数据清洗之 层次化索引

import pandas as pd
import numpy as np
import os

os.getcwd()

'D:\\Jupyter\\notebook\\Python数据清洗实战\\数据清洗之数据表处理'

os.chdir('D:\\Jupyter\\notebook\\Python数据清洗实战\\数据')

df = pd.read_csv('baby_trade_history.csv', dtype={'user_id':str}, index_col=[3,0])  # 把第四列和第一列当成行索引标签

df.head(5)

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<tr style="text-align: right;">

  <th></th>

  <th></th>

  <th>auction_id</th>

  <th>cat_id</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

</tr>

<tr>

  <th>cat1</th>

  <th>user_id</th>

  <th></th>

  <th></th>

  <th></th>

  <th></th>

  <th></th>

</tr>

<tr>

  <th>50022520</th>

  <th>786295544</th>

  <td>41098319944</td>

  <td>50014866</td>

  <td>21458:86755362;13023209:3593274;10984217:21985...</td>

  <td>2</td>

  <td>20140919</td>

</tr>

<tr>

  <th>28</th>

  <th>532110457</th>

  <td>17916191097</td>

  <td>50011993</td>

  <td>21458:11399317;1628862:3251296;21475:137325;16...</td>

  <td>1</td>

  <td>20131011</td>

</tr>

<tr>

  <th rowspan="2" valign="top">50014815</th>

  <th>249013725</th>

  <td>21896936223</td>

  <td>50012461</td>

  <td>21458:30992;1628665:92012;1628665:3233938;1628...</td>

  <td>1</td>

  <td>20131011</td>

</tr>

<tr>

  <th>917056007</th>

  <td>12515996043</td>

  <td>50018831</td>

  <td>21458:15841995;21956:3494076;27000458:59723383...</td>

  <td>2</td>

  <td>20141023</td>

</tr>

<tr>

  <th>50008168</th>

  <th>444069173</th>

  <td>20487688075</td>

  <td>50013636</td>

  <td>21458:30992;13658074:3323064;1628665:3233941;1...</td>

  <td>1</td>

  <td>20141103</td>

</tr>

# 取第一层索引为28，第二层索引为475046636 和 532110457的数据
# 取多个要以列表形式传入
df.loc[28].loc[[475046636, 532110457]]

.dataframe tbody tr th:only-of-type {

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.dataframe tbody tr th {

    vertical-align: top;

}

.dataframe thead th {

    text-align: right;

}

<tr style="text-align: right;">

  <th></th>

  <th>auction_id</th>

  <th>cat_id</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

</tr>

<tr>

  <th>user_id</th>

  <th></th>

  <th></th>

  <th></th>

  <th></th>

  <th></th>

</tr>

<tr>

  <th>475046636</th>

  <td>10368360710</td>

  <td>203527</td>

  <td>22724:40168;22729:40278;21458:21817;2770200:24...</td>

  <td>1</td>

  <td>20121101</td>

</tr>

<tr>

  <th>532110457</th>

  <td>17916191097</td>

  <td>50011993</td>

  <td>21458:11399317;1628862:3251296;21475:137325;16...</td>

  <td>1</td>

  <td>20131011</td>

</tr>

# 效果同上
df.loc[(28, [475046636, 532110457]), ['auction_id', 'cat_id']]

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.dataframe tbody tr th {

    vertical-align: top;

}

.dataframe thead th {

    text-align: right;

}

<tr style="text-align: right;">

  <th></th>

  <th></th>

  <th>auction_id</th>

  <th>cat_id</th>

</tr>

<tr>

  <th>cat1</th>

  <th>user_id</th>

  <th></th>

  <th></th>

</tr>

<tr>

  <th rowspan="2" valign="top">28</th>

  <th>532110457</th>

  <td>17916191097</td>

  <td>50011993</td>

</tr>

<tr>

  <th>475046636</th>

  <td>10368360710</td>

  <td>203527</td>

</tr>

# 取第一层索引为28和38的数据，以列表的形式传递
df.loc[([28, 38])]

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.dataframe tbody tr th {

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}

.dataframe thead th {

    text-align: right;

}

<tr style="text-align: right;">

  <th></th>

  <th></th>

  <th>auction_id</th>

  <th>cat_id</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

</tr>

<tr>

  <th>cat1</th>

  <th>user_id</th>

  <th></th>

  <th></th>

  <th></th>

  <th></th>

  <th></th>

</tr>

<tr>

  <th rowspan="3" valign="top">28</th>

  <th>532110457</th>

  <td>17916191097</td>

  <td>50011993</td>

  <td>21458:11399317;1628862:3251296;21475:137325;16...</td>

  <td>1</td>

  <td>20131011</td>

</tr>

<tr>

  <th>82830661</th>

  <td>19948600790</td>

  <td>50013874</td>

  <td>21458:11580;21475:137325</td>

  <td>1</td>

  <td>20121101</td>

</tr>

<tr>

  <th>475046636</th>

  <td>10368360710</td>

  <td>203527</td>

  <td>22724:40168;22729:40278;21458:21817;2770200:24...</td>

  <td>1</td>

  <td>20121101</td>

</tr>

<tr>

  <th>38</th>

  <th>734147966</th>

  <td>15307958346</td>

  <td>50018202</td>

  <td>21458:3270827;7361532:28710594;7397093:7536994...</td>

  <td>2</td>

  <td>20121101</td>

</tr>

<tr>

  <th rowspan="3" valign="top">28</th>

  <th>377550424</th>

  <td>15771663914</td>

  <td>50015841</td>

  <td>1628665:3233941;1628665:3233942;3914866:11580;...</td>

  <td>1</td>

  <td>20121123</td>

</tr>

<tr>

  <th>530850018</th>

  <td>22058239899</td>

  <td>50024147</td>

  <td>21458:205007542;43307470:5543413;2339128:62147...</td>

  <td>1</td>

  <td>20140210</td>

</tr>

<tr>

  <th>749507708</th>

  <td>19171641742</td>

  <td>50018860</td>

  <td>21458:3602856;1628665:3233941;1628665:3233942;...</td>

  <td>1</td>

  <td>20140210</td>

</tr>

<tr>

  <th>38</th>

  <th>469517728</th>

  <td>8232924597</td>

  <td>211122</td>

  <td>21458:21782;36786:42781029;13023102:6999219;22...</td>

  <td>6</td>

  <td>20140502</td>

</tr>

<tr>

  <th rowspan="2" valign="top">28</th>

  <th>938309370</th>

  <td>14149079479</td>

  <td>50023669</td>

  <td>21458:4204704;11820090:105550653;11644036:2861...</td>

  <td>1</td>

  <td>20120819</td>

</tr>

<tr>

  <th>14466144</th>

  <td>17610665576</td>

  <td>50011993</td>

  <td>21458:104000;21475:137325</td>

  <td>1</td>

  <td>20130327</td>

</tr>

<tr>

  <th>38</th>

  <th>177724549</th>

  <td>14228645401</td>

  <td>50018824</td>

  <td>21475:108284;6933666:96059;33595:16453265;2145...</td>

  <td>1</td>

  <td>20130327</td>

</tr>

<tr>

  <th rowspan="5" valign="top">28</th>

  <th>659020106</th>

  <td>40484992676</td>

  <td>50011993</td>

  <td>21458:16162126;13023209:10551667;122218042:605...</td>

  <td>1</td>

  <td>20140813</td>

</tr>

<tr>

  <th>18100946</th>

  <td>38451267766</td>

  <td>121540027</td>

  <td>21458:215485914;125501489:19689726;11945782:78...</td>

  <td>1</td>

  <td>20140911</td>

</tr>

<tr>

  <th>262519726</th>

  <td>19051046285</td>

  <td>121398041</td>

  <td>11666049:40203;21458:3961150;17472269:13302841...</td>

  <td>1</td>

  <td>20140703</td>

</tr>

<tr>

  <th>115566151</th>

  <td>14778919435</td>

  <td>50013187</td>

  <td>1628665:3233938;1628665:29796;1628665:133527;1...</td>

  <td>1</td>

  <td>20140113</td>

</tr>

<tr>

  <th>1714403831</th>

  <td>22443564698</td>

  <td>50014129</td>

  <td>21458:57737100;12102318:7282254;11945782:78135...</td>

  <td>1</td>

  <td>20131106</td>

</tr>

<tr>

  <th>38</th>

  <th>1970876909</th>

  <td>20197969079</td>

  <td>211122</td>

  <td>6940834:29865;21458:3270820;1629375:3253542;32...</td>

  <td>1</td>

  <td>20141017</td>

</tr>

<tr>

  <th rowspan="2" valign="top">28</th>

  <th>113473924</th>

  <td>15486726090</td>

  <td>50014250</td>

  <td>21458:30015090;1633959:43047819;1627584:28619;...</td>

  <td>1</td>

  <td>20120905</td>

</tr>

<tr>

  <th>348660284</th>

  <td>10896577394</td>

  <td>50014250</td>

  <td>1628665:29796;1628665:108579;1627584:11580;116...</td>

  <td>1</td>

  <td>20130525</td>

</tr>

<tr>

  <th>38</th>

  <th>151915451</th>

  <td>17305821144</td>

  <td>211122</td>

  <td>21458:21782;36786:42781029;6933553:3313169;130...</td>

  <td>2</td>

  <td>20140104</td>

</tr>

<tr>

  <th rowspan="2" valign="top">28</th>

  <th>745002413</th>

  <td>36815797313</td>

  <td>50023645</td>

  <td>1628665:82340;21475:11488282;21458:56610575;49...</td>

  <td>1</td>

  <td>20140104</td>

</tr>

<tr>

  <th>654037597</th>

  <td>13775864723</td>

  <td>50011993</td>

  <td>21458:116116655;1633959:3276615;1628862:50276;...</td>

  <td>1</td>

  <td>20130513</td>

</tr>

<tr>

  <th>38</th>

  <th>277279277</th>

  <td>18024521052</td>

  <td>211122</td>

  <td>21458:33516;33480:3238774;2653417:7353464;3359...</td>

  <td>12</td>

  <td>20130513</td>

</tr>

<tr>

  <th rowspan="32" valign="top">28</th>

  <th>1721792494</th>

  <td>36154660054</td>

  <td>50008845</td>

  <td>21458:3400531;5653832:7049425;13023209:7049425...</td>

  <td>1</td>

  <td>20140312</td>

</tr>

<tr>

  <th>56549058</th>

  <td>26930668292</td>

  <td>50003700</td>

  <td>21458:3351431;123273479:31526;1628665:3233941;...</td>

  <td>1</td>

  <td>20140312</td>

</tr>

<tr>

  <th>696527486</th>

  <td>37269469522</td>

  <td>50011993</td>

  <td>21458:118564374;13023209:547499553;122218042:3...</td>

  <td>1</td>

  <td>20140718</td>

</tr>

<tr>

  <th>643153890</th>

  <td>17954181229</td>

  <td>50003700</td>

  <td>123273479:41376163;21475:135183931;1628665:323...</td>

  <td>1</td>

  <td>20140718</td>

</tr>

<tr>

  <th>1097191176</th>

  <td>39095838474</td>

  <td>50015841</td>

  <td>1628665:3233941;1628665:3233942;1628665:323393...</td>

  <td>1</td>

  <td>20150203</td>

</tr>

<tr>

  <th>669370290</th>

  <td>40584762520</td>

  <td>121540027</td>

  <td>21458:215485914;125501489:598926205;11945782:7...</td>

  <td>1</td>

  <td>20141026</td>

</tr>

<tr>

  <th>371153601</th>

  <td>19893996909</td>

  <td>251725</td>

  <td>26318:52993;1627584:30689;11684888:104528258;2...</td>

  <td>1</td>

  <td>20121013</td>

</tr>

<tr>

  <th>...</th>

  <td>...</td>

  <td>...</td>

  <td>...</td>

  <td>...</td>

  <td>...</td>

</tr>

<tr>

  <th>173729389</th>

  <td>13926583575</td>

  <td>50012474</td>

  <td>21458:123482676;13023209:598168191;15673795:59...</td>

  <td>1</td>

  <td>20140501</td>

</tr>

<tr>

  <th>129479111</th>

  <td>39677605554</td>

  <td>50013187</td>

  <td>1628665:3233938;1628665:82340;1628665:29793;21...</td>

  <td>2</td>

  <td>20141229</td>

</tr>

<tr>

  <th>788572516</th>

  <td>18991044719</td>

  <td>121466040</td>

  <td>1628665:29784;1628665:29793;6907553:40595450;1...</td>

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  <td>20141129</td>

</tr>

<tr>

  <th>1031198213</th>

  <td>22309900818</td>

  <td>50011993</td>

  <td>21458:113001124;13023209:52138064;29183816:568...</td>

  <td>1</td>

  <td>20141129</td>

</tr>

<tr>

  <th>860528533</th>

  <td>14620263044</td>

  <td>50011993</td>

  <td>21458:115601688;1633959:138499706;1628862:3241...</td>

  <td>1</td>

  <td>20130925</td>

</tr>

<tr>

  <th>1614610402</th>

  <td>17337612618</td>

  <td>50016010</td>

  <td>21458:30992;3989588:31285;3998392:40870415;399...</td>

  <td>1</td>

  <td>20131223</td>

</tr>

<tr>

  <th>696020002</th>

  <td>23953356568</td>

  <td>50011993</td>

  <td>21458:34865735;1633959:3334315;1628862:33255;3...</td>

  <td>1</td>

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</tr>

<tr>

  <th>626575260</th>

  <td>38862644631</td>

  <td>50011993</td>

  <td>122218042:21671;21475:135183931;1628861:21671;...</td>

  <td>2</td>

  <td>20140625</td>

</tr>

<tr>

  <th>2139869403</th>

  <td>40339820434</td>

  <td>50011993</td>

  <td>122218042:117201278;21475:135183931;1628861:21...</td>

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  <td>20141210</td>

</tr>

<tr>

  <th>49369084</th>

  <td>37942071105</td>

  <td>50013190</td>

  <td>21458:211472372;44818245:223495732;1628665:323...</td>

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</tr>

<tr>

  <th>128447452</th>

  <td>37728960808</td>

  <td>50023670</td>

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<tr>

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</tr>

<tr>

  <th>45914430</th>

  <td>16066366006</td>

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</tr>

<tr>

  <th>1372572</th>

  <td>16915013171</td>

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<tr>

  <th>167288532</th>

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<tr>

  <th>2183330808</th>

  <td>19704833222</td>

  <td>50011993</td>

  <td>21458:119679150;13023209:4067094;122218042:216...</td>

  <td>1</td>

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<tr>

  <th>113446171</th>

  <td>37455013590</td>

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  <td>11760865:6120557;21458:3314841;122277597:2054;...</td>

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<tr>

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  <td>21458:91312408;138253785:653822445;135925585:1...</td>

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</tr>

<tr>

  <th>2212582093</th>

  <td>16684531006</td>

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  <td>21458:3532516;8088680:4482698;1628665:3233941;...</td>

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<tr>

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  <td>40599422228</td>

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  <td>1628665:3233941;1628665:29790;1628665:3233936;...</td>

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  <td>20150110</td>

</tr>

<tr>

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  <td>40798454555</td>

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  <td>122217795:29277;134943792:29774;134943792:2977...</td>

  <td>1</td>

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<tr>

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  <td>18176728510</td>

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  <td>1628665:3233941;1628665:3233942;1628665:323393...</td>

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</tr>

<tr>

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  <td>50012788</td>

  <td>21458:12376977;2112993:32075;1628665:92012;162...</td>

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<tr>

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  <td>17629938386</td>

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  <td>21458:3550980;29154281:231350353;11684888:1045...</td>

  <td>1</td>

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</tr>

<tr>

  <th>38</th>

  <th>1957645413</th>

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  <td>6940834:29865;1628149:137593;21475:114226;2275...</td>

  <td>1</td>

  <td>20140815</td>

</tr>