Pandas.ipynb

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      "name": "Untitled3.ipynb",
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/NehaAgarwal2598/Assignment-3/blob/master/Pandas.ipynb)"
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      "source": [
        "# Pandas\n",
        "\n",
        "Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.In this tutorial, we will learn the various features of Python Pandas and how to use them in practice.\n",
        "\n",
        "\n",
        "## Import pandas and numpy"
      ]
    },
    {
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        "id": "zWetN2Mxxc7G",
        "colab_type": "code",
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      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "outputId": "1f3e8c1b-da3f-473a-8f93-af084f7dc3ed"
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      "cell_type": "code",
      "source": [
        "a_ascii = ord('A')\n",
        "z_ascii = ord('Z')\n",
        "alphabets = [chr(i) for i in range(a_ascii, z_ascii+1)]\n",
        "\n",
        "print(alphabets)\n",
        "\n",
        "numbers = np.arange(26)\n",
        "\n",
        "print(numbers)\n",
        "\n",
        "print(type(alphabets), type(numbers))\n",
        "\n",
        "alpha_numbers = dict(zip(alphabets, numbers))\n",
        "\n",
        "print(alpha_numbers)\n",
        "\n",
        "print(type(alpha_numbers))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n",
            "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
            " 24 25]\n",
            "<class 'list'> <class 'numpy.ndarray'>\n",
            "{'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'H': 7, 'I': 8, 'J': 9, 'K': 10, 'L': 11, 'M': 12, 'N': 13, 'O': 14, 'P': 15, 'Q': 16, 'R': 17, 'S': 18, 'T': 19, 'U': 20, 'V': 21, 'W': 22, 'X': 23, 'Y': 24, 'Z': 25}\n",
            "<class 'dict'>\n"
          ],
          "name": "stdout"
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      "cell_type": "code",
      "source": [
        "series1 = pd.Series(alphabets)\n",
        "print(series1)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0     A\n",
            "1     B\n",
            "2     C\n",
            "3     D\n",
            "4     E\n",
            "5     F\n",
            "6     G\n",
            "7     H\n",
            "8     I\n",
            "9     J\n",
            "10    K\n",
            "11    L\n",
            "12    M\n",
            "13    N\n",
            "14    O\n",
            "15    P\n",
            "16    Q\n",
            "17    R\n",
            "18    S\n",
            "19    T\n",
            "20    U\n",
            "21    V\n",
            "22    W\n",
            "23    X\n",
            "24    Y\n",
            "25    Z\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "series2 = pd.Series(numbers)\n",
        "print(series2)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0      0\n",
            "1      1\n",
            "2      2\n",
            "3      3\n",
            "4      4\n",
            "5      5\n",
            "6      6\n",
            "7      7\n",
            "8      8\n",
            "9      9\n",
            "10    10\n",
            "11    11\n",
            "12    12\n",
            "13    13\n",
            "14    14\n",
            "15    15\n",
            "16    16\n",
            "17    17\n",
            "18    18\n",
            "19    19\n",
            "20    20\n",
            "21    21\n",
            "22    22\n",
            "23    23\n",
            "24    24\n",
            "25    25\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        "outputId": "30a9f9ad-1502-4436-daf4-0c9de06359ce"
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      "cell_type": "code",
      "source": [
        "series3 = pd.Series(alpha_numbers)\n",
        "print(series3)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A     0\n",
            "B     1\n",
            "C     2\n",
            "D     3\n",
            "E     4\n",
            "F     5\n",
            "G     6\n",
            "H     7\n",
            "I     8\n",
            "J     9\n",
            "K    10\n",
            "L    11\n",
            "M    12\n",
            "N    13\n",
            "O    14\n",
            "P    15\n",
            "Q    16\n",
            "R    17\n",
            "S    18\n",
            "T    19\n",
            "U    20\n",
            "V    21\n",
            "W    22\n",
            "X    23\n",
            "Y    24\n",
            "Z    25\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        },
        "outputId": "0748460a-db72-4e26-cc7a-5b915871f819"
      },
      "cell_type": "code",
      "source": [
        "#replace head() with head(n) where n can be any number between [0-25] and observe the output in deach case \n",
        "series3.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    0\n",
              "B    1\n",
              "C    2\n",
              "D    3\n",
              "E    4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 6
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    },
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        "outputId": "234de44b-4693-4cec-c8c3-c66d9297e907"
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      "cell_type": "code",
      "source": [
        "data = {'alphabets': alphabets, 'values': numbers}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "#Lets Change the column `values` to `alpha_numbers`\n",
        "\n",
        "#df.columns = ['alphabets', 'alpha_numbers']\n",
        "\n",
        "df"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alphabets</th>\n",
              "      <th>values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>10</th>\n",
              "      <td>K</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>L</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>M</td>\n",
              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>N</td>\n",
              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>O</td>\n",
              "      <td>14</td>\n",
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              "      <td>P</td>\n",
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              "      <th>17</th>\n",
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              "      <td>17</td>\n",
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              "      <th>18</th>\n",
              "      <td>S</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>T</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>U</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>V</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>W</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>X</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Y</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Z</td>\n",
              "      <td>25</td>\n",
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            "text/plain": [
              "   alphabets  values\n",
              "0          A       0\n",
              "1          B       1\n",
              "2          C       2\n",
              "3          D       3\n",
              "4          E       4\n",
              "5          F       5\n",
              "6          G       6\n",
              "7          H       7\n",
              "8          I       8\n",
              "9          J       9\n",
              "10         K      10\n",
              "11         L      11\n",
              "12         M      12\n",
              "13         N      13\n",
              "14         O      14\n",
              "15         P      15\n",
              "16         Q      16\n",
              "17         R      17\n",
              "18         S      18\n",
              "19         T      19\n",
              "20         U      20\n",
              "21         V      21\n",
              "22         W      22\n",
              "23         X      23\n",
              "24         Y      24\n",
              "25         Z      25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
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    {
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        },
        "outputId": "2c4a9a29-94ce-4bb2-eba0-e67c93538aa9"
      },
      "cell_type": "code",
      "source": [
        "# transpose\n",
        "\n",
        "df.T\n",
        "\n",
        "# there are many more operations which we can perform look at the documentation with the subsequent exercises we will learn more"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alphabets</th>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>E</td>\n",
              "      <td>F</td>\n",
              "      <td>G</td>\n",
              "      <td>H</td>\n",
              "      <td>I</td>\n",
              "      <td>J</td>\n",
              "      <td>...</td>\n",
              "      <td>Q</td>\n",
              "      <td>R</td>\n",
              "      <td>S</td>\n",
              "      <td>T</td>\n",
              "      <td>U</td>\n",
              "      <td>V</td>\n",
              "      <td>W</td>\n",
              "      <td>X</td>\n",
              "      <td>Y</td>\n",
              "      <td>Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>values</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>16</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>19</td>\n",
              "      <td>20</td>\n",
              "      <td>21</td>\n",
              "      <td>22</td>\n",
              "      <td>23</td>\n",
              "      <td>24</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          0  1  2  3  4  5  6  7  8  9  ...  16  17  18  19  20  21  22  23  \\\n",
              "alphabets  A  B  C  D  E  F  G  H  I  J ...   Q   R   S   T   U   V   W   X   \n",
              "values     0  1  2  3  4  5  6  7  8  9 ...  16  17  18  19  20  21  22  23   \n",
              "\n",
              "           24  25  \n",
              "alphabets   Y   Z  \n",
              "values     24  25  \n",
              "\n",
              "[2 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
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        "outputId": "b03ed129-c55c-4994-b855-6dfe4034f2d8"
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      "cell_type": "code",
      "source": [
        "ser = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))\n",
        "pos = [0, 4, 8, 14, 20]\n",
        "\n",
        "vowels = ser.take(pos)\n",
        "\n",
        "df = pd.DataFrame(vowels)#, columns=['vowels'])\n",
        "\n",
        "df.columns = ['vowels']\n",
        "\n",
        "#df.index = [0, 1, 2, 3, 4]\n",
        "\n",
        "df"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>i</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>o</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>u</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   vowels\n",
              "0       a\n",
              "4       e\n",
              "8       i\n",
              "14      o\n",
              "20      u"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "5KagP9PpgV2F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "aa231d5e-40a8-4d3a-c65a-36fc6fe4366f"
      },
      "cell_type": "code",
      "source": [
        "ser = pd.Series(['we', 'are', 'learning', 'pandas'])\n",
        "\n",
        "ser.map(lambda x : x.title())\n",
        "\n",
        "titles = [i.title() for i in ser]\n",
        "\n",
        "titles"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['We', 'Are', 'Learning', 'Pandas']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "h5R0JL2NjuFS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5f4f8a36-4549-4f8c-acc1-91c1738bb1a1"
      },
      "cell_type": "code",
      "source": [
        "my_index = [1, 2, 3, 4, 5]\n",
        "\n",
        "df1 = pd.DataFrame({'upper values': ['A', 'B', 'C', 'D', 'E'],\n",
        "                   'lower values': ['a', 'b', 'c', 'd', 'e']},\n",
        "                  index = my_index)\n",
        "\n",
        "df1"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "\n",
              "    .dataframe thead th {\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>lower values</th>\n",
              "      <th>upper values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>c</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>d</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>e</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  lower values upper values\n",
              "1            a            A\n",
              "2            b            B\n",
              "3            c            C\n",
              "4            d            D\n",
              "5            e            E"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "G_Frvc3mk93k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "aaf8c8ce-0765-4c02-c00d-b63e55088744"
      },
      "cell_type": "code",
      "source": [
        "new_index = [2, 5, 4, 3, 1]\n",
        "\n",
        "df1.reindex(index = new_index)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>lower values</th>\n",
              "      <th>upper values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>e</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>d</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>c</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  lower values upper values\n",
              "2            b            B\n",
              "5            e            E\n",
              "4            d            D\n",
              "3            c            C\n",
              "1            a            A"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "J82LU53m_OU0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Get to know your Data\n",
        "\n",
        "\n",
        "#### Import necessary modules\n"
      ]
    },
    {
      "metadata": {
        "id": "ZyO1UXL8mtSj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yXTzTowtnwGI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Loading CSV Data to a DataFrame"
      ]
    },
    {
      "metadata": {
        "id": "H1Bjlb5wm9f-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "iris_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KE-k7b_Mn5iN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### See the top 10 rows\n"
      ]
    },
    {
      "metadata": {
        "id": "HY2Ps7xMn4ao",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d67b4210-bb4b-45c6-d509-32f3cfeaf526"
      },
      "cell_type": "code",
      "source": [
        "iris_df.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "ZQXekIodqOZu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Find number of rows and columns\n"
      ]
    },
    {
      "metadata": {
        "id": "6Y-A-lbFqR82",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "59bc7283-b6e4-4103-e36a-8580c37911a7"
      },
      "cell_type": "code",
      "source": [
        "print(iris_df.shape)\n",
        "\n",
        "#first is row and second is column\n",
        "#select row by simple indexing\n",
        "\n",
        "#print(iris_df.shape[0])\n",
        "#print(iris_df.shape[1])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(150, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4ckCiGPhrC_t",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Print all columns"
      ]
    },
    {
      "metadata": {
        "id": "S6jgMyRDrF2a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "44673988-91d6-467d-8b8c-2a429624ad3f"
      },
      "cell_type": "code",
      "source": [
        "print(iris_df.columns)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
            "       'species'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kVav5-ACtIqS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Check Index\n"
      ]
    },
    {
      "metadata": {
        "id": "iu3I9zIGtLDX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0d772f6c-d7e8-4ec9-cb45-d9ac61397420"
      },
      "cell_type": "code",
      "source": [
        "print(iris_df.index)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RangeIndex(start=0, stop=150, step=1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "psCc7PborOCQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Right now the iris_data set has all the species grouped together let's shuffle it"
      ]
    },
    {
      "metadata": {
        "id": "Bxc8i6avrZPw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "b51db56f-9b4a-4b2f-8c4e-be225bafa609"
      },
      "cell_type": "code",
      "source": [
        "#generate a random permutaion on index\n",
        "\n",
        "print(iris_df.head())\n",
        "\n",
        "new_index = np.random.permutation(iris_df.index)\n",
        "iris_df = iris_df.reindex(index = new_index)\n",
        "\n",
        "print(iris_df.head())"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   sepal_length  sepal_width  petal_length  petal_width species\n",
            "0           5.1          3.5           1.4          0.2  setosa\n",
            "1           4.9          3.0           1.4          0.2  setosa\n",
            "2           4.7          3.2           1.3          0.2  setosa\n",
            "3           4.6          3.1           1.5          0.2  setosa\n",
            "4           5.0          3.6           1.4          0.2  setosa\n",
            "     sepal_length  sepal_width  petal_length  petal_width     species\n",
            "78            6.0          2.9           4.5          1.5  versicolor\n",
            "121           5.6          2.8           4.9          2.0   virginica\n",
            "43            5.0          3.5           1.6          0.6      setosa\n",
            "108           6.7          2.5           5.8          1.8   virginica\n",
            "58            6.6          2.9           4.6          1.3  versicolor\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "j32h8022sRT8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### We can also apply an operation on whole column of iris_df"
      ]
    },
    {
      "metadata": {
        "id": "seYXHXsYsYJI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "eb379004-47bf-4f60-b4ba-175e2697c365"
      },
      "cell_type": "code",
      "source": [
        "#original\n",
        "\n",
        "print(iris_df.head())\n",
        "\n",
        "iris_df['sepal_width'] *= 10\n",
        "\n",
        "#changed\n",
        "\n",
        "print(iris_df.head())\n",
        "\n",
        "#lets undo the operation\n",
        "\n",
        "iris_df['sepal_width'] /= 10\n",
        "\n",
        "print(iris_df.head())"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     sepal_length  sepal_width  petal_length  petal_width     species\n",
            "78            6.0          2.9           4.5          1.5  versicolor\n",
            "121           5.6          2.8           4.9          2.0   virginica\n",
            "43            5.0          3.5           1.6          0.6      setosa\n",
            "108           6.7          2.5           5.8          1.8   virginica\n",
            "58            6.6          2.9           4.6          1.3  versicolor\n",
            "     sepal_length  sepal_width  petal_length  petal_width     species\n",
            "78            6.0         29.0           4.5          1.5  versicolor\n",
            "121           5.6         28.0           4.9          2.0   virginica\n",
            "43            5.0         35.0           1.6          0.6      setosa\n",
            "108           6.7         25.0           5.8          1.8   virginica\n",
            "58            6.6         29.0           4.6          1.3  versicolor\n",
            "     sepal_length  sepal_width  petal_length  petal_width     species\n",
            "78            6.0          2.9           4.5          1.5  versicolor\n",
            "121           5.6          2.8           4.9          2.0   virginica\n",
            "43            5.0          3.5           1.6          0.6      setosa\n",
            "108           6.7          2.5           5.8          1.8   virginica\n",
            "58            6.6          2.9           4.6          1.3  versicolor\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "R-Ca-LBLzjiF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Show all the rows where sepal_width > 3.3"
      ]
    },
    {
      "metadata": {
        "id": "WJ7W-F-d0AoZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1165
        },
        "outputId": "09fb4b87-0377-4594-fda3-67eeaedf1754"
      },
      "cell_type": "code",
      "source": [
        "iris_df[iris_df['sepal_width']>3.3]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "        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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>5.3</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>7.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.2</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>5.2</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>7.2</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>6.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>5.5</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width     species\n",
              "43            5.0          3.5           1.6          0.6      setosa\n",
              "136           6.3          3.4           5.6          2.4   virginica\n",
              "20            5.4          3.4           1.7          0.2      setosa\n",
              "48            5.3          3.7           1.5          0.2      setosa\n",
              "39            5.1          3.4           1.5          0.2      setosa\n",
              "131           7.9          3.8           6.4          2.0   virginica\n",
              "10            5.4          3.7           1.5          0.2      setosa\n",
              "18            5.7          3.8           1.7          0.3      setosa\n",
              "11            4.8          3.4           1.6          0.2      setosa\n",
              "21            5.1          3.7           1.5          0.4      setosa\n",
              "46            5.1          3.8           1.6          0.2      setosa\n",
              "0             5.1          3.5           1.4          0.2      setosa\n",
              "28            5.2          3.4           1.4          0.2      setosa\n",
              "16            5.4          3.9           1.3          0.4      setosa\n",
              "148           6.2          3.4           5.4          2.3   virginica\n",
              "40            5.0          3.5           1.3          0.3      setosa\n",
              "27            5.2          3.5           1.5          0.2      setosa\n",
              "14            5.8          4.0           1.2          0.2      setosa\n",
              "19            5.1          3.8           1.5          0.3      setosa\n",
              "117           7.7          3.8           6.7          2.2   virginica\n",
              "24            4.8          3.4           1.9          0.2      setosa\n",
              "32            5.2          4.1           1.5          0.1      setosa\n",
              "15            5.7          4.4           1.5          0.4      setosa\n",
              "31            5.4          3.4           1.5          0.4      setosa\n",
              "109           7.2          3.6           6.1          2.5   virginica\n",
              "44            5.1          3.8           1.9          0.4      setosa\n",
              "85            6.0          3.4           4.5          1.6  versicolor\n",
              "36            5.5          3.5           1.3          0.2      setosa\n",
              "4             5.0          3.6           1.4          0.2      setosa\n",
              "6             4.6          3.4           1.4          0.3      setosa\n",
              "22            4.6          3.6           1.0          0.2      setosa\n",
              "5             5.4          3.9           1.7          0.4      setosa\n",
              "7             5.0          3.4           1.5          0.2      setosa\n",
              "17            5.1          3.5           1.4          0.3      setosa\n",
              "26            5.0          3.4           1.6          0.4      setosa\n",
              "33            5.5          4.2           1.4          0.2      setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "gH3DnhCq2Cbl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Club two filters together - Find all samples where sepal_width > 3.3 and species is versicolor"
      ]
    },
    {
      "metadata": {
        "id": "4U7ksr_R2H7M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "732f5979-ca02-46a4-b5be-da8180bcd6e1"
      },
      "cell_type": "code",
      "source": [
        "iris_df[(iris_df['sepal_width']>3.3) & (iris_df['species'] == 'versicolor')] "
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>6.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal_length  sepal_width  petal_length  petal_width     species\n",
              "85           6.0          3.4           4.5          1.6  versicolor"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "1lmnB3ot2u7I",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Sorting a column by value"
      ]
    },
    {
      "metadata": {
        "id": "K7KIj6fv2zWP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1969
        },
        "outputId": "83a21fb6-0211-4ccd-ccdc-f10e08f981a9"
      },
      "cell_type": "code",
      "source": [
        "iris_df.sort_values(by='sepal_width')#, ascending = False)\n",
        "#pass ascending = False for descending order"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>6.2</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>4.5</td>\n",
              "      <td>2.3</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>4.9</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.1</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.1</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>5.1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>4.9</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.7</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>6.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.6</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>7.7</td>\n",
              "      <td>2.6</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.2</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>5.2</td>\n",
              "      <td>2.7</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.3</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.2</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.6</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>6.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>5.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>7.2</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>5.3</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>7.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.2</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>5.2</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>5.5</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width     species\n",
              "60            5.0          2.0           3.5          1.0  versicolor\n",
              "119           6.0          2.2           5.0          1.5   virginica\n",
              "68            6.2          2.2           4.5          1.5  versicolor\n",
              "62            6.0          2.2           4.0          1.0  versicolor\n",
              "53            5.5          2.3           4.0          1.3  versicolor\n",
              "93            5.0          2.3           3.3          1.0  versicolor\n",
              "87            6.3          2.3           4.4          1.3  versicolor\n",
              "41            4.5          2.3           1.3          0.3      setosa\n",
              "57            4.9          2.4           3.3          1.0  versicolor\n",
              "80            5.5          2.4           3.8          1.1  versicolor\n",
              "81            5.5          2.4           3.7          1.0  versicolor\n",
              "69            5.6          2.5           3.9          1.1  versicolor\n",
              "98            5.1          2.5           3.0          1.1  versicolor\n",
              "106           4.9          2.5           4.5          1.7   virginica\n",
              "72            6.3          2.5           4.9          1.5  versicolor\n",
              "108           6.7          2.5           5.8          1.8   virginica\n",
              "146           6.3          2.5           5.0          1.9   virginica\n",
              "113           5.7          2.5           5.0          2.0   virginica\n",
              "89            5.5          2.5           4.0          1.3  versicolor\n",
              "79            5.7          2.6           3.5          1.0  versicolor\n",
              "118           7.7          2.6           6.9          2.3   virginica\n",
              "90            5.5          2.6           4.4          1.2  versicolor\n",
              "92            5.8          2.6           4.0          1.2  versicolor\n",
              "134           6.1          2.6           5.6          1.4   virginica\n",
              "101           5.8          2.7           5.1          1.9   virginica\n",
              "59            5.2          2.7           3.9          1.4  versicolor\n",
              "111           6.4          2.7           5.3          1.9   virginica\n",
              "82            5.8          2.7           3.9          1.2  versicolor\n",
              "83            6.0          2.7           5.1          1.6  versicolor\n",
              "94            5.6          2.7           4.2          1.3  versicolor\n",
              "..            ...          ...           ...          ...         ...\n",
              "28            5.2          3.4           1.4          0.2      setosa\n",
              "31            5.4          3.4           1.5          0.4      setosa\n",
              "11            4.8          3.4           1.6          0.2      setosa\n",
              "39            5.1          3.4           1.5          0.2      setosa\n",
              "85            6.0          3.4           4.5          1.6  versicolor\n",
              "24            4.8          3.4           1.9          0.2      setosa\n",
              "43            5.0          3.5           1.6          0.6      setosa\n",
              "0             5.1          3.5           1.4          0.2      setosa\n",
              "17            5.1          3.5           1.4          0.3      setosa\n",
              "36            5.5          3.5           1.3          0.2      setosa\n",
              "27            5.2          3.5           1.5          0.2      setosa\n",
              "40            5.0          3.5           1.3          0.3      setosa\n",
              "22            4.6          3.6           1.0          0.2      setosa\n",
              "109           7.2          3.6           6.1          2.5   virginica\n",
              "4             5.0          3.6           1.4          0.2      setosa\n",
              "21            5.1          3.7           1.5          0.4      setosa\n",
              "10            5.4          3.7           1.5          0.2      setosa\n",
              "48            5.3          3.7           1.5          0.2      setosa\n",
              "18            5.7          3.8           1.7          0.3      setosa\n",
              "19            5.1          3.8           1.5          0.3      setosa\n",
              "117           7.7          3.8           6.7          2.2   virginica\n",
              "131           7.9          3.8           6.4          2.0   virginica\n",
              "46            5.1          3.8           1.6          0.2      setosa\n",
              "44            5.1          3.8           1.9          0.4      setosa\n",
              "16            5.4          3.9           1.3          0.4      setosa\n",
              "5             5.4          3.9           1.7          0.4      setosa\n",
              "14            5.8          4.0           1.2          0.2      setosa\n",
              "32            5.2          4.1           1.5          0.1      setosa\n",
              "33            5.5          4.2           1.4          0.2      setosa\n",
              "15            5.7          4.4           1.5          0.4      setosa\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "9jg_Z4YCoMSV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### List all the unique species"
      ]
    },
    {
      "metadata": {
        "id": "M6EN78ufoJY7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cae20cb6-faec-49d8-8810-c6fc54c942db"
      },
      "cell_type": "code",
      "source": [
        "species = iris_df['species'].unique()\n",
        "\n",
        "print(species)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['versicolor' 'virginica' 'setosa']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wG1i5nxBodmB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Selecting a particular species using boolean mask (learnt in previous exercise)"
      ]
    },
    {
      "metadata": {
        "id": "gZvpbKBwoVUe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "df4ff2d0-9d60-4dc7-b7c1-1055c0099fe8"
      },
      "cell_type": "code",
      "source": [
        "setosa = iris_df[iris_df['species'] == species[0]]\n",
        "\n",
        "setosa.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>6.6</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>6.2</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.2</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal_length  sepal_width  petal_length  petal_width     species\n",
              "78           6.0          2.9           4.5          1.5  versicolor\n",
              "58           6.6          2.9           4.6          1.3  versicolor\n",
              "68           6.2          2.2           4.5          1.5  versicolor\n",
              "82           5.8          2.7           3.9          1.2  versicolor\n",
              "89           5.5          2.5           4.0          1.3  versicolor"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "7tumfZ3DotPG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "662d6678-ac35-4b15-8a11-540ef538064b"
      },
      "cell_type": "code",
      "source": [
        "# do the same for other 2 species \n",
        "versicolor = iris_df[iris_df['species'] == species[1]]\n",
        "\n",
        "versicolor.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>6.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "121           5.6          2.8           4.9          2.0  virginica\n",
              "108           6.7          2.5           5.8          1.8  virginica\n",
              "100           6.3          3.3           6.0          2.5  virginica\n",
              "136           6.3          3.4           5.6          2.4  virginica\n",
              "113           5.7          2.5           5.0          2.0  virginica"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "cUYm5UqVpDPy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "340897a3-525a-4f72-fdec-a541d5433c7d"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "virginica = iris_df[iris_df['species'] == species[2]]\n",
        "\n",
        "virginica.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>4.4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>5.3</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal_length  sepal_width  petal_length  petal_width species\n",
              "43           5.0          3.5           1.6          0.6  setosa\n",
              "20           5.4          3.4           1.7          0.2  setosa\n",
              "9            4.9          3.1           1.5          0.1  setosa\n",
              "38           4.4          3.0           1.3          0.2  setosa\n",
              "48           5.3          3.7           1.5          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "-y1wDc8SpdQs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Describe each created species to see the difference\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "eHrn3ZVRpOk5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "0deee989-05dc-4dd5-8e02-c709c99a9577"
      },
      "cell_type": "code",
      "source": [
        "setosa.describe()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.936000</td>\n",
              "      <td>2.770000</td>\n",
              "      <td>4.260000</td>\n",
              "      <td>1.326000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.516171</td>\n",
              "      <td>0.313798</td>\n",
              "      <td>0.469911</td>\n",
              "      <td>0.197753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.900000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.600000</td>\n",
              "      <td>2.525000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.900000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.300000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.600000</td>\n",
              "      <td>1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width\n",
              "count     50.000000    50.000000     50.000000    50.000000\n",
              "mean       5.936000     2.770000      4.260000     1.326000\n",
              "std        0.516171     0.313798      0.469911     0.197753\n",
              "min        4.900000     2.000000      3.000000     1.000000\n",
              "25%        5.600000     2.525000      4.000000     1.200000\n",
              "50%        5.900000     2.800000      4.350000     1.300000\n",
              "75%        6.300000     3.000000      4.600000     1.500000\n",
              "max        7.000000     3.400000      5.100000     1.800000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "GwJFT2GlpwUv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "f769b827-0624-4ba5-c3cf-031cb7557d40"
      },
      "cell_type": "code",
      "source": [
        "versicolor.describe()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50.00000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.58800</td>\n",
              "      <td>2.974000</td>\n",
              "      <td>5.552000</td>\n",
              "      <td>2.02600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.63588</td>\n",
              "      <td>0.322497</td>\n",
              "      <td>0.551895</td>\n",
              "      <td>0.27465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.90000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>1.40000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.22500</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.80000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.50000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.550000</td>\n",
              "      <td>2.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.90000</td>\n",
              "      <td>3.175000</td>\n",
              "      <td>5.875000</td>\n",
              "      <td>2.30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.90000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.50000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width\n",
              "count      50.00000    50.000000     50.000000     50.00000\n",
              "mean        6.58800     2.974000      5.552000      2.02600\n",
              "std         0.63588     0.322497      0.551895      0.27465\n",
              "min         4.90000     2.200000      4.500000      1.40000\n",
              "25%         6.22500     2.800000      5.100000      1.80000\n",
              "50%         6.50000     3.000000      5.550000      2.00000\n",
              "75%         6.90000     3.175000      5.875000      2.30000\n",
              "max         7.90000     3.800000      6.900000      2.50000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "Ad4qhSZLpztf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "dc25951b-48b4-47df-8c5e-835085ad5cd5"
      },
      "cell_type": "code",
      "source": [
        "virginica.describe()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50.00000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.00600</td>\n",
              "      <td>3.418000</td>\n",
              "      <td>1.464000</td>\n",
              "      <td>0.24400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.35249</td>\n",
              "      <td>0.381024</td>\n",
              "      <td>0.173511</td>\n",
              "      <td>0.10721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.30000</td>\n",
              "      <td>2.300000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.80000</td>\n",
              "      <td>3.125000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>0.20000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.00000</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.20000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.20000</td>\n",
              "      <td>3.675000</td>\n",
              "      <td>1.575000</td>\n",
              "      <td>0.30000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.80000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.60000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width\n",
              "count      50.00000    50.000000     50.000000     50.00000\n",
              "mean        5.00600     3.418000      1.464000      0.24400\n",
              "std         0.35249     0.381024      0.173511      0.10721\n",
              "min         4.30000     2.300000      1.000000      0.10000\n",
              "25%         4.80000     3.125000      1.400000      0.20000\n",
              "50%         5.00000     3.400000      1.500000      0.20000\n",
              "75%         5.20000     3.675000      1.575000      0.30000\n",
              "max         5.80000     4.400000      1.900000      0.60000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "Vdu0ulZWtr09",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Let's plot and see the difference"
      ]
    },
    {
      "metadata": {
        "id": "PEVMzRvpttmD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### import matplotlib.pyplot "
      ]
    },
    {
      "metadata": {
        "id": "rqDXuuAtt7C3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "outputId": "00e7b46e-378e-41cb-e39f-275ff332113a"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#hist creates a histogram there are many more plots(see the documentation) you can play with it.\n",
        "\n",
        "plt.hist(setosa['sepal_length'])\n",
        "plt.hist(versicolor['sepal_length'])\n",
        "plt.hist(virginica['sepal_length'])"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 4.,  1.,  6.,  5., 12.,  8.,  4.,  5.,  2.,  3.]),\n",
              " array([4.3 , 4.45, 4.6 , 4.75, 4.9 , 5.05, 5.2 , 5.35, 5.5 , 5.65, 5.8 ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFKCAYAAABcq1WoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAFGpJREFUeJzt3X9s1Hf9wPFX6cmwpWKHLQzdcFk0\ni9twkG0ZP92QH264uaErP8KQZCYiDGaCGdhgICExYeG7MN3cdEPmF0LCBgh8jZFFhMRsgEYMisnC\nwMTwY4Myys9CgO6+fxgacdDS6/V93PXx+Iv73PVzrzdvcs/eHb2WZbPZbAAAyXQr9AAA0NWILwAk\nJr4AkJj4AkBi4gsAiYkvACSWSXEnDQ2nUtxNXlRXV0RjY1Ohx+hUpb5G6yt+pb5G6yt+17LGmpqq\nq17nme9/yWTKCz1Cpyv1NVpf8Sv1NVpf8evoGsUXABITXwBITHwBIDHxBYDExBcAEhNfAEhMfAEg\nMfEFgMSuKb579uyJUaNGxcqVKyMi4v33349p06bFlClTYtq0adHQ0NCpQwJAKWkzvk1NTbFo0aIY\nPHhwy7GlS5dGXV1drFy5MkaPHh3Lly/v1CEBoJS0Gd/u3bvHq6++GrW1tS3HFixYEGPHjo2IiOrq\n6jh+/HjnTQgAJabN+GYymejRo8dlxyoqKqK8vDyam5tj1apV8cgjj3TagABQanL+rUbNzc3x7LPP\nxv3333/ZS9JXUl1dUVQftN3ab6K4Hr39jW+26/Z7rnBs6Ia1+RnmOlFse9hepb6+iNJfo/UVv46s\nMef4/vCHP4z+/fvH008/3eZti+lXS9XUVBXVr0DMl1Jac6nvYamvL6L012h9xe9a1pj3Xym4cePG\n+MQnPhGzZ8/O5csBoEtr85nv7t27Y/HixXHw4MHIZDKxadOm+PDDD+OGG26IJ598MiIibrvttli4\ncGFnzwoAJaHN+N55552xYsWKFLMAQJfgE64AIDHxBYDExBcAEhNfAEhMfAEgMfEFgMTEFwASE18A\nSEx8ASAx8QWAxMQXABITXwBITHwBIDHxBYDExBcAEhNfAEhMfAEgMfEFgMTEFwASE18ASEx8ASAx\n8QWAxMQXABITXwBITHwBIDHxBYDExBcAEhNfAEhMfAEgMfEFgMTEFwASE18ASEx8ASAx8QWAxMQX\nABITXwBITHwBIDHxBYDExBcAErum+O7ZsydGjRoVK1eujIiI999/P5588smYPHlyPPPMM3H+/PlO\nHRIASkmb8W1qaopFixbF4MGDW4795Cc/icmTJ8eqVauif//+sWbNmk4dEgBKSZvx7d69e7z66qtR\nW1vbcmzHjh3x1a9+NSIiHnzwwdi2bVvnTQgAJSbT5g0ymchkLr/Z2bNno3v37hER0bt372hoaOic\n6QCgBLUZ37Zks9k2b1NdXRGZTHlH7yqZmpqqQo/QLnvycI5iW3NbLq3nkTkbOvV+/u9/vtGp57+a\nUtuvK+mMNdat/l7ez5mLNya8XPJ7WOrri+jYGnOKb0VFRZw7dy569OgRhw8fvuwl6StpbGzKabhC\nqKmpioaGU4UeI7lSWnPKPSzE31tX+DfaFdZYyuvrCvt3LWtsLc45/ajRkCFDYtOmTRER8dZbb8Xw\n4cNzOQ0AdEltPvPdvXt3LF68OA4ePBiZTCY2bdoUS5YsiXnz5sXq1aujX79+8dhjj6WYFQBKQpvx\nvfPOO2PFihUfO758+fJOGQgASp1PuAKAxMQXABITXwBITHwBIDHxBYDExBcAEhNfAEhMfAEgMfEF\ngMTEFwASE18ASEx8ASAx8QWAxMQXABITXwBITHwBIDHxBYDExBcAEssUegCg9M38w7OFHgGuK575\nAkBi4gsAiYkvACQmvgCQmPgCQGLiCwCJiS8AJCa+AJCY+AJAYuILAImJLwAkJr4AkJj4AkBi4gsA\niYkvACQmvgCQmPgCQGLiCwCJiS8AJJbJ5YvOnDkTc+fOjRMnTsSFCxdi5syZMXz48HzPBgAlKaf4\n/vrXv45bb7015syZE4cPH45vf/vb8bvf/S7fswFAScrpZefq6uo4fvx4REScPHkyqqur8zoUAJSy\nnJ75jhs3LtatWxejR4+OkydPxs9//vN8zwUAJSun+G7YsCH69esXy5Yti3fffTfq6+tj3bp1V719\ndXVFZDLlOQ+ZWk1NVaFHaJc9+TjHd6Z1+BxDN6zt+CB5kmoPO/t+HpmzocPn+OR93hK6HhXb40x7\nlfr6Ijq2xpziu3Pnzhg2bFhERNx+++1x5MiRaG5ujvLyKwe2sbEp5wFTq6mpioaGU4UeoyhdL39v\nKffwelkzxaeU/+10hcfRa1lja3HO6T3f/v37x65duyIi4uDBg1FZWXnV8AIAl8vpme+ECROivr4+\npkyZEhcvXoyFCxfmeSwAKF05xbeysjJeeOGFfM8CAF2CT7gCgMTEFwASE18ASEx8ASAx8QWAxMQX\nABITXwBITHwBIDHxBYDExBcAEhNfAEhMfAEgMfEFgMTEFwASE18ASEx8ASAx8QWAxMQXABITXwBI\nTHwBIDHxBYDExBcAEhNfAEhMfAEgMfEFgMTEFwASE18ASEx8ASAx8QWAxMQXABITXwBITHwBIDHx\nBYDExBcAEhNfAEhMfAEgMfEFgMTEFwASE18ASCzn+G7cuDEeffTRGD9+fGzdujWPIwFAacspvo2N\njfHSSy/FqlWr4pVXXonNmzfney4AKFmZXL5o27ZtMXjw4OjZs2f07NkzFi1alO+5AKBk5RTfAwcO\nxLlz52L69Olx8uTJmDVrVgwePPiqt6+urohMpjznIVOrqam65tu+/Y1vdui+hm5Y26Gvj4jY0+Ez\n5Ed7/t7+0yNzNuR5knRyXTOU+r+d9qyvbvX3OnGSa/fGhJfbdfuO7GFO8Y2IOH78eLz44otx6NCh\nmDp1amzZsiXKysqueNvGxqacB0ytpqYqGhpOJbu/lPfV2UppLdeqK66Z/CjlfzupH0fzpT0zX8sa\nW4tzTu/59u7dOwYOHBiZTCZuueWWqKysjGPHjuVyKgDocnKK77Bhw2L79u3x0UcfRWNjYzQ1NUV1\ndXW+ZwOAkpTTy859+vSJsWPHRl1dXUREzJ8/P7p18yPDAHAtcn7Pd+LEiTFx4sR8zgIAXYKnqwCQ\nmPgCQGLiCwCJiS8AJCa+AJCY+AJAYuILAImJLwAkJr4AkJj4AkBi4gsAiYkvACQmvgCQmPgCQGLi\nCwCJiS8AJCa+AJCY+AJAYplCD9DV7fnOtEKPcF2Zt/d/O3yOFybX5mGSazPzD7/r1PN/8r5OPT0F\nUrf6e4UeISIiXhr5XKFH6LI88wWAxMQXABITXwBITHwBIDHxBYDExBcAEhNfAEhMfAEgMfEFgMTE\nFwASE18ASEx8ASAx8QWAxMQXABITXwBITHwBIDHxBYDExBcAEhNfAEisQ/E9d+5cjBo1KtatW5ev\neQCg5HUovi+//HL06tUrX7MAQJeQc3z37dsXe/fujQceeCCP4wBA6cs5vosXL4558+blcxYA6BIy\nuXzR+vXr4+67746bb775mm5fXV0RmUx5Lnd1VW9/45sdPsfQDWuveLympuqaz7Gnw1OUjj3fmZbT\n1/kWDgqjPY9119O5O0t7Z+7IGnOK79atW2P//v2xdevW+OCDD6J79+7Rt2/fGDJkyBVv39jYlPOA\nnamh4dTHjtXUVF3xOECp6azHumJ9HG3PzNeyxtbinFN8ly5d2vLnn/70p/HZz372quEFAC7n53wB\nILGcnvn+p1mzZuVjDgDoMjzzBYDExBcAEhNfAEhMfAEgMfEFgMTEFwASE18ASEx8ASAx8QWAxMQX\nABITXwBITHwBIDHxBYDExBcAEhNfAEhMfAEgMfEFgMTEFwASE18ASCxT6AHgevTMqiMdPscLk2vz\nMAlQijzzBYDExBcAEhNfAEhMfAEgMfEFgMTEFwASE18ASEx8ASAx8QWAxMQXABITXwBITHwBIDHx\nBYDExBcAEhNfAEhMfAEgMfEFgMTEFwASE18ASCyT6xc+99xz8Ze//CUuXrwY3/3ud2PMmDH5nAsA\nSlZO8d2+fXu89957sXr16mhsbIzHH39cfAHgGuUU33vvvTcGDBgQERGf+tSn4uzZs9Hc3Bzl5eV5\nHQ4ASlFO8S0vL4+KioqIiFizZk2MGDGi1fBWV1dEJpPfMO/Jwzlqaqradbyz5iC/nll1pNAjQFFo\nz2Pd9XTuztLemTuyxpzf842I+P3vfx9r1qyJX/7yl63errGxqSN302kaGk597FhNTdUVjwOUms56\nrCvWx9H2zHwta2wtzjnH949//GO88sor8dprr0VVVfF9hwMAhZJTfE+dOhXPPfdcvP766/HpT386\n3zMBQEnLKb6//e1vo7GxMb7//e+3HFu8eHH069cvb4MBQKnKKb4TJkyICRMm5HsWAOgSfMIVACQm\nvgCQmPgCQGLiCwCJiS8AJCa+AJCY+AJAYuILAImJLwAkJr4AkJj4AkBi4gsAiYkvACQmvgCQmPgC\nQGLiCwCJiS8AJCa+AJBYptADFNKe70z7+LH0YwAUxMw/PFvoEbosz3wBIDHxBYDExBcAEhNfAEhM\nfAEgMfEFgMTEFwASE18ASEx8ASAx8QWAxMQXABITXwBITHwBIDHxBYDExBcAEhNfAEhMfAEgMfEF\ngMTEFwASy+T6hT/+8Y9j165dUVZWFvX19TFgwIB8zgUAJSun+P7pT3+Kf/3rX7F69erYt29f1NfX\nx+rVq/M9GwCUpJxedt62bVuMGjUqIiJuu+22OHHiRJw+fTqvgwFAqcopvkePHo3q6uqWyzfeeGM0\nNDTkbSgAKGU5v+f7n7LZbKvX19RU5eNuLj/nhrV5Pyfk09BCDwB0qo60LadnvrW1tXH06NGWy0eO\nHImampqchwCAriSn+A4dOjQ2bdoUERH/+Mc/ora2Nnr27JnXwQCgVOX0svOgQYPijjvuiIkTJ0ZZ\nWVksWLAg33MBQMkqy7b1hi0AkFc+4QoAEhNfAEgsLz9qVMzOnTsXX//612PGjBkxfvz4luMjR46M\nvn37Rnl5eURELFmyJPr06VOoMdttx44d8cwzz8QXvvCFiIj44he/GD/60Y9arn/nnXfi+eefj/Ly\n8hgxYkTMnDmzUKPmpK31Ffv+XbJx48Z47bXXIpPJxOzZs+OBBx5oua7Y9zCi9fWVwh6++eabsXHj\nxpbLu3fvjr/+9a8tlzdu3Bi/+tWvolu3blFXVxdPPPFEIcbMWVvru+OOO2LQoEEtl19//fWW/SwG\nZ86ciblz58aJEyfiwoULMXPmzBg+fHjL9R3av2wX9/zzz2fHjx+fXbt27WXHH3zwwezp06cLNFXH\nbd++PTtr1qyrXv/QQw9lDx06lG1ubs5OmjQp+9577yWcruPaWl+x7182m80eO3YsO2bMmOypU6ey\nhw8fzs6fP/+y64t9D9taXyns4X/asWNHduHChS2Xz5w5kx0zZkz25MmT2bNnz2bHjRuXbWxsLOCE\nHfPf68tms9n77ruvQNPkx4oVK7JLlizJZrPZ7AcffJAdO3Zsy3Ud3b8u/bLzvn37Yu/evZd9t90V\n7N+/P3r16hU33XRTdOvWLb7yla/Etm3bCj0W/2Xbtm0xePDg6NmzZ9TW1saiRYtariuFPWxtfaXo\npZdeihkzZrRc3rVrV9x1111RVVUVPXr0iEGDBsXOnTsLOGHH/Pf6SkF1dXUcP348IiJOnjx52Sc7\ndnT/unR8Fy9eHPPmzbvq9QsWLIhJkybFkiVL2vwUr+vR3r17Y/r06TFp0qR4++23W443NDTEjTfe\n2HK5WD8e9Grru6TY9+/AgQNx7ty5mD59ekyePPmyuJbCHra2vkuKfQ8v+dvf/hY33XTTZR9GdPTo\n0aLfw0uutL6IiPPnz8ecOXNi4sSJsXz58gJNl7tx48bFoUOHYvTo0TFlypSYO3duy3Ud3b8u+57v\n+vXr4+67746bb775itfPnj07hg8fHr169YqZM2fGpk2b4mtf+1riKXP3+c9/Pp5++ul46KGHYv/+\n/TF16tR46623onv37oUeLS/aWl+x798lx48fjxdffDEOHToUU6dOjS1btkRZWVmhx8qb1tZXKnsY\nEbFmzZp4/PHHW71NMX9zcbX1Pfvss/Hoo49GWVlZTJkyJe6555646667CjBhbjZs2BD9+vWLZcuW\nxbvvvhv19fWxbt26K962vfvXZZ/5bt26NTZv3hx1dXXx5ptvxs9+9rN45513Wq5/7LHHonfv3pHJ\nZGLEiBGxZ8+eAk7bfn369ImHH344ysrK4pZbbonPfOYzcfjw4Yj4+MeDHj58OGpraws1ak5aW19E\n8e9fRETv3r1j4MCBkclk4pZbbonKyso4duxYRJTGHra2vojS2MNLduzYEQMHDrzs2JU+prfY9vCS\nK60vImLSpElRWVkZFRUVcf/99xfdHu7cuTOGDRsWERG33357HDlyJJqbmyOi4/vXZeO7dOnSWLt2\nbbzxxhvxxBNPxIwZM2LIkCEREXHq1Kl46qmn4vz58xER8ec//7nlf9UWi40bN8ayZcsi4t8vUX74\n4Yct/1P0c5/7XJw+fToOHDgQFy9ejC1btsTQocX1awBaW18p7F9ExLBhw2L79u3x0UcfRWNjYzQ1\nNbW851QKe9ja+kplDyP+/Y1RZWXlx151+vKXvxx///vf4+TJk3HmzJnYuXNn3HPPPQWaMndXW98/\n//nPmDNnTmSz2bh48WLs3Lmz6Pawf//+sWvXroiIOHjwYFRWVrb8b+2O7l+Xfdn5StatWxdVVVUx\nevToGDFiREyYMCFuuOGG+NKXvlR0L3eNHDkyfvCDH8TmzZvjwoULsXDhwvjNb37Tsr6FCxfGnDlz\nIiLi4YcfjltvvbXAE7dPW+sr9v2L+Pez+7Fjx0ZdXV1ERMyfPz/Wr19fMnvY1vpKYQ8jPv7+/C9+\n8Yu49957Y+DAgTFnzpx46qmnoqysLGbOnBlVVfn/DXCdrbX19e3bN771rW9Ft27dYuTIkTFgwIAC\nTtp+EyZMiPr6+pgyZUpcvHgxFi5cmLf98/GSAJBYl33ZGQAKRXwBIDHxBYDExBcAEhNfAEhMfAEg\nMfEFgMTEFwAS+3/wQITBxOH0DQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb3fe87c518>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2LTtpUJEibjg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Pandas Exercise :\n",
        "\n",
        "\n",
        "#### import necessary modules"
      ]
    },
    {
      "metadata": {
        "id": "c3_UBbMRhiKx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tp-cTCyWi8mR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Load url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\" to a dataframe named wine_df\n",
        "\n",
        "This is a wine dataset\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "DMojQY3thrRi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "wine_df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BF9MMjoZjSlg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### print first five rows"
      ]
    },
    {
      "metadata": {
        "id": "1vSMQdnHjYNU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "bcff5bdc-957f-4477-8fb6-42ae96485fe5"
      },
      "cell_type": "code",
      "source": [
        "print(wine_df.head())"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   1  14.23  1.71  2.43  15.6  127   2.8  3.06   .28  2.29  5.64  1.04  3.92  \\\n",
            "0  1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28  4.38  1.05  3.40   \n",
            "1  1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81  5.68  1.03  3.17   \n",
            "2  1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18  7.80  0.86  3.45   \n",
            "3  1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82  4.32  1.04  2.93   \n",
            "4  1  14.20  1.76  2.45  15.2  112  3.27  3.39  0.34  1.97  6.75  1.05  2.85   \n",
            "\n",
            "   1065  \n",
            "0  1050  \n",
            "1  1185  \n",
            "2  1480  \n",
            "3   735  \n",
            "4  1450  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Tet6P2DvjY3T",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### assign wine_df to a different variable wine_df_copy and then delete all odd rows of wine_df_copy\n",
        "\n",
        "[Hint](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop.html)"
      ]
    },
    {
      "metadata": {
        "id": "CMj3qSdJjx0u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "f1d7229b-92dc-44da-f2c1-6107aa684373"
      },
      "cell_type": "code",
      "source": [
        "wine_df_copy = wine_df.iloc[::2]\n",
        "\n",
        "wine_df_copy.head()\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "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></th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Malic acid</th>\n",
              "      <th>Ash</th>\n",
              "      <th>Alcalinity of ash</th>\n",
              "      <th>Magnesium</th>\n",
              "      <th>Total phenols</th>\n",
              "      <th>Flavanoids</th>\n",
              "      <th>Nonflavanoid phenols</th>\n",
              "      <th>Proanthocyanins</th>\n",
              "      <th>Color intensity</th>\n",
              "      <th>Hue</th>\n",
              "      <th>OD280/OD315 of diluted wines</th>\n",
              "      <th>Proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>14.20</td>\n",
              "      <td>1.76</td>\n",
              "      <td>2.45</td>\n",
              "      <td>15.2</td>\n",
              "      <td>112</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.97</td>\n",
              "      <td>6.75</td>\n",
              "      <td>1.05</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>14.06</td>\n",
              "      <td>2.15</td>\n",
              "      <td>2.61</td>\n",
              "      <td>17.6</td>\n",
              "      <td>121</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.51</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.25</td>\n",
              "      <td>5.05</td>\n",
              "      <td>1.06</td>\n",
              "      <td>3.58</td>\n",
              "      <td>1295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>13.86</td>\n",
              "      <td>1.35</td>\n",
              "      <td>2.27</td>\n",
              "      <td>16.0</td>\n",
              "      <td>98</td>\n",
              "      <td>2.98</td>\n",
              "      <td>3.15</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.85</td>\n",
              "      <td>7.22</td>\n",
              "      <td>1.01</td>\n",
              "      <td>3.55</td>\n",
              "      <td>1045</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  Total phenols  \\\n",
              "0  1    13.20        1.78  2.14               11.2        100           2.65   \n",
              "2  1    14.37        1.95  2.50               16.8        113           3.85   \n",
              "4  1    14.20        1.76  2.45               15.2        112           3.27   \n",
              "6  1    14.06        2.15  2.61               17.6        121           2.60   \n",
              "8  1    13.86        1.35  2.27               16.0         98           2.98   \n",
              "\n",
              "   Flavanoids  Nonflavanoid phenols  Proanthocyanins  Color intensity   Hue  \\\n",
              "0        2.76                  0.26             1.28             4.38  1.05   \n",
              "2        3.49                  0.24             2.18             7.80  0.86   \n",
              "4        3.39                  0.34             1.97             6.75  1.05   \n",
              "6        2.51                  0.31             1.25             5.05  1.06   \n",
              "8        3.15                  0.22             1.85             7.22  1.01   \n",
              "\n",
              "   OD280/OD315 of diluted wines  Proline  \n",
              "0                          3.40     1050  \n",
              "2                          3.45     1480  \n",
              "4                          2.85     1450  \n",
              "6                          3.58     1295  \n",
              "8                          3.55     1045  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "o6Cs6T1Rjz71",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Assign the columns as below:\n",
        "\n",
        "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it):  \n",
        "1) Alcohol  \n",
        "2) Malic acid  \n",
        "3) Ash  \n",
        "4) Alcalinity of ash  \n",
        "5) Magnesium  \n",
        "6) Total phenols  \n",
        "7) Flavanoids  \n",
        "8) Nonflavanoid phenols  \n",
        "9) Proanthocyanins  \n",
        "10)Color intensity  \n",
        "11)Hue  \n",
        "12)OD280/OD315 of diluted wines  \n",
        "13)Proline "
      ]
    },
    {
      "metadata": {
        "id": "my8HB4V4j779",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "3716de85-4ea3-4aaa-de05-829869427e88"
      },
      "cell_type": "code",
      "source": [
        "wine_df.columns = [' ', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', 'Proline']\n",
        "\n",
        "wine_df.head(10)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\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></th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Malic acid</th>\n",
              "      <th>Ash</th>\n",
              "      <th>Alcalinity of ash</th>\n",
              "      <th>Magnesium</th>\n",
              "      <th>Total phenols</th>\n",
              "      <th>Flavanoids</th>\n",
              "      <th>Nonflavanoid phenols</th>\n",
              "      <th>Proanthocyanins</th>\n",
              "      <th>Color intensity</th>\n",
              "      <th>Hue</th>\n",
              "      <th>OD280/OD315 of diluted wines</th>\n",
              "      <th>Proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>14.20</td>\n",
              "      <td>1.76</td>\n",
              "      <td>2.45</td>\n",
              "      <td>15.2</td>\n",
              "      <td>112</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.97</td>\n",
              "      <td>6.75</td>\n",
              "      <td>1.05</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>14.39</td>\n",
              "      <td>1.87</td>\n",
              "      <td>2.45</td>\n",
              "      <td>14.6</td>\n",
              "      <td>96</td>\n",
              "      <td>2.50</td>\n",
              "      <td>2.52</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.98</td>\n",
              "      <td>5.25</td>\n",
              "      <td>1.02</td>\n",
              "      <td>3.58</td>\n",
              "      <td>1290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>14.06</td>\n",
              "      <td>2.15</td>\n",
              "      <td>2.61</td>\n",
              "      <td>17.6</td>\n",
              "      <td>121</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.51</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.25</td>\n",
              "      <td>5.05</td>\n",
              "      <td>1.06</td>\n",
              "      <td>3.58</td>\n",
              "      <td>1295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>14.83</td>\n",
              "      <td>1.64</td>\n",
              "      <td>2.17</td>\n",
              "      <td>14.0</td>\n",
              "      <td>97</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.98</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.98</td>\n",
              "      <td>5.20</td>\n",
              "      <td>1.08</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>13.86</td>\n",
              "      <td>1.35</td>\n",
              "      <td>2.27</td>\n",
              "      <td>16.0</td>\n",
              "      <td>98</td>\n",
              "      <td>2.98</td>\n",
              "      <td>3.15</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.85</td>\n",
              "      <td>7.22</td>\n",
              "      <td>1.01</td>\n",
              "      <td>3.55</td>\n",
              "      <td>1045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>14.10</td>\n",
              "      <td>2.16</td>\n",
              "      <td>2.30</td>\n",
              "      <td>18.0</td>\n",
              "      <td>105</td>\n",
              "      <td>2.95</td>\n",
              "      <td>3.32</td>\n",
              "      <td>0.22</td>\n",
              "      <td>2.38</td>\n",
              "      <td>5.75</td>\n",
              "      <td>1.25</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1510</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  Total phenols  \\\n",
              "0  1    13.20        1.78  2.14               11.2        100           2.65   \n",
              "1  1    13.16        2.36  2.67               18.6        101           2.80   \n",
              "2  1    14.37        1.95  2.50               16.8        113           3.85   \n",
              "3  1    13.24        2.59  2.87               21.0        118           2.80   \n",
              "4  1    14.20        1.76  2.45               15.2        112           3.27   \n",
              "5  1    14.39        1.87  2.45               14.6         96           2.50   \n",
              "6  1    14.06        2.15  2.61               17.6        121           2.60   \n",
              "7  1    14.83        1.64  2.17               14.0         97           2.80   \n",
              "8  1    13.86        1.35  2.27               16.0         98           2.98   \n",
              "9  1    14.10        2.16  2.30               18.0        105           2.95   \n",
              "\n",
              "   Flavanoids  Nonflavanoid phenols  Proanthocyanins  Color intensity   Hue  \\\n",
              "0        2.76                  0.26             1.28             4.38  1.05   \n",
              "1        3.24                  0.30             2.81             5.68  1.03   \n",
              "2        3.49                  0.24             2.18             7.80  0.86   \n",
              "3        2.69                  0.39             1.82             4.32  1.04   \n",
              "4        3.39                  0.34             1.97             6.75  1.05   \n",
              "5        2.52                  0.30             1.98             5.25  1.02   \n",
              "6        2.51                  0.31             1.25             5.05  1.06   \n",
              "7        2.98                  0.29             1.98             5.20  1.08   \n",
              "8        3.15                  0.22             1.85             7.22  1.01   \n",
              "9        3.32                  0.22             2.38             5.75  1.25   \n",
              "\n",
              "   OD280/OD315 of diluted wines  Proline  \n",
              "0                          3.40     1050  \n",
              "1                          3.17     1185  \n",
              "2                          3.45     1480  \n",
              "3                          2.93      735  \n",
              "4                          2.85     1450  \n",
              "5                          3.58     1290  \n",
              "6                          3.58     1295  \n",
              "7                          2.85     1045  \n",
              "8                          3.55     1045  \n",
              "9                          3.17     1510  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "Zqi7hwWpkNbH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Set the values of the first 3 rows from alcohol as NaN\n",
        "\n",
        "Hint- Use iloc to select 3 rows of wine_df"
      ]
    },
    {
      "metadata": {
        "id": "buyT4vX4kPMl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "ec7814fd-5805-462d-bde5-700a2284b3bf"
      },
      "cell_type": "code",
      "source": [
        "wine_df.loc[:2, 'Alcohol'] = np.NaN\n",
        "wine_df.head()\n"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Malic acid</th>\n",
              "      <th>Ash</th>\n",
              "      <th>Alcalinity of ash</th>\n",
              "      <th>Magnesium</th>\n",
              "      <th>Total phenols</th>\n",
              "      <th>Flavanoids</th>\n",
              "      <th>Nonflavanoid phenols</th>\n",
              "      <th>Proanthocyanins</th>\n",
              "      <th>Color intensity</th>\n",
              "      <th>Hue</th>\n",
              "      <th>OD280/OD315 of diluted wines</th>\n",
              "      <th>Proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>14.20</td>\n",
              "      <td>1.76</td>\n",
              "      <td>2.45</td>\n",
              "      <td>15.2</td>\n",
              "      <td>112</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.97</td>\n",
              "      <td>6.75</td>\n",
              "      <td>1.05</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1450</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  Total phenols  \\\n",
              "0  1      NaN        1.78  2.14               11.2        100           2.65   \n",
              "1  1      NaN        2.36  2.67               18.6        101           2.80   \n",
              "2  1      NaN        1.95  2.50               16.8        113           3.85   \n",
              "3  1    13.24        2.59  2.87               21.0        118           2.80   \n",
              "4  1    14.20        1.76  2.45               15.2        112           3.27   \n",
              "\n",
              "   Flavanoids  Nonflavanoid phenols  Proanthocyanins  Color intensity   Hue  \\\n",
              "0        2.76                  0.26             1.28             4.38  1.05   \n",
              "1        3.24                  0.30             2.81             5.68  1.03   \n",
              "2        3.49                  0.24             2.18             7.80  0.86   \n",
              "3        2.69                  0.39             1.82             4.32  1.04   \n",
              "4        3.39                  0.34             1.97             6.75  1.05   \n",
              "\n",
              "   OD280/OD315 of diluted wines  Proline  \n",
              "0                          3.40     1050  \n",
              "1                          3.17     1185  \n",
              "2                          3.45     1480  \n",
              "3                          2.93      735  \n",
              "4                          2.85     1450  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "RQMNI2UHkP3o",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Create an array of 10 random numbers uptill 10 and assign it to a  variable named `random`"
      ]
    },
    {
      "metadata": {
        "id": "xunmCjaEmDwZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import random\n",
        "arr = random.sample(range(0, 10), 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hELUakyXmFSu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Use random numbers you generated as an index and assign NaN value to each of cell of the column alcohol"
      ]
    },
    {
      "metadata": {
        "id": "zMgaNnNHmP01",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "04045d1b-d45a-4655-c41b-f1648efba5ed"
      },
      "cell_type": "code",
      "source": [
        "wine_df.loc[arr, 'Alcohol'] = np.NaN\n",
        "wine_df.head(15)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "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></th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Malic acid</th>\n",
              "      <th>Ash</th>\n",
              "      <th>Alcalinity of ash</th>\n",
              "      <th>Magnesium</th>\n",
              "      <th>Total phenols</th>\n",
              "      <th>Flavanoids</th>\n",
              "      <th>Nonflavanoid phenols</th>\n",
              "      <th>Proanthocyanins</th>\n",
              "      <th>Color intensity</th>\n",
              "      <th>Hue</th>\n",
              "      <th>OD280/OD315 of diluted wines</th>\n",
              "      <th>Proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.76</td>\n",
              "      <td>2.45</td>\n",
              "      <td>15.2</td>\n",
              "      <td>112</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.97</td>\n",
              "      <td>6.75</td>\n",
              "      <td>1.05</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.87</td>\n",
              "      <td>2.45</td>\n",
              "      <td>14.6</td>\n",
              "      <td>96</td>\n",
              "      <td>2.50</td>\n",
              "      <td>2.52</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.98</td>\n",
              "      <td>5.25</td>\n",
              "      <td>1.02</td>\n",
              "      <td>3.58</td>\n",
              "      <td>1290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.15</td>\n",
              "      <td>2.61</td>\n",
              "      <td>17.6</td>\n",
              "      <td>121</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.51</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.25</td>\n",
              "      <td>5.05</td>\n",
              "      <td>1.06</td>\n",
              "      <td>3.58</td>\n",
              "      <td>1295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.64</td>\n",
              "      <td>2.17</td>\n",
              "      <td>14.0</td>\n",
              "      <td>97</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.98</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.98</td>\n",
              "      <td>5.20</td>\n",
              "      <td>1.08</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.35</td>\n",
              "      <td>2.27</td>\n",
              "      <td>16.0</td>\n",
              "      <td>98</td>\n",
              "      <td>2.98</td>\n",
              "      <td>3.15</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.85</td>\n",
              "      <td>7.22</td>\n",
              "      <td>1.01</td>\n",
              "      <td>3.55</td>\n",
              "      <td>1045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.16</td>\n",
              "      <td>2.30</td>\n",
              "      <td>18.0</td>\n",
              "      <td>105</td>\n",
              "      <td>2.95</td>\n",
              "      <td>3.32</td>\n",
              "      <td>0.22</td>\n",
              "      <td>2.38</td>\n",
              "      <td>5.75</td>\n",
              "      <td>1.25</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>14.12</td>\n",
              "      <td>1.48</td>\n",
              "      <td>2.32</td>\n",
              "      <td>16.8</td>\n",
              "      <td>95</td>\n",
              "      <td>2.20</td>\n",
              "      <td>2.43</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.57</td>\n",
              "      <td>5.00</td>\n",
              "      <td>1.17</td>\n",
              "      <td>2.82</td>\n",
              "      <td>1280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>13.75</td>\n",
              "      <td>1.73</td>\n",
              "      <td>2.41</td>\n",
              "      <td>16.0</td>\n",
              "      <td>89</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.81</td>\n",
              "      <td>5.60</td>\n",
              "      <td>1.15</td>\n",
              "      <td>2.90</td>\n",
              "      <td>1320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>14.75</td>\n",
              "      <td>1.73</td>\n",
              "      <td>2.39</td>\n",
              "      <td>11.4</td>\n",
              "      <td>91</td>\n",
              "      <td>3.10</td>\n",
              "      <td>3.69</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.40</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2.73</td>\n",
              "      <td>1150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>14.38</td>\n",
              "      <td>1.87</td>\n",
              "      <td>2.38</td>\n",
              "      <td>12.0</td>\n",
              "      <td>102</td>\n",
              "      <td>3.30</td>\n",
              "      <td>3.64</td>\n",
              "      <td>0.29</td>\n",
              "      <td>2.96</td>\n",
              "      <td>7.50</td>\n",
              "      <td>1.20</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>13.63</td>\n",
              "      <td>1.81</td>\n",
              "      <td>2.70</td>\n",
              "      <td>17.2</td>\n",
              "      <td>112</td>\n",
              "      <td>2.85</td>\n",
              "      <td>2.91</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.46</td>\n",
              "      <td>7.30</td>\n",
              "      <td>1.28</td>\n",
              "      <td>2.88</td>\n",
              "      <td>1310</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  Total phenols  \\\n",
              "0   1      NaN        1.78  2.14               11.2        100           2.65   \n",
              "1   1      NaN        2.36  2.67               18.6        101           2.80   \n",
              "2   1      NaN        1.95  2.50               16.8        113           3.85   \n",
              "3   1      NaN        2.59  2.87               21.0        118           2.80   \n",
              "4   1      NaN        1.76  2.45               15.2        112           3.27   \n",
              "5   1      NaN        1.87  2.45               14.6         96           2.50   \n",
              "6   1      NaN        2.15  2.61               17.6        121           2.60   \n",
              "7   1      NaN        1.64  2.17               14.0         97           2.80   \n",
              "8   1      NaN        1.35  2.27               16.0         98           2.98   \n",
              "9   1      NaN        2.16  2.30               18.0        105           2.95   \n",
              "10  1    14.12        1.48  2.32               16.8         95           2.20   \n",
              "11  1    13.75        1.73  2.41               16.0         89           2.60   \n",
              "12  1    14.75        1.73  2.39               11.4         91           3.10   \n",
              "13  1    14.38        1.87  2.38               12.0        102           3.30   \n",
              "14  1    13.63        1.81  2.70               17.2        112           2.85   \n",
              "\n",
              "    Flavanoids  Nonflavanoid phenols  Proanthocyanins  Color intensity   Hue  \\\n",
              "0         2.76                  0.26             1.28             4.38  1.05   \n",
              "1         3.24                  0.30             2.81             5.68  1.03   \n",
              "2         3.49                  0.24             2.18             7.80  0.86   \n",
              "3         2.69                  0.39             1.82             4.32  1.04   \n",
              "4         3.39                  0.34             1.97             6.75  1.05   \n",
              "5         2.52                  0.30             1.98             5.25  1.02   \n",
              "6         2.51                  0.31             1.25             5.05  1.06   \n",
              "7         2.98                  0.29             1.98             5.20  1.08   \n",
              "8         3.15                  0.22             1.85             7.22  1.01   \n",
              "9         3.32                  0.22             2.38             5.75  1.25   \n",
              "10        2.43                  0.26             1.57             5.00  1.17   \n",
              "11        2.76                  0.29             1.81             5.60  1.15   \n",
              "12        3.69                  0.43             2.81             5.40  1.25   \n",
              "13        3.64                  0.29             2.96             7.50  1.20   \n",
              "14        2.91                  0.30             1.46             7.30  1.28   \n",
              "\n",
              "    OD280/OD315 of diluted wines  Proline  \n",
              "0                           3.40     1050  \n",
              "1                           3.17     1185  \n",
              "2                           3.45     1480  \n",
              "3                           2.93      735  \n",
              "4                           2.85     1450  \n",
              "5                           3.58     1290  \n",
              "6                           3.58     1295  \n",
              "7                           2.85     1045  \n",
              "8                           3.55     1045  \n",
              "9                           3.17     1510  \n",
              "10                          2.82     1280  \n",
              "11                          2.90     1320  \n",
              "12                          2.73     1150  \n",
              "13                          3.00     1547  \n",
              "14                          2.88     1310  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "metadata": {
        "id": "PHyK_vRsmRwV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "####  How many missing values do we have? \n",
        "\n",
        "Hint: you can use isnull() and sum()"
      ]
    },
    {
      "metadata": {
        "id": "EnOYhmEqmfKp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "767ffaf7-7758-4394-9b18-8b240297c6d8"
      },
      "cell_type": "code",
      "source": [
        "wine_df.isnull().sum()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 0\n",
              "Alcohol                         10\n",
              "Malic acid                       0\n",
              "Ash                              0\n",
              "Alcalinity of ash                0\n",
              "Magnesium                        0\n",
              "Total phenols                    0\n",
              "Flavanoids                       0\n",
              "Nonflavanoid phenols             0\n",
              "Proanthocyanins                  0\n",
              "Color intensity                  0\n",
              "Hue                              0\n",
              "OD280/OD315 of diluted wines     0\n",
              "Proline                          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "-Fd4WBklmf1_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Delete the rows that contain missing values "
      ]
    },
    {
      "metadata": {
        "id": "As7IC6Ktms8-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "42fd938a-aaf9-421a-c574-b1ae84862997"
      },
      "cell_type": "code",
      "source": [
        "wine_df.dropna(inplace=True)\n",
        "wine_df.head(15)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "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></th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Malic acid</th>\n",
              "      <th>Ash</th>\n",
              "      <th>Alcalinity of ash</th>\n",
              "      <th>Magnesium</th>\n",
              "      <th>Total phenols</th>\n",
              "      <th>Flavanoids</th>\n",
              "      <th>Nonflavanoid phenols</th>\n",
              "      <th>Proanthocyanins</th>\n",
              "      <th>Color intensity</th>\n",
              "      <th>Hue</th>\n",
              "      <th>OD280/OD315 of diluted wines</th>\n",
              "      <th>Proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>14.12</td>\n",
              "      <td>1.48</td>\n",
              "      <td>2.32</td>\n",
              "      <td>16.8</td>\n",
              "      <td>95</td>\n",
              "      <td>2.20</td>\n",
              "      <td>2.43</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.57</td>\n",
              "      <td>5.00</td>\n",
              "      <td>1.17</td>\n",
              "      <td>2.82</td>\n",
              "      <td>1280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>13.75</td>\n",
              "      <td>1.73</td>\n",
              "      <td>2.41</td>\n",
              "      <td>16.0</td>\n",
              "      <td>89</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.81</td>\n",
              "      <td>5.60</td>\n",
              "      <td>1.15</td>\n",
              "      <td>2.90</td>\n",
              "      <td>1320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>14.75</td>\n",
              "      <td>1.73</td>\n",
              "      <td>2.39</td>\n",
              "      <td>11.4</td>\n",
              "      <td>91</td>\n",
              "      <td>3.10</td>\n",
              "      <td>3.69</td>\n",
              "      <td>0.43</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.40</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2.73</td>\n",
              "      <td>1150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>14.38</td>\n",
              "      <td>1.87</td>\n",
              "      <td>2.38</td>\n",
              "      <td>12.0</td>\n",
              "      <td>102</td>\n",
              "      <td>3.30</td>\n",
              "      <td>3.64</td>\n",
              "      <td>0.29</td>\n",
              "      <td>2.96</td>\n",
              "      <td>7.50</td>\n",
              "      <td>1.20</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>13.63</td>\n",
              "      <td>1.81</td>\n",
              "      <td>2.70</td>\n",
              "      <td>17.2</td>\n",
              "      <td>112</td>\n",
              "      <td>2.85</td>\n",
              "      <td>2.91</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.46</td>\n",
              "      <td>7.30</td>\n",
              "      <td>1.28</td>\n",
              "      <td>2.88</td>\n",
              "      <td>1310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>14.30</td>\n",
              "      <td>1.92</td>\n",
              "      <td>2.72</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.14</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.97</td>\n",
              "      <td>6.20</td>\n",
              "      <td>1.07</td>\n",
              "      <td>2.65</td>\n",
              "      <td>1280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1</td>\n",
              "      <td>13.83</td>\n",
              "      <td>1.57</td>\n",
              "      <td>2.62</td>\n",
              "      <td>20.0</td>\n",
              "      <td>115</td>\n",
              "      <td>2.95</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.72</td>\n",
              "      <td>6.60</td>\n",
              "      <td>1.13</td>\n",
              "      <td>2.57</td>\n",
              "      <td>1130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>14.19</td>\n",
              "      <td>1.59</td>\n",
              "      <td>2.48</td>\n",
              "      <td>16.5</td>\n",
              "      <td>108</td>\n",
              "      <td>3.30</td>\n",
              "      <td>3.93</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1.86</td>\n",
              "      <td>8.70</td>\n",
              "      <td>1.23</td>\n",
              "      <td>2.82</td>\n",
              "      <td>1680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1</td>\n",
              "      <td>13.64</td>\n",
              "      <td>3.10</td>\n",
              "      <td>2.56</td>\n",
              "      <td>15.2</td>\n",
              "      <td>116</td>\n",
              "      <td>2.70</td>\n",
              "      <td>3.03</td>\n",
              "      <td>0.17</td>\n",
              "      <td>1.66</td>\n",
              "      <td>5.10</td>\n",
              "      <td>0.96</td>\n",
              "      <td>3.36</td>\n",
              "      <td>845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>14.06</td>\n",
              "      <td>1.63</td>\n",
              "      <td>2.28</td>\n",
              "      <td>16.0</td>\n",
              "      <td>126</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.17</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.10</td>\n",
              "      <td>5.65</td>\n",
              "      <td>1.09</td>\n",
              "      <td>3.71</td>\n",
              "      <td>780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "      <td>12.93</td>\n",
              "      <td>3.80</td>\n",
              "      <td>2.65</td>\n",
              "      <td>18.6</td>\n",
              "      <td>102</td>\n",
              "      <td>2.41</td>\n",
              "      <td>2.41</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.98</td>\n",
              "      <td>4.50</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.52</td>\n",
              "      <td>770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1</td>\n",
              "      <td>13.71</td>\n",
              "      <td>1.86</td>\n",
              "      <td>2.36</td>\n",
              "      <td>16.6</td>\n",
              "      <td>101</td>\n",
              "      <td>2.61</td>\n",
              "      <td>2.88</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.69</td>\n",
              "      <td>3.80</td>\n",
              "      <td>1.11</td>\n",
              "      <td>4.00</td>\n",
              "      <td>1035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1</td>\n",
              "      <td>12.85</td>\n",
              "      <td>1.60</td>\n",
              "      <td>2.52</td>\n",
              "      <td>17.8</td>\n",
              "      <td>95</td>\n",
              "      <td>2.48</td>\n",
              "      <td>2.37</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.46</td>\n",
              "      <td>3.93</td>\n",
              "      <td>1.09</td>\n",
              "      <td>3.63</td>\n",
              "      <td>1015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1</td>\n",
              "      <td>13.50</td>\n",
              "      <td>1.81</td>\n",
              "      <td>2.61</td>\n",
              "      <td>20.0</td>\n",
              "      <td>96</td>\n",
              "      <td>2.53</td>\n",
              "      <td>2.61</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.66</td>\n",
              "      <td>3.52</td>\n",
              "      <td>1.12</td>\n",
              "      <td>3.82</td>\n",
              "      <td>845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1</td>\n",
              "      <td>13.05</td>\n",
              "      <td>2.05</td>\n",
              "      <td>3.22</td>\n",
              "      <td>25.0</td>\n",
              "      <td>124</td>\n",
              "      <td>2.63</td>\n",
              "      <td>2.68</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.58</td>\n",
              "      <td>1.13</td>\n",
              "      <td>3.20</td>\n",
              "      <td>830</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  Total phenols  \\\n",
              "10  1    14.12        1.48  2.32               16.8         95           2.20   \n",
              "11  1    13.75        1.73  2.41               16.0         89           2.60   \n",
              "12  1    14.75        1.73  2.39               11.4         91           3.10   \n",
              "13  1    14.38        1.87  2.38               12.0        102           3.30   \n",
              "14  1    13.63        1.81  2.70               17.2        112           2.85   \n",
              "15  1    14.30        1.92  2.72               20.0        120           2.80   \n",
              "16  1    13.83        1.57  2.62               20.0        115           2.95   \n",
              "17  1    14.19        1.59  2.48               16.5        108           3.30   \n",
              "18  1    13.64        3.10  2.56               15.2        116           2.70   \n",
              "19  1    14.06        1.63  2.28               16.0        126           3.00   \n",
              "20  1    12.93        3.80  2.65               18.6        102           2.41   \n",
              "21  1    13.71        1.86  2.36               16.6        101           2.61   \n",
              "22  1    12.85        1.60  2.52               17.8         95           2.48   \n",
              "23  1    13.50        1.81  2.61               20.0         96           2.53   \n",
              "24  1    13.05        2.05  3.22               25.0        124           2.63   \n",
              "\n",
              "    Flavanoids  Nonflavanoid phenols  Proanthocyanins  Color intensity   Hue  \\\n",
              "10        2.43                  0.26             1.57             5.00  1.17   \n",
              "11        2.76                  0.29             1.81             5.60  1.15   \n",
              "12        3.69                  0.43             2.81             5.40  1.25   \n",
              "13        3.64                  0.29             2.96             7.50  1.20   \n",
              "14        2.91                  0.30             1.46             7.30  1.28   \n",
              "15        3.14                  0.33             1.97             6.20  1.07   \n",
              "16        3.40                  0.40             1.72             6.60  1.13   \n",
              "17        3.93                  0.32             1.86             8.70  1.23   \n",
              "18        3.03                  0.17             1.66             5.10  0.96   \n",
              "19        3.17                  0.24             2.10             5.65  1.09   \n",
              "20        2.41                  0.25             1.98             4.50  1.03   \n",
              "21        2.88                  0.27             1.69             3.80  1.11   \n",
              "22        2.37                  0.26             1.46             3.93  1.09   \n",
              "23        2.61                  0.28             1.66             3.52  1.12   \n",
              "24        2.68                  0.47             1.92             3.58  1.13   \n",
              "\n",
              "    OD280/OD315 of diluted wines  Proline  \n",
              "10                          2.82     1280  \n",
              "11                          2.90     1320  \n",
              "12                          2.73     1150  \n",
              "13                          3.00     1547  \n",
              "14                          2.88     1310  \n",
              "15                          2.65     1280  \n",
              "16                          2.57     1130  \n",
              "17                          2.82     1680  \n",
              "18                          3.36      845  \n",
              "19                          3.71      780  \n",
              "20                          3.52      770  \n",
              "21                          4.00     1035  \n",
              "22                          3.63     1015  \n",
              "23                          3.82      845  \n",
              "24                          3.20      830  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "metadata": {
        "id": "DlpG8drhmz7W",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### BONUS: Play with the data set below"
      ]
    },
    {
      "metadata": {
        "id": "mD40T0Cnm5SA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "bb940ea4-50e4-474d-edb2-7d62f72d6920"
      },
      "cell_type": "code",
      "source": [
        "wine_df.describe()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "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></th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Malic acid</th>\n",
              "      <th>Ash</th>\n",
              "      <th>Alcalinity of ash</th>\n",
              "      <th>Magnesium</th>\n",
              "      <th>Total phenols</th>\n",
              "      <th>Flavanoids</th>\n",
              "      <th>Nonflavanoid phenols</th>\n",
              "      <th>Proanthocyanins</th>\n",
              "      <th>Color intensity</th>\n",
              "      <th>Hue</th>\n",
              "      <th>OD280/OD315 of diluted wines</th>\n",
              "      <th>Proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>167.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>12.936946</td>\n",
              "      <td>2.362575</td>\n",
              "      <td>2.361557</td>\n",
              "      <td>19.709581</td>\n",
              "      <td>99.197605</td>\n",
              "      <td>2.254671</td>\n",
              "      <td>1.964671</td>\n",
              "      <td>0.366826</td>\n",
              "      <td>1.565210</td>\n",
              "      <td>5.013772</td>\n",
              "      <td>0.951713</td>\n",
              "      <td>2.565449</td>\n",
              "      <td>717.347305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.760469</td>\n",
              "      <td>0.786493</td>\n",
              "      <td>1.145433</td>\n",
              "      <td>0.277395</td>\n",
              "      <td>3.275856</td>\n",
              "      <td>14.342724</td>\n",
              "      <td>0.618536</td>\n",
              "      <td>0.994313</td>\n",
              "      <td>0.126310</td>\n",
              "      <td>0.571145</td>\n",
              "      <td>2.371555</td>\n",
              "      <td>0.233859</td>\n",
              "      <td>0.703846</td>\n",
              "      <td>296.915081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.030000</td>\n",
              "      <td>0.740000</td>\n",
              "      <td>1.360000</td>\n",
              "      <td>10.600000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>1.280000</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>1.270000</td>\n",
              "      <td>278.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.330000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>2.210000</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>1.090000</td>\n",
              "      <td>0.270000</td>\n",
              "      <td>1.185000</td>\n",
              "      <td>3.065000</td>\n",
              "      <td>0.765000</td>\n",
              "      <td>1.845000</td>\n",
              "      <td>495.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>12.930000</td>\n",
              "      <td>1.860000</td>\n",
              "      <td>2.360000</td>\n",
              "      <td>19.500000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>2.030000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>1.530000</td>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>2.750000</td>\n",
              "      <td>650.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>13.580000</td>\n",
              "      <td>3.170000</td>\n",
              "      <td>2.555000</td>\n",
              "      <td>21.500000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>2.770000</td>\n",
              "      <td>0.450000</td>\n",
              "      <td>1.885000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>1.120000</td>\n",
              "      <td>3.135000</td>\n",
              "      <td>882.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>14.750000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.230000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>162.000000</td>\n",
              "      <td>3.880000</td>\n",
              "      <td>5.080000</td>\n",
              "      <td>0.660000</td>\n",
              "      <td>3.580000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.710000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1680.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Alcohol  Malic acid         Ash  Alcalinity of ash  \\\n",
              "count  167.000000  167.000000  167.000000  167.000000         167.000000   \n",
              "mean     2.000000   12.936946    2.362575    2.361557          19.709581   \n",
              "std      0.760469    0.786493    1.145433    0.277395           3.275856   \n",
              "min      1.000000   11.030000    0.740000    1.360000          10.600000   \n",
              "25%      1.000000   12.330000    1.580000    2.210000          17.500000   \n",
              "50%      2.000000   12.930000    1.860000    2.360000          19.500000   \n",
              "75%      3.000000   13.580000    3.170000    2.555000          21.500000   \n",
              "max      3.000000   14.750000    5.800000    3.230000          30.000000   \n",
              "\n",
              "        Magnesium  Total phenols  Flavanoids  Nonflavanoid phenols  \\\n",
              "count  167.000000     167.000000  167.000000            167.000000   \n",
              "mean    99.197605       2.254671    1.964671              0.366826   \n",
              "std     14.342724       0.618536    0.994313              0.126310   \n",
              "min     70.000000       0.980000    0.340000              0.130000   \n",
              "25%     88.000000       1.700000    1.090000              0.270000   \n",
              "50%     97.000000       2.200000    2.030000              0.340000   \n",
              "75%    107.000000       2.740000    2.770000              0.450000   \n",
              "max    162.000000       3.880000    5.080000              0.660000   \n",
              "\n",
              "       Proanthocyanins  Color intensity         Hue  \\\n",
              "count       167.000000       167.000000  167.000000   \n",
              "mean          1.565210         5.013772    0.951713   \n",
              "std           0.571145         2.371555    0.233859   \n",
              "min           0.410000         1.280000    0.480000   \n",
              "25%           1.185000         3.065000    0.765000   \n",
              "50%           1.530000         4.600000    0.960000   \n",
              "75%           1.885000         6.200000    1.120000   \n",
              "max           3.580000        13.000000    1.710000   \n",
              "\n",
              "       OD280/OD315 of diluted wines      Proline  \n",
              "count                    167.000000   167.000000  \n",
              "mean                       2.565449   717.347305  \n",
              "std                        0.703846   296.915081  \n",
              "min                        1.270000   278.000000  \n",
              "25%                        1.845000   495.000000  \n",
              "50%                        2.750000   650.000000  \n",
              "75%                        3.135000   882.500000  \n",
              "max                        4.000000  1680.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "id": "0kskZlnc3AOf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Bonus :\n",
        "            SORTING THE DATA SET  ACCORDING THEIR Alcohol value and Alcalinity of ash**"
      ]
    },
    {
      "metadata": {
        "id": "ggiVqoBp27sz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "206427fd-2616-4c08-9d75-900c1b2970aa"
      },
      "cell_type": "code",
      "source": [
        "data_sorted = wine_df.sort_values(['Alcohol','Alcalinity of ash'], ascending=False)\n",
        "data_sorted[['Alcohol','Alcalinity of ash']].head(10)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Alcohol</th>\n",
              "      <th>Alcalinity of ash</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>14.75</td>\n",
              "      <td>11.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>14.38</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14.38</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>14.34</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>14.30</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>14.22</td>\n",
              "      <td>16.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>14.22</td>\n",
              "      <td>13.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>14.21</td>\n",
              "      <td>18.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>14.19</td>\n",
              "      <td>16.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>14.16</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Alcohol  Alcalinity of ash\n",
              "12     14.75               11.4\n",
              "45     14.38               16.0\n",
              "13     14.38               12.0\n",
              "157    14.34               25.0\n",
              "15     14.30               20.0\n",
              "55     14.22               16.3\n",
              "38     14.22               13.2\n",
              "44     14.21               18.9\n",
              "17     14.19               16.5\n",
              "171    14.16               20.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    }
  ]
}