33. Pandas计算同比环比指标的3种方法.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas计算同比环比指标的3种方法\n",
    "\n",
    "#### 同比和环比：环比和同比用于描述统计数据的变化情况\n",
    "* 环比：表示本次统计段与相连的上次统计段之间的比较。\n",
    "    - 比如2010年中国第一季度GDP为G2010Q1亿元，第二季度GDP为G2010Q2亿元，则第二季度GDP环比增长（G2010Q2-G2010Q1)/G2010Q1；\n",
    "* 同比：即同期相比，表示某个特定统计段今年与去年之间的比较。\n",
    "    - 比如2009年中国第一季度GDP为G2009Q1亿元，则2010年第一季度的GDP同比增长为（G2010Q1-G2009Q1)/G2009Q1。\n",
    "\n",
    "<img src=\"./other_files/tongbi_huanbi.jpg\" style=\"margin-left:0px; \"/>\n",
    "\n",
    "\n",
    "演示步骤：\n",
    "0. 读取连续3年的天气数据\n",
    "1. 方法1：pandas.Series.pct_change\n",
    "2. 方法2：pandas.Series.shift\n",
    "3. 方法3：pandas.Series.diff\n",
    "\n",
    "pct_change、shift、diff，都实现了跨越多行的数据计算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. 读取连续3年的天气数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpath = \"./datas/beijing_tianqi/beijing_tianqi_2017-2019.csv\"\n",
    "df = pd.read_csv(fpath, index_col=\"ymd\", parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bWendu</th>\n",
       "      <th>yWendu</th>\n",
       "      <th>tianqi</th>\n",
       "      <th>fengxiang</th>\n",
       "      <th>fengli</th>\n",
       "      <th>aqi</th>\n",
       "      <th>aqiInfo</th>\n",
       "      <th>aqiLevel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ymd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>5℃</td>\n",
       "      <td>-3℃</td>\n",
       "      <td>霾~晴</td>\n",
       "      <td>南风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>450</td>\n",
       "      <td>严重污染</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>7℃</td>\n",
       "      <td>-6℃</td>\n",
       "      <td>晴~霾</td>\n",
       "      <td>南风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>246</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>5℃</td>\n",
       "      <td>-5℃</td>\n",
       "      <td>霾</td>\n",
       "      <td>南风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>320</td>\n",
       "      <td>严重污染</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           bWendu yWendu tianqi fengxiang fengli  aqi aqiInfo  aqiLevel\n",
       "ymd                                                                    \n",
       "2017-01-01     5℃    -3℃    霾~晴        南风   1-2级  450    严重污染         6\n",
       "2017-01-02     7℃    -6℃    晴~霾        南风   1-2级  246    重度污染         5\n",
       "2017-01-03     5℃    -5℃      霾        南风   1-2级  320    严重污染         6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 替换掉温度的后缀℃\n",
    "df[\"bWendu\"] = df[\"bWendu\"].str.replace(\"℃\", \"\").astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bWendu</th>\n",
       "      <th>yWendu</th>\n",
       "      <th>tianqi</th>\n",
       "      <th>fengxiang</th>\n",
       "      <th>fengli</th>\n",
       "      <th>aqi</th>\n",
       "      <th>aqiInfo</th>\n",
       "      <th>aqiLevel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ymd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>5</td>\n",
       "      <td>-3℃</td>\n",
       "      <td>霾~晴</td>\n",
       "      <td>南风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>450</td>\n",
       "      <td>严重污染</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>7</td>\n",
       "      <td>-6℃</td>\n",
       "      <td>晴~霾</td>\n",
       "      <td>南风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>246</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>5</td>\n",
       "      <td>-5℃</td>\n",
       "      <td>霾</td>\n",
       "      <td>南风</td>\n",
       "      <td>1-2级</td>\n",
       "      <td>320</td>\n",
       "      <td>严重污染</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            bWendu yWendu tianqi fengxiang fengli  aqi aqiInfo  aqiLevel\n",
       "ymd                                                                     \n",
       "2017-01-01       5    -3℃    霾~晴        南风   1-2级  450    严重污染         6\n",
       "2017-01-02       7    -6℃    晴~霾        南风   1-2级  246    重度污染         5\n",
       "2017-01-03       5    -5℃      霾        南风   1-2级  320    严重污染         6"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 新的df，为每个月的平均最高温\n",
    "df = df[[\"bWendu\"]].resample(\"M\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将索引按照日期升序排列\n",
    "df.sort_index(ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bWendu</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ymd</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>3.322581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-02-28</td>\n",
       "      <td>7.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>14.129032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>23.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>29.774194</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "               bWendu\n",
       "ymd                  \n",
       "2017-01-31   3.322581\n",
       "2017-02-28   7.642857\n",
       "2017-03-31  14.129032\n",
       "2017-04-30  23.700000\n",
       "2017-05-31  29.774194"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30',\n",
       "               '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31',\n",
       "               '2017-09-30', '2017-10-31', '2017-11-30', '2017-12-31',\n",
       "               '2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',\n",
       "               '2018-05-31', '2018-06-30', '2018-07-31', '2018-08-31',\n",
       "               '2018-09-30', '2018-10-31', '2018-11-30', '2018-12-31',\n",
       "               '2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',\n",
       "               '2019-05-31', '2019-06-30', '2019-07-31', '2019-08-31',\n",
       "               '2019-09-30', '2019-10-31', '2019-11-30', '2019-12-31'],\n",
       "              dtype='datetime64[ns]', name='ymd', freq='M')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13d8d77dc48>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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wI1FVoyxCddsAZbn2iDavWqiVOqpEibkAPjTm5nRLvy7TJ1Mtz0hCCHSzWKJ3Z1r6kRJdbL0WQnDT2lwO1nbjUkfoBc3rlZxvd+lyA89URYFacB28N2MugB9r6MHjlbpcwJwqIc5MXmoiDTr4JjGCk819AJqVD053Y0Uu4x4vr57XXw98vWrsGWZkwqPbCpSA5HgLOSnxutjME3MB/HBdN3FmwbYV+qj/nkuRI4l6HeTZjOBkUx8lWTZSk7RrizDV1hXpZNqsalfmAtQEFjB1XIESUOLQRyVKzAXwI3U9bCpMI9Gqv11e0xVl2tQMPAhSSqqae9lUqI/ZN4DZJLi+PIdXapyqSVmQ9LyFfrqSLBt1nUOaN7WKqQDuGp3gzcv9uk+fBBQ7bPSPTNA7pE6xn0tL7whdg+O6yH9PddO6HAbH3By+pDZlBeNcm3630E9XkpVM/8gEPRq/N2MqgAfy33poYBWMFZm+xZJ6tS17ToH892YdzcDBdzSXzWrmBbUrMyg17QO6X8AMKMnSx0JmTAXwI3U9WM0mtuig1CwYxQ5fO1TVV2NuVU19JMSZKM3V10fvhDgz15Rm89K5Dt0dxaU3gS30hgngk10Jtc2Dx1QAP3ypm03L0wzxEQ1827JNAuq71ELmXE4297I+P5W4MJ8+vxg3VuTQNThGVbNqEzwXI2yhn6ogPQmr2aRm4JHSPzLB2dZ+w6RPAOItZvLSVCnhXMbcHs62Dugu/x1wbVk2cWah0ijz0PMhDjMxmwQrMpM0LyWMmQB+rL4Hr8QwC5gBxQ4bDSqFMqvqNhfjbq+uKlCmSkmI48qV6qi1+VS3DZCaGEduin630E/nq0RRKZSIOFzXjdViYvNyfb7RZ1OU6Wtdqd78M6vyn2Ck5+t6XWkWjd3DtPXr4xguPapuc1G+TN9b6KcryUqmqWcYt8er2RhiJoAfqetmi4Hy3wFFDhuuUbfm5Up6dbK5j9yUBJalJmo9lFkF0jtV/moZ5e08BtlCP12Jw8aER9LcO6LZGGIigPcNj3OubYArS7RtM7oYgUoUlUaZWVVzn27TJwHly1KwWkzqvNNZNPm30JfruIXsTCZLCTVMo8wbwIUQhUKIV4QQ1UKIs0KIz/lvf0AIcVkIUeX/9e7wD3dxjtb3ICW6b2A1k8lacFWJ8g7dg2M0dg/rOn0CYLWYWJeXwskmNQOfidEWMANKHL6uhFo2nAumn6ob+KKU8oQQwg4cF0K85P+7b0kpHwrf8ELjcF038RYTGwtTtR7KghWm+0oJVS34O51q8Tew0vkMHHxplJ8daWTC49VluaOWAlvoV+ckaz2UBUm3WUlPitP0fMx5v5OklG1SyhP+P7uAaiA/3AMLpcOXutlWlE68xVj5b/DN3grSk1Rb2RmcbOrDbBKsL9D/D+bNy9MYc3upaVMHdExX3eaiJCvZcOtT4D9eTc8plKmEEEXAZuAN/033CyFOCyEeF0LoshC3d2icmnYXO4uNlz4JKFKlhDOqau6jNMeu+Qk8wQh8SlAbet6pus04W+inK3HYNN3ME3QAF0IkA78GPi+lHAB+AKwENgFtwDdmedzHhRCVQojKzs7I90Z+o97XSMiI+e+A4swkGrqGVSnhFF6vpKqpT/f574D8tESy7PEqDz7NwOgEl/tGKNNZG4RglWQl0+ka0+zgjqACuBAiDl/w/rmU8jcAUsoOKaVHSukFfgzsmOmxUspHpJTbpJTbsrKyQjXuoB2p6yExzswGnTT6X4wih43BMTddg6qUMKCuaxDXmNsQ+W/wndKzqTBNlRJOE0gp6f0Qh9kUT/ZE0WYWHkwVigAeA6qllN+ccvuyKXe7A3gz9MNbukD+22ox7sJR4AgnlUZ5ywn/TFavW+hnsnl5GnVdQ/QNqx/EAdUGOsRhJiuzAlViOg3gwG7gg8B100oG/10IcUYIcRq4FvhCOAe6GN2DY5zvcBlu+/x0xZnafpPoUVVzH/YEy2RXOCN4Kw+uZuEBNe0DpCUZawv9VMszfVViWi1kzrv6I6U8AMy0v/V/Qz+c0HqjvgcwXv+T6fLTEzGbhGpqNcXJJt8GHpPJOFuvNxSkYRK+sV9Tmq31cHThXJuL8twUQ22hnyreYqYwI4lLOp6BG9bhS90kWc1sMECZ2VzizCYK0xNpVOdjAjA87uZ8+4DuDnCYT3K8hTU59skDKGKdxyu50O4ybPokoMRh028O3MiO1HWzrSgjKjZOFDlsKoXid7qlH680Vv47YPPyNE419+FVBzzQ2D3k20Jv0AXMgJKsZOq7BjW5psaPbLPoGRrnonOQnSX6P30+GEWZvlpwVUr4Vg55o8Fm4ODLg/ePTKhj8vBt4AEM1wNlumKHjdEJL20Dke82GbUBPHDCx7o8Y6dPAoodNobHPXS6xrQeiuZONvVSlJlEhs2q9VAWbLIzoaoHp6Z9ALNJGG4L/XSBplb1GqRRojaA1/pXhVdlG/ubIyBQShjraRQp5eQCphGtzEomOd7CSbUjk+q2AUoMcgr9XFZm+WJMXVfkK1GiNoBfcg5is5pZlmrM8qTpAqWEsV4L3tY/itM1Zsj8N/iO4tpYmKpKCfGlUMoMnv8GyLbHY7OaNVnIjNoAXuscZGV2smHLk6bLS0sgzixivq1sYCu6UWfg4Bt7dZuLkXGP1kPRTP+Ibwu9UQ4xnosQgpKsZC5pUAse1QF8VVZ0pE8ALGYThelJMV8LXtXci9ViMnTlwubCdDxeyZut/VoPRTOTp9AbfAEzwHc+ppqBh4RrdIL2gVFWRkn+O0B1JfTNwNflpRi6NcImfwOuWD6hx6iHOMym2GGjtX+E0YnIfqoy7rtgDoEG69GygBlQlGmjsTt2uxJOeLycudxv2Px3gCM5nsKMxJjuTFjdNkB6Uhw5KfFaDyUkSrKSkTLya1RRGcBrndFVgRJQ7EhiZMJDx0BslhLWtLkYc3sNnf8O2FSYHtMLmdXtLsoMvIV+uhKNuhJGbQCPMwtWZCRpPZSQivVSwsBhCEbpAT6XzYVptPWP0t4f+c0fWvOdQm/cQxxmotUBx1EbwIsybViiYAv9VEUxXkp4sqkPR3I8+WmJWg9lyQI/hGLxhJ7G7iFGJ7yG74EyVZLVwrLUBDUDD4VLnYNRlz4ByEtLxGo2xWwlSlWz7wSeaPjYvTYvBavZFJN58GqDH+Iwm2KHLeJdCaMugI+5PTR2D0VlADebBMszY/OA477hceq6hqIi/w2+NqRr81JisjNhdZtvC320vUd9pYSDES0yiLoA3tA1jFdG3wJmQFFmUkymUAILftGQ/w7YVJjGmZZ+3B6v1kOJqJr26NhCP12JIxnXqJvuociduBR1ATxQgbIyijbxTBUoJYy1dqQnm/oQAkOfbTrd5uVpjEx4ON/h0nooEVXd5oqqBcyAtxYyIzfBisoALkQUB3CHjTG3l3YNWldqqaq5j9IcO8nx8x4iZRibC3317LGUBw9soY+mBcyAyaZWEaxEib4A3jlIfloiidbo+ngWEDgFO5YWMqWUVDUbtwPhbAozEsm0WWOqHrwmynZgTpWXlojVYqIugu/N6AvgzuisQAmYrAWPoTx4fdcQ/SMTUZX/Bl8TpE2FaTG1pT6whT7aKlDAV2RQlJmkZuCL5fFK6jqjq4nVdMtSEoi3xFYp4VsdCI29hX4mm5encalziP7hCa2HEhHHGnrJSYkn2x4dW+inK3Ekqxz4Yl3uHWHM7Y3qGbjJJFiRmRRTbWUrG3uxJ1hYHYXXNfBD6VRL9KdRxt1eXrvQyXVl2VFRyz+TkiwbTT3DTESosmjeAC6EKBRCvCKEqBZCnBVCfM5/e4YQ4iUhxEX/75pPj2o7fav50RzA4a3zMWPFicZetixPx2SKvjf9hsJUhIiNhczKhh5cY26uK8vReihhU5KVjNsrae6JzAQrmBm4G/iilLIc2Al8RgixFvg/wH4p5Wpgv/9rTUVrE6vpih02mrqH8cRAKWH/yAQXnC62rtB8fhAWKQlxrMpKjokt9ftrnFgtJnavytR6KGET6VLCeQO4lLJNSnnC/2cXUA3kA7cBT/nv9hRwe7gGGaxa5yCOZCtpScY77HYhVmTaGPd4ae0b0XooYVfV3IeURG0AB18e3PfvjN4fyFJK9ld3sGtlJknW6CkFnW6yK2GEzsdcUA5cCFEEbAbeAHKklG3gC/JAdqgHt1C1zsGorf+eqsjh67LY2B39efDjDT2YBGyMshLCqTYVptM7PBHV17Oua4iG7mH2lmkeJsIqLclKhs2qnxl4gBAiGfg18Hkp5cACHvdxIUSlEKKys7NzMWMMipQy6ksIA4pjqJTweFMv5ctSomoDz3SB8shoPqn+5WonANdGeQAH3yw8UrXgQQVwIUQcvuD9cynlb/w3dwghlvn/fhngnOmxUspHpJTbpJTbsrKyQjHmGXUOjjEw6o6JAJ5jTyAhLvpLCd0eL1VNfVGdPgFYk2MnyWqmKooXMvfXdFCWa6cgPbp69M8kkudjBlOFIoDHgGop5Ten/NXvgHv9f74XeCb0wwterCxggq+UsCjTFvUB/HyHi6FxT9QHcLNJsKEgNWo7E/aPTHCsoZfrYmD2Db5KlK7BMQZGw1/bH8wMfDfwQeA6IUSV/9e7gX8DbhBCXARu8H+tmUsxFMDBV0oY7SmU442+lMIWg5+BGYxNhemcax2I+KG4kfDahU48Xsne8hgJ4BE8Xm3exKKU8gAwWwHu3tAOZ/FqnYMkx1vITUnQeigRUeSwsb+mA7fHG3UnDwUcb/Tt2itIN/4JPPPZvDwNt1dytrWfrSsytB5OSL1c4yQ9KS4qd9LOpMRfSHHJORj2/j1R886v7RxkZZYtand4TVfsSGLCI2nti96uhMcbe9m6Ij0mrulm/xs92jb0eLySV847ubY0G3MUbsSayYrMJKxmExec4W8THD0B3DnIyhhJn4CvFhyitxKlY2CUlt6RmEifAGSnJJCXmsCpln6thxJSJ5t66Rue4LoYSZ8AxJlNlGTZuNCuAnhQBkYn6BgYi5n8N7xVStgYpQE8kP+O9gXMqdbmpXK2NboC+P4aJxaT4Oo14atA06OyXDvnVQAPzuQCZgxs4gnItseTZDVH7fmYxxt7ibeYqMhL1XooEVORl0J91xBDY26thxIyL1c72VGcQUpCnNZDiag1uXZa+0fDXokSFQE8lkoIA4QQrIjiUsLjjb1sLEjDaomKb9GgVOSlIKXvzMho0NwzzPkOV8yUD05VmuM7cehimI/Li4p3R23nIFazieUZ0b9JYKpiRxINUbj9enTCw9nWfrbEUPoEoCLf92njbGt0BPBXzvv29u0tj97ug7NZ4w/g59vD2xMlKgL4JecgRY6kqC2nm01Rpo3mnuGoO9X8dEs/Ex4ZU/lvgLzUBNKS4jh7OToC+P5qJyUO2+R6TSwpSE/EZjVzPsyfpqIi4sVKD5Tpihw23F5JS290dSWMxQVM8KXFKvJSONtm/IXMoTE3hy91x2T6BHzXck2unfMqhTK30QkPTT3DMbWAGRCtTa2ON/ZS4rCRYYvutsAzqchL5UL7YMROdAmXg7VdjHu8MVU+OF1pjq8SJZxtgg0fwBu6h/BKYqoGPGBFpi/nH00LmVJKTjT1xlz+O6AiL4Vxj5eLHZE7GDccXq5xYo+3sL0ounaVLkRprp3e4Qk6B8fC9hqGD+CxWIESkJUcj81qjqo+0vVdQ/QMjcdc+iQgUDZp5Hpwr1fyco2Tq0uziIuxdampApUoF8K4kGn4/91a5yBCEBMHOUwnhKDIYYuqWvBA/ntbjAbwYoeNxDizoStR3mztx+kai/rDG+azJtdfiRLGPHhUBPCC9EQS4sxaD0UTq7OTI7LjK1JONPWSkmCJyR/I4GstW77MzjkDB/D91U6EgGtKYzuAO5LjybRZw7qlPioCeCwuYAasy0+lfWAUpys6mlodb/Tlv6PxBPpgVeSlcq5tAK9BD61+ucbJluXpMbkIPV1prp0aNQOfmccrqesaisn8d8B6/+aPNy8bN2ca0D88wYWOQbbGSAOr2VTkpTA45qapx3hrGx0Do5y53B+z5YPTrcmxc7HDFbYfxoYO4C29w4y7vTEdwCvyUxECzrQY9738qA4AACAASURBVCN3wAn/mZBbi2I9gBt3R+YrNYHdlyqAg28GPjzu4XJfePZqGDqAx3IFSkByvIVih40zUTADP9HYi9kk2FgQvSfQB2NNbjIWkzBkJcr+Gif5aYmTFRixrtS/kFkTpjx4dATwrNj+ZtmQnxoVKZTjjb2UL7Nji+IT6IMRbzGzKjvZcDPw0QkPBy52cV1ZdkwcwhGM1f7J5YUw5cENH8AdyfGkJsVWq8rpomEh0+3xUtXcF/P574CKvFTDBfAjdd2MTHhievfldPaEOPLTEsNWKWbsAN45yKrs2GuUM100LGTWtLsYHvewNYZ37k1VkZdC1+AYzgHj/FB+ucZJYpyZK0sytR6KrpTl2tUMfDopZcw2sZouGhYyY7WB1Wwq8lIA4yxkSinZX+1k9ypHzO7JmM2aXDuXOsPT38awAbzTNYZr1B3TNeAB0bCQebyxl1z/uZAKrJ0M4Ma4ppf7RrjcN8K71ji0HorulObYmfDIsOyYnjeACyEeF0I4hRBvTrntASHEZSFElf/Xu0M+snm8VYES2wuYAUZfyIylE+iDYU+IY0VmkmFm4IHgtFpVn7zDW4c7hD6NEswM/Eng5hlu/5aUcpP/1/+Gdljzq+1UJYRTGXkhs63fN3tT6ZO3q8hLMVwAj8XDG+azMtuG2SS0CeBSyteAnpC/8hLVOgdJjreQkxKv9VB0wcgLmSca+wCV/56uIi+Vpp5h+kfCezBuKNR3DZFkNZNtV+/H6eItZoodtrA0tVpKDvx+IcRpf4ol4u+8WucgK7OT1UduPyMvZB5v7CUhzjSZ91V8Av8fRmhs1dA1RFGmTb0fZ1GaE55KlMUG8B8AK4FNQBvwjdnuKIT4uBCiUghR2dnZuciXe6dYb2I1XXK8hRKDLmQeb+plQ0FaTPeOnsk6A/UGr+8aUumTOZTm2mnqGWZ43B3S513UO0ZK2SGl9EgpvcCPgR1z3PcRKeU2KeW2rKysxY7zbQZGJ3C6xlT+e5r1+amcudyn9TAWZGTcw9nL/THb/3suWfZ4su3xup+BT3i8NPeOqAA+hzU5dqQk5CctLSqACyGWTfnyDuDN2e4bDqoHyszW5afSMTBmqIXM0y19uL2xdwJ9sIywkNncM4zHKylSAXxWpWE63CGYMsKngcNAqRCiRQjxEeDfhRBnhBCngWuBL4R0VPNQAXxmRlzIPN7k28CzWW2hn1FFXiq1nYOMTni0HsqsGroDFShJGo9Ev5ZnJJEQZwr54Q7zdg2SUt49w82PhXQUC3TJOYjVbKIwPVHLYejO1IXM68pytB5OUE409lKSFZsn0AejIi8Fj1dyvt3FxkJ9dmms6wwEcDWhmo3ZJFidbY/8DFyPqttdlGTZsKhFr7d5ayHTOHnwquZ+tqjZ96yM0Bu8oXuIlAQL6THeVG4+a3LsIa8FN1wE9HglJ/279pR38i1kGiOF0jM0TtfgGGW5avfebAozErEnWHRdidLQNUxxlirpnU9pbjJO1xi9Q+Mhe07DBfCa9gFcY262q651MzLSQuZF/8dJtZYxOyEEa5fpeyGzvmuI4kyV/55Paa6vrj+U9eCGC+DH6n2bQrcXqwA+kw3+02yMsJB5wb8YvUb1z5hTRV4qNe0DeHR4yPHohIfW/hFVgRKEwClFocyDGy+AN/aSl5pAfppawJxJRV6KYXZk1na4SI63sEx1IJxTRV4KoxNe6jpDW0McCo3dw0ipeqAEIyclnpQES0jz4IYK4FJKjtX3qNn3HGwGWsi86O/nrnKnc6vI129vcNXEKnhCCMpyU2I3hdLcM4LTNcY2lf+ek1EWMi90DE6eGajMbmVWMlaLSZcLmYEacJVCCc6a3GRq2l1IGZp0mKEC+NEGX/57hwrgc1pfkKb7hcxefwWKyn/PL85soizXrs8ZeOcQjmQrKQmqhDAYpTl2XKNu2kN0VJ6hAnhlQw+piXFq1jYPI+zIvBjYTZujrmUwAlvqQzVzC5X6btXEaiFCfbiDoQL40YYetq1Ix2RSOdO5BBYyT7foOYD7voHVDDw4a/NS6R+Z4HLfiNZDeZt6fxtZJTiBniihyoMbJoB3D45R1zmk8t9BCCxk6noG3jGIzWpWZ2AGaZ0ODzkeHHPT6RpT+e8FSEuykpMST02szcCPNfiaHu0oVjswg6H3hcyLThercuyqAiVIZbkpmIS+AniDvwKlRAXwBVkTwsMdDBPAKxt6sFpMrPPnd5W5TS5khmixJNRUBcrCJFrNrMxK5pyOKlECJYRqBr4wZbl2LnYMhmRjlmEC+LGGHjYVphFvMWs9FEMILGTqcRbeNzxOp2uMNWoBc0H01hs8MANXOfCFWZNjZ8ztpdFfgrkUhgjgw+Nu3mwdYHuRSp8Ea3JHpg4DeKACZXW2WsBciIq8VNr6R+kJYTOkpajvGmJZagKJVjWpWohQLmQaIoCfbOrD45WqgdUC6HkhM3Cs1Go1A1+QismFTH1c0/puVYGyGKuz7QgB59uX3hrBEAH8WEMPQsAW1UJ2QTYUpOlyBn6hw0WS1UxequpnsxBrdVaJ0tA1RHGWCuALlWg1syIjKXZm4McaeijPTVG7vRZosrWszhYya52+BUxVz78waUlW8tMSdRHA+4bH6R2eoFjNwBdlTY6dmvalX0fdB/AJj5eTTX0q/70Iel3IvNDhYpXKfy+KbyFT++upmlgtTWmunYbu4SWfdar7AH6udYDhcY/qQLgIelzI7B+ewKkqUBatIi+V+q4hhsbcmo5DlRAuTWmuHY9XTp4nuli6D+DH/A2s1ALmwtniLazMStbVQmZgC71awFycirwUpCRkO/kWq6FrCJPwnbauLNxbhzssLY1iiAC+PCOJnBS15Xox9LYjU5UQLk2gBC3Uh+MuVH33MAXpSVgtug8hulTksBFnFkuuRNH1/76UksqGXrap/Pei6W0h80KHi8Q4szpRaZEK0hNJjreEZAFsKeq7BlX6ZAnizCZWZiUvuRJl3gAuhHhcCOEUQrw55bYMIcRLQoiL/t/DEmHruoboHhpX/b+XQG8LmbXOQVbnqAqUxfKd6mKnpk27GbiUkoauYdUDZYlKc+3UtIU/hfIkcPO02/4PsF9KuRrY7/865Cr9+W/VgXDx9LaQ6atAUfnvpShb5itB06o3eOfgGINjborUSfRLsj4/ldb+UTqW8Ol43gAupXwN6Jl2823AU/4/PwXcvugRzOFofS8ZNisr1WaBRdPTQmb/yAQdA+oUnqUqzU1hYNRNW782abGGrmFAVaAsVaAwI1CosRiLzYHnSCnbAPy/Z892RyHEx4UQlUKIys7OzgW9SGWj7wAH1XJ0adbnp+ricIfaQAWKmoEvSbl/IVOrPPhbbWTVdVyKtXkpJMaZqfS3yl6MsC9iSikfkVJuk1Juy8rKCvpxzoFRGruH2aHqv5dsXX4qTpf2C5mTPVBUBcqSrPEH8GqN8uB1XUPEmQV5aaoybCnizCa2rEjTZAbeIYRYBuD/3bnoEcwicICDyn8vXWAh85TGs/ALHYMkxJkoSFcVKEuRkhBHQXqiZqWEDV1DLM9IwmLWdRGbIWxbkUF12wCu0YlFPX6xV+B3wL3+P98LPLPI55nVsYYeEuPMkx3YlMXbUJBKYpyZ1y8uLIUVahedvgVMVYGydGW5oemlsRj1Xeog41DZUZyBV8KJpr5FPT6YMsKngcNAqRCiRQjxEeDfgBuEEBeBG/xfh9Sxhh42L08jTv2UX7KEODN7VjvYd65D01PNL3YMskalT0KiLDeFS51DjLmX1ktjobxeSYNqIxsymwrTMJsEx+oXl0YJpgrlbinlMillnJSyQEr5mJSyW0q5V0q52v/74pM4M3CNTlDdNqC2z4fQ9eXZtPaPapY3HRidoH1glFVqC31IlC3z9dKodS69p/RCtA+MMub2qjayIWKLt1CRl7LoPLgup7cnmvrwStX/JJSuLfMVCu2v7tDk9QMLmGoGHhplGm2pn+xCqGbgIbO9KIOq5r5FfZrSZQCvbOjBbBJsXp6m9VCiRrY9gU2FaeyrCfl6c1BqVROrkCrKtGG1mCLe1GoygKsZeMhsL0pnzO3lzcsLX9PQZQA/Wt9DRV4KtniL1kOJKteXZ3OquQ+nK/LlhG9VoKjde6FgMZtYk5NM9RK3Yi9UfdcQCXEmcuyqhDBUApV2lYtIo+gugI+7vVQ196n0SRjsLc8B4OXqyM/CLzoHWZmVjFlVoIRMWW5KxGfgDV2+BUxVSRQ6juR4Shy2ReXBdRfAz1zuZ8ztVSfwhEFZrp38tET2aRHAO1xqC32IleXa6XSN0T04FrHXrO9WJYThsL0og8rGXrzehVWJ6S6ABz5GbF2hZuChJoRgb3k2B2o7l3yU00K4Rido6x9VTaxCrCzXt0ciUguZbo+Xpu5h1QMlDLYVpdM3PEFt58KqinQXwI819FDisJFlj9d6KFFpb3kOoxNeDl3qithrBg5xUDPw0Cpb5t9SH6EAfrlvBLdXqhl4GCy2sZWuArjXK6lsVAc4hNPOkgxsVjMvnYtcGqV2sgeKmoGHkiM5Hkdy/JJ7SgerTh1kHDYrMpPIsscveEOPrgL4RecgfcMTagEzjOItZq5ek8XLNZHblXmhw0W8xUShOj8x5Mpy7Zxf4qkuwQp0IVS7MENPCMH2ovTJHlDB0lUA//2ZNoSAq9cE37VQWbi95Tl0DIwtqu50MVQFSviU5do53+7Cs8DFr8Wo7xrCHm/BkWwN+2vFou1FGVzuG6G1byTox+gmgEspeabqMrtWZqoDjMPs2tIshIB9EdqV6atAUemTcChblsKY20tD91DYX6u+a4gih0315w+TxeTBdRPAq5r7aOwe5raN+VoPJeplJsezZXk6+2vCH8BdoxO09o+yWi1ghkUkt9Q3qBLCsCrLtWOzLuyAB90E8GeqWrFaTNy8PlfrocSE68tzePPyAG39wX9cW4xAsyW1gBkeq7J9qalwL2SOuT1c7h1RJYRhZDGb2LIi3XgzcLfHy3OnW9lblk1KQpzWw4kJ15cHmluFtxolUEKoZuDhkRBnpthhC3spYXPPMF6JOok+zLYXZXC+w0X/cHAHPOgigB+81E3X4Di3bVLpk0hZlZ3M8oyksHcnvNjhwmoxsVxVoIRNJA53qOv0V6CoAB5W24sykBKONwU3C9dFAH/m5GVSEixcW6aqTyIlsCvz4KVuhsfdYXsdVYESfmW5dpp7RhgcC991DCySqjay4bWpMA2LSQRdTqh5AB8Z9/DC2XbevX4Z8Raz1sOJKdeX5zDu9nLgYvh2ZV7sGFQVKGEWiS319V3DZNispCapFGc4JVrNrMtPDbozoeYB/KXqDobGPSp9ooEdxRnYEyxhKyccHHNzuW9ELWCGWWBLfTjTKPVdgxRlqjRYJOwozuBUc39Q/Yo0D+C/q7pMbkoCVxSr3ZeRFmc28a41Wbxc07ngLmjBqFULmBGRn5aIPd5CTRiPy2voGqbYoX4QR8K2FemMe7ycudw/7301DeC9Q+O8er6TWzflqf7CGrm+PIeuwTFOtSzuVOy5XPRv8VYz8PASQlDq35EZDsPjbtoHRil2qBl4JAQOeDgaRF8UTQP478+04fZKbtuUp+UwYto1pVmYTSIs5YQXnYOqAiVCypbZqW4fCEt/m4auYUBVoERKhs3KquzkoPLgmgbwZ6ouszo7mbXLUrQcRkxLS7KydUV6WPLgFztclDhsWMyaZ+qiXlluCq5RN639oT8ub7ICRQXwiNlelE5lY++8PW6W9M4SQjQIIc4IIaqEEJULeWxL7zDHGnq5fXO+6q2gsevLs6lpd9HSOxzS573oHFQ9wCOkPLCQGYYdmfWqC2HEbS/KwDXq5sI8nSZDMTW6Vkq5SUq5bSEPeqaqFYBbN6r0idau95+VGco0ytCYm5ZeVYESKYEflKE+I7N7cIyXznWQbY9Xh4xH0PYgDzrW5LNtoPPg1hXpqke0DpRkJVPisIU0jXKpU1WgRJI9IY6C9MSQBvA/Xujk5m+/zrnWAf72ptKQPa8yv4L0RHJTEjg6z4aepQZwCbwohDguhPj4THcQQnxcCFEphKjs7OwEfLOECx2D3K4WL3Vjb3k2R+q6cY0G14NhPoGKiNVqE0/ElOWmhCSFMjrh4avPnuXex4+SnhTHM/fv5q5thSEYoRIsIQTbitI5Vt8z58L0UgP4binlFuBPgM8IIa6efgcp5SNSym1Sym1ZWb6t8v9TdRmLSfCeDSqA68Xe8hwmPJLXQ7Qr87nTbWTb41mhPmFFTFmunbquIcbciz+wuqZ9gNu+d5AnDjZw364ifnf/HspVkYEmdhRn0D4wSkvv7B1DlxTApZSt/t+dwG+BHfM9xuuVPFvVytVrssiwqZM99GLbinRSE+NCkka51DnIHy90cs/OFaoCJYLKltnxeOXkBqqF8Holjx+o59bvHaR7aJwnPrSdB26tICFOtbfQyrYV/jx44+x58EW/u4QQNiGEPfBn4Ebgzfked7Shh9b+UVX7rTMWs4lrS7PYX+1kaIlNkX5yqAGr2cTdO5aHaHRKMAI9URa6I9M5MMp9Tx7ja8+d46pVDp7//FVcW5odjiEqC1Caa8ceb+Fo/ex58KVMj3KAA0KIU8BR4PdSyufne9AzVZdJspq5YW3OEl5aCYcPXllE/8gETx9tWvRzDIxO8KvjLdyycRlZ9vgQjk6ZT1FmEvEW04J6orx0roObv/06R+u7+afb1/HovdtwJKvrpgdmk2BrUfqclSiLrguSUtYBGxf2GPj96TZuXJtDklWVJOnN1hXp7FqZyY9eq+OenSsW9fH5V5UtDI17+NCu4jCMUJmLxWxidU5y0JUov6hs5ku/Ok1FXgrf/rNNrMpWFUN6s70og1fPn5/17yOaoHSNTjAw6ua2zarzoF7df90qOl1j/KKyecGP9XolPzncwJblaawvSA394JR5leWmBBXAD1zs4u9/c4arVjv4zad3qeCtU4F68NlENID3jUyQabNy1SpHJF9WWYArSzLZuiKdH756iXG3d0GP/eOFThq6h7lvt5p9a6Us106na4yuwbFZ73Ohw8WnfnaclVnJfP8vtqg+/Dq2oSAV6xyFABEN4AMjE9yyYZmqTNAxIQT3X7eK1v5RfnuyZUGPfeJQAzkp8fzJOnUwtVYCJX+zdSZ0ukb50BPHSLSaefxD29UZtDqXEGfmN5/eNevfRzSSSlDpEwO4Zk0W6/NTefjVS7g9wc3Ca52DvHahk3uuWEGc+gGtmdLc2bfUD4+7+ciTlfQOj/P4fdvJT0uM9PCURViXP3s6MqIriVazic2FaW+7bWJigpaWFkZHQ99FLRolJCRQUFBAXFz4Zk6BWfgnfnqc5063cXsQP3R/cthfOniFKh3UkiM5Hkdy/Dt2ZHq8kr96uoqzrf38+C+3zRkUFOOIaADPsse/o/NgS0sLdrudoqIi1ZVwHlJKuru7aWlpobg4vHnmG8pzKM2x871Xarl149wHbgRKB9+7MU+VoOlA+TL7O2bgX//9OfZVd/DVWyvYW65KeKNFRD/rzrTzcnR0lMzMTBW8gyCEIDMzMyKfVkwmwWeuW0Wtc5AXzrbPed9fVrYwPO7hvl1FYR+XMr+yXDsXOlyTvaSfOFjPEwcb+PDuYu5V1yiq6CJZqYJ38CL5f/We9csodtj47su1szbU8XglTx1qYNuKdFU6qBOluSmMub00dA/x0rkOvvbcOW5cm8P//55yrYemhJguAriiT2aT4NPXrORc2wCvnJ+5V/ir55009Qxz3+6iyA5OmVWZfyHzF5XN/NXTJ9mQn8q3/2wzZnXubNRRARxoaGhg3bp1b7vtmWee4fbbb5/8+l//9V9ZtWrV5NfPPvsst956a0hev6ioiK6u0HQBDLXbN+eTn5bId/bPPAt/8lADuSkJ3FShSgf1YlV2MmaT4Ed/rCPDZuXRe7eTaFW13tFIBfBZ7Nq1i8OHD09+ffjwYVJSUnA6fTPRQ4cOsXv3bq2GFzFxZhOfumYlVc19HKztftvfXexw8frFLj54pSod1JOEODOrspKxJ1h48kPbVU+aKKbedX5ut5t7772XDRs2cOedd2Kz2UhNTaW2thaAy5cv8/73v59Dhw4BvgC+a5evwP7FF1/kyiuvZMuWLdx1110MDvraeRYVFfGVr3yFLVu2sH79empqagDo7u7mxhtvZPPmzXziE5+YnNlO/yTw0EMP8cADD0Tqv2BWd20rICclnu++fPFttz91uAGrxcSfbVfN/vXmG3+6kV99cpc6ESnK6aqj1FefPcu51tAeyro2L4WvvLdi3vudP3+exx57jN27d/PhD3+Yhx9+mF27dnHo0CE8Hg+rV69m586dvPDCC9xyyy2cPn2a7du309XVxde//nX27duHzWbjwQcf5Jvf/CZf/vKXAXA4HJw4cYKHH36Yhx56iEcffZSvfvWr7Nmzhy9/+cv8/ve/55FHHgnpvznU4i1mPnH1Sr723DmO1vewoziD/pEJfnPiMrduzCNTlQ7qjqrzjg1qBu5XWFg4mRK55557OHDgALt37+bQoUMcOnSIK6+8kh07dvDGG29w8uRJSktLSUhI4MiRI5w7d47du3ezadMmnnrqKRobGyef933vex8AW7dupaGhAYDXXnuNe+65B4D3vOc9pKenR/Yfuwh371hOps3K917xfSL5ZWWzKh1UFI3pagYezEw5XKaX5wkh2LVrF9/97nfxeDx87GMfw263Mzo6yquvvjoZ7KWU3HDDDTz99NMzPm98vG92ajabcbvdb3v+6SwWC17vW1vX9bQ7NdFq5qNXlfDg8zWcbOrlJ4cb2V6UrmZ6iqIhNQP3a2pqmly0fPrpp9mzZw9r166ltbWV119/nc2bNwOwadMmfvjDH07mv3fu3MnBgwcnc+XDw8NcuHBhzte6+uqr+fnPfw7AH/7wB3p7fSdu5OTk4HQ66e7uZmxsjOeeey4s/9bFumfnclIT4/jMz0/4SgdVz29F0ZQK4H7l5eU89dRTbNiwgZ6eHj71qU8hhOCKK67A4XBM9h658sorqaurmwzgWVlZPPnkk9x9991s2LCBnTt3Ti5WzuYrX/kKr732Glu2bOHFF19k+XJf/5C4uDi+/OUvc8UVV3DLLbdQVlYW3n/0AtkT4vjQ7iJa+0dZlprAjRVqS7aiaEnMdWR9qG3btk1WVla+7bbq6mrKy9UOsYXQ8v+sf3iCvd/8I5++ZiUf3qNm4IoSCUKI41LKbdNv11UOXNG/1KQ4jv79XlT3A0XRngrgyoLN1ZlQUZTI0UUOPJJpHKNT/1eKogRoHsATEhLo7u5WgSkIgX7gCQkJWg9FURQdWFIKRQhxM/BtwAw8KqX8t4U+R0FBAS0tLXR2di5lKDEjcCKPoijKogO4EMIMfB+4AWgBjgkhfielPLeQ54mLiwv76TKKoijRaCkplB1ArZSyTko5Dvw3cFtohqUoiqLMZykBPB9onvJ1i/82RVEUJQKWEsBnqiV7x0qkEOLjQohKIUSlynMriqKEzlIWMVuAqY2gC4DW6XeSUj4CPAIghHAJIc4H8dypQH8I7qOeK3yv6QDmO0YoVv4vjPxcYOxrqdfnCvVrrp7xVinlon7hC/51QDFgBU4BFfM8pjLI534kFPdRzxXW15z3WsbQ/4Vhn8vo11KvzxWp11z0DFxK6RZC3A+8gK+M8HEp5dnFPt80z4boPuq5wveaoXq9YO+n5/8LIz9XsPQ6fr0+V0ReM6LNrIQQlXKGhiyK8ahrGT3UtTSuSO/E1PfZYcpCqGsZPdS1NKiIzsAVRVGU0NG8F4rRCSEG5/n7V4UQ6uOpAahrGR1i6TqqAK4oimJQYQng8/0EjDZCiGuEEM9N+fp7Qoj7NBxSyKhrGR3XUl3H6LiO06kZuKIoikGFLYALIZKFEPuFECeEEGeEELf5by8SQlQLIX4shDgrhHhRCJEYrnEoS6euZXRQ1zH6hHMGPgrcIaXcAlwLfEOIyZMUVwPfl1JWAH3A+8M4jkhw8/b/y2g7cUFdy+igrmOUCWcAF8C/CCFOA/vwdSrM8f9dvZSyyv/n40BRGMcRCY3AWiFEvBAiFdir9YBCTF3L6KCuY5QJ56HGfwFkAVullBNCiAbe+ik4NuV+HsCQH9eEEBZgTErZLIT4BXAauAic1HZkIaeuZXRQ1zHKhDOApwJO/zfKtcCKML6WViqASwBSyi8BX5p+BynlNREeUzioa0lUXEt1HYmK6zgp5AE88BMQ+DnwrBCiEqgCakL9WloSQnwS+Cvg81qPJVzUtYwO6jpGr5BvpRdCbAR+LKXcEdInViJOXcvooK5j9ArpIqb/J+DTwD+E8nmVyFPXMjqo6xjdVDMrRVEUg1rSDFwIUSiEeMW/CeCsEOJz/tszhBAvCSEu+n9P999eJoQ4LIQYE0L8zZTnKRVCVE35NSCEiJk8lh6E6lr6/+4L/ud4UwjxtBAiKmtw9SrE1/Jz/ut4Vr0n9WdJM3AhxDJgmZTyhBDCjq9+9HbgPqBHSvlvQoj/A6RLKf9OCJGNb+X7dqBXSvnQDM9pBi4DV0gpGxc9OGVBQnUthRD5wAFgrZRyxF/K9b9Syicj/6+KTSG8luuA/wZ2AOPA88CnpJQXI/6PUma0pBm4lLJNSnnC/2cXUI1vc8BtwFP+uz2F7xsDKaVTSnkMmJjjafcCl1TwjqwQX0sLkOivfkhihsOulfAJ4bUsB45IKYellG7gj8AdEfgnKEEK2SKmEKII2Ay8AeRIKdvA980EZC/gqf4M36KLopGlXEsp5WXgIaAJaAP6pZQvhnO8yuyW+L58E7haCJEphEgC3g0Uhm+0ykKFJIALIZKBXwOfl1IOLOF5rMCtwC9DMS5l4ZZ6Lf151duAYiAPsAkh7gntKJVgLPVaSimrgQeBl/ClT07h6zGi6MSSVmnKvgAAAm5JREFUA7gQIg7fN8nPpZS/8d/c4c/DBfJxziCf7k+AE1LKjqWOS1m4EF3L6/H11eiUUk4AvwF2hWvMysxC9b6UUj4mpdwipbwa6MG3LV3RiaVWoQjgMaBaSvnNKX/1O+Be/5/vBZ4J8invRqVPNBHCa9kE7BRCJPmfcy++HKwSIaF8X/oXOBFCLAfeh3p/6spSq1D2AK8DZwCv/+a/x5dv+wWwHN8b+i4pZY8QIheoBFL89x/EV60w4M+xNQMlUsr+RQ9KWZQQX8uvAh/A93H7JPBRKeXUZklKGIX4Wr4OZOJb4PxrKeX+iP5jlDmpjTyKoigGpY5UUxRFMSgVwBVFUQxKBXBFURSDUgFcURTFoFQAVxRFMSgVwBUlSEKIQa3HoChTqQCuKIpiUOE81FhRNCeE+CegS0r5bf/X/wx0AO/3/74J33b/M8Dn8J3GfruU8pIQohj4L3zvk+c1GL6izEnNwJVo9xj+7eNCCBO+bpeXgY34AvZ64IPAGv+ZkY8Cn/U/9tvAD6SU24H2CI9bUealArgS1aSUDUC3EGIzcCO+rf3dwDF/3+wx4BIQaHl7Bijy/3k3b/X++GmkxqwowVIpFCUWPIrvNJpc4HH/bVN7s3infO3l7e8L1WtC0S01A1diwW+Bm4HtwAsLeNxBfCkXgL8I9aAUZalUAFeinpRyHHgF+IWU0rOAh34O+IwQ4hiQGpbBKcoSqG6EStTzL16ewNc+VR1IoEQNNQNXopoQYi1QC+xXwVuJNmoGriiKYlBqBq4oimJQKoAriqIYlArgiqIoBqUCuKIoikGpAK4oimJQKoAriqIY1P8DuDzf3v2QEAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方法1：pandas.Series.pct_change\n",
    "\n",
    "pct_change方法直接算好了\"(新-旧)/旧\"的百分比\n",
    "\n",
    "官方文档地址：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.pct_change.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"bWendu_way1_huanbi\"] = df[\"bWendu\"].pct_change(periods=1)\n",
    "df[\"bWendu_way1_tongbi\"] = df[\"bWendu\"].pct_change(periods=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bWendu</th>\n",
       "      <th>bWendu_way1_huanbi</th>\n",
       "      <th>bWendu_way1_tongbi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ymd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>3.322581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-02-28</td>\n",
       "      <td>7.642857</td>\n",
       "      <td>1.300277</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>14.129032</td>\n",
       "      <td>0.848658</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>0.677397</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>29.774194</td>\n",
       "      <td>0.256295</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>30.966667</td>\n",
       "      <td>0.040051</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>31.612903</td>\n",
       "      <td>0.020869</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>30.129032</td>\n",
       "      <td>-0.046939</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>27.866667</td>\n",
       "      <td>-0.075089</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>17.225806</td>\n",
       "      <td>-0.381849</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-11-30</td>\n",
       "      <td>9.566667</td>\n",
       "      <td>-0.444632</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>4.483871</td>\n",
       "      <td>-0.531303</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>1.322581</td>\n",
       "      <td>-0.705036</td>\n",
       "      <td>-0.601942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>4.892857</td>\n",
       "      <td>2.699477</td>\n",
       "      <td>-0.359813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>14.129032</td>\n",
       "      <td>1.887685</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bWendu  bWendu_way1_huanbi  bWendu_way1_tongbi\n",
       "ymd                                                          \n",
       "2017-01-31   3.322581                 NaN                 NaN\n",
       "2017-02-28   7.642857            1.300277                 NaN\n",
       "2017-03-31  14.129032            0.848658                 NaN\n",
       "2017-04-30  23.700000            0.677397                 NaN\n",
       "2017-05-31  29.774194            0.256295                 NaN\n",
       "2017-06-30  30.966667            0.040051                 NaN\n",
       "2017-07-31  31.612903            0.020869                 NaN\n",
       "2017-08-31  30.129032           -0.046939                 NaN\n",
       "2017-09-30  27.866667           -0.075089                 NaN\n",
       "2017-10-31  17.225806           -0.381849                 NaN\n",
       "2017-11-30   9.566667           -0.444632                 NaN\n",
       "2017-12-31   4.483871           -0.531303                 NaN\n",
       "2018-01-31   1.322581           -0.705036           -0.601942\n",
       "2018-02-28   4.892857            2.699477           -0.359813\n",
       "2018-03-31  14.129032            1.887685            0.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方法2：pandas.Series.shift\n",
    "\n",
    "shift用于移动数据，但是保持索引不变\n",
    "\n",
    "官方文档地址：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.shift.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bWendu</th>\n",
       "      <th>bWendu</th>\n",
       "      <th>bWendu</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ymd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>3.322581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-02-28</td>\n",
       "      <td>7.642857</td>\n",
       "      <td>3.322581</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>14.129032</td>\n",
       "      <td>7.642857</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>14.129032</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>29.774194</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>30.966667</td>\n",
       "      <td>29.774194</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>31.612903</td>\n",
       "      <td>30.966667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>30.129032</td>\n",
       "      <td>31.612903</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>27.866667</td>\n",
       "      <td>30.129032</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>17.225806</td>\n",
       "      <td>27.866667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-11-30</td>\n",
       "      <td>9.566667</td>\n",
       "      <td>17.225806</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>4.483871</td>\n",
       "      <td>9.566667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>1.322581</td>\n",
       "      <td>4.483871</td>\n",
       "      <td>3.322581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>4.892857</td>\n",
       "      <td>1.322581</td>\n",
       "      <td>7.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>14.129032</td>\n",
       "      <td>4.892857</td>\n",
       "      <td>14.129032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bWendu     bWendu     bWendu\n",
       "ymd                                        \n",
       "2017-01-31   3.322581        NaN        NaN\n",
       "2017-02-28   7.642857   3.322581        NaN\n",
       "2017-03-31  14.129032   7.642857        NaN\n",
       "2017-04-30  23.700000  14.129032        NaN\n",
       "2017-05-31  29.774194  23.700000        NaN\n",
       "2017-06-30  30.966667  29.774194        NaN\n",
       "2017-07-31  31.612903  30.966667        NaN\n",
       "2017-08-31  30.129032  31.612903        NaN\n",
       "2017-09-30  27.866667  30.129032        NaN\n",
       "2017-10-31  17.225806  27.866667        NaN\n",
       "2017-11-30   9.566667  17.225806        NaN\n",
       "2017-12-31   4.483871   9.566667        NaN\n",
       "2018-01-31   1.322581   4.483871   3.322581\n",
       "2018-02-28   4.892857   1.322581   7.642857\n",
       "2018-03-31  14.129032   4.892857  14.129032"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 见识一下shift做了什么事情\n",
    "# 使用pd.concat合并Series列表变成一个大的df\n",
    "pd.concat(\n",
    "    [df[\"bWendu\"], \n",
    "     df[\"bWendu\"].shift(periods=1), \n",
    "     df[\"bWendu\"].shift(periods=12)],\n",
    "    axis=1\n",
    ").head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 环比\n",
    "series_shift1 = df[\"bWendu\"].shift(periods=1)\n",
    "df[\"bWendu_way2_huanbi\"] = (df[\"bWendu\"]-series_shift1)/series_shift1\n",
    "\n",
    "# 同比\n",
    "series_shift2 = df[\"bWendu\"].shift(periods=12)\n",
    "df[\"bWendu_way2_tongbi\"] = (df[\"bWendu\"]-series_shift2)/series_shift2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bWendu</th>\n",
       "      <th>bWendu_way1_huanbi</th>\n",
       "      <th>bWendu_way1_tongbi</th>\n",
       "      <th>bWendu_way2_huanbi</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>2017-01-31</td>\n",
       "      <td>3.322581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2017-02-28</td>\n",
       "      <td>7.642857</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.300277</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2017-03-31</td>\n",
       "      <td>14.129032</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.848658</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>0.677397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.677397</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>29.774194</td>\n",
       "      <td>0.256295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.256295</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>30.966667</td>\n",
       "      <td>0.040051</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040051</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>31.612903</td>\n",
       "      <td>0.020869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.020869</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>30.129032</td>\n",
       "      <td>-0.046939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.046939</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>27.866667</td>\n",
       "      <td>-0.075089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.075089</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>17.225806</td>\n",
       "      <td>-0.381849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.381849</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-11-30</td>\n",
       "      <td>9.566667</td>\n",
       "      <td>-0.444632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.444632</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>4.483871</td>\n",
       "      <td>-0.531303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.531303</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>1.322581</td>\n",
       "      <td>-0.705036</td>\n",
       "      <td>-0.601942</td>\n",
       "      <td>-0.705036</td>\n",
       "      <td>-0.601942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>4.892857</td>\n",
       "      <td>2.699477</td>\n",
       "      <td>-0.359813</td>\n",
       "      <td>2.699477</td>\n",
       "      <td>-0.359813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>14.129032</td>\n",
       "      <td>1.887685</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.887685</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bWendu  bWendu_way1_huanbi  bWendu_way1_tongbi  \\\n",
       "ymd                                                             \n",
       "2017-01-31   3.322581                 NaN                 NaN   \n",
       "2017-02-28   7.642857            1.300277                 NaN   \n",
       "2017-03-31  14.129032            0.848658                 NaN   \n",
       "2017-04-30  23.700000            0.677397                 NaN   \n",
       "2017-05-31  29.774194            0.256295                 NaN   \n",
       "2017-06-30  30.966667            0.040051                 NaN   \n",
       "2017-07-31  31.612903            0.020869                 NaN   \n",
       "2017-08-31  30.129032           -0.046939                 NaN   \n",
       "2017-09-30  27.866667           -0.075089                 NaN   \n",
       "2017-10-31  17.225806           -0.381849                 NaN   \n",
       "2017-11-30   9.566667           -0.444632                 NaN   \n",
       "2017-12-31   4.483871           -0.531303                 NaN   \n",
       "2018-01-31   1.322581           -0.705036           -0.601942   \n",
       "2018-02-28   4.892857            2.699477           -0.359813   \n",
       "2018-03-31  14.129032            1.887685            0.000000   \n",
       "\n",
       "            bWendu_way2_huanbi  bWendu_way2_tongbi  \n",
       "ymd                                                 \n",
       "2017-01-31                 NaN                 NaN  \n",
       "2017-02-28            1.300277                 NaN  \n",
       "2017-03-31            0.848658                 NaN  \n",
       "2017-04-30            0.677397                 NaN  \n",
       "2017-05-31            0.256295                 NaN  \n",
       "2017-06-30            0.040051                 NaN  \n",
       "2017-07-31            0.020869                 NaN  \n",
       "2017-08-31           -0.046939                 NaN  \n",
       "2017-09-30           -0.075089                 NaN  \n",
       "2017-10-31           -0.381849                 NaN  \n",
       "2017-11-30           -0.444632                 NaN  \n",
       "2017-12-31           -0.531303                 NaN  \n",
       "2018-01-31           -0.705036           -0.601942  \n",
       "2018-02-28            2.699477           -0.359813  \n",
       "2018-03-31            1.887685            0.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方法3. pandas.Series.diff\n",
    "\n",
    "pandas.Series.diff用于新值减去旧值\n",
    "\n",
    "官方文档：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.diff.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "  <tbody>\n",
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       "      <td>2017-01-31</td>\n",
       "      <td>3.322581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-02-28</td>\n",
       "      <td>7.642857</td>\n",
       "      <td>4.320276</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>14.129032</td>\n",
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       "      <td>2017-07-31</td>\n",
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       "      <td>0.646237</td>\n",
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       "    <tr>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>30.129032</td>\n",
       "      <td>-1.483871</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>27.866667</td>\n",
       "      <td>-2.262366</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>17.225806</td>\n",
       "      <td>-10.640860</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-11-30</td>\n",
       "      <td>9.566667</td>\n",
       "      <td>-7.659140</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>4.483871</td>\n",
       "      <td>-5.082796</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>1.322581</td>\n",
       "      <td>-3.161290</td>\n",
       "      <td>-2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>4.892857</td>\n",
       "      <td>3.570276</td>\n",
       "      <td>-2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>14.129032</td>\n",
       "      <td>9.236175</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bWendu     bWendu  bWendu\n",
       "ymd                                     \n",
       "2017-01-31   3.322581        NaN     NaN\n",
       "2017-02-28   7.642857   4.320276     NaN\n",
       "2017-03-31  14.129032   6.486175     NaN\n",
       "2017-04-30  23.700000   9.570968     NaN\n",
       "2017-05-31  29.774194   6.074194     NaN\n",
       "2017-06-30  30.966667   1.192473     NaN\n",
       "2017-07-31  31.612903   0.646237     NaN\n",
       "2017-08-31  30.129032  -1.483871     NaN\n",
       "2017-09-30  27.866667  -2.262366     NaN\n",
       "2017-10-31  17.225806 -10.640860     NaN\n",
       "2017-11-30   9.566667  -7.659140     NaN\n",
       "2017-12-31   4.483871  -5.082796     NaN\n",
       "2018-01-31   1.322581  -3.161290   -2.00\n",
       "2018-02-28   4.892857   3.570276   -2.75\n",
       "2018-03-31  14.129032   9.236175    0.00"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(\n",
    "    [df[\"bWendu\"], \n",
    "     df[\"bWendu\"].diff(periods=1), \n",
    "     df[\"bWendu\"].diff(periods=12)],\n",
    "    axis=1\n",
    ").head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 环比\n",
    "series_diff1 = df[\"bWendu\"].diff(periods=1)\n",
    "df[\"bWendu_way3_huanbi\"] = series_diff1/(df[\"bWendu\"]-series_diff1)\n",
    "\n",
    "# 同比\n",
    "series_diff2 = df[\"bWendu\"].diff(periods=12)\n",
    "df[\"bWendu_way3_tongbi\"] = series_diff2/(df[\"bWendu\"]-series_diff2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bWendu</th>\n",
       "      <th>bWendu_way1_huanbi</th>\n",
       "      <th>bWendu_way1_tongbi</th>\n",
       "      <th>bWendu_way2_huanbi</th>\n",
       "      <th>bWendu_way2_tongbi</th>\n",
       "      <th>bWendu_way3_huanbi</th>\n",
       "      <th>bWendu_way3_tongbi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ymd</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>3.322581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-02-28</td>\n",
       "      <td>7.642857</td>\n",
       "      <td>1.300277</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.300277</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.300277</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>14.129032</td>\n",
       "      <td>0.848658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.848658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.848658</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>0.677397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.677397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.677397</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>29.774194</td>\n",
       "      <td>0.256295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.256295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.256295</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>30.966667</td>\n",
       "      <td>0.040051</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040051</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040051</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>31.612903</td>\n",
       "      <td>0.020869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.020869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.020869</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>30.129032</td>\n",
       "      <td>-0.046939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.046939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.046939</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>27.866667</td>\n",
       "      <td>-0.075089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.075089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.075089</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>17.225806</td>\n",
       "      <td>-0.381849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.381849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.381849</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-11-30</td>\n",
       "      <td>9.566667</td>\n",
       "      <td>-0.444632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.444632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.444632</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>4.483871</td>\n",
       "      <td>-0.531303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.531303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.531303</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-31</td>\n",
       "      <td>1.322581</td>\n",
       "      <td>-0.705036</td>\n",
       "      <td>-0.601942</td>\n",
       "      <td>-0.705036</td>\n",
       "      <td>-0.601942</td>\n",
       "      <td>-0.705036</td>\n",
       "      <td>-0.601942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02-28</td>\n",
       "      <td>4.892857</td>\n",
       "      <td>2.699477</td>\n",
       "      <td>-0.359813</td>\n",
       "      <td>2.699477</td>\n",
       "      <td>-0.359813</td>\n",
       "      <td>2.699477</td>\n",
       "      <td>-0.359813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>14.129032</td>\n",
       "      <td>1.887685</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.887685</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.887685</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bWendu  bWendu_way1_huanbi  bWendu_way1_tongbi  \\\n",
       "ymd                                                             \n",
       "2017-01-31   3.322581                 NaN                 NaN   \n",
       "2017-02-28   7.642857            1.300277                 NaN   \n",
       "2017-03-31  14.129032            0.848658                 NaN   \n",
       "2017-04-30  23.700000            0.677397                 NaN   \n",
       "2017-05-31  29.774194            0.256295                 NaN   \n",
       "2017-06-30  30.966667            0.040051                 NaN   \n",
       "2017-07-31  31.612903            0.020869                 NaN   \n",
       "2017-08-31  30.129032           -0.046939                 NaN   \n",
       "2017-09-30  27.866667           -0.075089                 NaN   \n",
       "2017-10-31  17.225806           -0.381849                 NaN   \n",
       "2017-11-30   9.566667           -0.444632                 NaN   \n",
       "2017-12-31   4.483871           -0.531303                 NaN   \n",
       "2018-01-31   1.322581           -0.705036           -0.601942   \n",
       "2018-02-28   4.892857            2.699477           -0.359813   \n",
       "2018-03-31  14.129032            1.887685            0.000000   \n",
       "\n",
       "            bWendu_way2_huanbi  bWendu_way2_tongbi  bWendu_way3_huanbi  \\\n",
       "ymd                                                                      \n",
       "2017-01-31                 NaN                 NaN                 NaN   \n",
       "2017-02-28            1.300277                 NaN            1.300277   \n",
       "2017-03-31            0.848658                 NaN            0.848658   \n",
       "2017-04-30            0.677397                 NaN            0.677397   \n",
       "2017-05-31            0.256295                 NaN            0.256295   \n",
       "2017-06-30            0.040051                 NaN            0.040051   \n",
       "2017-07-31            0.020869                 NaN            0.020869   \n",
       "2017-08-31           -0.046939                 NaN           -0.046939   \n",
       "2017-09-30           -0.075089                 NaN           -0.075089   \n",
       "2017-10-31           -0.381849                 NaN           -0.381849   \n",
       "2017-11-30           -0.444632                 NaN           -0.444632   \n",
       "2017-12-31           -0.531303                 NaN           -0.531303   \n",
       "2018-01-31           -0.705036           -0.601942           -0.705036   \n",
       "2018-02-28            2.699477           -0.359813            2.699477   \n",
       "2018-03-31            1.887685            0.000000            1.887685   \n",
       "\n",
       "            bWendu_way3_tongbi  \n",
       "ymd                             \n",
       "2017-01-31                 NaN  \n",
       "2017-02-28                 NaN  \n",
       "2017-03-31                 NaN  \n",
       "2017-04-30                 NaN  \n",
       "2017-05-31                 NaN  \n",
       "2017-06-30                 NaN  \n",
       "2017-07-31                 NaN  \n",
       "2017-08-31                 NaN  \n",
       "2017-09-30                 NaN  \n",
       "2017-10-31                 NaN  \n",
       "2017-11-30                 NaN  \n",
       "2017-12-31                 NaN  \n",
       "2018-01-31           -0.601942  \n",
       "2018-02-28           -0.359813  \n",
       "2018-03-31            0.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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