第一次尝试

# 导入 pandas 模块
import pandas as pd   
#pandas是解决数据分析

# 设置直接显示图片
%matplotlib inline
# %为魔法命令。matplotlib为最著名的Python图表绘制扩展库

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

stock
#运行正常。显示出数据。它就是读取的stock.csv这个文件

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<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>

stock.head()
#head是显示头部数据，默认是前五个数据

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stock.head(3)
#（）里面可以设置你需要查找的数量，比如我需要查找前3个的数据，只需要在（）里输入3就可以了

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stock.tail()
#tail是显示尾部的数据，默认就是最后5个数据。（）也是同上面的一样，输入数字，代表是查找对应最后几个的数据。

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stock.shape
#shape表示数据的形状，行和列。本数据就是22行，5列

(22, 5)

stock.columns
#我的理解就是显示文件列的数据标题。

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

stock.index
#我的理解就是显示数据行的标题

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.info()
#我的理解就是像查看文件属性是一样的

<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.info(10)
#后面的（）有何意义呢？

<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()

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</style>
<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.plot()
#绘图，折线图

<matplotlib.axes._subplots.AxesSubplot at 0x9357400>

stock.plot(y='Open')
#()里设置要显示的折线图数据

<matplotlib.axes._subplots.AxesSubplot at 0x44bc9e8>

stock['Close']
#使用[]可以进行数据的索引。搜索某一项的数据

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['Open']

Date
2017-06-01    153.17
2017-06-02    153.58
2017-06-05    154.34
2017-06-06    153.90
2017-06-07    155.02
2017-06-08    155.25
2017-06-09    155.19
2017-06-12    145.74
2017-06-13    147.16
2017-06-14    147.50
2017-06-15    143.32
2017-06-16    143.78
2017-06-19    143.66
2017-06-20    146.87
2017-06-21    145.52
2017-06-22    145.77
2017-06-23    145.13
2017-06-26    147.17
2017-06-27    145.01
2017-06-28    144.49
2017-06-29    144.71
2017-06-30    144.45
Name: Open, dtype: float64

stock.Close
#不使用中括号也可以索引

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']
#可以精准索引到某一项

153.18000000000001

stock['Close']['2017-06-07']

155.37

stock['Close'][0]

153.18000000000001

stock['Close'][5]

154.99000000000001

stock['Close'][2]
#我的理解是后面的中括号里的数字是表示该数字的个数的下一个数的内容

153.93000000000001

stock[['Close']]
#这个跟前面的stock.['Open']差不多，只是显示出来不一样

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<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.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[['Open','Close']]
#搜索Open和Close这两项

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<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[:,'Open']
#跟上面使用中括号是一样的。

Date
2017-06-01    153.17
2017-06-02    153.58
2017-06-05    154.34
2017-06-06    153.90
2017-06-07    155.02
2017-06-08    155.25
2017-06-09    155.19
2017-06-12    145.74
2017-06-13    147.16
2017-06-14    147.50
2017-06-15    143.32
2017-06-16    143.78
2017-06-19    143.66
2017-06-20    146.87
2017-06-21    145.52
2017-06-22    145.77
2017-06-23    145.13
2017-06-26    147.17
2017-06-27    145.01
2017-06-28    144.49
2017-06-29    144.71
2017-06-30    144.45
Name: Open, dtype: float64

stock.loc['2017-06-01':'2017-06-05', 'Close']
#可以搜索某一个时间段的数据

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

stock.loc['2017-06-06':'2017-06-21', 'Close']

Date
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
Name: Close, dtype: float64

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

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

</style>
<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']]

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

#    ：表示的是从一项到另外一项里的所有内容。  ，表示这两项里的内容

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

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

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

stock.iloc[0,3]
#没懂

153.18000000000001

stock.iloc[0:2, 0:3]
#这个应该是查找数据的前2行，前3列

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Open</th>
<th>High</th>
<th>Low</th>
</tr>
<tr>
<th>Date</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>
</tr>
<tr>
<th>2017-06-02</th>
<td>153.58</td>
<td>155.45</td>
<td>152.89</td>
</tr>
</tbody>
</table>
</div>

stock.iloc[0:5, 0:4]
#对了就是的

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}


stock.iloc[0:2, :]
#这个是表示前2行，后面没有数字，则表示全部列吧

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

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.Open > 5e7
#5e7是表示5千万？

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    False
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: Open, dtype: bool

stock[stock.Volume > 5e7]

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

stock[stock.Volume < 5e7]

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

</style>
<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>2017-06-13</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>2017-06-19</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>

stock[stock.Volume> 5e7]
#加中括号应该是精确显示大于5千万的所有数据吧

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

stock.Open>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    False
2017-06-12    False
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: Open, dtype: bool

stock[stock.Close > stock.Open]

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

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

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

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

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

stock[(stock.Close > stock.Open) | (stock.Volume > 5e7)|(stock.Low)>5e7]
#自己弄了一个，感觉没对

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

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

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

stock['Close'].shift(1)
#没懂

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()

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

stock['Change'] = stock['Close'] - stock['Close'].shift(2)
stock.head()
#没懂

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

#第一题：2017年6月22日当天的收盘价是多少？

stock['Close']['2017-06-22']

145.63

#第二题： 成交量超过7千万的是哪一天？

stock.[stock.Volume>5e7]

  File "<ipython-input-62-699eabb80231>", line 1
    stock.[stock.Volume>5e7]
          ^
SyntaxError: invalid syntax

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

#第三题：在2017年6月28日，收盘价减去开盘价是多少？

stock['2017-06-28']['Close'-'Open']
#没做出来

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

KeyError                                  Traceback (most recent call last)

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2392             try:
-> 2393                 return self._engine.get_loc(key)
   2394             except KeyError:


pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5239)()


pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5085)()


pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20405)()


pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20359)()


KeyError: '2017-06-28'


During handling of the above exception, another exception occurred:


KeyError                                  Traceback (most recent call last)

<ipython-input-72-cd81ee331a30> in <module>()
----> 1 stock['2017-06-28']['Close'-'Open']


C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   2060             return self._getitem_multilevel(key)
   2061         else:
-> 2062             return self._getitem_column(key)
   2063 
   2064     def _getitem_column(self, key):


C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in _getitem_column(self, key)
   2067         # get column
   2068         if self.columns.is_unique:
-> 2069             return self._get_item_cache(key)
   2070 
   2071         # duplicate columns & possible reduce dimensionality


C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in _get_item_cache(self, item)
   1532         res = cache.get(item)
   1533         if res is None:
-> 1534             values = self._data.get(item)
   1535             res = self._box_item_values(item, values)
   1536             cache[item] = res


C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals.py in get(self, item, fastpath)
   3588 
   3589             if not isnull(item):
-> 3590                 loc = self.items.get_loc(item)
   3591             else:
   3592                 indexer = np.arange(len(self.items))[isnull(self.items)]


C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   2393                 return self._engine.get_loc(key)
   2394             except KeyError:
-> 2395                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2396 
   2397         indexer = self.get_indexer([key], method=method, tolerance=tolerance)


pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5239)()


pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5085)()


pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20405)()


pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20359)()


KeyError: '2017-06-28'

#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？

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

<div>
<style>
.dataframe thead tr:only-child th {
text-align: right;
}

.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}

#第五题： 课程中计算的涨跌幅change的最大值是多少？

stock.change

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

AttributeError                            Traceback (most recent call last)

<ipython-input-86-499bb8ff3134> in <module>()
----> 1 stock.change.max()


C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
   2968             if name in self._info_axis:
   2969                 return self[name]
-> 2970             return object.__getattribute__(self, name)
   2971 
   2972     def __setattr__(self, name, value):


AttributeError: 'DataFrame' object has no attribute 'change'

stock['Change'] =['stock.Open':'stock.Volume'>max]
#没做来

  File "<ipython-input-89-aa7cc373d074>", line 1
    stock['Change'] =['stock.Open':'stock.Volume'>max]
                                  ^
SyntaxError: invalid syntax

#第一课都走了一遍，还是学到一些东西，但是还是很多东西没有学懂。

最后编辑于：2017.12.10 07:02:16

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