{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from clickhouse_driver import Client\n",
    "#client = Client(host='localhost',password=\"click123!\",settings={\"use_numpy\":True})\n",
    "client = Client(host='localhost',password=\"click123!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = client.query_dataframe(\"select * from stock_cn where code=%(code)s order by date\", {\"code\":\"sh.600050\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>date</th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>adjustflag</th>\n",
       "      <th>turn</th>\n",
       "      <th>tradestatus</th>\n",
       "      <th>pctChg</th>\n",
       "      <th>isST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-01-04</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.80</td>\n",
       "      <td>121344910</td>\n",
       "      <td>346683264.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.866845</td>\n",
       "      <td>1</td>\n",
       "      <td>2.142855</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2.86</td>\n",
       "      <td>110247291</td>\n",
       "      <td>319527360.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.696112</td>\n",
       "      <td>1</td>\n",
       "      <td>1.398608</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-01-06</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.90</td>\n",
       "      <td>96796753</td>\n",
       "      <td>282116896.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.489181</td>\n",
       "      <td>1</td>\n",
       "      <td>0.344827</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-01-09</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.91</td>\n",
       "      <td>83990367</td>\n",
       "      <td>244001568.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.292159</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-01-10</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.91</td>\n",
       "      <td>40008102</td>\n",
       "      <td>115096856.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.615509</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.687284</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3638</th>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>4.68</td>\n",
       "      <td>4.69</td>\n",
       "      <td>4.62</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.69</td>\n",
       "      <td>79328390</td>\n",
       "      <td>368504352.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.259800</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.066100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3639</th>\n",
       "      <td>2020-12-22</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>4.62</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.54</td>\n",
       "      <td>4.64</td>\n",
       "      <td>113513761</td>\n",
       "      <td>520809376.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.371700</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.155200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3640</th>\n",
       "      <td>2020-12-23</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.55</td>\n",
       "      <td>4.49</td>\n",
       "      <td>4.51</td>\n",
       "      <td>4.54</td>\n",
       "      <td>93335737</td>\n",
       "      <td>421419616.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.305600</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.660800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3641</th>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.51</td>\n",
       "      <td>4.44</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.51</td>\n",
       "      <td>76268133</td>\n",
       "      <td>340610144.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.249800</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.330400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3642</th>\n",
       "      <td>2020-12-25</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.49</td>\n",
       "      <td>4.43</td>\n",
       "      <td>4.49</td>\n",
       "      <td>4.45</td>\n",
       "      <td>44819735</td>\n",
       "      <td>200330960.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.146800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.898900</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3643 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date       code  open  high   low  close  preclose     volume  \\\n",
       "0    2006-01-04  sh.600050  2.80  2.89  2.78   2.86      2.80  121344910   \n",
       "1    2006-01-05  sh.600050  2.86  2.92  2.85   2.90      2.86  110247291   \n",
       "2    2006-01-06  sh.600050  2.90  2.94  2.87   2.91      2.90   96796753   \n",
       "3    2006-01-09  sh.600050  2.91  2.93  2.88   2.91      2.91   83990367   \n",
       "4    2006-01-10  sh.600050  2.91  2.91  2.85   2.89      2.91   40008102   \n",
       "...         ...        ...   ...   ...   ...    ...       ...        ...   \n",
       "3638 2020-12-21  sh.600050  4.68  4.69  4.62   4.64      4.69   79328390   \n",
       "3639 2020-12-22  sh.600050  4.62  4.64  4.53   4.54      4.64  113513761   \n",
       "3640 2020-12-23  sh.600050  4.53  4.55  4.49   4.51      4.54   93335737   \n",
       "3641 2020-12-24  sh.600050  4.50  4.51  4.44   4.45      4.51   76268133   \n",
       "3642 2020-12-25  sh.600050  4.45  4.49  4.43   4.49      4.45   44819735   \n",
       "\n",
       "           amount  adjustflag      turn  tradestatus    pctChg  isST  \n",
       "0     346683264.0           3  1.866845            1  2.142855     0  \n",
       "1     319527360.0           3  1.696112            1  1.398608     0  \n",
       "2     282116896.0           3  1.489181            1  0.344827     0  \n",
       "3     244001568.0           3  1.292159            1  0.000000     0  \n",
       "4     115096856.0           3  0.615509            1 -0.687284     0  \n",
       "...           ...         ...       ...          ...       ...   ...  \n",
       "3638  368504352.0           3  0.259800            1 -1.066100     0  \n",
       "3639  520809376.0           3  0.371700            1 -2.155200     0  \n",
       "3640  421419616.0           3  0.305600            1 -0.660800     0  \n",
       "3641  340610144.0           3  0.249800            1 -1.330400     0  \n",
       "3642  200330960.0           3  0.146800            1  0.898900     0  \n",
       "\n",
       "[3643 rows x 14 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "result['date'] = pd.to_datetime(result['date'])\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2006-01-04\n",
       "1    2006-01-05\n",
       "2    2006-01-06\n",
       "3    2006-01-09\n",
       "4    2006-01-10\n",
       "5    2006-01-11\n",
       "6    2006-01-12\n",
       "7    2006-01-13\n",
       "8    2006-01-16\n",
       "9    2006-01-17\n",
       "10   2006-01-18\n",
       "11   2006-01-19\n",
       "12   2006-01-20\n",
       "13   2006-01-23\n",
       "14   2006-01-24\n",
       "15   2006-01-25\n",
       "16   2006-02-06\n",
       "17   2006-02-07\n",
       "18   2006-02-08\n",
       "19   2006-02-09\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = result['date'][:20]\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.80\n",
       "1     2.86\n",
       "2     2.90\n",
       "3     2.91\n",
       "4     2.91\n",
       "5     2.89\n",
       "6     2.83\n",
       "7     2.85\n",
       "8     2.84\n",
       "9     2.76\n",
       "10    2.75\n",
       "11    2.82\n",
       "12    2.85\n",
       "13    2.85\n",
       "14    2.83\n",
       "15    2.80\n",
       "16    2.84\n",
       "17    2.82\n",
       "18    2.72\n",
       "19    2.74\n",
       "Name: open, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openPrice = result['open'][:20]\n",
    "openPrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>adjustflag</th>\n",
       "      <th>turn</th>\n",
       "      <th>tradestatus</th>\n",
       "      <th>pctChg</th>\n",
       "      <th>isST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-01-04</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.80</td>\n",
       "      <td>121344910</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2.86</td>\n",
       "      <td>110247291</td>\n",
       "      <td>319527360.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.696112</td>\n",
       "      <td>1</td>\n",
       "      <td>1.398608</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-01-06</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.90</td>\n",
       "      <td>96796753</td>\n",
       "      <td>282116896.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.489181</td>\n",
       "      <td>1</td>\n",
       "      <td>0.344827</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006-01-09</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.91</td>\n",
       "      <td>83990367</td>\n",
       "      <td>244001568.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.292159</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006-01-10</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.91</td>\n",
       "      <td>40008102</td>\n",
       "      <td>115096856.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.615509</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.687284</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006-01-11</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.89</td>\n",
       "      <td>72812575</td>\n",
       "      <td>208591280.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.120193</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.730110</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006-01-12</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.84</td>\n",
       "      <td>63487193</td>\n",
       "      <td>181334960.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.976726</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2006-01-13</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.90</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.84</td>\n",
       "      <td>41028464</td>\n",
       "      <td>116972664.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.631207</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2006-01-16</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.84</td>\n",
       "      <td>67341655</td>\n",
       "      <td>188411488.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.036025</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.464787</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2006-01-17</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.73</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.77</td>\n",
       "      <td>53713708</td>\n",
       "      <td>148803136.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.826365</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.722021</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2006-01-18</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.75</td>\n",
       "      <td>87429754</td>\n",
       "      <td>246842592.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.345073</td>\n",
       "      <td>1</td>\n",
       "      <td>2.545452</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2006-01-19</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.82</td>\n",
       "      <td>74806906</td>\n",
       "      <td>212886336.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.150875</td>\n",
       "      <td>1</td>\n",
       "      <td>1.063829</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2006-01-20</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.79</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.85</td>\n",
       "      <td>91228551</td>\n",
       "      <td>257900592.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.403516</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2006-01-23</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.85</td>\n",
       "      <td>62699933</td>\n",
       "      <td>177741232.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.964614</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.701754</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2006-01-24</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.83</td>\n",
       "      <td>77417400</td>\n",
       "      <td>216744336.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.191037</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.060070</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2006-01-25</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.79</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.80</td>\n",
       "      <td>69115442</td>\n",
       "      <td>196750688.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.063314</td>\n",
       "      <td>1</td>\n",
       "      <td>1.785713</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2006-02-06</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.78</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.85</td>\n",
       "      <td>143182016</td>\n",
       "      <td>401863840.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.202800</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.052631</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2006-02-07</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.69</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2.82</td>\n",
       "      <td>254229177</td>\n",
       "      <td>697083968.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.911218</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.546096</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2006-02-08</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2.68</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2.72</td>\n",
       "      <td>181753445</td>\n",
       "      <td>492369920.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.796207</td>\n",
       "      <td>1</td>\n",
       "      <td>0.735293</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2006-02-09</td>\n",
       "      <td>sh.600050</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.68</td>\n",
       "      <td>2.74</td>\n",
       "      <td>174169047</td>\n",
       "      <td>467068832.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.679524</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.189779</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date       code  open  high   low  close  preclose     volume  \\\n",
       "0   2006-01-04  sh.600050  2.80  2.89  2.78   2.86      2.80  121344910   \n",
       "1   2006-01-05  sh.600050  2.86  2.92  2.85   2.90      2.86  110247291   \n",
       "2   2006-01-06  sh.600050  2.90  2.94  2.87   2.91      2.90   96796753   \n",
       "3   2006-01-09  sh.600050  2.91  2.93  2.88   2.91      2.91   83990367   \n",
       "4   2006-01-10  sh.600050  2.91  2.91  2.85   2.89      2.91   40008102   \n",
       "5   2006-01-11  sh.600050  2.89  2.91  2.83   2.84      2.89   72812575   \n",
       "6   2006-01-12  sh.600050  2.83  2.91  2.82   2.84      2.84   63487193   \n",
       "7   2006-01-13  sh.600050  2.85  2.90  2.83   2.84      2.84   41028464   \n",
       "8   2006-01-16  sh.600050  2.84  2.84  2.77   2.77      2.84   67341655   \n",
       "9   2006-01-17  sh.600050  2.76  2.81  2.73   2.75      2.77   53713708   \n",
       "10  2006-01-18  sh.600050  2.75  2.85  2.75   2.82      2.75   87429754   \n",
       "11  2006-01-19  sh.600050  2.82  2.88  2.81   2.85      2.82   74806906   \n",
       "12  2006-01-20  sh.600050  2.85  2.87  2.79   2.85      2.85   91228551   \n",
       "13  2006-01-23  sh.600050  2.85  2.88  2.80   2.83      2.85   62699933   \n",
       "14  2006-01-24  sh.600050  2.83  2.84  2.77   2.80      2.83   77417400   \n",
       "15  2006-01-25  sh.600050  2.80  2.88  2.79   2.85      2.80   69115442   \n",
       "16  2006-02-06  sh.600050  2.84  2.85  2.78   2.82      2.85  143182016   \n",
       "17  2006-02-07  sh.600050  2.82  2.82  2.69   2.72      2.82  254229177   \n",
       "18  2006-02-08  sh.600050  2.72  2.74  2.68   2.74      2.72  181753445   \n",
       "19  2006-02-09  sh.600050  2.74  2.74  2.65   2.68      2.74  174169047   \n",
       "\n",
       "         amount  adjustflag      turn  tradestatus    pctChg  isST  \n",
       "0   346683264.0           3  1.866845            1  2.142855     0  \n",
       "1   319527360.0           3  1.696112            1  1.398608     0  \n",
       "2   282116896.0           3  1.489181            1  0.344827     0  \n",
       "3   244001568.0           3  1.292159            1  0.000000     0  \n",
       "4   115096856.0           3  0.615509            1 -0.687284     0  \n",
       "5   208591280.0           3  1.120193            1 -1.730110     0  \n",
       "6   181334960.0           3  0.976726            1  0.000000     0  \n",
       "7   116972664.0           3  0.631207            1  0.000000     0  \n",
       "8   188411488.0           3  1.036025            1 -2.464787     0  \n",
       "9   148803136.0           3  0.826365            1 -0.722021     0  \n",
       "10  246842592.0           3  1.345073            1  2.545452     0  \n",
       "11  212886336.0           3  1.150875            1  1.063829     0  \n",
       "12  257900592.0           3  1.403516            1  0.000000     0  \n",
       "13  177741232.0           3  0.964614            1 -0.701754     0  \n",
       "14  216744336.0           3  1.191037            1 -1.060070     0  \n",
       "15  196750688.0           3  1.063314            1  1.785713     0  \n",
       "16  401863840.0           3  2.202800            1 -1.052631     0  \n",
       "17  697083968.0           3  3.911218            1 -3.546096     0  \n",
       "18  492369920.0           3  2.796207            1  0.735293     0  \n",
       "19  467068832.0           3  2.679524            1 -2.189779     0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>open</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.80</td>\n",
       "      <td>2006-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.86</td>\n",
       "      <td>2006-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.90</td>\n",
       "      <td>2006-01-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.91</td>\n",
       "      <td>2006-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.91</td>\n",
       "      <td>2006-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.89</td>\n",
       "      <td>2006-01-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.83</td>\n",
       "      <td>2006-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.85</td>\n",
       "      <td>2006-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.84</td>\n",
       "      <td>2006-01-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.76</td>\n",
       "      <td>2006-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.75</td>\n",
       "      <td>2006-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.82</td>\n",
       "      <td>2006-01-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.85</td>\n",
       "      <td>2006-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.85</td>\n",
       "      <td>2006-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.83</td>\n",
       "      <td>2006-01-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.80</td>\n",
       "      <td>2006-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.84</td>\n",
       "      <td>2006-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.82</td>\n",
       "      <td>2006-02-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.72</td>\n",
       "      <td>2006-02-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.74</td>\n",
       "      <td>2006-02-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    open       date\n",
       "0   2.80 2006-01-04\n",
       "1   2.86 2006-01-05\n",
       "2   2.90 2006-01-06\n",
       "3   2.91 2006-01-09\n",
       "4   2.91 2006-01-10\n",
       "5   2.89 2006-01-11\n",
       "6   2.83 2006-01-12\n",
       "7   2.85 2006-01-13\n",
       "8   2.84 2006-01-16\n",
       "9   2.76 2006-01-17\n",
       "10  2.75 2006-01-18\n",
       "11  2.82 2006-01-19\n",
       "12  2.85 2006-01-20\n",
       "13  2.85 2006-01-23\n",
       "14  2.83 2006-01-24\n",
       "15  2.80 2006-01-25\n",
       "16  2.84 2006-02-06\n",
       "17  2.82 2006-02-07\n",
       "18  2.72 2006-02-08\n",
       "19  2.74 2006-02-09"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[[\"open\",\"date\"]][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3643 entries, 0 to 3642\n",
      "Data columns (total 14 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   date         3643 non-null   datetime64[ns]\n",
      " 1   code         3643 non-null   object        \n",
      " 2   open         3643 non-null   float64       \n",
      " 3   high         3643 non-null   float64       \n",
      " 4   low          3643 non-null   float64       \n",
      " 5   close        3643 non-null   float64       \n",
      " 6   preclose     3643 non-null   float64       \n",
      " 7   volume       3643 non-null   int64         \n",
      " 8   amount       3643 non-null   float64       \n",
      " 9   adjustflag   3643 non-null   int64         \n",
      " 10  turn         3643 non-null   float64       \n",
      " 11  tradestatus  3643 non-null   int64         \n",
      " 12  pctChg       3643 non-null   float64       \n",
      " 13  isST         3643 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(8), int64(4), object(1)\n",
      "memory usage: 398.6+ KB\n"
     ]
    }
   ],
   "source": [
    "result.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f87264670>,\n",
       " <matplotlib.lines.Line2D at 0x7f872646d0>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(result[[\"open\",\"date\"]][:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = result['date'][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2006-01-04\n",
       "1    2006-01-05\n",
       "2    2006-01-06\n",
       "3    2006-01-09\n",
       "4    2006-01-10\n",
       "5    2006-01-11\n",
       "6    2006-01-12\n",
       "7    2006-01-13\n",
       "8    2006-01-16\n",
       "9    2006-01-17\n",
       "10   2006-01-18\n",
       "11   2006-01-19\n",
       "12   2006-01-20\n",
       "13   2006-01-23\n",
       "14   2006-01-24\n",
       "15   2006-01-25\n",
       "16   2006-02-06\n",
       "17   2006-02-07\n",
       "18   2006-02-08\n",
       "19   2006-02-09\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f872110d0>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(rotation=60)\n",
    "plt.plot(dates,np.arange(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'series1':[1,3,4,3,5],\n",
    "       'series2':[2,3,5,2,4],\n",
    "       'series3':[3,2,3,1,3]}\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>series1</th>\n",
       "      <th>series2</th>\n",
       "      <th>series3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   series1  series2  series3\n",
       "0        1        2        3\n",
       "1        3        3        2\n",
       "2        4        5        3\n",
       "3        3        2        1\n",
       "4        5        4        3"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f849c3610>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axis([0,5,0,7])\n",
    "plt.plot(x,df)\n",
    "plt.legend(data,loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = np.random.randint(0,100,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([6., 4., 9., 6., 4., 7., 2., 4., 5., 7., 3., 2., 2., 5., 5., 8., 9.,\n",
       "        5., 5., 2.]),\n",
       " array([ 0.  ,  4.95,  9.9 , 14.85, 19.8 , 24.75, 29.7 , 34.65, 39.6 ,\n",
       "        44.55, 49.5 , 54.45, 59.4 , 64.35, 69.3 , 74.25, 79.2 , 84.15,\n",
       "        89.1 , 94.05, 99.  ]),\n",
       " <BarContainer object of 20 artists>)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pop,bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = np.arange(5)\n",
    "values = [10,7,11,1,5]\n",
    "plt.bar(index, values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}