pandas_10min_class_15_29.ipynb

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      "name": "pandas_10min_class_15/29.ipynb",
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        "<a href=\"https://colab.research.google.com/github/xersuy/pandas_10min_study/blob/main/pandas_10min_class_15_29.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# Group Data\n",
        "**groupby 활용하여 다양한 데이터 집계를 활용하기**"
      ],
      "metadata": {
        "id": "MRxSap5TZAT2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "L9Mergc2aFOf"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = sns.load_dataset('mpg')\n",
        "df.head()"
      ],
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          "height": 392
        },
        "id": "o5NKo_lJaUyu",
        "outputId": "f7883f68-8011-490e-99b5-3c051aa3b6a0"
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      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>displacement</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>weight</th>\n",
              "      <th>acceleration</th>\n",
              "      <th>model_year</th>\n",
              "      <th>origin</th>\n",
              "      <th>name</th>\n",
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              "      <td>70</td>\n",
              "      <td>usa</td>\n",
              "      <td>chevrolet chevelle malibu</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15.0</td>\n",
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              "      <td>3693</td>\n",
              "      <td>11.5</td>\n",
              "      <td>70</td>\n",
              "      <td>usa</td>\n",
              "      <td>buick skylark 320</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>usa</td>\n",
              "      <td>plymouth satellite</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16.0</td>\n",
              "      <td>8</td>\n",
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              "      <td>amc rebel sst</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17.0</td>\n",
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              "      <td>10.5</td>\n",
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            "text/plain": [
              "    mpg  cylinders  displacement  ...  model_year  origin                       name\n",
              "0  18.0          8         307.0  ...          70     usa  chevrolet chevelle malibu\n",
              "1  15.0          8         350.0  ...          70     usa          buick skylark 320\n",
              "2  18.0          8         318.0  ...          70     usa         plymouth satellite\n",
              "3  16.0          8         304.0  ...          70     usa              amc rebel sst\n",
              "4  17.0          8         302.0  ...          70     usa                ford torino\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "origin 기준으로 그룹화를 하고 사이즈를 확인\n",
        "\"\"\"\n",
        "df.groupby(by='origin').size()"
      ],
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        "outputId": "9e45370f-72c3-47bf-a5d9-cff641c78ea9"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "origin\n",
              "europe     70\n",
              "japan      79\n",
              "usa       249\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "위 와 같이 사이즈를 구하는 방법\n",
        "\"\"\"\n",
        "df['origin'].value_counts()"
      ],
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        "id": "dBKUAzFMabLF",
        "outputId": "97b48f08-415d-4c9d-c031-bf233514fd51"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "usa       249\n",
              "japan      79\n",
              "europe     70\n",
              "Name: origin, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
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      "cell_type": "code",
      "source": [
        "df.groupby(by='origin')['cylinders'].size()"
      ],
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        },
        "id": "_mYm3s5SaONG",
        "outputId": "bf601c70-a61d-4dc0-8671-34f062c4681b"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "origin\n",
              "europe     70\n",
              "japan      79\n",
              "usa       249\n",
              "Name: cylinders, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(df.groupby(['origin', 'model_year'])['cylinders'].mean())"
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        "outputId": "04c91ab6-cf45-4ab7-a5c7-8d9093fdbf98"
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      "execution_count": 40,
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              "  "
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            "text/plain": [
              "                   cylinders\n",
              "origin model_year           \n",
              "europe 70           4.000000\n",
              "       71           4.000000\n",
              "       72           4.000000\n",
              "       73           4.000000\n",
              "       74           4.000000\n",
              "       75           4.000000\n",
              "       76           4.250000\n",
              "       77           4.000000\n",
              "       78           4.833333\n",
              "       79           4.250000\n",
              "       80           4.111111\n",
              "       81           4.500000\n",
              "       82           4.000000\n",
              "japan  70           4.000000\n",
              "       71           4.000000\n",
              "       72           3.800000\n",
              "       73           4.250000\n",
              "       74           4.000000\n",
              "       75           4.000000\n",
              "       76           4.500000\n",
              "       77           4.166667\n",
              "       78           4.000000\n",
              "       79           4.000000\n",
              "       80           4.076923\n",
              "       81           4.333333\n",
              "       82           4.000000\n",
              "usa    70           7.636364\n",
              "       71           6.200000\n",
              "       72           6.888889\n",
              "       73           7.241379\n",
              "       74           6.266667\n",
              "       75           6.400000\n",
              "       76           6.363636\n",
              "       77           6.222222\n",
              "       78           6.000000\n",
              "       79           6.260870\n",
              "       80           4.285714\n",
              "       81           4.923077\n",
              "       82           4.300000"
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              "                                                     [key], {});\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c047fa7a-a6b4-4ddf-9a88-658f2bad6871 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c047fa7a-a6b4-4ddf-9a88-658f2bad6871');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   a  b   c\n",
              "1  4  7  10\n",
              "2  4  7  10\n",
              "3  4  7  10"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.cumprod()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "dV_u4u6flRnL",
        "outputId": "4b71fff8-0f04-4f2b-d130-d147daf59f50"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-de63b1a6-9c4a-41f3-8d51-182b8619e10e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-de63b1a6-9c4a-41f3-8d51-182b8619e10e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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              "     a    b     c\n",
              "1    4    7    10\n",
              "2   20   56   110\n",
              "3  120  504  1320"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    }
  ]
}