Data 1_11 teach.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize':(9,6)}) # добавляет размер графика"
   ]
  },
  {
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   "source": [
    "#events_data_ = pd.read_csv(r'https://stepik.org/media/attachments/course/4852/event_data_train.zip') # тоже открывает zip\n",
    "#submissions_data_train_ = pd.read_csv(r'https://stepik.org/media/attachments/course/4852/submissions_data_train.zip')\n",
    "events_data = pd.read_csv('event_data_train.csv')\n",
    "submissions_data = pd.read_csv('submissions_data_train.csv')\n",
    "events_data.head(13)"
   ]
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    "events_data['data'] = pd.to_datetime(events_data.timestamp, unit='s') # unit='s' в секундах\n",
    "events_data.head()"
   ]
  },
  {
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       "Timestamp('2018-05-19 23:33:31')"
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      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data['day'] = events_data['data'].dt.date # events_data['day'] = events_data.data.dt.date\n",
    "events_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day\n2015-06-15    705\n2015-06-16    526\n2015-06-17    440\n2015-06-18    411\n2015-06-19    377\n2015-06-20    294\n2015-06-21    292\n2015-06-22    516\n2015-06-23    520\n2015-06-24    471\nName: user_id, dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data.groupby('day') \\\n",
    "    .user_id.nunique().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day         user_id\n2015-06-15  1046       567\n            26763      272\n            2446       241\n            1808       223\n            16503      210\n            5602       206\n            13793      205\n            11696      204\n            4406       194\n            5440       190\nName: user_id, dtype: int64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data.groupby('day') \\\n",
    "    .user_id.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4e6ea079e8>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XwuPxYNasWZg7d+6gDyIZXfGrIXpVjq74lXEJERFR1qTUx+Suu+7CXXfdpbtv/vz5mD9/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4e0c0fd390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "events_data.groupby('day') \\\n",
    "    .user_id.nunique().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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>user_id</th>\n",
       "      <th>passed_steps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<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>user_id</th>\n",
       "      <th>passed_steps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data[events_data.action == \"passed\"].groupby('user_id', as_index=False).agg({'step_id': 'count'}).rename(columns={'step_id': 'passed_steps'}).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17262, 2)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data[events_data.action == \"passed\"].groupby('user_id', as_index=False).agg({'step_id': 'count'}).rename(columns={'step_id': 'passed_steps'}).shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# events_data.query('action == \"passed\"') \\\n",
    "#     .groupby('user_id', as_index=False) \\\n",
    "#     .agg({'step_id': 'count'}).head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4e02234ba8>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FyAAAALMIGQAAYBYhAwAAzCJkAACAWYQMAAAwi5ABAABmETIAAMCsCYfMgw8+qPLycknS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4e6dd66860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# с учетом только тех пользователей, котрые прошли степ\n",
    "events_data.query('action == \"passed\"').groupby('user_id', as_index=False).agg({'step_id': 'count'}).rename(columns={'step_id': 'passed_steps'}).passed_steps.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "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>action</th>\n",
       "      <th>discovered</th>\n",
       "      <th>passed</th>\n",
       "      <th>started_attempt</th>\n",
       "      <th>viewed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91</td>\n",
       "      <td>87</td>\n",
       "      <td>30</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<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",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>action</th>\n",
       "      <th>discovered</th>\n",
       "      <th>passed</th>\n",
       "      <th>started_attempt</th>\n",
       "      <th>viewed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91</td>\n",
       "      <td>87</td>\n",
       "      <td>30</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data.pivot_table(index=\"user_id\", \n",
    "                        columns='action', \n",
    "                        values=\"step_id\", \n",
    "                        aggfunc='count', \n",
    "                        fill_value=0).head()\n",
    "                    \n",
    "# fill_value=0 - подставляет 0 вместо NaN\n",
    "# values=\"step_id\" - С какими значениями будем делать агрегационную функцию, step_id - это урок на курсе, \n",
    "# т е для каждого юзераподсчитываем количества уроков (пройденных, для какого кол-ва уроков было сделано столько попыток, сколько уроков просмотренных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "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>action</th>\n",
       "      <th>user_id</th>\n",
       "      <th>discovered</th>\n",
       "      <th>passed</th>\n",
       "      <th>started_attempt</th>\n",
       "      <th>viewed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "      <td>87</td>\n",
       "      <td>30</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<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>action</th>\n",
       "      <th>user_id</th>\n",
       "      <th>discovered</th>\n",
       "      <th>passed</th>\n",
       "      <th>started_attempt</th>\n",
       "      <th>viewed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "      <td>87</td>\n",
       "      <td>30</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_scores1 = events_data.pivot_table(index=\"user_id\", \n",
    "                        columns='action', \n",
    "                        values=\"step_id\", \n",
    "                        aggfunc='count', \n",
    "                        fill_value=0).reset_index().head()\n",
    "# .reset_index() - убирает индекс user_id - обновляет\n",
    "users_scores1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4e8fabce48>"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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URTQ2NgKwceNGKisraW1tZcGCBTQ0NEzOZJIkKeWNO2T+9V//lTvuuIO8vDyysrLYvn07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\nGTKSJClY/w9xsOh6dnRvRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4e7a170828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "events_data.pivot_table(index=\"user_id\", \n",
    "                        columns='action', \n",
    "                        values=\"step_id\", \n",
    "                        aggfunc='count', \n",
    "                        fill_value=0).reset_index().discovered.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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>user_id</th>\n",
       "      <th>day</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17632</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>1434340848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>12494</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>1434341931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>442</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>1434342029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>22254</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>1434342042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>6646</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>1434342230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<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>user_id</th>\n",
       "      <th>day</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17632</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>1434340848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>12494</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>1434341931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>442</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>1434342029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>22254</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>1434342042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>6646</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>1434342230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = events_data[['user_id','day','timestamp']].drop_duplicates(subset=['user_id','day']).head()\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n1                                         [1472827464]\n2                             [1514383364, 1519226966]\n3    [1434358476, 1441257725, 1441440209, 144153391...\n5                             [1466156809, 1499859621]\n7                                         [1521634660]\nName: timestamp, dtype: object"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data[['user_id','day','timestamp']].drop_duplicates(subset=['user_id','day']).groupby('user_id')['timestamp'].apply(list).head()\n",
    "# s2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n1                                                  []\n2                                           [4843602]\n3    [6899249, 182484, 93710, 2768870, 171400, 78712]\n5                                          [33702812]\n7                                                  []\nName: timestamp, dtype: object"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gap_data = events_data[['user_id','day','timestamp']].drop_duplicates(subset=['user_id','day']).groupby('user_id')['timestamp'].apply(list).apply(np.diff).head()\n",
    "gap_data\n",
    "# разность между прилайщими наблюдениями np.diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([array([], dtype=int32), array([4843602]),\n       array([6899249,  182484,   93710, 2768870,  171400,   78712]), ...,\n       array([   86128,   136759,    94899,  1422583, 14347289,   505061,\n          98252,    57019]),\n       array([1567990]), array([], dtype=int32)], dtype=object)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gap_data = events_data[['user_id','day','timestamp']].drop_duplicates(subset=['user_id','day']).groupby('user_id')['timestamp'].apply(list).apply(np.diff).values\n",
    "gap_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4843602, 6899249,  182484, ...,   98252,   57019, 1567990])"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate(gap_data, axis=0) # ось 0 - строкам\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "gap_data = pd.Series(np.concatenate(gap_data, axis=0))\n",
    "#gap_data = gap_data/ (24 * 60 * 60) # разница в днях, которая наблюдалась у пОльзователей в перерывах между заходами на курс\n",
    "#gap_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gap_data[gap_data < 200].hist() # большая часть промежутков в днях (gap_data) оказывается в диапазоне меньше 200 дней между двумя заходами пользователей \n",
    "# (рассчитано для каждого пользоватля"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gap_data.quantile(0.95) # только 5% пользователей возвращвется на курс в перерыве 59,76 = 60 дней ---> gap_data.quantile(0.95)--->59.760440972222156"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gap_data.quantile(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.325995370370403"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Итого:\n",
    "gap_data.quantile(0.9) / (24 * 60 * 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>viewed</td>\n",
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       "      <td>2018-05-19 23:33:31</td>\n",
       "      <td>2018-05-19</td>\n",
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       "</div>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Если пользователь не получил сертификат и больше чем 30 дней не был на платформе, то такой пользователь считается грохнувшимся\n",
    "# Нижний ряд - конечная финальная дата , от него будет считать отвалившихся пользоватей\n",
    "events_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1526772811"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#конечная финальная дата курса\n",
    "now = events_data.timestamp.max()\n",
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
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     "execution_count": 224,
     "metadata": {},
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    }
   ],
   "source": [
    "# максимальное время появления на курсе для каждого пользователя\n",
    "user_data = events_data.groupby('user_id', as_index=False).agg({'timestamp': 'max'}).rename(columns={'timestamp': 'last_timestamp'})\n",
    "user_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
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   "source": [
    "user_data['is_gone_user'] = now - user_data.last_timestamp\n",
    "user_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "# сколько дней не было на курсе , например, 30 дней (между 90% и 95%)\n",
    "drop_out_threshold = 30 * 24 * 60 *60"
   ]
  },
  {
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   "execution_count": 226,
   "metadata": {},
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     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# спрoсим, больше ли реальная разница (реальное время отсутствия на курсе) чем рассчитаноое нами время\n",
    "# если это так то пользователь будет записан в набор отвалившихся \"is_gone_user'\n",
    "user_data['is_gone_user'] = (now - user_data.last_timestamp) > drop_out_threshold\n",
    "user_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 227,
     "metadata": {},
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   ],
   "source": [
    "users_scores = submissions_data.pivot_table(index=\"user_id\", \n",
    "                        columns='submission_status', \n",
    "                        values=\"step_id\", \n",
    "                        aggfunc='count', \n",
    "                        fill_value=0).reset_index().head()\n",
    "# .reset_index() - убирает индекс user_id - обновляет\n",
    "users_scores"
   ]
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    "user_data.merge(users_scores).head()"
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     },
     "execution_count": 235,
     "metadata": {},
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    }
   ],
   "source": [
    "user_data = user_data.merge(users_scores, on='user_id',how='outer').fillna(0).head(20)\n",
    "user_data"
   ]
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  {
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   "execution_count": 234,
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   "outputs": [
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   ],
   "source": [
    "users_events_data = events_data.pivot_table(index=\"user_id\", \n",
    "                        columns='action', \n",
    "                        values=\"step_id\", \n",
    "                        aggfunc='count', \n",
    "                        fill_value=0).reset_index()\n",
    "users_events_data.head()"
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  {
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     "execution_count": 236,
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   "source": [
    "user_data = user_data.merge(users_events_data, how='outer')\n",
    "user_data.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
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      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_days = events_data.groupby('user_id').day.nunique().to_frame().reset_index()\n",
    "user_days.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data = user_data.merge(user_days, how='outer')\n",
    "user_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19234"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "user_data.user_id.nunique() # Проверяем, не потеряли ли кого-то"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19234"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "events_data.user_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 249,
     "metadata": {},
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   ],
   "source": [
    "# прошёл ли юзер успешно курс\n",
    "user_data['passed_course'] = user_data.passed > 170\n",
    "user_data.head()\n"
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  },
  {
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   "metadata": {},
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       "        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>user_id</th>\n",
       "      <th>last_timestamp</th>\n",
       "      <th>is_gone_user</th>\n",
       "      <th>correct</th>\n",
       "      <th>wrong</th>\n",
       "      <th>discovered</th>\n",
       "      <th>passed</th>\n",
       "      <th>started_attempt</th>\n",
       "      <th>viewed</th>\n",
       "      <th>day</th>\n",
       "      <th>passed cours</th>\n",
       "      <th>passed course</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passed_course</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>False</th>\n",
       "      <td>17809</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17809</td>\n",
       "      <td>17809</td>\n",
       "      <td>17809</td>\n",
       "      <td>17809</td>\n",
       "      <td>17809</td>\n",
       "      <td>17809</td>\n",
       "      <td>17809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1425</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1425</td>\n",
       "      <td>1425</td>\n",
       "      <td>1425</td>\n",
       "      <td>1425</td>\n",
       "      <td>1425</td>\n",
       "      <td>1425</td>\n",
       "      <td>1425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data.groupby('passed_course').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.001572238755685"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * 1425 / 17809 # процентное соотношение прошедших курс"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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