03. 확률변수와 분포.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 분포함수\n",
    "- 개념적 이해 필요 : 논문 해석\n",
    "- 테이블 데이터 전처리 등에서는 사용치 않음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import os\n",
    "\n",
    "import seaborn as sns\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coin_info():\n",
    "    return random.choice(['H', 'T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = list()\n",
    "for _ in range(1000):\n",
    "    samples.append(\"\".join([get_coin_info() for x in range(3)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HTT', 'HHH', 'HTT', 'TTH']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'HTT': 140,\n",
       "         'HHH': 141,\n",
       "         'TTH': 114,\n",
       "         'HHT': 119,\n",
       "         'THH': 130,\n",
       "         'THT': 126,\n",
       "         'TTT': 124,\n",
       "         'HTH': 106})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 특정 값이 들어갔을 때 head가 몇 번 나왔는지 확인\n",
    "# 확률 변수\n",
    "def coin_random_func(coin_sample):\n",
    "    return coin_sample.count(\"H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HTT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HHH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HTT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TTH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HHT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sample\n",
       "0    HTT\n",
       "1    HHH\n",
       "2    HTT\n",
       "3    TTH\n",
       "4    HHT"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(samples, columns=['sample'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['random_var'] = df['sample'].map(coin_random_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    380\n",
       "2    355\n",
       "3    141\n",
       "0    124\n",
       "Name: random_var, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['random_var'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.380\n",
       "2    0.355\n",
       "3    0.141\n",
       "0    0.124\n",
       "Name: random_var, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['random_var'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 확률분포함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.124\n",
       "1    0.380\n",
       "2    0.355\n",
       "3    0.141\n",
       "Name: random_var, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['random_var'].value_counts(normalize=True).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['random_var'].value_counts(normalize=True).sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 누적 분포 \n",
    "\n",
    "result = df['random_var'].value_counts(normalize=True).sort_index()\n",
    "result.cumsum().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['StudentsPerformance.csv']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join('data', 'StudentsPerformance.csv'))\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 연속형\n",
    "df['math score'].plot.kde()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bkjpj8EtSZ6Y6+Kd9aYgkD0ry70muS/LFJC8e2o9OclmSG4fXo5a61sWW5KAkVyf5yLA/1X1OcmSSDya5fvj7/tUO+vwnw3/X1yZ5b5JDp63PSd6WZGeSa2e1zdvHJOcNeXZDkt/a3/NObfB3sjTEXcBLq+qhwOOAFw593Ahsrao1wNZhf9q8GLhu1v609/kNwMeq6iHAIxn1fWr7nOR44I+Bmap6OKMLQZ7F9PX5HcDpe7TN2cfh/+1nAb88HPMPQ84t2NQGPx0sDVFVO6rqc8P29xiFwfGM+rlleNsW4KwlKbCRJA8EzgDOn9U8tX1Ocn/gCcBbAarqzqr6LlPc58HBwGFJDgYOZ3S/z1T1uao+DXx7j+b5+rgOeF9V3VFVNwFfZpRzCzbNwX888LVZ+7cMbVMpyWrgJOBy4Liq2gGjDwfg2CUsrYXXAy8DfjKrbZr7/GBgF/D2YXrr/CRHMMV9rqqvA38D3AzsAP6nqj7OFPd5lvn6uGiZNs3BP9bSENMgyX2BDwEvqarbl7qelpI8DdhZVVctdS0TdDBwMvCmqjoJ+AHLf4pjr4Z57XXACcDPA0ckec7SVrXkFi3Tpjn4u1gaIskhjEL/3VX14aH5tiQrh9+vBHYuVX0NnAKcmWQ7o+m7U5O8i+nu8y3ALVV1+bD/QUYfBNPc5ycDN1XVrqr6EfBh4NeY7j7vNl8fFy3Tpjn4p35piCRhNO97XVW9dtavLgXWD9vrgUsmXVsrVXVeVT2wqlYz+jv9t6p6DtPd528AX0ty4tC0FvgSU9xnRlM8j0ty+PDf+VpG32FNc593m6+PlwLPSnKfJCcAa4Ar9usMVTW1P8BTgf8C/ht45VLX06B/j2f0T73PA9cMP08Ffo7R1QA3Dq9HL3Wtjfr/ROAjw/ZU9xl4FLBt+Lu+GDiqgz6/BrgeuBa4ALjPtPUZeC+j7zB+xGhEf+7e+gi8csizG4Cn7O95XbJBkjozzVM9kqQ5GPyS1BmDX5I6Y/BLUmcMfknqjMEvSZ0x+CWpM/8HLD6/IVwBqqcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 연속형\n",
    "# xlabels => 확률 변수 \n",
    "# 표현 값이 확률\n",
    "df['math score'].plot.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eunbinpark/miniconda3/envs/da/lib/python3.8/site-packages/seaborn/distributions.py:2551: 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='math score', ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 분포 밀집도를 estimate 해서 보여줌\n",
    "sns.distplot(df['math score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eunbinpark/miniconda3/envs/da/lib/python3.8/site-packages/seaborn/distributions.py:2551: 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='math score', ylabel='Density'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 누적\n",
    "kwargs = {'cumulative' : True}\n",
    "sns.distplot(df['math score'],\n",
    "            hist_kws = kwargs,\n",
    "            kde_kws = kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='math score', ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df['math score'], cumulative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 4
}