04_univariable_number_distplot.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.distplot.html\n",
    "\n",
    "## 도수분포\n",
    "출처 : [도수 분포 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EB%8F%84%EC%88%98_%EB%B6%84%ED%8F%AC)\n",
    "\n",
    "도수 분포(度數分布, frequency distribution)는 표본의 다양한 산출 분포를 보여주는 목록, 표, 그래프이다. 표에 들어가는 각 항목은 특정 그룹이나 주기 안에 값이 발생한 빈도나 횟수를 포함하고 있으며 이러한 방식으로 표는 표본 값의 분포를 요약한다.\n",
    "\n",
    "## 히스토그램\n",
    "* 출처 : [히스토그램 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%ED%9E%88%EC%8A%A4%ED%86%A0%EA%B7%B8%EB%9E%A8)\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/c/c3/Histogram_of_arrivals_per_minute.svg/1920px-Histogram_of_arrivals_per_minute.svg.png\" width=\"400\">\n",
    "\n",
    "## 분포\n",
    "* 출처 : [확률 분포 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%ED%99%95%EB%A5%A0_%EB%B6%84%ED%8F%AC)\n",
    "* <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/8/8c/Standard_deviation_diagram.svg/700px-Standard_deviation_diagram.svg.png\" width=\"400\">\n",
    "\n",
    "\n",
    "\n",
    "### [6 시그마 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/6_%EC%8B%9C%EA%B7%B8%EB%A7%88)\n",
    "* 6 시그마는 모토로라가 등록한 상표이다. 시그마(σ)는 원래 정규분포에서 표준편차를 나타내며 6표준편차인 100만 개 중 3.4개의 불량률(Defects per million opportunities, DPMO)을 추구한다는 의미에서 나온 말이다. 실제로 ±6 시그마 수준은 100만 개중 0.002 개의 불량률로서, 6 시그마는 불량 제로를 추구하는 말이다.\n",
    "\n",
    "\n",
    "<img src=\"https://imgnews.pstatic.net/image/001/2020/06/10/AKR20200610092400004_01_i_P4_20200610135016535.jpg\" width=500>\n",
    "\n",
    "* 이미지 출처 : http://news.naver.com/main/read.nhn?mode=LSD&mid=sec&sid1=001&oid=001&aid=0011668723"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 라이브러리를 로드합니다.\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244, 7)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tips 데이터셋을 가져옵니다.\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "tips.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   total_bill  244 non-null    float64 \n",
      " 1   tip         244 non-null    float64 \n",
      " 2   sex         244 non-null    category\n",
      " 3   smoker      244 non-null    category\n",
      " 4   day         244 non-null    category\n",
      " 5   time        244 non-null    category\n",
      " 6   size        244 non-null    int64   \n",
      "dtypes: category(4), float64(2), int64(1)\n",
      "memory usage: 7.3 KB\n"
     ]
    }
   ],
   "source": [
    "# 데이터프레임의 info를 봅니다.\n",
    "# 이 때, 결측치가 있는지, 데이터의 타입이 무엇인지, \n",
    "# 데이터의 행과 열의 갯수는 몇 개인지, 메모리 사이즈는 어느정도인지를 봅니다.\n",
    "\n",
    "tips.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 시각화의 안 좋은 예"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.01\n",
       "1      1.66\n",
       "2      3.50\n",
       "3      3.31\n",
       "4      3.61\n",
       "       ... \n",
       "239    5.92\n",
       "240    2.00\n",
       "241    2.00\n",
       "242    1.75\n",
       "243    3.00\n",
       "Name: tip, Length: 244, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 기존 단변량 범주형 데이터에 적용했던 countplot 을 단변량 수치 데이터에 적용해 봅니다.\n",
    "tips[\"tip\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc45fae2310>"
      ]
     },
     "execution_count": 8,
     "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.countplot(data=tips, x=\"tip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# countplot은 등장하는 모든 값에 대해 빈도수를 세게 됩니다.\n",
    "# pandas 코드로 표현하면 value_counts 로 셀 수 있습니다.\n",
    "tips[\"tip\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 타입에 맞는 시각화 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc460b07850>"
      ]
     },
     "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": [
    "# distplot 으로 시각화를 합니다. \n",
    "# 히스토그램과 kdeplot을 함께 그립니다.\n",
    "sns.distplot(tips[\"tip\"], rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc460d78d90>"
      ]
     },
     "execution_count": 16,
     "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": [
    "# bin 의 갯수를 조절해 봅니다. bin 1개 일 때\n",
    "\n",
    "sns.distplot(tips[\"tip\"], bins=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc461997f90>"
      ]
     },
     "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": [
    "# bin 의 갯수를 조절해 봅니다. bin 10개 일 때\n",
    "\n",
    "sns.distplot(tips[\"tip\"], bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc461b8cf10>"
      ]
     },
     "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": [
    "# kde = False 옵션으로 그려봅니다.\n",
    "sns.distplot(tips[\"tip\"], kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc462664710>"
      ]
     },
     "execution_count": 29,
     "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": [
    "# hist = False 옵션으로 그려봅니다.\n",
    "sns.distplot(tips[\"tip\"], hist=False, rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4629e0250>"
      ]
     },
     "execution_count": 31,
     "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": [
    "# kdeplot 으로 그리기 \n",
    "# kde는 KDE(kernel density estimation)의 약자\n",
    "sns.kdeplot(tips[\"tip\"], shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc462b46fd0>"
      ]
     },
     "execution_count": 32,
     "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": [
    "\n",
    "sns.kdeplot(tips[\"tip\"], cumulative=True, shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc462d90a10>"
      ]
     },
     "execution_count": 34,
     "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": [
    "\n",
    "sns.kdeplot(tips[\"tip\"], shade=True, vertical=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = tips.hist()"
   ]
  },
  {
   "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": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "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"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}