euphoris/2015 Python 사용자 조사.ipynb

## 2015 Python 사용자 조사.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2015 Python 사용자 조사"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "한국어 Python 사용자들은 누구이며 어떻게 개발을 하고 있는지 알아보기 위해 2015년 8월 27일부터 29일까지 3일간 [설문조사](https://docs.google.com/forms/d/1rHOv9zDlzDb3sP4G4pdrNeyyd9K_slSU2zlVTNxxxk0/viewform)를 진행했습니다. 30일 PyCon.KR에서 발표한 분석 결과를 정리해서 공개합니다.\n",
    "\n",
    "문의 또는 수정/변경 요청은 유재명<euphoris@gmail.com>에게 해주십시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy\n",
    "import pandas\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pandas.read_csv('clean.csv')\n",
    "libs = data.columns.values[19:]\n",
    "m = data[libs].mean() * 100\n",
    "df = pandas.DataFrame({'라이브러리': libs, '비율': m})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e89a3940>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e89a3940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.set_context(\"notebook\")\n",
    "seaborn.set(font='NanumBarunGothic', font_scale=1.5)\n",
    "plt.figure(figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_ratio(libs, ylab='라이브러리'):\n",
    "    seaborn.barplot(x='비율', y='라이브러리', data=df.loc[libs])\n",
    "    seaborn.axlabel(xlabel = '비율 (%)', ylabel=ylab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python 사용"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 최근 1개월 내에 사용한 파이썬 구현"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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LhUKBGTNmYOXKlcjJydF6zOXLl7FgwQKEhYUhLy8P8+bNQ3x8PM6fP9+JlRMREXVtevOe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e8758b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ratio(['v2.5', 'v2.6', 'v2.7', 'v3.0', 'v3.1', 'v3.2', 'v3.3', 'v3.4', 'pypy',], '구현')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_fc(column):\n",
    "    m = data.groupby(column).agg({column: len}) / data[column].count() * 100\n",
    "    m['라이브러리'] = m.index\n",
    "    m = m.sort(column, ascending=False)\n",
    "    seaborn.barplot(y='라이브러리', x=column, data=m)\n",
    "    seaborn.axlabel('비율 (%)', column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 주로 사용하는 파이썬 에디터/IDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ryU27du1SUFCQfH19rVpHumPHjmnr1q0aMmRIrm/bYDAoMDBQW7du1enTp1Wm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\n5FQYUImIiIjIqTCgEhEREZFT+Q+zrNxplz9krQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e8648908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fc('editor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 개인 개발환경에서 주로 사용하는 운영체제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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36/6xsbE6cOCApk6dKrPZLC8vLw0YMECBgYGaPXu2JCkoKMgmzMbGxqpv3742V2t/+eUX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\nWIRZAAAAGNb/A7gL4W8wUxi3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e8567ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fc('os')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 최근 1년 내 사용한 라이브러리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DB 관련"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nTjx+/BiHDh3Cvn37cOvWLWzbtq1K54CIiKguU/i3EQcMGIDIyEi8fv0aAJCXl4eQkBD0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\nERFV0HfqLbuE//uhBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e859a978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ratio(['SQLAlchemy', 'MySQL-python','redis','DjangoORM','psycopg2','Storm'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 웹 개발"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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++AH29vYAgB07diAqKkpuvYmJCbKz+YZdIiIiqlkNXrbBi7d7XqSrq1vpM1OI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e84d37f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ratio(['flask', 'django', 'tornado', 'bottle', 'pyramid', 'falcon', 'wheezy',])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 과학 계산 및 데이터 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1KOfOnVPVqlWtApCfn59SUlKu+8QtW7ZUYmKixowZo9TUVPn6+io6OlphYWEK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\nAMBw/g8YnkmAf3eSYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e848a6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ratio(['numpy', 'scipy', 'pandas', 'scikit-learn', 'statmodels', 'sympy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 테스트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/79Spk+bPn6+lS5cqLi5Onp6eioiI0JAhQ6yb3LNa89b1hw8frmnTpunIkSMK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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e837a5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ratio(['requests', 'BeautifulSoup', 'lxml', 'selenium', 'html5lib', 'Scrapy', 'aiohttp', ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 개발 환경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pyJ947Obmpuzs7OKoBQAAoNgUOeT8dc4OAACAI7ilp6uuadu2rTXYXL58uVgLAgAAKA4F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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e82fe940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ratio(['pip', 'virtualenv', 'setuptools', 'anaconda', ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 연봉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def compare_user_group(lib, y='salary'):\n",
    "    m = data.groupby(lib).agg({y: numpy.mean})\n",
    "    m['_usage'] = m.index\n",
    "    seaborn.barplot(y=y, x='_usage', data=m)\n",
    "    seaborn.axlabel(xlabel=lib, ylabel=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 라이브러리와 연봉"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SQLAlchemy, virtualenv 등 일부 라이브러리는 사용자들의 연봉이 더 낮은 현상이 나타났습니다. 그 이유는..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ixYuVm5uriooKSdLWrVvlcDiUmpqqLl26KCEhQbNmzVJWVpY/lw4AQKvh98AQ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e831e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_user_group('SQLAlchemy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Pf/5zffHFF6qtrVVmZqbmzZunIUOGqEePHkpJSdHx48eVm5srSSosLFRhYaFS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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e826a048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_user_group('virtualenv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['total'] = data.career + data.usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_salary = data.loc[data.salary > 1000,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 경력과 연봉\n",
    "\n",
    "당연하지만 경력이 길 수록 연봉도 높아집니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s/OzqTQ4Z+bnWgZDhom5aMdZxWEsjgUMIIbLQ4BENCRpjSylF+4n+gZEMN56+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e839cb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.regplot(x=\"career\", x_jitter=.2, y=\"salary\", data=data_salary)\n",
    "seaborn.axlabel('경력', '연봉')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python을 오래 쓸 수록...\n",
    "\n",
    "재밌게도 Python을 오래 쓴 분들이 연봉이 더 높습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rFgDgF7/4Be68804IIfDuu+9GtPvJT36Curo6bN68GT/5yU/wwx/+EFu2bInO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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e8723c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.regplot(x=\"usage\", x_jitter=.2, y=\"salary\", data=data_salary)\n",
    "seaborn.axlabel('Python 쓴 햇수', '연봉')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 회사 규모\n",
    "\n",
    "좀 더 흥미롭게도 회사 규모와 연봉 사이에는 별 관계가 나타나지 않습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jv5psf2JQISIiiiEhBFq8Kry+oVNNtj8xqBAREcVIm1dB6xCsJtufGFSIiIgG2VCvJtuf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\nCjv1bgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e87240f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.regplot(x=\"company\", x_jitter=.2, y=\"salary\", data=data_salary)\n",
    "seaborn.axlabel('회사 규모(임직원 수)', '연봉')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아주 큰 회사들은 빼고 1000명 미만인 회사들만 보아도 그렇습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RjSCGSJ/ApWhubsYDDzyAf//3f8fEiRMBAK2trcjIyAgqm5ycjJaWFgBAS0sLkpOTg/b7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\nX1tbq+8biMMRHNyMZkJod5m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f/wna19raCovFErTdYrGgpaVFL5ORkRFUJjk5WS/T0tKC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\nonaUgBBCCCFE7SgBIYQQQoja/R+7uvKryjMsQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e866fdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.regplot(x=\"company\", x_jitter=.2, y=\"salary\", data=data_salary.loc[data_salary.company < 1000])\n",
    "seaborn.axlabel('회사 규모(임직원 수)', '연봉')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 회귀분석\n",
    "\n",
    "선형회귀분석이라는 통계적 기법을 이용해 경력, Python을 쓴 햇수, 회사 규모가 연봉을 예측하는데 어떻게 작용하는지 알아보겠습니다. 아래 두 번째 표에서 coef 열은 각 변수가 1 증가할 때마다 연봉이 평균적으로 얼마나 변하는지를 나타냅니다. 경력이 1년 증가하면 연봉은 164만원, Python을 1년 더 쓰면 165만원이 증가하는군요.\n",
    "\n",
    "Python을 한 번도 써본적 없는 경력 10년차 개발자와 Python을 10년 쓴 신입 개발자의 연봉이 평균적으로 비슷할 거라는 이야기입니다.\n",
    "\n",
    "회사 규모는 연봉을 예측하는데 역시 별 의미가 없네요. (유의수준 5%에서 통계적으로 유의미하지 않음)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>salary</td>      <th>  R-squared:         </th> <td>   0.270</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   8.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 31 Aug 2015</td> <th>  Prob (F-statistic):</th> <td>6.05e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:36:54</td>     <th>  Log-Likelihood:    </th> <td> -673.86</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    74</td>      <th>  AIC:               </th> <td>   1356.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    70</td>      <th>  BIC:               </th> <td>   1365.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td> 3069.0658</td> <td>  497.339</td> <td>    6.171</td> <td> 0.000</td> <td> 2077.155  4060.977</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>career</th>    <td>  164.0749</td> <td>   55.642</td> <td>    2.949</td> <td> 0.004</td> <td>   53.101   275.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>usage</th>     <td>  165.5141</td> <td>   67.498</td> <td>    2.452</td> <td> 0.017</td> <td>   30.893   300.135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>company</th>   <td>    0.0943</td> <td>    0.068</td> <td>    1.383</td> <td> 0.171</td> <td>   -0.042     0.230</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>25.106</td> <th>  Durbin-Watson:     </th> <td>   2.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  41.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.298</td> <th>  Prob(JB):          </th> <td>1.08e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.579</td> <th>  Cond. No.          </th> <td>7.64e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 salary   R-squared:                       0.270\n",
       "Model:                            OLS   Adj. R-squared:                  0.238\n",
       "Method:                 Least Squares   F-statistic:                     8.619\n",
       "Date:                Mon, 31 Aug 2015   Prob (F-statistic):           6.05e-05\n",
       "Time:                        23:36:54   Log-Likelihood:                -673.86\n",
       "No. Observations:                  74   AIC:                             1356.\n",
       "Df Residuals:                      70   BIC:                             1365.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   3069.0658    497.339      6.171      0.000      2077.155  4060.977\n",
       "career       164.0749     55.642      2.949      0.004        53.101   275.049\n",
       "usage        165.5141     67.498      2.452      0.017        30.893   300.135\n",
       "company        0.0943      0.068      1.383      0.171        -0.042     0.230\n",
       "==============================================================================\n",
       "Omnibus:                       25.106   Durbin-Watson:                   2.039\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               41.297\n",
       "Skew:                           1.298   Prob(JB):                     1.08e-09\n",
       "Kurtosis:                       5.579   Cond. No.                     7.64e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 7.64e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "smf.ols('salary ~ career + usage + company', data=data_salary).fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이걸 좀 직관적으로 보이기 위해 Python 쓴 햇수와 경력을 더해서 가로축으로 삼아 그래프를 그려봤습니다. 좀 더 패턴이 뚜렷해 보이지 않나요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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BCJHo2yEiSnp83xw+Q6Jv4HJ0dXXh0UcfxZe//GWUl5cDAHp6elBUVBQx1mKxoLu7GwDQ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\nhBBCiNlRwkEIIYQQs/t/Xf3uVCol6LoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e822c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.regplot(x=\"total\", x_jitter=.2, y=\"salary\", data=data_salary)\n",
    "seaborn.axlabel('Python 쓴 햇수 + 경력', '연봉')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SQLAlchemy 사용여부도 회귀분석에 같이 넣어보았습니다. 이렇게 하면 SQLAlchemy의 사용이 연봉에 미치는 효과가 유의수준 5%에서 통계적으로 유의미하지는 않은 것으로 나타났습니다. 이런 결과로 보건데 SQLAlchemy를 쓰면 연봉이 낮아지는 것처럼 보였던 것은 아마 경력이 짧거나 Python을 쓴지 오래되지 않은 분들이 SQLAlchemy를 주로 쓰기 때문에 그렇게 보인 것이 아닌가 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>salary</td>      <th>  R-squared:         </th> <td>   0.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   7.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 31 Aug 2015</td> <th>  Prob (F-statistic):</th> <td>4.58e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:36:54</td>     <th>  Log-Likelihood:    </th> <td> -796.65</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    86</td>      <th>  AIC:               </th> <td>   1603.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    81</td>      <th>  BIC:               </th> <td>   1616.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td> 2804.2040</td> <td>  666.509</td> <td>    4.207</td> <td> 0.000</td> <td> 1478.060  4130.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SQLAlchemy</th> <td> -793.3610</td> <td>  584.673</td> <td>   -1.357</td> <td> 0.179</td> <td>-1956.677   369.955</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>career</th>     <td>  100.4852</td> <td>   58.961</td> <td>    1.704</td> <td> 0.092</td> <td>  -16.828   217.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>usage</th>      <td>  266.9966</td> <td>   73.883</td> <td>    3.614</td> <td> 0.001</td> <td>  119.992   414.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>company</th>    <td>    0.1018</td> <td>    0.080</td> <td>    1.264</td> <td> 0.210</td> <td>   -0.058     0.262</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.996</td> <th>  Durbin-Watson:     </th> <td>   1.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.082</td> <th>  Jarque-Bera (JB):  </th> <td>   4.657</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.371</td> <th>  Prob(JB):          </th> <td>  0.0974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.865</td> <th>  Cond. No.          </th> <td>1.05e+04</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 salary   R-squared:                       0.265\n",
       "Model:                            OLS   Adj. R-squared:                  0.228\n",
       "Method:                 Least Squares   F-statistic:                     7.289\n",
       "Date:                Mon, 31 Aug 2015   Prob (F-statistic):           4.58e-05\n",
       "Time:                        23:36:54   Log-Likelihood:                -796.65\n",
       "No. Observations:                  86   AIC:                             1603.\n",
       "Df Residuals:                      81   BIC:                             1616.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   2804.2040    666.509      4.207      0.000      1478.060  4130.348\n",
       "SQLAlchemy  -793.3610    584.673     -1.357      0.179     -1956.677   369.955\n",
       "career       100.4852     58.961      1.704      0.092       -16.828   217.798\n",
       "usage        266.9966     73.883      3.614      0.001       119.992   414.001\n",
       "company        0.1018      0.080      1.264      0.210        -0.058     0.262\n",
       "==============================================================================\n",
       "Omnibus:                        4.996   Durbin-Watson:                   1.874\n",
       "Prob(Omnibus):                  0.082   Jarque-Bera (JB):                4.657\n",
       "Skew:                           0.371   Prob(JB):                       0.0974\n",
       "Kurtosis:                       3.865   Cond. No.                     1.05e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.05e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf.ols('salary ~ SQLAlchemy + career + usage + company', data=data).fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "virtualenv도 마찬가지입니다. SQLAlchemy와 virtualenv는 여러분의 연봉을 깎아먹지 않으니 안심하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>salary</td>      <th>  R-squared:         </th> <td>   0.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   7.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 31 Aug 2015</td> <th>  Prob (F-statistic):</th> <td>6.07e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:36:55</td>     <th>  Log-Likelihood:    </th> <td> -796.97</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    86</td>      <th>  AIC:               </th> <td>   1604.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    81</td>      <th>  BIC:               </th> <td>   1616.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td> 2875.2314</td> <td>  769.643</td> <td>    3.736</td> <td> 0.000</td> <td> 1343.883  4406.580</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>virtualenv</th> <td> -723.3209</td> <td>  651.652</td> <td>   -1.110</td> <td> 0.270</td> <td>-2019.904   573.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>career</th>     <td>   95.9677</td> <td>   60.345</td> <td>    1.590</td> <td> 0.116</td> <td>  -24.100   216.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>usage</th>      <td>  276.8826</td> <td>   74.184</td> <td>    3.732</td> <td> 0.000</td> <td>  129.280   424.485</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>company</th>    <td>    0.0973</td> <td>    0.082</td> <td>    1.183</td> <td> 0.240</td> <td>   -0.066     0.261</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.521</td> <th>  Durbin-Watson:     </th> <td>   1.879</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.104</td> <th>  Jarque-Bera (JB):  </th> <td>   3.892</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.387</td> <th>  Prob(JB):          </th> <td>   0.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.699</td> <th>  Cond. No.          </th> <td>1.23e+04</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 salary   R-squared:                       0.259\n",
       "Model:                            OLS   Adj. R-squared:                  0.223\n",
       "Method:                 Least Squares   F-statistic:                     7.086\n",
       "Date:                Mon, 31 Aug 2015   Prob (F-statistic):           6.07e-05\n",
       "Time:                        23:36:55   Log-Likelihood:                -796.97\n",
       "No. Observations:                  86   AIC:                             1604.\n",
       "Df Residuals:                      81   BIC:                             1616.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   2875.2314    769.643      3.736      0.000      1343.883  4406.580\n",
       "virtualenv  -723.3209    651.652     -1.110      0.270     -2019.904   573.262\n",
       "career        95.9677     60.345      1.590      0.116       -24.100   216.035\n",
       "usage        276.8826     74.184      3.732      0.000       129.280   424.485\n",
       "company        0.0973      0.082      1.183      0.240        -0.066     0.261\n",
       "==============================================================================\n",
       "Omnibus:                        4.521   Durbin-Watson:                   1.879\n",
       "Prob(Omnibus):                  0.104   Jarque-Bera (JB):                3.892\n",
       "Skew:                           0.387   Prob(JB):                        0.143\n",
       "Kurtosis:                       3.699   Cond. No.                     1.23e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.23e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smf.ols('salary ~ virtualenv + career + usage + company', data=data).fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "경력과 Python을 쓴 햇수도 그래프로 찍어봤습니다. 둘 사이에도 상당히 밀접한 관계가 있지만 예외도 많은 것을 볼 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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sr06S/hRVRUefK3y13zZJal9dZGpfnQ0lhdkeAaRdkiRxTEASGAdFRBnj9vrh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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc1e81a25f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.regplot(x=\"career\", x_jitter=.2, y=\"usage\", data=data)\n",
    "seaborn.axlabel('경력', 'Python 쓴 햇수')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3604925162478223"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.corrcoef(data.career, data.usage)[0,1]"
   ]
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}