HW_07_More_Pandas.ipynb

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        "<a href=\"https://colab.research.google.com/github/KCM2005/Kalenga-Mumba/blob/main/HW_07_More_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#Part 1: ride_sharing.csv\n",
        "import pandas as pd"
      ]
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      "source": [
        "df = pd.read_csv('ride_sharing.csv')\n",
        "df"
      ],
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              "         duration  station_A_id  \\\n",
              "0      12 minutes            81   \n",
              "1      24 minutes             3   \n",
              "2       8 minutes            67   \n",
              "3       4 minutes            16   \n",
              "4      11 minutes            22   \n",
              "...           ...           ...   \n",
              "25755  11 minutes            15   \n",
              "25756  10 minutes            15   \n",
              "25757  14 minutes            15   \n",
              "25758  14 minutes            15   \n",
              "25759  29 minutes            16   \n",
              "\n",
              "                                          station_A_name  station_B_id  \\\n",
              "0                                     Berry St at 4th St           323   \n",
              "1           Powell St BART Station (Market St at 4th St)           118   \n",
              "2      San Francisco Caltrain Station 2  (Townsend St...            23   \n",
              "3                                Steuart St at Market St            28   \n",
              "4                                  Howard St at Beale St           350   \n",
              "...                                                  ...           ...   \n",
              "25755  San Francisco Ferry Building (Harry Bridges Pl...            34   \n",
              "25756  San Francisco Ferry Building (Harry Bridges Pl...            34   \n",
              "25757  San Francisco Ferry Building (Harry Bridges Pl...            42   \n",
              "25758  San Francisco Ferry Building (Harry Bridges Pl...            42   \n",
              "25759                            Steuart St at Market St           115   \n",
              "\n",
              "                                      station_B_name  bike_id  user_type  \\\n",
              "0                                 Broadway at Kearny     5480          2   \n",
              "1                    Eureka Valley Recreation Center     5193          2   \n",
              "2                      The Embarcadero at Steuart St     3652          3   \n",
              "3                       The Embarcadero at Bryant St     1883          1   \n",
              "4                               8th St at Brannan St     4626          2   \n",
              "...                                              ...      ...        ...   \n",
              "25755                 Father Alfred E Boeddeker Park     5063          1   \n",
              "25756                 Father Alfred E Boeddeker Park     5411          2   \n",
              "25757  San Francisco City Hall (Polk St at Grove St)     5157          2   \n",
              "25758  San Francisco City Hall (Polk St at Grove St)     4438          2   \n",
              "25759                             Jackson Playground     1705          3   \n",
              "\n",
              "       user_birth_year user_gender  tire_size  \n",
              "0                 1959        Male         26  \n",
              "1                 1965        Male         27  \n",
              "2                 1993        Male         29  \n",
              "3                 1979        Male         26  \n",
              "4                 1994        Male         27  \n",
              "...                ...         ...        ...  \n",
              "25755             2000        Male         26  \n",
              "25756             1998        Male         26  \n",
              "25757             1995        Male         26  \n",
              "25758             1995        Male         27  \n",
              "25759             1990        Male         27  \n",
              "\n",
              "[25760 rows x 10 columns]"
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              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
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              "      const buttonEl =\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 25760,\n  \"fields\": [\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 172,\n        \"samples\": [\n          \"103 minutes\",\n          \"142 minutes\",\n          \"180 minutes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"station_A_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 3,\n        \"max\": 81,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          21,\n          3,\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"station_A_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Montgomery St BART Station (Market St at 2nd St)\",\n          \"Powell St BART Station (Market St at 4th St)\",\n          \"San Francisco Caltrain (Townsend St at 4th St)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"station_B_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105,\n        \"min\": 3,\n        \"max\": 383,\n        \"num_unique_values\": 152,\n        \"samples\": [\n          37,\n          147,\n          375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"station_B_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 152,\n        \"samples\": [\n          \"2nd St at Folsom St\",\n          \"29th St at Tiffany Ave\",\n          \"Grove St at Masonic Ave\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bike_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1576,\n        \"min\": 11,\n        \"max\": 6638,\n        \"num_unique_values\": 1805,\n        \"samples\": [\n          861,\n          465,\n          1768\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_birth_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 1901,\n        \"max\": 2001,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          1939,\n          1901,\n          1959\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Male\",\n          \"Female\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tire_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 26,\n        \"max\": 29,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          26,\n          27,\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rows = df.shape[0]\n",
        "print(\"There are\",rows, \"rows\")"
      ],
      "metadata": {
        "id": "TonxSfGsx-An",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "875fa8e7-b721-41f7-9ea8-93ebec8cb2ea"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 25760 rows\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols = df.shape[1]\n",
        "print(\"There are\",cols, \"columns\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PaV9omxNWWAl",
        "outputId": "9ff017b3-c61b-47b9-ca5c-358a1f342a89"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 10 columns\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "duration_column = df.duration\n",
        "duration_column = duration_column.astype(int)\n",
        "df[\"duration\"] = duration_column"
      ],
      "metadata": {
        "id": "7kG8-nc1dcs2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "id": "t5jZhRDKegUj",
        "outputId": "283adf14-97af-4b52-f7a7-5329a235411d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       duration  station_A_id  \\\n",
              "0            12            81   \n",
              "1            24             3   \n",
              "2             8            67   \n",
              "3             4            16   \n",
              "4            11            22   \n",
              "...         ...           ...   \n",
              "25755        11            15   \n",
              "25756        10            15   \n",
              "25757        14            15   \n",
              "25758        14            15   \n",
              "25759        29            16   \n",
              "\n",
              "                                          station_A_name  station_B_id  \\\n",
              "0                                     Berry St at 4th St           323   \n",
              "1           Powell St BART Station (Market St at 4th St)           118   \n",
              "2      San Francisco Caltrain Station 2  (Townsend St...            23   \n",
              "3                                Steuart St at Market St            28   \n",
              "4                                  Howard St at Beale St           350   \n",
              "...                                                  ...           ...   \n",
              "25755  San Francisco Ferry Building (Harry Bridges Pl...            34   \n",
              "25756  San Francisco Ferry Building (Harry Bridges Pl...            34   \n",
              "25757  San Francisco Ferry Building (Harry Bridges Pl...            42   \n",
              "25758  San Francisco Ferry Building (Harry Bridges Pl...            42   \n",
              "25759                            Steuart St at Market St           115   \n",
              "\n",
              "                                      station_B_name  bike_id  user_type  \\\n",
              "0                                 Broadway at Kearny     5480          2   \n",
              "1                    Eureka Valley Recreation Center     5193          2   \n",
              "2                      The Embarcadero at Steuart St     3652          3   \n",
              "3                       The Embarcadero at Bryant St     1883          1   \n",
              "4                               8th St at Brannan St     4626          2   \n",
              "...                                              ...      ...        ...   \n",
              "25755                 Father Alfred E Boeddeker Park     5063          1   \n",
              "25756                 Father Alfred E Boeddeker Park     5411          2   \n",
              "25757  San Francisco City Hall (Polk St at Grove St)     5157          2   \n",
              "25758  San Francisco City Hall (Polk St at Grove St)     4438          2   \n",
              "25759                             Jackson Playground     1705          3   \n",
              "\n",
              "       user_birth_year user_gender  tire_size  \n",
              "0                 1959        Male         26  \n",
              "1                 1965        Male         27  \n",
              "2                 1993        Male         29  \n",
              "3                 1979        Male         26  \n",
              "4                 1994        Male         27  \n",
              "...                ...         ...        ...  \n",
              "25755             2000        Male         26  \n",
              "25756             1998        Male         26  \n",
              "25757             1995        Male         26  \n",
              "25758             1995        Male         27  \n",
              "25759             1990        Male         27  \n",
              "\n",
              "[25760 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-01cdb5e0-a297-4e29-88e3-dba6123570c2\" class=\"colab-df-container\">\n",
              "    <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",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>station_A_id</th>\n",
              "      <th>station_A_name</th>\n",
              "      <th>station_B_id</th>\n",
              "      <th>station_B_name</th>\n",
              "      <th>bike_id</th>\n",
              "      <th>user_type</th>\n",
              "      <th>user_birth_year</th>\n",
              "      <th>user_gender</th>\n",
              "      <th>tire_size</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>81</td>\n",
              "      <td>Berry St at 4th St</td>\n",
              "      <td>323</td>\n",
              "      <td>Broadway at Kearny</td>\n",
              "      <td>5480</td>\n",
              "      <td>2</td>\n",
              "      <td>1959</td>\n",
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              "      <td>26</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
              "      <td>118</td>\n",
              "      <td>Eureka Valley Recreation Center</td>\n",
              "      <td>5193</td>\n",
              "      <td>2</td>\n",
              "      <td>1965</td>\n",
              "      <td>Male</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>67</td>\n",
              "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
              "      <td>23</td>\n",
              "      <td>The Embarcadero at Steuart St</td>\n",
              "      <td>3652</td>\n",
              "      <td>3</td>\n",
              "      <td>1993</td>\n",
              "      <td>Male</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>Steuart St at Market St</td>\n",
              "      <td>28</td>\n",
              "      <td>The Embarcadero at Bryant St</td>\n",
              "      <td>1883</td>\n",
              "      <td>1</td>\n",
              "      <td>1979</td>\n",
              "      <td>Male</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>22</td>\n",
              "      <td>Howard St at Beale St</td>\n",
              "      <td>350</td>\n",
              "      <td>8th St at Brannan St</td>\n",
              "      <td>4626</td>\n",
              "      <td>2</td>\n",
              "      <td>1994</td>\n",
              "      <td>Male</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25755</th>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
              "      <td>34</td>\n",
              "      <td>Father Alfred E Boeddeker Park</td>\n",
              "      <td>5063</td>\n",
              "      <td>1</td>\n",
              "      <td>2000</td>\n",
              "      <td>Male</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25756</th>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
              "      <td>34</td>\n",
              "      <td>Father Alfred E Boeddeker Park</td>\n",
              "      <td>5411</td>\n",
              "      <td>2</td>\n",
              "      <td>1998</td>\n",
              "      <td>Male</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25757</th>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
              "      <td>42</td>\n",
              "      <td>San Francisco City Hall (Polk St at Grove St)</td>\n",
              "      <td>5157</td>\n",
              "      <td>2</td>\n",
              "      <td>1995</td>\n",
              "      <td>Male</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25758</th>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
              "      <td>42</td>\n",
              "      <td>San Francisco City Hall (Polk St at Grove St)</td>\n",
              "      <td>4438</td>\n",
              "      <td>2</td>\n",
              "      <td>1995</td>\n",
              "      <td>Male</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25759</th>\n",
              "      <td>29</td>\n",
              "      <td>16</td>\n",
              "      <td>Steuart St at Market St</td>\n",
              "      <td>115</td>\n",
              "      <td>Jackson Playground</td>\n",
              "      <td>1705</td>\n",
              "      <td>3</td>\n",
              "      <td>1990</td>\n",
              "      <td>Male</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>25760 rows × 10 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01cdb5e0-a297-4e29-88e3-dba6123570c2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-01cdb5e0-a297-4e29-88e3-dba6123570c2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-01cdb5e0-a297-4e29-88e3-dba6123570c2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3c2a874f-1077-4717-86ae-203d52e5459c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3c2a874f-1077-4717-86ae-203d52e5459c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3c2a874f-1077-4717-86ae-203d52e5459c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_79cf50dd-b22f-4c39-8bcf-5111b0031373\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_79cf50dd-b22f-4c39-8bcf-5111b0031373 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 25760,\n  \"fields\": [\n    {\n      \"column\": \"duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 1,\n        \"max\": 1372,\n        \"num_unique_values\": 172,\n        \"samples\": [\n          103,\n          142,\n          180\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"station_A_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 3,\n        \"max\": 81,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          21,\n          3,\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"station_A_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Montgomery St BART Station (Market St at 2nd St)\",\n          \"Powell St BART Station (Market St at 4th St)\",\n          \"San Francisco Caltrain (Townsend St at 4th St)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"station_B_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105,\n        \"min\": 3,\n        \"max\": 383,\n        \"num_unique_values\": 152,\n        \"samples\": [\n          37,\n          147,\n          375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"station_B_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 152,\n        \"samples\": [\n          \"2nd St at Folsom St\",\n          \"29th St at Tiffany Ave\",\n          \"Grove St at Masonic Ave\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bike_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1576,\n        \"min\": 11,\n        \"max\": 6638,\n        \"num_unique_values\": 1805,\n        \"samples\": [\n          861,\n          465,\n          1768\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_birth_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 1901,\n        \"max\": 2001,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          1939,\n          1901,\n          1959\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Male\",\n          \"Female\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tire_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 26,\n        \"max\": 29,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          26,\n          27,\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zu-KlkbPZP0a",
        "outputId": "24f2ccf3-dfe2-47f4-b843-30814a17c1ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "duration            int64\n",
              "station_A_id        int64\n",
              "station_A_name     object\n",
              "station_B_id        int64\n",
              "station_B_name     object\n",
              "bike_id             int64\n",
              "user_type           int64\n",
              "user_birth_year     int64\n",
              "user_gender        object\n",
              "tire_size           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_value = df[\"duration\"].min()\n",
        "max_value = df[\"duration\"].max()\n",
        "\n",
        "print(\"Minimum value:\", min_value)\n",
        "print(\"Maximum value:\", max_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnkcZXmIXnyb",
        "outputId": "b1aed9bd-61d0-452d-e88b-f940fdd60fa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum value: 1\n",
            "Maximum value: 1372\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({\n",
        "    'duration' : [5, 20, 10, 15, 30, 60],\n",
        "    'station_A': ['Long Beach', 'Downtown Berkely', '12th St Oakland', 'Palo Alto', 'Santa Barbra', 'Concord'],\n",
        "    'station_B': ['Market Street', 'Embacarendo Center', 'Pleasant Hill', 'Civic Center', '16th St Mission', 'Union City'],\n",
        "    'user_rider' : ['female', 'female', 'female', 'male', 'male', 'female'],\n",
        "    'tire_size': [28, 26, 27, 26, 21, 26]\n",
        "})\n",
        "df.query('duration < 60')\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pKNHbqTrZmGT",
        "outputId": "974fe395-8f9a-4782-b9de-57fcf0b125fd"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   duration         station_A           station_B user_rider  tire_size\n",
            "0         5        Long Beach       Market Street     female         28\n",
            "1        20  Downtown Berkely  Embacarendo Center     female         26\n",
            "2        10   12th St Oakland       Pleasant Hill     female         27\n",
            "3        15         Palo Alto        Civic Center       male         26\n",
            "4        30      Santa Barbra     16th St Mission       male         21\n",
            "5        60           Concord          Union City     female         26\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_value = df[\"duration\"].min()\n",
        "max_value = df[\"duration\"].max()\n",
        "\n",
        "print(\"Minimum value:\", min_value)\n",
        "print(\"Maximum value:\", max_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bc6qBE2ph9wZ",
        "outputId": "132b3fff-622c-4079-abdf-96db5c2d44b4"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum value: 5\n",
            "Maximum value: 60\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[df['tire_size'] == 26]\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pRVW5H5hHeoe",
        "outputId": "cf1eee79-d4d8-446d-c64e-71b6ec386989"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   duration         station_A           station_B user_rider  tire_size\n",
            "1        20  Downtown Berkely  Embacarendo Center     female         26\n",
            "3        15         Palo Alto        Civic Center       male         26\n",
            "5        60           Concord          Union City     female         26\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[(df['user_rider'] == 'female') & (df['tire_size'] == 26) & (df['duration'] <= 60)]\n",
        "count = len(df)\n",
        "print(count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPY-2SHpIDr-",
        "outputId": "84d0f4f8-72bc-4f04-e181-b53b5a488e6e"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Part 2: more-messy-data.csv\n",
        "df = pd.read_csv('more-messy-data.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "cjiNN9vVIS0N",
        "outputId": "ff76fcda-06cb-499a-cd72-590337c197f7"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id  sepal_length  sepal_width  petal_length petal_width     species\n",
              "0       1           3.5          2.9           1.4         0.5   virginica\n",
              "1       2           3.6          3.2           3.0         0.5      setosa\n",
              "2       3           3.8          NaN           2.2         1.5      setosa\n",
              "3       4           5.8          2.7           2.6         1.2   virginica\n",
              "4       5           4.9          3.6           3.0         1.2   virginica\n",
              "...   ...           ...          ...           ...         ...         ...\n",
              "1311  412           NaN          3.6           2.9         0.5  Versicolor\n",
              "1312  413           5.8          2.7           2.0         0.1  Versicolor\n",
              "1313  414           3.3          3.7           1.3         2.8  Versicolor\n",
              "1314  415           3.1          NaN           1.1         NaN  Versicolor\n",
              "1315  416           4.3          2.5           1.3           1  Versicolor\n",
              "\n",
              "[1316 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1a427ac-eadb-4fdd-9abd-8dafedc8b0f7\" class=\"colab-df-container\">\n",
              "    <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",
              "    .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>id</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>1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3.6</td>\n",
              "      <td>3.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.5</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1.2</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1311</th>\n",
              "      <td>412</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.6</td>\n",
              "      <td>2.9</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1312</th>\n",
              "      <td>413</td>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1313</th>\n",
              "      <td>414</td>\n",
              "      <td>3.3</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.3</td>\n",
              "      <td>2.8</td>\n",
              "      <td>Versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1314</th>\n",
              "      <td>415</td>\n",
              "      <td>3.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1315</th>\n",
              "      <td>416</td>\n",
              "      <td>4.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "      <td>Versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1316 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1316,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 275,\n        \"min\": 1,\n        \"max\": 1000,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          522,\n          738,\n          741\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8525554120707532,\n        \"min\": 3.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          5.1,\n          4.4,\n          5.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5764604875212629,\n        \"min\": 2.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          2.9,\n          3.8,\n          3.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5767541494601741,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1.4,\n          1.5,\n          2.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"0\",\n          \"gg29\",\n          \"0.8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Setosan\",\n          \"setosa\",\n          \"Virginia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_rows = df[df.duplicated()]\n",
        "print(\"Number of duplicate rows in DF:\", len(duplicate_rows))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PfoRty3zInfd",
        "outputId": "83f7205b-7b38-4316-a765-e05da6e7439a"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows in DF: 287\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop_duplicates()"
      ],
      "metadata": {
        "id": "trwbjUEKIogN"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sepal_length_mean = df['sepal_length'].mean()\n",
        "sepal_width_mean = df['sepal_width'].mean()\n",
        "petal_length_mean = df['petal_length'].mean()\n",
        "petal_width_mean = df['petal_width'].mean()\n",
        "\n",
        "df['sepal_length'].fillna(sepal_length_mean, inplace=True)\n",
        "df['sepal_width'].fillna(sepal_width_mean, inplace=True)\n",
        "df['petal_length'].fillna(petal_length_mean, inplace=True)\n",
        "df['petal_width'].fillna(petal_width_mean, inplace=True)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "S8sB6IMOIqi8",
        "outputId": "e2ce62bf-bba0-4479-c668-40801d55daf2"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id  sepal_length  sepal_width  petal_length petal_width     species\n",
              "0       1      3.500000     2.900000           1.4         0.5   virginica\n",
              "1       2      3.600000     3.200000           3.0         0.5      setosa\n",
              "2       3      3.800000     2.994451           2.2         1.5      setosa\n",
              "3       4      5.800000     2.700000           2.6         1.2   virginica\n",
              "4       5      4.900000     3.600000           3.0         1.2   virginica\n",
              "...   ...           ...          ...           ...         ...         ...\n",
              "1310  411      4.800000     2.600000           1.9         0.9  Versicolor\n",
              "1311  412      4.518834     3.600000           2.9         0.5  Versicolor\n",
              "1312  413      5.800000     2.700000           2.0         0.1  Versicolor\n",
              "1313  414      3.300000     3.700000           1.3         2.8  Versicolor\n",
              "1315  416      4.300000     2.500000           1.3           1  Versicolor\n",
              "\n",
              "[948 rows x 6 columns]"
            ],
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>sepal_length</th>\n",
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              "      <td>2.700000</td>\n",
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              "      <td>1.2</td>\n",
              "      <td>virginica</td>\n",
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              "      <th>4</th>\n",
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              "    <tr>\n",
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              "      <td>416</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "      <td>Versicolor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>948 rows × 6 columns</p>\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "      }\n",
              "\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
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              "\n",
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              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 948,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 286,\n        \"min\": 1,\n        \"max\": 1000,\n        \"num_unique_values\": 923,\n        \"samples\": [\n          358,\n          932,\n          33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8223537364415853,\n        \"min\": 3.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          4.2,\n          3.1,\n          5.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5278871299282232,\n        \"min\": 2.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          2.9,\n          2.4,\n          2.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5477289311365691,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          1.4,\n          2.5,\n          2.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"0\",\n          \"gg29\",\n          \"0.8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Setosan\",\n          \"setosa\",\n          \"Virginia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values = df.isnull().sum()\n",
        "print(missing_values)\n",
        "df.dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "brbA9aRBJ0M6",
        "outputId": "b38f68fd-fb83-41bc-de89-a01fb7bfb6af"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id               0\n",
            "sepal_length     0\n",
            "sepal_width      0\n",
            "petal_length     0\n",
            "petal_width      0\n",
            "species         61\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id  sepal_length  sepal_width  petal_length petal_width  \\\n",
              "0       1      3.500000     2.900000           1.4         0.5   \n",
              "1       2      3.600000     3.200000           3.0         0.5   \n",
              "2       3      3.800000     2.994451           2.2         1.5   \n",
              "3       4      5.800000     2.700000           2.6         1.2   \n",
              "4       5      4.900000     3.600000           3.0         1.2   \n",
              "...   ...           ...          ...           ...         ...   \n",
              "1310  411      4.800000     2.600000           1.9         0.9   \n",
              "1311  412      4.518834     3.600000           2.9         0.5   \n",
              "1312  413      5.800000     2.700000           2.0         0.1   \n",
              "1313  414      3.300000     3.700000           1.3         2.8   \n",
              "1315  416      4.300000     2.500000           1.3           1   \n",
              "\n",
              "                 species  \n",
              "0      virginica_species  \n",
              "1         setosa_species  \n",
              "2         setosa_species  \n",
              "3      virginica_species  \n",
              "4      virginica_species  \n",
              "...                  ...  \n",
              "1310  Versicolor_species  \n",
              "1311  Versicolor_species  \n",
              "1312  Versicolor_species  \n",
              "1313  Versicolor_species  \n",
              "1315  Versicolor_species  \n",
              "\n",
              "[887 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7839564d-27b5-4007-81a7-df857c77d777\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\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>id</th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
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              "      <th>0</th>\n",
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              "      <td>3.500000</td>\n",
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              "      <td>0.5</td>\n",
              "      <td>virginica_species</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>setosa_species</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>2.994451</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.5</td>\n",
              "      <td>setosa_species</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1.2</td>\n",
              "      <td>virginica_species</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>virginica_species</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1310</th>\n",
              "      <td>411</td>\n",
              "      <td>4.800000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.9</td>\n",
              "      <td>Versicolor_species</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1311</th>\n",
              "      <td>412</td>\n",
              "      <td>4.518834</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>2.9</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Versicolor_species</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1312</th>\n",
              "      <td>413</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Versicolor_species</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1313</th>\n",
              "      <td>414</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>3.700000</td>\n",
              "      <td>1.3</td>\n",
              "      <td>2.8</td>\n",
              "      <td>Versicolor_species</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1315</th>\n",
              "      <td>416</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "      <td>Versicolor_species</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  }\n",
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              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 887,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288,\n        \"min\": 1,\n        \"max\": 1000,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          830,\n          705,\n          137\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8191405631692812,\n        \"min\": 3.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          5.9,\n          3.1,\n          5.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5264680608391417,\n        \"min\": 2.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          2.9,\n          2.4,\n          2.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5475881221078964,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          1.4,\n          2.5,\n          2.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"0\",\n          \"gg29\",\n          \"0.8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Setosan\",\n          \"setosa_species\",\n          \"Virginia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "number_of_different_species = df['species'].count()\n",
        "print(\"Number of different species:\", number_of_different_species)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwU0rOLoJLEr",
        "outputId": "ee87689b-1350-4abf-87d1-80b335e17d01"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of different species: 887\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['species'] = df['species'].replace('setosa', 'setosa_species')\n",
        "df['species'] = df['species'].replace('virginica', 'virginica_species')\n",
        "df['species'] = df['species'].replace('Versicolor', 'Versicolor_species')\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ayScBgTiJMEc",
        "outputId": "a0740283-e98d-4915-d2fa-ea2862eefd1e"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id  sepal_length  sepal_width  petal_length petal_width  \\\n",
              "0       1      3.500000     2.900000           1.4         0.5   \n",
              "1       2      3.600000     3.200000           3.0         0.5   \n",
              "2       3      3.800000     2.994451           2.2         1.5   \n",
              "3       4      5.800000     2.700000           2.6         1.2   \n",
              "4       5      4.900000     3.600000           3.0         1.2   \n",
              "...   ...           ...          ...           ...         ...   \n",
              "1310  411      4.800000     2.600000           1.9         0.9   \n",
              "1311  412      4.518834     3.600000           2.9         0.5   \n",
              "1312  413      5.800000     2.700000           2.0         0.1   \n",
              "1313  414      3.300000     3.700000           1.3         2.8   \n",
              "1315  416      4.300000     2.500000           1.3           1   \n",
              "\n",
              "                 species  \n",
              "0      virginica_species  \n",
              "1         setosa_species  \n",
              "2         setosa_species  \n",
              "3      virginica_species  \n",
              "4      virginica_species  \n",
              "...                  ...  \n",
              "1310  Versicolor_species  \n",
              "1311  Versicolor_species  \n",
              "1312  Versicolor_species  \n",
              "1313  Versicolor_species  \n",
              "1315  Versicolor_species  \n",
              "\n",
              "[948 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b30afacf-18eb-49b3-abaf-e10b5246a126\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "      <th>id</th>\n",
              "      <th>sepal_length</th>\n",
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              "      <th>0</th>\n",
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              "      <td>0.5</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3.600000</td>\n",
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              "      <td>3.0</td>\n",
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              "      <td>setosa_species</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>2.994451</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.5</td>\n",
              "      <td>setosa_species</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1.2</td>\n",
              "      <td>virginica_species</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1310</th>\n",
              "      <td>411</td>\n",
              "      <td>4.800000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.9</td>\n",
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              "      <th>1311</th>\n",
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              "      <td>2.8</td>\n",
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              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "      <td>Versicolor_species</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>948 rows × 6 columns</p>\n",
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              "\n",
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              "      display:flex;\n",
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              "    }\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-b30afacf-18eb-49b3-abaf-e10b5246a126 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b30afacf-18eb-49b3-abaf-e10b5246a126');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-65eeb2e1-c224-480a-8688-0d5e8a345f73\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-65eeb2e1-c224-480a-8688-0d5e8a345f73')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-65eeb2e1-c224-480a-8688-0d5e8a345f73 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_cc2b0e62-beab-44bd-b2f8-7bbf761e9041\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_cc2b0e62-beab-44bd-b2f8-7bbf761e9041 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 948,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 286,\n        \"min\": 1,\n        \"max\": 1000,\n        \"num_unique_values\": 923,\n        \"samples\": [\n          358,\n          932,\n          33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8223537364415853,\n        \"min\": 3.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          4.2,\n          3.1,\n          5.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5278871299282232,\n        \"min\": 2.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          2.9,\n          2.4,\n          2.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5477289311365691,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          1.4,\n          2.5,\n          2.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"0\",\n          \"gg29\",\n          \"0.8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Setosan\",\n          \"setosa_species\",\n          \"Virginia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    }
  ]
}