003P_Pandas.ipynb

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      "cell_type": "markdown",
      "source": [
        "# **Python Pandas (DataFrame)**"
      ],
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      "source": [
        "### **Overview:**\n",
        "- Pandas is a Python module that simplifies data science and analysis.\n",
        "- It provides easy and effective tools for working with structured data.\n",
        "- It's widely used in data manipulation, cleaning, and analysis.\n",
        "- Pandas is based on **numpy** so many properties of numpy can be used here (2D array)"
      ],
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      "cell_type": "markdown",
      "source": [
        "### Importing Pandas library"
      ],
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      "source": [
        "# installation\n",
        "!pip install pandas"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# alias 'pd' for convenience.\n",
        "import pandas as pd"
      ],
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      "source": [
        "### Creating Series\n",
        "- `pd.Series(data, index)`: Creating a Series object with data and optional custom index."
      ],
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        "id": "RHx8FSORzjsU"
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      "source": [
        "number_series = pd.Series([2, 3, 5, 6, 8],\n",
        "                          index=['a', 'b',\"c\"'d','e','f'],name='Number')\n",
        "print(number_series)"
      ],
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          "text": [
            "a     2\n",
            "b     3\n",
            "cd    5\n",
            "e     6\n",
            "f     8\n",
            "Name: Number, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating DataFrame:\n",
        "- `pd.DataFrame(data, columns)`:Creating a DataFrame with data and optional custom column names."
      ],
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      "source": [
        "# Creating a DataFrame from a list of tuples,list of list,dict, etc.\n",
        "weather_data = [('1/1/2017', 32, 6, 'Rain'), ('1/2/2017', 35, 7, 'Sunny')]\n",
        "\n",
        "pd.DataFrame(weather_data, columns=['day', 'temperature', 'windspeed', 'event'])"
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            "text/plain": [
              "        day  temperature  windspeed  event\n",
              "0  1/1/2017           32          6   Rain\n",
              "1  1/2/2017           35          7  Sunny"
            ],
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        "series_dict = {'First Series' : pd.Series([10, 20, 30, 40]),\n",
        "               'Second Series' : pd.Series([10, 20, 30, 40])}\n",
        "\n",
        "pd.DataFrame(series_dict)"
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            "text/plain": [
              "   First Series  Second Series\n",
              "0            10             10\n",
              "1            20             20\n",
              "2            30             30\n",
              "3            40             40"
            ],
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              "     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-d74f5d6c-df4b-435c-9ca6-98421e063498 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Input and Output**:\n",
        "\n",
        "- `pd.read_csv(file_path)`:\n",
        "  - Reading data from a CSV file.\n",
        "- `pd.read_excel(file_path)`:\n",
        "  - Reading data from an Excel file.\n",
        "- `df.to_csv(file_path)`:\n",
        "  - Writing data to a CSV file.\n",
        "- `df.to_excel(file_path)`:\n",
        "  - Writing data to an Excel file."
      ],
      "metadata": {
        "id": "O4F51HhV0ulw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Give file path as string\n",
        "file_path=\"/content/drive/MyDrive/Projects/EDA(Haberman Dataset)/haberman.csv\"\n",
        "df=pd.read_csv(file_path)"
      ],
      "metadata": {
        "id": "WC43-zyd1BHV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_path=\"/content/sample_data/\"+\"modified_data.csv\"\n",
        "df.to_csv(file_path)"
      ],
      "metadata": {
        "id": "4UpamrNu3kvO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "MKXq7gYm6LQD",
        "outputId": "c0c48755-be6a-4ddb-8bc2-11bb8d67aa7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  year  nodes  status\n",
              "0     30    64      1       1\n",
              "1     30    62      3       1\n",
              "2     30    65      0       1\n",
              "3     31    59      2       1\n",
              "4     31    65      4       1\n",
              "..   ...   ...    ...     ...\n",
              "301   75    62      1       1\n",
              "302   76    67      0       1\n",
              "303   77    65      3       1\n",
              "304   78    65      1       2\n",
              "305   83    58      2       2\n",
              "\n",
              "[306 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8b5b4cc6-2c39-4011-b838-8ac028aacad0\" 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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>year</th>\n",
              "      <th>nodes</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30</td>\n",
              "      <td>62</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30</td>\n",
              "      <td>65</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>59</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31</td>\n",
              "      <td>65</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>75</td>\n",
              "      <td>62</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>76</td>\n",
              "      <td>67</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>303</th>\n",
              "      <td>77</td>\n",
              "      <td>65</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>304</th>\n",
              "      <td>78</td>\n",
              "      <td>65</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>83</td>\n",
              "      <td>58</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>306 rows × 4 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-8b5b4cc6-2c39-4011-b838-8ac028aacad0')\"\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-8b5b4cc6-2c39-4011-b838-8ac028aacad0 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-8b5b4cc6-2c39-4011-b838-8ac028aacad0');\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-0d9c9892-0ca5-4cd4-a2a0-26705043a8ed\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0d9c9892-0ca5-4cd4-a2a0-26705043a8ed')\"\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-0d9c9892-0ca5-4cd4-a2a0-26705043a8ed button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PxJJJzui7Ojn",
        "outputId": "2ee2ebd8-9c1f-4ef3-ee03-85306bfd59b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Basic Operations**:\n",
        "\n",
        "- `df.head(n)`:\n",
        "  - Viewing the first n rows of a DataFrame.\n",
        "\n",
        "- `df.tail(n)`:\n",
        "  - Viewing the last n rows of a DataFrame.\n",
        "\n",
        "- `df.describe()`:\n",
        "  - Generating summary statistics of the DataFrame.\n",
        "\n",
        "- `df.info()`:\n",
        "  - Displaying information about the DataFrame's data types and missing values.\n",
        "\n",
        "- `df.shape`:\n",
        "  - Retrieving the number of rows and columns in a DataFrame.\n",
        "\n"
      ],
      "metadata": {
        "id": "COsEavV77RIt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "1m1_jZjg7oyA",
        "outputId": "7a644511-00ea-4beb-fef6-889d5dca1536"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  year  nodes  status\n",
              "0   30    64      1       1\n",
              "1   30    62      3       1\n",
              "2   30    65      0       1"
            ],
            "text/html": [
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-092c8ffe-5d9a-4a8d-84cc-3b49fb075af6');\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",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-789fa100-79bd-4ac9-b59d-a5293ae4813f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "wbSIfyOv7tl3",
        "outputId": "4d45b2bc-295c-4535-b520-33ef14179f20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  year  nodes  status\n",
              "303   77    65      3       1\n",
              "304   78    65      1       2\n",
              "305   83    58      2       2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d3a7bcf7-adea-4508-aaaa-fc7e6454e1c5\" class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-d3a7bcf7-adea-4508-aaaa-fc7e6454e1c5 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-d3a7bcf7-adea-4508-aaaa-fc7e6454e1c5');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    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-0832b070-dce9-422c-8c2f-b78e3b202de4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "rQ35fWGu7vcK",
        "outputId": "a04a3b1c-767a-406e-dc82-5ab38d0c3558"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age        year       nodes      status\n",
              "count  306.000000  306.000000  306.000000  306.000000\n",
              "mean    52.457516   62.852941    4.026144    1.264706\n",
              "std     10.803452    3.249405    7.189654    0.441899\n",
              "min     30.000000   58.000000    0.000000    1.000000\n",
              "25%     44.000000   60.000000    0.000000    1.000000\n",
              "50%     52.000000   63.000000    1.000000    1.000000\n",
              "75%     60.750000   65.750000    4.000000    2.000000\n",
              "max     83.000000   69.000000   52.000000    2.000000"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>year</th>\n",
              "      <th>nodes</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>306.000000</td>\n",
              "      <td>306.000000</td>\n",
              "      <td>306.000000</td>\n",
              "      <td>306.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>52.457516</td>\n",
              "      <td>62.852941</td>\n",
              "      <td>4.026144</td>\n",
              "      <td>1.264706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.803452</td>\n",
              "      <td>3.249405</td>\n",
              "      <td>7.189654</td>\n",
              "      <td>0.441899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>44.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>52.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.750000</td>\n",
              "      <td>65.750000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>83.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-47fe9238-766b-48ca-a2c8-48cf0fc14e06 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-47fe9238-766b-48ca-a2c8-48cf0fc14e06');\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",
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              "\n",
              "\n",
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              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    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",
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              "\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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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-12688315-68a5-4619-b875-98b11be358ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6g9UPIIl7yEQ",
        "outputId": "9cf60bdf-5771-4c76-bda3-bee2dc351c13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 306 entries, 0 to 305\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype\n",
            "---  ------  --------------  -----\n",
            " 0   age     306 non-null    int64\n",
            " 1   year    306 non-null    int64\n",
            " 2   nodes   306 non-null    int64\n",
            " 3   status  306 non-null    int64\n",
            "dtypes: int64(4)\n",
            "memory usage: 9.7 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zW9oWwK_71Lm",
        "outputId": "7dbd59e9-e222-4a45-b840-4366573b92cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(306, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Subset dataframe**:\n",
        "\n",
        "- `df[column_name]`:\n",
        "  - Accessing a specific column by its name.\n",
        "\n",
        "- `df[['column1', 'column2']]`:\n",
        "  - Selecting multiple columns.(Give list of columns you want to select)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "4ZaAtLlw8cJQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"age\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5TvYDf6U_n7W",
        "outputId": "ae3f56d3-be12-4ad1-dd4c-6a0374b910bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    30\n",
              "1    30\n",
              "2    30\n",
              "3    31\n",
              "4    31\n",
              "Name: age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(df[\"age\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehZ9coJC_013",
        "outputId": "b39e1d69-e5f9-43bc-d2ab-8e9c0e0a1a06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"age\",\"year\"]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hy8bolaG_r0e",
        "outputId": "c3b9c783-c8fa-4708-edb6-f8fb0a908e9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  year\n",
              "0   30    64\n",
              "1   30    62\n",
              "2   30    65\n",
              "3   31    59\n",
              "4   31    65"
            ],
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              "      <td>62</td>\n",
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              "\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-bc332204-441c-4d6e-9bea-bd8e99ef6908 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-bc332204-441c-4d6e-9bea-bd8e99ef6908');\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-70c326b9-3786-4978-ad33-1d6bd7b4e988\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-70c326b9-3786-4978-ad33-1d6bd7b4e988')\"\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-70c326b9-3786-4978-ad33-1d6bd7b4e988 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(df[[\"age\",\"year\"]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jGerOx5R_33w",
        "outputId": "0645d766-509b-4ac9-cc12-6fc0a9ea4c9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loc and iLoc\n",
        "\n",
        "- `df.loc[row_label, column_label]`:\n",
        "  - Accessing data using labels.\n",
        "  - row_label and column_label specify the labels for row and column, respectively.\n",
        "\n",
        "- `df.iloc[row_index, column_index]`:\n",
        "  - Accessing data using integer-based indexing start from zero\n",
        "  - row_index and column_index are the integer-based row and column indices.\n",
        "\n",
        "\n",
        "\n",
        "- Slicing a DataFrame.\n",
        "  - `df.iloc[start_row_index :end_row_index , start_col_number +1 : end_col_number +1 ]`:\n",
        "  - `df.loc[start_row_name: end_row_name , start_col_name : end_col_name]`"
      ],
      "metadata": {
        "id": "Gdq7WgkXAPVy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[2,\"age\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kFy3wUQe_-1H",
        "outputId": "7d9e54f8-db34-4a30-871f-0fd66cc8034a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[2,0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OdMdi8eiAvv2",
        "outputId": "9c37f091-b409-42ce-9b20-cc52b82c5caa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[0:2,\"age\":\"nodes\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "_DZ-YzFpBYzH",
        "outputId": "48c340ef-a549-4838-908f-f13f75f5ddbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  year  nodes\n",
              "0   30    64      1\n",
              "1   30    62      3\n",
              "2   30    65      0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-970c716d-1915-4261-a24d-1fc3cf154000\" 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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>year</th>\n",
              "      <th>nodes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30</td>\n",
              "      <td>62</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30</td>\n",
              "      <td>65</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-970c716d-1915-4261-a24d-1fc3cf154000')\"\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-970c716d-1915-4261-a24d-1fc3cf154000 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-970c716d-1915-4261-a24d-1fc3cf154000');\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-5698cc9b-c9f4-40c5-905c-ccfd4482b496\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5698cc9b-c9f4-40c5-905c-ccfd4482b496')\"\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-5698cc9b-c9f4-40c5-905c-ccfd4482b496 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[0:3,0:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "bLPGGMRmBjPO",
        "outputId": "9d551a26-1269-4c28-c8dd-bfea584aaa67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  year  nodes\n",
              "0   30    64      1\n",
              "1   30    62      3\n",
              "2   30    65      0"
            ],
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "    60% {\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Aggregate Functions and Condition :"
      ],
      "metadata": {
        "id": "CQvJYgT5C3CE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####  `df.aggregate_function()` :\n",
        "  - we can use to find aggregates [mean(),max(),min(),count(),median(),var(),std(),quantile(),etc] of columns\n",
        "\n",
        "  - to find specific columns `df[list of columns].aggregate_function()`\n",
        "\n",
        "#### `df[df['column_name'] > value]`:\n",
        "  - Filtering data based on a condition.\n",
        "  - we can use various condition with aggregate functions\n"
      ],
      "metadata": {
        "id": "JTXcGAVx_lH_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "cUfvWK-3D2j1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ByRuLDUR-qK2",
        "outputId": "5d7899f5-89fa-4f17-d34c-59d674d7fa2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age       52.457516\n",
              "year      62.852941\n",
              "nodes      4.026144\n",
              "status     1.264706\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"age\"].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWu-Qm6QEc3a",
        "outputId": "c27d7f18-0a34-4cb0-9133-792423a68d0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We get boolean values when apply condition on dataframe\n",
        "df[\"age\"] > 77"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGHcq16-EhYl",
        "outputId": "dd6a9d4b-8ca9-4396-cf91-eaf0c755c9c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      False\n",
              "1      False\n",
              "2      False\n",
              "3      False\n",
              "4      False\n",
              "       ...  \n",
              "301    False\n",
              "302    False\n",
              "303    False\n",
              "304     True\n",
              "305     True\n",
              "Name: age, Length: 306, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# When we put condtions inside dataframe we get all rows with true condition\n",
        "# Just like masking in numpy\n",
        "df[df[\"age\"]>77]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "04n9tinbE_OP",
        "outputId": "7b04446f-dab7-4e77-adc8-aa27beddf6d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  year  nodes  status\n",
              "304   78    65      1       2\n",
              "305   83    58      2       2"
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cd9bff98-618d-4b9f-85c1-0a67f9622e07');\n",
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              "          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",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "\n",
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              "      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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df[\"age\"] == df[\"age\"].max()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "l95gebdUFfiW",
        "outputId": "bf96de8e-d64a-47dc-8c0b-4aefe9ab6d09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  year  nodes  status\n",
              "305   83    58      2       2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b5a97b91-aa25-49ec-ac34-c2d021ffbe9e\" class=\"colab-df-container\">\n",
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              "  </thead>\n",
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              "      <th>305</th>\n",
              "      <td>83</td>\n",
              "      <td>58</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-b5a97b91-aa25-49ec-ac34-c2d021ffbe9e 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-b5a97b91-aa25-49ec-ac34-c2d021ffbe9e');\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",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Exploring Data:**"
      ],
      "metadata": {
        "id": "3aCA88zBLhR2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Get unique values in a column\n",
        "  - `df['column_name'].unique()`\n",
        "\n",
        "- Get the number of unique values\n",
        "  - `df['column_name'].nunique()`\n",
        "\n",
        "- Get value counts of each category\n",
        "  - `df['column_name'].value_counts()`\n",
        "\n",
        "- Check for missing values\n",
        "  - `df.isnull()`\n"
      ],
      "metadata": {
        "id": "uAlBemq8LqhO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"status\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PYHbE8Rnpe4M",
        "outputId": "1a94c5e3-4dc9-4639-ea7c-7b3a318ae745"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"status\"].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7zjrM9qpqPc",
        "outputId": "6c86e793-d47b-40c9-fdd8-0f315374a3fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"status\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6fmp1B2qA8l",
        "outputId": "e5044aca-8201-4c8a-d0f2-fff210ac7e82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    225\n",
              "2     81\n",
              "Name: status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing Values or Null values\n",
        "import numpy as np\n",
        "a=np.nan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OOM6iGlrQ-z",
        "outputId": "a35e868e-c220-4882-dd42-e6bb3e0f900e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# It show True when there is any missing(NAN) value at that position\n",
        "# We can use condition on top of it\n",
        "df.isnull()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MX0zDb6yqRlR",
        "outputId": "b4ad145d-ca6c-4599-d78f-7ec05bebb680"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age   year  nodes  status\n",
              "0  False  False  False   False\n",
              "1  False  False  False   False\n",
              "2  False  False  False   False\n",
              "3  False  False  False   False\n",
              "4  False  False  False   False"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a075f1a9-6ffa-4a77-8d24-37105251e7c8\" 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",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
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              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a075f1a9-6ffa-4a77-8d24-37105251e7c8')\"\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-a075f1a9-6ffa-4a77-8d24-37105251e7c8 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-a075f1a9-6ffa-4a77-8d24-37105251e7c8');\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-0c2f32db-2030-4ca8-9675-5d4ceb1f9424\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0c2f32db-2030-4ca8-9675-5d4ceb1f9424')\"\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-0c2f32db-2030-4ca8-9675-5d4ceb1f9424 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# If any null value present in dataset return true\n",
        "df.isnull().values.any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_4r6tXWrvk4",
        "outputId": "b3f59305-dacd-4d94-ec12-8e616c9a17d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the null values in single dataframe column\n",
        "df[\"age\"].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPJd4vITsEZJ",
        "outputId": "03ab7e89-2a22-4509-f21e-0c38db7d8faa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Cleaning and Preparation**"
      ],
      "metadata": {
        "id": "uT7NMhq3G0-h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Remove rows or columns\n",
        "  - `df.drop(labels, axis, inplace=False)`:\n",
        "  - Removing rows (axis=0) or columns(axis=1) from a DataFrame.\n",
        "  -`inplace` : parameter is often used in various Pandas methods and functions to specify whether the changes you make should be applied directly to the original DataFrame (when inplace=True) or if a new DataFrame with the modifications should be returned (when inplace=False, which is the default behavior).\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Zs222_4HHGTx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Replacing missing values with a specified value.:\n",
        "  - `df.fillna(value,inplace=True)`\n",
        "      \n",
        "- Removing duplicate rows:\n",
        "  - `df.drop_duplicates()`\n",
        "\n",
        "- Renaming columns:\n",
        "  - `df.rename(columns)`\n",
        "  - give columns=dictionary of old and new names of columns\n",
        "\n",
        "- Setting a new index for the DataFrame:\n",
        "  - `df.set_index(keys)`\n",
        "\n",
        "- Resetting the index of a DataFrame:\n",
        "  - `df.reset_index()`\n",
        "\n",
        "- Sorting the DataFrame by column(s):\n",
        "  - `df.sort_values(by, ascending)`\n",
        "\n",
        "- Replace\n",
        "  -  `df[\"columns\"].replace(\"old\",\"new\",inplace=True)`\n",
        "  -  `df[\"columns\"].replace(\"dict of old and new values\")`"
      ],
      "metadata": {
        "id": "hXc_2eHoKVzq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(labels=[\"age\",\"year\"],axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "LwKj8LdYKWnz",
        "outputId": "1f5b5160-9a1f-4a75-cb8c-be6156fe9320"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   nodes  status\n",
              "0      1       1\n",
              "1      3       1\n",
              "2      0       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-104ee590-323e-41c1-b712-89055e01e1c0\" 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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>nodes</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-104ee590-323e-41c1-b712-89055e01e1c0')\"\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",
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              "  </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-104ee590-323e-41c1-b712-89055e01e1c0 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-104ee590-323e-41c1-b712-89055e01e1c0');\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-8665a18b-b5e2-4c46-b3e1-319ddc7c4ecc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8665a18b-b5e2-4c46-b3e1-319ddc7c4ecc')\"\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-8665a18b-b5e2-4c46-b3e1-319ddc7c4ecc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(labels=[0,2,4,6],axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "mPY2NqEcKvqW",
        "outputId": "7b02f372-93ae-4cbe-8a91-e1f0e1c887bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  year  nodes  status\n",
              "1   30    62      3       1\n",
              "3   31    59      2       1\n",
              "5   33    58     10       1"
            ],
            "text/html": [
              "\n",
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              "    </button>\n",
              "\n",
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              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\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-22849ddb-f155-4669-9200-14602416af29 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-22849ddb-f155-4669-9200-14602416af29');\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-4304f111-5871-4183-a24c-11ec11bf4df7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4304f111-5871-4183-a24c-11ec11bf4df7')\"\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",
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              "        <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-4304f111-5871-4183-a24c-11ec11bf4df7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns={\"age\":\"AGE\"},inplace=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "oPD2uetCtEno",
        "outputId": "31d31696-0699-4d98-f019-c6c59f9f507b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   AGE  year  nodes  status\n",
              "0   30    64      1       1\n",
              "1   30    62      3       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b0f3b73c-1555-40a7-a85b-7653d52acc3d\" 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",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>AGE</th>\n",
              "      <th>year</th>\n",
              "      <th>nodes</th>\n",
              "      <th>status</th>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            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",
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              "  </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-b0f3b73c-1555-40a7-a85b-7653d52acc3d 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-b0f3b73c-1555-40a7-a85b-7653d52acc3d');\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-003743ab-de70-4ec0-9ee8-31d9d3114387\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-003743ab-de70-4ec0-9ee8-31d9d3114387')\"\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-003743ab-de70-4ec0-9ee8-31d9d3114387 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create and Add columns\n",
        "df[\"+10\"]=[i*10 for i in range(306)]"
      ],
      "metadata": {
        "id": "ZScsBluxv-M_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "feyOzB5fyRaD",
        "outputId": "79ecb710-a68e-428c-e4f4-685a33ab37c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  year  nodes  status  +10\n",
              "0   30    64      1       1    0\n",
              "1   30    62      3       1   10"
            ],
            "text/html": [
              "\n",
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              "      <td>1</td>\n",
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              "  </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",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-b123402e-69a2-4478-ab9c-ba4a476bb17e 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-b123402e-69a2-4478-ab9c-ba4a476bb17e');\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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2cec56bd-04d1-4b0d-aecf-a925436cab93')\"\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",
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              "    </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",
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              "    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",
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              "    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-2cec56bd-04d1-4b0d-aecf-a925436cab93 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set column as index\n",
        "df.set_index(\"+10\",inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "B4FQYi8MyVSk",
        "outputId": "5b65b8b7-fc27-4d07-9702-a94e822079c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  year  nodes  status\n",
              "+10                          \n",
              "0     30    64      1       1\n",
              "10    30    62      3       1\n",
              "20    30    65      0       1\n",
              "30    31    59      2       1\n",
              "40    31    65      4       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f4b31b7c-b56b-426d-8046-49ed056fac59\" class=\"colab-df-container\">\n",
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              "      <th>20</th>\n",
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              "  </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",
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              "      width: 32px;\n",
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              "\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",
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              "\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",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f4b31b7c-b56b-426d-8046-49ed056fac59 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-f4b31b7c-b56b-426d-8046-49ed056fac59');\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-387d29da-b32a-4b2c-bc25-a4dd13a87ce4\">\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
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              "  </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",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    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",
              "      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-387d29da-b32a-4b2c-bc25-a4dd13a87ce4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.reset_index(inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EcfHbTndtcJf",
        "outputId": "ea8a1a05-1148-4618-fa92-53bcca089a43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   +10  age  year  nodes  status\n",
              "0    0   30    64      1       1\n",
              "1   10   30    62      3       1\n",
              "2   20   30    65      0       1\n",
              "3   30   31    59      2       1\n",
              "4   40   31    65      4       1"
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              "      const buttonEl =\n",
              "        document.querySelector('#df-c23552f9-74a7-4293-8e03-18f8c24685aa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "  }\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\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-a8ce303a-069c-4368-b06d-cf0d25b8639d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(labels=\"+10\",axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "Fm4EsIOitFQ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# First it will sort by \"nodes\" then with \"age\"\n",
        "df.sort_values(by=[\"nodes\",\"age\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5Cy2dgDvy8uA",
        "outputId": "5c677d15-aad0-4c64-bbe3-0364eb645b6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  year  nodes  status\n",
              "2    30    65      0       1\n",
              "6    33    60      0       1\n",
              "7    34    59      0       2\n",
              "13   34    60      0       1\n",
              "15   35    63      0       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5fd99284-4265-482b-b244-35bfc465356f\" 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",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>year</th>\n",
              "      <th>nodes</th>\n",
              "      <th>status</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>34</td>\n",
              "      <td>59</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>34</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>35</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5fd99284-4265-482b-b244-35bfc465356f')\"\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-5fd99284-4265-482b-b244-35bfc465356f 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-5fd99284-4265-482b-b244-35bfc465356f');\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-4eff7bce-642d-4766-aed1-0fbf63727efa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4eff7bce-642d-4766-aed1-0fbf63727efa')\"\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-4eff7bce-642d-4766-aed1-0fbf63727efa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"status\"].replace({1:\"a\",2:\"b\"},inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2Rt7IA-52lAE",
        "outputId": "575ff950-8c61-4c1c-8b4f-f8beee989d36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  year  nodes status\n",
              "0   30    64      1      a\n",
              "1   30    62      3      a\n",
              "2   30    65      0      a\n",
              "3   31    59      2      a\n",
              "4   31    65      4      a"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1a6d51ff-37b0-44b4-95bd-9a05003720c7\" 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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>year</th>\n",
              "      <th>nodes</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30</td>\n",
              "      <td>62</td>\n",
              "      <td>3</td>\n",
              "      <td>a</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30</td>\n",
              "      <td>65</td>\n",
              "      <td>0</td>\n",
              "      <td>a</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>59</td>\n",
              "      <td>2</td>\n",
              "      <td>a</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31</td>\n",
              "      <td>65</td>\n",
              "      <td>4</td>\n",
              "      <td>a</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a6d51ff-37b0-44b4-95bd-9a05003720c7')\"\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-1a6d51ff-37b0-44b4-95bd-9a05003720c7 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-1a6d51ff-37b0-44b4-95bd-9a05003720c7');\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-61931ff5-d520-4702-944c-6723fa897422\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-61931ff5-d520-4702-944c-6723fa897422')\"\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-61931ff5-d520-4702-944c-6723fa897422 button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Adding New Rows and Columns"
      ],
      "metadata": {
        "id": "EP_srgv53SsV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Add New columns\n",
        "  - `df[\"New_Column\"]=list of data`\n",
        "  - `df[\"Modified_Column\"]= df[\"old_columns\"] (with some operations)`\n",
        "\n",
        "\n",
        "- Add New rows  \n",
        "  - df.append(new_row , ignore_index=True)\n",
        "  - new_row= dictionary of keys as column names and values we want to add\n",
        "  - `ignore_index=True` : argument reindexes the DataFrame with a new index after appending the new row.\n",
        "\n",
        "- Apply Method\n",
        "  - `df.apply(function,axis)`\n",
        "  - Create new columns (axis=1) by using function on old columns values row wise\n",
        "\n",
        "- eval method and function:\n",
        "  - `df.eval(\"condition\")`  :  to filter rows based on a condition , (condition will be in str format)\n",
        "  - **`eval()`**  function : eval() is a powerful, yet potentially risky, built-in function in Python. It allows you to evaluate a string as a Python expression or code. While it can be handy for dynamically executing code, it's essential to be cautious because it can introduce security risks if used with untrusted input."
      ],
      "metadata": {
        "id": "vLq3xZxj4h8J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create new column\n",
        "df[\"birth\"]= 1900 +df[\"year\"]-df[\"age\"]\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tXw13lQjheQ7",
        "outputId": "01b6e2fa-5d1a-44e5-d125-b4043a6783ac"
      },
      "execution_count": null,
      "outputs": [
        {
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              "     age  year  nodes status  birth\n",
              "301   75    62      1      a   1887\n",
              "302   76    67      0      a   1891\n",
              "303   77    65      3      a   1888\n",
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          },
          "metadata": {},
          "execution_count": 48
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    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(labels=\"birth\",axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "JgAGlAF0qA_v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use Apply method\n",
        "\n",
        "def age_in_months(y):\n",
        "  return 12 * y\n",
        "\n",
        "df[\"age_M\"]=df[\"year\"].apply(age_in_months)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "M6UEEHkXrd6q",
        "outputId": "69f62f25-db91-4ccb-8a9a-1197ff28d772"
      },
      "execution_count": null,
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        {
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            "text/plain": [
              "   age  year  nodes status  age_M\n",
              "0   30    64      1      a    768\n",
              "1   30    62      3      a    744\n",
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              "<div id=\"df-aaa728ce-5315-474b-b623-5c5d1c08e0e3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aaa728ce-5315-474b-b623-5c5d1c08e0e3')\"\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    display: none;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
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              "    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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              "      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-aaa728ce-5315-474b-b623-5c5d1c08e0e3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply with lambda function\n",
        "\n",
        "df[\"birth\"]=df.apply(lambda x: 1900+x[\"year\"]-x[\"age\"] , axis=1)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "V7Lj_-Igw25g",
        "outputId": "588cedd9-38d6-4af6-8f02-f520aad02d16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  year  nodes status  age_M  birth\n",
              "0   30    64      1      a    768   1934\n",
              "1   30    62      3      a    744   1932\n",
              "2   30    65      0      a    780   1935\n",
              "3   31    59      2      a    708   1928\n",
              "4   31    65      4      a    780   1934"
            ],
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-4f7ab16a-28c3-43b9-9db6-738d2b8a2fef 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-4f7ab16a-28c3-43b9-9db6-738d2b8a2fef');\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",
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              "\n",
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              "    </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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    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",
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              "    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-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\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-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-792ba491-4cb2-4c00-a36a-ece4b0c35914 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(labels=[\"birth\",\"age_M\"],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "1_hewggq2Rpt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use eval()\n",
        "df[df.eval(\"age>75\")]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "1ARaDbUC2fb1",
        "outputId": "80104b63-e881-4719-c97a-7b3d45bd8fb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  year  nodes  status\n",
              "302   76    67      0       1\n",
              "303   77    65      3       1\n",
              "304   78    65      1       2\n",
              "305   83    58      2       2"
            ],
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              "\n",
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              "<style scoped>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>year</th>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "            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-40f5d6ee-224e-442e-abe2-8136ba9b89a9 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-40f5d6ee-224e-442e-abe2-8136ba9b89a9');\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-e741eeb9-1610-4bf0-83b3-92f109d3637c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e741eeb9-1610-4bf0-83b3-92f109d3637c')\"\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-e741eeb9-1610-4bf0-83b3-92f109d3637c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval(\"[1,2,4]\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8-RwKPLEJYc",
        "outputId": "fc7a0fd1-767e-43f5-83ff-195c113cea01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 4]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval(\"100+10\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKTR9RlGEQY0",
        "outputId": "4f0f65b8-eec9-4747-8916-a55d9d9d05fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "110"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Reshaping Data**\n",
        "\n",
        "- Append dataframe on rows or columns:\n",
        "  - pd.concat( [list of dataframe to add] ,axis , ignore_index)\n"
      ],
      "metadata": {
        "id": "Dz1D91sJIsrd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "5JVwNAhXG51B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- To join Two or more dataframes\n",
        "  - pd.merge( [list of dataframes] , how , on ]"
      ],
      "metadata": {
        "id": "X5CRS1_XKEgn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "HkLXqwlXJ9JI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Datetime** Module"
      ],
      "metadata": {
        "id": "BgGP2M5uwa4c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "### **1. datetime Module:**\n",
        "\n",
        "-   Returns current date and time.\n",
        "  -`datetime.datetime.now()`\n",
        "\n",
        "\n",
        "- Creates a datetime object:\n",
        "  - `datetime.datetime(year, month, day, hour, minute, second)`\n",
        "\n"
      ],
      "metadata": {
        "id": "nWgLSWtY5CDh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import datetime"
      ],
      "metadata": {
        "id": "IYtkxVCAxiHk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datetime.datetime.now()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eo-u1JiYzLK6",
        "outputId": "17a0cd76-76c8-49f8-9e98-6544a19bb6a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2023, 11, 10, 5, 55, 36, 749498)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datetime.datetime(2030,1,1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDk2VIiEzNte",
        "outputId": "a1a590b3-43d6-49e3-d9b7-396e5a9e0965"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2030, 1, 1, 0, 0)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2. pandas Library:**\n",
        "\n",
        "-  Converts a column to datetime format.\n",
        "  - `pd.to_datetime()`\n",
        "\n",
        "- Extracting components:\n",
        "\n",
        "   \n",
        "  - `df['year'] = df['date'].dt.year`\n",
        "  - `df['month'] = df['date'].dt.month`\n",
        "  \n",
        "\n",
        "- Generates a range of dates.:\n",
        "  - `pd.date_range(start,period,freq)`\n",
        "  \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "2YqBIiUR5uZj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame([(\"02/02/21\",\"Mon\",60),(\"20/12/12\",\"Tue\",70),(\"06/06/06\",\"Wed\",65)],\n",
        "                columns=[\"date\",\"day\",\"percentage\"],index=[1,2,3])\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9tBLTRTxAQX",
        "outputId": "9dadf042-aa37-431a-f961-c655b2f10c27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 3 entries, 1 to 3\n",
            "Data columns (total 3 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   date        3 non-null      object\n",
            " 1   day         3 non-null      object\n",
            " 2   percentage  3 non-null      int64 \n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 96.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert column datatype to datetime format\n",
        "df[\"date\"]=pd.to_datetime(df[\"date\"])\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TdNxcqOyy8Jp",
        "outputId": "507ac5dc-8fe1-4781-b250-d2fc86e3c9ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 3 entries, 1 to 3\n",
            "Data columns (total 3 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   date        3 non-null      datetime64[ns]\n",
            " 1   day         3 non-null      object        \n",
            " 2   percentage  3 non-null      int64         \n",
            "dtypes: datetime64[ns](1), int64(1), object(1)\n",
            "memory usage: 96.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract day,month,year\n",
        "df[\"year\"]=df[\"date\"].dt.year\n",
        "df[\"month\"]=df[\"date\"].dt.month\n",
        "df[\"day\"]=df[\"date\"].dt.day\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "gPHf08ZJ7reP",
        "outputId": "78d0af46-06b7-4089-ce00-f534e7fc27b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        date  day  percentage  year  month\n",
              "1 2021-02-02    2          60  2021      2\n",
              "2 2012-12-20   20          70  2012     12\n",
              "3 2006-06-06    6          65  2006      6"
            ],
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3. `strftime` and `strptime`**:\n",
        "\n",
        "- `strftime`: Converts datetime to a string with a specified format."
      ],
      "metadata": {
        "id": "NSdB2zBH9azR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "current_datetime=datetime.datetime.now()\n",
        "formatted_date = current_datetime.strftime('This is the Date : %Y-%m-%d  & Time : %H:%M:%S')\n",
        "print(formatted_date)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "miWAcC-z9dZ2",
        "outputId": "0ad3a820-f8d9-4202-e3f2-80a122116223"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is the Date : 2023-11-10  & Time : 06:42:15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- `strptime`: Parses a string into a datetime object using a specified format."
      ],
      "metadata": {
        "id": "-BqWW3S5-EwT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "parsed_datetime = datetime.datetime.strptime('2023-11-09', '%Y-%m-%d')\n",
        "print(parsed_datetime)\n",
        "# for above example\n",
        "parsed_datetime = datetime.datetime.strptime(formatted_date, 'This is the Date : %Y-%m-%d  & Time : %H:%M:%S')\n",
        "print(parsed_datetime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmsfpAXQ-Gez",
        "outputId": "e2ccf1a1-b699-4a29-b0fb-76638630e976"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-11-09 00:00:00\n",
            "2023-11-10 06:42:15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "### 4. `timedelta`:\n",
        "\n",
        "- `datetime.timedelta(days=x, hours=y, minutes=z)`: Represents a duration of time.\n"
      ],
      "metadata": {
        "id": "UsoyEocQ6MHD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "time_difference = datetime.timedelta(days=5, hours=3)\n",
        "new_datetime = current_datetime + time_difference\n",
        "print(new_datetime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25f3lVNH-hyg",
        "outputId": "505eebab-664d-4a80-810c-811849cf7829"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-11-15 09:42:15.490466\n"
          ]
        }
      ]
    }
  ]
}