Game Analyst in Saber Interactive from Chigvintseva.ipynb

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    "\"\"\"\n",
    "Имеются данные социальной игры в жанре match-3 на платформе Facebook.\n",
    "\n",
    "Данные для тестового задания брать тут:\n",
    "https://docs.google.com/spreadsheets/d/1Pmx-AWbYoCaee7FQZ06gkUbAzotgg2wLD7v2XfEXW4Y/edit?usp=sharing\n",
    "\n",
    "На странице FirstEnter приложенной таблицы представлены данные о прохождении игроками туториала.\n",
    "Туториал состоит из 8 шагов, выполняемых последовательно при первом входе в игру.\n",
    "MaxAchievedStep: 0-8 - последний достигнутый игроком шаг туториала.\n",
    "\n",
    "    0 - регистрация игрока\n",
    "    1 - первый матч 3-х фишек\n",
    "    2 - второй матч 3-х фишек\n",
    "    3 - свободный ход (не ограничен туториалом)\n",
    "    4 - окно награды за уровень с кнопкой “далее”\n",
    "    5 - матч из 4-х фишек\n",
    "    6 - матч бонуса (уничтожить линию)\n",
    "    7 - свободный ход\n",
    "    8 - конец уровня и конец туториала\n",
    "\n",
    "На странице RetentionRate представлены данные о дате и времени входа игроков. \n",
    "Одинаковый UserId - один игрок. \n",
    "Данные об источнике инсталла можно получить из таблицы FirstEnter (столбец RefPlace). \n",
    "\"\"\""
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       "            Plotly.purge(gd);\n",
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "#Задание:\n",
    "\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "data = pd.read_excel('Trainee Analyst Test DataSet.xlsx', ['FirstEnter', 'RetentionRate']) #считываем оба листа таблицы\n",
    "\n",
    "#1. Построить график воронки туториала по шагам. Для построения можно использовать любой известный вам инструмент\n",
    "        #Необходимо посчитать, сколько людей выполнило каждый из шагов и \n",
    "        #показать отношение количества людей, прошедших отдельные шаги туториала к количеству людей, стартовавших туториал.\n",
    "\n",
    "#группируем игроков по шагам:\n",
    "pivot = data['FirstEnter'].pivot_table(index ='Max AchievedStep', values = 'PlayerUid', aggfunc='count')  \n",
    "\n",
    "#считаем общее число стартовавших туториал:\n",
    "total_begin = int(pivot.sum())  \n",
    "\n",
    "# добавляем новый столбец Total_Step = кол-во игроков, выполнивших данный этап\n",
    "pivot['Total_Step'] = pivot['PlayerUid'][::-1].cumsum()[::-1]  \n",
    "\n",
    "#добавляем новый столбец Rate = отношение количества людей, прошедших отдельные шаги туториала к количеству людей, стартовавших туториал\n",
    "pivot['Rate'] = round(pivot['Total_Step']/total_begin,2)  \n",
    "\n",
    "#добавляем новый столбец Step - расшифровка каждого шага\n",
    "pivot['Step'] = [\"0 - регистрация игрока\", \"1 - первый матч 3-х фишек\", \"2 - второй матч 3-х фишек\", \n",
    "          \"3 - свободный ход (не ограничен туториалом)\", \"4 - окно награды за уровень с кнопкой Далее\",\n",
    "         \"5 - матч из 4-х фишек\",\"6 - матч бонуса (уничтожить линию)\",\"7 - свободный ход\",\n",
    "         \"8 - конец уровня и конец туториала\"]\n",
    "\n",
    "#выводим саму воронку\n",
    "fig = px.funnel(pivot, x='Total_Step', y='Step')\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "показатель Retention Day 1 = 8.56 %\n"
     ]
    }
   ],
   "source": [
    "#2. Посчитать показатель Retention Day 1.\n",
    "        #Retention Day X - это доля пользователей, которые вошли в приложение в конкретный день X после первого входа, \n",
    "        #т.е. Retention Day 1 - отношение количества людей, которые зашли на следующий день после первого визита \n",
    "        #к общему количеству совершивших первый вход.\n",
    "\n",
    "data['RetentionRate']['Days']=data['RetentionRate']['DateTime'] - data['RetentionRate']['Registered']\n",
    "pivot2 = data['RetentionRate'].pivot_table(columns = 'Days', index ='PlayerUid', values = 'DateTime', aggfunc='count')  #группируем \n",
    "pivot2.columns = ['days_0','days_1','days_2','days_3','days_4','days_5']\n",
    "Retention_Day_1 = round(pivot2.days_1.count()/pivot2.days_0.count()*100, 2)\n",
    "print('показатель Retention Day 1 =', Retention_Day_1, '%')"
   ]
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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