04_univariable_number_boxplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 단변수\n",
    "* 변수가 하나일 때\n",
    "* 단변수(Univariable) - 1변수(독립변수가 하나)\n",
    "* 단변량(Univariate) - 1변량(종속변수가 하나)\n",
    "* 출처 : [독립변수와 종속변수 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EB%8F%85%EB%A6%BD%EB%B3%80%EC%88%98%EC%99%80_%EC%A2%85%EC%86%8D%EB%B3%80%EC%88%98)\n",
    "    * 실험이나 모델링에서 사용되는 변수는 독립 변수(independent variable), 종속 변수(dependent variable) 혹은 기타로 분류할 수 있다. 독립 변수는 입력값이나 원인을 나타내며, 종속 변수는 결과물이나 효과를 나타낸다. \n",
    "    * 기계 학습 혹은 패턴 인식에서는 특징 (feature) 이라고도 한다. 그냥 단순히 입력 변수(input variable)이라고도 한다.\n",
    "    * 독립 변수는 설명 변수(Explanatory variable)라고 하기도 한다. 독립 변수로 취급하는 것의 수량이 통계적으로 독립적이지 않을 경우에 한하여 더 선호된다.\n",
    "\n",
    "### 수치형\n",
    "\n",
    "* 수치형 vs 범주형에 따라 표현이 달라지고 여기에서는 수치형 변수를 봅니다.\n",
    "* https://seaborn.pydata.org/generated/seaborn.boxplot.html\n",
    "\n",
    "\n",
    "### box-and-whisker plot | diagram\n",
    "\n",
    "* [상자 수염 그림 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EC%83%81%EC%9E%90_%EC%88%98%EC%97%BC_%EA%B7%B8%EB%A6%BC)\n",
    "* 가공하지 않은 자료 그대로를 이용하여 그린 것이 아니라, 자료로부터 얻어낸 통계량인 5가지 요약 수치로 그린다.\n",
    "* 5가지 요약 수치란 기술통계학에서 자료의 정보를 알려주는 아래의 다섯 가지 수치를 의미한다.\n",
    "\n",
    "\n",
    "1. 최솟값\n",
    "1. 제 1사분위수\n",
    "1. 제 2사분위수( ), 즉 중앙값\n",
    "1. 제 3 사분위 수( )\n",
    "1. 최댓값\n",
    "\n",
    "* Box plot 이해하기 : \n",
    "    * [박스 플롯에 대하여 :: -[|]- Box and Whisker](https://boxnwhis.kr/2019/02/19/boxplot.html)\n",
    "    * [Understanding Boxplots – Towards Data Science](https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 라이브러리를 로드합니다.\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244, 7)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tips 데이터셋을 가져옵니다.\n",
    "\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "tips.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## distplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b2b1c9a10>"
      ]
     },
     "execution_count": 3,
     "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.distplot(tips[\"tip\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b2bd556d0>"
      ]
     },
     "execution_count": 4,
     "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": [
    "# boxplot 으로 시각화 합니다.\n",
    "sns.distplot(tips[\"tip\"])\n",
    "sns.boxplot(tips[\"tip\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## boxenplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b2bd9a890>"
      ]
     },
     "execution_count": 5,
     "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.boxenplot(tips[\"tip\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## violinplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b2bf83890>"
      ]
     },
     "execution_count": 6,
     "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.violinplot(tips[\"tip\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## subplots\n",
    "* distplot, boxplot, boxenplot, violinplot, swarmplot 을 그립니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b2c18f650>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(nrows=5, ncols=1, figsize=(15, 8))\n",
    "\n",
    "sns.distplot(tips[\"tip\"], ax=axes[0])\n",
    "sns.boxplot(tips[\"tip\"], ax=axes[1])\n",
    "sns.boxenplot(tips[\"tip\"], ax=axes[2])\n",
    "sns.violinplot(tips[\"tip\"], ax=axes[3])\n",
    "sns.swarmplot(tips[\"tip\"], ax=axes[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 이상치(outlier) 구하기\n",
    "* https://ko.wikipedia.org/wiki/%EC%83%81%EC%9E%90_%EC%88%98%EC%97%BC_%EA%B7%B8%EB%A6%BC\n",
    "* IQR = Q3 - Q1\n",
    "* outlier\n",
    "    * Q3 + 1.5(IQR)\n",
    "    * Q1 - 1.5(IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    244.000000\n",
       "mean       2.998279\n",
       "std        1.383638\n",
       "min        1.000000\n",
       "25%        2.000000\n",
       "50%        2.900000\n",
       "75%        3.562500\n",
       "max       10.000000\n",
       "Name: tip, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc = tips[\"tip\"].describe()\n",
    "desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5624999999999996"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR = desc[\"75%\"] - desc[\"25%\"]\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5624999999999996"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR = tips[\"tip\"].quantile(0.75) - tips[\"tip\"].quantile(0.25)\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.343749999999999"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR_15 = IQR * 1.5\n",
    "IQR_15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.906249999999998"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc[\"75%\"] + IQR_15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3437499999999991"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc[\"25%\"] - IQR_15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [The Datasaurus Dozen - Same Stats, Different Graphs | Autodesk Research](https://www.autodeskresearch.com/publications/samestats)\n",
    "\n",
    "<img src=\"https://d2f99xq7vri1nk.cloudfront.net/BoxViolinSmaller.gif\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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  "varInspector": {
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}