pandas_10min_class_17_29.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pandas_10min_class_17/29.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOoDlmmGUQ87JbKeNL9PJVR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/xersuy/pandas_10min_study/blob/main/pandas_10min_class_17_29.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 시각화 Plotting 기능 활용하기\n",
        "**Pandas로 Series와 DataFrame데이터 시각화 하기**\n",
        "\n",
        "\n",
        "참고하기 좋음\n",
        "https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#visualization\n"
      ],
      "metadata": {
        "id": "RicL75BL_00V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n"
      ],
      "metadata": {
        "id": "ZK9K2MlN_QCN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "# 한글폰트 사용시 그래프에서 마이너스 폰트 깨지는 문제에 대한 대처\n",
        "mpl.rcParams['axes.unicode_minus'] = False\n",
        "\n"
      ],
      "metadata": {
        "id": "R-TTzlHJkw2K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame"
      ],
      "metadata": {
        "id": "zflmlUt4lRGr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### kind : str\n",
        "  df.plot 도움말 참고\n",
        "\n",
        "\n",
        "    The kind of plot to produce:\n",
        "\n",
        "    - 'line' : line plot (default)\n",
        "    - 'bar' : vertical bar plot\n",
        "    - 'barh' : horizontal bar plot\n",
        "    - 'hist' : histogram\n",
        "    - 'box' : boxplot\n",
        "    - 'kde' : Kernel Density Estimation plot\n",
        "    - 'density' : same as 'kde'\n",
        "    - 'area' : area plot\n",
        "    - 'pie' : pie plot\n",
        "    - 'scatter' : scatter plot\n",
        "    - 'hexbin' : hexbin plot.\n"
      ],
      "metadata": {
        "id": "HsCkao7Kl7pn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ts = pd.Series(np.random.randn(1000), index=pd.date_range(\"1/1/2000\", periods=1000))\n",
        "\n",
        "ts.plot()"
      ],
      "metadata": {
        "id": "EKZMbPQwnPvN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "fdbf01a5-fb01-4196-86d4-e661d15f718f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f08be88d4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ts = ts.cumsum()\n",
        "ts.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "G1jMfm9wnkOF",
        "outputId": "66f8a977-204a-4400-81db-3af6f82ba963"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f08bd788c10>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list(\"ABCD\"))\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OG_xyDRFnvB7",
        "outputId": "e8c151d3-5fc1-415f-fc72-a369109bcebe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f37b2a26-ae94-4b80-962b-38550c7e4552\">\n",
              "    <div 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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-01</th>\n",
              "      <td>0.739035</td>\n",
              "      <td>-0.195841</td>\n",
              "      <td>0.926046</td>\n",
              "      <td>-0.215451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-02</th>\n",
              "      <td>0.671180</td>\n",
              "      <td>1.065816</td>\n",
              "      <td>0.216501</td>\n",
              "      <td>0.758043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>-0.844334</td>\n",
              "      <td>1.894383</td>\n",
              "      <td>1.611378</td>\n",
              "      <td>-0.016870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>-1.380673</td>\n",
              "      <td>-0.497657</td>\n",
              "      <td>0.207160</td>\n",
              "      <td>0.594930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>-0.221180</td>\n",
              "      <td>-0.853813</td>\n",
              "      <td>0.509148</td>\n",
              "      <td>1.146693</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>2002-09-22</th>\n",
              "      <td>0.723121</td>\n",
              "      <td>-1.065180</td>\n",
              "      <td>0.197199</td>\n",
              "      <td>-1.036356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-09-23</th>\n",
              "      <td>1.748763</td>\n",
              "      <td>-1.667281</td>\n",
              "      <td>1.309128</td>\n",
              "      <td>1.128757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-09-24</th>\n",
              "      <td>1.783072</td>\n",
              "      <td>-0.924484</td>\n",
              "      <td>1.335910</td>\n",
              "      <td>2.698305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-09-25</th>\n",
              "      <td>0.235437</td>\n",
              "      <td>-0.154917</td>\n",
              "      <td>-1.167482</td>\n",
              "      <td>-0.284753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002-09-26</th>\n",
              "      <td>0.502947</td>\n",
              "      <td>0.003295</td>\n",
              "      <td>0.502644</td>\n",
              "      <td>0.881360</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f37b2a26-ae94-4b80-962b-38550c7e4552')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-f37b2a26-ae94-4b80-962b-38550c7e4552 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-f37b2a26-ae94-4b80-962b-38550c7e4552');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                   A         B         C         D\n",
              "2000-01-01  0.739035 -0.195841  0.926046 -0.215451\n",
              "2000-01-02  0.671180  1.065816  0.216501  0.758043\n",
              "2000-01-03 -0.844334  1.894383  1.611378 -0.016870\n",
              "2000-01-04 -1.380673 -0.497657  0.207160  0.594930\n",
              "2000-01-05 -0.221180 -0.853813  0.509148  1.146693\n",
              "...              ...       ...       ...       ...\n",
              "2002-09-22  0.723121 -1.065180  0.197199 -1.036356\n",
              "2002-09-23  1.748763 -1.667281  1.309128  1.128757\n",
              "2002-09-24  1.783072 -0.924484  1.335910  2.698305\n",
              "2002-09-25  0.235437 -0.154917 -1.167482 -0.284753\n",
              "2002-09-26  0.502947  0.003295  0.502644  0.881360\n",
              "\n",
              "[1000 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "iMauzv_Fn3TQ",
        "outputId": "45daf97d-6170-4998-aac6-396781dea7cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f08bd3b35d0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.cumsum()\n",
        "df.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "kzrapE1En_-z",
        "outputId": "d38ff3f3-ef70-4b52-82b3-b3597c989f06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f08bdc4bc10>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.DataFrame(np.random.randn(1000, 2), columns=[\"B\", \"C\"]).cumsum()\n",
        "df3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uizRnXjUoC6j",
        "outputId": "98d614be-87a1-4201-b15d-2a653ef32d42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.381060</td>\n",
              "      <td>0.281302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.318095</td>\n",
              "      <td>0.391837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.287017</td>\n",
              "      <td>0.912604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.137834</td>\n",
              "      <td>1.441803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.521834</td>\n",
              "      <td>0.192541</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\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",
              "      fill: #174EA6;\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-07d0c7d4-93de-42c5-8839-6bb0877db6e0 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-07d0c7d4-93de-42c5-8839-6bb0877db6e0');\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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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          B         C\n",
              "0 -0.381060  0.281302\n",
              "1 -0.318095  0.391837\n",
              "2  0.287017  0.912604\n",
              "3  0.137834  1.441803\n",
              "4 -0.521834  0.192541"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3[\"A\"] = pd.Series(list(range(len(df))))\n",
        "df3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cX1xs-XGoW0w",
        "outputId": "76f19252-1e8c-4730-d33c-22bc6963a874"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7c6d9cb8-7dfd-43a6-9c2b-fa2379e9cfa5\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>A</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.381060</td>\n",
              "      <td>0.281302</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.318095</td>\n",
              "      <td>0.391837</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.287017</td>\n",
              "      <td>0.912604</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.137834</td>\n",
              "      <td>1.441803</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.521834</td>\n",
              "      <td>0.192541</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c6d9cb8-7dfd-43a6-9c2b-fa2379e9cfa5')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-7c6d9cb8-7dfd-43a6-9c2b-fa2379e9cfa5 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-7c6d9cb8-7dfd-43a6-9c2b-fa2379e9cfa5');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          B         C  A\n",
              "0 -0.381060  0.281302  0\n",
              "1 -0.318095  0.391837  1\n",
              "2  0.287017  0.912604  2\n",
              "3  0.137834  1.441803  3\n",
              "4 -0.521834  0.192541  4"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3.plot(x=\"A\", y='B')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "6bRllLoEokj8",
        "outputId": "3b1f522d-2f40-4a63-93a5-b13d9e3a767d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f08bd049c90>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}