draw_cdf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.ticker as mtick\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data example\n",
    "* 첫번째 열은 무시하도록 되어 있음\n",
    "    * 아래 예시의 경우 value, value2 값만 가지고 cdf를 그리게 됨\n",
    "* 수정하려면 하단 main의 cols 할당 부분에 [1:]을 지워주면 됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>value</th>\n",
       "      <th>value2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>c</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>d</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>c</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>d</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>c</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>d</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>b</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>c</td>\n",
       "      <td>10</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>d</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>k</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>z</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  value  value2\n",
       "0        a      3       1\n",
       "1        b      5       2\n",
       "2        a      3       5\n",
       "3        b      5       3\n",
       "4        a      3       2\n",
       "5        b      5       1\n",
       "6        a      3       6\n",
       "7        b      5       7\n",
       "8        a      3      12\n",
       "9        b      5      44\n",
       "10       a      3      22\n",
       "11       b      5      66\n",
       "12       a      3     111\n",
       "13       b      5       2\n",
       "14       c     10       5\n",
       "15       d     12       6\n",
       "16       b      5      12\n",
       "17       c     10      16\n",
       "18       d     12      20\n",
       "19       b      5      80\n",
       "20       c     10       5\n",
       "21       d     12      10\n",
       "22       b      5      15\n",
       "23       c     10     122\n",
       "24       d     12       6\n",
       "25       k      1       0\n",
       "26       z      0       0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./temp.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# parameters\n",
    "* 축 설명 폰트 크기, 그래프 크기, 그래프 색깔, 그래프 굵기\n",
    "* 제목, 제목 크기\n",
    "* legend 위치\n",
    "* 축 이름\n",
    "* 축 수치 글씨 크기\n",
    "* 읽을 데이터 위치, 저장 위치\n",
    "* 기본적으로 legend 이름은 column 값을 따르는데, 수정하려면 값 넣으면 됨 (list로)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "## , in ticks\n",
    "fmt = '{x:,.0f}'\n",
    "tick = mtick.StrMethodFormatter(fmt)\n",
    "\n",
    "### Main features\n",
    "\n",
    "font_size = 16 # axis title size\n",
    "fig_size = (6,4) #width,height\n",
    "colors = ['b','r','g','y'] #color pick\n",
    "line_width = 3\n",
    "title = \"title_example\"\n",
    "title_size = 22\n",
    "\n",
    "yaxis_fontsize = 12 # axis number size\n",
    "xaxis_fontsize = 12\n",
    "\n",
    "# best, upper right, upper left, lower left, lower right, right, center left, center right, lower center, upper center, center\n",
    "legend_loc = \"lower right\"\n",
    "\n",
    "xaxis = \"x_example\"\n",
    "yaxis = \"y_example\"\n",
    "\n",
    "data_path = \"./temp.csv\"\n",
    "save_path = \"./fig1.png\"\n",
    "\n",
    "legend_name=[]\n",
    "legend_fontsize=12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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CK9OG+v3wgZ3HESLKMTa6HIGj6sfcEw0HCJnHypWDQeKFF0zNwksn9OjR9H7yk9RvtZV7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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e64902e0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv(data_path)\n",
    "\n",
    "cols = data.columns[1:]\n",
    "fig, ax = plt.subplots(figsize=fig_size)\n",
    "\n",
    "if len(legend_name)==0:\n",
    "    legend_name = cols\n",
    "    \n",
    "for idx,col in enumerate(cols):\n",
    "    cdf = data.groupby([col]).size().reset_index(name=\"counts\")\n",
    "    sums = sum(cdf['counts'])\n",
    "    cdf['cdf'] = cdf.counts.cumsum()/sums\n",
    "    \n",
    "    ax.plot(cdf[col],cdf['cdf'],linewidth=line_width,color=colors[idx],label=legend_name[idx])\n",
    "    ax.xaxis.set_major_formatter(tick) \n",
    "plt.title(title,fontsize=title_size)\n",
    "plt.legend(loc=legend_loc,fontsize=legend_fontsize)\n",
    "plt.xlabel(xaxis,fontsize=font_size)\n",
    "plt.ylabel(yaxis,fontsize=font_size)\n",
    "\n",
    "plt.xticks(fontsize=xaxis_fontsize)\n",
    "plt.yticks(np.arange(0,1.1,0.1),fontsize=yaxis_fontsize)\n",
    "\n",
    "plt.grid(alpha=.4, linewidth=.5, linestyle='--')\n",
    "plt.savefig(save_path,format='png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}