01.2 - pandas.ipynb

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    "<div style=\"clear:both\"></div>\n",
    "Rafał Nowak<br/>\n",
    "<a href=\"https://github.com/rnoxy\">github.com/rnoxy</a>"
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   "source": [
    "# `pandas`\n"
   ]
  },
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   "source": [
    "# Instalacja pakietu"
   ]
  },
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     "text": [
      "Requirement already satisfied: pandas in /Users/rno/opt/miniconda3/envs/nn/lib/python3.9/site-packages (1.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/rno/opt/miniconda3/envs/nn/lib/python3.9/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/rno/opt/miniconda3/envs/nn/lib/python3.9/site-packages (from pandas) (2022.1)\n",
      "Requirement already satisfied: numpy>=1.20.0 in /Users/rno/opt/miniconda3/envs/nn/lib/python3.9/site-packages (from pandas) (1.22.4)\n",
      "Requirement already satisfied: six>=1.5 in /Users/rno/opt/miniconda3/envs/nn/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pandas"
   ]
  },
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   "cell_type": "markdown",
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   },
   "source": [
    "# Import\n"
   ]
  },
  {
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   "execution_count": 2,
   "metadata": {
    "cell_id": "00005-177d07be-8f42-446d-96dc-d49291b09e7b",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
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    "deepnote_cell_type": "code",
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    "execution_start": 1654345204594,
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   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00006-1ca893a8-c21a-4dc0-8a2a-c274ed56b922",
    "colab_type": "text",
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    "deepnote_cell_height": 81.953125,
    "deepnote_cell_type": "markdown",
    "id": "dA4e878Tn8Qv"
   },
   "source": [
    "# Podstawowe zbiory danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "cell_id": "87a6b1823b9f45f6b5de58f3fade1996",
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    "deepnote_table_state": {
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    "execution_millis": 10,
    "execution_start": 1654345249886,
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    "source_hash": "7dff1d38",
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   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    data={\n",
    "        \"a\": [3, 4, 5],\n",
    "        \"b\": [1, 3, 90],\n",
    "        \"category\": [\"cat\", \"dog\", \"car\"]\n",
    "    }\n",
    ")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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    "cell_id": "90396fd4e35d479882eb07204b486d76",
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     "pageIndex": 0,
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     "sortBy": [
      {
       "id": "b",
       "type": "asc"
      }
     ]
    },
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 34,
    "execution_start": 1654345254748,
    "output_cleared": false,
    "source_hash": "f804c160",
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>90</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   a   b category\n",
       "0  3   1      cat\n",
       "1  4   3      dog\n",
       "2  5  90      car"
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     },
     "execution_count": 4,
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     "output_type": "execute_result"
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   ],
   "source": [
    "df"
   ]
  },
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    "tags": []
   },
   "source": [
    "# Wczytywanie danych z pliku\n",
    "\n",
    "## Zbiór danych *Wine+Quality*\n",
    "\n",
    "źródło zbioru danych: https://archive.ics.uci.edu/ml/datasets/Wine+Quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-06-17 19:48:34--  https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 264426 (258K) [application/x-httpd-php]\n",
      "Saving to: ‘datasets/winequality-white.csv’\n",
      "\n",
      "datasets/winequalit 100%[===================>] 258,23K   342KB/s    in 0,8s    \n",
      "\n",
      "2022-06-17 19:48:36 (342 KB/s) - ‘datasets/winequality-white.csv’ saved [264426/264426]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget \\\n",
    "   https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv \\\n",
    "   -O datasets/winequality-white.csv"
   ]
  },
  {
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   "execution_count": 6,
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    "execution_millis": 119,
    "execution_start": 1654345458582,
    "source_hash": "62cf0b8a",
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   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
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       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
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       "      <td>3.00</td>\n",
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       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.99400</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.99510</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.99560</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.99560</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4893</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.039</td>\n",
       "      <td>24.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99114</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4894</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>57.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4895</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.041</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.99254</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4896</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.022</td>\n",
       "      <td>20.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.98869</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>12.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.020</td>\n",
       "      <td>22.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98941</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4898 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0               7.0              0.27         0.36            20.7      0.045   \n",
       "1               6.3              0.30         0.34             1.6      0.049   \n",
       "2               8.1              0.28         0.40             6.9      0.050   \n",
       "3               7.2              0.23         0.32             8.5      0.058   \n",
       "4               7.2              0.23         0.32             8.5      0.058   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "4893            6.2              0.21         0.29             1.6      0.039   \n",
       "4894            6.6              0.32         0.36             8.0      0.047   \n",
       "4895            6.5              0.24         0.19             1.2      0.041   \n",
       "4896            5.5              0.29         0.30             1.1      0.022   \n",
       "4897            6.0              0.21         0.38             0.8      0.020   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    45.0                 170.0  1.00100  3.00       0.45   \n",
       "1                    14.0                 132.0  0.99400  3.30       0.49   \n",
       "2                    30.0                  97.0  0.99510  3.26       0.44   \n",
       "3                    47.0                 186.0  0.99560  3.19       0.40   \n",
       "4                    47.0                 186.0  0.99560  3.19       0.40   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
       "4894                 57.0                 168.0  0.99490  3.15       0.46   \n",
       "4895                 30.0                 111.0  0.99254  2.99       0.46   \n",
       "4896                 20.0                 110.0  0.98869  3.34       0.38   \n",
       "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol  quality  \n",
       "0         8.8        6  \n",
       "1         9.5        6  \n",
       "2        10.1        6  \n",
       "3         9.9        6  \n",
       "4         9.9        6  \n",
       "...       ...      ...  \n",
       "4893     11.2        6  \n",
       "4894      9.6        5  \n",
       "4895      9.4        6  \n",
       "4896     12.8        7  \n",
       "4897     11.8        6  \n",
       "\n",
       "[4898 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/winequality-white.csv', sep=';')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cell_id": "9afafd4d4b84460fb25e7639ba1e28fc",
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     21.1875
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    "execution_start": 1654345553858,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cell_id": "0f0d7505b4254ffe870fa3de4d51a5bc",
    "deepnote_cell_height": 175.765625,
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     78.8125
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    "execution_millis": 8,
    "execution_start": 1654345555192,
    "output_cleared": false,
    "source_hash": "25d43fa0",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
       "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
       "       'pH', 'sulphates', 'alcohol', 'quality'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    "cell_id": "7ef31b43a8914c6dbd0fe43db71d6d03",
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    "execution_millis": 113,
    "execution_start": 1654345563627,
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    "source_hash": "f88152d9",
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "      <td>4898.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.854788</td>\n",
       "      <td>0.278241</td>\n",
       "      <td>0.334192</td>\n",
       "      <td>6.391415</td>\n",
       "      <td>0.045772</td>\n",
       "      <td>35.308085</td>\n",
       "      <td>138.360657</td>\n",
       "      <td>0.994027</td>\n",
       "      <td>3.188267</td>\n",
       "      <td>0.489847</td>\n",
       "      <td>10.514267</td>\n",
       "      <td>5.877909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.843868</td>\n",
       "      <td>0.100795</td>\n",
       "      <td>0.121020</td>\n",
       "      <td>5.072058</td>\n",
       "      <td>0.021848</td>\n",
       "      <td>17.007137</td>\n",
       "      <td>42.498065</td>\n",
       "      <td>0.002991</td>\n",
       "      <td>0.151001</td>\n",
       "      <td>0.114126</td>\n",
       "      <td>1.230621</td>\n",
       "      <td>0.885639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.987110</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.300000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.036000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.991723</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>0.043000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>0.993740</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>0.996100</td>\n",
       "      <td>3.280000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.200000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>0.346000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1.038980</td>\n",
       "      <td>3.820000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    4898.000000       4898.000000  4898.000000     4898.000000   \n",
       "mean        6.854788          0.278241     0.334192        6.391415   \n",
       "std         0.843868          0.100795     0.121020        5.072058   \n",
       "min         3.800000          0.080000     0.000000        0.600000   \n",
       "25%         6.300000          0.210000     0.270000        1.700000   \n",
       "50%         6.800000          0.260000     0.320000        5.200000   \n",
       "75%         7.300000          0.320000     0.390000        9.900000   \n",
       "max        14.200000          1.100000     1.660000       65.800000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  4898.000000          4898.000000           4898.000000  4898.000000   \n",
       "mean      0.045772            35.308085            138.360657     0.994027   \n",
       "std       0.021848            17.007137             42.498065     0.002991   \n",
       "min       0.009000             2.000000              9.000000     0.987110   \n",
       "25%       0.036000            23.000000            108.000000     0.991723   \n",
       "50%       0.043000            34.000000            134.000000     0.993740   \n",
       "75%       0.050000            46.000000            167.000000     0.996100   \n",
       "max       0.346000           289.000000            440.000000     1.038980   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  4898.000000  4898.000000  4898.000000  4898.000000  \n",
       "mean      3.188267     0.489847    10.514267     5.877909  \n",
       "std       0.151001     0.114126     1.230621     0.885639  \n",
       "min       2.720000     0.220000     8.000000     3.000000  \n",
       "25%       3.090000     0.410000     9.500000     5.000000  \n",
       "50%       3.180000     0.470000    10.400000     6.000000  \n",
       "75%       3.280000     0.550000    11.400000     6.000000  \n",
       "max       3.820000     1.080000    14.200000     9.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
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    "execution_millis": 4,
    "execution_start": 1653857581535,
    "source_hash": "aa0a3b2f",
    "tags": []
   },
   "source": [
    "## Zbiór danych *AirQuality*\n",
    "źródło danych: https://archive.ics.uci.edu/ml/datasets/Air+quality\n",
    "\n",
    "*Uwaga*: warto zauważyć, że w zbiorze znajdują się puste komórki --> **brakujące dane** (ang. *missing data*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-06-17 19:48:36--  https://archive.ics.uci.edu/ml/machine-learning-databases/00360/AirQualityUCI.zip\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1543989 (1,5M) [application/x-httpd-php]\n",
      "Saving to: ‘datasets/AirQualityUCI.zip’\n",
      "\n",
      "datasets/AirQuality 100%[===================>]   1,47M  1,04MB/s    in 1,4s    \n",
      "\n",
      "2022-06-17 19:48:38 (1,04 MB/s) - ‘datasets/AirQualityUCI.zip’ saved [1543989/1543989]\n",
      "\n",
      "Archive:  datasets/AirQualityUCI.zip\n",
      "  inflating: datasets/AirQualityUCI.csv  \n",
      "  inflating: datasets/AirQualityUCI.xlsx  \n"
     ]
    }
   ],
   "source": [
    "!wget \\\n",
    "  https://archive.ics.uci.edu/ml/machine-learning-databases/00360/AirQualityUCI.zip \\\n",
    "  -O datasets/AirQualityUCI.zip\n",
    "!unzip -o datasets/AirQualityUCI.zip -d datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cell_id": "4406d4b519494706a5d4add51cdf5c61",
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    },
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     "filters": [],
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    },
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    "execution_millis": 9,
    "execution_start": 1654345585758,
    "output_cleared": true,
    "source_hash": "f3f07c5f",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"datasets/AirQualityUCI.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PT08.S1(CO)</th>\n",
       "      <th>NMHC(GT)</th>\n",
       "      <th>PT08.S2(NMHC)</th>\n",
       "      <th>NOx(GT)</th>\n",
       "      <th>PT08.S3(NOx)</th>\n",
       "      <th>NO2(GT)</th>\n",
       "      <th>PT08.S4(NO2)</th>\n",
       "      <th>PT08.S5(O3)</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9357.000000</td>\n",
       "      <td>9357.000000</td>\n",
       "      <td>9357.000000</td>\n",
       "      <td>9357.000000</td>\n",
       "      <td>9357.000000</td>\n",
       "      <td>9357.000000</td>\n",
       "      <td>9357.000000</td>\n",
       "      <td>9357.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1048.990061</td>\n",
       "      <td>-159.090093</td>\n",
       "      <td>894.595276</td>\n",
       "      <td>168.616971</td>\n",
       "      <td>794.990168</td>\n",
       "      <td>58.148873</td>\n",
       "      <td>1391.479641</td>\n",
       "      <td>975.072032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>329.832710</td>\n",
       "      <td>139.789093</td>\n",
       "      <td>342.333252</td>\n",
       "      <td>257.433866</td>\n",
       "      <td>321.993552</td>\n",
       "      <td>126.940455</td>\n",
       "      <td>467.210125</td>\n",
       "      <td>456.938184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-200.000000</td>\n",
       "      <td>-200.000000</td>\n",
       "      <td>-200.000000</td>\n",
       "      <td>-200.000000</td>\n",
       "      <td>-200.000000</td>\n",
       "      <td>-200.000000</td>\n",
       "      <td>-200.000000</td>\n",
       "      <td>-200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>921.000000</td>\n",
       "      <td>-200.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>637.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>1185.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1053.000000</td>\n",
       "      <td>-200.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>794.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1446.000000</td>\n",
       "      <td>942.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1221.000000</td>\n",
       "      <td>-200.000000</td>\n",
       "      <td>1105.000000</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>960.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>1662.000000</td>\n",
       "      <td>1255.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2040.000000</td>\n",
       "      <td>1189.000000</td>\n",
       "      <td>2214.000000</td>\n",
       "      <td>1479.000000</td>\n",
       "      <td>2683.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>2775.000000</td>\n",
       "      <td>2523.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PT08.S1(CO)     NMHC(GT)  PT08.S2(NMHC)      NOx(GT)  PT08.S3(NOx)  \\\n",
       "count  9357.000000  9357.000000    9357.000000  9357.000000   9357.000000   \n",
       "mean   1048.990061  -159.090093     894.595276   168.616971    794.990168   \n",
       "std     329.832710   139.789093     342.333252   257.433866    321.993552   \n",
       "min    -200.000000  -200.000000    -200.000000  -200.000000   -200.000000   \n",
       "25%     921.000000  -200.000000     711.000000    50.000000    637.000000   \n",
       "50%    1053.000000  -200.000000     895.000000   141.000000    794.000000   \n",
       "75%    1221.000000  -200.000000    1105.000000   284.000000    960.000000   \n",
       "max    2040.000000  1189.000000    2214.000000  1479.000000   2683.000000   \n",
       "\n",
       "           NO2(GT)  PT08.S4(NO2)  PT08.S5(O3)  Unnamed: 15  Unnamed: 16  \n",
       "count  9357.000000   9357.000000  9357.000000          0.0          0.0  \n",
       "mean     58.148873   1391.479641   975.072032          NaN          NaN  \n",
       "std     126.940455    467.210125   456.938184          NaN          NaN  \n",
       "min    -200.000000   -200.000000  -200.000000          NaN          NaN  \n",
       "25%      53.000000   1185.000000   700.000000          NaN          NaN  \n",
       "50%      96.000000   1446.000000   942.000000          NaN          NaN  \n",
       "75%     133.000000   1662.000000  1255.000000          NaN          NaN  \n",
       "max     340.000000   2775.000000  2523.000000          NaN          NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "# `pandas` (`df.values`) --> `numpy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cell_id": "24fd1802f57243c2b51fb8ca55d14546",
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    "execution_start": 1654345677927,
    "output_cleared": true,
    "source_hash": "1ce4c102",
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   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"datasets/winequality-white.csv\", sep=\";\")\n",
    "\n",
    "data = df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "cell_id": "0a944aa78e1c44068f590eb1257a2e79",
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     "h": 5,
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     "y": 0
    },
    "deepnote_cell_height": 80.953125,
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 7,
    "execution_start": 1654343504769,
    "output_cleared": true,
    "source_hash": "20f83e23",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data.shape=(4898, 12), type=<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(f\"data.shape={data.shape}, type={type(data)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cell_id": "43baac30993348af80f68f7ab973e522",
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     "h": 5,
     "w": 12,
     "x": 0,
     "y": 0
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    "deepnote_cell_type": "code",
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     136.421875
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    "execution_millis": 36,
    "execution_start": 1654343507603,
    "output_cleared": true,
    "source_hash": "f85a7cba",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['10/03/2004', '18.00.00', '2,6', ..., '0,7578', nan, nan],\n",
       "       ['10/03/2004', '19.00.00', '2', ..., '0,7255', nan, nan],\n",
       "       ['10/03/2004', '20.00.00', '2,2', ..., '0,7502', nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"datasets/AirQualityUCI.csv\", sep=\";\").values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00031-971725ab-d206-4b76-ba56-4880e80f9f3d",
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     "y": 174
    },
    "deepnote_cell_height": 134.34375,
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "# Materiały dodatkowe\n",
    "* Poritosh Mahto, *Pandas For Machine Learning*, https://medium.com/mlpoint/pandas-for-machine-learning-53846bc9a98b"
   ]
  }
 ],
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