colabでスプシを読み込む.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/gr39my/for_qiita/blob/main/colab%E3%81%A7%E3%82%B9%E3%83%97%E3%82%B7%E3%82%92%E8%AA%AD%E3%81%BF%E8%BE%BC%E3%82%80.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. データ読み込み"
      ],
      "metadata": {
        "id": "gH7CnD9A-KqT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "pd.set_option('display.max_columns', None)\n",
        "\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "\n",
        "!pip install japanize-matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import japanize_matplotlib\n",
        "japanize_matplotlib.japanize()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCqF7i350PPE",
        "outputId": "d7ee07d9-83fe-434c-ad22-7d2cbbd1bf7f"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: japanize-matplotlib in /usr/local/lib/python3.10/dist-packages (1.1.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from japanize-matplotlib) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->japanize-matplotlib) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->japanize-matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->japanize-matplotlib) (4.51.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->japanize-matplotlib) (1.4.5)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib->japanize-matplotlib) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->japanize-matplotlib) (24.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->japanize-matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->japanize-matplotlib) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->japanize-matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->japanize-matplotlib) (1.16.0)\n"
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      "execution_count": null,
      "metadata": {
        "id": "jyOqYmHCzwnA"
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      "source": [
        "# 認証のためのコード\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n",
        "import gspread\n",
        "from google.auth import default\n",
        "creds, _ = default()\n",
        "\n",
        "gc = gspread.authorize(creds)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## スプレッドシートを開く（シートURLから）\n",
        "url = \"https://docs.google.com/spreadsheets/d/12GsIwLvAX8vGNA1m1XWM_YupRG2oGIJOhQ15KiDeocs/edit#gid=0\"\n",
        "ss = gc.open_by_url(url)\n",
        "\n",
        "# シートを特定する（シート名で特定）\n",
        "st = ss.worksheet(\"シート1\")\n",
        "\n",
        "# スプレッドシートのデータをPandasのDataframeに変換する。\n",
        "rows = st.get_all_values()\n",
        "df = pd.DataFrame.from_records(rows[1:], columns=rows[0])\n"
      ],
      "metadata": {
        "id": "aDDDmesa0ECY"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 206
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        "id": "600htHE-z9oG",
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              "         日付    体重       備考\n",
              "0  2024/6/1  65.0         \n",
              "1  2024/6/2  64.7         \n",
              "2  2024/6/3  64.8         \n",
              "3  2024/6/4  65.2  たくさん食べた\n",
              "4  2024/6/5  63.9         "
            ],
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              "      <td>2024/6/3</td>\n",
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              "      <th>3</th>\n",
              "      <td>2024/6/4</td>\n",
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              "      <th>4</th>\n",
              "      <td>2024/6/5</td>\n",
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "# 2. データ型の変更"
      ],
      "metadata": {
        "id": "i72-clgM-PFT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bvXcIdI1QoY",
        "outputId": "e5c6fe40-d20d-4973-e122-6f71419816df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14 entries, 0 to 13\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   日付      14 non-null     object\n",
            " 1   体重      14 non-null     object\n",
            " 2   備考      14 non-null     object\n",
            "dtypes: object(3)\n",
            "memory usage: 464.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# floatに変更\n",
        "num_cols = ['体重']\n",
        "df[num_cols] = df[num_cols].astype(float)\n",
        "\n",
        "# datetimeに変更\n",
        "datetime_cols = ['日付']\n",
        "for col in datetime_cols:\n",
        "    df[col] = pd.to_datetime(df[col], format='%Y/%m/%d', errors='coerce')\n"
      ],
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        "id": "lJ9dYVQez42n"
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      "source": [
        "df.info()"
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      "metadata": {
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        "id": "fF_rxuUC9Wbu",
        "outputId": "5883de33-abb2-4194-953d-71da462ecc34"
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      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14 entries, 0 to 13\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype         \n",
            "---  ------  --------------  -----         \n",
            " 0   日付      14 non-null     datetime64[ns]\n",
            " 1   体重      14 non-null     float64       \n",
            " 2   備考      14 non-null     object        \n",
            "dtypes: datetime64[ns](1), float64(1), object(1)\n",
            "memory usage: 464.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. 可視化"
      ],
      "metadata": {
        "id": "qgkyUIaz-SHY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 折れ線グラフの作成\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(df['日付'], df['体重'], marker='o', linestyle='-', color='b')\n",
        "\n",
        "# グラフのタイトルとラベルを設定\n",
        "plt.title('日付と体重の推移')\n",
        "plt.xlabel('日付')\n",
        "plt.ylabel('体重 (kg)')\n",
        "\n",
        "# グラフを表示\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "ssYuIL1P9YhF",
        "outputId": "36cf2b4f-7643-4fe0-93bf-dd8b9a09374f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M8oBhb8o-gV1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}