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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "authorship_tag": "ABX9TyPCVTB3RATGHAn9Gv4Ne7d1", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/kun432/07b1df826819d8938c51b1df2e96611a/-concat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "zennの記事を元に以下を追加してみた。\n", | |
| "\n", | |
| "- 各コーナーでの内外の位置取り\n", | |
| "- matplotlibでかんたんな可視化\n", | |
| "- pandas-1.4.0でappendが廃止されることに伴い、concatに書き換え\n", | |
| "\n", | |
| "参考）pyparsingで競馬のコーナー通過順位をパース\n", | |
| "https://zenn.dev/moripon/articles/ed5caa9c1d621e\n" | |
| ], | |
| "metadata": { | |
| "id": "qngtoogbjC2m" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install japanize-matplotlib\n", | |
| "!pip install 'pandas>1.4.0'" | |
| ], | |
| "metadata": { | |
| "id": "46uGuY2ObEWu", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "7d3daaa6-4336-42bb-999c-e1133f51b5c7" | |
| }, | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", | |
| "Collecting japanize-matplotlib\n", | |
| " Downloading japanize-matplotlib-1.1.3.tar.gz (4.1 MB)\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.1/4.1 MB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from japanize-matplotlib) (3.2.2)\n", | |
| "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib->japanize-matplotlib) (0.11.0)\n", | |
| "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.8/dist-packages (from matplotlib->japanize-matplotlib) (1.21.6)\n", | |
| "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->japanize-matplotlib) (1.4.4)\n", | |
| "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->japanize-matplotlib) (3.0.9)\n", | |
| "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->japanize-matplotlib) (2.8.2)\n", | |
| "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib->japanize-matplotlib) (1.15.0)\n", | |
| "Building wheels for collected packages: japanize-matplotlib\n", | |
| " Building wheel for japanize-matplotlib (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for japanize-matplotlib: filename=japanize_matplotlib-1.1.3-py3-none-any.whl size=4120275 sha256=f29c5e94197668dc3230867b591f20bbf976b35ebf916b975309857672192dea\n", | |
| " Stored in directory: /root/.cache/pip/wheels/4f/ca/96/4cc5e192421cceb077fbf4ffec533382edd416fd3fa0af0bbd\n", | |
| "Successfully built japanize-matplotlib\n", | |
| "Installing collected packages: japanize-matplotlib\n", | |
| "Successfully installed japanize-matplotlib-1.1.3\n", | |
| "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", | |
| "Collecting pandas>1.4.0\n", | |
| " Downloading pandas-1.5.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB)\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.2/12.2 MB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25hRequirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.8/dist-packages (from pandas>1.4.0) (2022.7)\n", | |
| "Requirement already satisfied: numpy>=1.20.3 in /usr/local/lib/python3.8/dist-packages (from pandas>1.4.0) (1.21.6)\n", | |
| "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.8/dist-packages (from pandas>1.4.0) (2.8.2)\n", | |
| "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.8.1->pandas>1.4.0) (1.15.0)\n", | |
| "Installing collected packages: pandas\n", | |
| " Attempting uninstall: pandas\n", | |
| " Found existing installation: pandas 1.3.5\n", | |
| " Uninstalling pandas-1.3.5:\n", | |
| " Successfully uninstalled pandas-1.3.5\n", | |
| "Successfully installed pandas-1.5.2\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "id": "UCipf59VE4EB" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "import pyparsing as pp\n", | |
| "import matplotlib.cm as cm\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import japanize_matplotlib" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# DataFrame列名定義\n", | |
| "columns = ['diff', 'horse_no', 'side']\n", | |
| "\n", | |
| "# 差の定数(unit:馬身)\n", | |
| "DIFF_GROUP = 0.3\n", | |
| "DIFF_MIN = 1.5\n", | |
| "DIFF_MID = 3.0\n", | |
| "DIFF_MUCH = 6.0\n", | |
| "\n", | |
| "class ParsePass():\n", | |
| " \n", | |
| " def __init__(self):\n", | |
| " \n", | |
| " # 馬番\n", | |
| " horse_no = pp.Word(pp.nums).setParseAction(self._horse_no_action)\n", | |
| " \n", | |
| " # 馬群\n", | |
| " group = pp.Suppress(pp.Literal('(')) + \\\n", | |
| " pp.Optional(pp.delimitedList(pp.Word(pp.nums), delim=',')) + \\\n", | |
| " pp.Suppress(pp.Literal(')'))\n", | |
| " group.ignore('*')\n", | |
| " group.setParseAction(self._group_action)\n", | |
| "\n", | |
| " # 情報要素\n", | |
| " element = (group | horse_no)\n", | |
| " \n", | |
| " # 前走馬との差\n", | |
| " diff_min = pp.Suppress(pp.Optional(pp.Literal(','))).setParseAction(self._diff_min_action) + element\n", | |
| " diff_mid = pp.Suppress(pp.Literal('-')).setParseAction(self._diff_mid_action) + element\n", | |
| " diff_much = pp.Suppress(pp.Literal('=')).setParseAction(self._diff_much_action) + element\n", | |
| "\n", | |
| " # 全体定義\n", | |
| " self._passing_order = element + pp.ZeroOrMore( diff_mid | diff_much | diff_min )\n", | |
| " \n", | |
| " def _horse_no_action(self, token):\n", | |
| " \n", | |
| " df_append = pd.DataFrame(data=[[self._diff, token[0], 1]], columns=columns)\n", | |
| " self._data = pd.concat([self._data, df_append], ignore_index=True, axis=0).drop_duplicates().reset_index(drop=True)\n", | |
| " return\n", | |
| "\n", | |
| " def _group_action(self, token):\n", | |
| " \n", | |
| " for i, no in enumerate(token):\n", | |
| " df_append = pd.DataFrame(data=[[self._diff, no, 1+i]], columns=columns)\n", | |
| " self._data = pd.concat([self._data, df_append], ignore_index=True, axis=0).drop_duplicates().reset_index(drop=True)\n", | |
| " self._diff += DIFF_GROUP\n", | |
| " self._diff -= DIFF_GROUP\n", | |
| " return\n", | |
| " \n", | |
| " def _diff_min_action(self, token):\n", | |
| " \n", | |
| " self._diff += DIFF_MIN\n", | |
| " return\n", | |
| " \n", | |
| " def _diff_mid_action(self, token):\n", | |
| " \n", | |
| " self._diff += DIFF_MID\n", | |
| " return\n", | |
| " \n", | |
| " def _diff_much_action(self, token):\n", | |
| " \n", | |
| " self._diff += DIFF_MUCH\n", | |
| " return\n", | |
| " \n", | |
| " def parse(self, pass_str):\n", | |
| " \n", | |
| " # 初期化\n", | |
| " self._data = pd.DataFrame(columns=columns)\n", | |
| " self._diff = 0\n", | |
| " # parse\n", | |
| " self._passing_order.parseString(pass_str)\n", | |
| " # index調整\n", | |
| " self._data.index = np.arange(1, len(self._data)+1)\n", | |
| " self._data.index.name = 'rank'\n", | |
| "\n", | |
| " return(self._data)" | |
| ], | |
| "metadata": { | |
| "id": "mvMLNW17E_0B" | |
| }, | |
| "execution_count": 23, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "pass_data = '(*12,10,15)5-(1,13)-(2,4)3(6,9)(14,11)(8,7)'#@param {type: \"string\"}" | |
| ], | |
| "metadata": { | |
| "id": "KndSiMxMkMCh" | |
| }, | |
| "execution_count": 24, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "pass_parsing = ParsePass()\n", | |
| "df = pass_parsing.parse(pass_data)\n", | |
| "df" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 551 | |
| }, | |
| "id": "yV6efWYeFLG7", | |
| "outputId": "eab0e5af-8be6-4ebb-a098-0d0549c018df" | |
| }, | |
| "execution_count": 25, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " diff horse_no side\n", | |
| "rank \n", | |
| "1 0 12 1\n", | |
| "2 0.3 10 2\n", | |
| "3 0.6 15 3\n", | |
| "4 2.1 5 1\n", | |
| "5 5.1 1 1\n", | |
| "6 5.4 13 2\n", | |
| "7 8.4 2 1\n", | |
| "8 8.7 4 2\n", | |
| "9 10.2 3 1\n", | |
| "10 11.7 6 1\n", | |
| "11 12.0 9 2\n", | |
| "12 13.5 14 1\n", | |
| "13 13.8 11 2\n", | |
| "14 15.3 8 1\n", | |
| "15 15.6 7 2" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-4289b435-d45d-4e6f-98e5-02e0129dd756\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>diff</th>\n", | |
| " <th>horse_no</th>\n", | |
| " <th>side</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>rank</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0</td>\n", | |
| " <td>12</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>0.3</td>\n", | |
| " <td>10</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>0.6</td>\n", | |
| " <td>15</td>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>2.1</td>\n", | |
| " <td>5</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>5.1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>5.4</td>\n", | |
| " <td>13</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>8.4</td>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>8.7</td>\n", | |
| " <td>4</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>10.2</td>\n", | |
| " <td>3</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>11.7</td>\n", | |
| " <td>6</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>12.0</td>\n", | |
| " <td>9</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>12</th>\n", | |
| " <td>13.5</td>\n", | |
| " <td>14</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>13</th>\n", | |
| " <td>13.8</td>\n", | |
| " <td>11</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14</th>\n", | |
| " <td>15.3</td>\n", | |
| " <td>8</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>15</th>\n", | |
| " <td>15.6</td>\n", | |
| " <td>7</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
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| " 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", | |
| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 25 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "plt.figure(figsize=(df[\"diff\"].max(),df[\"side\"].max()))\n", | |
| "plt.scatter(df[\"diff\"], df[\"side\"], s=100)\n", | |
| "plt.xlabel(\"前 ← 前後 → 後\", fontsize=15)\n", | |
| "plt.ylabel(\"内 ← 内外 → 外\", fontsize=15)\n", | |
| "plt.title(\"隊列\", fontsize=20)\n", | |
| "plt.yticks([0,1,2,3,4], color=\"w\") " | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 357 | |
| }, | |
| "id": "yac18U0njctj", | |
| "outputId": "eaf7e9a4-89d0-4734-901c-0eb0f3266618" | |
| }, | |
| "execution_count": 22, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "([<matplotlib.axis.YTick at 0x7f8b1ac7c190>,\n", | |
| " <matplotlib.axis.YTick at 0x7f8b1ac7c280>,\n", | |
| " <matplotlib.axis.YTick at 0x7f8b1ab239a0>,\n", | |
| " <matplotlib.axis.YTick at 0x7f8b1ab3d310>,\n", | |
| " <matplotlib.axis.YTick at 0x7f8b1ab3d820>],\n", | |
| " <a list of 5 Text major ticklabel objects>)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 22 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1123.2x216 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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