analysis/基金年度收益.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "from configure.settings import DBSelector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mongo = DBSelector().mongo('qq')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "document=mongo['db_stock']['ttjj_rank_2021_12_15']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_list = document.find({},{'_id':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(result_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>type</th>\n",
       "      <th>crawl_date</th>\n",
       "      <th>基金代码</th>\n",
       "      <th>基金简称</th>\n",
       "      <th>缩写</th>\n",
       "      <th>日期</th>\n",
       "      <th>单位净值</th>\n",
       "      <th>累计净值</th>\n",
       "      <th>日增长率(%)</th>\n",
       "      <th>近1周增幅</th>\n",
       "      <th>...</th>\n",
       "      <th>成立来</th>\n",
       "      <th>成立日期</th>\n",
       "      <th>购买手续费折扣</th>\n",
       "      <th>自定义</th>\n",
       "      <th>手续费原价？</th>\n",
       "      <th>手续费折后？</th>\n",
       "      <th>布吉岛1</th>\n",
       "      <th>布吉岛2</th>\n",
       "      <th>布吉岛3</th>\n",
       "      <th>布吉岛4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>指数</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>163114</td>\n",
       "      <td>申万菱信中证环保产业指数(LOF)A</td>\n",
       "      <td>SWLXZZHBCYZSLOFA</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>1.8665</td>\n",
       "      <td>3.1318</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.64</td>\n",
       "      <td>...</td>\n",
       "      <td>172.33</td>\n",
       "      <td>2014-05-30</td>\n",
       "      <td>1</td>\n",
       "      <td>73.4827</td>\n",
       "      <td>1.20%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>1</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>1</td>\n",
       "      <td>100.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>指数</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>010419</td>\n",
       "      <td>申万菱信中证环保产业指数(LOF)C</td>\n",
       "      <td>SWLXZZHBCYZSLOFC</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>1.8141</td>\n",
       "      <td>1.8141</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.64</td>\n",
       "      <td>...</td>\n",
       "      <td>81.41</td>\n",
       "      <td>2020-10-23</td>\n",
       "      <td>1</td>\n",
       "      <td>72.9856</td>\n",
       "      <td></td>\n",
       "      <td>0.00%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>指数</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>010805</td>\n",
       "      <td>东财新能源车A</td>\n",
       "      <td>DCXNYCA</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>1.6324</td>\n",
       "      <td>1.6324</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>...</td>\n",
       "      <td>63.24</td>\n",
       "      <td>2020-12-16</td>\n",
       "      <td>1</td>\n",
       "      <td>63.24</td>\n",
       "      <td>1.20%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>1</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>指数</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>160634</td>\n",
       "      <td>鹏华中证环保产业指数(LOF)</td>\n",
       "      <td>PHZZHBCYZSLOF</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>1.8140</td>\n",
       "      <td>1.2930</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>38.21</td>\n",
       "      <td>2015-06-16</td>\n",
       "      <td>1</td>\n",
       "      <td>70.9709</td>\n",
       "      <td>1.20%</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>1</td>\n",
       "      <td>0.12%</td>\n",
       "      <td>1</td>\n",
       "      <td>87.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>指数</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>010806</td>\n",
       "      <td>东财新能源车C</td>\n",
       "      <td>DCXNYCC</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>1.6259</td>\n",
       "      <td>1.6259</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>...</td>\n",
       "      <td>62.59</td>\n",
       "      <td>2020-12-16</td>\n",
       "      <td>1</td>\n",
       "      <td>62.59</td>\n",
       "      <td></td>\n",
       "      <td>0.00%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  type  crawl_date    基金代码                基金简称                缩写          日期  \\\n",
       "0   指数  2021-12-15  163114  申万菱信中证环保产业指数(LOF)A  SWLXZZHBCYZSLOFA  2021-12-15   \n",
       "1   指数  2021-12-15  010419  申万菱信中证环保产业指数(LOF)C  SWLXZZHBCYZSLOFC  2021-12-15   \n",
       "2   指数  2021-12-15  010805             东财新能源车A           DCXNYCA  2021-12-15   \n",
       "3   指数  2021-12-15  160634     鹏华中证环保产业指数(LOF)     PHZZHBCYZSLOF  2021-12-15   \n",
       "4   指数  2021-12-15  010806             东财新能源车C           DCXNYCC  2021-12-15   \n",
       "\n",
       "     单位净值    累计净值 日增长率(%)  近1周增幅  ...     成立来        成立日期 购买手续费折扣      自定义  \\\n",
       "0  1.8665  3.1318   -0.05   0.64  ...  172.33  2014-05-30       1  73.4827   \n",
       "1  1.8141  1.8141   -0.04   0.64  ...   81.41  2020-10-23       1  72.9856   \n",
       "2  1.6324  1.6324   -1.08  -1.92  ...   63.24  2020-12-16       1    63.24   \n",
       "3  1.8140  1.2930   -0.11   0.33  ...   38.21  2015-06-16       1  70.9709   \n",
       "4  1.6259  1.6259   -1.09  -1.93  ...   62.59  2020-12-16       1    62.59   \n",
       "\n",
       "  手续费原价？ 手续费折后？ 布吉岛1   布吉岛2 布吉岛3    布吉岛4  \n",
       "0  1.20%  0.12%    1  0.12%    1  100.45  \n",
       "1         0.00%                           \n",
       "2  1.20%  0.12%    1  0.12%    1          \n",
       "3  1.20%  0.12%    1  0.12%    1   87.07  \n",
       "4         0.00%                           \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop_duplicates(['基金代码'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11575"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11575 entries, 0 to 11574\n",
      "Data columns (total 23 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   type        11575 non-null  object\n",
      " 1   crawl_date  11575 non-null  object\n",
      " 2   基金代码        11575 non-null  object\n",
      " 3   基金简称        11575 non-null  object\n",
      " 4   缩写          11575 non-null  object\n",
      " 5   日期          11575 non-null  object\n",
      " 6   单位净值        11575 non-null  object\n",
      " 7   累计净值        11575 non-null  object\n",
      " 8   日增长率(%)     11575 non-null  object\n",
      " 9   近1周增幅       11575 non-null  object\n",
      " 10  近1月增幅       11575 non-null  object\n",
      " 11  近3月增幅       11575 non-null  object\n",
      " 12  近6月增幅       11575 non-null  object\n",
      " 13  近1年增幅       11575 non-null  object\n",
      " 14  近2年增幅       11575 non-null  object\n",
      " 15  近3年增幅       11575 non-null  object\n",
      " 16  今年来         11575 non-null  object\n",
      " 17  成立来         11575 non-null  object\n",
      " 18  成立日期        11575 non-null  object\n",
      " 19  购买手续费折扣     11575 non-null  object\n",
      " 20  自定义         11575 non-null  object\n",
      " 21  手续费原价？      11575 non-null  object\n",
      " 22  手续费折后？      11575 non-null  object\n",
      "dtypes: object(23)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convertor(x):\n",
    "    try:\n",
    "        x=float(x)\n",
    "    except:\n",
    "        x=-9999\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df['今年来']=df['今年来'].map(convertor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['布吉岛1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['布吉岛2']\n",
    "del df['布吉岛3']\n",
    "del df['布吉岛4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values(by='今年来',ascending=True).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 换手率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "turnover_db = DBSelector().mongo('qq')['db_stock']['turnover_2021_12_15']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_db = DBSelector().mongo('qq')['db_stock']['ttjj_rank_2021_12_15']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_list=[]\n",
    "for item in turnover_db.find():\n",
    "    item_dict={}\n",
    "    item_dict['code']=item['code']\n",
    "    \n",
    "    rate_list = item['turnover_rate']\n",
    "    item_dict['kind']=item['kind']\n",
    "    turnover_num=None\n",
    "    sum_turnover=[]\n",
    "    for r in rate_list:\n",
    "        # r.get('REPORTDATE')=='2021-06-30':\n",
    "        turnover_num=r.get('STOCKTURNOVER')\n",
    "        sum_turnover.append(turnover_num)\n",
    "    t_mean=np.mean(sum_turnover)    \n",
    "    item_dict['turnover']=t_mean\n",
    "    result_list.append(item_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "turnover_rate_df = pd.DataFrame(result_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9600"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(turnover_rate_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9600 entries, 0 to 9599\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   code      9600 non-null   object \n",
      " 1   kind      9600 non-null   object \n",
      " 2   turnover  5567 non-null   float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 225.1+ KB\n"
     ]
    }
   ],
   "source": [
    "turnover_rate_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>turnover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2146</th>\n",
       "      <td>005856</td>\n",
       "      <td>4338.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2145</th>\n",
       "      <td>005855</td>\n",
       "      <td>4338.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2084</th>\n",
       "      <td>001569</td>\n",
       "      <td>2996.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3786</th>\n",
       "      <td>519097</td>\n",
       "      <td>2706.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>003238</td>\n",
       "      <td>2528.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9595</th>\n",
       "      <td>013573</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9596</th>\n",
       "      <td>013228</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9597</th>\n",
       "      <td>970013</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9598</th>\n",
       "      <td>013428</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9599</th>\n",
       "      <td>014035</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9600 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        code  turnover\n",
       "2146  005856   4338.81\n",
       "2145  005855   4338.81\n",
       "2084  001569   2996.75\n",
       "3786  519097   2706.39\n",
       "3879  003238   2528.70\n",
       "...      ...       ...\n",
       "9595  013573       NaN\n",
       "9596  013228       NaN\n",
       "9597  970013       NaN\n",
       "9598  013428       NaN\n",
       "9599  014035       NaN\n",
       "\n",
       "[9600 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turnover_rate_df.sort_values('turnover',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "turnover_rate_df=turnover_rate_df[turnover_rate_df['turnover'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>kind</th>\n",
       "      <th>turnover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163114</td>\n",
       "      <td>指数</td>\n",
       "      <td>79.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>010419</td>\n",
       "      <td>指数</td>\n",
       "      <td>79.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160634</td>\n",
       "      <td>指数</td>\n",
       "      <td>85.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>164304</td>\n",
       "      <td>指数</td>\n",
       "      <td>91.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>501057</td>\n",
       "      <td>指数</td>\n",
       "      <td>143.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     code kind  turnover\n",
       "0  163114   指数     79.45\n",
       "1  010419   指数     79.45\n",
       "3  160634   指数     85.84\n",
       "5  164304   指数     91.52\n",
       "6  501057   指数    143.73"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turnover_rate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5567"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(turnover_rate_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2146</th>\n",
       "      <td>005856</td>\n",
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       "      <td>4338.81</td>\n",
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       "    <tr>\n",
       "      <th>2145</th>\n",
       "      <td>005855</td>\n",
       "      <td>混合</td>\n",
       "      <td>4338.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2084</th>\n",
       "      <td>001569</td>\n",
       "      <td>混合</td>\n",
       "      <td>2996.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3786</th>\n",
       "      <td>519097</td>\n",
       "      <td>混合</td>\n",
       "      <td>2706.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>003238</td>\n",
       "      <td>混合</td>\n",
       "      <td>2528.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>7319</th>\n",
       "      <td>008874</td>\n",
       "      <td>债券</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4322</th>\n",
       "      <td>167501</td>\n",
       "      <td>lof</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6876</th>\n",
       "      <td>550018</td>\n",
       "      <td>债券</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7205</th>\n",
       "      <td>550019</td>\n",
       "      <td>债券</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4255</th>\n",
       "      <td>160618</td>\n",
       "      <td>lof</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5567 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        code kind  turnover\n",
       "2146  005856   混合   4338.81\n",
       "2145  005855   混合   4338.81\n",
       "2084  001569   混合   2996.75\n",
       "3786  519097   混合   2706.39\n",
       "3879  003238   混合   2528.70\n",
       "...      ...  ...       ...\n",
       "7319  008874   债券      0.15\n",
       "4322  167501  lof      0.12\n",
       "6876  550018   债券      0.10\n",
       "7205  550019   债券      0.10\n",
       "4255  160618  lof      0.05\n",
       "\n",
       "[5567 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turnover_rate_df.sort_values('turnover',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>turnover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5567.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>210.741636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>256.180416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57.045000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>128.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>279.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4338.810000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          turnover\n",
       "count  5567.000000\n",
       "mean    210.741636\n",
       "std     256.180416\n",
       "min       0.050000\n",
       "25%      57.045000\n",
       "50%     128.570000\n",
       "75%     279.850000\n",
       "max    4338.810000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turnover_rate_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "turnover_backup=turnover_rate_df.copy()\n",
    "\n",
    "for index,item in turnover_rate_df[turnover_rate_df['kind']=='债券'].iterrows():\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dup=turnover_rate_df.duplicated('code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>kind</th>\n",
       "      <th>turnover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [code, kind, turnover]\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turnover_rate_df[dup]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "turnover_rate_df=turnover_rate_df[turnover_rate_df['kind']!='债券']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4971"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(turnover_rate_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "turnover_rate_df=turnover_rate_df[turnover_rate_df['kind']!='fof']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "混合      3443\n",
       "指数       805\n",
       "股票       484\n",
       "qdii     113\n",
       "lof       88\n",
       "fof       38\n",
       "Name: kind, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turnover_rate_df['kind'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_code = turnover_rate_df[turnover_rate_df['kind']=='股票']['code'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['005669', '004784', '000729', '001245', '009147', '000828',\n",
       "       '001877', '005927', '005928', '006081', '006080', '008009',\n",
       "       '000756', '005037', '005038', '004925', '005825', '009954',\n",
       "       '006257', '001410', '003853', '003834', '005885', '005983',\n",
       "       '001476', '540008', '001643', '005161', '000409', '001644',\n",
       "       '001790', '001404', '007590', '001576', '000696', '671030',\n",
       "       '005628', '001718', '003145', '004812', '001166', '010128',\n",
       "       '005009', '005629', '160919', '010129', '004813', '001070',\n",
       "       '000592', '001167', '001158', '006926', '009398', '000985',\n",
       "       '006265', '009491', '000309', '005569', '003984', '009492',\n",
       "       '001072', '519975', '005570', '001054', '003985', '001677',\n",
       "       '005457', '005802', '001719', '007137', '004350', '004616',\n",
       "       '003069', '001628', '005310', '007138', '001039', '001825',\n",
       "       '002168', '001208', '005763', '002210', '008177', '005968',\n",
       "       '001097', '000866', '001521', '004640', '005962', '002871',\n",
       "       '003865', '001672', '002872', '005969', '007831', '008445',\n",
       "       '320020', '003624', '007832', '005963', '001917', '003625',\n",
       "       '001028', '007950', '004997', '001975', '000326', '008085',\n",
       "       '006251', '206012', '001956', '008084', '005402', '000628',\n",
       "       '009513', '162208', '010003', '000586', '000549', '009514',\n",
       "       '010004', '005189', '000991', '001725', '005188', '007903',\n",
       "       '001637', '007674', '007685', '001496', '001692', '007675',\n",
       "       '001396', '001616', '008128', '004292', '008129', '000916',\n",
       "       '001528', '002289', '000978', '009891', '003298', '005505',\n",
       "       '001707', '002861', '006478', '003299', '005506', '000803',\n",
       "       '006346', '001645', '006751', '519935', '006347', '000457',\n",
       "       '001520', '003053', '001171', '001749', '009043', '320022',\n",
       "       '003956', '003054', '003416', '006195', '000418', '002980',\n",
       "       '004352', '001050', '000471', '001490', '007952', '001736',\n",
       "       '000893', '001236', '001008', '001541', '006502', '002556',\n",
       "       '001043', '001188', '005495', '004698', '006503', '163110',\n",
       "       '005496', '000867', '000513', '001291', '001126', '340006',\n",
       "       '006692', '008871', '005660', '000594', '008294', '005035',\n",
       "       '005661', '009341', '000697', '008872', '002595', '005328',\n",
       "       '001717', '006693', '005036', '010141', '001714', '000082',\n",
       "       '005635', '009069', '001938', '008295', '009070', '004730',\n",
       "       '008134', '010142', '005960', '008537', '004683', '005826',\n",
       "       '005235', '001036', '004241', '005636', '009476', '000524',\n",
       "       '005236', '008538', '450009', '162107', '005961', '010013',\n",
       "       '009658', '001048', '009557', '000831', '007133', '001186',\n",
       "       '000793', '001726', '007134', '009775', '006803', '376510',\n",
       "       '001409', '001482', '000925', '673090', '001195', '580008',\n",
       "       '007388', '007470', '001705', '001651', '540010', '007471',\n",
       "       '000971', '164205', '007389', '001849', '004716', '001473',\n",
       "       '001583', '519714', '519965', '009126', '009874', '001974',\n",
       "       '003745', '004858', '003230', '004891', '001009', '009127',\n",
       "       '001042', '001542', '004892', '003190', '003231', '001733',\n",
       "       '009106', '007750', '000979', '002229', '009107', '009113',\n",
       "       '003191', '004040', '009875', '005894', '000577', '009876',\n",
       "       '001577', '004041', '002952', '004686', '001416', '006868',\n",
       "       '001277', '001040', '006002', '004403', '008314', '006193',\n",
       "       '006003', '004484', '000996', '005267', '004404', '004485',\n",
       "       '008315', '006194', '006604', '000778', '001421', '360001',\n",
       "       '006605', '001163', '001313', '008736', '001319', '000411',\n",
       "       '005502', '002334', '008618', '008283', '008619', '008737',\n",
       "       '007853', '002335', '001605', '006138', '008234', '003232',\n",
       "       '009805', '005259', '009537', '540006', '001223', '001105',\n",
       "       '003233', '004075', '009538', '005106', '000688', '540009',\n",
       "       '004450', '005662', '519193', '000884', '008303', '001781',\n",
       "       '005663', '007965', '000854', '003634', '008304', '008958',\n",
       "       '007966', '519606', '004606', '001104', '008261', '006675',\n",
       "       '001044', '001766', '002621', '005303', '001685', '009550',\n",
       "       '009240', '000761', '004476', '008166', '009877', '519673',\n",
       "       '005268', '005620', '009162', '008262', '006676', '001579',\n",
       "       '007548', '000913', '005970', '001626', '002697', '005304',\n",
       "       '006603', '001703', '540007', '009551', '167702', '008167',\n",
       "       '005621', '167703', '000711', '001230', '002300', '004851',\n",
       "       '006981', '001162', '008286', '001638', '008264', '399011',\n",
       "       '000955', '009414', '007005', '160133', '008860', '008382',\n",
       "       '009988', '008265', '001178', '005237', '001878', '002387',\n",
       "       '005238', '000746', '006228', '008412', '000960', '007512',\n",
       "       '004098', '009601', '000751', '001047', '009899', '008934',\n",
       "       '005612', '006229', '008413', '009602', '001915', '008768',\n",
       "       '004805', '008935', '004189', '008769', '007523', '000780',\n",
       "       '110022', '007524', '008854', '008107', '008855', '006106',\n",
       "       '009863', '000974', '006818', '002121', '006819', '009265',\n",
       "       '008075', '007287', '004995', '160127', '002332', '001193',\n",
       "       '005777', '007288', '008076', '002333', '006923', '006924',\n",
       "       '006911', '006957', '009017', '009896', '005707', '009897',\n",
       "       '006671', '006781', '160322', '001764', '009733', '009734',\n",
       "       '008709', '009898', '005209', '005210'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_code = turnover_rate_df[turnover_rate_df['kind']=='指数']['code'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(index_code)&set(stock_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_list=[]\n",
    "for item in rank_db.find({},{'_id':0}):\n",
    "    rank_list.append(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_df = pd.DataFrame(rank_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "fund_dup_code = rank_df[rank_df.duplicated('基金代码')]['基金代码'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1975"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fund_dup_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_df_new=rank_df[(rank_df['type']=='股票')|(rank_df['type']=='混合')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7181"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rank_df_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
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       "Empty DataFrame\n",
       "Columns: [type, crawl_date, 基金代码, 基金简称, 缩写, 日期, 单位净值, 累计净值, 日增长率(%), 近1周增幅, 近1月增幅, 近3月增幅, 近6月增幅, 近1年增幅, 近2年增幅, 近3年增幅, 今年来, 成立来, 成立日期, 购买手续费折扣, 自定义, 手续费原价？, 手续费折后？, 布吉岛1, 布吉岛2, 布吉岛3, 布吉岛4]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 27 columns]"
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     },
     "execution_count": 76,
     "metadata": {},
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   ],
   "source": [
    "rank_df_new[rank_df_new.duplicated(['基金代码'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_fund_turnover = pd.merge(rank_df_new,turnover_rate_df,how='inner',left_on='基金代码',right_on='code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "         type  crawl_date   基金简称     缩写     日期   单位净值   累计净值  日增长率(%)  近1周增幅  \\\n",
       "基金代码                                                                           \n",
       "000001  False       False  False  False  False  False  False    False  False   \n",
       "000006  False       False  False  False  False  False  False    False  False   \n",
       "000008  False       False  False  False  False  False  False    False  False   \n",
       "000011  False       False  False  False  False  False  False    False  False   \n",
       "000017  False       False  False  False  False  False  False    False  False   \n",
       "...       ...         ...    ...    ...    ...    ...    ...      ...    ...   \n",
       "970069  False       False  False  False  False  False  False    False  False   \n",
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       "970074  False       False  False  False  False  False  False    False  False   \n",
       "970093  False       False  False  False  False  False  False    False  False   \n",
       "970095  False       False  False  False  False  False  False    False  False   \n",
       "\n",
       "        近1月增幅  ...    成立来   成立日期  购买手续费折扣    自定义  手续费原价？  手续费折后？   布吉岛1  \\\n",
       "基金代码           ...                                                        \n",
       "000001  False  ...  False  False    False  False   False   False  False   \n",
       "000006  False  ...  False  False    False  False   False   False  False   \n",
       "000008  False  ...  False  False    False  False   False   False  False   \n",
       "000011  False  ...  False  False    False  False   False   False  False   \n",
       "000017  False  ...  False  False    False  False   False   False  False   \n",
       "...       ...  ...    ...    ...      ...    ...     ...     ...    ...   \n",
       "970069  False  ...  False  False    False  False   False   False  False   \n",
       "970073  False  ...  False  False    False  False   False   False  False   \n",
       "970074  False  ...  False  False    False  False   False   False  False   \n",
       "970093  False  ...  False  False    False  False   False   False  False   \n",
       "970095  False  ...  False  False    False  False   False   False  False   \n",
       "\n",
       "         布吉岛2   布吉岛3   布吉岛4  \n",
       "基金代码                         \n",
       "000001  False  False  False  \n",
       "000006  False  False  False  \n",
       "000008  False  False  False  \n",
       "000011  False  False  False  \n",
       "000017  False  False  False  \n",
       "...       ...    ...    ...  \n",
       "970069  False  False  False  \n",
       "970073  False  False  False  \n",
       "970074  False  False  False  \n",
       "970093  False  False  False  \n",
       "970095  False  False  False  \n",
       "\n",
       "[7181 rows x 26 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_df_new.groupby('基金代码').count()>1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7181"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rank_df_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4933"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(turnover_rate_df['code'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4933"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(turnover_rate_df['code'].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4793"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rank_fund_turnover)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>type</th>\n",
       "      <th>crawl_date</th>\n",
       "      <th>基金代码</th>\n",
       "      <th>基金简称</th>\n",
       "      <th>缩写</th>\n",
       "      <th>日期</th>\n",
       "      <th>单位净值</th>\n",
       "      <th>累计净值</th>\n",
       "      <th>日增长率(%)</th>\n",
       "      <th>近1周增幅</th>\n",
       "      <th>...</th>\n",
       "      <th>自定义</th>\n",
       "      <th>手续费原价？</th>\n",
       "      <th>手续费折后？</th>\n",
       "      <th>布吉岛1</th>\n",
       "      <th>布吉岛2</th>\n",
       "      <th>布吉岛3</th>\n",
       "      <th>布吉岛4</th>\n",
       "      <th>code</th>\n",
       "      <th>kind</th>\n",
       "      <th>turnover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>股票</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>005669</td>\n",
       "      <td>前海开源公用事业股票</td>\n",
       "      <td>QHKYGYSYGP</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>3.6923</td>\n",
       "      <td>3.6923</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.67</td>\n",
       "      <td>...</td>\n",
       "      <td>145.3355</td>\n",
       "      <td>1.50%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>005669</td>\n",
       "      <td>股票</td>\n",
       "      <td>114.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>股票</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>004784</td>\n",
       "      <td>招商稳健优选股票</td>\n",
       "      <td>ZSWJYXGP</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>3.7029</td>\n",
       "      <td>3.7029</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.47</td>\n",
       "      <td>...</td>\n",
       "      <td>85.6276</td>\n",
       "      <td>1.50%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>004784</td>\n",
       "      <td>股票</td>\n",
       "      <td>338.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>股票</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>000729</td>\n",
       "      <td>建信中小盘先锋股票A</td>\n",
       "      <td>JXZXPXFGPA</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>4.2240</td>\n",
       "      <td>4.2240</td>\n",
       "      <td>0.91</td>\n",
       "      <td>2.20</td>\n",
       "      <td>...</td>\n",
       "      <td>78.0776</td>\n",
       "      <td>1.50%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>1</td>\n",
       "      <td>200.64</td>\n",
       "      <td>000729</td>\n",
       "      <td>股票</td>\n",
       "      <td>631.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>股票</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>001245</td>\n",
       "      <td>工银生态环境股票</td>\n",
       "      <td>GYSTHJGP</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>2.9740</td>\n",
       "      <td>2.9740</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>...</td>\n",
       "      <td>84.1486</td>\n",
       "      <td>1.50%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>1</td>\n",
       "      <td>239.50</td>\n",
       "      <td>001245</td>\n",
       "      <td>股票</td>\n",
       "      <td>235.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>股票</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>009147</td>\n",
       "      <td>建信新能源行业股票</td>\n",
       "      <td>JXXNYHYGP</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>2.8684</td>\n",
       "      <td>2.8684</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>-1.81</td>\n",
       "      <td>...</td>\n",
       "      <td>89.2834</td>\n",
       "      <td>1.50%</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15%</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>009147</td>\n",
       "      <td>股票</td>\n",
       "      <td>88.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  type  crawl_date    基金代码        基金简称          缩写          日期    单位净值  \\\n",
       "0   股票  2021-12-15  005669  前海开源公用事业股票  QHKYGYSYGP  2021-12-15  3.6923   \n",
       "1   股票  2021-12-15  004784    招商稳健优选股票    ZSWJYXGP  2021-12-15  3.7029   \n",
       "2   股票  2021-12-15  000729  建信中小盘先锋股票A  JXZXPXFGPA  2021-12-15  4.2240   \n",
       "3   股票  2021-12-15  001245    工银生态环境股票    GYSTHJGP  2021-12-15  2.9740   \n",
       "4   股票  2021-12-15  009147   建信新能源行业股票   JXXNYHYGP  2021-12-15  2.8684   \n",
       "\n",
       "     累计净值 日增长率(%)  近1周增幅  ...       自定义 手续费原价？ 手续费折后？ 布吉岛1   布吉岛2 布吉岛3  \\\n",
       "0  3.6923    0.89   1.67  ...  145.3355  1.50%  0.15%    1  0.15%    1   \n",
       "1  3.7029   -0.10   0.47  ...   85.6276  1.50%  0.15%    1  0.15%    1   \n",
       "2  4.2240    0.91   2.20  ...   78.0776  1.50%  0.15%    1  0.15%    1   \n",
       "3  2.9740   -0.77  -1.03  ...   84.1486  1.50%  0.15%    1  0.15%    1   \n",
       "4  2.8684   -1.08  -1.81  ...   89.2834  1.50%  0.15%    1  0.15%    1   \n",
       "\n",
       "     布吉岛4    code kind turnover  \n",
       "0          005669   股票   114.20  \n",
       "1          004784   股票   338.84  \n",
       "2  200.64  000729   股票   631.63  \n",
       "3  239.50  001245   股票   235.40  \n",
       "4          009147   股票    88.56  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_fund_turnover.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_fund_turnover_simple=rank_fund_turnover[['type','基金代码','基金简称','今年来','成立来','turnover','累计净值']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>type</th>\n",
       "      <th>基金代码</th>\n",
       "      <th>基金简称</th>\n",
       "      <th>今年来</th>\n",
       "      <th>成立来</th>\n",
       "      <th>turnover</th>\n",
       "      <th>累计净值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>股票</td>\n",
       "      <td>005669</td>\n",
       "      <td>前海开源公用事业股票</td>\n",
       "      <td>122.24</td>\n",
       "      <td>269.23</td>\n",
       "      <td>114.20</td>\n",
       "      <td>3.6923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>股票</td>\n",
       "      <td>004784</td>\n",
       "      <td>招商稳健优选股票</td>\n",
       "      <td>69.90</td>\n",
       "      <td>270.29</td>\n",
       "      <td>338.84</td>\n",
       "      <td>3.7029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>股票</td>\n",
       "      <td>000729</td>\n",
       "      <td>建信中小盘先锋股票A</td>\n",
       "      <td>67.22</td>\n",
       "      <td>322.40</td>\n",
       "      <td>631.63</td>\n",
       "      <td>4.2240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>股票</td>\n",
       "      <td>001245</td>\n",
       "      <td>工银生态环境股票</td>\n",
       "      <td>66.24</td>\n",
       "      <td>197.40</td>\n",
       "      <td>235.40</td>\n",
       "      <td>2.9740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>股票</td>\n",
       "      <td>009147</td>\n",
       "      <td>建信新能源行业股票</td>\n",
       "      <td>65.94</td>\n",
       "      <td>186.84</td>\n",
       "      <td>88.56</td>\n",
       "      <td>2.8684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  type    基金代码        基金简称     今年来     成立来  turnover    累计净值\n",
       "0   股票  005669  前海开源公用事业股票  122.24  269.23    114.20  3.6923\n",
       "1   股票  004784    招商稳健优选股票   69.90  270.29    338.84  3.7029\n",
       "2   股票  000729  建信中小盘先锋股票A   67.22  322.40    631.63  4.2240\n",
       "3   股票  001245    工银生态环境股票   66.24  197.40    235.40  2.9740\n",
       "4   股票  009147   建信新能源行业股票   65.94  186.84     88.56  2.8684"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_fund_turnover_simple.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4793 entries, 0 to 4792\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   type      4793 non-null   object \n",
      " 1   基金代码      4793 non-null   object \n",
      " 2   基金简称      4793 non-null   object \n",
      " 3   今年来       4793 non-null   object \n",
      " 4   成立来       4793 non-null   object \n",
      " 5   turnover  4793 non-null   float64\n",
      " 6   累计净值      4793 non-null   object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 299.6+ KB\n"
     ]
    }
   ],
   "source": [
    "rank_fund_turnover_simple.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-80-3a6386e959a2>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rank_fund_turnover_simple['今年来']=rank_fund_turnover_simple['今年来'].map(convertor)\n",
      "<ipython-input-80-3a6386e959a2>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rank_fund_turnover_simple['成立来']=rank_fund_turnover_simple['成立来'].map(convertor)\n"
     ]
    }
   ],
   "source": [
    "rank_fund_turnover_simple['今年来']=rank_fund_turnover_simple['今年来'].map(convertor)\n",
    "rank_fund_turnover_simple['成立来']=rank_fund_turnover_simple['成立来'].map(convertor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4793 entries, 0 to 4792\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   type      4793 non-null   object \n",
      " 1   基金代码      4793 non-null   object \n",
      " 2   基金简称      4793 non-null   object \n",
      " 3   今年来       4793 non-null   float64\n",
      " 4   成立来       4793 non-null   float64\n",
      " 5   turnover  4793 non-null   float64\n",
      " 6   累计净值      4793 non-null   object \n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 299.6+ KB\n"
     ]
    }
   ],
   "source": [
    "rank_fund_turnover_simple.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows',None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_fund_turnover_simple.sort_values(by='成立来',ascending=False).head(50).to_excel('111.xlsx',encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      50.000000\n",
       "mean      190.172400\n",
       "std       192.410815\n",
       "min        17.960000\n",
       "25%        79.732500\n",
       "50%       117.380000\n",
       "75%       243.692500\n",
       "max      1202.400000\n",
       "Name: turnover, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_fund_turnover_simple.sort_values(by='成立来',ascending=False).head(50)['turnover'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>基金代码</th>\n",
       "      <th>基金简称</th>\n",
       "      <th>今年来</th>\n",
       "      <th>成立来</th>\n",
       "      <th>turnover</th>\n",
       "      <th>累计净值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>股票</td>\n",
       "      <td>164818</td>\n",
       "      <td>工银中证传媒指数(LOF)A</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-72.25</td>\n",
       "      <td>99.48</td>\n",
       "      <td>0.2898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>股票</td>\n",
       "      <td>160639</td>\n",
       "      <td>鹏华中证高铁产业指数(LOF)</td>\n",
       "      <td>5.34</td>\n",
       "      <td>-62.43</td>\n",
       "      <td>133.19</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>股票</td>\n",
       "      <td>160135</td>\n",
       "      <td>南方中证高铁产业指数(LOF)</td>\n",
       "      <td>4.80</td>\n",
       "      <td>-54.22</td>\n",
       "      <td>141.80</td>\n",
       "      <td>1.0137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>股票</td>\n",
       "      <td>162413</td>\n",
       "      <td>华宝中证1000指数</td>\n",
       "      <td>20.19</td>\n",
       "      <td>-50.22</td>\n",
       "      <td>48.37</td>\n",
       "      <td>0.4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>股票</td>\n",
       "      <td>161123</td>\n",
       "      <td>易方达中证万得并购重组(LOF)</td>\n",
       "      <td>11.27</td>\n",
       "      <td>-45.25</td>\n",
       "      <td>144.18</td>\n",
       "      <td>0.5358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>股票</td>\n",
       "      <td>168203</td>\n",
       "      <td>中融国证钢铁行业指数(LOF)</td>\n",
       "      <td>45.16</td>\n",
       "      <td>-42.13</td>\n",
       "      <td>429.07</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>股票</td>\n",
       "      <td>164908</td>\n",
       "      <td>交银中证环境治理(LOF)A</td>\n",
       "      <td>18.90</td>\n",
       "      <td>-41.62</td>\n",
       "      <td>282.21</td>\n",
       "      <td>0.5838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>股票</td>\n",
       "      <td>165523</td>\n",
       "      <td>信诚中证信息安全指数(LOF)A</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-39.21</td>\n",
       "      <td>107.45</td>\n",
       "      <td>0.6175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4117</th>\n",
       "      <td>混合</td>\n",
       "      <td>001239</td>\n",
       "      <td>长盛国企改革混合</td>\n",
       "      <td>1.56</td>\n",
       "      <td>-35.10</td>\n",
       "      <td>288.23</td>\n",
       "      <td>0.6490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>股票</td>\n",
       "      <td>502013</td>\n",
       "      <td>长盛中证申万一带一路指数(LOF)</td>\n",
       "      <td>5.39</td>\n",
       "      <td>-34.49</td>\n",
       "      <td>103.28</td>\n",
       "      <td>1.4976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>股票</td>\n",
       "      <td>501031</td>\n",
       "      <td>汇添富中证环境治理指数C</td>\n",
       "      <td>20.91</td>\n",
       "      <td>-33.79</td>\n",
       "      <td>446.01</td>\n",
       "      <td>0.6621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>股票</td>\n",
       "      <td>501030</td>\n",
       "      <td>汇添富中证环境治理指数A</td>\n",
       "      <td>21.37</td>\n",
       "      <td>-33.22</td>\n",
       "      <td>446.01</td>\n",
       "      <td>0.6678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>股票</td>\n",
       "      <td>165524</td>\n",
       "      <td>信诚中证智能家居指数(LOF)A</td>\n",
       "      <td>8.41</td>\n",
       "      <td>-33.04</td>\n",
       "      <td>79.12</td>\n",
       "      <td>0.6571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>股票</td>\n",
       "      <td>160420</td>\n",
       "      <td>华安创业板50指数</td>\n",
       "      <td>21.89</td>\n",
       "      <td>-31.02</td>\n",
       "      <td>70.33</td>\n",
       "      <td>0.6898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>股票</td>\n",
       "      <td>161030</td>\n",
       "      <td>富国中证体育产业指数(LOF)A</td>\n",
       "      <td>5.74</td>\n",
       "      <td>-30.46</td>\n",
       "      <td>279.48</td>\n",
       "      <td>0.6870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>股票</td>\n",
       "      <td>161027</td>\n",
       "      <td>富国中证全指证券公司指数(LOF)A</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-29.34</td>\n",
       "      <td>49.32</td>\n",
       "      <td>0.6580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>股票</td>\n",
       "      <td>001899</td>\n",
       "      <td>东海社会安全</td>\n",
       "      <td>-2.87</td>\n",
       "      <td>-29.00</td>\n",
       "      <td>32.04</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>股票</td>\n",
       "      <td>162412</td>\n",
       "      <td>华宝医疗ETF联接A</td>\n",
       "      <td>-13.86</td>\n",
       "      <td>-27.86</td>\n",
       "      <td>98.93</td>\n",
       "      <td>0.6909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>股票</td>\n",
       "      <td>001241</td>\n",
       "      <td>国寿安保中证500ETF联接</td>\n",
       "      <td>15.52</td>\n",
       "      <td>-27.68</td>\n",
       "      <td>50.74</td>\n",
       "      <td>0.7233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>股票</td>\n",
       "      <td>006817</td>\n",
       "      <td>泰康港股通地产指数C</td>\n",
       "      <td>-20.46</td>\n",
       "      <td>-27.66</td>\n",
       "      <td>260.72</td>\n",
       "      <td>0.7234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1039</th>\n",
       "      <td>股票</td>\n",
       "      <td>160629</td>\n",
       "      <td>鹏华中证传媒指数(LOF)</td>\n",
       "      <td>-4.37</td>\n",
       "      <td>-27.65</td>\n",
       "      <td>46.28</td>\n",
       "      <td>1.2140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>股票</td>\n",
       "      <td>160633</td>\n",
       "      <td>鹏华券商A</td>\n",
       "      <td>-1.89</td>\n",
       "      <td>-27.46</td>\n",
       "      <td>55.78</td>\n",
       "      <td>0.6790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>股票</td>\n",
       "      <td>006816</td>\n",
       "      <td>泰康港股通地产指数A</td>\n",
       "      <td>-20.16</td>\n",
       "      <td>-26.95</td>\n",
       "      <td>260.72</td>\n",
       "      <td>0.7305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>股票</td>\n",
       "      <td>161720</td>\n",
       "      <td>招商中证全指证券公司指数(LOF)A</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-26.22</td>\n",
       "      <td>70.44</td>\n",
       "      <td>0.7749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>股票</td>\n",
       "      <td>161036</td>\n",
       "      <td>富国中证娱乐主题指数增强(LOF)A</td>\n",
       "      <td>-10.57</td>\n",
       "      <td>-25.88</td>\n",
       "      <td>58.15</td>\n",
       "      <td>0.7412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>股票</td>\n",
       "      <td>001409</td>\n",
       "      <td>工银互联网加股票</td>\n",
       "      <td>9.06</td>\n",
       "      <td>-25.40</td>\n",
       "      <td>87.19</td>\n",
       "      <td>0.7460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>股票</td>\n",
       "      <td>167503</td>\n",
       "      <td>安信中证一带一路主题指数</td>\n",
       "      <td>34.13</td>\n",
       "      <td>-24.94</td>\n",
       "      <td>88.54</td>\n",
       "      <td>0.8060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4819</th>\n",
       "      <td>混合</td>\n",
       "      <td>519651</td>\n",
       "      <td>银河转型混合</td>\n",
       "      <td>-9.18</td>\n",
       "      <td>-24.80</td>\n",
       "      <td>237.80</td>\n",
       "      <td>0.7520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>股票</td>\n",
       "      <td>010593</td>\n",
       "      <td>南方医药创新股票C</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-23.77</td>\n",
       "      <td>59.35</td>\n",
       "      <td>0.7623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>股票</td>\n",
       "      <td>010592</td>\n",
       "      <td>南方医药创新股票A</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-23.41</td>\n",
       "      <td>59.35</td>\n",
       "      <td>0.7659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5307</th>\n",
       "      <td>混合</td>\n",
       "      <td>011163</td>\n",
       "      <td>博时港股通领先趋势混合C</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-21.75</td>\n",
       "      <td>187.56</td>\n",
       "      <td>0.7825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>股票</td>\n",
       "      <td>008709</td>\n",
       "      <td>银河龙头股票</td>\n",
       "      <td>-22.86</td>\n",
       "      <td>-21.71</td>\n",
       "      <td>254.63</td>\n",
       "      <td>0.7829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3241</th>\n",
       "      <td>混合</td>\n",
       "      <td>001184</td>\n",
       "      <td>易方达新常态灵活配置混合</td>\n",
       "      <td>6.65</td>\n",
       "      <td>-21.40</td>\n",
       "      <td>63.24</td>\n",
       "      <td>0.7860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>股票</td>\n",
       "      <td>009898</td>\n",
       "      <td>民生加银医药健康股票</td>\n",
       "      <td>-23.54</td>\n",
       "      <td>-21.24</td>\n",
       "      <td>244.58</td>\n",
       "      <td>0.7876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>混合</td>\n",
       "      <td>011162</td>\n",
       "      <td>博时港股通领先趋势混合A</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-21.22</td>\n",
       "      <td>187.56</td>\n",
       "      <td>0.7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>股票</td>\n",
       "      <td>010204</td>\n",
       "      <td>中银港股通优势成长股票</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-21.00</td>\n",
       "      <td>104.44</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>股票</td>\n",
       "      <td>007357</td>\n",
       "      <td>创金合信港股通量化股票C</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-21.00</td>\n",
       "      <td>528.40</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5169</th>\n",
       "      <td>混合</td>\n",
       "      <td>011158</td>\n",
       "      <td>弘毅远方港股通智选领航混合C</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>-20.80</td>\n",
       "      <td>301.19</td>\n",
       "      <td>0.7920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168</th>\n",
       "      <td>股票</td>\n",
       "      <td>004643</td>\n",
       "      <td>南方房地产ETF联接C</td>\n",
       "      <td>-8.68</td>\n",
       "      <td>-20.75</td>\n",
       "      <td>5.81</td>\n",
       "      <td>0.7925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>股票</td>\n",
       "      <td>009734</td>\n",
       "      <td>创金合信港股通大消费精选股票C</td>\n",
       "      <td>-22.84</td>\n",
       "      <td>-20.54</td>\n",
       "      <td>245.15</td>\n",
       "      <td>0.7946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     type    基金代码                基金简称      今年来    成立来  turnover    累计净值\n",
       "944    股票  164818      工银中证传媒指数(LOF)A    -2.01 -72.25     99.48  0.2898\n",
       "656    股票  160639     鹏华中证高铁产业指数(LOF)     5.34 -62.43    133.19    0.38\n",
       "678    股票  160135     南方中证高铁产业指数(LOF)     4.80 -54.22    141.80  1.0137\n",
       "270    股票  162413          华宝中证1000指数    20.19 -50.22     48.37  0.4850\n",
       "492    股票  161123    易方达中证万得并购重组(LOF)    11.27 -45.25    144.18  0.5358\n",
       "43     股票  168203     中融国证钢铁行业指数(LOF)    45.16 -42.13    429.07    1.35\n",
       "295    股票  164908      交银中证环境治理(LOF)A    18.90 -41.62    282.21  0.5838\n",
       "856    股票  165523    信诚中证信息安全指数(LOF)A    -0.32 -39.21    107.45  0.6175\n",
       "4117   混合  001239            长盛国企改革混合     1.56 -35.10    288.23  0.6490\n",
       "652    股票  502013   长盛中证申万一带一路指数(LOF)     5.39 -34.49    103.28  1.4976\n",
       "254    股票  501031        汇添富中证环境治理指数C    20.91 -33.79    446.01  0.6621\n",
       "243    股票  501030        汇添富中证环境治理指数A    21.37 -33.22    446.01  0.6678\n",
       "570    股票  165524    信诚中证智能家居指数(LOF)A     8.41 -33.04     79.12  0.6571\n",
       "234    股票  160420           华安创业板50指数    21.89 -31.02     70.33  0.6898\n",
       "636    股票  161030    富国中证体育产业指数(LOF)A     5.74 -30.46    279.48  0.6870\n",
       "922    股票  161027  富国中证全指证券公司指数(LOF)A    -1.67 -29.34     49.32  0.6580\n",
       "984    股票  001899              东海社会安全    -2.87 -29.00     32.04    0.71\n",
       "1249   股票  162412          华宝医疗ETF联接A   -13.86 -27.86     98.93  0.6909\n",
       "388    股票  001241      国寿安保中证500ETF联接    15.52 -27.68     50.74  0.7233\n",
       "1275   股票  006817          泰康港股通地产指数C   -20.46 -27.66    260.72  0.7234\n",
       "1039   股票  160629       鹏华中证传媒指数(LOF)    -4.37 -27.65     46.28  1.2140\n",
       "934    股票  160633               鹏华券商A    -1.89 -27.46     55.78  0.6790\n",
       "1273   股票  006816          泰康港股通地产指数A   -20.16 -26.95    260.72  0.7305\n",
       "950    股票  161720  招商中证全指证券公司指数(LOF)A    -2.08 -26.22     70.44  0.7749\n",
       "1200   股票  161036  富国中证娱乐主题指数增强(LOF)A   -10.57 -25.88     58.15  0.7412\n",
       "558    股票  001409            工银互联网加股票     9.06 -25.40     87.19  0.7460\n",
       "91     股票  167503        安信中证一带一路主题指数    34.13 -24.94     88.54  0.8060\n",
       "4819   混合  519651              银河转型混合    -9.18 -24.80    237.80  0.7520\n",
       "1328   股票  010593           南方医药创新股票C -9999.00 -23.77     59.35  0.7623\n",
       "1321   股票  010592           南方医药创新股票A -9999.00 -23.41     59.35  0.7659\n",
       "5307   混合  011163        博时港股通领先趋势混合C -9999.00 -21.75    187.56  0.7825\n",
       "1280   股票  008709              银河龙头股票   -22.86 -21.71    254.63  0.7829\n",
       "3241   混合  001184        易方达新常态灵活配置混合     6.65 -21.40     63.24  0.7860\n",
       "1281   股票  009898          民生加银医药健康股票   -23.54 -21.24    244.58  0.7876\n",
       "5108   混合  011162        博时港股通领先趋势混合A -9999.00 -21.22    187.56  0.7878\n",
       "1314   股票  010204         中银港股通优势成长股票 -9999.00 -21.00    104.44    0.79\n",
       "1287   股票  007357        创金合信港股通量化股票C -9999.00 -21.00    528.40    0.79\n",
       "5169   混合  011158      弘毅远方港股通智选领航混合C -9999.00 -20.80    301.19  0.7920\n",
       "1168   股票  004643         南方房地产ETF联接C    -8.68 -20.75      5.81  0.7925\n",
       "1279   股票  009734     创金合信港股通大消费精选股票C   -22.84 -20.54    245.15  0.7946"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_fund_turnover_simple.sort_values(by='成立来',ascending=True).head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_fund_turnover_simple.sort_values(by='成立来',ascending=True).head(50).to_excel('222.xlsx',encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     50.000000\n",
       "mean     170.341600\n",
       "std      136.754242\n",
       "min        5.810000\n",
       "25%       60.322500\n",
       "50%      121.350000\n",
       "75%      253.402500\n",
       "max      528.400000\n",
       "Name: turnover, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_fund_turnover_simple.sort_values(by='成立来',ascending=True).head(50)['turnover'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_fund_turnover_simple_new=rank_fund_turnover_simple[rank_fund_turnover_simple['今年来']!=-9999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     50.000000\n",
       "mean     164.931800\n",
       "std      162.702669\n",
       "min        3.500000\n",
       "25%       56.372500\n",
       "50%      101.380000\n",
       "75%      245.150000\n",
       "max      715.890000\n",
       "Name: turnover, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_fund_turnover_simple_new.sort_values(by='成立来',ascending=True).head(50)['turnover'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_fund_turnover_simple_new.sort_values(by='成立来',ascending=True).head(50).to_excel(\"333.xlsx\",encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\xda'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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