沖縄市場月報位置調整.ipynb

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  "metadata": {
    "colab": {
      "name": "沖縄市場月報位置調整.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNMjakN2y1PG6wTYMz+Zw2E",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/imabari/ImabariScraping/blob/master/%E6%B2%96%E7%B8%84%E5%B8%82%E5%A0%B4%E6%9C%88%E5%A0%B1%E4%BD%8D%E7%BD%AE%E8%AA%BF%E6%95%B4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4xmBi-ZEqXI",
        "outputId": "19157be7-022e-4e72-8351-c7e367611af9"
      },
      "source": [
        "!pip install pdfplumber"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pdfplumber\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4f/18/2ac9c819831087db6b5ef78ea66aa0ae8cd4b468c51845316f6a6e31c036/pdfplumber-0.5.26.tar.gz (43kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 2.9MB/s \n",
            "\u001b[?25hCollecting pdfminer.six==20200517\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/c0/ef1c8758bbd86edb10b5443700aac97d0ba27a9ca2e7696db8cd1fdbd5a8/pdfminer.six-20200517-py3-none-any.whl (5.6MB)\n",
            "\u001b[K     |████████████████████████████████| 5.6MB 5.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: Pillow>=7.0.0 in /usr/local/lib/python3.6/dist-packages (from pdfplumber) (7.0.0)\n",
            "Collecting Wand\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/98/08/096b76e9211ca5ef338791100b76375555cb4082a53496b1c1d5897ee13c/Wand-0.6.5-py2.py3-none-any.whl (138kB)\n",
            "\u001b[K     |████████████████████████████████| 143kB 42.6MB/s \n",
            "\u001b[?25hCollecting pycryptodome\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ad/16/9627ab0493894a11c68e46000dbcc82f578c8ff06bc2980dcd016aea9bd3/pycryptodome-3.10.1-cp35-abi3-manylinux2010_x86_64.whl (1.9MB)\n",
            "\u001b[K     |████████████████████████████████| 1.9MB 42.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: chardet; python_version > \"3.0\" in /usr/local/lib/python3.6/dist-packages (from pdfminer.six==20200517->pdfplumber) (3.0.4)\n",
            "Requirement already satisfied: sortedcontainers in /usr/local/lib/python3.6/dist-packages (from pdfminer.six==20200517->pdfplumber) (2.3.0)\n",
            "Building wheels for collected packages: pdfplumber\n",
            "  Building wheel for pdfplumber (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pdfplumber: filename=pdfplumber-0.5.26-cp36-none-any.whl size=31747 sha256=78bf25e4aea9f2e070a8cb84969c826348c540ab9734d22358d0941ebf802195\n",
            "  Stored in directory: /root/.cache/pip/wheels/5a/4a/09/8f7b594a0d7ed1ca5fe27078a6a96e7439167a502074d87d1b\n",
            "Successfully built pdfplumber\n",
            "Installing collected packages: pycryptodome, pdfminer.six, Wand, pdfplumber\n",
            "Successfully installed Wand-0.6.5 pdfminer.six-20200517 pdfplumber-0.5.26 pycryptodome-3.10.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3TsVN_lEybD"
      },
      "source": [
        "import pathlib"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbtQPRvkE51j"
      },
      "source": [
        "import pandas as pd\r\n",
        "import pdfplumber\r\n",
        "import requests\r\n",
        "from bs4 import BeautifulSoup"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwAkAIEYE8QL"
      },
      "source": [
        "def fetch_file(url, dir=\".\"):\r\n",
        "\r\n",
        "    r = requests.get(url)\r\n",
        "    r.raise_for_status()\r\n",
        "\r\n",
        "    p = pathlib.Path(dir, pathlib.PurePath(url).name)\r\n",
        "    p.parent.mkdir(parents=True, exist_ok=True)\r\n",
        "\r\n",
        "    with p.open(mode=\"wb\") as fw:\r\n",
        "        fw.write(r.content)\r\n",
        "    return p"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMggw1dAE8tr"
      },
      "source": [
        "def snap_adjustment(s, limit=5):\r\n",
        "\r\n",
        "    count = s.value_counts().sort_index()\r\n",
        "\r\n",
        "    index = 0\r\n",
        "    value = 0\r\n",
        "\r\n",
        "    for i, v in count.items():\r\n",
        "\r\n",
        "        if (i - index) < limit:\r\n",
        "\r\n",
        "            if v > value:\r\n",
        "                s = s.replace(index, i)\r\n",
        "                index = i\r\n",
        "                value = v\r\n",
        "\r\n",
        "            else:\r\n",
        "                s = s.replace(i, index)\r\n",
        "\r\n",
        "        else:\r\n",
        "            index = i\r\n",
        "            value = v\r\n",
        "\r\n",
        "    return s"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLuY_P-RE2yL"
      },
      "source": [
        "url = \"https://www.pref.okinawa.jp/site/norin/oroshiuri/documents/documents/geppou3104.pdf\""
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svsEsCn-FG6L"
      },
      "source": [
        "p = fetch_file(url)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzdtWSirFHqD"
      },
      "source": [
        "with pdfplumber.open(p) as pdf:\r\n",
        "\r\n",
        "    dfs = []\r\n",
        "\r\n",
        "    # ページ範囲指定（0スタート）\r\n",
        "    for page in pdf.pages[32:35]:\r\n",
        "\r\n",
        "        crop = page.within_bbox((0, 105, page.width, 800))\r\n",
        "\r\n",
        "        df_tmp = (\r\n",
        "            pd.DataFrame(crop.extract_words(keep_blank_chars=True))\r\n",
        "            .astype({\"x0\": float, \"x1\": float, \"top\": float, \"bottom\": float})\r\n",
        "            .sort_values([\"top\", \"x0\"])\r\n",
        "        )\r\n",
        "\r\n",
        "        df_tmp[\"top\"] = snap_adjustment(df_tmp[\"top\"])\r\n",
        "        df_tmp[\"page\"] = page.page_number\r\n",
        "\r\n",
        "        dfs.append(df_tmp)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45lb-UvpF5E7"
      },
      "source": [
        "df = pd.concat(dfs)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4tlkgQFYFoMU",
        "outputId": "9bc0c18e-dd60-454f-9bc1-e7bed1a0a418"
      },
      "source": [
        "df"
      ],
      "execution_count": 10,
      "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>text</th>\n",
              "      <th>x0</th>\n",
              "      <th>x1</th>\n",
              "      <th>top</th>\n",
              "      <th>bottom</th>\n",
              "      <th>upright</th>\n",
              "      <th>direction</th>\n",
              "      <th>page</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>品　　　目</td>\n",
              "      <td>90.84</td>\n",
              "      <td>128.243</td>\n",
              "      <td>111.14</td>\n",
              "      <td>120.50</td>\n",
              "      <td>True</td>\n",
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              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>県　　内</td>\n",
              "      <td>156.26</td>\n",
              "      <td>187.448</td>\n",
              "      <td>111.14</td>\n",
              "      <td>120.50</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>単価</td>\n",
              "      <td>208.10</td>\n",
              "      <td>226.820</td>\n",
              "      <td>111.14</td>\n",
              "      <td>120.50</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>県　　外</td>\n",
              "      <td>249.41</td>\n",
              "      <td>280.598</td>\n",
              "      <td>111.14</td>\n",
              "      <td>120.50</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>単価</td>\n",
              "      <td>300.89</td>\n",
              "      <td>319.610</td>\n",
              "      <td>111.14</td>\n",
              "      <td>120.50</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>1,728</td>\n",
              "      <td>266.93</td>\n",
              "      <td>287.550</td>\n",
              "      <td>625.18</td>\n",
              "      <td>634.54</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>216</td>\n",
              "      <td>308.81</td>\n",
              "      <td>322.850</td>\n",
              "      <td>625.18</td>\n",
              "      <td>634.54</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>1,728</td>\n",
              "      <td>436.42</td>\n",
              "      <td>457.040</td>\n",
              "      <td>625.18</td>\n",
              "      <td>634.54</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>216</td>\n",
              "      <td>474.46</td>\n",
              "      <td>488.500</td>\n",
              "      <td>625.18</td>\n",
              "      <td>634.54</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>end</td>\n",
              "      <td>399.79</td>\n",
              "      <td>413.793</td>\n",
              "      <td>637.78</td>\n",
              "      <td>647.14</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>593 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      text      x0       x1     top  bottom  upright  direction  page\n",
              "0    品　　　目   90.84  128.243  111.14  120.50     True          1    33\n",
              "1     県　　内  156.26  187.448  111.14  120.50     True          1    33\n",
              "2       単価  208.10  226.820  111.14  120.50     True          1    33\n",
              "3     県　　外  249.41  280.598  111.14  120.50     True          1    33\n",
              "4       単価  300.89  319.610  111.14  120.50     True          1    33\n",
              "..     ...     ...      ...     ...     ...      ...        ...   ...\n",
              "176  1,728  266.93  287.550  625.18  634.54     True          1    35\n",
              "177    216  308.81  322.850  625.18  634.54     True          1    35\n",
              "178  1,728  436.42  457.040  625.18  634.54     True          1    35\n",
              "179    216  474.46  488.500  625.18  634.54     True          1    35\n",
              "180    end  399.79  413.793  637.78  647.14     True          1    35\n",
              "\n",
              "[593 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HexVWFbNtFN"
      },
      "source": [
        "# テキスト処理\r\n",
        "df[\"text\"] = df[\"text\"].str.replace(\"　\", \"\").str.replace(\",\", \"\")"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sddbr_kVIYfN"
      },
      "source": [
        "df[\"center\"] = df.loc[:, [\"x0\", \"x1\"]].median(axis=1)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PSsCQXjitS6"
      },
      "source": [
        "# 位置調整\r\n",
        "\r\n",
        "snap_adjustmentのlimitで結合範囲を指定"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bIHKg9QmJxId"
      },
      "source": [
        "## 左基準"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWwLy_2xJBgt"
      },
      "source": [
        "df[\"x0\"] = snap_adjustment(df[\"x0\"])        "
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "U54h2KwbIIJc",
        "outputId": "bfb5142f-d37f-453a-8235-eba3ddd24507"
      },
      "source": [
        "df.plot.scatter(x=\"x0\", y=\"top\", ylim=(892, 0), figsize=(6, 8))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd63ab7f940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGt__zx_J_It"
      },
      "source": [
        "## 中央基準"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05a0cMoSKu99"
      },
      "source": [
        "df[\"center\"] = snap_adjustment(df[\"center\"], 25)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "U7QZ1nobIdwE",
        "outputId": "f3eed5f7-df73-452d-a7ee-b3505bad4522"
      },
      "source": [
        "df.plot.scatter(x=\"center\", y=\"top\", ylim=(892, 0), figsize=(6, 8))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd63aae1828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aqMfQEAsJbHM"
      },
      "source": [
        "## 右基準"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhHn0LK0JTUd"
      },
      "source": [
        "df[\"x1\"] = snap_adjustment(df[\"x1\"])"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "sA3Qf-FFH438",
        "outputId": "4d2ef481-a241-4d8f-de16-90834690789e"
      },
      "source": [
        "df.plot.scatter(x=\"x1\", y=\"top\", ylim=(892, 0), figsize=(6, 8))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd63a60fa20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-h2XQJPFxaT"
      },
      "source": [
        "table = (\r\n",
        "    df.pivot_table(\r\n",
        "        index=[\"page\", \"top\"],\r\n",
        "        columns=\"center\",   # 基準を指定：\"x0\", \"x1\", \"center\"\r\n",
        "        values=\"text\",\r\n",
        "        aggfunc=lambda x: \"\".join(str(v) for v in x),\r\n",
        "    )\r\n",
        ").values\r\n",
        "\r\n",
        "df1 = pd.DataFrame(table).dropna(thresh=2).dropna(how=\"all\", axis=1)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4Ldd0DfjEtmk",
        "outputId": "17f6bf33-ed19-419d-fdae-22f7c4e04170"
      },
      "source": [
        "df1"
      ],
      "execution_count": 20,
      "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>品目</td>\n",
              "      <td>県内</td>\n",
              "      <td>単価</td>\n",
              "      <td>県外</td>\n",
              "      <td>単価</td>\n",
              "      <td>外国</td>\n",
              "      <td>単価</td>\n",
              "      <td>総計</td>\n",
              "      <td>単価</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>キク</td>\n",
              "      <td>78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12895</td>\n",
              "      <td>165</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12895</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ベゴニア類</td>\n",
              "      <td>34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4218</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4252</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>6426</td>\n",
              "      <td>189</td>\n",
              "      <td>1306246</td>\n",
              "      <td>310</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1312672</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12960</td>\n",
              "      <td>589</td>\n",
              "      <td>48600</td>\n",
              "      <td>386</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>61560</td>\n",
              "      <td>416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>ハイドランジア</td>\n",
              "      <td>18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8756</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8774</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11286</td>\n",
              "      <td>627</td>\n",
              "      <td>8862941</td>\n",
              "      <td>1012</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8874227</td>\n",
              "      <td>1011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>プリムラ類</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1728</td>\n",
              "      <td>216</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1728</td>\n",
              "      <td>216</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>135 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           0      1    2        3     4    5    6        7     8\n",
              "0         品目     県内   単価       県外    単価   外国   単価       総計    単価\n",
              "1         キク     78  NaN      NaN   NaN  NaN  NaN       78   NaN\n",
              "2        NaN  12895  165      NaN   NaN  NaN  NaN    12895   165\n",
              "3      ベゴニア類     34  NaN     4218   NaN  NaN  NaN     4252   NaN\n",
              "4        NaN   6426  189  1306246   310  NaN  NaN  1312672   309\n",
              "..       ...    ...  ...      ...   ...  ...  ...      ...   ...\n",
              "130      NaN  12960  589    48600   386  NaN  NaN    61560   416\n",
              "131  ハイドランジア     18  NaN     8756   NaN  NaN  NaN     8774   NaN\n",
              "132      NaN  11286  627  8862941  1012  NaN  NaN  8874227  1011\n",
              "133    プリムラ類    NaN  NaN        8   NaN  NaN  NaN        8   NaN\n",
              "134      NaN    NaN  NaN     1728   216  NaN  NaN     1728   216\n",
              "\n",
              "[135 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mqpl5aKfK_vt"
      },
      "source": [
        "df1.to_csv(\"test.csv\", encoding=\"utf_8_sig\")"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Yvx-QJgZJVx"
      },
      "source": [
        "# 前処理"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXbTJ5rcO-kO"
      },
      "source": [
        "df2 = df1[df1[0] != \"品目\"].copy()"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "EmYjL6bhP-yu",
        "outputId": "dd6817d2-95b1-49ca-9204-8eb8feefb34d"
      },
      "source": [
        "df2"
      ],
      "execution_count": 23,
      "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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              "\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>0</th>\n",
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              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
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              "      <th>6</th>\n",
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              "      <th>8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>キク</td>\n",
              "      <td>78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12895</td>\n",
              "      <td>165</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12895</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ベゴニア類</td>\n",
              "      <td>34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4218</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4252</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>6426</td>\n",
              "      <td>189</td>\n",
              "      <td>1306246</td>\n",
              "      <td>310</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1312672</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>インパチェンス</td>\n",
              "      <td>804</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>874</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>NaN</td>\n",
              "      <td>12960</td>\n",
              "      <td>589</td>\n",
              "      <td>48600</td>\n",
              "      <td>386</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>61560</td>\n",
              "      <td>416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>ハイドランジア</td>\n",
              "      <td>18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8756</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8774</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11286</td>\n",
              "      <td>627</td>\n",
              "      <td>8862941</td>\n",
              "      <td>1012</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8874227</td>\n",
              "      <td>1011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>プリムラ類</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1728</td>\n",
              "      <td>216</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1728</td>\n",
              "      <td>216</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>134 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           0      1    2        3     4    5    6        7     8\n",
              "1         キク     78  NaN      NaN   NaN  NaN  NaN       78   NaN\n",
              "2        NaN  12895  165      NaN   NaN  NaN  NaN    12895   165\n",
              "3      ベゴニア類     34  NaN     4218   NaN  NaN  NaN     4252   NaN\n",
              "4        NaN   6426  189  1306246   310  NaN  NaN  1312672   309\n",
              "5    インパチェンス    804  NaN       70   NaN  NaN  NaN      874   NaN\n",
              "..       ...    ...  ...      ...   ...  ...  ...      ...   ...\n",
              "130      NaN  12960  589    48600   386  NaN  NaN    61560   416\n",
              "131  ハイドランジア     18  NaN     8756   NaN  NaN  NaN     8774   NaN\n",
              "132      NaN  11286  627  8862941  1012  NaN  NaN  8874227  1011\n",
              "133    プリムラ類    NaN  NaN        8   NaN  NaN  NaN        8   NaN\n",
              "134      NaN    NaN  NaN     1728   216  NaN  NaN     1728   216\n",
              "\n",
              "[134 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-4StRmVP6OO"
      },
      "source": [
        "df2[0] = df2[0].fillna(method=\"ffill\")"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "cjpWFvmVQNju",
        "outputId": "f6c826a7-daaa-41bf-a172-601c344c2113"
      },
      "source": [
        "df_even = (\r\n",
        "    df2[::2]\r\n",
        "    .set_axis(\r\n",
        "        [\r\n",
        "            \"品名\",\r\n",
        "            \"県内_数量\",\r\n",
        "            \"県内_単価\",\r\n",
        "            \"県外_数量\",\r\n",
        "            \"県外_単価\",\r\n",
        "            \"外国_数量\",\r\n",
        "            \"外国_単価\",\r\n",
        "            \"総計_数量\",\r\n",
        "            \"総計_単価\",\r\n",
        "        ],\r\n",
        "        axis=1,\r\n",
        "    )\r\n",
        "    .dropna(how=\"all\", axis=1)\r\n",
        ")\r\n",
        "df_even"
      ],
      "execution_count": 25,
      "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>品名</th>\n",
              "      <th>県内_数量</th>\n",
              "      <th>県外_数量</th>\n",
              "      <th>総計_数量</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>キク</td>\n",
              "      <td>78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ベゴニア類</td>\n",
              "      <td>34</td>\n",
              "      <td>4218</td>\n",
              "      <td>4252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>インパチェンス</td>\n",
              "      <td>804</td>\n",
              "      <td>70</td>\n",
              "      <td>874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ガーベラ</td>\n",
              "      <td>96</td>\n",
              "      <td>2432</td>\n",
              "      <td>2528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ゼラニウム</td>\n",
              "      <td>416</td>\n",
              "      <td>126</td>\n",
              "      <td>542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>クレマチス</td>\n",
              "      <td>NaN</td>\n",
              "      <td>252</td>\n",
              "      <td>252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>デンドロビウム</td>\n",
              "      <td>NaN</td>\n",
              "      <td>603</td>\n",
              "      <td>603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>ツツジ</td>\n",
              "      <td>22</td>\n",
              "      <td>126</td>\n",
              "      <td>148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>ハイドランジア</td>\n",
              "      <td>18</td>\n",
              "      <td>8756</td>\n",
              "      <td>8774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>プリムラ類</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>67 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          品名 県内_数量 県外_数量 総計_数量\n",
              "1         キク    78   NaN    78\n",
              "3      ベゴニア類    34  4218  4252\n",
              "5    インパチェンス   804    70   874\n",
              "7       ガーベラ    96  2432  2528\n",
              "9      ゼラニウム   416   126   542\n",
              "..       ...   ...   ...   ...\n",
              "125    クレマチス   NaN   252   252\n",
              "127  デンドロビウム   NaN   603   603\n",
              "129      ツツジ    22   126   148\n",
              "131  ハイドランジア    18  8756  8774\n",
              "133    プリムラ類   NaN     8     8\n",
              "\n",
              "[67 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "TiBgOmbyQOUe",
        "outputId": "d3beea9d-c07e-4dfa-b0ab-60b310c540a5"
      },
      "source": [
        "df_odd = (\r\n",
        "    df2[1::2]\r\n",
        "    .set_axis(\r\n",
        "        [\r\n",
        "            \"品名\",\r\n",
        "            \"県内_金額\",\r\n",
        "            \"県内_単価\",\r\n",
        "            \"県外_金額\",\r\n",
        "            \"県外_単価\",\r\n",
        "            \"外国_金額\",\r\n",
        "            \"外国_単価\",\r\n",
        "            \"総計_金額\",\r\n",
        "            \"総計_単価\",\r\n",
        "        ],\r\n",
        "        axis=1,\r\n",
        "    )\r\n",
        "    .dropna(how=\"all\", axis=1)\r\n",
        ")\r\n",
        "df_odd"
      ],
      "execution_count": 26,
      "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>品名</th>\n",
              "      <th>県内_金額</th>\n",
              "      <th>県内_単価</th>\n",
              "      <th>県外_金額</th>\n",
              "      <th>県外_単価</th>\n",
              "      <th>総計_金額</th>\n",
              "      <th>総計_単価</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>キク</td>\n",
              "      <td>12895</td>\n",
              "      <td>165</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12895</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ベゴニア類</td>\n",
              "      <td>6426</td>\n",
              "      <td>189</td>\n",
              "      <td>1306246</td>\n",
              "      <td>310</td>\n",
              "      <td>1312672</td>\n",
              "      <td>309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>インパチェンス</td>\n",
              "      <td>100607</td>\n",
              "      <td>125</td>\n",
              "      <td>19440</td>\n",
              "      <td>278</td>\n",
              "      <td>120047</td>\n",
              "      <td>137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ガーベラ</td>\n",
              "      <td>8294</td>\n",
              "      <td>86</td>\n",
              "      <td>543593</td>\n",
              "      <td>224</td>\n",
              "      <td>551887</td>\n",
              "      <td>218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>ゼラニウム</td>\n",
              "      <td>55037</td>\n",
              "      <td>132</td>\n",
              "      <td>57240</td>\n",
              "      <td>454</td>\n",
              "      <td>112277</td>\n",
              "      <td>207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>クレマチス</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>282552</td>\n",
              "      <td>1121</td>\n",
              "      <td>282552</td>\n",
              "      <td>1121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>デンドロビウム</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>499111</td>\n",
              "      <td>828</td>\n",
              "      <td>499111</td>\n",
              "      <td>828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>ツツジ</td>\n",
              "      <td>12960</td>\n",
              "      <td>589</td>\n",
              "      <td>48600</td>\n",
              "      <td>386</td>\n",
              "      <td>61560</td>\n",
              "      <td>416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>ハイドランジア</td>\n",
              "      <td>11286</td>\n",
              "      <td>627</td>\n",
              "      <td>8862941</td>\n",
              "      <td>1012</td>\n",
              "      <td>8874227</td>\n",
              "      <td>1011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>プリムラ類</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1728</td>\n",
              "      <td>216</td>\n",
              "      <td>1728</td>\n",
              "      <td>216</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>67 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          品名   県内_金額 県内_単価    県外_金額 県外_単価    総計_金額 総計_単価\n",
              "2         キク   12895   165      NaN   NaN    12895   165\n",
              "4      ベゴニア類    6426   189  1306246   310  1312672   309\n",
              "6    インパチェンス  100607   125    19440   278   120047   137\n",
              "8       ガーベラ    8294    86   543593   224   551887   218\n",
              "10     ゼラニウム   55037   132    57240   454   112277   207\n",
              "..       ...     ...   ...      ...   ...      ...   ...\n",
              "126    クレマチス     NaN   NaN   282552  1121   282552  1121\n",
              "128  デンドロビウム     NaN   NaN   499111   828   499111   828\n",
              "130      ツツジ   12960   589    48600   386    61560   416\n",
              "132  ハイドランジア   11286   627  8862941  1012  8874227  1011\n",
              "134    プリムラ類     NaN   NaN     1728   216     1728   216\n",
              "\n",
              "[67 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddugehznQROW"
      },
      "source": [
        "df3 = (\r\n",
        "    pd.merge(df_even, df_odd, on=\"品名\")\r\n",
        "    .set_index(\"品名\")\r\n",
        "    .reindex(\r\n",
        "        columns=[\r\n",
        "            \"県内_数量\",\r\n",
        "            \"県内_単価\",\r\n",
        "            \"県内_金額\",\r\n",
        "            \"県外_数量\",\r\n",
        "            \"県外_単価\",\r\n",
        "            \"県外_金額\",\r\n",
        "            \"外国_数量\",\r\n",
        "            \"外国_単価\",\r\n",
        "            \"外国_金額\",\r\n",
        "            \"総計_数量\",\r\n",
        "            \"総計_単価\",\r\n",
        "            \"総計_金額\",\r\n",
        "        ]\r\n",
        "    )\r\n",
        ")"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "-JziEPwBQUb3",
        "outputId": "b09c8e6a-2799-4a42-b4b9-a46659a03a87"
      },
      "source": [
        "df3"
      ],
      "execution_count": 28,
      "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>県内_数量</th>\n",
              "      <th>県内_単価</th>\n",
              "      <th>県内_金額</th>\n",
              "      <th>県外_数量</th>\n",
              "      <th>県外_単価</th>\n",
              "      <th>県外_金額</th>\n",
              "      <th>外国_数量</th>\n",
              "      <th>外国_単価</th>\n",
              "      <th>外国_金額</th>\n",
              "      <th>総計_数量</th>\n",
              "      <th>総計_単価</th>\n",
              "      <th>総計_金額</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>品名</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>キク</th>\n",
              "      <td>78</td>\n",
              "      <td>165</td>\n",
              "      <td>12895</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78</td>\n",
              "      <td>165</td>\n",
              "      <td>12895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ベゴニア類</th>\n",
              "      <td>34</td>\n",
              "      <td>189</td>\n",
              "      <td>6426</td>\n",
              "      <td>4218</td>\n",
              "      <td>310</td>\n",
              "      <td>1306246</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4252</td>\n",
              "      <td>309</td>\n",
              "      <td>1312672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>インパチェンス</th>\n",
              "      <td>804</td>\n",
              "      <td>125</td>\n",
              "      <td>100607</td>\n",
              "      <td>70</td>\n",
              "      <td>278</td>\n",
              "      <td>19440</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>874</td>\n",
              "      <td>137</td>\n",
              "      <td>120047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ガーベラ</th>\n",
              "      <td>96</td>\n",
              "      <td>86</td>\n",
              "      <td>8294</td>\n",
              "      <td>2432</td>\n",
              "      <td>224</td>\n",
              "      <td>543593</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2528</td>\n",
              "      <td>218</td>\n",
              "      <td>551887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ゼラニウム</th>\n",
              "      <td>416</td>\n",
              "      <td>132</td>\n",
              "      <td>55037</td>\n",
              "      <td>126</td>\n",
              "      <td>454</td>\n",
              "      <td>57240</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>542</td>\n",
              "      <td>207</td>\n",
              "      <td>112277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>クレマチス</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>252</td>\n",
              "      <td>1121</td>\n",
              "      <td>282552</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>252</td>\n",
              "      <td>1121</td>\n",
              "      <td>282552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>デンドロビウム</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>603</td>\n",
              "      <td>828</td>\n",
              "      <td>499111</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>603</td>\n",
              "      <td>828</td>\n",
              "      <td>499111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ツツジ</th>\n",
              "      <td>22</td>\n",
              "      <td>589</td>\n",
              "      <td>12960</td>\n",
              "      <td>126</td>\n",
              "      <td>386</td>\n",
              "      <td>48600</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>148</td>\n",
              "      <td>416</td>\n",
              "      <td>61560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ハイドランジア</th>\n",
              "      <td>18</td>\n",
              "      <td>627</td>\n",
              "      <td>11286</td>\n",
              "      <td>8756</td>\n",
              "      <td>1012</td>\n",
              "      <td>8862941</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8774</td>\n",
              "      <td>1011</td>\n",
              "      <td>8874227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>プリムラ類</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>216</td>\n",
              "      <td>1728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>216</td>\n",
              "      <td>1728</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>67 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        県内_数量 県内_単価   県内_金額 県外_数量 県外_単価  ... 外国_単価  外国_金額  総計_数量  総計_単価    総計_金額\n",
              "品名                                       ...                                    \n",
              "キク         78   165   12895   NaN   NaN  ...   NaN    NaN     78    165    12895\n",
              "ベゴニア類      34   189    6426  4218   310  ...   NaN    NaN   4252    309  1312672\n",
              "インパチェンス   804   125  100607    70   278  ...   NaN    NaN    874    137   120047\n",
              "ガーベラ       96    86    8294  2432   224  ...   NaN    NaN   2528    218   551887\n",
              "ゼラニウム     416   132   55037   126   454  ...   NaN    NaN    542    207   112277\n",
              "...       ...   ...     ...   ...   ...  ...   ...    ...    ...    ...      ...\n",
              "クレマチス     NaN   NaN     NaN   252  1121  ...   NaN    NaN    252   1121   282552\n",
              "デンドロビウム   NaN   NaN     NaN   603   828  ...   NaN    NaN    603    828   499111\n",
              "ツツジ        22   589   12960   126   386  ...   NaN    NaN    148    416    61560\n",
              "ハイドランジア    18   627   11286  8756  1012  ...   NaN    NaN   8774   1011  8874227\n",
              "プリムラ類     NaN   NaN     NaN     8   216  ...   NaN    NaN      8    216     1728\n",
              "\n",
              "[67 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YyJXS-jpQWYP"
      },
      "source": [
        "df3.to_csv(\"okinawa.csv\", encoding=\"utf_8_sig\")"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Y8xh701WQYXu",
        "outputId": "1eb1073f-9285-4ed1-f68d-2e55566f552e"
      },
      "source": [
        "from google.colab import files\r\n",
        "\r\n",
        "files.download(\"okinawa.csv\")"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_100bf879-d595-4a96-91e2-a18e18f66c5b\", \"okinawa.csv\", 4113)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}