27-Kernel_Density_Estimate_plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#density-plot\n",
    "\n",
    "# Kernel Density Estimate plot \n",
    "* [Kernel density estimation - Wikipedia](https://en.wikipedia.org/wiki/Kernel_density_estimation)\n",
    "* 통계에서 커널 밀도 추정 (KDE)은 임의 변수의 확률 밀도 함수 (PDF)를 추정하는 비모수적 방법이다. 이 함수는 가우스 커널을 사용하며 자동 대역폭 결정을 포함한다.\n",
    "* [비모수 통계 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EB%B9%84%EB%AA%A8%EC%88%98_%ED%86%B5%EA%B3%84)\n",
    "* 커널 밀도 추정치는 히스토그램 과 밀접한 관련이 있지만 적절한 커널을 사용하여 매끄럽고 연속성과 같은 속성을 부여 할 수 있다.\n",
    "* 커널 함수는 원점을 중심으로 대칭이며 적분값이 1인 함수다.\n",
    "\n",
    "\n",
    "### 확률 밀도 함수( probability density function )\n",
    "* [확률 밀도 함수 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%ED%99%95%EB%A5%A0_%EB%B0%80%EB%8F%84_%ED%95%A8%EC%88%98)\n",
    "* [확률 변수 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%ED%99%95%EB%A5%A0_%EB%B3%80%EC%88%98)\n",
    "\n",
    "\n",
    "### 정규분포\n",
    "\n",
    "* 확률론과 통계학에서, 정규 분포(normal distribution) 또는 가우시안 분포(Gaussian distribution)는 연속 확률 분포의 하나이다. 정규분포는 수집된 자료의 분포를 근사하는 데에 자주 사용되며, 이것은 중심극한정리에 의하여 독립적인 확률변수들의 평균은 정규분포에 가까워지는 성질이 있기 때문이다. - 출처 : [정규 분포 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EC%A0%95%EA%B7%9C_%EB%B6%84%ED%8F%AC)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# 한글폰트 사용시 그래프에서 마이너스 폰트 깨지는 문제에 대한 대처\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser = pd.Series(np.random.randn(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1147fb400>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD8CAYAAACcjGjIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUyLW4KQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120e4240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ser.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1148e0eb8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD8CAYAAABpcuN4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148ed7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ser.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1149bf6a0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD8CAYAAABpcuN4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114902320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ser.plot.kde()"
   ]
  },
  {
   "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": "3.6.1"
  },
  "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": 2
}