diff --git a/EDA.ipynb b/EDA.ipynb
index bc217d904b024f1ffc1ed4920cd489a37469a527..75a3b680fa5aa28a7cac2460d0097efda94cf0c5 100644
--- a/EDA.ipynb
+++ b/EDA.ipynb
@@ -347,6 +347,756 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Opracował: Jakub Szczegiecki"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from plotnine import ggplot, aes, geom_point, geom_line, ggtitle\n",
+    "from sklearn.decomposition import PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>PROXIMITY</th>\n",
+       "      <th>TIMESTAMP</th>\n",
+       "      <th>MEASUREMENT</th>\n",
+       "      <th>EPC</th>\n",
+       "      <th>EAN</th>\n",
+       "      <th>ITEMID</th>\n",
+       "      <th>STYLECOLOR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-70.4</td>\n",
+       "      <td>2021-10-26T09:46:33.735</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3035684754340E0000B594FD</td>\n",
+       "      <td>5902805533040</td>\n",
+       "      <td>2127269</td>\n",
+       "      <td>RH267-85J</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-61.8</td>\n",
+       "      <td>2021-10-26T09:46:33.747</td>\n",
+       "      <td>1</td>\n",
+       "      <td>303568480C2B874000B59A39</td>\n",
+       "      <td>5902851445731</td>\n",
+       "      <td>2217401</td>\n",
+       "      <td>RS483-99X</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-74.4</td>\n",
+       "      <td>2021-10-26T09:46:33.751</td>\n",
+       "      <td>1</td>\n",
+       "      <td>303568480C357A0000B59999</td>\n",
+       "      <td>5902851547602</td>\n",
+       "      <td>2227511</td>\n",
+       "      <td>SB281-90M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-78.0</td>\n",
+       "      <td>2021-10-26T09:46:33.754</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30356847541DA78000B5BA4D</td>\n",
+       "      <td>5902805303667</td>\n",
+       "      <td>2104706</td>\n",
+       "      <td>RJ369-87X</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-71.1</td>\n",
+       "      <td>2021-10-26T09:46:33.758</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30356847542A2B0000B5B215</td>\n",
+       "      <td>5902805431803</td>\n",
+       "      <td>2117629</td>\n",
+       "      <td>RM119-93X</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502684</th>\n",
+       "      <td>-59.9</td>\n",
+       "      <td>2021-10-26T10:52:08.744</td>\n",
+       "      <td>43</td>\n",
+       "      <td>303568458835010000B5BA58</td>\n",
+       "      <td>5902690542769</td>\n",
+       "      <td>2028744</td>\n",
+       "      <td>QY337-00X</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502685</th>\n",
+       "      <td>-79.9</td>\n",
+       "      <td>2021-10-26T10:52:08.745</td>\n",
+       "      <td>43</td>\n",
+       "      <td>513568458843D94000B5B5DE</td>\n",
+       "      <td>5902690694772</td>\n",
+       "      <td>2044040</td>\n",
+       "      <td>QZ555-20X</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502686</th>\n",
+       "      <td>-66.8</td>\n",
+       "      <td>2021-10-26T10:52:08.745</td>\n",
+       "      <td>43</td>\n",
+       "      <td>30356847541DA7C000B5BADD</td>\n",
+       "      <td>5902805303674</td>\n",
+       "      <td>2104707</td>\n",
+       "      <td>RJ369-87X</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502687</th>\n",
+       "      <td>-60.8</td>\n",
+       "      <td>2021-10-26T10:52:08.747</td>\n",
+       "      <td>43</td>\n",
+       "      <td>303568458835010000B5BA58</td>\n",
+       "      <td>5902690542769</td>\n",
+       "      <td>2028744</td>\n",
+       "      <td>QY337-00X</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502688</th>\n",
+       "      <td>-75.7</td>\n",
+       "      <td>2021-10-26T10:52:08.748</td>\n",
+       "      <td>43</td>\n",
+       "      <td>303568480C34550000B5A5E7</td>\n",
+       "      <td>5902851535883</td>\n",
+       "      <td>2226337</td>\n",
+       "      <td>RV167-87X</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>502689 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        PROXIMITY                TIMESTAMP  MEASUREMENT  \\\n",
+       "0           -70.4  2021-10-26T09:46:33.735            1   \n",
+       "1           -61.8  2021-10-26T09:46:33.747            1   \n",
+       "2           -74.4  2021-10-26T09:46:33.751            1   \n",
+       "3           -78.0  2021-10-26T09:46:33.754            1   \n",
+       "4           -71.1  2021-10-26T09:46:33.758            1   \n",
+       "...           ...                      ...          ...   \n",
+       "502684      -59.9  2021-10-26T10:52:08.744           43   \n",
+       "502685      -79.9  2021-10-26T10:52:08.745           43   \n",
+       "502686      -66.8  2021-10-26T10:52:08.745           43   \n",
+       "502687      -60.8  2021-10-26T10:52:08.747           43   \n",
+       "502688      -75.7  2021-10-26T10:52:08.748           43   \n",
+       "\n",
+       "                             EPC            EAN   ITEMID STYLECOLOR  \n",
+       "0       3035684754340E0000B594FD  5902805533040  2127269  RH267-85J  \n",
+       "1       303568480C2B874000B59A39  5902851445731  2217401  RS483-99X  \n",
+       "2       303568480C357A0000B59999  5902851547602  2227511  SB281-90M  \n",
+       "3       30356847541DA78000B5BA4D  5902805303667  2104706  RJ369-87X  \n",
+       "4       30356847542A2B0000B5B215  5902805431803  2117629  RM119-93X  \n",
+       "...                          ...            ...      ...        ...  \n",
+       "502684  303568458835010000B5BA58  5902690542769  2028744  QY337-00X  \n",
+       "502685  513568458843D94000B5B5DE  5902690694772  2044040  QZ555-20X  \n",
+       "502686  30356847541DA7C000B5BADD  5902805303674  2104707  RJ369-87X  \n",
+       "502687  303568458835010000B5BA58  5902690542769  2028744  QY337-00X  \n",
+       "502688  303568480C34550000B5A5E7  5902851535883  2226337  RV167-87X  \n",
+       "\n",
+       "[502689 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('C:/Users/jakub/Documents/query_main.csv', sep = ';', header = None)\n",
+    "df.columns = ['PROXIMITY', 'TIMESTAMP', 'MEASUREMENT', 'EPC', 'EAN', 'ITEMID', 'STYLECOLOR']\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>timestamp</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>minute</th>\n",
+       "      <th>second</th>\n",
+       "      <th>microsecond</th>\n",
+       "      <th>delta_ms</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-10-26 09:46:33.735</td>\n",
+       "      <td>9</td>\n",
+       "      <td>46</td>\n",
+       "      <td>33</td>\n",
+       "      <td>735000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-10-26 09:46:33.747</td>\n",
+       "      <td>9</td>\n",
+       "      <td>46</td>\n",
+       "      <td>33</td>\n",
+       "      <td>747000</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-10-26 09:46:33.751</td>\n",
+       "      <td>9</td>\n",
+       "      <td>46</td>\n",
+       "      <td>33</td>\n",
+       "      <td>751000</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-10-26 09:46:33.754</td>\n",
+       "      <td>9</td>\n",
+       "      <td>46</td>\n",
+       "      <td>33</td>\n",
+       "      <td>754000</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-10-26 09:46:33.758</td>\n",
+       "      <td>9</td>\n",
+       "      <td>46</td>\n",
+       "      <td>33</td>\n",
+       "      <td>758000</td>\n",
+       "      <td>23.0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502684</th>\n",
+       "      <td>2021-10-26 10:52:08.744</td>\n",
+       "      <td>10</td>\n",
+       "      <td>52</td>\n",
+       "      <td>8</td>\n",
+       "      <td>744000</td>\n",
+       "      <td>3935009.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502685</th>\n",
+       "      <td>2021-10-26 10:52:08.745</td>\n",
+       "      <td>10</td>\n",
+       "      <td>52</td>\n",
+       "      <td>8</td>\n",
+       "      <td>745000</td>\n",
+       "      <td>3935010.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502686</th>\n",
+       "      <td>2021-10-26 10:52:08.745</td>\n",
+       "      <td>10</td>\n",
+       "      <td>52</td>\n",
+       "      <td>8</td>\n",
+       "      <td>745000</td>\n",
+       "      <td>3935010.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502687</th>\n",
+       "      <td>2021-10-26 10:52:08.747</td>\n",
+       "      <td>10</td>\n",
+       "      <td>52</td>\n",
+       "      <td>8</td>\n",
+       "      <td>747000</td>\n",
+       "      <td>3935012.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502688</th>\n",
+       "      <td>2021-10-26 10:52:08.748</td>\n",
+       "      <td>10</td>\n",
+       "      <td>52</td>\n",
+       "      <td>8</td>\n",
+       "      <td>748000</td>\n",
+       "      <td>3935013.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>502689 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     timestamp  hour  minute  second  microsecond   delta_ms\n",
+       "0      2021-10-26 09:46:33.735     9      46      33       735000        0.0\n",
+       "1      2021-10-26 09:46:33.747     9      46      33       747000       12.0\n",
+       "2      2021-10-26 09:46:33.751     9      46      33       751000       16.0\n",
+       "3      2021-10-26 09:46:33.754     9      46      33       754000       19.0\n",
+       "4      2021-10-26 09:46:33.758     9      46      33       758000       23.0\n",
+       "...                        ...   ...     ...     ...          ...        ...\n",
+       "502684 2021-10-26 10:52:08.744    10      52       8       744000  3935009.0\n",
+       "502685 2021-10-26 10:52:08.745    10      52       8       745000  3935010.0\n",
+       "502686 2021-10-26 10:52:08.745    10      52       8       745000  3935010.0\n",
+       "502687 2021-10-26 10:52:08.747    10      52       8       747000  3935012.0\n",
+       "502688 2021-10-26 10:52:08.748    10      52       8       748000  3935013.0\n",
+       "\n",
+       "[502689 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "timestamp1 = df[\"TIMESTAMP\"].astype('datetime64[ns]')\n",
+    "\n",
+    "hour = timestamp1.astype('datetime64[ns]').dt.hour.astype(int)\n",
+    "minute = timestamp1.astype('datetime64[ns]').dt.minute.astype(int)\n",
+    "second = timestamp1.astype('datetime64[ns]').dt.second.astype(int)\n",
+    "microsecond = timestamp1.astype('datetime64[ns]').dt.microsecond.astype(int)\n",
+    "\n",
+    "millisecond = microsecond/1000+second*1000+minute*60000+hour*3600000\n",
+    "delta_ms = millisecond - millisecond[0]\n",
+    "delta_ms\n",
+    "\n",
+    "timestamp = pd.DataFrame()\n",
+    "timestamp = pd.concat([timestamp1, hour, minute, second, microsecond, delta_ms], axis = 1)\n",
+    "timestamp.columns = ['timestamp', 'hour', 'minute', 'second', 'microsecond', 'delta_ms']\n",
+    "\n",
+    "timestamp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\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>PROXIMITY</th>\n",
+       "      <th>NUMBER</th>\n",
+       "      <th>EAN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-75.210592</td>\n",
+       "      <td>4003</td>\n",
+       "      <td>5902805820515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-75.055416</td>\n",
+       "      <td>7902</td>\n",
+       "      <td>5902805820546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-75.673864</td>\n",
+       "      <td>6780</td>\n",
+       "      <td>5902851535869</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-76.017081</td>\n",
+       "      <td>11937</td>\n",
+       "      <td>5902851535913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-76.254074</td>\n",
+       "      <td>8283</td>\n",
+       "      <td>5902975218037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>-74.921590</td>\n",
+       "      <td>7531</td>\n",
+       "      <td>5902805820447</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>-75.528464</td>\n",
+       "      <td>7237</td>\n",
+       "      <td>5902851852638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>-76.882319</td>\n",
+       "      <td>10967</td>\n",
+       "      <td>5902975217993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>-76.228160</td>\n",
+       "      <td>3196</td>\n",
+       "      <td>5902975236970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>-76.337086</td>\n",
+       "      <td>6439</td>\n",
+       "      <td>5902975236994</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>76 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    PROXIMITY  NUMBER            EAN\n",
+       "0  -75.210592    4003  5902805820515\n",
+       "1  -75.055416    7902  5902805820546\n",
+       "2  -75.673864    6780  5902851535869\n",
+       "3  -76.017081   11937  5902851535913\n",
+       "4  -76.254074    8283  5902975218037\n",
+       "..        ...     ...            ...\n",
+       "71 -74.921590    7531  5902805820447\n",
+       "72 -75.528464    7237  5902851852638\n",
+       "73 -76.882319   10967  5902975217993\n",
+       "74 -76.228160    3196  5902975236970\n",
+       "75 -76.337086    6439  5902975236994\n",
+       "\n",
+       "[76 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('C:/Users/jakub/Documents/query4.csv', sep = ';')\n",
+    "df['EAN'] = df['EAN'].astype(str)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([], [])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x=df['EAN'], y=df['NUMBER'], c=df['NUMBER'])\n",
+    "plt.xlabel('Tag')\n",
+    "plt.ylabel('Liczba odczytĂłw')\n",
+    "plt.title('Liczba odczytĂłw dla danego tagu')\n",
+    "plt.xticks([])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax1 = plt.subplots()\n",
+    "\n",
+    "ax2 = ax1.twinx()\n",
+    "\n",
+    "ax1.bar(df['EAN'], df['NUMBER'], color='darkred', alpha=0.3, width = 0.5)\n",
+    "ax1.set_xticklabels([])\n",
+    "ax1.set_xlabel('Tag')\n",
+    "ax1.set_ylabel(\"Liczba odczytĂłw\" ,color='darkred')\n",
+    "    \n",
+    "ax2.scatter(x=df['EAN'], y=df['PROXIMITY'], color='b')\n",
+    "ax2.set_xticklabels([])\n",
+    "ax2.set_ylim(-110,-25)\n",
+    "ax2.set_ylabel(\"Średnia siła odczytu\",color='b')\n",
+    "\n",
+    "plt.title('Liczba odczytów i średnia siła pomiaru z podziałem na tagi')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>PROXIMITY</th>\n",
+       "      <th>NUMBER</th>\n",
+       "      <th>ClassName</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-75.313509</td>\n",
+       "      <td>10674</td>\n",
+       "      <td>knitwear</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-75.236090</td>\n",
+       "      <td>26874</td>\n",
+       "      <td>ladies'_und/socks</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-74.951699</td>\n",
+       "      <td>160731</td>\n",
+       "      <td>t-shirts_s_s</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-76.448187</td>\n",
+       "      <td>38199</td>\n",
+       "      <td>jeans_others</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-75.847888</td>\n",
+       "      <td>43547</td>\n",
+       "      <td>trousers</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-75.493705</td>\n",
+       "      <td>222664</td>\n",
+       "      <td>t-shirts</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PROXIMITY  NUMBER          ClassName\n",
+       "0 -75.313509   10674           knitwear\n",
+       "1 -75.236090   26874  ladies'_und/socks\n",
+       "2 -74.951699  160731       t-shirts_s_s\n",
+       "3 -76.448187   38199       jeans_others\n",
+       "4 -75.847888   43547           trousers\n",
+       "5 -75.493705  222664           t-shirts"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('C:/Users/jakub/Documents/query5.csv', sep = ';')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-18-3c518dfe74d5>:6: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax1 = plt.subplots()\n",
+    "\n",
+    "ax2 = ax1.twinx()\n",
+    "\n",
+    "ax1.bar(df['ClassName'], df['NUMBER'], color='darkred', alpha=0.3)\n",
+    "ax1.set_xticklabels(labels = df['ClassName'], rotation = 90)\n",
+    "ax1.set_ylim(0,280000)\n",
+    "ax1.set_xlabel(\"Klasa\")\n",
+    "for x,y in zip(df['ClassName'],df['NUMBER']):\n",
+    "    label = '{:d}'.format(y)\n",
+    "    ax1.annotate(label, \n",
+    "                 (x,y), \n",
+    "                 textcoords=\"offset points\", \n",
+    "                 xytext=(0,10), \n",
+    "                 ha='center',\n",
+    "                 fontsize = 10) \n",
+    "ax1.set_ylabel(\"Liczba odczytĂłw\" ,color='darkred')\n",
+    "    \n",
+    "ax2.scatter(x=df['ClassName'], y=df['PROXIMITY'], color='b')\n",
+    "ax2.set_ylim(-110,-25)\n",
+    "ax2.set_ylabel(\"Średnia siła odczytu\",color='b')\n",
+    "\n",
+    "plt.title('Liczba odczytów i średnia siła pomiaru z podziałem na klasy')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -365,7 +1115,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/query4.csv b/query4.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d520c7916c8b39d4ea0d37600ec2406a97bb6e06
--- /dev/null
+++ b/query4.csv
@@ -0,0 +1,77 @@
+ďťżPROXIMITY;NUMBER;EAN
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+-75.055416;7902;5902805820546
+-75.673864;6780;5902851535869
+-76.017081;11937;5902851535913
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+-75.428973;5788;5902805219692
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+-76.845064;12420;5902805533040
+-78.004046;3163;5902805533279
+-77.475953;3514;5902805716498
+-76.856280;3065;5902805716535
+-76.370902;2959;5902805820409
+-74.921590;7531;5902805820447
+-75.528464;7237;5902851852638
+-76.882319;10967;5902975217993
+-76.228160;3196;5902975236970
+-76.337086;6439;5902975236994
diff --git a/query5.csv b/query5.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0906163e03296fbab7334fcf76529a152328c55e
--- /dev/null
+++ b/query5.csv
@@ -0,0 +1,7 @@
+ďťżPROXIMITY;NUMBER;ClassName
+-75.313509;10674;knitwear
+-75.236090;26874;ladies'_und/socks
+-74.951699;160731;t-shirts_s_s
+-76.448187;38199;jeans_others
+-75.847888;43547;trousers
+-75.493705;222664;t-shirts