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Shinichi-Nakagawa/npb2023_teamstats.ipynb

Created September 24, 2023 09:13
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyOeBKTFecgVAQDiif36VJ2l",
"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/Shinichi-Nakagawa/c104e5988069e6b3c3bc33d78a0b5d41/npb2023_teamstats.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# プロ野球チーム成績からチームごとの指標を計算・可視化\n",
"\n",
"- チーム打撃・チーム投球成績をスクレイピング\n",
"- データ型を指定してCSVとして保存\n",
"- Plotlyで可視化"
],
"metadata": {
"id": "4a_TuwVg425x"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "JKzLztF94NH4"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"pd.options.display.max_rows = 12\n",
"pd.options.display.max_columns = 50\n",
"from datetime import datetime\n",
"\n",
"now = datetime.now()\n"
]
},
{
"cell_type": "code",
"source": [
"!pip install requests-html"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JP4e1y8S4SEW",
"outputId": "63b0b460-eb8c-4e88-f212-fbf337cce062"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: requests-html in /usr/local/lib/python3.10/dist-packages (0.10.0)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from requests-html) (2.31.0)\n",
"Requirement already satisfied: pyquery in /usr/local/lib/python3.10/dist-packages (from requests-html) (2.0.0)\n",
"Requirement already satisfied: fake-useragent in /usr/local/lib/python3.10/dist-packages (from requests-html) (1.2.1)\n",
"Requirement already satisfied: parse in /usr/local/lib/python3.10/dist-packages (from requests-html) (1.19.1)\n",
"Requirement already satisfied: bs4 in /usr/local/lib/python3.10/dist-packages (from requests-html) (0.0.1)\n",
"Requirement already satisfied: w3lib in /usr/local/lib/python3.10/dist-packages (from requests-html) (2.1.2)\n",
"Requirement already satisfied: pyppeteer>=0.0.14 in /usr/local/lib/python3.10/dist-packages (from requests-html) (1.0.2)\n",
"Requirement already satisfied: appdirs<2.0.0,>=1.4.3 in /usr/local/lib/python3.10/dist-packages (from pyppeteer>=0.0.14->requests-html) (1.4.4)\n",
"Requirement already satisfied: certifi>=2021 in /usr/local/lib/python3.10/dist-packages (from pyppeteer>=0.0.14->requests-html) (2023.7.22)\n",
"Requirement already satisfied: importlib-metadata>=1.4 in /usr/local/lib/python3.10/dist-packages (from pyppeteer>=0.0.14->requests-html) (6.8.0)\n",
"Requirement already satisfied: pyee<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from pyppeteer>=0.0.14->requests-html) (8.2.2)\n",
"Requirement already satisfied: tqdm<5.0.0,>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from pyppeteer>=0.0.14->requests-html) (4.66.1)\n",
"Requirement already satisfied: urllib3<2.0.0,>=1.25.8 in /usr/local/lib/python3.10/dist-packages (from pyppeteer>=0.0.14->requests-html) (1.26.16)\n",
"Requirement already satisfied: websockets<11.0,>=10.0 in /usr/local/lib/python3.10/dist-packages (from pyppeteer>=0.0.14->requests-html) (10.4)\n",
"Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from bs4->requests-html) (4.11.2)\n",
"Requirement already satisfied: lxml>=2.1 in /usr/local/lib/python3.10/dist-packages (from pyquery->requests-html) (4.9.3)\n",
"Requirement already satisfied: cssselect>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from pyquery->requests-html) (1.2.0)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->requests-html) (3.2.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->requests-html) (3.4)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=1.4->pyppeteer>=0.0.14->requests-html) (3.16.2)\n",
"Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->bs4->requests-html) (2.5)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!python --version"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fc3LAHmr4Z6p",
"outputId": "ff86ec04-851e-43f7-bc3c-dab89df97604"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Python 3.10.12\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## チーム成績の取得とデータ整形"
],
"metadata": {
"id": "u_pMYz7u5V2w"
}
},
{
"cell_type": "code",
"source": [
"# 取得対象のチーム成績\n",
"\n",
"TEAM_STATS_CENTRAL_PITCHING = \"https://npb.jp/bis/2023/stats/tmp_c.html\"\n",
"TEAM_STATS_CENTRAL_BATTING = \"https://npb.jp/bis/2023/stats/tmb_c.html\""
],
"metadata": {
"id": "7VfVrvE04de_"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# ひとまず取得\n",
"from requests_html import HTMLSession\n",
"session = HTMLSession()"
],
"metadata": {
"id": "j1bFHn2o5FiU"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# 投手&打者成績を取得する\n",
"from typing import Dict, Union, List\n",
"\n",
"COLUMNS_PITCING = ['team', 'era', 'g', 'win', 'lose', 'sv', 'hld', 'hldp', 'cg', 'sho', 'no_walks', 'win_p', 'batter_faces', 'ip', 'ip_2', 'h', 'hr', 'bb', 'ibb', 'hbp', 'so', 'wp', 'balk', 'r', 'er']\n",
"COLUMNS_BATTING = ['team', 'ba', 'g', 'pa', 'ab', 'r', 'h', '_2b', '_3b', 'hr', 'tb', 'rbi', 'sb', 'cs', 'sh', 'sf', 'bb', 'ibb', 'hbp', 'so', 'dp', 'slg', 'obp']\n",
"\n",
"def stats(url: str, columns: List[str], session: HTMLSession) -> Dict[str, Union[str, int, float]]:\n",
" r = session.get(url)\n",
" rows: Dict[str, Union[str, int, float]] = {}\n",
" table = r.html.xpath(\"/html/body/div/div/div/div[3]/table\", first=True)\n",
" for i, tr in enumerate(table.find(\"tr\")):\n",
" # header is skip\n",
" if i == 0:\n",
" continue\n",
" values = {}\n",
" for ii, td in enumerate(tr.find('td')):\n",
" if ii == 0:\n",
" _a = td.find('a', first=True)\n",
" values[columns[ii]] = str(_a.text).replace('\\u3000', '')\n",
" else:\n",
" values[columns[ii]] = td.text\n",
" rows[values['team']] = values\n",
" return rows\n"
],
"metadata": {
"id": "450Uyp_076n_"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"stats_p = stats(TEAM_STATS_CENTRAL_PITCHING, COLUMNS_PITCING, session)\n",
"stats_b = stats(TEAM_STATS_CENTRAL_BATTING, COLUMNS_BATTING, session)"
],
"metadata": {
"id": "OykoFv6rBXnz"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "JbH28iGdJ2Cr"
}
},
{
"cell_type": "code",
"source": [
"# pandasに読み込んで整形する\n",
"# 投手成績はひと工夫入れる\n",
"_stats_p = {}\n",
"for k, v in stats_p.items():\n",
" v['ip'] = v['ip'] + v['ip_2']\n",
" del v['ip_2']\n",
" _stats_p[k] = v\n",
"\n",
"df_stats_p = pd.DataFrame(_stats_p.values()).astype({'era': float, 'g': int, 'win': int, 'lose': int, 'sv': int, 'hld': int, 'hldp': int, 'cg': int, 'sho': int, 'no_walks': int, 'win_p': float, 'batter_faces': int, 'ip': float, 'h': int, 'hr': int, 'bb': int, 'ibb': int, 'hbp': int, 'so': int, 'wp': int, 'balk': int, 'r': int, 'er': int})"
],
"metadata": {
"id": "CQC6u3ngF88I"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_stats_p"
],
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"height": 238
},
"id": "irC7spIMGsHM",
"outputId": "4a25829d-d405-4081-86c2-585ed7044afd"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" team era g win lose sv hld hldp cg sho no_walks win_p \\\n",
"0 阪神 2.64 135 83 48 43 123 144 10 17 23 0.634 \n",
"1 中日 3.17 135 52 79 35 136 157 4 7 8 0.397 \n",
"2 DeNA 3.20 135 69 63 38 111 131 8 12 16 0.523 \n",
"3 広島 3.24 138 72 62 44 121 147 9 13 11 0.537 \n",
"4 巨人 3.52 136 67 67 29 118 144 5 15 8 0.500 \n",
"5 ヤクルト 3.73 137 54 80 32 129 144 3 15 9 0.403 \n",
"\n",
" batter_faces ip h hr bb ibb hbp so wp balk r er \n",
"0 4919 1216.1 1043 83 295 16 40 958 19 2 398 357 \n",
"1 5086 1214.1 1065 74 418 18 48 1007 31 0 484 428 \n",
"2 5073 1218.1 1145 114 341 26 41 1099 27 0 473 433 \n",
"3 5170 1230.0 1153 107 386 27 40 936 30 3 494 443 \n",
"4 5069 1215.1 1082 128 390 17 62 1020 26 6 502 476 \n",
"5 5134 1215.2 1175 138 373 41 62 912 32 1 552 504 "
],
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" <td>中日</td>\n",
" <td>3.17</td>\n",
" <td>135</td>\n",
" <td>52</td>\n",
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" <td>DeNA</td>\n",
" <td>3.20</td>\n",
" <td>135</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>38</td>\n",
" <td>111</td>\n",
" <td>131</td>\n",
" <td>8</td>\n",
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" <td>1218.1</td>\n",
" <td>1145</td>\n",
" <td>114</td>\n",
" <td>341</td>\n",
" <td>26</td>\n",
" <td>41</td>\n",
" <td>1099</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>473</td>\n",
" <td>433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>広島</td>\n",
" <td>3.24</td>\n",
" <td>138</td>\n",
" <td>72</td>\n",
" <td>62</td>\n",
" <td>44</td>\n",
" <td>121</td>\n",
" <td>147</td>\n",
" <td>9</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>0.537</td>\n",
" <td>5170</td>\n",
" <td>1230.0</td>\n",
" <td>1153</td>\n",
" <td>107</td>\n",
" <td>386</td>\n",
" <td>27</td>\n",
" <td>40</td>\n",
" <td>936</td>\n",
" <td>30</td>\n",
" <td>3</td>\n",
" <td>494</td>\n",
" <td>443</td>\n",
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" <td>巨人</td>\n",
" <td>3.52</td>\n",
" <td>136</td>\n",
" <td>67</td>\n",
" <td>67</td>\n",
" <td>29</td>\n",
" <td>118</td>\n",
" <td>144</td>\n",
" <td>5</td>\n",
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" <td>8</td>\n",
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" <td>1215.1</td>\n",
" <td>1082</td>\n",
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" <td>17</td>\n",
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" <td>26</td>\n",
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" <th>5</th>\n",
" <td>ヤクルト</td>\n",
" <td>3.73</td>\n",
" <td>137</td>\n",
" <td>54</td>\n",
" <td>80</td>\n",
" <td>32</td>\n",
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" <td>144</td>\n",
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" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
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" 20% {\n",
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" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
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" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-f8a2ba54-a6e0-4451-8703-595675f362ef button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"df_stats_b = pd.DataFrame(stats_b.values()).astype({'ba': float, 'g': int, 'pa': int, 'ab': int, 'r': int, 'h': int, '_2b': int, '_3b': int, 'hr': int, 'tb': int, 'rbi': int, 'sb': int, 'cs': int, 'sh': int, 'sf': int, 'bb': int, 'ibb': int, 'hbp': int, 'so': int, 'dp': int, 'slg': float, 'obp': float})"
],
"metadata": {
"id": "pOCM8mnuGxTk"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df_stats_b"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"id": "hmnR7UhQHGJ8",
"outputId": "49035f47-1a04-4c76-b571-756df5e357c2"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" team ba g pa ab r h _2b _3b hr tb rbi sb cs \\\n",
"0 巨人 0.254 136 5104 4607 511 1170 197 14 161 1878 499 46 29 \n",
"1 阪神 0.249 135 5187 4514 536 1123 173 34 77 1595 515 73 26 \n",
"2 DeNA 0.248 135 5054 4535 502 1125 219 18 104 1692 486 29 25 \n",
"3 広島 0.247 138 5067 4573 482 1129 191 16 94 1634 466 75 50 \n",
"4 ヤクルト 0.240 137 5134 4513 513 1082 203 9 121 1666 489 61 20 \n",
"5 中日 0.237 135 4968 4530 374 1073 182 19 67 1494 355 33 23 \n",
"\n",
" sh sf bb ibb hbp so dp slg obp \n",
"0 87 30 343 36 37 1055 91 0.408 0.309 \n",
"1 102 44 475 34 52 1104 82 0.353 0.324 \n",
"2 99 30 338 22 52 798 95 0.373 0.306 \n",
"3 92 20 335 25 47 994 111 0.357 0.304 \n",
"4 108 32 432 22 49 1058 110 0.369 0.311 \n",
"5 86 21 294 17 37 978 90 0.330 0.288 "
],
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" <td>4573</td>\n",
" <td>482</td>\n",
" <td>1129</td>\n",
" <td>191</td>\n",
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" <td>94</td>\n",
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" </tr>\n",
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" <td>0.240</td>\n",
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" <td>1082</td>\n",
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" <td>0.311</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>中日</td>\n",
" <td>0.237</td>\n",
" <td>135</td>\n",
" <td>4968</td>\n",
" <td>4530</td>\n",
" <td>374</td>\n",
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" 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",
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" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-32535c6b-8a04-4b95-98c5-f529d2080436');\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",
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" + ' 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",
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"\n",
"\n",
"<div id=\"df-381b1b63-48b2-4e7d-999a-f9642486027c\">\n",
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" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
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" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
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" }\n",
"\n",
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" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
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" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
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" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
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"\n",
" <script>\n",
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" </div>\n"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"# 必要な指標を作り込む（打撃）\n",
"df_stats_b['ops'] = round(df_stats_b['obp'] + df_stats_b['slg'], 3)\n",
"df_stats_b['ab_k'] = round(df_stats_b['ab'] / df_stats_b['so'], 2)\n",
"df_stats_b['ab_bb'] = round(df_stats_b['ab'] / df_stats_b['bb'], 2)\n",
"df_stats_b['bb_per_so'] = round((df_stats_b['bb'] + df_stats_b['hbp'] + df_stats_b['ibb']) / df_stats_b['so'], 3)"
],
"metadata": {
"id": "N-Wu8VzbKL59"
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"execution_count": 12,
"outputs": []
},
{
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"df_stats_b"
],
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"id": "9IolahbJMQdh",
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" team ba g pa ab r h _2b _3b hr tb rbi sb cs \\\n",
"0 巨人 0.254 136 5104 4607 511 1170 197 14 161 1878 499 46 29 \n",
"1 阪神 0.249 135 5187 4514 536 1123 173 34 77 1595 515 73 26 \n",
"2 DeNA 0.248 135 5054 4535 502 1125 219 18 104 1692 486 29 25 \n",
"3 広島 0.247 138 5067 4573 482 1129 191 16 94 1634 466 75 50 \n",
"4 ヤクルト 0.240 137 5134 4513 513 1082 203 9 121 1666 489 61 20 \n",
"5 中日 0.237 135 4968 4530 374 1073 182 19 67 1494 355 33 23 \n",
"\n",
" sh sf bb ibb hbp so dp slg obp ops ab_k ab_bb \\\n",
"0 87 30 343 36 37 1055 91 0.408 0.309 0.717 4.37 13.43 \n",
"1 102 44 475 34 52 1104 82 0.353 0.324 0.677 4.09 9.50 \n",
"2 99 30 338 22 52 798 95 0.373 0.306 0.679 5.68 13.42 \n",
"3 92 20 335 25 47 994 111 0.357 0.304 0.661 4.60 13.65 \n",
"4 108 32 432 22 49 1058 110 0.369 0.311 0.680 4.27 10.45 \n",
"5 86 21 294 17 37 978 90 0.330 0.288 0.618 4.63 15.41 \n",
"\n",
" bb_per_so \n",
"0 0.394 \n",
"1 0.508 \n",
"2 0.516 \n",
"3 0.409 \n",
"4 0.475 \n",
"5 0.356 "
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" <td>0.661</td>\n",
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},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"# 必要な指標を作り込む（投手）\n",
"# memo: fipの補正値は適当（今年は打低すぎるので2.8とかでもいいかも)\n",
"df_stats_p['fip'] = round((3 * (df_stats_p['bb'] + df_stats_p['hbp'] - df_stats_p['ibb']) + (13 * df_stats_p['hr']) - (2 * df_stats_p['so'])) / df_stats_p['ip'] + 3, 2)\n",
"df_stats_p['so_p'] = round(((df_stats_p['so'] * 9) /df_stats_p['ip']), 2)\n",
"df_stats_p['bb_p'] = round(((df_stats_p['bb'] * 9) /df_stats_p['ip']), 2)\n",
"df_stats_p"
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"id": "OCXwYiPnMRZg",
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" team era g win lose sv hld hldp cg sho no_walks win_p \\\n",
"0 阪神 2.64 135 83 48 43 123 144 10 17 23 0.634 \n",
"1 中日 3.17 135 52 79 35 136 157 4 7 8 0.397 \n",
"2 DeNA 3.20 135 69 63 38 111 131 8 12 16 0.523 \n",
"3 広島 3.24 138 72 62 44 121 147 9 13 11 0.537 \n",
"4 巨人 3.52 136 67 67 29 118 144 5 15 8 0.500 \n",
"5 ヤクルト 3.73 137 54 80 32 129 144 3 15 9 0.403 \n",
"\n",
" batter_faces ip h hr bb ibb hbp so wp balk r er \\\n",
"0 4919 1216.1 1043 83 295 16 40 958 19 2 398 357 \n",
"1 5086 1214.1 1065 74 418 18 48 1007 31 0 484 428 \n",
"2 5073 1218.1 1145 114 341 26 41 1099 27 0 473 433 \n",
"3 5170 1230.0 1153 107 386 27 40 936 30 3 494 443 \n",
"4 5069 1215.1 1082 128 390 17 62 1020 26 6 502 476 \n",
"5 5134 1215.2 1175 138 373 41 62 912 32 1 552 504 \n",
"\n",
" fip so_p bb_p \n",
"0 3.10 7.09 2.18 \n",
"1 3.24 7.46 3.10 \n",
"2 3.29 8.12 2.52 \n",
"3 3.58 6.85 2.82 \n",
"4 3.76 7.55 2.89 \n",
"5 3.95 6.75 2.76 "
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" <th>0</th>\n",
" <td>阪神</td>\n",
" <td>2.64</td>\n",
" <td>135</td>\n",
" <td>83</td>\n",
" <td>48</td>\n",
" <td>43</td>\n",
" <td>123</td>\n",
" <td>144</td>\n",
" <td>10</td>\n",
" <td>17</td>\n",
" <td>23</td>\n",
" <td>0.634</td>\n",
" <td>4919</td>\n",
" <td>1216.1</td>\n",
" <td>1043</td>\n",
" <td>83</td>\n",
" <td>295</td>\n",
" <td>16</td>\n",
" <td>40</td>\n",
" <td>958</td>\n",
" <td>19</td>\n",
" <td>2</td>\n",
" <td>398</td>\n",
" <td>357</td>\n",
" <td>3.10</td>\n",
" <td>7.09</td>\n",
" <td>2.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>中日</td>\n",
" <td>3.17</td>\n",
" <td>135</td>\n",
" <td>52</td>\n",
" <td>79</td>\n",
" <td>35</td>\n",
" <td>136</td>\n",
" <td>157</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>0.397</td>\n",
" <td>5086</td>\n",
" <td>1214.1</td>\n",
" <td>1065</td>\n",
" <td>74</td>\n",
" <td>418</td>\n",
" <td>18</td>\n",
" <td>48</td>\n",
" <td>1007</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>484</td>\n",
" <td>428</td>\n",
" <td>3.24</td>\n",
" <td>7.46</td>\n",
" <td>3.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>DeNA</td>\n",
" <td>3.20</td>\n",
" <td>135</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>38</td>\n",
" <td>111</td>\n",
" <td>131</td>\n",
" <td>8</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>0.523</td>\n",
" <td>5073</td>\n",
" <td>1218.1</td>\n",
" <td>1145</td>\n",
" <td>114</td>\n",
" <td>341</td>\n",
" <td>26</td>\n",
" <td>41</td>\n",
" <td>1099</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>473</td>\n",
" <td>433</td>\n",
" <td>3.29</td>\n",
" <td>8.12</td>\n",
" <td>2.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>広島</td>\n",
" <td>3.24</td>\n",
" <td>138</td>\n",
" <td>72</td>\n",
" <td>62</td>\n",
" <td>44</td>\n",
" <td>121</td>\n",
" <td>147</td>\n",
" <td>9</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>0.537</td>\n",
" <td>5170</td>\n",
" <td>1230.0</td>\n",
" <td>1153</td>\n",
" <td>107</td>\n",
" <td>386</td>\n",
" <td>27</td>\n",
" <td>40</td>\n",
" <td>936</td>\n",
" <td>30</td>\n",
" <td>3</td>\n",
" <td>494</td>\n",
" <td>443</td>\n",
" <td>3.58</td>\n",
" <td>6.85</td>\n",
" <td>2.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>巨人</td>\n",
" <td>3.52</td>\n",
" <td>136</td>\n",
" <td>67</td>\n",
" <td>67</td>\n",
" <td>29</td>\n",
" <td>118</td>\n",
" <td>144</td>\n",
" <td>5</td>\n",
" <td>15</td>\n",
" <td>8</td>\n",
" <td>0.500</td>\n",
" <td>5069</td>\n",
" <td>1215.1</td>\n",
" <td>1082</td>\n",
" <td>128</td>\n",
" <td>390</td>\n",
" <td>17</td>\n",
" <td>62</td>\n",
" <td>1020</td>\n",
" <td>26</td>\n",
" <td>6</td>\n",
" <td>502</td>\n",
" <td>476</td>\n",
" <td>3.76</td>\n",
" <td>7.55</td>\n",
" <td>2.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ヤクルト</td>\n",
" <td>3.73</td>\n",
" <td>137</td>\n",
" <td>54</td>\n",
" <td>80</td>\n",
" <td>32</td>\n",
" <td>129</td>\n",
" <td>144</td>\n",
" <td>3</td>\n",
" <td>15</td>\n",
" <td>9</td>\n",
" <td>0.403</td>\n",
" <td>5134</td>\n",
" <td>1215.2</td>\n",
" <td>1175</td>\n",
" <td>138</td>\n",
" <td>373</td>\n",
" <td>41</td>\n",
" <td>62</td>\n",
" <td>912</td>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" <td>552</td>\n",
" <td>504</td>\n",
" <td>3.95</td>\n",
" <td>6.75</td>\n",
" <td>2.76</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0797bb5-90d7-42e6-aaf9-89f267c1d611')\"\n",
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" </svg>\n",
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"\n",
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" .colab-df-container {\n",
" display:flex;\n",
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"\n",
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" background-color: #E8F0FE;\n",
" border: none;\n",
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"\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",
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"\n",
" .colab-df-buttons div {\n",
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"\n",
" [theme=dark] .colab-df-convert {\n",
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" 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-d0797bb5-90d7-42e6-aaf9-89f267c1d611 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-d0797bb5-90d7-42e6-aaf9-89f267c1d611');\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",
"\n",
"\n",
"<div id=\"df-96e5dc05-e3de-4ef6-bac7-7738c7025101\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-96e5dc05-e3de-4ef6-bac7-7738c7025101')\"\n",
" title=\"Suggest charts.\"\n",
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" width=\"24px\">\n",
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"</svg>\n",
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"\n",
"<style>\n",
" .colab-df-quickchart {\n",
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" .colab-df-quickchart {\n",
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" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-96e5dc05-e3de-4ef6-bac7-7738c7025101 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
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"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"# 計算したデータを保存（必要なときだけ有効化）\n",
"\n",
"# df_stats_b.to_csv(f'{now.strftime(\"%Y%m%d_%H%M%S\")}_team_stats_batting.csv')\n",
"# df_stats_p.to_csv(f'{now.strftime(\"%Y%m%d_%H%M%S\")}_team_stats_pitcing.csv')"
],
"metadata": {
"id": "1X5ENewMNN3N"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## 成績の可視化"
],
"metadata": {
"id": "EJYNVyRL5feF"
}
},
{
"cell_type": "code",
"source": [
"# Plotlyで可視化\n",
"!pip install plotly"
],
"metadata": {
"id": "673O5VslWJtp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c75caaf9-c480-4501-d6af-0ec5626b088f"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.15.0)\n",
"Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (8.2.3)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (23.1)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import plotly.express as px\n",
"\n",
"WIDTH = 1400\n",
"HEIGHT = 700\n",
"\n",
"fig = px.bar(df_stats_b.sort_values(['ab_bb'], ascending=False), x=['ab_k', 'ab_bb'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=\"【チーム打撃成績】三振および四球獲得までの平均打数\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（ab_bbの昇順）')\n",
"fig.update_xaxes(title='ab_k（三振するまでの平均打数）, ab_bb（四球獲得までの平均打数）', dtick=2, range=(0, 18))\n",
"fig.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 717
},
"id": "JBaL0L2O4ZeZ",
"outputId": "5a58c51d-1aa1-4f42-a99f-aaaaf10688a2"
},
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
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},
{
"cell_type": "code",
"source": [
"fig = px.bar(df_stats_p.sort_values(['bb_p'], ascending=False), x=['so_p', 'bb_p'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=\"【チーム投手成績】奪三振率と与四球率の比較\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（与四球率の昇順）')\n",
"fig.update_xaxes(title='so_p（奪三振率）, bb_p（与四球率）', dtick=1, range=(0, 9))\n",
"fig.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 717
},
"id": "9QvKLZV34xnE",
"outputId": "ea3e98ad-4ae5-4b53-f129-6710f2fa4685"
},
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "wmLKpx8gCBN6"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## 以下, スライド素材用のデータ"
],
"metadata": {
"id": "UsUM_0NgGdC0"
}
},
{
"cell_type": "code",
"source": [
"import plotly.express as px\n",
"fig = px.bar(df_stats_b.sort_values(['ab_bb'], ascending=False), x=['ab_bb'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=\"【チーム打撃成績】四球獲得に要した打数（少ない=多く四球を獲得しているので優秀）\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（数字が優秀な順）')\n",
"fig.update_xaxes(title='四球獲得までの平均打数（ab_bb）', dtick=2, range=(0, 18))\n",
"fig.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 717
},
"outputId": "6b7f8326-5b76-4bb9-8200-334f016e5c87",
"id": "eOQlkn_AGZig"
},
"execution_count": 19,
"outputs": [
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{
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"metadata": {
"id": "1j1Xkc_pGavG"
},
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{
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"source": [
"import plotly.express as px\n",
"fig = px.bar(df_stats_b.sort_values(['ab_k'], ascending=True), x=['ab_k'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=\"【チーム打撃成績】三振するまでの平均打数（多い=三振が少ない）\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（数字が優秀な順）')\n",
"fig.update_xaxes(title='三振するまでの平均打数（ab_k）', dtick=1, range=(0, 9))\n",
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],
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"height": 717
},
"outputId": "8af577de-bc98-4396-ae6c-f1e704bc5304",
"id": "C8Ot08jqGa-E"
},
"execution_count": 20,
"outputs": [
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"source": [
"fig = px.bar(df_stats_p.sort_values(['so_p'], ascending=True), x=['so_p'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=\"【チーム投手成績】奪三振率（高いと優秀）\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（数字が優秀な順）')\n",
"fig.update_xaxes(title='奪三振率（so_p）', dtick=1, range=(0, 9))\n",
"fig.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 717
},
"id": "D5Kiz3vsHlt4",
"outputId": "b1f9aabf-e615-474b-af53-d30f81c0b98e"
},
"execution_count": 21,
"outputs": [
{
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"source": [
"fig = px.bar(df_stats_p.sort_values(['bb_p'], ascending=False), x=['bb_p'], y='team', barmode='group',)\n",
"fig.update_layout(\n",
" title=\"【チーム投手成績】与四球率（低いと優秀）\",\n",
" # autosize=True, # 画面に合わせたい時に有効化\n",
" width=WIDTH,\n",
" height=HEIGHT,\n",
" legend_title=None\n",
")\n",
"fig.update_yaxes(title='チーム名（数字が優秀な順）')\n",
"fig.update_xaxes(title='与四球率（bb_p）', dtick=1, range=(0, 6))\n",
"fig.show()"
],
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 717
},
"id": "ov6fAwzFJOxH",
"outputId": "f45a93f1-8928-4620-af75-6701042402c6"
},
"execution_count": 22,
"outputs": [
{
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},
{
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"source": [],
"metadata": {
"id": "IykavUTpK7WH"
},
"execution_count": 22,
"outputs": []
}
]
}
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