{
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   "id": "27eae913-1e8f-4f96-aaff-3242bf13145c",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-05-27T12:18:20.036467Z",
     "shell.execute_reply.started": "2026-05-27T12:18:19.741460Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e424351e-1575-4347-a650-f3039cc9ea13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-27T12:19:38.064321Z",
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     "shell.execute_reply.started": "2026-05-27T12:19:38.064292Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://data.hsbo.de/Bestellungen_Shop.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e15cb3a8-6972-43bf-a882-055edf857022",
   "metadata": {
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    }
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Datum</th>\n",
       "      <th>BestellungNr</th>\n",
       "      <th>Kundengruppe</th>\n",
       "      <th>Altersgruppe</th>\n",
       "      <th>Artikel</th>\n",
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       "      <th>4</th>\n",
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       "      <td>2024-06-11</td>\n",
       "      <td>229</td>\n",
       "      <td>Mitarbeiter</td>\n",
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       "      <td>0.05</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0       Datum  BestellungNr Kundengruppe Altersgruppe  Artikel  \\\n",
       "0           0  2024-06-10           211     KigaKind          0-5        1   \n",
       "1           1  2024-06-10           215  Mitarbeiter        31-40        1   \n",
       "2           2  2024-06-10           219    Schulkind         6-10        1   \n",
       "3           3  2024-06-10           222     KigaKind          0-5        3   \n",
       "4           4  2024-06-11           229  Mitarbeiter        41-50        1   \n",
       "\n",
       "   Betrag  \n",
       "0    0.15  \n",
       "1    0.10  \n",
       "2    0.15  \n",
       "3    0.15  \n",
       "4    0.05  "
      ]
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     "metadata": {},
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    }
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "894991e9-e081-47a9-8f8a-78dda5032c9d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-27T12:19:54.715666Z",
     "iopub.status.busy": "2026-05-27T12:19:54.715370Z",
     "iopub.status.idle": "2026-05-27T12:19:54.726805Z",
     "shell.execute_reply": "2026-05-27T12:19:54.725762Z",
     "shell.execute_reply.started": "2026-05-27T12:19:54.715637Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                                                   5151\n",
       "Datum           2024-06-102024-06-102024-06-102024-06-102024-0...\n",
       "BestellungNr                                                30092\n",
       "Kundengruppe    KigaKindMitarbeiterSchulkindKigaKindMitarbeite...\n",
       "Altersgruppe    0-531-406-100-541-5031-4051-6031-4021-3041-502...\n",
       "Artikel                                                       110\n",
       "Betrag                                                       11.4\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
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  {
   "cell_type": "code",
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   "id": "bb3cdbc1-5757-4ef8-8766-a36043fce1ba",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ABCDEF'"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "\"ABC\" + \"DEF\""
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8b27aab9-9776-4cb0-9031-4d3091a9460e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-27T12:23:25.156008Z",
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     "shell.execute_reply.started": "2026-05-27T12:23:25.155979Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ArrowStringArray>\n",
       "['0-5', '31-40', '6-10', '41-50', '51-60', '21-30', '11-20', '> 60']\n",
       "Length: 8, dtype: str"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['Altersgruppe'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "73bb5f28-4fc2-4e0d-9723-d42211097e25",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2026-05-27T12:25:30.388410Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1.0784313725490196)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Artikel'].mean()"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0df8568b-f354-4e39-ba8a-8ec5f47f1cd6",
   "metadata": {},
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eadebcf6-740a-4e9c-98dc-41b36f1b6e85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-27T12:35:43.562384Z",
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     "shell.execute_reply.started": "2026-05-27T12:35:43.562354Z"
    }
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>BestellungNr</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.500000</td>\n",
       "      <td>295.019608</td>\n",
       "      <td>1.078431</td>\n",
       "      <td>0.111765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.588849</td>\n",
       "      <td>41.151731</td>\n",
       "      <td>0.363870</td>\n",
       "      <td>0.041730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.250000</td>\n",
       "      <td>262.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.500000</td>\n",
       "      <td>300.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.750000</td>\n",
       "      <td>329.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  BestellungNr     Artikel      Betrag\n",
       "count  102.000000    102.000000  102.000000  102.000000\n",
       "mean    50.500000    295.019608    1.078431    0.111765\n",
       "std     29.588849     41.151731    0.363870    0.041730\n",
       "min      0.000000    211.000000    1.000000    0.050000\n",
       "25%     25.250000    262.250000    1.000000    0.062500\n",
       "50%     50.500000    300.500000    1.000000    0.100000\n",
       "75%     75.750000    329.750000    1.000000    0.150000\n",
       "max    101.000000    364.000000    3.000000    0.150000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ec18b2f-cee4-446b-85e0-ecfa7f761ebd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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