P118-P121_example2_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 교과서 121페이지 예제2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex =[(25,7),(35,11),(45,17),(55,29),(65,19),(75,12),(85,5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makelist(ex):\n",
    "    result=[]\n",
    "    for i,j in ex:\n",
    "        result+=[i]*j\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mylist=makelist(ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.8"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(mylist) #평균"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235.96"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(mylist) # 분산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.36098955145794"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(mylist)  # 표준 편차"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 교과서 118페이지 예제3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex2 =[1.2, 4.7, 7.4, 10.2, 12.6, 14.1, 15.9, 17.6, 19.7, 20.8,\n",
    "      1.7, 5.3, 7.5, 10.7, 12.7, 14.6, 16.4, 18.5, 19.9, 21.3,\n",
    "      1.8, 5.6, 8.2, 10.9, 12.9, 14.8, 16.8, 18.9, 20.0, 22.4,\n",
    "      3.4, 6.8, 8.4, 12.3, 13.1, 15.4, 17.2, 19.0, 20.4, 22.5,\n",
    "      4.1, 7.1, 9.0, 12.4, 13.7, 15.7, 17.4, 19.3, 20.7, 22.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ex2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.314"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(ex2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.111170427994952"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(ex2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.7"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(ex2)  # 최댓값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(ex2) # 최솟깂"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.25"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(ex2, 25)  # 1사분위 수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    50.000000\n",
       "mean     13.314000\n",
       "std       6.173214\n",
       "min       1.200000\n",
       "25%       8.250000\n",
       "50%      13.900000\n",
       "75%      18.800000\n",
       "max      22.700000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(ex2)\n",
    "s.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "sns.distplot(ex2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}