록-크몽안내   30   \n",
       "49  3011         kmongNotification_detail                메뉴목록-크몽안내-상세   30   \n",
       "50  3000                     notification                       알림-알림   30   \n",
       "51  3001              notification_detail                    알림-알림-상세   30   \n",
       "52  9910                              NPS                         NPS   99   \n",
       "\n",
       "            funnel name  funnel desc  \n",
       "0                  home            홈  \n",
       "1              category         카테고리  \n",
       "2              category         카테고리  \n",
       "3                search           검색  \n",
       "4                search           검색  \n",
       "5                search           검색  \n",
       "6                   gig           상품  \n",
       "7                   gig           상품  \n",
       "8                   gig           상품  \n",
       "9                 login          로그인  \n",
       "10                login          로그인  \n",
       "11                login          로그인  \n",
       "12                inbox          메시지  \n",
       "13                inbox          메시지  \n",
       "14                inbox          메시지  \n",
       "15            bookmarks          찜하기  \n",
       "16            bookmarks          찜하기  \n",
       "17                order           결제  \n",
       "18                order           결제  \n",
       "19                order           결제  \n",
       "20             thankyou         결제완료  \n",
       "21                  new         신규등록  \n",
       "22                  new         신규등록  \n",
       "23                  new         신규등록  \n",
       "24              account  계정관리 - 회원공통  \n",
       "25              account  계정관리 - 회원공통  \n",
       "26              account  계정관리 - 회원공통  \n",
       "27              account  계정관리 - 회원공통  \n",
       "28              account  계정관리 - 회원공통  \n",
       "29       account_seller   계정관리 - 판매자  \n",
       "30  transaction_history         거래관리  \n",
       "31  transaction_history         거래관리  \n",
       "32  transaction_history         거래관리  \n",
       "33  transaction_history         거래관리  \n",
       "34  transaction_history         거래관리  \n",
       "35  transaction_history         거래관리  \n",
       "36  transaction_history         거래관리  \n",
       "37  transaction_history         거래관리  \n",
       "38  transaction_history         거래관리  \n",
       "39  transaction_history         거래관리  \n",
       "40  transaction_history         거래관리  \n",
       "41  transaction_history         거래관리  \n",
       "42                 info      소개 및 안내  \n",
       "43                 info      소개 및 안내  \n",
       "44                 info      소개 및 안내  \n",
       "45                 info      소개 및 안내  \n",
       "46     referral_program     친구초대프로그램  \n",
       "47                event      이벤트프로모션  \n",
       "48         notification           알림  \n",
       "49         notification           알림  \n",
       "50         notification           알림  \n",
       "51         notification           알림  \n",
       "52               others           기타  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# pickle을 활용해 퍼널 데이터를 읽어옵니다.\n",
    "# 그리고 이 데이터를 funnel이라는 이름의 변수에 저장합니다.\n",
    "funnel = pickle.load(open('Funnel_ID_app.p','rb'))\n",
    "\n",
    "# funnel 변수에 할당된 데이터의 행렬 사이즈를 출력합니다.\n",
    "# 출력은 (row, column) 으로 표시됩니다.\n",
    "print(funnel.shape)\n",
    "print(type(funnel))\n",
    "\n",
    "# funnel 데이터 전체를 출력합니다.\n",
    "funnel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "마찬가지로 사용자가 어떤 상품탭에 들어갔는지에 대한 상품 분류 정보를 가지고 있는 categories.p 데이터도 pickle을 통해 읽어오겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(245, 9)\n"
     ]
    },
    {
     "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>depth</th>\n",
       "      <th>categoryid</th>\n",
       "      <th>categoryname</th>\n",
       "      <th>cat1_id</th>\n",
       "      <th>cat2_id</th>\n",
       "      <th>cat3_id</th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>디자인</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>디자인</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>마케팅</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>마케팅</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>번역·통역</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>번역·통역</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>문서작성</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>문서작성</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>IT·프로그래밍</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IT·프로그래밍</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>3</td>\n",
       "      <td>72501</td>\n",
       "      <td>모델</td>\n",
       "      <td>7</td>\n",
       "      <td>725.0</td>\n",
       "      <td>72501.0</td>\n",
       "      <td>콘텐츠 제작</td>\n",
       "      <td>엔터테이너</td>\n",
       "      <td>모델</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>3</td>\n",
       "      <td>72502</td>\n",
       "      <td>MC</td>\n",
       "      <td>7</td>\n",
       "      <td>725.0</td>\n",
       "      <td>72502.0</td>\n",
       "      <td>콘텐츠 제작</td>\n",
       "      <td>엔터테이너</td>\n",
       "      <td>MC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>3</td>\n",
       "      <td>72503</td>\n",
       "      <td>배우</td>\n",
       "      <td>7</td>\n",
       "      <td>725.0</td>\n",
       "      <td>72503.0</td>\n",
       "      <td>콘텐츠 제작</td>\n",
       "      <td>엔터테이너</td>\n",
       "      <td>배우</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>3</td>\n",
       "      <td>72504</td>\n",
       "      <td>공연</td>\n",
       "      <td>7</td>\n",
       "      <td>725.0</td>\n",
       "      <td>72504.0</td>\n",
       "      <td>콘텐츠 제작</td>\n",
       "      <td>엔터테이너</td>\n",
       "      <td>공연</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>3</td>\n",
       "      <td>72505</td>\n",
       "      <td>기타</td>\n",
       "      <td>7</td>\n",
       "      <td>725.0</td>\n",
       "      <td>72505.0</td>\n",
       "      <td>콘텐츠 제작</td>\n",
       "      <td>엔터테이너</td>\n",
       "      <td>기타</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>245 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     depth  categoryid categoryname  cat1_id  cat2_id  cat3_id      cat1  \\\n",
       "0        1           1          디자인        1      NaN      NaN       디자인   \n",
       "1        1           2          마케팅        2      NaN      NaN       마케팅   \n",
       "2        1           3        번역·통역        3      NaN      NaN     번역·통역   \n",
       "3        1           4         문서작성        4      NaN      NaN      문서작성   \n",
       "4        1           6     IT·프로그래밍        6      NaN      NaN  IT·프로그래밍   \n",
       "..     ...         ...          ...      ...      ...      ...       ...   \n",
       "240      3       72501           모델        7    725.0  72501.0    콘텐츠 제작   \n",
       "241      3       72502           MC        7    725.0  72502.0    콘텐츠 제작   \n",
       "242      3       72503           배우        7    725.0  72503.0    콘텐츠 제작   \n",
       "243      3       72504           공연        7    725.0  72504.0    콘텐츠 제작   \n",
       "244      3       72505           기타        7    725.0  72505.0    콘텐츠 제작   \n",
       "\n",
       "      cat2 cat3  \n",
       "0      NaN  NaN  \n",
       "1      NaN  NaN  \n",
       "2      NaN  NaN  \n",
       "3      NaN  NaN  \n",
       "4      NaN  NaN  \n",
       "..     ...  ...  \n",
       "240  엔터테이너   모델  \n",
       "241  엔터테이너   MC  \n",
       "242  엔터테이너   배우  \n",
       "243  엔터테이너   공연  \n",
       "244  엔터테이너   기타  \n",
       "\n",
       "[245 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories = pickle.load(open('categories.p','rb'))\n",
    "\n",
    "print(categories.shape)\n",
    "categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 로그를 사용자별로 정렬한 뒤 살펴볼 수 있도록 정리해주세요.\n",
    "\n",
    "로그데이터를 정리했으면 이를 정렬 및 분리를 하고 싶습니다. 기준은 다음과 같습니다.\n",
    "\n",
    "  * 사용자 아이디(```userid```)마다 그룹핑이 되어있어야 합니다.\n",
    "  * 그룹핑이 된 사용자를 기준으로, 최신 로그가 위로 올라와야 합니다.\n",
    "  \n",
    "또한 정리한 로그 데이터를 활용해 다음의 기능을 하는 함수를 구현했으면 좋겠습니다.\n",
    "\n",
    "  * 특정 사용자 아이디(```userid```) 또는, 특정 사용자 아이디를 리스트로 여러 개 넣으면, 해당 사용자들의 로그 데이터만 별도록 색인할 수 있어야 합니다.\n",
    "  * 특정 컬럼명을 리스트로 넣으면 해당 컬럼만 별도로 색인할 수 있어야 합니다.\n",
    "  * 특정 ```viewid```를 리스트로 넣으면 그 ```viewid```에 해당하는 로그 데이터만 별도로 색인할 수 있어야 합니다.\n",
    "  \n",
    "최종적으로 다음의 결과가 나와야 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table border=\"1\" class=\"dataframe\">\n",
    "   <thead>\n",
    "      <tr style=\"text-align: right;\">\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th>eventdatetime</th>\n",
    "         <th>devicemanufacturer</th>\n",
    "         <th>devicetype</th>\n",
    "         <th>ostype</th>\n",
    "         <th>osversion</th>\n",
    "         <th>appversion</th>\n",
    "         <th>eventcategory</th>\n",
    "         <th>viewid</th>\n",
    "         <th>viewaction</th>\n",
    "         <th>funnel desc</th>\n",
    "         <th>viewid desc</th>\n",
    "         <th>categoryname</th>\n",
    "         <th>category1</th>\n",
    "         <th>category2</th>\n",
    "         <th>category3</th>\n",
    "         <th>sourcetype</th>\n",
    "         <th>channel</th>\n",
    "         <th>params_campaign</th>\n",
    "         <th>params_medium</th>\n",
    "         <th>params_term</th>\n",
    "         <th>isfirstactivity</th>\n",
    "         <th>isfirstgoalactivity</th>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>userid</th>\n",
    "         <th>rowuuid</th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "      </tr>\n",
    "   </thead>\n",
    "   <tbody>\n",
    "      <tr>\n",
    "         <th>00000000-0000-0000-0000-000000000000</th>\n",
    "         <th>2461124f-cd69-4a4d-8c38-3a51dc3087e2</th>\n",
    "         <td>2018-09-29 07:32:23</td>\n",
    "         <td>Apple</td>\n",
    "         <td>iPhone</td>\n",
    "         <td>iOS</td>\n",
    "         <td>11.4.1</td>\n",
    "         <td>4.0.4</td>\n",
    "         <td>install</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>False</td>\n",
    "         <td>False</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th rowspan=\"4\" valign=\"top\">001B9A43-5F55-4145-84B5-6495DF1EAF80</th>\n",
    "         <th>9e8a9758-b454-4f32-a905-112cd04e615b</th>\n",
    "         <td>2018-09-29 11:07:35</td>\n",
    "         <td>Apple</td>\n",
    "         <td>iPhone</td>\n",
    "         <td>iOS</td>\n",
    "         <td>11.4.1</td>\n",
    "         <td>4.0.4</td>\n",
    "         <td>background</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>ea568a15-b66a-4618-97bf-ad9c9a8e40c9</th>\n",
    "         <td>2018-09-29 11:07:34</td>\n",
    "         <td>Apple</td>\n",
    "         <td>iPhone</td>\n",
    "         <td>iOS</td>\n",
    "         <td>11.4.1</td>\n",
    "         <td>4.0.4</td>\n",
    "         <td>launch</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>False</td>\n",
    "         <td>False</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>d276e79c-fc5f-4fc6-acea-c62ff38e7225</th>\n",
    "         <td>2018-09-29 02:56:41</td>\n",
    "         <td>Apple</td>\n",
    "         <td>iPhone</td>\n",
    "         <td>iOS</td>\n",
    "         <td>11.4.1</td>\n",
    "         <td>4.0.4</td>\n",
    "         <td>background</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>db1ab8b6-2782-4158-a95c-f64cf8219b60</th>\n",
    "         <td>2018-09-29 02:56:21</td>\n",
    "         <td>Apple</td>\n",
    "         <td>iPhone</td>\n",
    "         <td>iOS</td>\n",
    "         <td>11.4.1</td>\n",
    "         <td>4.0.4</td>\n",
    "         <td>goal</td>\n",
    "         <td>gig_detail</td>\n",
    "         <td>view</td>\n",
    "         <td>상품</td>\n",
    "         <td>상품상세</td>\n",
    "         <td>로고디자인</td>\n",
    "         <td>디자인</td>\n",
    "         <td>로고디자인</td>\n",
    "         <td>NaN</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>True</td>\n",
    "         <td>False</td>\n",
    "      </tr>\n",
    "   </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "만일 특정 사용자, 가령 ```user_id```가 **fff40190-d751-425a-9813-2284072e47f5**인 사용자의 로그 데이터만 색인하면 다음의 결과가 나와야 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table border=\"1\" class=\"dataframe\">\n",
    "   <thead>\n",
    "      <tr style=\"text-align: right;\">\n",
    "         <th></th>\n",
    "         <th>eventdatetime</th>\n",
    "         <th>devicemanufacturer</th>\n",
    "         <th>devicetype</th>\n",
    "         <th>ostype</th>\n",
    "         <th>osversion</th>\n",
    "         <th>appversion</th>\n",
    "         <th>eventcategory</th>\n",
    "         <th>viewid</th>\n",
    "         <th>viewaction</th>\n",
    "         <th>funnel desc</th>\n",
    "         <th>viewid desc</th>\n",
    "         <th>categoryname</th>\n",
    "         <th>category1</th>\n",
    "         <th>category2</th>\n",
    "         <th>category3</th>\n",
    "         <th>sourcetype</th>\n",
    "         <th>channel</th>\n",
    "         <th>params_campaign</th>\n",
    "         <th>params_medium</th>\n",
    "         <th>params_term</th>\n",
    "         <th>isfirstactivity</th>\n",
    "         <th>isfirstgoalactivity</th>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>rowuuid</th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "      </tr>\n",
    "   </thead>\n",
    "   <tbody>\n",
    "      <tr>\n",
    "         <th>12dc5a10-2713-4a96-9693-3e29ca1a0f5c</th>\n",
    "         <td>2018-09-28 00:03:19</td>\n",
    "         <td>Samsung</td>\n",
    "         <td>SM-G950N</td>\n",
    "         <td>Android</td>\n",
    "         <td>8.0.0</td>\n",
    "         <td>3.3.3</td>\n",
    "         <td>background</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>f55223e0-52f5-41c2-8f0b-41c562baa3fc</th>\n",
    "         <td>2018-09-28 00:03:16</td>\n",
    "         <td>Samsung</td>\n",
    "         <td>SM-G950N</td>\n",
    "         <td>Android</td>\n",
    "         <td>8.0.0</td>\n",
    "         <td>3.3.3</td>\n",
    "         <td>goal</td>\n",
    "         <td>inbox_detail</td>\n",
    "         <td>view</td>\n",
    "         <td>메시지</td>\n",
    "         <td>메시지목록-상세</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>False</td>\n",
    "         <td>False</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>457da225-eaf2-4ffe-94e0-d3ffb7b93490</th>\n",
    "         <td>2018-09-28 00:02:41</td>\n",
    "         <td>Samsung</td>\n",
    "         <td>SM-G950N</td>\n",
    "         <td>Android</td>\n",
    "         <td>8.0.0</td>\n",
    "         <td>3.3.3</td>\n",
    "         <td>goal</td>\n",
    "         <td>inbox_detail</td>\n",
    "         <td>view</td>\n",
    "         <td>메시지</td>\n",
    "         <td>메시지목록-상세</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>False</td>\n",
    "         <td>False</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>d1da5023-8ef1-46ae-bd79-ab9208854c73</th>\n",
    "         <td>2018-09-28 00:02:41</td>\n",
    "         <td>Samsung</td>\n",
    "         <td>SM-G950N</td>\n",
    "         <td>Android</td>\n",
    "         <td>8.0.0</td>\n",
    "         <td>3.3.3</td>\n",
    "         <td>launchInSession</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>20b8dfa9-4ac9-4b53-87d4-86db737fd7ef</th>\n",
    "         <td>2018-09-27 23:51:25</td>\n",
    "         <td>Samsung</td>\n",
    "         <td>SM-G950N</td>\n",
    "         <td>Android</td>\n",
    "         <td>8.0.0</td>\n",
    "         <td>3.3.3</td>\n",
    "         <td>background</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>a9d0ac01-b715-46f2-b92d-8570dfbc2cca</th>\n",
    "         <td>2018-09-27 23:51:08</td>\n",
    "         <td>Samsung</td>\n",
    "         <td>SM-G950N</td>\n",
    "         <td>Android</td>\n",
    "         <td>8.0.0</td>\n",
    "         <td>3.3.3</td>\n",
    "         <td>goal</td>\n",
    "         <td>inbox_detail</td>\n",
    "         <td>view</td>\n",
    "         <td>메시지</td>\n",
    "         <td>메시지목록-상세</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>False</td>\n",
    "         <td>False</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>af9381ed-25ca-4ba2-9a99-9a06f4c9bc26</th>\n",
    "         <td>2018-09-27 23:50:22</td>\n",
    "         <td>Samsung</td>\n",
    "         <td>SM-G950N</td>\n",
    "         <td>Android</td>\n",
    "         <td>8.0.0</td>\n",
    "         <td>3.3.3</td>\n",
    "         <td>launch</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>False</td>\n",
    "         <td>False</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>a773ad20-fb9c-4c6d-96fd-1861dec0cea8</th>\n",
    "         <td>2018-09-27 23:50:22</td>\n",
    "         <td>Samsung</td>\n",
    "         <td>SM-G950N</td>\n",
    "         <td>Android</td>\n",
    "         <td>8.0.0</td>\n",
    "         <td>3.3.3</td>\n",
    "         <td>goal</td>\n",
    "         <td>inbox_detail</td>\n",
    "         <td>view</td>\n",
    "         <td>메시지</td>\n",
    "         <td>메시지목록-상세</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>unattributed</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>False</td>\n",
    "         <td>False</td>\n",
    "      </tr>\n",
    "   </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또한 가령 ```user_id```가 **fff40190-d751-425a-9813-2284072e47f5**인 사용자의 로그 데이터 중에서, ```eventdatetime```, ```eventcategory```, ```viewid```, ```viewaction```, ```funnel desc```, ```viewid desc``` 컬럼만 출력하길 원한다면 다음의 결과가 나와야 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table border=\"1\" class=\"dataframe\" style=\"float: left;\">\n",
    "   <thead>\n",
    "      <tr style=\"text-align: right;\">\n",
    "         <th></th>\n",
    "         <th>eventdatetime</th>\n",
    "         <th>eventcategory</th>\n",
    "         <th>viewid</th>\n",
    "         <th>viewaction</th>\n",
    "         <th>funnel desc</th>\n",
    "         <th>viewid desc</th>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>rowuuid</th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "         <th></th>\n",
    "      </tr>\n",
    "   </thead>\n",
    "   <tbody>\n",
    "      <tr>\n",
    "         <th>12dc5a10-2713-4a96-9693-3e29ca1a0f5c</th>\n",
    "         <td>2018-09-28 00:03:19</td>\n",
    "         <td>background</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>f55223e0-52f5-41c2-8f0b-41c562baa3fc</th>\n",
    "         <td>2018-09-28 00:03:16</td>\n",
    "         <td>goal</td>\n",
    "         <td>inbox_detail</td>\n",
    "         <td>view</td>\n",
    "         <td>메시지</td>\n",
    "         <td>메시지목록-상세</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>457da225-eaf2-4ffe-94e0-d3ffb7b93490</th>\n",
    "         <td>2018-09-28 00:02:41</td>\n",
    "         <td>goal</td>\n",
    "         <td>inbox_detail</td>\n",
    "         <td>view</td>\n",
    "         <td>메시지</td>\n",
    "         <td>메시지목록-상세</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>d1da5023-8ef1-46ae-bd79-ab9208854c73</th>\n",
    "         <td>2018-09-28 00:02:41</td>\n",
    "         <td>launchInSession</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>20b8dfa9-4ac9-4b53-87d4-86db737fd7ef</th>\n",
    "         <td>2018-09-27 23:51:25</td>\n",
    "         <td>background</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>a9d0ac01-b715-46f2-b92d-8570dfbc2cca</th>\n",
    "         <td>2018-09-27 23:51:08</td>\n",
    "         <td>goal</td>\n",
    "         <td>inbox_detail</td>\n",
    "         <td>view</td>\n",
    "         <td>메시지</td>\n",
    "         <td>메시지목록-상세</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>af9381ed-25ca-4ba2-9a99-9a06f4c9bc26</th>\n",
    "         <td>2018-09-27 23:50:22</td>\n",
    "         <td>launch</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "         <td>NaN</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <th>a773ad20-fb9c-4c6d-96fd-1861dec0cea8</th>\n",
    "         <td>2018-09-27 23:50:22</td>\n",
    "         <td>goal</td>\n",
    "         <td>inbox_detail</td>\n",
    "         <td>view</td>\n",
    "         <td>메시지</td>\n",
    "         <td>메시지목록-상세</td>\n",
    "      </tr>\n",
    "   </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "마지막으로 ```user_id```가 **fff40190-d751-425a-9813-2284072e47f5**인 사용자의 로그 데이터 중에서, ```viewid```가 \"home\", \"inbox_detail\", \"inbox\", \"gig_detail\", \"thankyou\" 인 로그 데이터만 출력하길 원한다면 다음의 결과가 나와야 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table border=\"1\" class=\"dataframe\">  <thead>    <tr style=\"text-align: right;\">      <th></th>      <th>eventdatetime</th>      <th>devicemanufacturer</th>      <th>devicetype</th>      <th>ostype</th>      <th>osversion</th>      <th>appversion</th>      <th>eventcategory</th>      <th>viewid</th>      <th>viewaction</th>      <th>funnel desc</th>      <th>viewid desc</th>      <th>categoryname</th>      <th>category1</th>      <th>category2</th>      <th>category3</th>      <th>sourcetype</th>      <th>channel</th>      <th>params_campaign</th>      <th>params_medium</th>      <th>params_term</th>      <th>isfirstactivity</th>      <th>isfirstgoalactivity</th>    </tr>    <tr>      <th>rowuuid</th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>      <th></th>    </tr>  </thead>  <tbody>    <tr>      <th>f55223e0-52f5-41c2-8f0b-41c562baa3fc</th>      <td>2018-09-28 00:03:16</td>      <td>Samsung</td>      <td>SM-G950N</td>      <td>Android</td>      <td>8.0.0</td>      <td>3.3.3</td>      <td>goal</td>      <td>inbox_detail</td>      <td>view</td>      <td>메시지</td>      <td>메시지목록-상세</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>unattributed</td>      <td>unattributed</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>False</td>      <td>False</td>    </tr>    <tr>      <th>457da225-eaf2-4ffe-94e0-d3ffb7b93490</th>      <td>2018-09-28 00:02:41</td>      <td>Samsung</td>      <td>SM-G950N</td>      <td>Android</td>      <td>8.0.0</td>      <td>3.3.3</td>      <td>goal</td>      <td>inbox_detail</td>      <td>view</td>      <td>메시지</td>      <td>메시지목록-상세</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>unattributed</td>      <td>unattributed</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>False</td>      <td>False</td>    </tr>    <tr>      <th>a9d0ac01-b715-46f2-b92d-8570dfbc2cca</th>      <td>2018-09-27 23:51:08</td>      <td>Samsung</td>      <td>SM-G950N</td>      <td>Android</td>      <td>8.0.0</td>      <td>3.3.3</td>      <td>goal</td>      <td>inbox_detail</td>      <td>view</td>      <td>메시지</td>      <td>메시지목록-상세</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>unattributed</td>      <td>unattributed</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>False</td>      <td>False</td>    </tr>    <tr>      <th>a773ad20-fb9c-4c6d-96fd-1861dec0cea8</th>      <td>2018-09-27 23:50:22</td>      <td>Samsung</td>      <td>SM-G950N</td>      <td>Android</td>      <td>8.0.0</td>      <td>3.3.3</td>      <td>goal</td>      <td>inbox_detail</td>      <td>view</td>      <td>메시지</td>      <td>메시지목록-상세</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>unattributed</td>      <td>unattributed</td>      <td>NaN</td>      <td>NaN</td>      <td>NaN</td>      <td>False</td>      <td>False</td>    </tr>  </tbody></table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음의 기능을 구현한 ```find_log```라는 함수를 구현해주세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find_log라는 함수를 정의합니다. 함수에는 여러 옵션이 들어가는데,\n",
    "\n",
    "# user_id에는 사용자 아이디(user_id) 하나 또는 리스트를 넣을 수 있고, 지정한 사용자 아이디에 해당하는 로그 데이터를 반환합니다.\n",
    "# 이 인자의 기본 값은 None입니다. 이 경우 모든 사용자의 로그 데이터를 반환합니다.\n",
    "\n",
    "# columns에는 컬럼명 하나 또는 리스트를 넣을 수 있고, 로그 데이터에서 지정한 컬럼만 반환합니다.\n",
    "# 이 인자의 기본 값은 None입니다. 이 경우 모든 컬럼을 반환합니다.\n",
    "\n",
    "# viewid에는 viewid 하나 또는 리스트를 넣을 수 있고, 로그 데이터에서 지정한 viewid만을 반환합니다.\n",
    "# 이 인자의 기본 값은 None입니다. 이 경우 모든 viewid를 반환합니다.\n",
    "\n",
    "def find_log(user_id = None, columns = None, viewid = None):\n",
    "    # ToDo: 여기에 코드를 작성하여 find_log라는 함수를 완성해주세요.\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def find_log(user_id = None, columns = None, viewid = None):\n",
    "    log_temp = log.copy()\n",
    "    log_temp['eventdatetime'] = pd.to_datetime(log_temp['eventdatetime'])\n",
    "    # 설정된 viewid에 따라 데이터프레임에 viewid가 매칭되었는지를 파악해주는 칼럼 추가 - 아니라면 pass\n",
    "    if viewid != None:\n",
    "        if type(viewid) == str:\n",
    "            viewid = [viewid]\n",
    "        log_temp = log.copy()\n",
    "        log_temp['inappeventcategory'] = log_temp['inappeventcategory'].fillna('null')\n",
    "        log_temp['viewid_matching'] = pd.Series(len(set(lists) & set(viewid)) > 0 for lists in log_temp['inappeventcategory'].str.split('.'))\n",
    "        log_temp = log_temp.loc[log_temp[\"viewid_matching\"] == True]  # 행들을 가져옴\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "    \n",
    "    \n",
    "    # user_id에 따른 행 커팅\n",
    "    if user_id != None:\n",
    "        if type(user_id) == str:\n",
    "            user_id = [user_id]\n",
    "        log_temp = log_temp.loc[log_temp['canonicaldeviceuuid'].isin(user_id)]\n",
    "        log_temp = log_temp.set_index('rowuuid').sort_values(by=\"eventdatetime\", ascending=True)\n",
    "    else:\n",
    "        log_temp = log_temp.set_index([\"canonicaldeviceuuid\",\"rowuuid\"]).sort_values(by=\"eventdatetime\", ascending=True)\n",
    "    \n",
    "    \n",
    "    # columns에 따른 칼럼 커팅\n",
    "    if columns != None:\n",
    "        if type(columns) == str:\n",
    "            columns = [columns]\n",
    "        log_temp = log_temp[columns]\n",
    "        \n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "    return log_temp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### find_log 함수 test 하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>devicemanufacturer</th>\n",
       "      <th>osversion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rowuuid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8d247c42-d833-4f1a-83cd-3037065b8e9e</th>\n",
       "      <td>samsung</td>\n",
       "      <td>Android8.0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     devicemanufacturer     osversion\n",
       "rowuuid                                                              \n",
       "8d247c42-d833-4f1a-83cd-3037065b8e9e            samsung  Android8.0.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "test_id_list = ['F36FAA62-ADAC-4AA5-9B00-1FD6CB7EE957','A9E5778A-8F3D-4597-9718-74BF953A9F64']\n",
    "test_viewid_list = [\"home\", \"inbox_detail\", \"inbox\", \"gig_detail\", \"thankyou\"]\n",
    "find_log(user_id = \"ba111d43-85e2-4e97-a59b-6151a1610d47\", columns = [\"devicemanufacturer\", \"osversion\"], viewid = \"thankyou\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 최근 48시간의 페이지뷰의 변화량을 출력해주세요.\n",
    "\n",
    "정리한 로그 데이터를 바탕으로, 로그가 기록된 2018년 09월 28일부터 29일까지의 48시간동안 발생한 페이지뷰의 변화량을 출력하고 싶습니다. 사용자가 서비스를 한 번 이용할 때 마다 페이지뷰가 한 번 올라갔다고 가정합니다. (즉, 전체 데이터의 개수인 434,244개를 최근 48시간동안의 페이지뷰라고 가정하겠습니다) 가령 다음의 그림이 나와야 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 뷰 카테고리별 체류시간 출력 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log 데이터의 event datetime 을 datetime 데이터로\n",
    "log.eventdatetime = pd.to_datetime(log.eventdatetime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 전처리 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#log[\"eventdatetime\"] = log[\"eventdatetime\"].apply(lambda t: t.replace(second=0))\n",
    "log_data = log.copy()\n",
    "\n",
    "# 필요한 데이터만 남기고 drop\n",
    "log_data = log_data.drop([\"eventcategory\", \"isfirstactivity\", \"apppackagename\", \"sourcetype\", \"channel\", \"params_campaign\", \"params_medium\", \"params_term\", \"inappeventlabel\", \"isfirstgoalactivity\", \"event_rank\"], axis=1)\n",
    "\n",
    "\n",
    "# \"canonicaldeviceuuid\" 칼럼의 암호화된 user_id는 너무 길고 복잡하니 정수 넘버를 부여한 암호로 변경\n",
    "user_id_temp = log_data[\"canonicaldeviceuuid\"].unique()\n",
    "n_user_id = range(len(user_id_temp))\n",
    "id_zip = zip(user_id_temp, n_user_id)\n",
    "id_dict = dict(id_zip)\n",
    "log_data['n_user_id'] = log_data['canonicaldeviceuuid'].map(id_dict)\n",
    "log_data = log_data.drop(['canonicaldeviceuuid', 'devicemanufacturer', 'rowuuid'], axis=1) # 불필요해진 데이터는 삭제\n",
    "\n",
    "# 뷰 카테고리가 NaN으로 기입된 항목은 제외\n",
    "log_data = log_data.loc[~log_data['inappeventcategory'].isnull()]\n",
    "\n",
    "# duration 데이터를 추출하기 위해 (1) 유저별로 정렬을 한 다음 유저의 뷰 데이터를 시간 순으로 (eventdatetime) 정렬\n",
    "log_data_duration = log_data.sort_values(by=[\"n_user_id\", 'eventdatetime'])\n",
    "\n",
    "# duration 추출을 위해 [다음 행동의 시각 - 현재 행동의 시각] 을 데이터로 기록\n",
    "log_data_duration['timestamp_after'] = log_data_duration['eventdatetime'].shift(-1)\n",
    "log_data_duration['log_duration'] = (log_data_duration['timestamp_after'] - log_data_duration['eventdatetime'])\n",
    "\n",
    "log_data_duration['next'] = log_data_duration['n_user_id'].shift(-1)\n",
    "\n",
    "\n",
    "# view id 탭에서 .view와 .click을 제거해서 통일, 직관적인 이름으로 변경\n",
    "log_data_duration['inappeventcategory'] = pd.Series(item_list[0] for item_list in log_data_duration['inappeventcategory'].str.split('.'))\n",
    "log_data_duration.rename(columns = {'inappeventcategory': 'what_user_view'}, inplace = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 문제상황 처리\n",
    "1. n_user_id 가 1 -> 2 로 변경되는 순간, 접속시각 차이가 커서 duration이 지나치게 높게 나타나는 현상\n",
    "2. n_user_id 가 1 = 1로 같아도 잠시 접속을 이탈했다가 다시 접속하면 duration이 높게 나타나는 현상"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1번 상황 처리\n",
    "\n",
    "# 1-1 아이다가 바뀌지 않는 상황 ( == )은 duration을 timedelta 상황에서 \"분\" 단위로 변경\n",
    "\n",
    "log_data_duration.loc[log_data_duration['n_user_id'] == log_data_duration['next'], 'log_duration'] = pd.Series(log_data_duration.loc[log_data_duration['n_user_id'] == log_data_duration['next'], 'log_duration'].dt.total_seconds() / 60)\n",
    "\n",
    "# 1-2 아이디가 바뀌는 상황 ( != )은 해당 유저의 마지막 활동이므로 그때의 duration은 0을 강제 입력\n",
    "log_data_duration.loc[log_data_duration['n_user_id'] != log_data_duration['next'], 'log_duration'] = 0\n",
    "\n",
    "\n",
    "# float으로 단위 맞춰줌\n",
    "log_data_duration['log_duration'] = log_data_duration['log_duration'].astype('float')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2번 상황 처리\n",
    "\n",
    "# 단순 로그뷰만으로는 실제 이용자가 화면에 오래 머무른 것인지, 이탈한 것인지를 명확히 확인하기 어려움\n",
    "# 분위수를 이용해 이상치를 설정하고, 이상치를 벗어나는 값(cut_point)은 이탈에 따른 duration 증가로 판단, 0을 집어넣음\n",
    "\n",
    "def robust_duration(category, percentile):\n",
    "    cut_point = np.percentile(log_data_duration.loc[log_data_duration['what_user_view'] == category, \"log_duration\"], percentile)\n",
    "    log_data_duration.loc[(log_data_duration['what_user_view'] == category) & (log_data_duration[\"log_duration\"] > cut_point), \"log_duration\"] = 0\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### robust 처리 전 후 비교하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "before_data = log_data_duration.pivot_table(index='what_user_view', values='log_duration', aggfunc='mean')\n",
    "\n",
    "# 지금까지 나왔던 \"what_user_view\"의 항목을을 for 돌려서 robust_duration 함수를 수행시킴.\n",
    "# 분위수는 98%로 설정\n",
    "\n",
    "\n",
    "for category in log_data_duration['what_user_view'].unique():\n",
    "    robust_duration(category, 98)\n",
    "\n",
    "after_data = log_data_duration.pivot_table(index='what_user_view', values='log_duration', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>before_mean</th>\n",
       "      <th>after_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>what_user_view</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>account_setting_AuthStatus</th>\n",
       "      <td>97.653077</td>\n",
       "      <td>52.752051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kmongCash_history_refundRequest</th>\n",
       "      <td>79.777083</td>\n",
       "      <td>38.056944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mobileAuth</th>\n",
       "      <td>29.322789</td>\n",
       "      <td>9.499830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>signup</th>\n",
       "      <td>44.807377</td>\n",
       "      <td>8.021311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kmongNotification_detail</th>\n",
       "      <td>32.459756</td>\n",
       "      <td>6.854878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_payment</th>\n",
       "      <td>16.348000</td>\n",
       "      <td>5.910222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bookmarks_portfolio</th>\n",
       "      <td>20.908790</td>\n",
       "      <td>5.299429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>search</th>\n",
       "      <td>21.188069</td>\n",
       "      <td>5.177553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kmongNotification</th>\n",
       "      <td>23.460131</td>\n",
       "      <td>5.012192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buyer_order_track_cancel</th>\n",
       "      <td>14.470390</td>\n",
       "      <td>5.007979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 before_mean  after_mean\n",
       "what_user_view                                          \n",
       "account_setting_AuthStatus         97.653077   52.752051\n",
       "kmongCash_history_refundRequest    79.777083   38.056944\n",
       "mobileAuth                         29.322789    9.499830\n",
       "signup                             44.807377    8.021311\n",
       "kmongNotification_detail           32.459756    6.854878\n",
       "order_payment                      16.348000    5.910222\n",
       "bookmarks_portfolio                20.908790    5.299429\n",
       "search                             21.188069    5.177553\n",
       "kmongNotification                  23.460131    5.012192\n",
       "buyer_order_track_cancel           14.470390    5.007979"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparing = pd.merge(before_data, after_data, left_index=True, right_index=True,how='right').sort_values(by='log_duration_y', ascending=False).head(10)\n",
    "comparing.columns = ['before_mean', 'after_mean']\n",
    "comparing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "같은 하루 중 유저의 중복 접속에 의한 접속시간 증가를 제거하니 뷰 별 접속 시간 평균이 많이 robust 해졌다.\n",
    "\n",
    "또한 자신의 계정 설정에서 접속해 있는 시간이 가장 크며\n",
    "\n",
    "크몽 캐시 환불 요청을 할 때 역시 평균 40분 가량이 소모되는 긴 작업임을 알 수 있다.\n",
    "\n",
    "모바일 인증 시간도 10분 정도 평균 잡아먹는다\n",
    "\n",
    "회원가입은 8분 정도 잡아먹는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>log_duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>appversion</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3.3.2</th>\n",
       "      <td>4.508694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.3.3</th>\n",
       "      <td>4.068989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.3.5</th>\n",
       "      <td>3.291099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.4.0</th>\n",
       "      <td>1.748988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0.2</th>\n",
       "      <td>4.357586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0.3</th>\n",
       "      <td>4.747755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0.4</th>\n",
       "      <td>4.133249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0.5</th>\n",
       "      <td>3.040685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            log_duration\n",
       "appversion              \n",
       "3.3.2           4.508694\n",
       "3.3.3           4.068989\n",
       "3.3.5           3.291099\n",
       "3.4.0           1.748988\n",
       "4.0.2           4.357586\n",
       "4.0.3           4.747755\n",
       "4.0.4           4.133249\n",
       "4.0.5           3.040685"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_data_duration.pivot_table(index = 'appversion', values='log_duration', aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 다른 경우 아예 제거해버리는 방법도 있다\n",
    "#indexNames = log_data_bounce.loc[log_data_bounce['n_user_id'] != log_data_bounce['next']].index\n",
    "#log_data_bounce_temp = log_data_bounce.drop(indexNames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 그림에 추가로, 다음의 조건에 해당하는 데이터만 별도로 페이지뷰를 출력할 수 있도록 할 것입니다.\n",
    "\n",
    "  1. OS(```ostype```)별 페이지뷰, 가령 iOS와 Android마다의 페이지뷰의 변화량을 알 수 있어야 합니다.\n",
    "  1. OS버전(```osversion```)별 페이지뷰, 가령 특정 버전(ex: 11.4.1)마다의 페이지뷰의 변화량을 알 수 있어야 합니다.\n",
    "  1. 디바이스(```devicetype```)별 페이지뷰, 가령 특정 디바이스(ex: iPhone)마다의 페이지뷰의 변화량을 알 수 있어야 합니다.\n",
    "  1. 어플리케이션 버전(```appversion```)별 페이지뷰, 가령 특정 버전(ex: 4.0.4)마다의 페이지뷰의 변화량을 알 수 있어야 합니다.\n",
    "  \n",
    "이 시각화를 출력했을 때, 특정 조건에서 페이지뷰가 급감한다면 해당 OS나 디바이스에서 구동하는 어플리케이션에 버그가 있을 가능성이 있습니다. 이 경우 운영팀/개발팀에게 빠르게 이 사실을 전달하여 디버깅을 해야 할 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "가령 전체 페이지뷰 대비 아이폰(iPhone)에서 동작하는 어플리케이션의 페이지뷰의 변화량은 다음과 같습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x215313128>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또한 전체 페이지뷰 대비 특정 버전(ex: 4.0.4)에서 동작하는 페이지뷰의 변화량은 다음과 같습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x215177358>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1728x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음의 기능을 구현하는 ```plot_pageview```라는 함수를 구현해주세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_pageview라는 함수를 정의합니다. 함수에는 여러 옵션이 들어가는데,\n",
    "# ostype, osversion, devicetype, appversion 모두 하나의 문자열 또는 리스트를 넣을 수 있습니다.\n",
    "# 이 경우 인자로 넣은 정보에 해당하는 조건만 별도로 시각화를 합니다.\n",
    "# 이 인자의 기본 값은 None입니다. 이 경우 해당 조건으로 별도 시각화를 하지 않습니다.\n",
    "def plot_pageview(ostype = None, osversion = None, devicetype = None, appversion = None):\n",
    "    # ToDo: 여기에 코드를 작성하여 find_log라는 함수를 완성해주세요.\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 자유 주제 - 데이터를 활용하여 자유롭게 시각화를 해주세요.\n",
    "\n",
    "마지막 문제는 자유 주제입니다. 특정한 형식에 구애받지 않고 자유롭게 데이터 분석과 시각화를 통해 데이터에서 인사이트를 추출해주세요.\n",
    "\n",
    "크몽의 데이터에는 아직 우리가 분석하지 않은 미지의 요소들이 있습니다. 몇 가지 예시를 적어보자면 다음과 같습니다.\n",
    "\n",
    "  * 시간별 이탈률 분석(bounce rate), 내지는 특정 페이지나 디바이스별 이탈률을 분석할 수도 있습니다.\n",
    "  * 퍼널 분석(Funnel analysis), 사용자가 서비스에 방문해서 목표를 달성할 때 까지의 모든 과정을 시각화하고 분석할 수도 있습니다. ([참고](https://www.slideshare.net/LeeGwan/ss-42159541))\n",
    "  * 목표(ex: 상품 구매)를 달성하는 사용자와 달성하지 않는 사용자의 차이를 분석할 수 있습니다.\n",
    "  \n",
    "주어진 내용을 바탕으로 자유롭게 분석 및 시각화를 한 뒤 제출하세요. 문제를 푸는 지원자의 분석 스킬, 그리고 상상력과 창의력을 최대한 활용하기를 기대합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code here!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 과제 제출\n",
    "\n",
    "과제를 모두 푼 분들은 datascience@dsschool.co.kr 에 결과를 제출해주세요. 제출할 때는 다음의 자료가 필요합니다.\n",
    "\n",
    "1. 과제를 푼 쥬피터 노트북. ipynb 파일로 보내주시면 됩니다.\n",
    "1. 과제를 푼 지원자의 이력을 알 수 있는 이력서와 포트폴리오, 기타 본인의 커리어와 능력을 어필할 수 있는 자료.\n",
    "\n",
    "과제에 대해 궁금한 점이 있다면 datascience@dsschool.co.kr로 문의 메일을 보내주세요. 다만 면접용 과제이니만큼 과제 풀이에 대한 힌트를 드릴 수는 없으며, 동일한 이유로 과제에 대한 솔루션을 제공해드릴 수 없는 점 양해 부탁드립니다. (컬럼이나 데이터, 그리고 문제에 대한 부연설명 요청 등은 언제든지 환영합니다)\n",
    "\n",
    "수업 내용, 과제에 대한 문의사항은 datascience@dsschool.co.kr로, 수료증이나 기수 변경, 기타 영수증 발급은 support@dsschool.co.kr 로 보내주세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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