07. 확률분포 - 포아송 분포 지수분포.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "metric-breach",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from random import random\n",
    "from scipy.special import comb\n",
    "import scipy as sp\n",
    "\n",
    "sns.set(style='ticks', color_codes = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "greek-return",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08422433748856832"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pmf로 표현\n",
    "# 확률변수 2 ( 실책을 2번 했다~ 실제로 발생한 사건)\n",
    "# 평균 한 게임당 실책을 5번했다~ \n",
    "\n",
    "sp.stats.poisson.pmf(k = 2, mu = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "measured-copyright",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rvs_poisson = sp.stats.poisson.rvs(mu=5, size=1000)\n",
    "np.unique(rvs_poisson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "rapid-namibia",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eunbinpark/miniconda3/envs/da/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(rvs_poisson, bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "consistent-ceramic",
   "metadata": {},
   "source": [
    "### 문제 \n",
    "1. 평균 실책 횟수 3회\n",
    "2. 한번이라도 실책을 하는 경기를 치룰 확률은?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "complimentary-valentine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.049787068367863944"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 한 번도 실책하지 않을 확률\n",
    "sp.stats.poisson.pmf(k = 0, mu = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "sufficient-question",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.950212931632136"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - sp.stats.poisson.pmf(k = 0, mu = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "stopped-cambridge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41876, 9)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/football_games.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "personalized-housing",
   "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>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date home_team away_team  home_score  away_score tournament     city  \\\n",
       "0  1872-11-30  Scotland   England           0           0   Friendly  Glasgow   \n",
       "1  1873-03-08   England  Scotland           4           2   Friendly   London   \n",
       "2  1874-03-07  Scotland   England           2           1   Friendly  Glasgow   \n",
       "3  1875-03-06   England  Scotland           2           2   Friendly   London   \n",
       "4  1876-03-04  Scotland   England           3           0   Friendly  Glasgow   \n",
       "\n",
       "    country  neutral  \n",
       "0  Scotland    False  \n",
       "1   England    False  \n",
       "2  Scotland    False  \n",
       "3   England    False  \n",
       "4  Scotland    False  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "european-harvard",
   "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>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2921</th>\n",
       "      <td>1949-01-02</td>\n",
       "      <td>China PR</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>So Kon Po</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>1949-01-16</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Saigon</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>1949-01-25</td>\n",
       "      <td>Macau</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Macau</td>\n",
       "      <td>Macau</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3093</th>\n",
       "      <td>1950-04-16</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>Chinese Taipei</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Wan Chai</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3467</th>\n",
       "      <td>1953-04-05</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>So Kon Po</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>False</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>41585</th>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>EAFF Championship</td>\n",
       "      <td>Busan</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41587</th>\n",
       "      <td>2019-12-15</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>China PR</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>EAFF Championship</td>\n",
       "      <td>Busan</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41589</th>\n",
       "      <td>2019-12-18</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>EAFF Championship</td>\n",
       "      <td>Busan</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41813</th>\n",
       "      <td>2020-11-14</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Wiener Neustadt</td>\n",
       "      <td>Austria</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41848</th>\n",
       "      <td>2020-11-17</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Maria Enzersdorf</td>\n",
       "      <td>Austria</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>864 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    home_team       away_team  home_score  away_score  \\\n",
       "2921   1949-01-02     China PR     South Korea           3           2   \n",
       "2923   1949-01-16      Vietnam     South Korea           3           3   \n",
       "2924   1949-01-25        Macau     South Korea           1           5   \n",
       "3093   1950-04-16  South Korea  Chinese Taipei           3           1   \n",
       "3467   1953-04-05    Hong Kong     South Korea           4           0   \n",
       "...           ...          ...             ...         ...         ...   \n",
       "41585  2019-12-11  South Korea       Hong Kong           2           0   \n",
       "41587  2019-12-15  South Korea        China PR           1           0   \n",
       "41589  2019-12-18  South Korea           Japan           1           0   \n",
       "41813  2020-11-14       Mexico     South Korea           3           2   \n",
       "41848  2020-11-17  South Korea           Qatar           2           1   \n",
       "\n",
       "              tournament              city      country  neutral  \n",
       "2921            Friendly         So Kon Po    Hong Kong     True  \n",
       "2923            Friendly            Saigon      Vietnam    False  \n",
       "2924            Friendly             Macau        Macau    False  \n",
       "3093            Friendly          Wan Chai    Hong Kong     True  \n",
       "3467            Friendly         So Kon Po    Hong Kong    False  \n",
       "...                  ...               ...          ...      ...  \n",
       "41585  EAFF Championship             Busan  South Korea    False  \n",
       "41587  EAFF Championship             Busan  South Korea    False  \n",
       "41589  EAFF Championship             Busan  South Korea    False  \n",
       "41813           Friendly   Wiener Neustadt      Austria     True  \n",
       "41848           Friendly  Maria Enzersdorf      Austria     True  \n",
       "\n",
       "[864 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_condition = (df['home_team'].str.contains(\"South Korea\")) |  (df['away_team'].str.contains(\"South Korea\"))\n",
    "df[sk_condition]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "demonstrated-celtic",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_a = df[df['home_team'].str.contains(\"South Korea\")][['date', 'home_score']]\n",
    "df_b = df[df['away_team'].str.contains(\"South Korea\")][['date', 'away_score']]\n",
    "\n",
    "df_korea = pd.concat([df_a, df_b])\n",
    "df_korea['score'] = df_korea['away_score'].add(df_korea['home_score'], fill_value = 0)\n",
    "df_korea = df_korea[['date', 'score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "living-provincial",
   "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>date</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3093</th>\n",
       "      <td>1950-04-16</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3468</th>\n",
       "      <td>1953-04-08</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3667</th>\n",
       "      <td>1954-06-20</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3946</th>\n",
       "      <td>1956-04-21</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009</th>\n",
       "      <td>1956-08-26</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41169</th>\n",
       "      <td>2019-09-10</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41365</th>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41440</th>\n",
       "      <td>2019-11-14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41547</th>\n",
       "      <td>2019-11-19</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41813</th>\n",
       "      <td>2020-11-14</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>864 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  score\n",
       "3093   1950-04-16    3.0\n",
       "3468   1953-04-08    2.0\n",
       "3667   1954-06-20    0.0\n",
       "3946   1956-04-21    3.0\n",
       "4009   1956-08-26    2.0\n",
       "...           ...    ...\n",
       "41169  2019-09-10    2.0\n",
       "41365  2019-10-15    0.0\n",
       "41440  2019-11-14    0.0\n",
       "41547  2019-11-19    0.0\n",
       "41813  2020-11-14    2.0\n",
       "\n",
       "[864 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 각 날짜 별 한국이 몇 골을 넣었는지에 대한 데이터\n",
    "# 포아송 분포에서... 한 게임 당 3골 > 람다 3 \n",
    "# 한 골이라도 넣을 확률은? \n",
    "df_korea "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "conservative-corps",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eunbinpark/miniconda3/envs/da/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='score'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_korea['score'], int(df_korea['score'].max()), kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "specialized-precipitation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     0.218750\n",
       "1.0     0.292824\n",
       "2.0     0.248843\n",
       "3.0     0.119213\n",
       "4.0     0.065972\n",
       "5.0     0.027778\n",
       "6.0     0.009259\n",
       "7.0     0.004630\n",
       "8.0     0.006944\n",
       "9.0     0.004630\n",
       "16.0    0.001157\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 포아송 분포가 잘 맞는지 확인해야함\n",
    "(df_korea['score'].value_counts(normalize = True)).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "independent-shakespeare",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t0.17297129387175522\n",
      "1\t0.30350055730275566\n",
      "2\t0.2662655352262602\n",
      "3\t0.15573246581906267\n",
      "4\t0.0683131997053527\n",
      "5\t0.023972872859563593\n",
      "6\t0.007010585504455706\n",
      "7\t0.0017572830067385673\n",
      "8\t0.0003854226039085165\n",
      "9\t7.514154674965426e-05\n",
      "10\t1.3184558434314329e-05\n",
      "11\t2.103092443857379e-06\n",
      "12\t3.075123596535291e-07\n",
      "13\t4.150540751733897e-08\n",
      "14\t5.201901272841069e-09\n",
      "15\t6.084940069156672e-10\n",
      "16\t6.673010087414279e-11\n"
     ]
    }
   ],
   "source": [
    "mu = df_korea['score'].mean()\n",
    "result = list()\n",
    "\n",
    "for k in range(0, 17): # 16골까지\n",
    "    value = sp.stats.poisson.pmf(k = k, mu = mu)\n",
    "    result.append(value)\n",
    "    print(f\"{k}\\t{value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "southwest-stamp",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 11 artists>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 실제 값 \n",
    "true_values = (df_korea['score'].value_counts(normalize=True)).sort_values()\n",
    "\n",
    "# 포아송 분포의 프로그램의 값\n",
    "plt.bar(range(0, 17), result, alpha=0.3)\n",
    "\n",
    "plt.bar(true_values.index, true_values, alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "super-concert",
   "metadata": {},
   "source": [
    "### 3골 이상 넣을 확률"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "public-identifier",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.742737386400771"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(result[:3]) # ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "listed-virus",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}