Sample Code/Day_26_SampleCode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 今天的範例，帶大家操作投影片中，如何描述所收集到資料男生身高的資料"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z9YHBcEGYdBV"
   },
   "source": [
    "### 範例資料\n",
    "產出男生的身高資料\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "collapsed": true,
    "executionInfo": {
     "elapsed": 1558,
     "status": "ok",
     "timestamp": 1578021044012,
     "user": {
      "displayName": "Mora chen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB40f7sDArbZ5_DYq02nNcnLD0Ryaf7AhsASSQeLQ=s64",
      "userId": "03171203089166907199"
     },
     "user_tz": -480
    },
    "id": "yz28_IgkYdBW",
    "outputId": "a12f486c-18b3-4fb5-d06c-f162aebd9444"
   },
   "outputs": [],
   "source": [
    "# library\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import math\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#輸入資料\n",
    "boys=[164, 176, 169, 169, 165, 175, 159, 151, 144, 160, 183, 165, 156, 170,\n",
    " 164, 173, 165, 163, 177, 171]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男孩身高平均= 165.95\n",
      "statistics_mean_boy= 165.95\n"
     ]
    }
   ],
   "source": [
    "# 計算統計量_平均數的方法\n",
    "mean_boy=np.mean(boys)\n",
    "print(\"男孩身高平均=\",mean_boy)\n",
    "\n",
    "statistics_mean_boy=statistics.mean(boys)\n",
    "print(\"statistics_mean_boy=\",statistics_mean_boy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "np_median_boy= 165.0\n",
      "statistics_median_boy= 165.0\n"
     ]
    }
   ],
   "source": [
    "# 計算統計量_中位數的方法\n",
    "np_median_boy=np.median(boys,axis=None)\n",
    "print(\"np_median_boy=\",np_median_boy)\n",
    "\n",
    "\n",
    "statistics_median_boy=statistics.median(boys)\n",
    "print(\"statistics_median_boy=\",statistics_median_boy)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男孩身高眾數= ModeResult(mode=array([165]), count=array([3]))\n",
      "男孩身高眾數= 165\n",
      "statistics_mode_boy= 165\n"
     ]
    }
   ],
   "source": [
    "# 統計量_眾數\n",
    "# 統計量的眾數，如果有多個眾數，取最小的值當眾數。\n",
    "\n",
    "mode_boy=stats.mode(boys,axis=None)\n",
    "print(\"男孩身高眾數=\",mode_boy)\n",
    "print(\"男孩身高眾數=\",mode_boy[0][0])\n",
    "\n",
    "\n",
    "# 統計量_眾數\n",
    "statistics_mode_boy=statistics.mode(boys)\n",
    "print(\"statistics_mode_boy=\",statistics_mode_boy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39\n"
     ]
    }
   ],
   "source": [
    "#全距\n",
    "#rangeV=max(boys)-min(boys)\n",
    "def rangeV(x): \n",
    "  return(max(x)-min(x))\n",
    "    \n",
    "print(rangeV(boys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男孩身高標準差= 84.89210526315789\n",
      "男孩身高標準差= 84.89210526315789\n"
     ]
    }
   ],
   "source": [
    "# 計算變異數的方法\n",
    "print(\"男孩身高變異數=\",statistics.variance(boys))\n",
    "print(\"男孩身高變異數=\",np.var(boys,ddof=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男孩身高標準差= 9.213691185575838\n",
      "statistics_mean_boy= 9.213691185575838\n"
     ]
    }
   ],
   "source": [
    "# 統計量_標準差的方法\n",
    "#樣本標準差\n",
    "#ddof=1, 回傳 sample standard deviation 樣本標準差，分母(n-1)，無偏估計\n",
    "std_boy=np.std(boys,ddof=1)\n",
    "print(\"男孩身高標準差=\",std_boy)\n",
    "\n",
    "statistics_stdev_boy=statistics.stdev(boys)\n",
    "print(\"statistics_mean_boy=\",statistics_stdev_boy)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90百分位數= 176.1\n",
      "50百分位數= 165.0\n",
      "20百分位數= 159.8\n",
      "20百分位數= 159.8\n"
     ]
    }
   ],
   "source": [
    "# python 百分位數\n",
    "#np\n",
    "print(\"90百分位數=\",np.percentile(boys, 90))\n",
    "print(\"50百分位數=\",np.percentile(boys, 50))\n",
    "print(\"20百分位數=\",np.percentile(boys, 20))\n",
    "#stat\n",
    "print(\"20百分位數=\",stats.scoreatpercentile(boys, 20))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.47132127317376954\n",
      "0.19395882957876331\n"
     ]
    }
   ],
   "source": [
    "#計算峰度和偏度\n",
    "print(stats.skew(boys))\n",
    "print(stats.kurtosis(boys))\n",
    "\n",
    "# pandas和 stat 接近\n",
    "# python的峰帶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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amVlLHQW6pPGkMP96RHyrQZO1wLTK8lRg3cjLMzOzTnXyLhcBXwNWRcQXmzRbAJye3+1y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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#最後，畫圖看分布\n",
    "plt.hist(boys,alpha=.4,bins=40)\n",
    "plt.title('boy,skewness={0},kurtosis={1}'.format(round(stats.skew(boys),2),round(stats.kurtosis(boys),2)))\n",
    "plt.axvline(x=mean_boy)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Day33.YOLO 細節理解 - 網路輸出的後處理_HW.ipynb",
   "provenance": []
  },
  "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}