Statistics-Week7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b372d515",
   "metadata": {},
   "source": [
    "## 변수의 성격\n",
    "\n",
    "##### 변수는 크게 **숫자**, **문자** 로 구분된다.\n",
    "\n",
    "### Categorical (=nominal) Variable\n",
    "    - 순서가 없이 이름이 있는 변수를 말한다. \n",
    "    - Ex. [검은색, 분홍색, 흰색], [남자, 여자]\n",
    "### Ordinal Variable\n",
    "    - 순서가 있는 이름을 말한다.\n",
    "    - Ex. [초등학교, 중학교, 고등학교], [차상위, 중위, 상위]\n",
    "### Interval Variable\n",
    "    - 간격이 있는 변수\n",
    "    - Ex. ['Bad', 'soso' ,'good']\n",
    "### Continuous Variavle\n",
    "    - 연속적인 변수\n",
    "    - Ex. 키, 몸무게"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd1c42fa",
   "metadata": {},
   "source": [
    "## Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3473dbb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "random.seed(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "061efb6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5507979 , 0.70814782],\n",
       "       [0.29090474, 0.51082761],\n",
       "       [0.89294695, 0.89629309]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(3)\n",
    "\n",
    "np.random.random((3, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c63a721",
   "metadata": {},
   "source": [
    "- `np.random.rand`는 uniform distrubution<br>\n",
    "- `np.random.randn`은 standard normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1afb9da7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 5]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.sample([1, 2, 3, 4, 5, 6], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "161d98a3",
   "metadata": {},
   "source": [
    "### Uniform Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e476cbd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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3, 3, 3, 4, 5, 4, 3, 2, 3, 1, 1, 5, 5, 5, 3, 5, 4, 4, 3, 6, 6, 2, 3, 6, 2, 5, 1, 1, 2, 2, 6, 4, 4, 1, 2, 3, 1, 6, 3, 2, 4, 6, 5, 5, 5, 3, 2, 6, 6, 1, 5, 2, 2, 1, 3, 1, 3, 6, 3, 2, 6, 6, 3, 1, 2, 5, 6, 5, 6, 2, 1, 3, 5, 4, 3, 3, 6, 4, 6, 5, 4, 3, 4, 5, 2, 3, 2, 4, 1, 6, 5, 2, 5, 5, 2, 5, 3, 6, 4, 3, 2, 1, 5, 2, 6, 4, 3, 3, 2, 6, 1, 2, 5, 3, 4, 2, 2, 6, 5, 2, 2, 5, 2, 1, 5, 6, 1, 6, 1, 4, 3, 2, 1, 6, 4, 4, 4, 3, 4, 2, 2, 1, 6, 5, 3, 1, 6, 5, 6, 3, 5, 3, 3, 6, 1, 5, 6, 4, 1, 5, 5, 3, 1, 2, 1, 5, 5, 3, 3, 3, 5, 4, 3, 3, 2, 5, 2, 6, 3, 3, 2, 1, 2, 1, 1, 4, 1, 6, 2, 3, 2, 6, 1, 6, 1, 2, 1, 2, 2, 3, 6, 4, 4, 6, 2, 4, 1, 4, 1, 5, 2, 1, 1, 2, 5, 5, 3, 6, 2, 4, 6, 1, 2, 3, 5, 1, 3, 4, 3, 1, 4, 5, 1, 1, 6, 4, 4, 4, 2, 3, 6, 4, 1, 2, 1, 6, 3, 2, 5, 4, 3, 3, 6, 6, 2, 5, 4, 2, 3, 1, 6, 1, 6, 6, 5, 5, 1, 5, 1, 2, 6, 6, 4, 4, 1, 1, 5, 3, 1, 5, 2, 1, 2, 4, 2, 4, 5, 3, 4, 4, 1, 4, 3, 5, 3, 4, 5, 5, 6, 6, 3, 4, 2, 4, 3, 3, 5, 1, 6, 4, 1, 4, 4, 2, 5, 4, 5, 6, 6, 3, 6, 4, 3, 2, 1, 4, 2, 5, 3, 4, 2, 4, 3, 3, 1, 3, 2, 2, 3, 4, 4, 1, 3, 1, 5, 1, 4, 6, 4, 5, 4, 2, 5, 6, 2, 5, 6, 5, 2, 2, 6, 4, 6, 6, 3, 2, 6, 5, 2, 4, 1, 2, 3, 1, 5, 5, 4, 3, 3, 1, 2, 6, 4, 3, 1, 6, 6, 4, 4, 6, 3, 6, 1, 6, 1, 2, 4, 3, 4, 5, 2, 1, 6, 6, 5, 3, 6, 5, 4, 3, 5, 6, 5, 4, 4, 4, 6, 1, 1, 1, 2, 6, 2, 1, 4, 3, 4, 3, 3, 4, 4, 5, 5, 4, 6, 4, 5, 4, 4, 1, 6, 6, 4, 1, 3, 1, 1, 4, 2, 2, 2, 2, 4, 3, 4, 1, 5, 4, 3, 3, 4, 1, 4, 1, 5, 6, 5, 5, 1, 6, 2, 4, 4, 1, 2, 2, 3, 2, 4, 2, 3, 2, 2, 2, 6, 4, 2, 6, 3, 1, 1, 1, 3, 3, 3, 6, 5, 6, 2, 1, 4, 5, 5, 3, 1, 2, 6, 3, 4, 6, 5, 6, 6, 4, 6, 6, 4, 1, 5, 1, 2, 6, 2, 2, 2, 6, 2, 5, 2, 5, 5, 1, 6, 1, 6, 6, 5, 4, 1, 4, 4, 6, 4, 3, 6, 3, 1, 1, 5, 2, 2, 5, 3, 2, 1, 6, 2, 2, 5, 5, 1, 4, 5, 6, 5, 6, 1, 4, 3, 3, 6, 2, 2, 5, 1, 5, 4, 1, 5, 6, 5, 2, 2, 6, 6, 3, 6, 1, 6, 6, 3, 2, 1, 5, 6, 2, 4, 3, 4, 3, 3, 5, 2, 6, 5, 6, 2, 1, 3, 3, 3, 1, 1, 6, 1, 1, 2, 5, 3, 3, 6, 1, 1, 4, 4, 3, 2, 4, 3, 6, 6, 3, 4, 4, 4, 6, 1, 3, 5, 3, 5, 2, 5, 1, 6, 2, 6, 4, 2, 6, 5, 5, 6, 2, 4, 6, 3, 1, 3, 3, 6, 6, 4, 4, 5, 6, 1, 2, 4, 1, 4, 6, 3, 2, 1, 1, 3, 3, 3, 4, 1, 1, 6, 3, 1, 5, 3, 2, 1, 6, 3, 2, 5, 1, 5, 1, 2, 6, 2, 2, 2, 1, 1, 3, 1, 1, 1, 2, 6, 1, 4, 5, 1, 3, 3, 1, 4, 4, 2, 2, 1, 6, 5, 1, 2, 5, 3, 4, 1, 4, 5, 2, 5, 3, 4, 3, 3, 4, 4, 1, 6, 5, 3, 6, 4, 4, 2, 1, 6, 1, 5, 5, 3, 5, 1, 5, 3, 3, 4, 6, 6, 3, 2, 5, 2, 2, 2, 2, 1, 3, 2, 1, 2, 4, 1, 4, 4, 2, 1, 4, 5, 2, 3, 1, 6, 3, 4, 4, 3, 6, 3, 2, 5, 5, 2, 5, 5, 4, 2, 2, 4, 4, 3, 2, 6, 6, 1, 6, 5, 4, 5, 4, 6, 3, 1, 1, 5, 1, 4, 2, 4, 3, 6, 1, 6, 3, 6, 5, 1, 3, 5, 5, 2, 3, 2, 3, 6, 6, 5, 1, 2, 6, 5, 2, 2, 6, 1, 3, 4, 4, 6, 3, 6, 4, 1, 5, 1, 3, 5, 1, 6, 3, 5, 5, 4, 3, 4, 4, 2, 5, 5, 4, 5, 4, 1, 3, 4, 1, 2, 6, 5, 5, 1, 5, 2, 3, 3, 2, 6, 4, 5, 6, 5, 5, 6, 5, 1, 3, 5, 2, 5, 3, 4, 2, 4, 1, 4, 5, 2, 3, 6, 2, 3, 3, 6, 4, 5, 5, 2, 3, 4, 3, 3, 5, 6, 3, 1, 3, 3, 6, 1, 5, 2, 5, 4, 6, 6, 4, 1, 5, 2, 1, 2, 4, 6, 1, 5, 3, 1, 4, 3, 2, 5, 3, 5, 2, 1, 3, 5, 2, 2, 4, 1, 5, 3, 2, 2, 3, 4, 1, 4, 3, 2, 4, 4, 5, 2, 3, 1, 5, 1, 3, 6, 1, 1, 5, 6, 6, 2, 6, 4, 6, 2, 3, 6, 2, 1, 5, 5, 6, 6, 6, 1, 6, 1, 1, 2, 2, 4, 4, 3, 1, 6, 6, 6, 4, 4, 3, 2, 4, 3, 5, 6, 3, 6, 2, 4, 5, 4, 6, 4, 2, 6, 1, 3, 5, 3, 2, 3, 5, 1, 3, 2, 5, 4, 5, 1, 2, 2, 5, 1, 4, 5, 4, 3, 2, 6, 6, 3, 4, 2, 3, 6, 2, 1, 6, 1, 1, 3, 5, 3, 1, 5, 1, 4, 2, 1, 1, 3, 6, 1, 6, 2, 3, 3, 3, 5, 5, 3, 2, 3, 1, 4, 2, 4, 4, 3, 4, 4, 5, 6, 6, 2, 4, 4, 6, 4, 4, 5, 4, 3, 5, 3, 3, 6, 4, 5, 2, 6, 6, 4, 3, 1, 4, 5, 5, 6, 2, 5, 5, 6, 1, 3, 5, 5, 6, 5, 3, 4, 4, 3, 3, 5, 3, 2, 4, 3, 5, 1, 4, 4, 6, 1, 5, 3, 1, 3, 4, 2, 1, 2, 2, 5, 1, 1, 4, 6, 3, 2, 2, 5, 6, 1, 5, 3, 4, 4, 5, 3, 4, 3, 5, 2, 2, 5, 2, 1, 3, 5, 1, 5, 4, 2, 2, 3, 3, 6, 4, 2, 4, 3, 4, 3, 1, 5, 4, 5, 3, 4, 4, 5, 3, 6, 2, 3, 3, 6, 3, 5, 2, 1, 1, 3, 5, 5, 4, 3, 6, 4, 3, 6, 3, 1, 3, 3, 2, 4, 4, 2, 2, 1, 4, 6, 4, 3, 2, 6, 6, 5, 4, 5, 1, 3, 5, 1, 4, 5, 4, 2, 4, 1, 4, 1, 5, 1, 2, 5, 2, 4, 5, 2, 3, 2, 6, 5, 4, 4, 2, 6, 5, 6, 6, 1, 4, 5, 5, 6, 4, 6, 1, 5, 3, 2, 1, 4, 6, 1, 3, 6, 2, 5, 6, 3, 1, 3, 3, 3, 2, 6, 3, 6, 4, 4, 4, 4, 6, 4, 6, 2, 1, 4, 1, 6, 1, 6, 1, 1, 4, 3, 4, 5, 3, 3, 5, 1, 4, 1, 4, 6, 2, 1, 5, 5, 5, 3, 2, 4, 6, 3, 4, 1, 5, 2, 3, 2, 2, 2, 5, 6, 4, 1, 1, 6, 1, 1, 5, 6, 3, 4, 5, 3, 6, 1, 5, 6, 4, 4, 4, 1, 6, 2, 1, 3, 2, 4, 2, 2, 5, 3, 5, 1, 1, 1, 6, 4, 2, 3, 2, 3, 3, 1, 1, 3, 1, 4, 1, 1, 2, 5, 1, 5, 2, 3, 3, 3, 5, 3, 4, 4, 4, 4, 5, 2, 1, 4, 6, 2, 4, 2, 2, 3, 4, 2, 3, 1, 5, 5, 3, 1, 6, 4, 3, 3, 5, 3, 5, 4, 5, 6, 1, 2, 5, 4, 2, 3, 4, 3, 4, 1, 2, 4, 4, 3, 4, 6, 4, 6, 1, 4, 5, 2, 1, 6, 2, 6, 6, 5, 6, 3, 3, 5, 2, 4, 6, 5, 2, 4, 1, 3, 6, 1, 4, 3, 1, 6, 6, 1, 6, 6, 2, 4, 3, 4, 3, 4, 1, 6, 3, 1, 2, 6, 4, 1, 3, 3, 1, 4, 2, 3, 4, 1, 6, 3, 1, 3, 5, 2, 3, 1, 2, 3, 1, 3, 5, 2, 6, 4, 2, 1, 1, 5, 6, 3, 5, 3, 6, 5, 2, 1, 3, 1, 3, 5, 2, 5, 1, 6, 4, 3, 4, 1, 6, 5, 5, 6, 5, 2, 1, 4, 1, 4, 3, 2, 3, 1, 1, 5, 3, 5, 4, 2, 6, 4, 3, 2, 4, 6, 1, 5, 4, 6, 4, 1, 3, 4, 3, 1, 2, 6, 3, 5, 2, 4, 6, 4, 6, 5, 1, 4, 4, 1, 2, 3, 4, 6, 3, 5, 5, 1, 4, 1, 1, 4, 2, 1, 3, 1, 5, 3, 2, 3, 4, 4, 3, 1, 5, 2, 6, 5, 3, 3, 4, 4, 1, 6, 2, 6, 6, 6, 2, 6, 1, 2, 5, 2, 4, 6, 2, 5, 4, 2, 1, 4, 6, 1, 4, 4, 2, 1, 2, 5, 5, 6, 1, 2, 3, 4, 1, 2, 6, 6, 1, 4, 3, 1, 4, 6, 2, 5, 5, 4, 3, 5, 1, 2, 2, 6, 4, 2, 4, 5, 2, 2, 1, 4, 5, 1, 5, 2, 1, 6, 5, 2, 4, 4, 5, 1, 3, 2, 5, 1, 3, 5, 2, 1, 5, 3, 6, 2, 1, 5, 3, 4, 2, 1, 5, 2, 4, 4, 1, 2, 3, 4, 3, 5, 4, 4, 4, 3, 2, 5, 1, 5, 2, 3, 6, 2, 2, 3, 5, 2, 6, 1, 5, 6, 5, 6, 4, 5, 2, 2, 1, 4, 6, 1, 4, 6, 3, 6, 2, 5, 4, 3, 3, 5, 3, 2, 4, 1, 5, 5, 1, 1, 2, 1, 5, 3, 4, 3, 5, 2, 3, 4, 1, 4, 3, 6, 6, 2, 3, 3, 4, 5, 5, 2, 5, 5, 6, 5, 3, 6, 5, 5, 6, 3, 4, 1, 5, 5, 1, 6, 5, 5, 6, 5, 1, 3, 2, 4, 6, 6, 2, 2, 1, 5, 1, 6, 3, 6, 3, 5, 6, 6, 4, 1, 5, 3, 2, 5, 5, 6, 1, 6, 6, 1, 2, 3, 4, 2, 4, 5, 1, 5, 2, 3, 4, 6, 4, 3, 3, 4, 3, 1, 5, 4, 5, 4, 6, 6, 1, 4, 5, 4, 2, 6, 2, 4, 6, 3, 1, 5, 3, 1, 4, 2, 4, 5, 4, 4, 3, 3, 6, 6, 5, 2, 6, 3, 5, 6, 4, 5, 1, 5, 4, 6, 2, 6, 2, 3, 3, 1, 5, 4, 3, 2, 2, 1, 4, 6, 3, 2, 1, 5, 3, 2, 1, 2, 3, 2, 2, 3, 2, 2, 5, 5, 3, 3, 2, 5, 5, 1, 3, 4, 6, 6, 5, 6, 3, 3, 4, 2, 2, 4, 2, 3, 1, 5, 5, 5, 5, 5, 6, 4, 6, 1, 5, 2, 3, 1, 5, 1, 4, 3, 5, 3, 5, 4, 2, 3, 3, 5, 5, 3, 4, 4, 1, 5, 1, 4, 1, 3, 3, 3, 5, 5, 6, 5, 1, 1, 4, 1, 1, 1, 1, 3, 3, 2, 1, 5, 4, 4, 5, 4, 1, 6, 4, 2, 5, 2, 4, 2, 1, 3, 5, 5, 1, 6, 3, 3, 4, 4, 4, 5, 4, 2, 4, 5, 6, 3, 5, 5, 5, 3, 1, 4, 2, 1, 3, 4, 1, 6, 5, 1, 5, 3, 5, 6, 3, 6, 3, 6, 6, 1, 1, 3, 4, 6, 2, 4, 5, 2, 1, 2, 2, 6, 5, 4, 6, 4, 4, 6, 4, 2, 6, 6, 6, 3, 1, 5, 2, 6, 1, 5, 2, 3, 3, 4, 3, 6, 1, 5, 5, 5, 5, 1, 4, 3, 3, 6, 1, 4, 3, 6, 6, 4, 1, 4, 2, 4, 3, 5, 1, 1, 6, 1, 6, 6, 6, 3, 1, 2, 1, 1, 4, 5, 4, 4, 3, 1, 6, 3, 6, 2, 3, 1, 3, 4, 5, 5, 1, 3, 1, 2, 5, 4, 6, 3, 3, 3, 6, 5, 1, 1, 1, 4, 6, 5, 4, 3, 1, 3, 2, 4, 4, 2, 6, 4, 1, 2, 3, 5, 1, 1, 2, 4, 2, 3, 2, 1, 1, 5, 4, 6, 3, 3, 3, 3, 6, 4, 1, 6, 2, 6, 2, 6, 1, 1, 5, 1, 2, 1, 5, 3, 4, 5, 1, 1, 3, 5, 2, 5, 4, 3, 2, 5, 2, 1, 3, 1, 1, 2, 6, 2, 5, 3, 6, 3, 6, 2, 4, 3, 5, 5, 1, 2, 3, 1, 5, 1, 5, 5, 1, 6, 5, 2, 3, 1, 3, 4, 4, 3, 1, 4, 2, 4, 3, 5, 6, 3, 4, 3, 6, 5, 4, 1, 2, 5, 4, 6, 2, 4, 3, 2, 2, 4, 1, 1, 4, 2, 3, 4, 4, 6, 1, 4, 4, 1, 2, 3, 4, 2, 2, 5, 2, 2, 6, 5, 2, 2, 1, 2, 3, 1, 2, 4, 5, 3, 4, 3, 4, 1, 1, 6, 6, 4, 5, 5, 3, 2, 3, 5, 4, 2, 3, 2, 1, 1, 5, 2, 2, 4, 4, 2, 2, 4, 3, 5, 2, 3, 6, 5, 2, 6, 1, 2, 5, 5, 4, 5, 2, 2, 3, 1, 1, 4, 2, 6, 1, 6, 1, 6, 6, 2, 4, 4, 2, 3, 1, 1, 3, 3, 1, 1, 4, 4, 4, 5, 4, 1, 5, 5, 4, 6, 6, 5, 3, 3, 5, 1, 2, 5, 3, 2, 5, 2, 6, 5, 3, 6, 5, 3, 2, 1, 5, 5, 4, 5, 6, 1, 6, 6, 1, 6, 5, 1, 1, 5, 5, 5, 4, 3, 5, 1, 3, 3, 1, 3, 5, 2, 2, 3, 4, 5, 2, 2, 1, 1, 1, 5, 2, 3, 6, 4, 2, 3, 1, 5, 4, 1, 6, 3, 6, 1, 3, 1, 3, 4, 4, 5, 5, 1, 4, 4, 2, 3, 5, 1, 6, 2, 1, 5, 1, 1, 2, 6, 2, 4, 5, 4, 2, 4, 6, 3, 2, 3, 3, 2, 4, 6, 6, 6, 3, 2, 6, 4, 6, 5, 2, 6, 2, 6, 1, 3, 4, 6, 6, 5, 6, 4, 3, 3, 3, 1, 5, 3, 3, 2, 3, 6, 3, 3, 1, 5, 4, 2, 5, 3, 1, 3, 1, 4, 6, 4, 2, 6, 4, 1, 6, 2, 6, 4, 3, 4, 3, 6, 5, 4, 6, 3, 3, 4, 3, 6, 6, 4, 3, 1, 4, 2, 5, 4, 5, 5, 5, 2, 4, 6, 4, 3, 4, 3, 6, 6, 5, 1, 1, 4, 6, 6, 2, 1, 2, 2, 5, 2, 4, 1, 5, 2, 5, 1, 2, 6, 6, 4, 5, 6, 2, 2, 5, 1, 5, 3, 4, 3, 4, 2, 6, 1, 1, 3, 6, 2, 2, 3, 4, 1, 5, 4, 5, 3, 4, 4, 5, 4, 3, 6, 1, 2, 1, 5, 1, 6, 3, 6, 5, 6, 1, 2, 1, 5, 4, 3, 4, 1, 2, 6, 2, 6, 5, 2, 5, 3, 5, 3, 2, 2, 5, 2, 4, 6, 3, 3, 5, 5, 6, 1, 4, 6, 6, 6, 1, 2, 5, 5, 6, 6, 4, 1, 1, 5, 2, 1, 5, 4, 4, 6, 2, 2, 2, 5, 1, 4, 3, 3, 2, 4, 4, 1, 1, 6, 6, 6, 1, 1, 2, 2, 3, 1, 2, 3, 4, 1, 2, 5, 5, 1, 4, 5, 2, 3, 3, 5, 1, 3, 2, 4, 2, 4, 5, 5, 6, 5, 1, 5, 2, 4, 3, 3, 4, 6, 5, 2, 1, 2, 6, 6, 6, 2, 6, 2, 6, 6, 6, 4, 6, 5, 3, 6, 5, 4, 5, 5, 3, 3, 4, 6, 6, 1, 5, 6, 5, 2, 1, 5, 2, 6, 4, 5, 3, 1, 2, 2, 2, 6, 4, 6, 1, 5, 1, 1, 1, 2, 3, 3, 4, 5, 2, 6, 3, 4, 6, 5, 6, 5, 3, 4, 2, 4, 2, 5, 4, 2, 3, 5, 4, 2, 4, 4, 1, 3, 3, 2, 2, 1, 3, 5, 3, 4, 2, 6, 3, 1, 3, 6, 6, 4, 6, 4, 5, 3, 4, 1, 1, 5, 5, 1, 6, 3, 1, 2, 5, 1, 1, 4, 5, 6, 2, 1, 1, 3, 1, 4, 5, 1, 6, 4, 4, 5, 2, 3, 4, 3, 3, 1, 5, 6, 2, 4, 5, 5, 5, 1, 5, 5, 6, 3, 2, 5, 5, 4, 1, 5, 3, 4, 5, 3, 4, 4, 4, 2, 1, 2, 3, 3, 4, 1, 3, 1, 4, 3, 4, 5, 3, 2, 3, 1, 4, 3, 2, 1, 3, 2, 5, 1, 1, 6, 3, 4, 4, 1, 2, 6, 5, 2, 1, 4, 4, 4, 2, 3, 5, 5, 5, 6, 1, 2, 2, 5, 6, 2, 6, 2, 6, 3, 5, 1, 2, 2, 6, 6, 6, 6, 1, 4, 1, 2, 4, 4, 4, 2, 1, 3, 3, 6, 2, 2, 6, 6, 1, 2, 2, 1, 5, 2, 1, 4, 5, 1, 5, 2, 5, 1, 2, 3, 5, 2, 2, 5, 3, 3, 6, 3, 1, 6, 2, 6, 3, 1, 4, 6, 4, 3, 2, 6, 6, 6, 4, 6, 1, 6, 2, 3, 1, 5, 3, 3, 1, 5, 3, 1, 2, 2, 4, 1, 4, 6, 4, 6, 5, 5, 1, 3, 1, 6, 4, 2, 1, 3, 3, 4, 2, 4, 1, 4, 4, 5, 3, 6, 2, 6, 3, 5, 2, 5, 3, 1, 6, 2, 2, 3, 5, 3, 5, 4, 1, 6, 2, 1, 4, 3, 5, 6, 6, 3, 4, 1, 1, 5, 6, 4, 1, 5, 3, 5, 4, 4, 1, 1, 3, 4, 6, 1, 1, 6, 5, 4, 2, 3, 5, 2, 2, 2, 1, 1, 2, 3, 2, 5, 3, 2, 6, 1, 6, 1, 2, 4, 3, 3, 2, 4, 6, 6, 4, 5, 6, 1, 6, 1, 1, 2, 5, 4, 5, 5, 5, 5, 1, 3, 1, 6, 6, 5, 4, 6, 5, 4, 2, 3, 2, 5, 3, 4, 2, 5, 5, 4, 2, 6, 1, 1, 1, 2, 3, 4, 3, 1, 3, 1, 5, 1, 6, 5, 2, 6, 3, 5, 4, 2, 3, 3, 5, 6, 6, 1, 5, 6, 1, 6, 4, 3, 1, 2, 2, 2, 1, 4, 5, 2, 5, 2, 6, 5, 2, 3, 3, 2, 2, 4, 6, 5, 5, 4, 2, 3, 3, 1, 1, 5, 6, 5, 2, 5, 5, 3, 2, 2, 6, 6, 2, 2, 4, 3, 3, 5, 1, 4, 5, 5, 3, 2, 4, 3, 3, 6, 2, 4, 6, 2, 3, 3, 2, 2, 6, 3, 1, 2, 1, 4, 2, 5, 4, 3, 6, 2, 5, 2, 2, 4, 4, 3, 6, 3, 6, 4, 5, 4, 6, 3, 5, 5, 1, 3, 5, 1, 4, 1, 6, 6, 4, 3, 4, 5, 2, 3, 3, 3, 4, 4, 5, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "dice = [random.randrange(1, 6+1) for _ in range(10000)]\n",
    "print(dice)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1525ee0",
   "metadata": {},
   "source": [
    "#### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cb1cc0b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dice 1: 1658회\n",
      "Dice 2: 1667회\n",
      "Dice 3: 1764회\n",
      "Dice 4: 1696회\n",
      "Dice 5: 1636회\n",
      "Dice 6: 1579회\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 7):\n",
    "    print(f\"Dice {i}: {dice.count(i)}회\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9068c6f5",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(dice)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f03dfac2",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.hist(dice, bins=np.arange(0.5, 7))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "877338a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "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": [
    "import pandas as pd\n",
    "\n",
    "pd.Series(dice).hist(bins=np.arange(0.5, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65eec762",
   "metadata": {},
   "source": [
    "### Describe\n",
    "\n",
    "통계량을 구해보자. describe()는 기술통계량 평균, 최소값, 표준편차, 건수를 출력한다.<br> \n",
    "DataFrame 또는 컬럼을 Series로 변환한 후 describe() 를 적용할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1c7250b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10000.000000\n",
       "mean         3.472200\n",
       "std          1.688879\n",
       "min          1.000000\n",
       "25%          2.000000\n",
       "50%          3.000000\n",
       "75%          5.000000\n",
       "max          6.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "s = pd.Series(dice)\n",
    "s.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c9dd46c",
   "metadata": {},
   "source": [
    "### 정규 분포\n",
    "\n",
    "#### Sampling of Gaussian distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ceb9cdfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "comp1 = np.random.normal(0, 1, size=200000)\n",
    "comp2 = np.random.normal(10, 2, size=200000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e5c1de68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  3  4 14 22 17 18 13  7  1] [-2.97712531 -2.42822109 -1.87931687 -1.33041266 -0.78150844 -0.23260422\n",
      "  0.3163      0.86520422  1.41410844  1.96301266  2.51191688]\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(100)\n",
    "count, binends = np.histogram(x)\n",
    "print(count, binends)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "48456e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  3.,  4., 14., 22., 17., 18., 13.,  7.,  1.]),\n",
       " array([-2.97712531, -2.42822109, -1.87931687, -1.33041266, -0.78150844,\n",
       "        -0.23260422,  0.3163    ,  0.86520422,  1.41410844,  1.96301266,\n",
       "         2.51191688]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAKsElEQVR4nO3dUYhlh13H8d/fpvrQCqZkGmMNjkgoDVJTWWKhIimxmjbStELBPGjAwlpIoIU+uG3ACFJYKVZBRIgkJA9pVEhDgimaGAqxYIubEtoN25pQtpo2ZDcGbcQHSfP3YW9ws53NzM69s3f/k88HlnvvmTNz/oddvpw5e8+51d0BYJ4fWfcAAOyOgAMMJeAAQwk4wFACDjDURedzY5dccklvbm6ez00CjPf4448/390bZy4/rwHf3NzMkSNHzucmAcarqu9stdwpFIChBBxgKAEHGErAAYYScIChBBxgKAEHGErAAYYScIChzuuVmLCdzUMPrWW7xw9fv5btwjIcgQMMJeAAQwk4wFACDjCUgAMMJeAAQwk4wFACDjCUgAMMJeAAQwk4wFACDjCUgAMMJeAAQ20b8Kq6vKq+VFXHqurJqvr4YvlbquqRqnpq8Xjx3o8LwCt2cgT+UpJPdvc7krw7yc1VdWWSQ0ke7e4rkjy6eA3AebJtwLv72e7+2uL5i0mOJXlbkhuS3L1Y7e4kH9qjGQHYwjmdA6+qzSTvSvLVJJd297PJqcgneevKpwPgrHYc8Kp6c5L7knyiu79/Dt93sKqOVNWRkydP7mZGALawo4BX1RtzKt73dPcXFoufq6rLFl+/LMmJrb63u2/v7gPdfWBjY2MVMwOQnb0LpZLckeRYd3/utC89mOSmxfObkjyw+vEAOJudfCr9e5L8dpJvVNUTi2WfTnI4yd9W1UeT/FuSj+zJhABsaduAd/eXk9RZvnztascBYKdciQkwlIADDCXgAEMJOMBQAg4wlIADDCXgAEMJOMBQAg4wlIADDCXgAEMJOMBQAg4w1E5uJwvsoc1DD61lu8cPX7+W7bI6jsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhnIlJmR9V0PCMhyBAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDbRvwqrqzqk5U1dHTlv1hVX23qp5Y/PnA3o4JwJl2cgR+V5Lrtlj+p9191eLPF1c7FgDb2Tbg3f1YkhfOwywAnINlzoHfUlVfX5xiuXhlEwGwI7sN+F8m+bkkVyV5NsmfnG3FqjpYVUeq6sjJkyd3uTkAzrSrgHf3c939g+5+OclfJbn6Nda9vbsPdPeBjY2N3c4JwBl2FfCquuy0lx9OcvRs6wKwN7b9VPqqujfJNUkuqapnktyW5JqquipJJzme5Pf2bkQAtrJtwLv7xi0W37EHswBwDlyJCTCUgAMMJeAAQwk4wFDb/icmrz+bhx5a9wjADjgCBxhKwAGGEnCAoQQcYCgBBxhKwAGGEnCAoQQcYCgX8sDr1Dov2Dp++Pq1bXs/cQQOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMNS2Aa+qO6vqRFUdPW3ZW6rqkap6avF48d6OCcCZdnIEfleS685YdijJo919RZJHF68BOI+2DXh3P5bkhTMW35Dk7sXzu5N8aLVjAbCd3Z4Dv7S7n02SxeNbz7ZiVR2sqiNVdeTkyZO73BwAZ9rz/8Ts7tu7+0B3H9jY2NjrzQG8buw24M9V1WVJsng8sbqRANiJ3Qb8wSQ3LZ7flOSB1YwDwE7t5G2E9yb55yRvr6pnquqjSQ4neV9VPZXkfYvXAJxHF223QnffeJYvXbviWQA4B67EBBhKwAGGEnCAoQQcYCgBBxhKwAGGEnCAoQQcYCgBBxhKwAGGEnCAoQQcYCgBBxhKwAGGEnCAoQQcYCgBBxhKwAGGEnCAoQQcYCgBBxhKwAGGumjdAwCvP5uHHlrLdo8fvn4t290rjsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGCope4HXlXHk7yY5AdJXuruA6sYCoDtreIDHd7b3c+v4OcAcA6cQgEYatmAd5KHq+rxqjq41QpVdbCqjlTVkZMnTy65OQBesWzA39Pdv5jk/UlurqpfOXOF7r69uw9094GNjY0lNwfAK5YKeHd/b/F4Isn9Sa5exVAAbG/XAa+qN1XVj7/yPMmvJTm6qsEAeG3LvAvl0iT3V9UrP+fz3f33K5kKgG3tOuDd/e0kv7DCWQA4B95GCDCUgAMMJeAAQ63iUnr2yOahh9Y9AnABcwQOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJQrMXfAFZHAhcgROMBQAg4wlIADDCXgAEMJOMBQAg4wlIADDCXgAEMJOMBQAg4wlIADDCXgAEMJOMBQAg4wlIADDCXgAEMJOMBQYz6Rx6fiAMtaZ0eOH75+5T/TETjAUAIOMJSAAwwl4ABDCTjAUAIOMNRSAa+q66rqW1X1dFUdWtVQAGxv1wGvqjck+Ysk709yZZIbq+rKVQ0GwGtb5gj86iRPd/e3u/t/k/x1khtWMxYA21nmSsy3Jfn3014/k+SXzlypqg4mObh4+d9V9a1dbOuSJM/v4vsm2M/7luzv/dvP+5bs7/077/tWf7zUt//MVguXCXhtsax/aEH37UluX2I7qaoj3X1gmZ9xodrP+5bs7/3bz/uW7O/92y/7tswplGeSXH7a659O8r3lxgFgp5YJ+L8kuaKqfraqfjTJbyV5cDVjAbCdXZ9C6e6XquqWJP+Q5A1J7uzuJ1c22astdQrmAref9y3Z3/u3n/ct2d/7ty/2rbp/6LQ1AAO4EhNgKAEHGGpMwKvqj6rq61X1RFU9XFU/te6ZVqWqPltV31zs3/1V9RPrnmmVquojVfVkVb1cVePfupXs79tIVNWdVXWiqo6ue5ZVq6rLq+pLVXVs8W/y4+ueaRljAp7ks939zu6+KsnfJfmDNc+zSo8k+fnufmeSf03yqTXPs2pHk/xmksfWPcgqvA5uI3FXkuvWPcQeeSnJJ7v7HUneneTmyX93YwLe3d8/7eWbssVFQ1N198Pd/dLi5Vdy6j31+0Z3H+vu3VyBe6Ha17eR6O7Hkryw7jn2Qnc/291fWzx/McmxnLqqfKQxH2qcJFX1mSS/k+S/krx3zePsld9N8jfrHoLXtKPbSHBhq6rNJO9K8tU1j7JrF1TAq+ofk/zkFl+6tbsf6O5bk9xaVZ9KckuS287rgEvYbt8W69yaU7/i3XM+Z1uFnezfPrKj20hw4aqqNye5L8knzvjtfpQLKuDd/as7XPXzSR7KoIBvt29VdVOS30hybQ98c/45/N3tB24jMVhVvTGn4n1Pd39h3fMsY8w58Kq64rSXH0zyzXXNsmpVdV2S30/ywe7+n3XPw7bcRmKoqqokdyQ51t2fW/c8yxpzJWZV3Zfk7UleTvKdJB/r7u+ud6rVqKqnk/xYkv9YLPpKd39sjSOtVFV9OMmfJ9lI8p9JnujuX1/rUEuqqg8k+bP8/20kPrPeiVanqu5Nck1O3XL1uSS3dfcdax1qRarql5P8U5Jv5FRLkuTT3f3F9U21e2MCDsCrjTmFAsCrCTjAUAIOMJSAAwwl4ABDCTjAUAIOMNT/AYSsOuV9bGaoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e5bbed82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "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": [
    "pd.Series(np.concatenate([comp1, comp2])).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "944eeb0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MLKv/R44cWXabE23Hhtmx7HfqjPHte9yvOaz83I/rtV7KOOf/ZDvevK6G9/04DTP+kQMjyeeB3wJ+tar+7BgHAIs9UceoH6vN0YWq3cBugI0bN9b09PRxen20mZkZltvmRLtx5+Nj2e+ODbPceeCE/Lth2V774vRY9jtoped+XPO8lHHO/8l2vD9fq+F9P07DjH+kT0kl+T7mwuJrVfWNVn6rnWai3b/d6oeAiwaarwXebPW1i9SPapNkDXAW8O4ofZYkDWeUT0kFuA94qaq+MvDUY8C2trwNeHSgvrV98uli5i5uP9NOX32Q5Kq2zRsWtJnf1nXAU+06hyRphY1yLPoF4JeBA0m+22q/BtwO7E1yE/A6cD1AVb2QZC/wInOfsLqlqj5p7W4G7gfOAJ5oN5gLpAeTHGTuyGLrCP2VJI1g6MCoqv/G4tcYADYt0WYXsGuR+rPA5YvUP6QFjiRpvPymtySpi4EhSepiYEiSuhgYkqQup+Y3dqRjWDfw5bkdG2ZX3ZfppNXKIwxJUhcDQ5LUxcCQJHUxMCRJXQwMSVIXA0OS1MXAkCR1MTAkSV0MDElSFwNDktTFwJAkdTEwJEldDAxJUhcDQ5LUxcCQJHUxMCRJXQwMSVIXA0OS1MXAkCR1MTAkSV0MDElSFwNDktTFwJAkdTEwJEldDAxJUpc14+6AJJ0M63Y+fsznd2yY5cbjrDOs126/5qRsd9wMDI3N8d7QklYXA2MB/xKTpMV9Jq5hJNmc5OUkB5PsHHd/JGkSrfrASHIa8G+AvwtcCvxikkvH2ytJmjyrPjCAK4GDVfVHVfXnwEPAljH3SZImTqpq3H04piTXAZur6h+2x78M/HRVfWlgne3A9vbwR4GXl7mb84B3TkB3P4smeezg+Cd5/JM8dvj0+P9aVf3wsRp8Fi56Z5HaUSlXVbuB3UPvIHm2qjYO2/6zbJLHDo5/ksc/yWOH4cb/WTgldQi4aODxWuDNMfVFkibWZyEw/gBYn+TiJN8PbAUeG3OfJGnirPpTUlU1m+RLwO8BpwFfraoXTvBuhj6ddQqY5LGD45/k8U/y2GGI8a/6i96SpNXhs3BKSpK0ChgYkqQuBkaT5J8l+eMk3223nx93n062Sf/JlSSvJTnQ5vvZcffnZEry1SRvJ3l+oHZOkieTvNLuzx5nH0+mJcY/Ee/5JBcl+f0kLyV5IcmXW33Z829gHO2uqvqJdvtP4+7MyeRPrvx/f6fN96n+efz7gc0LajuBfVW1HtjXHp+q7ufT44fJeM/PAjuq6seAq4Bb2nt92fNvYEwuf3JlglTVt4B3F5S3AHva8h7g2pXs00paYvwToaoOV9V32vIHwEvAhQwx/wbG0b6U5L+3w9dT9vC8uRB4Y+DxoVabJAV8M8lz7edlJs1UVR2Gub9UgPPH3J9xmKT3PEnWAT8J7GeI+Z+owEjyX5I8v8htC3Av8NeBnwAOA3eOs68r4Lg/uTIBvlBVP8XcablbkvzMuDukFTVR7/kknwd+C/jVqvqzYbax6r+4dyJV1c/2rJfk3wK/e5K7M24T/5MrVfVmu387yW8zd5ruW+Pt1Yp6K8kFVXU4yQXA2+Pu0Eqqqrfml0/193yS72MuLL5WVd9o5WXP/0QdYRxLe8Hm/X3g+aXWPUVM9E+uJPlckh+aXwZ+jlN/zhd6DNjWlrcBj46xLytuUt7zSQLcB7xUVV8ZeGrZ8+83vZskDzJ3aFrAa8A/mj+/d6pqHyP8df7iJ1d2jbdHKyfJjwC/3R6uAX7zVB5/kq8D08z9pPVbwG3A7wB7gb8KvA5cX1Wn5IXhJcY/zQS855P8beC/AgeA/9vKv8bcdYxlzb+BIUnq4ikpSVIXA0OS1MXAkCR1MTAkSV0MDElSFwNDktTFwJAkdfl/xwBFP5KKx1IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(np.concatenate([comp1, comp2])).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bdd27ad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bigdata_analysis",
   "language": "python",
   "name": "bigdata_analysis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}