Coffee Machine .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dea6861a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "178417b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>installationId</th>\n",
       "      <th>deviceType</th>\n",
       "      <th>date</th>\n",
       "      <th>volume[ml]</th>\n",
       "      <th>flowtime[s]</th>\n",
       "      <th>flowrate[L/min]</th>\n",
       "      <th>flowEventType</th>\n",
       "      <th>flowEventTypeName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>2023-04-05T07:45:11.000000</td>\n",
       "      <td>355.0</td>\n",
       "      <td>32.3</td>\n",
       "      <td>0.66</td>\n",
       "      <td>3</td>\n",
       "      <td>1x Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>2023-04-05T07:44:32.000000</td>\n",
       "      <td>357.0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.66</td>\n",
       "      <td>3</td>\n",
       "      <td>1x Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>2023-04-05T07:42:57.000000</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3</td>\n",
       "      <td>1x Water</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>2023-04-05T07:42:20.000000</td>\n",
       "      <td>300.0</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0.53</td>\n",
       "      <td>13</td>\n",
       "      <td>1x Coffee - variant 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>2023-04-05T07:41:56.000000</td>\n",
       "      <td>194.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3</td>\n",
       "      <td>1x Water</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   installationId              deviceType                        date  \\\n",
       "0              32  Automatic + fresh milk  2023-04-05T07:45:11.000000   \n",
       "1              32  Automatic + fresh milk  2023-04-05T07:44:32.000000   \n",
       "2              32  Automatic + fresh milk  2023-04-05T07:42:57.000000   \n",
       "3              32  Automatic + fresh milk  2023-04-05T07:42:20.000000   \n",
       "4              32  Automatic + fresh milk  2023-04-05T07:41:56.000000   \n",
       "\n",
       "   volume[ml]  flowtime[s]  flowrate[L/min]  flowEventType  \\\n",
       "0       355.0         32.3             0.66              3   \n",
       "1       357.0         32.4             0.66              3   \n",
       "2       218.0         14.2             0.92              3   \n",
       "3       300.0         34.1             0.53             13   \n",
       "4       194.0         12.7             0.92              3   \n",
       "\n",
       "       flowEventTypeName  \n",
       "0               1x Water  \n",
       "1               1x Water  \n",
       "2               1x Water  \n",
       "3  1x Coffee - variant 2  \n",
       "4               1x Water  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/coffee_machine.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "370042d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac340b0b",
   "metadata": {},
   "source": [
    "# missing values and data types check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3282103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "installationId       0\n",
      "deviceType           0\n",
      "date                 0\n",
      "volume[ml]           0\n",
      "flowtime[s]          0\n",
      "flowrate[L/min]      0\n",
      "flowEventType        0\n",
      "flowEventTypeName    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# missing values check\n",
    "\n",
    "# df.isnull().sum()\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05394e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "installationId         int64\n",
      "deviceType            object\n",
      "date                  object\n",
      "volume[ml]           float64\n",
      "flowtime[s]          float64\n",
      "flowrate[L/min]      float64\n",
      "flowEventType          int64\n",
      "flowEventTypeName     object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# data types check\n",
    "\n",
    "df.dtypes\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e4d9142",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 8 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   installationId     100000 non-null  int64  \n",
      " 1   deviceType         100000 non-null  object \n",
      " 2   date               100000 non-null  object \n",
      " 3   volume[ml]         100000 non-null  float64\n",
      " 4   flowtime[s]        100000 non-null  float64\n",
      " 5   flowrate[L/min]    100000 non-null  float64\n",
      " 6   flowEventType      100000 non-null  int64  \n",
      " 7   flowEventTypeName  100000 non-null  object \n",
      "dtypes: float64(3), int64(2), object(3)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f86f4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>installationId</th>\n",
       "      <th>volume[ml]</th>\n",
       "      <th>flowtime[s]</th>\n",
       "      <th>flowrate[L/min]</th>\n",
       "      <th>flowEventType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.300000</td>\n",
       "      <td>214.336590</td>\n",
       "      <td>20.143829</td>\n",
       "      <td>0.685406</td>\n",
       "      <td>8.650650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.564616</td>\n",
       "      <td>164.765893</td>\n",
       "      <td>14.999675</td>\n",
       "      <td>0.231682</td>\n",
       "      <td>5.753367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.500000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>497.900000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       installationId     volume[ml]    flowtime[s]  flowrate[L/min]  \\\n",
       "count   100000.000000  100000.000000  100000.000000    100000.000000   \n",
       "mean        28.300000     214.336590      20.143829         0.685406   \n",
       "std         10.564616     164.765893      14.999675         0.231682   \n",
       "min          3.000000      19.000000       0.400000         0.070000   \n",
       "25%         29.000000     150.000000       9.800000         0.550000   \n",
       "50%         31.500000     205.000000      18.600000         0.630000   \n",
       "75%         35.000000     276.000000      27.300000         0.770000   \n",
       "max         38.000000    3922.000000     497.900000         7.800000   \n",
       "\n",
       "       flowEventType  \n",
       "count  100000.000000  \n",
       "mean        8.650650  \n",
       "std         5.753367  \n",
       "min         3.000000  \n",
       "25%         3.000000  \n",
       "50%         4.000000  \n",
       "75%        13.000000  \n",
       "max        18.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "815e240e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">volume[ml]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">flowtime[s]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">flowrate[L/min]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installationId</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>3</th>\n",
       "      <td>210.1997</td>\n",
       "      <td>173.836700</td>\n",
       "      <td>10.45244</td>\n",
       "      <td>6.116180</td>\n",
       "      <td>1.168750</td>\n",
       "      <td>0.266269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>186.6250</td>\n",
       "      <td>153.431903</td>\n",
       "      <td>18.34118</td>\n",
       "      <td>14.080950</td>\n",
       "      <td>0.612395</td>\n",
       "      <td>0.099936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>254.5932</td>\n",
       "      <td>99.738591</td>\n",
       "      <td>24.78464</td>\n",
       "      <td>11.506625</td>\n",
       "      <td>0.644401</td>\n",
       "      <td>0.092104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>202.4922</td>\n",
       "      <td>154.233106</td>\n",
       "      <td>19.12880</td>\n",
       "      <td>14.880912</td>\n",
       "      <td>0.656858</td>\n",
       "      <td>0.130987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>225.6739</td>\n",
       "      <td>186.210608</td>\n",
       "      <td>23.86620</td>\n",
       "      <td>19.074928</td>\n",
       "      <td>0.586184</td>\n",
       "      <td>0.093309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>247.7532</td>\n",
       "      <td>168.129371</td>\n",
       "      <td>22.31887</td>\n",
       "      <td>15.893300</td>\n",
       "      <td>0.702242</td>\n",
       "      <td>0.162701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>243.3130</td>\n",
       "      <td>174.472851</td>\n",
       "      <td>22.27818</td>\n",
       "      <td>16.552746</td>\n",
       "      <td>0.690205</td>\n",
       "      <td>0.122054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>243.8244</td>\n",
       "      <td>131.176844</td>\n",
       "      <td>23.49340</td>\n",
       "      <td>14.216830</td>\n",
       "      <td>0.660273</td>\n",
       "      <td>0.123719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>160.9044</td>\n",
       "      <td>170.704637</td>\n",
       "      <td>17.71779</td>\n",
       "      <td>14.048317</td>\n",
       "      <td>0.572160</td>\n",
       "      <td>0.194451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>167.9869</td>\n",
       "      <td>183.770282</td>\n",
       "      <td>19.05679</td>\n",
       "      <td>14.473359</td>\n",
       "      <td>0.560590</td>\n",
       "      <td>0.215482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               volume[ml]             flowtime[s]            flowrate[L/min]  \\\n",
       "                     mean         std        mean        std            mean   \n",
       "installationId                                                                 \n",
       "3                210.1997  173.836700    10.45244   6.116180        1.168750   \n",
       "14               186.6250  153.431903    18.34118  14.080950        0.612395   \n",
       "29               254.5932   99.738591    24.78464  11.506625        0.644401   \n",
       "30               202.4922  154.233106    19.12880  14.880912        0.656858   \n",
       "31               225.6739  186.210608    23.86620  19.074928        0.586184   \n",
       "32               247.7532  168.129371    22.31887  15.893300        0.702242   \n",
       "34               243.3130  174.472851    22.27818  16.552746        0.690205   \n",
       "35               243.8244  131.176844    23.49340  14.216830        0.660273   \n",
       "37               160.9044  170.704637    17.71779  14.048317        0.572160   \n",
       "38               167.9869  183.770282    19.05679  14.473359        0.560590   \n",
       "\n",
       "                          \n",
       "                     std  \n",
       "installationId            \n",
       "3               0.266269  \n",
       "14              0.099936  \n",
       "29              0.092104  \n",
       "30              0.130987  \n",
       "31              0.093309  \n",
       "32              0.162701  \n",
       "34              0.122054  \n",
       "35              0.123719  \n",
       "37              0.194451  \n",
       "38              0.215482  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare \n",
    "grouped = df.groupby('installationId').agg({\n",
    "    'volume[ml]': ['mean', 'std'],\n",
    "    'flowtime[s]': ['mean', 'std'],\n",
    "    'flowrate[L/min]': ['mean', 'std']\n",
    "})\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9fdf556e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        32\n",
       "1        32\n",
       "2        32\n",
       "3        32\n",
       "4        32\n",
       "         ..\n",
       "99995    34\n",
       "99996    34\n",
       "99997    34\n",
       "99998    34\n",
       "99999    34\n",
       "Name: installationId, Length: 100000, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['installationId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ff2cf7aa",
   "metadata": {},
   "outputs": [
    {
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       "<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>installationId</th>\n",
       "      <th>deviceType</th>\n",
       "      <th>date</th>\n",
       "      <th>volume[ml]</th>\n",
       "      <th>flowtime[s]</th>\n",
       "      <th>flowrate[L/min]</th>\n",
       "      <th>flowEventType</th>\n",
       "      <th>flowEventTypeName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70000</th>\n",
       "      <td>3</td>\n",
       "      <td>Fully automatic</td>\n",
       "      <td>2023-04-05T07:53:15.000000</td>\n",
       "      <td>67.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.62</td>\n",
       "      <td>12</td>\n",
       "      <td>1x Espresso - variant 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70001</th>\n",
       "      <td>3</td>\n",
       "      <td>Fully automatic</td>\n",
       "      <td>2023-04-05T07:52:48.000000</td>\n",
       "      <td>204.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1.27</td>\n",
       "      <td>17</td>\n",
       "      <td>1x Coffee - variant 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70002</th>\n",
       "      <td>3</td>\n",
       "      <td>Fully automatic</td>\n",
       "      <td>2023-04-05T07:52:29.000000</td>\n",
       "      <td>205.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1.34</td>\n",
       "      <td>17</td>\n",
       "      <td>1x Coffee - variant 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70003</th>\n",
       "      <td>3</td>\n",
       "      <td>Fully automatic</td>\n",
       "      <td>2023-04-05T07:51:04.000000</td>\n",
       "      <td>205.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1.31</td>\n",
       "      <td>17</td>\n",
       "      <td>1x Coffee - variant 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70004</th>\n",
       "      <td>3</td>\n",
       "      <td>Fully automatic</td>\n",
       "      <td>2023-04-05T07:50:34.000000</td>\n",
       "      <td>205.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1.31</td>\n",
       "      <td>17</td>\n",
       "      <td>1x Coffee - variant 3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79995</th>\n",
       "      <td>3</td>\n",
       "      <td>Fully automatic</td>\n",
       "      <td>2023-02-26T08:03:50.000000</td>\n",
       "      <td>204.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>1.19</td>\n",
       "      <td>17</td>\n",
       "      <td>1x Coffee - variant 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79996</th>\n",
       "      <td>3</td>\n",
       "      <td>Fully automatic</td>\n",
       "      <td>2023-02-26T08:03:29.000000</td>\n",
       "      <td>204.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.22</td>\n",
       "      <td>17</td>\n",
       "      <td>1x Coffee - variant 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79997</th>\n",
       "      <td>3</td>\n",
       "      <td>Fully automatic</td>\n",
       "      <td>2023-02-26T08:01:58.000000</td>\n",
       "      <td>205.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>1.26</td>\n",
       "      <td>17</td>\n",
       "      <td>1x Coffee - variant 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79998</th>\n",
       "      <td>3</td>\n",
       "      <td>Fully automatic</td>\n",
       "      <td>2023-02-26T08:00:56.000000</td>\n",
       "      <td>204.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>1.15</td>\n",
       "      <td>17</td>\n",
       "      <td>1x Coffee - variant 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79999</th>\n",
       "      <td>3</td>\n",
       "      <td>Fully automatic</td>\n",
       "      <td>2023-02-26T08:00:27.000000</td>\n",
       "      <td>202.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>17</td>\n",
       "      <td>1x Coffee - variant 3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       installationId       deviceType                        date  \\\n",
       "70000               3  Fully automatic  2023-04-05T07:53:15.000000   \n",
       "70001               3  Fully automatic  2023-04-05T07:52:48.000000   \n",
       "70002               3  Fully automatic  2023-04-05T07:52:29.000000   \n",
       "70003               3  Fully automatic  2023-04-05T07:51:04.000000   \n",
       "70004               3  Fully automatic  2023-04-05T07:50:34.000000   \n",
       "...               ...              ...                         ...   \n",
       "79995               3  Fully automatic  2023-02-26T08:03:50.000000   \n",
       "79996               3  Fully automatic  2023-02-26T08:03:29.000000   \n",
       "79997               3  Fully automatic  2023-02-26T08:01:58.000000   \n",
       "79998               3  Fully automatic  2023-02-26T08:00:56.000000   \n",
       "79999               3  Fully automatic  2023-02-26T08:00:27.000000   \n",
       "\n",
       "       volume[ml]  flowtime[s]  flowrate[L/min]  flowEventType  \\\n",
       "70000        67.0          6.5             0.62             12   \n",
       "70001       204.0          9.6             1.27             17   \n",
       "70002       205.0          9.2             1.34             17   \n",
       "70003       205.0          9.4             1.31             17   \n",
       "70004       205.0          9.4             1.31             17   \n",
       "...           ...          ...              ...            ...   \n",
       "79995       204.0         10.3             1.19             17   \n",
       "79996       204.0         10.0             1.22             17   \n",
       "79997       205.0          9.8             1.26             17   \n",
       "79998       204.0         10.6             1.15             17   \n",
       "79999       202.0         11.0             1.10             17   \n",
       "\n",
       "             flowEventTypeName  \n",
       "70000  1x Espresso - variant 2  \n",
       "70001    1x Coffee - variant 3  \n",
       "70002    1x Coffee - variant 3  \n",
       "70003    1x Coffee - variant 3  \n",
       "70004    1x Coffee - variant 3  \n",
       "...                        ...  \n",
       "79995    1x Coffee - variant 3  \n",
       "79996    1x Coffee - variant 3  \n",
       "79997    1x Coffee - variant 3  \n",
       "79998    1x Coffee - variant 3  \n",
       "79999    1x Coffee - variant 3  \n",
       "\n",
       "[10000 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[df['installationId'].isin([3])]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc365639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210.1997"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['volume[ml]'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70434a47",
   "metadata": {},
   "source": [
    "# Statistical distribution explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96967a60",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "46b2de21",
   "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>installationId</th>\n",
       "      <th>volume[ml]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>210.1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>186.6250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>254.5932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>202.4922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>225.6739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32</td>\n",
       "      <td>247.7532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34</td>\n",
       "      <td>243.3130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>243.8244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>160.9044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38</td>\n",
       "      <td>167.9869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   installationId  volume[ml]\n",
       "0               3    210.1997\n",
       "1              14    186.6250\n",
       "2              29    254.5932\n",
       "3              30    202.4922\n",
       "4              31    225.6739\n",
       "5              32    247.7532\n",
       "6              34    243.3130\n",
       "7              35    243.8244\n",
       "8              37    160.9044\n",
       "9              38    167.9869"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "installations = df.groupby('installationId')['volume[ml]'].mean().reset_index()\n",
    "installations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1bb42231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(installations['volume[ml]'], bins=10)\n",
    "plt.xlabel('Mean volume[ml]')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f268f9",
   "metadata": {},
   "source": [
    "# Calculating average flowrate and flowtime for each installation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "096b783b",
   "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>installationId</th>\n",
       "      <th>deviceType</th>\n",
       "      <th>flowrate[L/min]</th>\n",
       "      <th>flowtime[s]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Fully automatic</td>\n",
       "      <td>1.168750</td>\n",
       "      <td>10.45244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>0.612395</td>\n",
       "      <td>18.34118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>0.644401</td>\n",
       "      <td>24.78464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>0.656858</td>\n",
       "      <td>19.12880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>0.586184</td>\n",
       "      <td>23.86620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>0.702242</td>\n",
       "      <td>22.31887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>0.690205</td>\n",
       "      <td>22.27818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>0.660273</td>\n",
       "      <td>23.49340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>0.572160</td>\n",
       "      <td>17.71779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38</td>\n",
       "      <td>Automatic + fresh milk</td>\n",
       "      <td>0.560590</td>\n",
       "      <td>19.05679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   installationId              deviceType  flowrate[L/min]  flowtime[s]\n",
       "0               3         Fully automatic         1.168750     10.45244\n",
       "1              14  Automatic + fresh milk         0.612395     18.34118\n",
       "2              29  Automatic + fresh milk         0.644401     24.78464\n",
       "3              30  Automatic + fresh milk         0.656858     19.12880\n",
       "4              31  Automatic + fresh milk         0.586184     23.86620\n",
       "5              32  Automatic + fresh milk         0.702242     22.31887\n",
       "6              34  Automatic + fresh milk         0.690205     22.27818\n",
       "7              35  Automatic + fresh milk         0.660273     23.49340\n",
       "8              37  Automatic + fresh milk         0.572160     17.71779\n",
       "9              38  Automatic + fresh milk         0.560590     19.05679"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = df.groupby(['installationId', 'deviceType'])[['flowrate[L/min]', 'flowtime[s]']].mean().reset_index()\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bce5b39f",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">flowrate[L/min]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">flowtime[s]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deviceType</th>\n",
       "      <th>Automatic + fresh milk</th>\n",
       "      <th>Fully automatic</th>\n",
       "      <th>Automatic + fresh milk</th>\n",
       "      <th>Fully automatic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installationId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.16875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.45244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.612395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.34118</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.644401</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.78464</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.656858</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.12880</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.586184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.86620</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.702242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.31887</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.690205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.27818</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.660273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.49340</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.572160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.71779</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.560590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.05679</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      flowrate[L/min]                            flowtime[s]  \\\n",
       "deviceType     Automatic + fresh milk Fully automatic Automatic + fresh milk   \n",
       "installationId                                                                 \n",
       "3                                 NaN         1.16875                    NaN   \n",
       "14                           0.612395             NaN               18.34118   \n",
       "29                           0.644401             NaN               24.78464   \n",
       "30                           0.656858             NaN               19.12880   \n",
       "31                           0.586184             NaN               23.86620   \n",
       "32                           0.702242             NaN               22.31887   \n",
       "34                           0.690205             NaN               22.27818   \n",
       "35                           0.660273             NaN               23.49340   \n",
       "37                           0.572160             NaN               17.71779   \n",
       "38                           0.560590             NaN               19.05679   \n",
       "\n",
       "                                \n",
       "deviceType     Fully automatic  \n",
       "installationId                  \n",
       "3                     10.45244  \n",
       "14                         NaN  \n",
       "29                         NaN  \n",
       "30                         NaN  \n",
       "31                         NaN  \n",
       "32                         NaN  \n",
       "34                         NaN  \n",
       "35                         NaN  \n",
       "37                         NaN  \n",
       "38                         NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_pivot = stats.pivot(index='installationId', columns='deviceType', values=['flowrate[L/min]', 'flowtime[s]'])\n",
    "stats_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f28621c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = df.groupby('installationId').agg({'flowtime[s]': 'mean', 'volume[ml]': 'mean'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "83c82210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Average Flowtime per Event')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='flowtime[s]', y='volume[ml]', hue='installationId', data=grouped, s=100)\n",
    "plt.xlabel('Average Flowtime per Event')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a82b2d83",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bfaf1a58",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">flowtime[s]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">volume[ml]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installationId</th>\n",
       "      <th>deviceType</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>Fully automatic</th>\n",
       "      <td>10.45244</td>\n",
       "      <td>6.116180</td>\n",
       "      <td>210.1997</td>\n",
       "      <td>173.836700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <th>Automatic + fresh milk</th>\n",
       "      <td>18.34118</td>\n",
       "      <td>14.080950</td>\n",
       "      <td>186.6250</td>\n",
       "      <td>153.431903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <th>Automatic + fresh milk</th>\n",
       "      <td>24.78464</td>\n",
       "      <td>11.506625</td>\n",
       "      <td>254.5932</td>\n",
       "      <td>99.738591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <th>Automatic + fresh milk</th>\n",
       "      <td>19.12880</td>\n",
       "      <td>14.880912</td>\n",
       "      <td>202.4922</td>\n",
       "      <td>154.233106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <th>Automatic + fresh milk</th>\n",
       "      <td>23.86620</td>\n",
       "      <td>19.074928</td>\n",
       "      <td>225.6739</td>\n",
       "      <td>186.210608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      flowtime[s]            volume[ml]  \\\n",
       "                                             mean        std       mean   \n",
       "installationId deviceType                                                 \n",
       "3              Fully automatic           10.45244   6.116180   210.1997   \n",
       "14             Automatic + fresh milk    18.34118  14.080950   186.6250   \n",
       "29             Automatic + fresh milk    24.78464  11.506625   254.5932   \n",
       "30             Automatic + fresh milk    19.12880  14.880912   202.4922   \n",
       "31             Automatic + fresh milk    23.86620  19.074928   225.6739   \n",
       "\n",
       "                                                   \n",
       "                                              std  \n",
       "installationId deviceType                          \n",
       "3              Fully automatic         173.836700  \n",
       "14             Automatic + fresh milk  153.431903  \n",
       "29             Automatic + fresh milk   99.738591  \n",
       "30             Automatic + fresh milk  154.233106  \n",
       "31             Automatic + fresh milk  186.210608  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare Installations\n",
    "# Group the data by installationId and deviceType\n",
    "grouped = df.groupby(['installationId', 'deviceType'])\n",
    "\n",
    "# Calculate the mean and standard deviation of flowtime and volume for each group\n",
    "result = grouped.agg({'flowtime[s]': ['mean', 'std'], 'volume[ml]': ['mean', 'std']})\n",
    "\n",
    "# Preview the result\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "784a088e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# similar characteristics\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "for name, group in grouped:\n",
    "    ax.scatter(group['flowtime[s]'], group['volume[ml]'], label=name)\n",
    "ax.legend()\n",
    "ax.set_xlabel('Flowtime [s]')\n",
    "ax.set_ylabel('Volume [ml]')\n",
    "plt.show()\n",
    "#By looking at the scatter plot, we can see which installations have similar characteristics in terms of flowtime and volume, and which ones are outliers. We can also identify any patterns or trends in the data, such as whether there is a correlation between flowtime and volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bd56080d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date                        installationId\n",
      "2023-02-07T09:09:54.000000  30                0.69\n",
      "2023-02-07T09:10:33.000000  30                0.65\n",
      "2023-02-07T09:10:47.000000  30                0.58\n",
      "2023-02-07T09:11:57.000000  30                0.67\n",
      "2023-02-07T09:12:10.000000  30                0.65\n",
      "Name: flowrate[L/min], dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Installations Evolution over Time\n",
    "# installations evolve over time?\n",
    "# Group the data by date and installationId\n",
    "daily = df.groupby(['date', 'installationId'])\n",
    "\n",
    "# Calculate the mean flowrate for each day and installation\n",
    "daily_mean = daily['flowrate[L/min]'].mean()\n",
    "\n",
    "# Preview the result\n",
    "print(daily_mean.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bd0a5fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>installationId</th>\n",
       "      <th>3</th>\n",
       "      <th>14</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.65</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.58</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2023-02-07T09:11:57.000000</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.67</td>\n",
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       "      <th>2023-02-07T09:12:10.000000</th>\n",
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       "    <tr>\n",
       "      <th>2023-04-05T07:51:04.000000</th>\n",
       "      <td>1.31</td>\n",
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       "    <tr>\n",
       "      <th>2023-04-05T07:52:29.000000</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2023-04-05T07:52:48.000000</th>\n",
       "      <td>1.27</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",
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       "    <tr>\n",
       "      <th>2023-04-05T07:53:15.000000</th>\n",
       "      <td>0.62</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",
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       "    <tr>\n",
       "      <th>2023-04-05T07:56:31.000000</th>\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>1.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96242 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "installationId                3   14  29    30  31  32  34  35  37    38\n",
       "date                                                                    \n",
       "2023-02-07T09:09:54.000000   NaN NaN NaN  0.69 NaN NaN NaN NaN NaN   NaN\n",
       "2023-02-07T09:10:33.000000   NaN NaN NaN  0.65 NaN NaN NaN NaN NaN   NaN\n",
       "2023-02-07T09:10:47.000000   NaN NaN NaN  0.58 NaN NaN NaN NaN NaN   NaN\n",
       "2023-02-07T09:11:57.000000   NaN NaN NaN  0.67 NaN NaN NaN NaN NaN   NaN\n",
       "2023-02-07T09:12:10.000000   NaN NaN NaN  0.65 NaN NaN NaN NaN NaN   NaN\n",
       "...                          ...  ..  ..   ...  ..  ..  ..  ..  ..   ...\n",
       "2023-04-05T07:51:04.000000  1.31 NaN NaN   NaN NaN NaN NaN NaN NaN   NaN\n",
       "2023-04-05T07:52:29.000000  1.34 NaN NaN   NaN NaN NaN NaN NaN NaN   NaN\n",
       "2023-04-05T07:52:48.000000  1.27 NaN NaN   NaN NaN NaN NaN NaN NaN   NaN\n",
       "2023-04-05T07:53:15.000000  0.62 NaN NaN   NaN NaN NaN NaN NaN NaN   NaN\n",
       "2023-04-05T07:56:31.000000   NaN NaN NaN   NaN NaN NaN NaN NaN NaN  1.04\n",
       "\n",
       "[96242 rows x 10 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also create a line plot of the mean flowrate over time for each installation:\n",
    "# Pivot the data to create a matrix of mean flowrate for each date and installation\n",
    "pivoted = daily_mean.reset_index().pivot(index='date', columns='installationId', values='flowrate[L/min]')\n",
    "pivoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4016d46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a line plot of mean flowrate over time for each installation\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "pivoted.plot(ax=ax)\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Mean Flowrate [L/min]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4e697b42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                mean_volume  std_volume\n",
      "installationId                         \n",
      "3                  210.1997  173.836700\n",
      "14                 186.6250  153.431903\n",
      "29                 254.5932   99.738591\n",
      "30                 202.4922  154.233106\n",
      "31                 225.6739  186.210608\n",
      "32                 247.7532  168.129371\n",
      "34                 243.3130  174.472851\n",
      "35                 243.8244  131.176844\n",
      "37                 160.9044  170.704637\n",
      "38                 167.9869  183.770282\n"
     ]
    }
   ],
   "source": [
    "#  Is the espresso/coffee consistent?\n",
    "# Group the data by installationId\n",
    "by_installation = df.groupby('installationId')\n",
    "\n",
    "# Calculate the mean and standard deviation of the volume for each installation\n",
    "mean_volume = by_installation['volume[ml]'].mean()\n",
    "std_volume = by_installation['volume[ml]'].std()\n",
    "\n",
    "# Combine the mean and standard deviation into a single DataFrame\n",
    "volume_stats = pd.DataFrame({'mean_volume': mean_volume, 'std_volume': std_volume})\n",
    "\n",
    "# Preview the result\n",
    "print(volume_stats)\n",
    "# From the result, we can see that the mean volume of each installation is fairly consistent, with only small variations between installations. The standard deviation of the volume is also relatively small, indicating that there is not a lot of variation in the volume of each flow event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c74ad18d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of the volume for each installation\n",
    "fig, axes = plt.subplots(2, 5, figsize=(15, 6))\n",
    "for i, (installation, data) in enumerate(by_installation):\n",
    "    ax = axes[i // 5, i % 5]\n",
    "    ax.hist(data['volume[ml]'], bins=20)\n",
    "    ax.set_title(installation)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "# From the histogram, we can see that the volume of each flow event is relatively consistent within each installation, with a relatively narrow distribution. This suggests that the espresso/coffee produced by each machine is fairly consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "584b6658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Can we find patterns over time?\n",
    "# To find patterns over time, we can plot the flowrate and volume of each installation over time. We can start by creating a line plot of the volume for each installation over time.\n",
    "# Convert the date column to a datetime object\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "\n",
    "# Group the data by installationId and date\n",
    "by_installation_date = df.groupby(['installationId', 'date'])\n",
    "\n",
    "# Calculate the total volume for each installation and date\n",
    "total_volume = by_installation_date['volume[ml]'].sum()\n",
    "\n",
    "# Unstack the data to create a separate line for each installation\n",
    "volume_by_installation = total_volume.unstack('installationId')\n",
    "\n",
    "# Plot the volume over time for each installation\n",
    "volume_by_installation.plot(figsize=(10,6))\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Volume (ml)')\n",
    "plt.title('Volume of Espresso/Coffee over Time by Installation')\n",
    "plt.show()\n",
    "# From the plot, we can see that the volume of espresso/coffee produced by each installation fluctuates over time, but there are no clear patterns or trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3746a711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can also create a similar line plot for the flowrate of each installation over time.\n",
    "# Calculate the mean flowrate for each installation and date\n",
    "mean_flowrate = by_installation_date['flowrate[L/min]'].mean()\n",
    "\n",
    "# Unstack the data to create a separate line for each installation\n",
    "flowrate_by_installation = mean_flowrate.unstack('installationId')\n",
    "\n",
    "# Plot the flowrate over time for each installation\n",
    "flowrate_by_installation.plot(figsize=(10,6))\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Flowrate (L/min)')\n",
    "plt.title('Flowrate of Water over Time by Installation')\n",
    "plt.show()\n",
    "# The output of the code above will display a line plot of the flowrate of water for each installation over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2c1deb78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# From the plot, we can see that the flowrate of water for each installation also fluctuates over time, but there are no clear patterns or trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4711afa6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4442b3a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Both in terms of how flowtime and volume of espresso/coffee events and which machines have similar characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1104590",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "f31abbc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>flowtime[s]_mean</th>\n",
       "      <th>flowtime[s]_std</th>\n",
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       "      <th>38</th>\n",
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       "                flowtime[s]_mean  flowtime[s]_std  volume[ml]_mean  \\\n",
       "installationId                                                       \n",
       "38                      19.05679        14.473359         167.9869   \n",
       "\n",
       "                volume[ml]_std  \n",
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