我有一个产生以下内容的数据框a：
a = instruments['corn'].contracts().reset_index()
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Date</th>
<th>Change</th>
<th>Contract</th>
<th>High</th>
<th>Last</th>
<th>Low</th>
<th>Open</th>
<th>Prev. Day Open Interest</th>
<th>Settle</th>
<th>Volatility</th>
<th>Volume</th>
<th>Expiration</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1979-03-22</td>
<td>0.0</td>
<td>198005</td>
<td>273.50</td>
<td>0.0</td>
<td>273.00</td>
<td>273.50</td>
<td>1390.0</td>
<td>273.25</td>
<td>0.000000</td>
<td>1390.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>1</th>
<td>1979-03-23</td>
<td>0.0</td>
<td>198005</td>
<td>273.25</td>
<td>0.0</td>
<td>272.50</td>
<td>273.25</td>
<td>1435.0</td>
<td>272.50</td>
<td>0.000000</td>
<td>45.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>2</th>
<td>1979-03-26</td>
<td>0.0</td>
<td>198005</td>
<td>274.50</td>
<td>0.0</td>
<td>273.25</td>
<td>273.50</td>
<td>1580.0</td>
<td>274.50</td>
<td>0.000000</td>
<td>195.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>3</th>
<td>1979-03-27</td>
<td>0.0</td>
<td>198005</td>
<td>274.50</td>
<td>0.0</td>
<td>273.50</td>
<td>273.50</td>
<td>1710.0</td>
<td>274.50</td>
<td>0.000000</td>
<td>375.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>4</th>
<td>1979-03-28</td>
<td>0.0</td>
<td>198005</td>
<td>277.75</td>
<td>0.0</td>
<td>275.00</td>
<td>275.00</td>
<td>2365.0</td>
<td>277.75</td>
<td>0.000000</td>
<td>795.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>5</th>
<td>1979-03-29</td>
<td>0.0</td>
<td>198005</td>
<td>279.00</td>
<td>0.0</td>
<td>277.00</td>
<td>277.00</td>
<td>3130.0</td>
<td>277.00</td>
<td>0.000000</td>
<td>1890.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>6</th>
<td>1979-03-30</td>
<td>0.0</td>
<td>198005</td>
<td>278.50</td>
<td>0.0</td>
<td>277.00</td>
<td>277.00</td>
<td>3550.0</td>
<td>278.50</td>
<td>0.000000</td>
<td>620.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>7</th>
<td>1979-04-02</td>
<td>0.0</td>
<td>198005</td>
<td>281.00</td>
<td>0.0</td>
<td>279.00</td>
<td>279.00</td>
<td>4550.0</td>
<td>279.75</td>
<td>0.000000</td>
<td>1100.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>8</th>
<td>1979-04-03</td>
<td>0.0</td>
<td>198005</td>
<td>279.50</td>
<td>0.0</td>
<td>277.25</td>
<td>279.50</td>
<td>4685.0</td>
<td>278.25</td>
<td>0.000000</td>
<td>355.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>9</th>
<td>1979-04-04</td>
<td>0.0</td>
<td>198005</td>
<td>278.75</td>
<td>0.0</td>
<td>278.00</td>
<td>278.00</td>
<td>5095.0</td>
<td>278.50</td>
<td>0.000000</td>
<td>670.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>10</th>
<td>1979-04-05</td>
<td>0.0</td>
<td>198005</td>
<td>279.25</td>
<td>0.0</td>
<td>278.50</td>
<td>278.50</td>
<td>5325.0</td>
<td>279.00</td>
<td>0.000000</td>
<td>230.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>11</th>
<td>1979-04-06</td>
<td>0.0</td>
<td>198005</td>
<td>280.00</td>
<td>0.0</td>
<td>278.00</td>
<td>278.50</td>
<td>5380.0</td>
<td>280.00</td>
<td>0.000000</td>
<td>395.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>12</th>
<td>1979-04-09</td>
<td>0.0</td>
<td>198005</td>
<td>281.50</td>
<td>0.0</td>
<td>279.50</td>
<td>279.50</td>
<td>5925.0</td>
<td>280.50</td>
<td>0.000000</td>
<td>630.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>13</th>
<td>1979-04-10</td>
<td>0.0</td>
<td>198005</td>
<td>280.00</td>
<td>0.0</td>
<td>278.75</td>
<td>280.00</td>
<td>6075.0</td>
<td>280.00</td>
<td>0.000000</td>
<td>365.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>14</th>
<td>1979-04-11</td>
<td>0.0</td>
<td>198005</td>
<td>281.00</td>
<td>0.0</td>
<td>279.00</td>
<td>280.00</td>
<td>5755.0</td>
<td>280.50</td>
<td>0.000000</td>
<td>1520.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>15</th>
<td>1979-04-12</td>
<td>0.0</td>
<td>198005</td>
<td>280.25</td>
<td>0.0</td>
<td>278.25</td>
<td>280.25</td>
<td>5735.0</td>
<td>278.25</td>
<td>0.000000</td>
<td>320.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>16</th>
<td>1979-04-16</td>
<td>0.0</td>
<td>198005</td>
<td>277.25</td>
<td>0.0</td>
<td>275.75</td>
<td>276.00</td>
<td>6110.0</td>
<td>276.50</td>
<td>0.000000</td>
<td>860.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>17</th>
<td>1979-04-17</td>
<td>0.0</td>
<td>198005</td>
<td>278.75</td>
<td>0.0</td>
<td>276.75</td>
<td>276.75</td>
<td>6555.0</td>
<td>278.75</td>
<td>0.000000</td>
<td>965.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>18</th>
<td>1979-04-18</td>
<td>0.0</td>
<td>198005</td>
<td>278.50</td>
<td>0.0</td>
<td>277.25</td>
<td>278.00</td>
<td>6630.0</td>
<td>278.00</td>
<td>0.000000</td>
<td>120.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>19</th>
<td>1979-04-19</td>
<td>0.0</td>
<td>198005</td>
<td>278.00</td>
<td>0.0</td>
<td>276.25</td>
<td>277.25</td>
<td>6795.0</td>
<td>277.00</td>
<td>0.000000</td>
<td>1315.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>20</th>
<td>1979-04-20</td>
<td>0.0</td>
<td>198005</td>
<td>277.00</td>
<td>0.0</td>
<td>275.75</td>
<td>276.25</td>
<td>6910.0</td>
<td>277.00</td>
<td>0.000000</td>
<td>255.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>21</th>
<td>1979-04-23</td>
<td>0.0</td>
<td>198005</td>
<td>278.50</td>
<td>0.0</td>
<td>276.25</td>
<td>276.25</td>
<td>8580.0</td>
<td>278.50</td>
<td>0.000000</td>
<td>2290.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>22</th>
<td>1979-04-24</td>
<td>0.0</td>
<td>198005</td>
<td>282.00</td>
<td>0.0</td>
<td>279.00</td>
<td>279.50</td>
<td>9160.0</td>
<td>282.00</td>
<td>0.000000</td>
<td>790.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>23</th>
<td>1979-04-25</td>
<td>0.0</td>
<td>198005</td>
<td>282.50</td>
<td>0.0</td>
<td>280.50</td>
<td>280.75</td>
<td>10500.0</td>
<td>281.25</td>
<td>0.000000</td>
<td>1870.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>24</th>
<td>1979-04-26</td>
<td>0.0</td>
<td>198005</td>
<td>283.00</td>
<td>0.0</td>
<td>279.75</td>
<td>280.75</td>
<td>10780.0</td>
<td>282.00</td>
<td>0.000000</td>
<td>1950.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>25</th>
<td>1979-04-27</td>
<td>0.0</td>
<td>198005</td>
<td>284.75</td>
<td>0.0</td>
<td>282.25</td>
<td>283.00</td>
<td>10485.0</td>
<td>282.25</td>
<td>0.000000</td>
<td>1880.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>26</th>
<td>1979-04-30</td>
<td>0.0</td>
<td>198005</td>
<td>287.25</td>
<td>0.0</td>
<td>282.50</td>
<td>282.75</td>
<td>11765.0</td>
<td>287.25</td>
<td>0.000000</td>
<td>5070.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>27</th>
<td>1979-05-01</td>
<td>0.0</td>
<td>198005</td>
<td>289.00</td>
<td>0.0</td>
<td>286.50</td>
<td>287.00</td>
<td>13000.0</td>
<td>288.25</td>
<td>0.000000</td>
<td>2360.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>28</th>
<td>1979-05-02</td>
<td>0.0</td>
<td>198005</td>
<td>295.00</td>
<td>0.0</td>
<td>289.00</td>
<td>289.00</td>
<td>14730.0</td>
<td>290.25</td>
<td>0.000000</td>
<td>3200.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>29</th>
<td>1979-05-03</td>
<td>0.0</td>
<td>198005</td>
<td>291.75</td>
<td>0.0</td>
<td>285.00</td>
<td>291.75</td>
<td>15100.0</td>
<td>287.00</td>
<td>0.000000</td>
<td>1680.0</td>
<td>1980-05-14</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>115809</th>
<td>1990-08-08</td>
<td>0.0</td>
<td>199009</td>
<td>253.75</td>
<td>0.0</td>
<td>251.75</td>
<td>253.75</td>
<td>161630.0</td>
<td>252.25</td>
<td>0.015012</td>
<td>38160.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115810</th>
<td>1990-08-09</td>
<td>0.0</td>
<td>199009</td>
<td>252.25</td>
<td>0.0</td>
<td>250.00</td>
<td>251.50</td>
<td>159955.0</td>
<td>250.25</td>
<td>0.014661</td>
<td>30105.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115811</th>
<td>1990-08-10</td>
<td>0.0</td>
<td>199009</td>
<td>254.00</td>
<td>0.0</td>
<td>249.75</td>
<td>252.50</td>
<td>159195.0</td>
<td>250.25</td>
<td>0.014339</td>
<td>39585.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115812</th>
<td>1990-08-13</td>
<td>0.0</td>
<td>199009</td>
<td>249.50</td>
<td>0.0</td>
<td>244.00</td>
<td>249.50</td>
<td>158425.0</td>
<td>245.25</td>
<td>0.014102</td>
<td>43025.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115813</th>
<td>1990-08-14</td>
<td>0.0</td>
<td>199009</td>
<td>245.50</td>
<td>0.0</td>
<td>243.75</td>
<td>244.25</td>
<td>155785.0</td>
<td>245.50</td>
<td>0.014326</td>
<td>40915.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115814</th>
<td>1990-08-15</td>
<td>0.0</td>
<td>199009</td>
<td>247.75</td>
<td>0.0</td>
<td>244.50</td>
<td>246.25</td>
<td>154925.0</td>
<td>247.50</td>
<td>0.014211</td>
<td>36445.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115815</th>
<td>1990-08-16</td>
<td>0.0</td>
<td>199009</td>
<td>250.00</td>
<td>0.0</td>
<td>247.50</td>
<td>248.00</td>
<td>153660.0</td>
<td>249.00</td>
<td>0.014025</td>
<td>44395.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115816</th>
<td>1990-08-17</td>
<td>0.0</td>
<td>199009</td>
<td>254.00</td>
<td>0.0</td>
<td>247.75</td>
<td>248.75</td>
<td>152610.0</td>
<td>250.75</td>
<td>0.013697</td>
<td>49635.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115817</th>
<td>1990-08-20</td>
<td>0.0</td>
<td>199009</td>
<td>254.00</td>
<td>0.0</td>
<td>251.00</td>
<td>251.00</td>
<td>146950.0</td>
<td>252.25</td>
<td>0.013579</td>
<td>36695.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115818</th>
<td>1990-08-21</td>
<td>0.0</td>
<td>199009</td>
<td>253.75</td>
<td>0.0</td>
<td>250.25</td>
<td>252.00</td>
<td>144820.0</td>
<td>251.75</td>
<td>0.013266</td>
<td>51935.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115819</th>
<td>1990-08-22</td>
<td>0.0</td>
<td>199009</td>
<td>254.75</td>
<td>0.0</td>
<td>251.50</td>
<td>252.50</td>
<td>141515.0</td>
<td>253.75</td>
<td>0.012918</td>
<td>50210.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115820</th>
<td>1990-08-23</td>
<td>0.0</td>
<td>199009</td>
<td>260.00</td>
<td>0.0</td>
<td>255.00</td>
<td>255.00</td>
<td>138385.0</td>
<td>259.00</td>
<td>0.012822</td>
<td>50155.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115821</th>
<td>1990-08-24</td>
<td>0.0</td>
<td>199009</td>
<td>260.00</td>
<td>0.0</td>
<td>257.25</td>
<td>258.00</td>
<td>121575.0</td>
<td>257.75</td>
<td>0.012794</td>
<td>38135.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115822</th>
<td>1990-08-27</td>
<td>0.0</td>
<td>199009</td>
<td>256.50</td>
<td>0.0</td>
<td>251.50</td>
<td>256.00</td>
<td>112135.0</td>
<td>252.00</td>
<td>0.012563</td>
<td>34850.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115823</th>
<td>1990-08-28</td>
<td>0.0</td>
<td>199009</td>
<td>254.00</td>
<td>0.0</td>
<td>251.00</td>
<td>251.00</td>
<td>104110.0</td>
<td>253.75</td>
<td>0.012990</td>
<td>44680.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115824</th>
<td>1990-08-29</td>
<td>0.0</td>
<td>199009</td>
<td>255.50</td>
<td>0.0</td>
<td>249.75</td>
<td>254.75</td>
<td>91055.0</td>
<td>250.00</td>
<td>0.013190</td>
<td>64930.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115825</th>
<td>1990-08-30</td>
<td>0.0</td>
<td>199009</td>
<td>249.50</td>
<td>0.0</td>
<td>245.00</td>
<td>249.25</td>
<td>77335.0</td>
<td>245.25</td>
<td>0.013716</td>
<td>73035.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115826</th>
<td>1990-08-31</td>
<td>0.0</td>
<td>199009</td>
<td>248.00</td>
<td>0.0</td>
<td>241.50</td>
<td>246.00</td>
<td>65310.0</td>
<td>243.25</td>
<td>0.013584</td>
<td>53775.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115827</th>
<td>1990-09-04</td>
<td>0.0</td>
<td>199009</td>
<td>243.00</td>
<td>0.0</td>
<td>239.50</td>
<td>243.00</td>
<td>56660.0</td>
<td>240.75</td>
<td>0.013397</td>
<td>30185.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115828</th>
<td>1990-09-05</td>
<td>0.0</td>
<td>199009</td>
<td>243.75</td>
<td>0.0</td>
<td>241.50</td>
<td>241.75</td>
<td>52140.0</td>
<td>243.25</td>
<td>0.013030</td>
<td>23890.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115829</th>
<td>1990-09-06</td>
<td>0.0</td>
<td>199009</td>
<td>244.50</td>
<td>0.0</td>
<td>241.75</td>
<td>244.50</td>
<td>42070.0</td>
<td>243.00</td>
<td>0.013099</td>
<td>24200.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115830</th>
<td>1990-09-07</td>
<td>0.0</td>
<td>199009</td>
<td>242.00</td>
<td>0.0</td>
<td>238.50</td>
<td>242.00</td>
<td>34570.0</td>
<td>238.75</td>
<td>0.012864</td>
<td>29765.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115831</th>
<td>1990-09-10</td>
<td>0.0</td>
<td>199009</td>
<td>240.50</td>
<td>0.0</td>
<td>235.00</td>
<td>237.00</td>
<td>31165.0</td>
<td>239.50</td>
<td>0.013181</td>
<td>8510.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115832</th>
<td>1990-09-11</td>
<td>0.0</td>
<td>199009</td>
<td>241.50</td>
<td>0.0</td>
<td>239.00</td>
<td>240.00</td>
<td>28485.0</td>
<td>241.50</td>
<td>0.013361</td>
<td>6850.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115833</th>
<td>1990-09-12</td>
<td>0.0</td>
<td>199009</td>
<td>241.50</td>
<td>0.0</td>
<td>238.75</td>
<td>240.75</td>
<td>25310.0</td>
<td>239.25</td>
<td>0.013057</td>
<td>7865.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115834</th>
<td>1990-09-13</td>
<td>0.0</td>
<td>199009</td>
<td>234.00</td>
<td>0.0</td>
<td>231.75</td>
<td>233.00</td>
<td>21410.0</td>
<td>233.25</td>
<td>0.014449</td>
<td>8975.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115835</th>
<td>1990-09-14</td>
<td>0.0</td>
<td>199009</td>
<td>233.50</td>
<td>0.0</td>
<td>231.75</td>
<td>233.00</td>
<td>19690.0</td>
<td>232.75</td>
<td>0.014074</td>
<td>4505.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115836</th>
<td>1990-09-17</td>
<td>0.0</td>
<td>199009</td>
<td>235.75</td>
<td>0.0</td>
<td>232.25</td>
<td>233.75</td>
<td>16135.0</td>
<td>235.75</td>
<td>0.013777</td>
<td>9515.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115837</th>
<td>1990-09-18</td>
<td>0.0</td>
<td>199009</td>
<td>237.75</td>
<td>0.0</td>
<td>236.00</td>
<td>236.25</td>
<td>8635.0</td>
<td>237.00</td>
<td>0.013788</td>
<td>11545.0</td>
<td>1990-09-14</td>
</tr>
<tr>
<th>115838</th>
<td>1990-09-19</td>
<td>0.0</td>
<td>199009</td>
<td>237.00</td>
<td>0.0</td>
<td>232.50</td>
<td>237.00</td>
<td>980.0</td>
<td>234.25</td>
<td>0.013469</td>
<td>10070.0</td>
<td>1990-09-14</td>
</tr>
</tbody>
</table>
对于每一天，我都试图获得最高音量的合约字段。
我差不多了：
a.groupby(['Date'],as_index=False).agg({'Volume':np.max})
生成一个包含天数和最高卷的数据框，但我无法弄清楚如何获得相应的合同，因为它没有映射到原始（非分组）数据框的索引。
如何获得相应的合约字段？
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Date</th>
<th>Volume</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1959-07-01</td>
<td>541.0</td>
</tr>
<tr>
<th>1</th>
<td>1959-07-02</td>
<td>1156.0</td>
</tr>
<tr>
<th>2</th>
<td>1959-07-06</td>
<td>1495.0</td>
</tr>
<tr>
<th>3</th>
<td>1959-07-07</td>
<td>1204.0</td>
</tr>
<tr>
<th>4</th>
<td>1959-07-08</td>
<td>1244.0</td>
</tr>
<tr>
<th>5</th>
<td>1959-07-09</td>
<td>921.0</td>
</tr>
<tr>
<th>6</th>
<td>1959-07-10</td>
<td>1473.0</td>
</tr>
<tr>
<th>7</th>
<td>1959-07-13</td>
<td>1457.0</td>
</tr>
<tr>
<th>8</th>
<td>1959-07-14</td>
<td>1511.0</td>
</tr>
<tr>
<th>9</th>
<td>1959-07-15</td>
<td>1778.0</td>
</tr>
<tr>
<th>10</th>
<td>1959-07-16</td>
<td>1057.0</td>
</tr>
<tr>
<th>11</th>
<td>1959-07-17</td>
<td>671.0</td>
</tr>
<tr>
<th>12</th>
<td>1959-07-20</td>
<td>868.0</td>
</tr>
<tr>
<th>13</th>
<td>1959-07-21</td>
<td>1539.0</td>
</tr>
<tr>
<th>14</th>
<td>1959-07-22</td>
<td>1089.0</td>
</tr>
<tr>
<th>15</th>
<td>1959-07-23</td>
<td>924.0</td>
</tr>
<tr>
<th>16</th>
<td>1959-07-24</td>
<td>1399.0</td>
</tr>
<tr>
<th>17</th>
<td>1959-07-27</td>
<td>680.0</td>
</tr>
<tr>
<th>18</th>
<td>1959-07-28</td>
<td>664.0</td>
</tr>
<tr>
<th>19</th>
<td>1959-07-29</td>
<td>744.0</td>
</tr>
<tr>
<th>20</th>
<td>1959-07-30</td>
<td>599.0</td>
</tr>
<tr>
<th>21</th>
<td>1959-07-31</td>
<td>1030.0</td>
</tr>
<tr>
<th>22</th>
<td>1959-08-03</td>
<td>623.0</td>
</tr>
<tr>
<th>23</th>
<td>1959-08-04</td>
<td>898.0</td>
</tr>
<tr>
<th>24</th>
<td>1959-08-05</td>
<td>1006.0</td>
</tr>
<tr>
<th>25</th>
<td>1959-08-06</td>
<td>495.0</td>
</tr>
<tr>
<th>26</th>
<td>1959-08-07</td>
<td>1851.0</td>
</tr>
<tr>
<th>27</th>
<td>1959-08-10</td>
<td>1229.0</td>
</tr>
<tr>
<th>28</th>
<td>1959-08-11</td>
<td>909.0</td>
</tr>
<tr>
<th>29</th>
<td>1959-08-12</td>
<td>535.0</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>14304</th>
<td>2016-04-14</td>
<td>277581.0</td>
</tr>
<tr>
<th>14305</th>
<td>2016-04-15</td>
<td>281613.0</td>
</tr>
</tbody>
</table>
答案 0 :(得分：0)
我认为您可以首先通过汇总idxmax找到具有最大值的列Volume的索引，然后按loc选择df：
df.loc[df.groupby("Date")["Volume"].idxmax()]
如果需要Contract：
print (df.loc[df.groupby("Date")["Volume"].idxmax(), 'Contract'])
另一个解决方案首先是Volume列df.sort_values(by="Volume", ascending=False).groupby("Date", as_index=False).first()
，然后是sort_values，汇总groupby：
import pandas as pd
df = pd.DataFrame({'Prev. Day Open Interest': {115809: 161630.0, 115813: 155785.0, 115814: 154925.0, 115815: 153660.0, 115823: 104110.0, 115824: 91055.0, 115825: 77335.0, 115826: 65310.0, 115827: 56660.0, 115828: 52140.0, 115835: 19690.0, 115838: 980.0}, 'Date': {115809: '1990-08-08', 115813: '1990-08-08', 115814: '1990-08-15', 115815: '1990-08-15', 115823: '1990-08-23', 115824: '1990-08-23', 115825: '1990-08-30', 115826: '1990-08-30', 115827: '1990-09-04', 115828: '1990-09-04', 115835: '1990-09-11', 115838: '1990-09-11'}, 'Settle': {115809: 252.25, 115813: 245.5, 115814: 247.5, 115815: 249.0, 115823: 253.75, 115824: 250.0, 115825: 245.25, 115826: 243.25, 115827: 240.75, 115828: 243.25, 115835: 232.75, 115838: 234.25}, 'Expiration': {115809: '1990-09-14', 115813: '1990-09-14', 115814: '1990-09-14', 115815: '1990-09-14', 115823: '1990-09-14', 115824: '1990-09-14', 115825: '1990-09-14', 115826: '1990-09-14', 115827: '1990-09-14', 115828: '1990-09-14', 115835: '1990-09-14', 115838: '1990-09-14'}, 'Volume': {115809: 38160.0, 115813: 40915.0, 115814: 36445.0, 115815: 44395.0, 115823: 44680.0, 115824: 64930.0, 115825: 73035.0, 115826: 53775.0, 115827: 30185.0, 115828: 23890.0, 115835: 4505.0, 115838: 10070.0}, 'Volatility': {115809: 0.015012000000000001, 115813: 0.014325999999999998, 115814: 0.014211000000000001, 115815: 0.014024999999999999, 115823: 0.01299, 115824: 0.01319, 115825: 0.013715999999999999, 115826: 0.013584000000000001, 115827: 0.013396999999999999, 115828: 0.013030000000000002, 115835: 0.014074000000000001, 115838: 0.013469}, 'Last': {115809: 0.0, 115813: 0.0, 115814: 0.0, 115815: 0.0, 115823: 0.0, 115824: 0.0, 115825: 0.0, 115826: 0.0, 115827: 0.0, 115828: 0.0, 115835: 0.0, 115838: 0.0}, 'High': {115809: 253.75, 115813: 245.5, 115814: 247.75, 115815: 250.0, 115823: 254.0, 115824: 255.5, 115825: 249.5, 115826: 248.0, 115827: 243.0, 115828: 243.75, 115835: 233.5, 115838: 237.0}, 'Low': {115809: 251.75, 115813: 243.75, 115814: 244.5, 115815: 247.5, 115823: 251.0, 115824: 249.75, 115825: 245.0, 115826: 241.5, 115827: 239.5, 115828: 241.5, 115835: 231.75, 115838: 232.5}, 'Contract': {115809: 199001, 115813: 199002, 115814: 199003, 115815: 199004, 115823: 199005, 115824: 199006, 115825: 199007, 115826: 199008, 115827: 199009, 115828: 199010, 115835: 199011, 115838: 199012}, 'Open': {115809: 253.75, 115813: 244.25, 115814: 246.25, 115815: 248.0, 115823: 251.0, 115824: 254.75, 115825: 249.25, 115826: 246.0, 115827: 243.0, 115828: 241.75, 115835: 233.0, 115838: 237.0}, 'Change': {115809: 0.0, 115813: 0.0, 115814: 0.0, 115815: 0.0, 115823: 0.0, 115824: 0.0, 115825: 0.0, 115826: 0.0, 115827: 0.0, 115828: 0.0, 115835: 0.0, 115838: 0.0}})
print (df)
Change Contract Date Expiration High Last Low Open Prev. Day Open Interest Settle Volatility Volume
115809 0.0 199001 1990-08-08 1990-09-14 253.75 0.0 251.75 253.75 161630.0 252.25 0.015012 38160.0
115813 0.0 199002 1990-08-08 1990-09-14 245.50 0.0 243.75 244.25 155785.0 245.50 0.014326 40915.0
115814 0.0 199003 1990-08-15 1990-09-14 247.75 0.0 244.50 246.25 154925.0 247.50 0.014211 36445.0
115815 0.0 199004 1990-08-15 1990-09-14 250.00 0.0 247.50 248.00 153660.0 249.00 0.014025 44395.0
115823 0.0 199005 1990-08-23 1990-09-14 254.00 0.0 251.00 251.00 104110.0 253.75 0.012990 44680.0
115824 0.0 199006 1990-08-23 1990-09-14 255.50 0.0 249.75 254.75 91055.0 250.00 0.013190 64930.0
115825 0.0 199007 1990-08-30 1990-09-14 249.50 0.0 245.00 249.25 77335.0 245.25 0.013716 73035.0
115826 0.0 199008 1990-08-30 1990-09-14 248.00 0.0 241.50 246.00 65310.0 243.25 0.013584 53775.0
115827 0.0 199009 1990-09-04 1990-09-14 243.00 0.0 239.50 243.00 56660.0 240.75 0.013397 30185.0
115828 0.0 199010 1990-09-04 1990-09-14 243.75 0.0 241.50 241.75 52140.0 243.25 0.013030 23890.0
115835 0.0 199011 1990-09-11 1990-09-14 233.50 0.0 231.75 233.00 19690.0 232.75 0.014074 4505.0
115838 0.0 199012 1990-09-11 1990-09-14 237.00 0.0 232.50 237.00 980.0 234.25 0.013469 10070.0
print (df.loc[df.groupby("Date")["Volume"].idxmax()])
Change Contract Date Expiration High Last Low Open Prev. Day Open Interest Settle Volatility Volume
115813 0.0 199002 1990-08-08 1990-09-14 245.5 0.0 243.75 244.25 155785.0 245.50 0.014326 40915.0
115815 0.0 199004 1990-08-15 1990-09-14 250.0 0.0 247.50 248.00 153660.0 249.00 0.014025 44395.0
115824 0.0 199006 1990-08-23 1990-09-14 255.5 0.0 249.75 254.75 91055.0 250.00 0.013190 64930.0
115825 0.0 199007 1990-08-30 1990-09-14 249.5 0.0 245.00 249.25 77335.0 245.25 0.013716 73035.0
115827 0.0 199009 1990-09-04 1990-09-14 243.0 0.0 239.50 243.00 56660.0 240.75 0.013397 30185.0
115838 0.0 199012 1990-09-11 1990-09-14 237.0 0.0 232.50 237.00 980.0 234.25 0.013469 10070.0
print (df.sort_values(by="Volume", ascending=False).groupby("Date", as_index=False).first())
Date Change Contract Expiration High Last Low Open Prev. Day Open Interest Settle Volatility Volume
0 1990-08-08 0.0 199002 1990-09-14 245.5 0.0 243.75 244.25 155785.0 245.50 0.014326 40915.0
1 1990-08-15 0.0 199004 1990-09-14 250.0 0.0 247.50 248.00 153660.0 249.00 0.014025 44395.0
2 1990-08-23 0.0 199006 1990-09-14 255.5 0.0 249.75 254.75 91055.0 250.00 0.013190 64930.0
3 1990-08-30 0.0 199007 1990-09-14 249.5 0.0 245.00 249.25 77335.0 245.25 0.013716 73035.0
4 1990-09-04 0.0 199009 1990-09-14 243.0 0.0 239.50 243.00 56660.0 240.75 0.013397 30185.0
5 1990-09-11 0.0 199012 1990-09-14 237.0 0.0 232.50 237.00 980.0 234.25 0.013469 10070.0
示例（某些数据已更改）：
print (df.loc[df.groupby("Date")["Volume"].idxmax(), 'Contract'])
115813 199002
115815 199004
115824 199006
115825 199007
115827 199009
115838 199012
Name: Contract, dtype: int64
print (df.sort_values(by="Volume", ascending=False).groupby("Date", as_index=False).first()['Contract'])
0 199002
1 199004
2 199006
3 199007
4 199009
5 199012
Name: Contract, dtype: int64
<?php
$products;//What ever you have assigned to this...
$size = count($products); // size of products array
$tmp = PHP_INT_MAX; // Set this to the highest possible counter value.
for($i = 0; $i < $size; $i++)
{
//If a new lowest counter has been found empty array and append this value
if($products[$i]["counter"] < $tmp)
{
$lowest = array();
$lowest[0] = $products[$i]["item"];
$tmp = $products[$i]["counter"];
}
//If counter is equally low append value
if($products[$i]["counter"] == $tmp)
{
$lowest[] = $products[$i]["item"];
}
}
echo array_rand($lowest);
?>