material/library/pandas_for_deep_learning.ipynb

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      "name": "pandas_for_deep_learning.ipynb",
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIE8gatIZ8tJ"
      },
      "source": [
        "# Pandas for Deep Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6V3ksncYtk6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1164398a-6931-435a-89c4-fe57b3306e06"
      },
      "source": [
        "!pip install panda"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting panda\n",
            "  Downloading https://files.pythonhosted.org/packages/79/03/74996420528fe488ce17c42b6400531c8067d7eb661c304fa3aa8fdad17c/panda-0.3.1.tar.gz\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from panda) (56.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from panda) (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->panda) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->panda) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->panda) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->panda) (2.10)\n",
            "Building wheels for collected packages: panda\n",
            "  Building wheel for panda (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for panda: filename=panda-0.3.1-cp37-none-any.whl size=7260 sha256=6af0b3358915038a264a3a36d4be097b506c5cd3e2d9e073a66746a955ca500c\n",
            "  Stored in directory: /root/.cache/pip/wheels/c6/c8/45/06ed898b0bb401c1ff207dbb05b1587ff28860a236d98b1996\n",
            "Successfully built panda\n",
            "Installing collected packages: panda\n",
            "Successfully installed panda-0.3.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjC3MLqEZmVQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "850bb3e5-beb6-4dcf-d709-2e814ddac6ca"
      },
      "source": [
        "import pandas as pd\n",
        "print(pd.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.1.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-bS2LfQhuuD"
      },
      "source": [
        "# 데이터 준비"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7_9lDXebUnt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e446880-0066-40df-e8d7-55837dc73206"
      },
      "source": [
        "!rm -rf RegularSeasonCompactResults.csv\n",
        "!wget https://raw.githubusercontent.com/adeshpande3/Pandas-Tutorial/master/RegularSeasonCompactResults.csv\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-10 13:02:27--  https://raw.githubusercontent.com/adeshpande3/Pandas-Tutorial/master/RegularSeasonCompactResults.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4115852 (3.9M) [text/plain]\n",
            "Saving to: ‘RegularSeasonCompactResults.csv’\n",
            "\n",
            "RegularSeasonCompac 100%[===================>]   3.92M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2021-05-10 13:02:27 (41.9 MB/s) - ‘RegularSeasonCompactResults.csv’ saved [4115852/4115852]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0zmA0yNbjm-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aaf5b6cc-6211-4d6a-dffc-e66b6a86edbe"
      },
      "source": [
        "!head -10 RegularSeasonCompactResults.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Season,Daynum,Wteam,Wscore,Lteam,Lscore,Wloc,Numot\n",
            "1985,20,1228,81,1328,64,N,0\n",
            "1985,25,1106,77,1354,70,H,0\n",
            "1985,25,1112,63,1223,56,H,0\n",
            "1985,25,1165,70,1432,54,H,0\n",
            "1985,25,1192,86,1447,74,H,0\n",
            "1985,25,1218,79,1337,78,H,0\n",
            "1985,25,1228,64,1226,44,N,0\n",
            "1985,25,1242,58,1268,56,N,0\n",
            "1985,25,1260,98,1133,80,H,0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vA0h51xqhxJP"
      },
      "source": [
        "# 데이터 로딩"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IyfgKhSPZmyY"
      },
      "source": [
        "df = pd.read_csv('RegularSeasonCompactResults.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBhyYvYOcckR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "5c28a59b-d733-4246-b406-35aafd6dac45"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Daynum</th>\n",
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              "      <th>Lteam</th>\n",
              "      <th>Lscore</th>\n",
              "      <th>Wloc</th>\n",
              "      <th>Numot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1985</td>\n",
              "      <td>20</td>\n",
              "      <td>1228</td>\n",
              "      <td>81</td>\n",
              "      <td>1328</td>\n",
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              "      <td>0</td>\n",
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              "      <td>25</td>\n",
              "      <td>1106</td>\n",
              "      <td>77</td>\n",
              "      <td>1354</td>\n",
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              "      <th>2</th>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1112</td>\n",
              "      <td>63</td>\n",
              "      <td>1223</td>\n",
              "      <td>56</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1165</td>\n",
              "      <td>70</td>\n",
              "      <td>1432</td>\n",
              "      <td>54</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1192</td>\n",
              "      <td>86</td>\n",
              "      <td>1447</td>\n",
              "      <td>74</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>145284</th>\n",
              "      <td>2016</td>\n",
              "      <td>132</td>\n",
              "      <td>1114</td>\n",
              "      <td>70</td>\n",
              "      <td>1419</td>\n",
              "      <td>50</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>145285</th>\n",
              "      <td>2016</td>\n",
              "      <td>132</td>\n",
              "      <td>1163</td>\n",
              "      <td>72</td>\n",
              "      <td>1272</td>\n",
              "      <td>58</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145286</th>\n",
              "      <td>2016</td>\n",
              "      <td>132</td>\n",
              "      <td>1246</td>\n",
              "      <td>82</td>\n",
              "      <td>1401</td>\n",
              "      <td>77</td>\n",
              "      <td>N</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145287</th>\n",
              "      <td>2016</td>\n",
              "      <td>132</td>\n",
              "      <td>1277</td>\n",
              "      <td>66</td>\n",
              "      <td>1345</td>\n",
              "      <td>62</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>145288</th>\n",
              "      <td>2016</td>\n",
              "      <td>132</td>\n",
              "      <td>1386</td>\n",
              "      <td>87</td>\n",
              "      <td>1433</td>\n",
              "      <td>74</td>\n",
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              "        Season  Daynum  Wteam  Wscore  Lteam  Lscore Wloc  Numot\n",
              "0         1985      20   1228      81   1328      64    N      0\n",
              "1         1985      25   1106      77   1354      70    H      0\n",
              "2         1985      25   1112      63   1223      56    H      0\n",
              "3         1985      25   1165      70   1432      54    H      0\n",
              "4         1985      25   1192      86   1447      74    H      0\n",
              "...        ...     ...    ...     ...    ...     ...  ...    ...\n",
              "145284    2016     132   1114      70   1419      50    N      0\n",
              "145285    2016     132   1163      72   1272      58    N      0\n",
              "145286    2016     132   1246      82   1401      77    N      1\n",
              "145287    2016     132   1277      66   1345      62    N      0\n",
              "145288    2016     132   1386      87   1433      74    N      0\n",
              "\n",
              "[145289 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FeTX6zr1bXey",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "c66d7012-130a-4f1b-8e60-429be27ed569"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <th>Season</th>\n",
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              "      <th>Wscore</th>\n",
              "      <th>Lteam</th>\n",
              "      <th>Lscore</th>\n",
              "      <th>Wloc</th>\n",
              "      <th>Numot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1985</td>\n",
              "      <td>20</td>\n",
              "      <td>1228</td>\n",
              "      <td>81</td>\n",
              "      <td>1328</td>\n",
              "      <td>64</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1106</td>\n",
              "      <td>77</td>\n",
              "      <td>1354</td>\n",
              "      <td>70</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1112</td>\n",
              "      <td>63</td>\n",
              "      <td>1223</td>\n",
              "      <td>56</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1165</td>\n",
              "      <td>70</td>\n",
              "      <td>1432</td>\n",
              "      <td>54</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1192</td>\n",
              "      <td>86</td>\n",
              "      <td>1447</td>\n",
              "      <td>74</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Season  Daynum  Wteam  Wscore  Lteam  Lscore Wloc  Numot\n",
              "0    1985      20   1228      81   1328      64    N      0\n",
              "1    1985      25   1106      77   1354      70    H      0\n",
              "2    1985      25   1112      63   1223      56    H      0\n",
              "3    1985      25   1165      70   1432      54    H      0\n",
              "4    1985      25   1192      86   1447      74    H      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hyt8V0nzcIGF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "9b88b565-ad71-4c19-b3b0-43c640b9b0dd"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Season</th>\n",
              "      <th>Daynum</th>\n",
              "      <th>Wteam</th>\n",
              "      <th>Wscore</th>\n",
              "      <th>Lteam</th>\n",
              "      <th>Lscore</th>\n",
              "      <th>Wloc</th>\n",
              "      <th>Numot</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>145284</th>\n",
              "      <td>2016</td>\n",
              "      <td>132</td>\n",
              "      <td>1114</td>\n",
              "      <td>70</td>\n",
              "      <td>1419</td>\n",
              "      <td>50</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145285</th>\n",
              "      <td>2016</td>\n",
              "      <td>132</td>\n",
              "      <td>1163</td>\n",
              "      <td>72</td>\n",
              "      <td>1272</td>\n",
              "      <td>58</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145286</th>\n",
              "      <td>2016</td>\n",
              "      <td>132</td>\n",
              "      <td>1246</td>\n",
              "      <td>82</td>\n",
              "      <td>1401</td>\n",
              "      <td>77</td>\n",
              "      <td>N</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145287</th>\n",
              "      <td>2016</td>\n",
              "      <td>132</td>\n",
              "      <td>1277</td>\n",
              "      <td>66</td>\n",
              "      <td>1345</td>\n",
              "      <td>62</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145288</th>\n",
              "      <td>2016</td>\n",
              "      <td>132</td>\n",
              "      <td>1386</td>\n",
              "      <td>87</td>\n",
              "      <td>1433</td>\n",
              "      <td>74</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Season  Daynum  Wteam  Wscore  Lteam  Lscore Wloc  Numot\n",
              "145284    2016     132   1114      70   1419      50    N      0\n",
              "145285    2016     132   1163      72   1272      58    N      0\n",
              "145286    2016     132   1246      82   1401      77    N      1\n",
              "145287    2016     132   1277      66   1345      62    N      0\n",
              "145288    2016     132   1386      87   1433      74    N      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yw_DDZIicMit",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6667d2d9-ce35-4570-e99b-c28c84635cfc"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(145289, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bF9T8oDOkuAm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba650aca-e98e-4a84-ba4e-445b5bb44bfe"
      },
      "source": [
        "df.index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=145289, step=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfrSWFOecMlE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3caa0972-832b-4ae6-ed3b-5d38ea01833d"
      },
      "source": [
        "df.columns.tolist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Season', 'Daynum', 'Wteam', 'Wscore', 'Lteam', 'Lscore', 'Wloc', 'Numot']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fSze9X1ckyaW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c61d15f9-84c5-42ab-b2d0-10c64e15ae0f"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 145289 entries, 0 to 145288\n",
            "Data columns (total 8 columns):\n",
            " #   Column  Non-Null Count   Dtype \n",
            "---  ------  --------------   ----- \n",
            " 0   Season  145289 non-null  int64 \n",
            " 1   Daynum  145289 non-null  int64 \n",
            " 2   Wteam   145289 non-null  int64 \n",
            " 3   Wscore  145289 non-null  int64 \n",
            " 4   Lteam   145289 non-null  int64 \n",
            " 5   Lscore  145289 non-null  int64 \n",
            " 6   Wloc    145289 non-null  object\n",
            " 7   Numot   145289 non-null  int64 \n",
            "dtypes: int64(7), object(1)\n",
            "memory usage: 8.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tc5JleNDcMpj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "15818dd7-1ee1-492e-f350-3d79611c9b83"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Season</th>\n",
              "      <th>Daynum</th>\n",
              "      <th>Wteam</th>\n",
              "      <th>Wscore</th>\n",
              "      <th>Lteam</th>\n",
              "      <th>Lscore</th>\n",
              "      <th>Numot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>145289.000000</td>\n",
              "      <td>145289.000000</td>\n",
              "      <td>145289.000000</td>\n",
              "      <td>145289.000000</td>\n",
              "      <td>145289.000000</td>\n",
              "      <td>145289.000000</td>\n",
              "      <td>145289.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2001.574834</td>\n",
              "      <td>75.223816</td>\n",
              "      <td>1286.720646</td>\n",
              "      <td>76.600321</td>\n",
              "      <td>1282.864064</td>\n",
              "      <td>64.497009</td>\n",
              "      <td>0.044387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.233342</td>\n",
              "      <td>33.287418</td>\n",
              "      <td>104.570275</td>\n",
              "      <td>12.173033</td>\n",
              "      <td>104.829234</td>\n",
              "      <td>11.380625</td>\n",
              "      <td>0.247819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1985.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1101.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>1101.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1994.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>1198.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>1191.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2002.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>1284.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>1280.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2010.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>1379.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>1375.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>1464.000000</td>\n",
              "      <td>186.000000</td>\n",
              "      <td>1464.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Season         Daynum  ...         Lscore          Numot\n",
              "count  145289.000000  145289.000000  ...  145289.000000  145289.000000\n",
              "mean     2001.574834      75.223816  ...      64.497009       0.044387\n",
              "std         9.233342      33.287418  ...      11.380625       0.247819\n",
              "min      1985.000000       0.000000  ...      20.000000       0.000000\n",
              "25%      1994.000000      47.000000  ...      57.000000       0.000000\n",
              "50%      2002.000000      78.000000  ...      64.000000       0.000000\n",
              "75%      2010.000000     103.000000  ...      72.000000       0.000000\n",
              "max      2016.000000     132.000000  ...     150.000000       6.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7JGcSQAcMrq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "806a75ee-e09e-4707-eac9-242ed61a2dd2"
      },
      "source": [
        "df.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Season    2016\n",
              "Daynum     132\n",
              "Wteam     1464\n",
              "Wscore     186\n",
              "Lteam     1464\n",
              "Lscore     150\n",
              "Wloc         N\n",
              "Numot        6\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJJvIyqCh1kX"
      },
      "source": [
        "# 참조 방법\n",
        "```\n",
        "# Direct Access\n",
        "df.Wteam : Series  <------ 권장\n",
        "df.Wteam[82] : value\n",
        "df.Wteam[82:85] : Series\n",
        "df['Wteam'] : Series  \n",
        "df['Wteam'][82] : value\n",
        "df['Wteam'][82:85] : Series\n",
        "df[82] # ERROR\n",
        "df[82:85] : DataFrame\n",
        "df[82:85]['Wteam'] : Series\n",
        "df[[True, True, True, ....]] : DataFrame\n",
        "\n",
        "# by loc(location) : 컬럼을 컬럼명으로\n",
        "df.loc[82] : Series\n",
        "df.loc[82:85] : DataFrame\n",
        "df.loc[[82,83,84,85]] : DataFrame\n",
        "df.loc[(82,83,84,85)] : ERROR\n",
        "df.loc[82,83,84,85] : ERROR\n",
        "df.loc[82]['Wteam'] : value <-------- 권장\n",
        "df.loc[82,'Wteam'] : value <--------- 권장\n",
        "df.loc[82][2] : value\n",
        "df.loc[82,2] : ERROR\n",
        "df.loc['Wteam'] : ERROR\n",
        "\n",
        "# by iloc(integer location) : 컬럼을 컬럼 인덱스로\n",
        "df.iloc[82] : Series\n",
        "df.iloc[82:85] : DataFrame\n",
        "df.iloc[[82,83,84,85]] : DataFrame\n",
        "df.iloc[(82,83,84,85)] : ERROR\n",
        "df.iloc[82,83,84,85] : ERROR\n",
        "df.iloc[82]['Wteam'] : value\n",
        "df.iloc[82,'Wteam] : ERROR\n",
        "df.iloc[82][2] : value  <-------- 권장\n",
        "df.iloc[82,2] : value  <--------- 권장\n",
        "df.iloc['Wteam'] : ERROR\n",
        "```\n",
        "\n",
        "정리하면\n",
        "\n",
        "```\n",
        "특정 칼럼 df.Wteam\n",
        "\n",
        "loc 사용하여 참조  : df.loc[82, 'Wteam']\n",
        "iloc 사용하여 참조 : df.iloc[82, 2] \n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sKEOLZnvcMuC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ffaf2bc-b1b1-4029-9e5d-cce99d3361f1"
      },
      "source": [
        "df['Wteam']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         1228\n",
              "1         1106\n",
              "2         1112\n",
              "3         1165\n",
              "4         1192\n",
              "          ... \n",
              "145284    1114\n",
              "145285    1163\n",
              "145286    1246\n",
              "145287    1277\n",
              "145288    1386\n",
              "Name: Wteam, Length: 145289, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fm18-8CWcMyu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56a37a35-3d0b-4040-fde3-11c3cf6ebed6"
      },
      "source": [
        "df['Wteam'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1464"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "id": "ldR59h3lBrBZ",
        "outputId": "53b667e0-7220-4c01-d873-60cf319777ec"
      },
      "source": [
        "df[82:85]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Season</th>\n",
              "      <th>Daynum</th>\n",
              "      <th>Wteam</th>\n",
              "      <th>Wscore</th>\n",
              "      <th>Lteam</th>\n",
              "      <th>Lscore</th>\n",
              "      <th>Wloc</th>\n",
              "      <th>Numot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>1985</td>\n",
              "      <td>28</td>\n",
              "      <td>1464</td>\n",
              "      <td>71</td>\n",
              "      <td>1134</td>\n",
              "      <td>58</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>1985</td>\n",
              "      <td>29</td>\n",
              "      <td>1116</td>\n",
              "      <td>59</td>\n",
              "      <td>1416</td>\n",
              "      <td>45</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>1985</td>\n",
              "      <td>29</td>\n",
              "      <td>1124</td>\n",
              "      <td>99</td>\n",
              "      <td>1341</td>\n",
              "      <td>77</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Season  Daynum  Wteam  Wscore  Lteam  Lscore Wloc  Numot\n",
              "82    1985      28   1464      71   1134      58    H      0\n",
              "83    1985      29   1116      59   1416      45    N      0\n",
              "84    1985      29   1124      99   1341      77    H      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XyssEc6ZcM1I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2c55b2b-6c90-4ef0-c036-7ac2b1db0e87"
      },
      "source": [
        "df['Wteam'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1286.7206464357246"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEJNux1HeRnd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cafe426e-50bc-4520-d8eb-2ced60724c43"
      },
      "source": [
        "# df[82] # NOT SUPPORT\n",
        "df.iloc[82]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Season    1985\n",
              "Daynum      28\n",
              "Wteam     1464\n",
              "Wscore      71\n",
              "Lteam     1134\n",
              "Lscore      58\n",
              "Wloc         H\n",
              "Numot        0\n",
              "Name: 82, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qp3sGHZU9u5g",
        "outputId": "bd13ece1-d7fc-40ee-84f2-4f86add0061a"
      },
      "source": [
        "df.loc[82]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Season    1985\n",
              "Daynum      28\n",
              "Wteam     1464\n",
              "Wscore      71\n",
              "Lteam     1134\n",
              "Lscore      58\n",
              "Wloc         H\n",
              "Numot        0\n",
              "Name: 82, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPQvTrWX-acf",
        "outputId": "3d12651c-b4ab-45b4-af66-7f994ebe69e0"
      },
      "source": [
        "df.loc[82]['Wteam']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1464"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "buPHbtTieRsd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43a10e5d-85b5-42e2-9ed1-635f5ca4aad1"
      },
      "source": [
        "df.iloc[82]['Wteam']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1464"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6fbeWyb-nhd",
        "outputId": "dbb42842-829d-442f-dcde-714312cf997b"
      },
      "source": [
        "df.loc[82, 'Wteam']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1464"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UfXIowMv-pnN",
        "outputId": "65940ca8-fed8-406b-a924-b383147ce7eb"
      },
      "source": [
        "# df.iloc[82, 'Wteam'] # Not Support\n",
        "df.iloc[82, 2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1464"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3phnrVbFhJNX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df23c25a-dc51-483f-908d-63b582771470"
      },
      "source": [
        "df['Wteam'][82]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1464"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rpl6VHNoc9Xf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22c652f1-4118-4eb1-d58c-7fe0a983d831"
      },
      "source": [
        "df['Wteam'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1181    819\n",
              "1242    804\n",
              "1246    765\n",
              "1314    761\n",
              "1112    746\n",
              "       ... \n",
              "1101     18\n",
              "1446     14\n",
              "1118      6\n",
              "1289      6\n",
              "1327      3\n",
              "Name: Wteam, Length: 364, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTCd4mTkHSb3"
      },
      "source": [
        "# 데이터 구조와 참조 방법"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OnQEKZBtKYoF"
      },
      "source": [
        "## 마스크 타입 필터"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHfamfU-IZVH",
        "outputId": "ec2efa5d-39a0-45f4-9751-f4077df328c4"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "# 필터 선언\n",
        "filtered_mask = pd.Series([True, False, True, False])\n",
        "print(\"filtered_mask legnth =\",len(filtered_mask))\n",
        "print(filtered_mask)\n",
        "print()\n",
        "\n",
        "# 필터링된 DF\n",
        "print(df[filtered_mask])\n",
        "print()\n",
        "\n",
        "# 대상 항목만 값 설정\n",
        "df.loc[filtered_mask, 'BBB'] = -1\n",
        "print(df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "filtered_mask legnth = 4\n",
            "0     True\n",
            "1    False\n",
            "2     True\n",
            "3    False\n",
            "dtype: bool\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "2    6   30  -30\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   -1  100\n",
            "1    5   20   50\n",
            "2    6   -1  -30\n",
            "3    7   40  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBqR2ZjYNaZL"
      },
      "source": [
        "## 인덱스 타입 필터"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAM3g7YaNdp4",
        "outputId": "7d994ad3-fc10-4fb4-8e9d-1ac7d5b897b8"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "# 필터 선언\n",
        "filtered_index = np.array([0, 2])\n",
        "print(\"filtered_index legnth =\",len(filtered_index))\n",
        "print(filtered_index)\n",
        "print()\n",
        "\n",
        "# 필터링된 DF\n",
        "print(df.loc[filtered_index])\n",
        "print()\n",
        "\n",
        "# 대상 항목만 값 설정\n",
        "df.loc[filtered_index, 'BBB'] = -1\n",
        "print(df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "filtered_index legnth = 2\n",
            "[0 2]\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "2    6   30  -30\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   -1  100\n",
            "1    5   20   50\n",
            "2    6   -1  -30\n",
            "3    7   40  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MuvgO3CvLDAY"
      },
      "source": [
        "## 단일 조건"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJBot4eFG_3x",
        "outputId": "bfa978f9-a831-4f73-b9bd-5de26f168b55"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "# 조건으로 필터\n",
        "filtered_mask = df.AAA>=5\n",
        "print(\"filtered_mask legnth =\",len(filtered_mask))\n",
        "print(filtered_mask)\n",
        "print()\n",
        "\n",
        "# 필터링된 DF\n",
        "print(df[filtered_mask])\n",
        "print()\n",
        "\n",
        "# 대상 항목만 값 설정\n",
        "df.loc[filtered_mask, 'BBB'] = -1\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "filtered_mask legnth = 4\n",
            "0    False\n",
            "1     True\n",
            "2     True\n",
            "3     True\n",
            "Name: AAA, dtype: bool\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   -1   50\n",
            "2    6   -1  -30\n",
            "3    7   -1  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RR185aXmLJKI"
      },
      "source": [
        "## 복수 조건 - 마스크 사용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f5CVtOqHVasc",
        "outputId": "61e40717-4d80-4b2a-ea4d-4e289af27b70"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# 복수 조건으로 필터링된 DF\n",
        "filtered_mask = (df.AAA>=5) & (df.BBB<40)\n",
        "\n",
        "print(\"filtered_mask legnth =\",len(filtered_mask))\n",
        "print(filtered_mask)\n",
        "print()\n",
        "\n",
        "# 대상 항목만 값 설정\n",
        "df.loc[filtered_mask, 'BBB'] = -1\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "filtered_mask legnth = 4\n",
            "0    False\n",
            "1     True\n",
            "2     True\n",
            "3    False\n",
            "dtype: bool\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   -1   50\n",
            "2    6   -1  -30\n",
            "3    7   40  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4OnI1vskQFqA"
      },
      "source": [
        "## 복수 조건 - numpy 검색후 인덱스 사용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A57oUcphJ5A8",
        "outputId": "554f4dcf-2ab4-4a17-d69c-772433d8e5a3"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# 복수 조건\n",
        "filtered_index = np.where( (df.AAA>=5) & (df.BBB<40) )[0]\n",
        "print(\"filtered_index legnth =\",len(filtered_index))\n",
        "print(filtered_index)\n",
        "print()\n",
        "\n",
        "# 필터링된 DF\n",
        "print(df.loc[filtered_index])\n",
        "print()\n",
        "\n",
        "# 대상 항목만 값 설정\n",
        "df.loc[filtered_index, 'BBB'] = -1\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "filtered_index legnth = 2\n",
            "[1 2]\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   -1   50\n",
            "2    6   -1  -30\n",
            "3    7   40  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g51lSpRo3nD_"
      },
      "source": [
        "# HowTo\n",
        "\n",
        "많은 부분을 https://pandas.pydata.org/pandas-docs/stable/user_guide/cookbook.html#에서 가져옴"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q82Zg_m9dAVa"
      },
      "source": [
        "## DF 구하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OjS25gZk8-uY"
      },
      "source": [
        "### 조건에 맞는 행으로 구성된 DF 구하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PtatCyRE7Q6-",
        "outputId": "69288779-2c74-4204-e8c1-5d267b451864"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "print(df.loc[df.AAA>=5])\n",
        "print()\n",
        "\n",
        "print(df.loc[df.AAA>=5].loc[df.BBB<=30])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "1    5   20   50\n",
            "2    6   30  -30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2iOzDYDdEfL"
      },
      "source": [
        "### 특정 컬럼으로 구성된 DF 구하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fuzuqp-7dJLx",
        "outputId": "8fcc55b9-88f4-48c3-b7e8-97176c8ded44"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "print(df[\"AAA\"])\n",
        "print(\"type =\", type(df[\"AAA\"])) # Series이다.\n",
        "print()\n",
        "\n",
        "print(df[[\"AAA\"]]) # 컬럼명의 리스트를 주면 DataFrame이다.\n",
        "print(\"type =\", type(df[[\"AAA\"]]))\n",
        "print()\n",
        "\n",
        "print(df[[\"AAA\", \"CCC\"]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "0    4\n",
            "1    5\n",
            "2    6\n",
            "3    7\n",
            "Name: AAA, dtype: int64\n",
            "type = <class 'pandas.core.series.Series'>\n",
            "\n",
            "   AAA\n",
            "0    4\n",
            "1    5\n",
            "2    6\n",
            "3    7\n",
            "type = <class 'pandas.core.frame.DataFrame'>\n",
            "\n",
            "   AAA  CCC\n",
            "0    4  100\n",
            "1    5   50\n",
            "2    6  -30\n",
            "3    7  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HUR9zH8X9CCo"
      },
      "source": [
        "## 특정 조건에 맞는 행의 값 변경"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VvbqWu2iSxsE"
      },
      "source": [
        "### 단일 조건"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QxRROe5N9Lj1",
        "outputId": "5d9dd736-0cc3-4cb4-9eea-09fbfa23450e"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df.loc[df.AAA>=5, \"BBB\"] = -1\n",
        "print(df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   -1   50\n",
            "2    6   -1  -30\n",
            "3    7   -1  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXIc9D-RSwE8"
      },
      "source": [
        "### 복수 조건"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYxQKNgwQZiw",
        "outputId": "2c37643e-f2fa-44c5-c31f-c2f1e3ff70b2"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "filtered_index = df.loc[df.AAA>=5].loc[df.BBB<=30].index\n",
        "print(\"filtered_index =\", filtered_index)\n",
        "print()\n",
        "\n",
        "df.loc[filtered_index, 'BBB'] = -1\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "filtered_index = Int64Index([1, 2], dtype='int64')\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   -1   50\n",
            "2    6   -1  -30\n",
            "3    7   40  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3AvPlzcgS1IO"
      },
      "source": [
        "### 복수 컬럼"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zFS70S1S5om",
        "outputId": "765697bf-3f7d-440e-c5bc-c3e89c44c1dd"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df.loc[df.AAA>=5, [\"BBB\",\"CCC\"]] = -1\n",
        "print(df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   -1   -1\n",
            "2    6   -1   -1\n",
            "3    7   -1   -1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6yNPCCCTzeB"
      },
      "source": [
        "### 특정 조건으로 값 이분 설정 - 각각 설정\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "vbMXo9fOT4p1",
        "outputId": "19d688d4-582d-4288-cd36-a27242e9b13a"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "\n",
        "df.loc[df.AAA>=5, .BBB] = 999\n",
        "print(df)\n",
        "\n",
        "df.loc[df.AAA<5, .BBB] = -999\n",
        "print(df)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-268-627430d1bc08>\"\u001b[0;36m, line \u001b[0;32m9\u001b[0m\n\u001b[0;31m    df.loc[df.AAA>=5, .BBB] = 999\u001b[0m\n\u001b[0m                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHjph1dCUEl_"
      },
      "source": [
        "### 특정 조건으로 값 이분 설정 - numpy 사용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6NDrhafwUD1a",
        "outputId": "36644086-bc0a-4c73-983f-5e4ad182b648"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df.BBB = np.where(df.AAA>=5, 999, -999)\n",
        "print(df)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4 -999  100\n",
            "1    5  999   50\n",
            "2    6  999  -30\n",
            "3    7  999  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "szVTefUubLV3"
      },
      "source": [
        "## 검색"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gz_Jv9sUZZok"
      },
      "source": [
        "### 값 리스트에 포함되어 있는지 검색"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SueBbTrbap3Y",
        "outputId": "21e97a53-ab95-46ad-a78c-903619b5dc29"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "print(df[df.AAA.isin([4, 5, 7])])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "3    7   40  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-C7so7jZvyy"
      },
      "source": [
        "### 특정 인덱스 필터링"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CuP77zjvZeMo",
        "outputId": "a693ce52-1521-40be-f1a6-e1ec5eef6279"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "print(df[df.index.isin([0, 1, 3])])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "3    7   40  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIbJ1GeDaEF1"
      },
      "source": [
        "### 짝수 인덱스 필터링"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tqd0LQQ9Z0l8",
        "outputId": "8a621777-61f5-400f-a242-6de7179b7d67"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "print(df[df.index%2!=0])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "1    5   20   50\n",
            "3    7   40  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8hMVknvajKR"
      },
      "source": [
        "## 컬럼 추가\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bbPwA4sbtkO"
      },
      "source": [
        "### 특정 컬럼 복사"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-MIWNDy8bwLe",
        "outputId": "bf4bf472-ba32-434b-ad98-0ffb4a943805"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df[\"AAA_copied\"] = df[\"AAA\"]\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC  AAA_copied\n",
            "0    4   10  100           4\n",
            "1    5   20   50           5\n",
            "2    6   30  -30           6\n",
            "3    7   40  -50           7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ILsBkoPlbaov"
      },
      "source": [
        "### 모든 컬럼 복사"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvvtLKuhZuCI",
        "outputId": "92cb9504-001e-45bc-997f-156814464312"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "source_cols = df.columns\n",
        "new_cols = [str(x) + \"_copied\" for x in source_cols]\n",
        "df[new_cols] = df[source_cols]\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC  AAA_copied  BBB_copied  CCC_copied\n",
            "0    4   10  100           4          10         100\n",
            "1    5   20   50           5          20          50\n",
            "2    6   30  -30           6          30         -30\n",
            "3    7   40  -50           7          40         -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O76tPHPLb9j5"
      },
      "source": [
        "### 인코딩된 컬럼 추가"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fifAcS3ZeKQ",
        "outputId": "26111e23-c67c-4e21-ba47-a92687f1f58f"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "def encode(AAA):\n",
        "  if AAA==4 : return \"A\"\n",
        "  if AAA==6 : return \"B\"\n",
        "  return \"C\"\n",
        "\n",
        "df[\"AAA_encoded\"] = df[[\"AAA\"]].applymap(encode)\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC AAA_encoded\n",
            "0    4   10  100           A\n",
            "1    5   20   50           C\n",
            "2    6   30  -30           B\n",
            "3    7   40  -50           C\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjMz3OuAkHOF"
      },
      "source": [
        "### 특정 컬럼 값 변경해서"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5czufvMkM_X",
        "outputId": "362d9d9d-a1c3-4179-ddb2-23017970061e"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df[\"AAA_doubled\"] = df[\"AAA\"]*2.\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC  AAA_doubled\n",
            "0    4   10  100          8.0\n",
            "1    5   20   50         10.0\n",
            "2    6   30  -30         12.0\n",
            "3    7   40  -50         14.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wu5F85SdkeeS"
      },
      "source": [
        "### 특정 위치에"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_gV0V4skiOp",
        "outputId": "e2d80c12-9f63-4f64-b0d0-2b6ece1e644f"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df.insert(1, 'AAA_doubled', df.AAA*2.)\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  AAA_doubled  BBB  CCC\n",
            "0    4          8.0   10  100\n",
            "1    5         10.0   20   50\n",
            "2    6         12.0   30  -30\n",
            "3    7         14.0   40  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPwwTZnak4Wa"
      },
      "source": [
        "## 컬럼 삭제"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oNsPewewlIMG"
      },
      "source": [
        "### 컬럼 이름으로 1개"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yvGjk8pck6MV",
        "outputId": "10a3e4e0-5574-4fd0-cb87-0ad3763ec44e"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df.drop(columns=\"BBB\", inplace=True)\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  CCC\n",
            "0    4  100\n",
            "1    5   50\n",
            "2    6  -30\n",
            "3    7  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVdNvY7olLnF"
      },
      "source": [
        "### 컬럼이름으로 여러개"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0_fCRaplEQI",
        "outputId": "f5d3aad3-fd6e-4198-a0f1-dcc79a339d37"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df.drop(columns=[\"BBB\", \"CCC\"], inplace=True)\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA\n",
            "0    4\n",
            "1    5\n",
            "2    6\n",
            "3    7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BIGJxXaun52T"
      },
      "source": [
        "## 데이터 추가"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aNJPaTZBqqyI"
      },
      "source": [
        "### row 1개 추가\n",
        "\n",
        "하지 않는다.\n",
        "\n",
        "만약 필요하다면 list에 추가하고 최종적으로 DataFrmae 생성.\n",
        "\n",
        "```\n",
        "data = []\n",
        "for a, b, c in some_function_that_yields_data():\n",
        "    data.append([a, b, c])\n",
        "\n",
        "df = pd.DataFrame(data, columns=['A', 'B', 'C'])\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61hPdisKoDN5"
      },
      "source": [
        "### dataframe 전체 추가"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3fvauA9eoJYw",
        "outputId": "abaa368e-5e61-40dc-bcb4-da909efc85db"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "df2 = pd.DataFrame(\n",
        "        {\"AAA\": [8, 9], \"BBB\": [80, 90], \"CCC\": [80, 90]}\n",
        ")\n",
        "\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "print(df2)\n",
        "print()\n",
        "\n",
        "df = df.append(df2) # inplace 못한다.\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    8   80   80\n",
            "1    9   90   90\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "0    8   80   80\n",
            "1    9   90   90\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LILrO1Xfo9mf"
      },
      "source": [
        "### dataframe 3개 이상 합치기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z2VrSJaopBI3",
        "outputId": "80d435eb-b2b5-4508-974a-e2e9b48cba17"
      },
      "source": [
        "df1 = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5], \"BBB\": [10, 20], \"CCC\": [100, 50]}\n",
        ")\n",
        "\n",
        "df2 = pd.DataFrame(\n",
        "        {\"AAA\": [6, 7], \"BBB\": [30, 40], \"CCC\": [-30, -50]}\n",
        ")\n",
        "\n",
        "df3 = pd.DataFrame(\n",
        "        {\"AAA\": [8, 9], \"BBB\": [80, 90], \"CCC\": [80, 90]}\n",
        ")\n",
        "\n",
        "print(df1)\n",
        "print()\n",
        "\n",
        "print(df2)\n",
        "print()\n",
        "\n",
        "print(df3)\n",
        "print()\n",
        "\n",
        "df = pd.concat([df1, df2, df3], ignore_index=True)\n",
        "\n",
        "# append() 반복해 쓰면 효율이 떨어진다. \n",
        "#df = df1.append(df2).append(df3)\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    6   30  -30\n",
            "1    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    8   80   80\n",
            "1    9   90   90\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "0    6   30  -30\n",
            "1    7   40  -50\n",
            "0    8   80   80\n",
            "1    9   90   90\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3YOgKZtmJK4"
      },
      "source": [
        "## 데이터 삭제"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AFbs05BfmP0K"
      },
      "source": [
        "### row index로 1개"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ilSsnCsZk6Jb",
        "outputId": "147b5743-1f28-46fa-d7ee-392b7e582deb"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df.drop(1, inplace=True)\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YCiJTVSmb3n"
      },
      "source": [
        "### row index로 여러개"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ADQkGE2_k6FK",
        "outputId": "7088fc0a-c1ac-4ea0-f285-3f7607ea8364"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df.drop([1,3], inplace=True)\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "2    6   30  -30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TqT2xntWnLB3",
        "outputId": "4e6d60d7-7285-4913-c790-02de92dd5a33"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "target_indexes = [1,3]\n",
        "\n",
        "df.drop(target_indexes, inplace=True)\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "2    6   30  -30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6mgNjz_amimK"
      },
      "source": [
        "### 특정 조건으로"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BQkB7uccnJIu",
        "outputId": "e1a04b92-6979-4b2d-b886-2c82c219442f"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "target_indexes = df.loc[df.AAA>5].index\n",
        "print(target_indexes)\n",
        "print()\n",
        "\n",
        "df.drop(target_indexes, inplace=True)\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "Int64Index([2, 3], dtype='int64')\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NfOe1uJmlvn",
        "outputId": "8af8ed38-1162-4f4a-f1f1-e4e09796abdb"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df.drop(df.loc[df.AAA>5].index, inplace=True)\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1R0C_K2hd-N_"
      },
      "source": [
        "## 기타"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-3_xP4jexkv"
      },
      "source": [
        "### 특정 칼럼으로 소팅"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkfC2s3Ge0CG",
        "outputId": "5b081180-b97c-4bf9-c84b-eb5a973963bb"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 35, 10], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "print(\"sort by BBB\")\n",
        "print(df.sort_values(by=\"BBB\"))\n",
        "print()\n",
        "\n",
        "print(\"sort by BBB, CCC\")\n",
        "print(df.sort_values(by=[\"BBB\", \"CCC\"]))\n",
        "print()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   35  -30\n",
            "3    7   10  -50\n",
            "\n",
            "sort by BBB\n",
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "3    7   10  -50\n",
            "1    5   20   50\n",
            "2    6   35  -30\n",
            "\n",
            "sort by BBB, CCC\n",
            "   AAA  BBB  CCC\n",
            "3    7   10  -50\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   35  -30\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiIuGSadXOC4"
      },
      "source": [
        "### 특정 컬럼 값이 특정 값에 가까운 순서로 소팅"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNeH3HicXTRu",
        "outputId": "7d9db61e-91d3-4e95-da5f-bece7ed26e68"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "target_value = 43.0\n",
        "\n",
        "abs_value = (df.CCC - target_value).abs()\n",
        "print(\"abs_value\")\n",
        "print(abs_value)\n",
        "print()\n",
        "\n",
        "print(\"abs_value.argsort()\")\n",
        "print(abs_value.argsort())\n",
        "print()\n",
        "\n",
        "print(df.loc[(df.CCC - target_value).abs().argsort()])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n",
            "\n",
            "abs_value\n",
            "0    57.0\n",
            "1     7.0\n",
            "2    73.0\n",
            "3    93.0\n",
            "Name: CCC, dtype: float64\n",
            "\n",
            "abs_value.argsort()\n",
            "0    1\n",
            "1    0\n",
            "2    2\n",
            "3    3\n",
            "Name: CCC, dtype: int64\n",
            "\n",
            "   AAA  BBB  CCC\n",
            "1    5   20   50\n",
            "0    4   10  100\n",
            "2    6   30  -30\n",
            "3    7   40  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2RZ1XYdleBPq"
      },
      "source": [
        "### group by로 묶어서 group중 min 찾기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zaVOOS6bZeGQ",
        "outputId": "d4516191-60cd-4c17-cfc6-5a97e102d08f"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "   {\"AAA\": [1, 1, 1, 2, 2, 2, 3, 3], \"BBB\": [2, 1, 3, 4, 5, 1, 2, 3]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "print(df.loc[df.groupby(\"AAA\")[\"BBB\"].idxmin()])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB\n",
            "0    1    2\n",
            "1    1    1\n",
            "2    1    3\n",
            "3    2    4\n",
            "4    2    5\n",
            "5    2    1\n",
            "6    3    2\n",
            "7    3    3\n",
            "\n",
            "   AAA  BBB\n",
            "1    1    1\n",
            "5    2    1\n",
            "6    3    2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUniVvQZsuun"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nvDYBiiDA6QO"
      },
      "source": [
        "## 코드성 데이터 인코딩\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zt43M-T2By-J"
      },
      "source": [
        "### category 타입으로"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbQbR2vdBDfT"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "   {\"AAA\": [1, 1, 1, 2, 2, 2, 3, 3], \"BBB\": [\"A\", \"B\", \"C\", \"A\", \"A\", \"C\", \"B\", \"A\"]}\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2kIePNwBk9Z",
        "outputId": "f3b3c46a-fa29-407c-f1c3-3c94474c25d0"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8 entries, 0 to 7\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   AAA     8 non-null      int64 \n",
            " 1   BBB     8 non-null      object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 256.0+ bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvpI4eewBbtK"
      },
      "source": [
        "df.BBB = df.BBB.astype('category')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPadMZMRBjCT",
        "outputId": "953639ab-5d73-425e-a142-4ff9ef572b46"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8 entries, 0 to 7\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype   \n",
            "---  ------  --------------  -----   \n",
            " 0   AAA     8 non-null      int64   \n",
            " 1   BBB     8 non-null      category\n",
            "dtypes: category(1), int64(1)\n",
            "memory usage: 304.0 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TIslm4b0Bq37"
      },
      "source": [
        "df['BBB_code'] = df.BBB.cat.codes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "1-EmRWhZBu2j",
        "outputId": "2c46794b-39b7-4c21-ebdc-beb4b07ea4bf"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAA</th>\n",
              "      <th>BBB</th>\n",
              "      <th>BBB_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>B</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>B</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   AAA BBB  BBB_code\n",
              "0    1   A         0\n",
              "1    1   B         1\n",
              "2    1   C         2\n",
              "3    2   A         0\n",
              "4    2   A         0\n",
              "5    2   C         2\n",
              "6    3   B         1\n",
              "7    3   A         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6udsK7sB5Yv"
      },
      "source": [
        "### 특정 카테고리 리스트로"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYri1TmJB9n8"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "   {\"AAA\": [1, 1, 1, 2, 2, 2, 3, 3], \"BBB\": [\"A\", \"B\", \"C\", \"A\", \"A\", \"C\", \"B\", \"A\"]}\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fNtWbYH7B9n-",
        "outputId": "c5762e06-b794-4dea-d2ec-20ee5b68d3a6"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8 entries, 0 to 7\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   AAA     8 non-null      int64 \n",
            " 1   BBB     8 non-null      object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 256.0+ bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9dhMvSWB_lB"
      },
      "source": [
        "types = [ \"C\", \"B\", \"A\" ]\n",
        "df.BBB = pd.Categorical(df.BBB, categories=types, ordered=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-gio5FYFcU3"
      },
      "source": [
        "df['BBB_code'] = df.BBB.cat.codes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "6Nl9ol53FdQB",
        "outputId": "d9af4e65-64fd-4bab-a993-e88a45f16da9"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAA</th>\n",
              "      <th>BBB</th>\n",
              "      <th>BBB_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>B</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>B</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   AAA BBB  BBB_code\n",
              "0    1   A         2\n",
              "1    1   B         1\n",
              "2    1   C         0\n",
              "3    2   A         2\n",
              "4    2   A         2\n",
              "5    2   C         0\n",
              "6    3   B         1\n",
              "7    3   A         2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Scnzu0qx1YB6"
      },
      "source": [
        "### 특정 카테고리 코드 맵으로"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8q8yOy0p00xp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82217b2d-e07c-4080-b907-850385a39c26"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "   {\"AAA\": [1, 1, 1, 2, 2, 2, 3, 3], \"BBB\": [\"A\", \"B\", \"C\", \"A\", \"A\", \"C\", \"B\", \"A\"]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df['BBB_coded'] = df['BBB'].map({\"A\":0, \"B\":1, \"C\":2})\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA BBB\n",
            "0    1   A\n",
            "1    1   B\n",
            "2    1   C\n",
            "3    2   A\n",
            "4    2   A\n",
            "5    2   C\n",
            "6    3   B\n",
            "7    3   A\n",
            "\n",
            "   AAA BBB  BBB_coded\n",
            "0    1   A          0\n",
            "1    1   B          1\n",
            "2    1   C          2\n",
            "3    2   A          0\n",
            "4    2   A          0\n",
            "5    2   C          2\n",
            "6    3   B          1\n",
            "7    3   A          0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXj1-ag3393F"
      },
      "source": [
        "## 결측치 확인"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ztN1KqPZ6ZTL"
      },
      "source": [
        "### 결측치 존재 여부 확인"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qF5Koa0M4q25",
        "outputId": "392c3dcb-198f-49d4-83c3-6f8d67f53ec2"
      },
      "source": [
        "df = pd.DataFrame([[np.nan, 2, np.nan, 0],\n",
        "                   [3, 4, np.nan, 1],\n",
        "                   [np.nan, np.nan, np.nan, 5],\n",
        "                   [np.nan, 3, np.nan, 4]],\n",
        "                  columns=list('ABCD'))\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "# NaN 인 곳을 True로\n",
        "print(\"df.isnull()\")\n",
        "print(df.isnull())\n",
        "print()\n",
        "\n",
        "# 컬럼 별로 True가 한 개라도 있는 지\n",
        "print(\"df.isnull().any()\")\n",
        "print(df.isnull().any())\n",
        "print()\n",
        "\n",
        "# 전체에서 True가 한 개라도 있는 지\n",
        "print(\"df.isnull().any().any()\")\n",
        "print(df.isnull().any().any())\n",
        "print()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     A    B   C  D\n",
            "0  NaN  2.0 NaN  0\n",
            "1  3.0  4.0 NaN  1\n",
            "2  NaN  NaN NaN  5\n",
            "3  NaN  3.0 NaN  4\n",
            "\n",
            "df.isnull()\n",
            "       A      B     C      D\n",
            "0   True  False  True  False\n",
            "1  False  False  True  False\n",
            "2   True   True  True  False\n",
            "3   True  False  True  False\n",
            "\n",
            "df.isnull().any()\n",
            "A     True\n",
            "B     True\n",
            "C     True\n",
            "D    False\n",
            "dtype: bool\n",
            "\n",
            "df.isnull().any().any()\n",
            "True\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ecK0DE5Y6cIA"
      },
      "source": [
        "### 결측치 갯수 확인"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CL_tkafL4qzz",
        "outputId": "f2726fca-0077-4992-9afc-08ea965d5c3d"
      },
      "source": [
        "df = pd.DataFrame([[np.nan, 2, np.nan, 0],\n",
        "                   [3, 4, np.nan, 1],\n",
        "                   [np.nan, np.nan, np.nan, 5],\n",
        "                   [np.nan, 3, np.nan, 4]],\n",
        "                  columns=list('ABCD'))\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "# NaN 인 곳을 True로\n",
        "print(\"df.isnull()\")\n",
        "print(df.isnull())\n",
        "print()\n",
        "\n",
        "# 컬럼 별 True 갯수\n",
        "print(\"df.isnull().sum()\")\n",
        "print(df.isnull().sum())\n",
        "print()\n",
        "\n",
        "# 전체 True 갯수\n",
        "print(\"df.isnull().sum().sum()\")\n",
        "print(df.isnull().sum().sum())\n",
        "print()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     A    B   C  D\n",
            "0  NaN  2.0 NaN  0\n",
            "1  3.0  4.0 NaN  1\n",
            "2  NaN  NaN NaN  5\n",
            "3  NaN  3.0 NaN  4\n",
            "\n",
            "df.isnull()\n",
            "       A      B     C      D\n",
            "0   True  False  True  False\n",
            "1  False  False  True  False\n",
            "2   True   True  True  False\n",
            "3   True  False  True  False\n",
            "\n",
            "df.isnull().sum()\n",
            "A    3\n",
            "B    1\n",
            "C    4\n",
            "D    0\n",
            "dtype: int64\n",
            "\n",
            "df.isnull().sum().sum()\n",
            "8\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMuvRq207Xxy"
      },
      "source": [
        "### 각 컬럼별 결측 비율"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5gvYa1j74qvS",
        "outputId": "8390ce38-1c3d-4888-e7f9-aefbb7acaf9d"
      },
      "source": [
        "df = pd.DataFrame([[np.nan, 2, np.nan, 0],\n",
        "                   [3, 4, np.nan, 1],\n",
        "                   [np.nan, np.nan, np.nan, 5],\n",
        "                   [np.nan, 3, np.nan, 4]],\n",
        "                  columns=list('ABCD'))\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "# NaN 인 곳을 True로\n",
        "print(\"df.isnull()\")\n",
        "print(df.isnull())\n",
        "print()\n",
        "\n",
        "# 컬럼 별 True 갯수\n",
        "print(\"df.isnull().sum()\")\n",
        "print(df.isnull().sum())\n",
        "print()\n",
        "\n",
        "# 컬럼 별 결측치 비율\n",
        "print(df.isnull().sum()/(len(df))*100)\n",
        "print()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     A    B   C  D\n",
            "0  NaN  2.0 NaN  0\n",
            "1  3.0  4.0 NaN  1\n",
            "2  NaN  NaN NaN  5\n",
            "3  NaN  3.0 NaN  4\n",
            "\n",
            "df.isnull()\n",
            "       A      B     C      D\n",
            "0   True  False  True  False\n",
            "1  False  False  True  False\n",
            "2   True   True  True  False\n",
            "3   True  False  True  False\n",
            "\n",
            "df.isnull().sum()\n",
            "A    3\n",
            "B    1\n",
            "C    4\n",
            "D    0\n",
            "dtype: int64\n",
            "\n",
            "A     75.0\n",
            "B     25.0\n",
            "C    100.0\n",
            "D      0.0\n",
            "dtype: float64\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-JBQdBG78rG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7g4h0ioE78oS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZuCcOiS78iT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAST44ri78fk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7xa2Z-HD78aK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-D_o465F4qpy"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PYb8ku_7v3yF"
      },
      "source": [
        "## 결측치 처리\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JFzIqEpQ82BW"
      },
      "source": [
        "### 결측치 있는 데이터 버리기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ln_2FwVM78tq",
        "outputId": "d2881ee6-41f3-44f9-d4ba-bd6c8920c019"
      },
      "source": [
        "df = pd.DataFrame([[1, 2, 3, 0],\n",
        "                   [3, 4, 5, 1],\n",
        "                   [np.nan, np.nan, np.nan, 5],\n",
        "                   [2, 3, 1, 4]],\n",
        "                  columns=list('ABCD'))\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "print(df.dropna())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     A    B    C  D\n",
            "0  1.0  2.0  3.0  0\n",
            "1  3.0  4.0  5.0  1\n",
            "2  NaN  NaN  NaN  5\n",
            "3  2.0  3.0  1.0  4\n",
            "\n",
            "     A    B    C  D\n",
            "0  1.0  2.0  3.0  0\n",
            "1  3.0  4.0  5.0  1\n",
            "3  2.0  3.0  1.0  4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G9I3c18q9Abs"
      },
      "source": [
        "### 결측치 있는 컬럼 버리기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uJNFhfHa9C0Q",
        "outputId": "a7417284-83ae-4a20-c28c-12623da03cb4"
      },
      "source": [
        "df = pd.DataFrame([[1, 2, np.nan, 0],\n",
        "                   [3, 4, 5, 1],\n",
        "                   [3, 4, np.nan, 5],\n",
        "                   [2, 3, np.nan, 4]],\n",
        "                  columns=list('ABCD'))\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "\n",
        "print(df.dropna(axis=1))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   A  B    C  D\n",
            "0  1  2  NaN  0\n",
            "1  3  4  5.0  1\n",
            "2  3  4  NaN  5\n",
            "3  2  3  NaN  4\n",
            "\n",
            "   A  B  D\n",
            "0  1  2  0\n",
            "1  3  4  1\n",
            "2  3  4  5\n",
            "3  2  3  4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nnQLMCN1zmN0"
      },
      "source": [
        "### 특정값으로 채우기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfBzGaP6wb9G",
        "outputId": "00e1c457-c6cc-456e-b2a2-81591963f512"
      },
      "source": [
        "df = pd.DataFrame([[np.nan, 2, np.nan, 0],\n",
        "                   [3, 4, np.nan, 1],\n",
        "                   [np.nan, np.nan, np.nan, 5],\n",
        "                   [np.nan, 3, np.nan, 4]],\n",
        "                  columns=list('ABCD'))\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "filled_df = df.fillna(0)\n",
        "\n",
        "print(filled_df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     A    B   C  D\n",
            "0  NaN  2.0 NaN  0\n",
            "1  3.0  4.0 NaN  1\n",
            "2  NaN  NaN NaN  5\n",
            "3  NaN  3.0 NaN  4\n",
            "\n",
            "     A    B    C  D\n",
            "0  0.0  2.0  0.0  0\n",
            "1  3.0  4.0  0.0  1\n",
            "2  0.0  0.0  0.0  5\n",
            "3  0.0  3.0  0.0  4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FAMB9A6O2Nfd"
      },
      "source": [
        "### 컬럼별 특정 값으로 채우기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UG-hyVn90AS9",
        "outputId": "1a09c539-e704-4e26-89cb-38f2039ca37f"
      },
      "source": [
        "df = pd.DataFrame([[np.nan, 2, np.nan, 0],\n",
        "                   [3, 4, np.nan, 1],\n",
        "                   [np.nan, np.nan, np.nan, 5],\n",
        "                   [np.nan, 3, np.nan, 4]],\n",
        "                  columns=list('ABCD'))\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "\n",
        "values = {'A': 0, 'B': 1, 'C': 2, 'D': 3}\n",
        "filled_df = df.fillna(value=values)\n",
        "\n",
        "print(filled_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     A    B   C  D\n",
            "0  NaN  2.0 NaN  0\n",
            "1  3.0  4.0 NaN  1\n",
            "2  NaN  NaN NaN  5\n",
            "3  NaN  3.0 NaN  4\n",
            "\n",
            "     A    B    C  D\n",
            "0  0.0  2.0  2.0  0\n",
            "1  3.0  4.0  2.0  1\n",
            "2  0.0  1.0  2.0  5\n",
            "3  0.0  3.0  2.0  4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yg8VavqF1rAw"
      },
      "source": [
        "### 앞의 값으로 채우기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7_3fdE5V0AY8",
        "outputId": "ed9ddc32-0561-4eb9-b96c-fbceb3f4eb5d"
      },
      "source": [
        "df = pd.DataFrame([[np.nan, 2, np.nan, 0],\n",
        "                   [3, 4, np.nan, 1],\n",
        "                   [np.nan, np.nan, np.nan, 5],\n",
        "                   [np.nan, 3, np.nan, 4]],\n",
        "                  columns=list('ABCD'))\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "filled_df = df.fillna(method=\"ffill\")\n",
        "\n",
        "print(filled_df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     A    B   C  D\n",
            "0  NaN  2.0 NaN  0\n",
            "1  3.0  4.0 NaN  1\n",
            "2  NaN  NaN NaN  5\n",
            "3  NaN  3.0 NaN  4\n",
            "\n",
            "     A    B   C  D\n",
            "0  NaN  2.0 NaN  0\n",
            "1  3.0  4.0 NaN  1\n",
            "2  3.0  4.0 NaN  5\n",
            "3  3.0  3.0 NaN  4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6FAQV8g1tNX"
      },
      "source": [
        "### 뒤의 값으로 채우기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yinoi2As1TlY",
        "outputId": "c55642ea-638a-465d-b191-2fc1150cf26f"
      },
      "source": [
        "df = pd.DataFrame([[np.nan, 2, np.nan, 0],\n",
        "                   [3, 4, np.nan, 1],\n",
        "                   [np.nan, np.nan, np.nan, 5],\n",
        "                   [np.nan, 3, np.nan, 4]],\n",
        "                  columns=list('ABCD'))\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "filled_df = df.fillna(method=\"bfill\")\n",
        "\n",
        "print(filled_df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     A    B   C  D\n",
            "0  NaN  2.0 NaN  0\n",
            "1  3.0  4.0 NaN  1\n",
            "2  NaN  NaN NaN  5\n",
            "3  NaN  3.0 NaN  4\n",
            "\n",
            "     A    B   C  D\n",
            "0  3.0  2.0 NaN  0\n",
            "1  3.0  4.0 NaN  1\n",
            "2  NaN  3.0 NaN  5\n",
            "3  NaN  3.0 NaN  4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JQkZetsf23-k"
      },
      "source": [
        "### 컬럼 별 평균값으로 채우기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eF1Tj5tn0AN0",
        "outputId": "de8ce1e0-3277-44f5-a534-ae4d088314e9"
      },
      "source": [
        "df = pd.DataFrame([[np.nan, 2, np.nan, 0],\n",
        "                   [3, 4, np.nan, 1],\n",
        "                   [np.nan, np.nan, np.nan, 5],\n",
        "                   [np.nan, 3, np.nan, 4]],\n",
        "                  columns=list('ABCD'))\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "print(\"df.mean()\")\n",
        "print(df.mean())\n",
        "print()\n",
        "\n",
        "filled_df = df.fillna(df.mean())\n",
        "\n",
        "print(filled_df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     A    B   C  D\n",
            "0  NaN  2.0 NaN  0\n",
            "1  3.0  4.0 NaN  1\n",
            "2  NaN  NaN NaN  5\n",
            "3  NaN  3.0 NaN  4\n",
            "\n",
            "df.mean()\n",
            "A    3.0\n",
            "B    3.0\n",
            "C    NaN\n",
            "D    2.5\n",
            "dtype: float64\n",
            "\n",
            "     A    B   C  D\n",
            "0  3.0  2.0 NaN  0\n",
            "1  3.0  4.0 NaN  1\n",
            "2  3.0  3.0 NaN  5\n",
            "3  3.0  3.0 NaN  4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tH1Y11ERz_kC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ka26g5JasuVC",
        "outputId": "5559756b-a1e2-4de3-8196-742d12f6220d"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "df = pd.DataFrame(\n",
        "   np.random.randn(6, 1),\n",
        "   index=pd.date_range(\"2013-08-01\", periods=6, freq=\"B\"),\n",
        "   columns=list(\"A\"),\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df.loc[df.index[3], \"A\"] = np.nan\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df = df.ffill()\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                   A\n",
            "2013-08-01  0.052441\n",
            "2013-08-02  0.187507\n",
            "2013-08-05  1.421545\n",
            "2013-08-06  0.876818\n",
            "2013-08-07  0.988105\n",
            "2013-08-08  0.292646\n",
            "\n",
            "                   A\n",
            "2013-08-01  0.052441\n",
            "2013-08-02  0.187507\n",
            "2013-08-05  1.421545\n",
            "2013-08-06       NaN\n",
            "2013-08-07  0.988105\n",
            "2013-08-08  0.292646\n",
            "\n",
            "                   A\n",
            "2013-08-01  0.052441\n",
            "2013-08-02  0.187507\n",
            "2013-08-05  1.421545\n",
            "2013-08-06  1.421545\n",
            "2013-08-07  0.988105\n",
            "2013-08-08  0.292646\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qNM3ZgoH_deZ"
      },
      "source": [
        "## 정규화"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XfLouyoH_mI2"
      },
      "source": [
        "### Normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BxNAmKQdd_M",
        "outputId": "70b49ad3-140a-40b0-e7b8-41cde706fbc1"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 35, 10], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df2 = (df - df.min()) / (df.max() - df.min())\n",
        "print(df2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   35  -30\n",
            "3    7   10  -50\n",
            "\n",
            "        AAA  BBB       CCC\n",
            "0  0.000000  0.0  1.000000\n",
            "1  0.333333  0.4  0.666667\n",
            "2  0.666667  1.0  0.133333\n",
            "3  1.000000  0.0  0.000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xYODK-Yk_pdu"
      },
      "source": [
        "### Standardization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BZNn6VkVf7YK",
        "outputId": "ad755e30-ade3-4f86-8969-e5840e39d154"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 35, 10], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df2 = (df - df.mean()) / df.std()\n",
        "print(df2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   35  -30\n",
            "3    7   10  -50\n",
            "\n",
            "        AAA       BBB       CCC\n",
            "0 -1.161895 -0.740613  1.179575\n",
            "1 -0.387298  0.105802  0.464681\n",
            "2  0.387298  1.375424 -0.679149\n",
            "3  1.161895 -0.740613 -0.965107\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L3enOD5lI1AF"
      },
      "source": [
        "### 특정 칼럼만"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUWLreM-_faa",
        "outputId": "72f5fc18-b55f-4870-c78e-d0ea58e9387c"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 35, 10], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "\n",
        "print(df)\n",
        "print()\n",
        "\n",
        "df2 = (df - df.min()) / (df.max() - df.min())\n",
        "print(df2)\n",
        "\n",
        "target_columns = [ \"AAA\", \"BBB\" ]\n",
        "df[target_columns] = df2[target_columns]\n",
        "\n",
        "print(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   AAA  BBB  CCC\n",
            "0    4   10  100\n",
            "1    5   20   50\n",
            "2    6   35  -30\n",
            "3    7   10  -50\n",
            "\n",
            "        AAA  BBB       CCC\n",
            "0  0.000000  0.0  1.000000\n",
            "1  0.333333  0.4  0.666667\n",
            "2  0.666667  1.0  0.133333\n",
            "3  1.000000  0.0  0.000000\n",
            "        AAA  BBB  CCC\n",
            "0  0.000000  0.0  100\n",
            "1  0.333333  0.4   50\n",
            "2  0.666667  1.0  -30\n",
            "3  1.000000  0.0  -50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9QmEZPqlA8O1"
      },
      "source": [
        "# 시각화\n",
        "\n",
        "https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html 참조함."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DbLb8nYe3cfx"
      },
      "source": [
        "## box plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kUI6XHVy3ehs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "b639d6a3-9d8a-4cae-e824-7a4e3119da9b"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "np.random.seed(1234)\n",
        "df = pd.DataFrame(np.random.randn(10, 4),\n",
        "                  columns=['Col1', 'Col2', 'Col3', 'Col4'])\n",
        "\n",
        "df.boxplot(column=['Col1', 'Col2', 'Col3'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcbhY_Ve36Fi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "2c1caac5-475d-422f-b3e2-646210791e6f"
      },
      "source": [
        "df = pd.DataFrame(np.random.randn(10, 2),\n",
        "                  columns=['Col1', 'Col2'])\n",
        "df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A',\n",
        "                     'B', 'B', 'B', 'B', 'B'])\n",
        "\n",
        "df.boxplot(by='X')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZF4AxIdbBZpA"
      },
      "source": [
        "## seris plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYmH1phh46gB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "26e2ee4b-09d3-4ce5-e317-bc5cf0c1d01a"
      },
      "source": [
        "x = np.cumsum(np.random.normal(loc=1, scale=5, size=50))\n",
        "s = pd.Series(x)\n",
        "\n",
        "s.plot()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCB1bvPMDsFf"
      },
      "source": [
        "## histogram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "TdSdgKhYD1SA",
        "outputId": "74c429a6-7198-4b8b-f706-f62f9d3bbf44"
      },
      "source": [
        "x = np.random.normal(loc=1, scale=5, size=50)\n",
        "s = pd.Series(x)\n",
        "\n",
        "s.hist()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYy-yAIJ5bMV"
      },
      "source": [
        "## scatter matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddjXc1C55ddQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "51ba3918-9737-45b5-8c71-674f9d2280da"
      },
      "source": [
        "df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])\n",
        "\n",
        "pd.plotting.scatter_matrix(df, alpha=0.2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56rZWzjU7z7d"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSXL2MjJ8uBG"
      },
      "source": [
        "np.random.seed(5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "89jyRmSN_jMh"
      },
      "source": [
        "## DataFrame.plot의 함수들\n",
        "\n",
        "df.plot.xxx() 함수들은\n",
        "\n",
        "df.plot(kind=\"xxx\")와 동일하다.\n",
        "\n",
        "예를 들어 df.plot.bar()는 df.plot(kind=\"bar\")와 동일하다.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHYIW3bl_mw1"
      },
      "source": [
        "### bar()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "YvqD2CHo-UiM",
        "outputId": "426c7380-16f3-4889-ee77-cd4c91e29435"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.plot.bar()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "koNwVbjd_ong"
      },
      "source": [
        "### barh()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "jE1viGpO-adQ",
        "outputId": "5c00f8ae-2d84-4231-ff3b-2ed85f310d21"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.plot.barh()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MF4z0yT_p9g"
      },
      "source": [
        "### box()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "wA-yL5Un-eLF",
        "outputId": "29408e3a-fe53-4087-d4c0-24347cebe42a"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.plot.box()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JjQn0kC_rYi"
      },
      "source": [
        "### density()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "sQMB0FwU-h_n",
        "outputId": "935c1a4f-bd4c-493e-de0b-2b263d5730b5"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.plot.density()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ORVjKIzg_tBs"
      },
      "source": [
        "### hist()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "vPg1gqna-lTc",
        "outputId": "77de2d6c-0fce-4dd4-9482-222bec56379f"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.plot.hist()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MdSuOpcGAF6Y"
      },
      "source": [
        "### kde()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "HPAGPHyS-sCt",
        "outputId": "b67e8062-3ef8-4e33-92f1-f6c0e0b9b496"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.plot.kde()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E7fp5h31AH5G"
      },
      "source": [
        "### line()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "stuGd8fe-vBD",
        "outputId": "a06ea9a5-7602-4da5-f775-773e32446924"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.plot.line()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ddZh3FuIAJSs"
      },
      "source": [
        "### pie()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "pitDKaGf-ycy",
        "outputId": "94e56ad1-f48b-4af0-dfc6-c3f602e607b9"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(3,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.plot.pie(y=\"A\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLn55I8nALP8"
      },
      "source": [
        "### scatter()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "jRyjxZE_-7AQ",
        "outputId": "bf166005-fb43-4618-e547-8d2390206c94"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.plot.scatter(x=\"A\", y=\"B\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpWhCEK3A9xg"
      },
      "source": [
        "## Series.plot의 함수들\n",
        "\n",
        "s.plot.xxx() 함수들은\n",
        "\n",
        "s.plot(kind=\"xxx\")와 동일하다.\n",
        "\n",
        "예를 들어 s.plot.bar()는 s.plot(kind=\"bar\")와 동일하다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "rkPJpTYcBuN4",
        "outputId": "380c8581-2a0a-4ac1-8310-440b4a0f4dfb"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.A.plot()\n",
        "plt.show()\n",
        "\n",
        "# df.A.plot.line()과 같다."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f381eeb5690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wja1ga16A9xh"
      },
      "source": [
        "### bar()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "l4_51Vn4A9xh",
        "outputId": "0e75d4f5-e6d4-4493-b0e4-f9f209f173ea"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.A.plot.bar()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24Hn04K_A9xi"
      },
      "source": [
        "### barh()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "RZ4XJLJ0A9xi",
        "outputId": "e4643359-6401-47db-dfa6-99341232cc68"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.A.plot.barh()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DkxtU7V4A9xj"
      },
      "source": [
        "### box()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "d51iAVlmA9xj",
        "outputId": "7694fd7c-4abd-49a3-f45b-0ce050625b7f"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.A.plot.box()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KpkK4AAuA9xj"
      },
      "source": [
        "### density()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "xcFxvyvNA9xj",
        "outputId": "66d9ec08-7553-4a6c-f61b-c8fe484ee768"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.A.plot.density()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQCm98QsA9xk"
      },
      "source": [
        "### hist()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "0jZ43AtaA9xk",
        "outputId": "28ed1c19-a099-4ca4-c8d6-386c8b542c39"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(20,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.A.plot.hist()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZyqcpPnzA9xk"
      },
      "source": [
        "### kde()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "QEYYh7wGA9xk",
        "outputId": "34602470-9ab2-466a-cb8d-944cbfaa8376"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.A.plot.kde()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yR0EjblQA9xl"
      },
      "source": [
        "### line()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "SgXXLkiaA9xl",
        "outputId": "2db26d3f-99b3-442d-ea9c-09a2f5f94df6"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(10,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.A.plot.line()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3fZW7s7A9xl"
      },
      "source": [
        "### pie()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "AoPKA6NiA9xm",
        "outputId": "63af91db-03b8-46b5-b0a7-aa5ad1c69d19"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(3,4)), columns=list(\"ABCD\"))\n",
        "\n",
        "df.A.plot.pie(y=\"A\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mqbHAUYkA2bz"
      },
      "source": [
        "## 기타"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpHpAiEyAO5r"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "d_-PdvA-9CCR",
        "outputId": "a99fb07e-1441-441f-dd81-7d2b43a5bdb0"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(1000,4)), index=ts.index, columns=list(\"ABCD\"))\n",
        "\n",
        "plt.figure();\n",
        "df.iloc[5].plot(kind=\"hist\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "rEgQCdPI9llY",
        "outputId": "7352e1b2-d6a6-4a9d-d1e1-a98bf168743c"
      },
      "source": [
        "df = pd.DataFrame(np.random.randint(10, size=(1000,4)), index=ts.index, columns=list(\"ABCD\"))\n",
        "\n",
        "plt.figure();\n",
        "df.iloc[5].plot(kind=\"box\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-82-ef610b815b58>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    df.iloc[5].plot(kind=\"box\", 'A');\u001b[0m\n\u001b[0m                               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "zv5Ensvn9FDN",
        "outputId": "edc06e4d-ca9f-46f7-8e0e-7cb5fdf99508"
      },
      "source": [
        "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list(\"ABCD\"))\n",
        "df = df.cumsum()\n",
        "\n",
        "plt.figure();\n",
        "df.iloc[5].plot(kind=\"box\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wChDRQpa82cA"
      },
      "source": [
        "### column별 historgram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lgu86-2hBC6P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "e58f676e-2fdd-401f-c2bf-3bf9bb41f7e9"
      },
      "source": [
        "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list(\"ABCD\"))\n",
        "df = df.cumsum()\n",
        "\n",
        "df.diff().hist()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9u2uVKtBC2x"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "90gH4dG3BCtw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfUSF6aQBCiY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGsory8vjutx"
      },
      "source": [
        "# 기타 기능들\n",
        "\n",
        "https://www.datacamp.com/community/blog/python-pandas-cheat-sheet 참조"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sFamy1a1i5Gp"
      },
      "source": [
        "## numpy로 변환"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rt2wLbhRiEaT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed394421-1e07-45da-fcf5-31bc62943cd0"
      },
      "source": [
        "df = pd.DataFrame(\n",
        "        {\"AAA\": [4, 5, 6, 7], \"BBB\": [10, 20, 30, 40], \"CCC\": [100, 50, -30, -50]}\n",
        ")\n",
        "n = df.to_numpy()\n",
        "print(n)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  4  10 100]\n",
            " [  5  20  50]\n",
            " [  6  30 -30]\n",
            " [  7  40 -50]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nODR3tKojFCb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43gLO1ICjaa7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o6XBP5MijbCo"
      },
      "source": [
        "## 데이터 저장\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AcNMVpCjdQQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "outputId": "4bea05be-bd3f-4535-adf0-35209c415788"
      },
      "source": [
        "df.to_csv('saved.csv')\n",
        "!head -10 saved.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ",Season,Daynum,Wteam,Wscore,Lteam,Lscore,Wloc,Numot\n",
            "0,1985,20,1228,81,1328,64,N,0\n",
            "1,1985,25,1106,77,1354,70,H,0\n",
            "2,1985,25,1112,63,1223,56,H,0\n",
            "3,1985,25,1165,70,1432,54,H,0\n",
            "4,1985,25,1192,86,1447,74,H,0\n",
            "5,1985,25,1218,79,1337,78,H,0\n",
            "6,1985,25,1228,64,1226,44,N,0\n",
            "7,1985,25,1242,58,1268,56,N,0\n",
            "8,1985,25,1260,98,1133,80,H,0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "liVCABFKkAM0"
      },
      "source": [
        "##  엑셀 파일 읽기/쓰기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0W1IxT0okD5R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "471e8afb-3cf9-4809-833e-30187245d967"
      },
      "source": [
        "df.to_excel(\"saved.xlsx\")\n",
        "!ls -al "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 14540\n",
            "drwxr-xr-x 1 root root    4096 Jul 16 16:15 .\n",
            "drwxr-xr-x 1 root root    4096 Jul 16 09:17 ..\n",
            "drwxr-xr-x 1 root root    4096 Jul 11 16:05 .config\n",
            "-rw-r--r-- 1 root root     150 Jul 16 12:16 data1.csv\n",
            "-rw-r--r-- 1 root root      18 Jul 16 12:25 data2.csv\n",
            "-rw-r--r-- 1 root root 4115852 Jul 16 15:39 RegularSeasonCompactResults.csv\n",
            "drwxr-xr-x 1 root root    4096 Jul  3 16:14 sample_data\n",
            "drwxr-xr-x 6 root root    4096 Jul 16 09:32 samplemod\n",
            "-rw-r--r-- 1 root root 5021766 Jul 16 16:12 saved.csv\n",
            "-rw-r--r-- 1 root root 5715985 Jul 16 16:15 saved.xlsx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-BqpbwqkKzX"
      },
      "source": [
        "xlsx = pd.ExcelFile('saved.xlsx')\n",
        "df2 = pd.read_excel(xlsx)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3znoNI5KkWZ5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "914100b5-0e54-4114-cc64-c83b37a221c2"
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Season</th>\n",
              "      <th>Daynum</th>\n",
              "      <th>Wteam</th>\n",
              "      <th>Wscore</th>\n",
              "      <th>Lteam</th>\n",
              "      <th>Lscore</th>\n",
              "      <th>Wloc</th>\n",
              "      <th>Numot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1985</td>\n",
              "      <td>20</td>\n",
              "      <td>1228</td>\n",
              "      <td>81</td>\n",
              "      <td>1328</td>\n",
              "      <td>64</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1106</td>\n",
              "      <td>77</td>\n",
              "      <td>1354</td>\n",
              "      <td>70</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1112</td>\n",
              "      <td>63</td>\n",
              "      <td>1223</td>\n",
              "      <td>56</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1165</td>\n",
              "      <td>70</td>\n",
              "      <td>1432</td>\n",
              "      <td>54</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1985</td>\n",
              "      <td>25</td>\n",
              "      <td>1192</td>\n",
              "      <td>86</td>\n",
              "      <td>1447</td>\n",
              "      <td>74</td>\n",
              "      <td>H</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  Season  Daynum  Wteam  Wscore  Lteam  Lscore Wloc  Numot\n",
              "0           0    1985      20   1228      81   1328      64    N      0\n",
              "1           1    1985      25   1106      77   1354      70    H      0\n",
              "2           2    1985      25   1112      63   1223      56    H      0\n",
              "3           3    1985      25   1165      70   1432      54    H      0\n",
              "4           4    1985      25   1192      86   1447      74    H      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1eC2SURkXOR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}