01_fenxi/600519_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 导入所需要的库\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 股票代号\n",
    "stock_0 = 'SH600519' #贵州茅台\n",
    "\n",
    "# 输入年份（如年报数据只到2016年，则输入2017）\n",
    "year = 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 读取csv文件，转化为DataFrame格式\n",
    "def get_all_data(stock_id):\n",
    "    # 读取利润表数据\n",
    "    lrb = pd.read_csv(\n",
    "        r'data/%s_lrb.csv' % (stock_id),\n",
    "        encoding='utf-8',\n",
    "        header=0,\n",
    "        index_col=None)\n",
    "\n",
    "    list_lrb = []\n",
    "    for i in lrb['报表期截止日']:\n",
    "        str_i = str(i)\n",
    "        # i_ = i[:4] + '-' + i[4:6] + '-' + i[6:8]\n",
    "        list_lrb.append(str(i))\n",
    "    list_lrb_0 = []\n",
    "    for i in list_lrb:\n",
    "        i_ = i[:4] + '-' + i[4:6] + '-' + i[6:8]\n",
    "        list_lrb_0.append(i_)\n",
    "\n",
    "    lrb['报告时间'] = [pd.to_datetime(t) for t in list_lrb_0]\n",
    "\n",
    "    lrb.index = lrb['报告时间']\n",
    "\n",
    "    data = lrb[::-1]\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 根据月份筛选数据，如12月为年报数据\n",
    "def get_data_month(data, month):\n",
    "    data_month = data[data.index.month == month]\n",
    "    return data_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 筛选、计算需要的数据\n",
    "def get_data_ratio(data):\n",
    "    result = pd.DataFrame()\n",
    "    \n",
    "    result['营业收入'] = data['营业收入']\n",
    "    result['归母净利润'] = data['归属于母公司所有者的净利润']\n",
    "    result['营业利润'] = data['营业利润']\n",
    "      \n",
    "    result['营业利润率'] = data['营业利润'] / data['营业收入']\n",
    "    result['净利率'] = data['归属于母公司所有者的净利润'] / data['营业收入']\n",
    "    result['毛利率'] = (data['营业收入'] - data['营业成本']) / data['营业收入']\n",
    "    \n",
    "    result.index = data['报告时间']\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 根据数据进行作图\n",
    "def data_plot(data, ratio, legend='贵州茅台', kind='bar'):\n",
    "    l_0 = len(data)\n",
    "    s_0 = list(range(l_0))\n",
    "    x_0 = np.array(s_0)\n",
    "    y_0 = tuple([str(i) for i in range(year - l_0, year)])\n",
    "    \n",
    "    data[ratio].plot(kind=kind)\n",
    "    plt.title(ratio)\n",
    "#     plt.ylabel(ratio)\n",
    "#     plt.legend([legend], loc='upper left')\n",
    "    plt.legend([legend])\n",
    "    plt.xticks(x_0, y_0)\n",
    "    plt.grid(color='#95a5a6', linestyle='--', linewidth=1, axis='y',alpha=0.4)\n",
    "    plt.savefig(r'pic/%s.png' % (ratio,))\n",
    "#     plt.show();\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 读取茅台利润表数据\n",
    "data = get_all_data(stock_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将年报数据筛选出来\n",
    "data_year = get_data_month(data, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 筛选所需数据\n",
    "result = get_data_ratio(data_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ZAd0kQ0aVbkwhUly6TICISIKoqIuIJIiKuohIgqioi4gkiD4olUj0AafI/kFH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\n425uVAEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b0bb76198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 作图并保存\n",
    "data_plot(result, '营业收入')\n",
    "# 图片显示出来\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b0c147e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 作图并保存\n",
    "data_plot(result, '归母净利润')\n",
    "# 图片显示出来\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "12px",
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