02_syouritu.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f19a350-7a0a-4f51-b1d9-a75c772cb063",
   "metadata": {},
   "source": [
    "# About\n",
    "\n",
    "勝率の単純集計"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfaaf571-3756-43af-8622-a316ecb18636",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import collections\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(font_scale=1.5)\n",
    "c_list = sns.color_palette().as_hex()\n",
    "color_num = len(c_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fcfd1ad7-ce3c-4292-b5af-6eb8c354f5c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dct_player = {\n",
    "    \"さくま\": \"sakuma\", \"ようきひ\":\"yohkihi\", \"ガキ\": \"gaki\", \"エンマ\": \"enma\", \n",
    "}\n",
    "pl_color = {\n",
    "    \"sakuma\": c_list[0], \"yohkihi\":c_list[1], \"gaki\": c_list[2], \"enma\": c_list[3], \n",
    "}\n",
    "\n",
    "s = \"\"\"\n",
    "さくま:ガキ:ようきひ\n",
    "さくま:ようきひ:ガキ\n",
    "ガキ:ようきひ:さくま\n",
    "ようきひ:ガキ:さくま\n",
    "ようきひ:さくま:ガキ\n",
    "ようきひ:ガキ:さくま\n",
    "ようきひ:ガキ:さくま\n",
    "ようきひ:さくま:ガキ\n",
    "ようきひ:さくま:ガキ\n",
    "さくま:ガキ:ようきひ\n",
    "ガキ:さくま:ようきひ\n",
    "ようきひ:さくま:ガキ\n",
    "ようきひ:さくま:ガキ\n",
    "ようきひ:さくま:ガキ\n",
    "さくま:ようきひ:ガキ\n",
    "さくま:ガキ:ようきひ\n",
    "さくま:ようきひ:ガキ\n",
    "さくま:ガキ:ようきひ\n",
    "ようきひ:ガキ:さくま\n",
    "ようきひ:さくま:ガキ\n",
    "ようきひ:さくま:ガキ\n",
    "ようきひ:さくま:ガキ\n",
    "ようきひ:さくま:ガキ\n",
    "ようきひ:さくま:ガキ\n",
    "ようきひ:さくま:ガキ\n",
    "ようきひ:さくま:ガキ\n",
    "ようきひ:さくま:ガキ\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cdaaaaf4-ce14-4008-b270-a15f4d5133fe",
   "metadata": {},
   "outputs": [
    {
     "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>さくま</td>\n",
       "      <td>ガキ</td>\n",
       "      <td>ようきひ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>さくま</td>\n",
       "      <td>ようきひ</td>\n",
       "      <td>ガキ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ガキ</td>\n",
       "      <td>ようきひ</td>\n",
       "      <td>さくま</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ようきひ</td>\n",
       "      <td>ガキ</td>\n",
       "      <td>さくま</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ようきひ</td>\n",
       "      <td>さくま</td>\n",
       "      <td>ガキ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1     2     3\n",
       "0   さくま    ガキ  ようきひ\n",
       "1   さくま  ようきひ    ガキ\n",
       "2    ガキ  ようきひ   さくま\n",
       "3  ようきひ    ガキ   さくま\n",
       "4  ようきひ   さくま    ガキ"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst_rank = []\n",
    "for t in s.strip().split(\"\\n\"):\n",
    "    rank = t.split(\":\")\n",
    "    lst_rank.append({1:rank[0], 2:rank[1], 3:rank[2]})\n",
    "df_rank = pd.DataFrame(lst_rank)\n",
    "df_rank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "036ea50d-18f9-4b9f-a1da-db5230898a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'さくま': 7, 'ガキ': 2, 'ようきひ': 18})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt_top = collections.Counter(df_rank[1])\n",
    "cnt_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6a3107a-556f-4692-a7f9-582782331604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rank.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c83eb001-9b4e-4349-ba31-9b25a1440249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 4))\n",
    "ax = fig.subplots(1, 1)\n",
    "\n",
    "players = [dct_player[k] for k in cnt_top.keys()]\n",
    "colors = [pl_color[p] for p in players]\n",
    "ax.bar(players, cnt_top.values(), \n",
    "       color=colors);\n",
    "for k, v in cnt_top.items():\n",
    "    ax.text(dct_player[k], v, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3200b12f-8027-4eb2-aaaa-e46a3bb9b993",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}