大数据作业初稿

# 导入 pandas 模块
import pandas as pd   

# 设置直接显示图片
%matplotlib inline

stock = pd.read_csv('stock.csv', parse_dates=True, index_col='Date')

stock

<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Open</th>
<th>High</th>
<th>Low</th>
<th>Close</th>
<th>Volume</th>
</tr>
<tr>
<th>Date</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2017-06-01</th>
<td>153.17</td>
<td>153.33</td>
<td>152.22</td>
<td>153.18</td>
<td>16404088</td>
</tr>
<tr>
<th>2017-06-02</th>
<td>153.58</td>
<td>155.45</td>
<td>152.89</td>
<td>155.45</td>
<td>27770715</td>
</tr>
<tr>
<th>2017-06-05</th>
<td>154.34</td>
<td>154.45</td>
<td>153.46</td>
<td>153.93</td>
<td>25331662</td>
</tr>
<tr>
<th>2017-06-06</th>
<td>153.90</td>
<td>155.81</td>
<td>153.78</td>
<td>154.45</td>
<td>26624926</td>
</tr>
<tr>
<th>2017-06-07</th>
<td>155.02</td>
<td>155.98</td>
<td>154.48</td>
<td>155.37</td>
<td>21069647</td>
</tr>
<tr>
<th>2017-06-08</th>
<td>155.25</td>
<td>155.54</td>
<td>154.40</td>
<td>154.99</td>
<td>21250798</td>
</tr>
<tr>
<th>2017-06-09</th>
<td>155.19</td>
<td>155.19</td>
<td>146.02</td>
<td>148.98</td>
<td>64882657</td>
</tr>
<tr>
<th>2017-06-12</th>
<td>145.74</td>
<td>146.09</td>
<td>142.51</td>
<td>145.42</td>
<td>72307330</td>
</tr>
<tr>
<th>2017-06-13</th>
<td>147.16</td>
<td>147.45</td>
<td>145.15</td>
<td>146.59</td>
<td>34165445</td>
</tr>
<tr>
<th>2017-06-14</th>
<td>147.50</td>
<td>147.50</td>
<td>143.84</td>
<td>145.16</td>
<td>31531232</td>
</tr>
<tr>
<th>2017-06-15</th>
<td>143.32</td>
<td>144.48</td>
<td>142.21</td>
<td>144.29</td>
<td>32165373</td>
</tr>
<tr>
<th>2017-06-16</th>
<td>143.78</td>
<td>144.50</td>
<td>142.20</td>
<td>142.27</td>
<td>50361093</td>
</tr>
<tr>
<th>2017-06-19</th>
<td>143.66</td>
<td>146.74</td>
<td>143.66</td>
<td>146.34</td>
<td>32541404</td>
</tr>
<tr>
<th>2017-06-20</th>
<td>146.87</td>
<td>146.87</td>
<td>144.94</td>
<td>145.01</td>
<td>24900073</td>
</tr>
<tr>
<th>2017-06-21</th>
<td>145.52</td>
<td>146.07</td>
<td>144.61</td>
<td>145.87</td>
<td>21265751</td>
</tr>
<tr>
<th>2017-06-22</th>
<td>145.77</td>
<td>146.70</td>
<td>145.12</td>
<td>145.63</td>
<td>19106294</td>
</tr>
<tr>
<th>2017-06-23</th>
<td>145.13</td>
<td>147.16</td>
<td>145.11</td>
<td>146.28</td>
<td>35439389</td>
</tr>
<tr>
<th>2017-06-26</th>
<td>147.17</td>
<td>148.28</td>
<td>145.38</td>
<td>145.82</td>
<td>25692361</td>
</tr>
<tr>
<th>2017-06-27</th>
<td>145.01</td>
<td>146.16</td>
<td>143.62</td>
<td>143.73</td>
<td>24761891</td>
</tr>
<tr>
<th>2017-06-28</th>
<td>144.49</td>
<td>146.11</td>
<td>143.16</td>
<td>145.83</td>
<td>22082432</td>
</tr>
<tr>
<th>2017-06-29</th>
<td>144.71</td>
<td>145.13</td>
<td>142.28</td>
<td>143.68</td>
<td>31499368</td>
</tr>
<tr>
<th>2017-06-30</th>
<td>144.45</td>
<td>144.96</td>
<td>143.78</td>
<td>144.02</td>
<td>23024107</td>
</tr>
</tbody>
</table>
</div>

import pandas as pd
import datetime
import pandas_datareader.data as web

# 设置获取的时间区间
start = datetime.datetime(2017,6,1)
end = datetime.datetime(2017,6,30)

# 从google获取苹果的股票数据
apple = web.DataReader("AAPL", "google", start, end)

---------------------------------------------------------------------------

ModuleNotFoundError                       Traceback (most recent call last)

<ipython-input-10-37ab99df9f31> in <module>()
      1 import pandas as pd
      2 import datetime
----> 3 import pandas_datareader.data as web
      4 
      5 # 设置获取的时间区间


ModuleNotFoundError: No module named 'pandas_datareader'

stock.head()

stock.head(3)

###看尾部5行
stock.tail()

#### 总体信息,没有输括号
stock.info

<bound method DataFrame.info of               Open    High     Low   Close    Volume
Date                                                
2017-06-01  153.17  153.33  152.22  153.18  16404088
2017-06-02  153.58  155.45  152.89  155.45  27770715
2017-06-05  154.34  154.45  153.46  153.93  25331662
2017-06-06  153.90  155.81  153.78  154.45  26624926
2017-06-07  155.02  155.98  154.48  155.37  21069647
2017-06-08  155.25  155.54  154.40  154.99  21250798
2017-06-09  155.19  155.19  146.02  148.98  64882657
2017-06-12  145.74  146.09  142.51  145.42  72307330
2017-06-13  147.16  147.45  145.15  146.59  34165445
2017-06-14  147.50  147.50  143.84  145.16  31531232
2017-06-15  143.32  144.48  142.21  144.29  32165373
2017-06-16  143.78  144.50  142.20  142.27  50361093
2017-06-19  143.66  146.74  143.66  146.34  32541404
2017-06-20  146.87  146.87  144.94  145.01  24900073
2017-06-21  145.52  146.07  144.61  145.87  21265751
2017-06-22  145.77  146.70  145.12  145.63  19106294
2017-06-23  145.13  147.16  145.11  146.28  35439389
2017-06-26  147.17  148.28  145.38  145.82  25692361
2017-06-27  145.01  146.16  143.62  143.73  24761891
2017-06-28  144.49  146.11  143.16  145.83  22082432
2017-06-29  144.71  145.13  142.28  143.68  31499368
2017-06-30  144.45  144.96  143.78  144.02  23024107>

#### 总体信息
stock.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 22 entries, 2017-06-01 to 2017-06-30
Data columns (total 5 columns):
Open      22 non-null float64
High      22 non-null float64
Low       22 non-null float64
Close     22 non-null float64
Volume    22 non-null int64
dtypes: float64(4), int64(1)
memory usage: 1.0 KB

stock.describe()
#### 尝试了不输入圆括号，结果就是显示明细

<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Open</th>
<th>High</th>
<th>Low</th>
<th>Close</th>
<th>Volume</th>
</tr>
</thead>
<tbody>
<tr>
<th>count</th>
<td>22.000000</td>
<td>22.000000</td>
<td>22.000000</td>
<td>22.000000</td>
<td>2.200000e+01</td>
</tr>
<tr>
<th>mean</th>
<td>148.215000</td>
<td>149.088636</td>
<td>146.582727</td>
<td>147.831364</td>
<td>3.109900e+07</td>
</tr>
<tr>
<th>std</th>
<td>4.450871</td>
<td>4.337027</td>
<td>4.507623</td>
<td>4.434315</td>
<td>1.416738e+07</td>
</tr>
<tr>
<th>min</th>
<td>143.320000</td>
<td>144.480000</td>
<td>142.200000</td>
<td>142.270000</td>
<td>1.640409e+07</td>
</tr>
<tr>
<th>25%</th>
<td>144.785000</td>
<td>146.095000</td>
<td>143.630000</td>
<td>145.047500</td>
<td>2.231785e+07</td>
</tr>
<tr>
<th>50%</th>
<td>146.320000</td>
<td>147.015000</td>
<td>145.025000</td>
<td>145.850000</td>
<td>2.615864e+07</td>
</tr>
<tr>
<th>75%</th>
<td>153.477500</td>
<td>154.170000</td>
<td>150.670000</td>
<td>152.130000</td>
<td>3.244740e+07</td>
</tr>
<tr>
<th>max</th>
<td>155.250000</td>
<td>155.980000</td>
<td>154.480000</td>
<td>155.450000</td>
<td>7.230733e+07</td>
</tr>
</tbody>
</table>
</div>

stock

stock.shape

(22, 5)

stock.columns

Index(['Open', 'High', 'Low', 'Close', 'Volume'], dtype='object')

stock.index

DatetimeIndex(['2017-06-01', '2017-06-02', '2017-06-05', '2017-06-06',
               '2017-06-07', '2017-06-08', '2017-06-09', '2017-06-12',
               '2017-06-13', '2017-06-14', '2017-06-15', '2017-06-16',
               '2017-06-19', '2017-06-20', '2017-06-21', '2017-06-22',
               '2017-06-23', '2017-06-26', '2017-06-27', '2017-06-28',
               '2017-06-29', '2017-06-30'],
              dtype='datetime64[ns]', name='Date', freq=None)

stock.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x23bdac3a3c8>

stock.plot(y='Open')

<matplotlib.axes._subplots.AxesSubplot at 0x23bdb08e748>

stock['Close']

Date
2017-06-01    153.18
2017-06-02    155.45
2017-06-05    153.93
2017-06-06    154.45
2017-06-07    155.37
2017-06-08    154.99
2017-06-09    148.98
2017-06-12    145.42
2017-06-13    146.59
2017-06-14    145.16
2017-06-15    144.29
2017-06-16    142.27
2017-06-19    146.34
2017-06-20    145.01
2017-06-21    145.87
2017-06-22    145.63
2017-06-23    146.28
2017-06-26    145.82
2017-06-27    143.73
2017-06-28    145.83
2017-06-29    143.68
2017-06-30    144.02
Name: Close, dtype: float64

stock.Close

Date
2017-06-01    153.18
2017-06-02    155.45
2017-06-05    153.93
2017-06-06    154.45
2017-06-07    155.37
2017-06-08    154.99
2017-06-09    148.98
2017-06-12    145.42
2017-06-13    146.59
2017-06-14    145.16
2017-06-15    144.29
2017-06-16    142.27
2017-06-19    146.34
2017-06-20    145.01
2017-06-21    145.87
2017-06-22    145.63
2017-06-23    146.28
2017-06-26    145.82
2017-06-27    143.73
2017-06-28    145.83
2017-06-29    143.68
2017-06-30    144.02
Name: Close, dtype: float64

stock['Close']['2017-06-01']

153.18000000000001

stock.Close['2017-06-01']

153.18000000000001

stock['Close'][0]

153.18000000000001

stock[['Close']]

<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Close</th>
</tr>
<tr>
<th>Date</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2017-06-01</th>
<td>153.18</td>
</tr>
<tr>
<th>2017-06-02</th>
<td>155.45</td>
</tr>
<tr>
<th>2017-06-05</th>
<td>153.93</td>
</tr>
<tr>
<th>2017-06-06</th>
<td>154.45</td>
</tr>
<tr>
<th>2017-06-07</th>
<td>155.37</td>
</tr>
<tr>
<th>2017-06-08</th>
<td>154.99</td>
</tr>
<tr>
<th>2017-06-09</th>
<td>148.98</td>
</tr>
<tr>
<th>2017-06-12</th>
<td>145.42</td>
</tr>
<tr>
<th>2017-06-13</th>
<td>146.59</td>
</tr>
<tr>
<th>2017-06-14</th>
<td>145.16</td>
</tr>
<tr>
<th>2017-06-15</th>
<td>144.29</td>
</tr>
<tr>
<th>2017-06-16</th>
<td>142.27</td>
</tr>
<tr>
<th>2017-06-19</th>
<td>146.34</td>
</tr>
<tr>
<th>2017-06-20</th>
<td>145.01</td>
</tr>
<tr>
<th>2017-06-21</th>
<td>145.87</td>
</tr>
<tr>
<th>2017-06-22</th>
<td>145.63</td>
</tr>
<tr>
<th>2017-06-23</th>
<td>146.28</td>
</tr>
<tr>
<th>2017-06-26</th>
<td>145.82</td>
</tr>
<tr>
<th>2017-06-27</th>
<td>143.73</td>
</tr>
<tr>
<th>2017-06-28</th>
<td>145.83</td>
</tr>
<tr>
<th>2017-06-29</th>
<td>143.68</td>
</tr>
<tr>
<th>2017-06-30</th>
<td>144.02</td>
</tr>
</tbody>
</table>
</div>

stock[['Open','Close']]

<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Open</th>
<th>Close</th>
</tr>
<tr>
<th>Date</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2017-06-01</th>
<td>153.17</td>
<td>153.18</td>
</tr>
<tr>
<th>2017-06-02</th>
<td>153.58</td>
<td>155.45</td>
</tr>
<tr>
<th>2017-06-05</th>
<td>154.34</td>
<td>153.93</td>
</tr>
<tr>
<th>2017-06-06</th>
<td>153.90</td>
<td>154.45</td>
</tr>
<tr>
<th>2017-06-07</th>
<td>155.02</td>
<td>155.37</td>
</tr>
<tr>
<th>2017-06-08</th>
<td>155.25</td>
<td>154.99</td>
</tr>
<tr>
<th>2017-06-09</th>
<td>155.19</td>
<td>148.98</td>
</tr>
<tr>
<th>2017-06-12</th>
<td>145.74</td>
<td>145.42</td>
</tr>
<tr>
<th>2017-06-13</th>
<td>147.16</td>
<td>146.59</td>
</tr>
<tr>
<th>2017-06-14</th>
<td>147.50</td>
<td>145.16</td>
</tr>
<tr>
<th>2017-06-15</th>
<td>143.32</td>
<td>144.29</td>
</tr>
<tr>
<th>2017-06-16</th>
<td>143.78</td>
<td>142.27</td>
</tr>
<tr>
<th>2017-06-19</th>
<td>143.66</td>
<td>146.34</td>
</tr>
<tr>
<th>2017-06-20</th>
<td>146.87</td>
<td>145.01</td>
</tr>
<tr>
<th>2017-06-21</th>
<td>145.52</td>
<td>145.87</td>
</tr>
<tr>
<th>2017-06-22</th>
<td>145.77</td>
<td>145.63</td>
</tr>
<tr>
<th>2017-06-23</th>
<td>145.13</td>
<td>146.28</td>
</tr>
<tr>
<th>2017-06-26</th>
<td>147.17</td>
<td>145.82</td>
</tr>
<tr>
<th>2017-06-27</th>
<td>145.01</td>
<td>143.73</td>
</tr>
<tr>
<th>2017-06-28</th>
<td>144.49</td>
<td>145.83</td>
</tr>
<tr>
<th>2017-06-29</th>
<td>144.71</td>
<td>143.68</td>
</tr>
<tr>
<th>2017-06-30</th>
<td>144.45</td>
<td>144.02</td>
</tr>
</tbody>
</table>
</div>

stock.loc['2017-06-01', 'Close']

153.18000000000001

stock.loc[:, 'Close']

Date
2017-06-01    153.18
2017-06-02    155.45
2017-06-05    153.93
2017-06-06    154.45
2017-06-07    155.37
2017-06-08    154.99
2017-06-09    148.98
2017-06-12    145.42
2017-06-13    146.59
2017-06-14    145.16
2017-06-15    144.29
2017-06-16    142.27
2017-06-19    146.34
2017-06-20    145.01
2017-06-21    145.87
2017-06-22    145.63
2017-06-23    146.28
2017-06-26    145.82
2017-06-27    143.73
2017-06-28    145.83
2017-06-29    143.68
2017-06-30    144.02
Name: Close, dtype: float64

stock.loc['2017-06-01':'2017-06-05', 'Close']

Date
2017-06-01    153.18
2017-06-02    155.45
2017-06-05    153.93
Name: Close, dtype: float64

stock.loc['2017-06-01':'2017-06-05', 'Open':'Close']

<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Open</th>
<th>High</th>
<th>Low</th>
<th>Close</th>
</tr>
<tr>
<th>Date</th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2017-06-01</th>
<td>153.17</td>
<td>153.33</td>
<td>152.22</td>
<td>153.18</td>
</tr>
<tr>
<th>2017-06-02</th>
<td>153.58</td>
<td>155.45</td>
<td>152.89</td>
<td>155.45</td>
</tr>
<tr>
<th>2017-06-05</th>
<td>154.34</td>
<td>154.45</td>
<td>153.46</td>
<td>153.93</td>
</tr>
</tbody>
</table>
</div>

stock.loc['2017-06-01':'2017-06-05', ['Open', 'Close']]

stock.iloc[0,3]

153.18000000000001

stock.iloc[0:2, 0:3]

stock.iloc[0:2, :]

stock.Volume > 5e7

Date
2017-06-01    False
2017-06-02    False
2017-06-05    False
2017-06-06    False
2017-06-07    False
2017-06-08    False
2017-06-09     True
2017-06-12     True
2017-06-13    False
2017-06-14    False
2017-06-15    False
2017-06-16     True
2017-06-19    False
2017-06-20    False
2017-06-21    False
2017-06-22    False
2017-06-23    False
2017-06-26    False
2017-06-27    False
2017-06-28    False
2017-06-29    False
2017-06-30    False
Name: Volume, dtype: bool

stock[stock.Volume > 5e7]

stock[stock.Close > stock.Open]

stock[(stock.Close > stock.Open) & (stock.Volume > 3e7)]

stock[(stock.Close > stock.Open) | (stock.Volume > 5e7)]

stock['fluctuation'] = stock['High'] - stock['Low']
stock.head()

stock['Close'].shift(1)

Date
2017-06-01       NaN
2017-06-02    153.18
2017-06-05    155.45
2017-06-06    153.93
2017-06-07    154.45
2017-06-08    155.37
2017-06-09    154.99
2017-06-12    148.98
2017-06-13    145.42
2017-06-14    146.59
2017-06-15    145.16
2017-06-16    144.29
2017-06-19    142.27
2017-06-20    146.34
2017-06-21    145.01
2017-06-22    145.87
2017-06-23    145.63
2017-06-26    146.28
2017-06-27    145.82
2017-06-28    143.73
2017-06-29    145.83
2017-06-30    143.68
Name: Close, dtype: float64

stock['Change'] = stock['Close'] - stock['Close'].shift(1)
stock.head()

本课作业

说明：以下问题请编写代码获得结果。

第一题：2017年6月22日当天的收盘价是多少？

##我自己输入的是这条：stock.loc['2017-06-22','close']，结果错了.参照前面的作业，输入后面这条
stock['Close']['2017-06-22']

145.63

第二题：成交量超过7千万的是哪一天？

###我自己先尝试乱输入看看呢:stock['volume'>7e5]
####错得离谱，两点。 1.7千万的科学计数，是7e7； 2.stock.volume>7e7,才是正确的表示，
stock.Volume> 7e7

Date
2017-06-01    False
2017-06-02    False
2017-06-05    False
2017-06-06    False
2017-06-07    False
2017-06-08    False
2017-06-09    False
2017-06-12     True
2017-06-13    False
2017-06-14    False
2017-06-15    False
2017-06-16    False
2017-06-19    False
2017-06-20    False
2017-06-21    False
2017-06-22    False
2017-06-23    False
2017-06-26    False
2017-06-27    False
2017-06-28    False
2017-06-29    False
2017-06-30    False
Name: Volume, dtype: bool

###规则化一下呢,答案是6月12号
stock[stock.Volume> 7e7]

第三题：在2017年6月28日，收盘价减去开盘价是多少？

###我自己尝试着输入看看：stock[stock.Date='2017-06-28',stock.Close-stock.Open]，结果错得离谱
stock['Close']-stock['Open']
###使用笨办法，直接算出所有的来，但是不知道如何筛选。应该可以把这段运算赋值给一个“差值”，然后在从“差值”中筛选

Date
2017-06-01    0.01
2017-06-02    1.87
2017-06-05   -0.41
2017-06-06    0.55
2017-06-07    0.35
2017-06-08   -0.26
2017-06-09   -6.21
2017-06-12   -0.32
2017-06-13   -0.57
2017-06-14   -2.34
2017-06-15    0.97
2017-06-16   -1.51
2017-06-19    2.68
2017-06-20   -1.86
2017-06-21    0.35
2017-06-22   -0.14
2017-06-23    1.15
2017-06-26   -1.35
2017-06-27   -1.28
2017-06-28    1.34
2017-06-29   -1.03
2017-06-30   -0.43
dtype: float64

答案是1.34

第四题：开盘价低于144并且成交量大于5千万那一天的收盘价是多少？
参考此条stock[(stock.Close > stock.Open) & (stock.Volume > 3e7)]

stock[(stock.Open<144)&(stock.Volume>5e7)]

stock['Change'] = stock['Close'] - stock['Close'].shift(1)
stock

<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Open</th>
<th>High</th>
<th>Low</th>
<th>Close</th>
<th>Volume</th>
<th>fluctuation</th>
<th>Change</th>
</tr>
<tr>
<th>Date</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2017-06-01</th>
<td>153.17</td>
<td>153.33</td>
<td>152.22</td>
<td>153.18</td>
<td>16404088</td>
<td>1.11</td>
<td>NaN</td>
</tr>
<tr>
<th>2017-06-02</th>
<td>153.58</td>
<td>155.45</td>
<td>152.89</td>
<td>155.45</td>
<td>27770715</td>
<td>2.56</td>
<td>2.27</td>
</tr>
<tr>
<th>2017-06-05</th>
<td>154.34</td>
<td>154.45</td>
<td>153.46</td>
<td>153.93</td>
<td>25331662</td>
<td>0.99</td>
<td>-1.52</td>
</tr>
<tr>
<th>2017-06-06</th>
<td>153.90</td>
<td>155.81</td>
<td>153.78</td>
<td>154.45</td>
<td>26624926</td>
<td>2.03</td>
<td>0.52</td>
</tr>
<tr>
<th>2017-06-07</th>
<td>155.02</td>
<td>155.98</td>
<td>154.48</td>
<td>155.37</td>
<td>21069647</td>
<td>1.50</td>
<td>0.92</td>
</tr>
<tr>
<th>2017-06-08</th>
<td>155.25</td>
<td>155.54</td>
<td>154.40</td>
<td>154.99</td>
<td>21250798</td>
<td>1.14</td>
<td>-0.38</td>
</tr>
<tr>
<th>2017-06-09</th>
<td>155.19</td>
<td>155.19</td>
<td>146.02</td>
<td>148.98</td>
<td>64882657</td>
<td>9.17</td>
<td>-6.01</td>
</tr>
<tr>
<th>2017-06-12</th>
<td>145.74</td>
<td>146.09</td>
<td>142.51</td>
<td>145.42</td>
<td>72307330</td>
<td>3.58</td>
<td>-3.56</td>
</tr>
<tr>
<th>2017-06-13</th>
<td>147.16</td>
<td>147.45</td>
<td>145.15</td>
<td>146.59</td>
<td>34165445</td>
<td>2.30</td>
<td>1.17</td>
</tr>
<tr>
<th>2017-06-14</th>
<td>147.50</td>
<td>147.50</td>
<td>143.84</td>
<td>145.16</td>
<td>31531232</td>
<td>3.66</td>
<td>-1.43</td>
</tr>
<tr>
<th>2017-06-15</th>
<td>143.32</td>
<td>144.48</td>
<td>142.21</td>
<td>144.29</td>
<td>32165373</td>
<td>2.27</td>
<td>-0.87</td>
</tr>
<tr>
<th>2017-06-16</th>
<td>143.78</td>
<td>144.50</td>
<td>142.20</td>
<td>142.27</td>
<td>50361093</td>
<td>2.30</td>
<td>-2.02</td>
</tr>
<tr>
<th>2017-06-19</th>
<td>143.66</td>
<td>146.74</td>
<td>143.66</td>
<td>146.34</td>
<td>32541404</td>
<td>3.08</td>
<td>4.07</td>
</tr>
<tr>
<th>2017-06-20</th>
<td>146.87</td>
<td>146.87</td>
<td>144.94</td>
<td>145.01</td>
<td>24900073</td>
<td>1.93</td>
<td>-1.33</td>
</tr>
<tr>
<th>2017-06-21</th>
<td>145.52</td>
<td>146.07</td>
<td>144.61</td>
<td>145.87</td>
<td>21265751</td>
<td>1.46</td>
<td>0.86</td>
</tr>
<tr>
<th>2017-06-22</th>
<td>145.77</td>
<td>146.70</td>
<td>145.12</td>
<td>145.63</td>
<td>19106294</td>
<td>1.58</td>
<td>-0.24</td>
</tr>
<tr>
<th>2017-06-23</th>
<td>145.13</td>
<td>147.16</td>
<td>145.11</td>
<td>146.28</td>
<td>35439389</td>
<td>2.05</td>
<td>0.65</td>
</tr>
<tr>
<th>2017-06-26</th>
<td>147.17</td>
<td>148.28</td>
<td>145.38</td>
<td>145.82</td>
<td>25692361</td>
<td>2.90</td>
<td>-0.46</td>
</tr>
<tr>
<th>2017-06-27</th>
<td>145.01</td>
<td>146.16</td>
<td>143.62</td>
<td>143.73</td>
<td>24761891</td>
<td>2.54</td>
<td>-2.09</td>
</tr>
<tr>
<th>2017-06-28</th>
<td>144.49</td>
<td>146.11</td>
<td>143.16</td>
<td>145.83</td>
<td>22082432</td>
<td>2.95</td>
<td>2.10</td>
</tr>
<tr>
<th>2017-06-29</th>
<td>144.71</td>
<td>145.13</td>
<td>142.28</td>
<td>143.68</td>
<td>31499368</td>
<td>2.85</td>
<td>-2.15</td>
</tr>
<tr>
<th>2017-06-30</th>
<td>144.45</td>
<td>144.96</td>
<td>143.78</td>
<td>144.02</td>
<td>23024107</td>
<td>1.18</td>
<td>0.34</td>
</tr>
</tbody>
</table>
</div>

大数据作业 初稿

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