数据清洗之数据筛选

原创

ruochen

修改于 2021-05-13 14:33:10

2.3K0

数据常用筛选方法

在数据中，选择需要的行或者列
基础索引方式，就是直接引用
ioc行索引名称或者条件，列索引名称或者标签
iloc行索引位置，列索引位置

import pandas as pd
import os
import numpy as np

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', encoding='utf-8', dtype={'user_id':str})

df

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>user_id</th>

  <th>auction_id</th>

  <th>cat_id</th>

  <th>cat1</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>786295544</td>

  <td>41098319944</td>

  <td>50014866</td>

  <td>50022520</td>

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

  <td>2</td>

  <td>20140919</td>

</tr>

<tr>

  <th>1</th>

  <td>532110457</td>

  <td>17916191097</td>

  <td>50011993</td>

  <td>28</td>

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

  <td>1</td>

  <td>20131011</td>

</tr>

<tr>

  <th>2</th>

  <td>249013725</td>

  <td>21896936223</td>

  <td>50012461</td>

  <td>50014815</td>

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

  <td>1</td>

  <td>20131011</td>

</tr>

<tr>

  <th>3</th>

  <td>917056007</td>

  <td>12515996043</td>

  <td>50018831</td>

  <td>50014815</td>

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

  <td>2</td>

  <td>20141023</td>

</tr>

<tr>

  <th>4</th>

  <td>444069173</td>

  <td>20487688075</td>

  <td>50013636</td>

  <td>50008168</td>

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

  <td>1</td>

  <td>20141103</td>

</tr>

<tr>

  <th>5</th>

  <td>152298847</td>

  <td>41840167463</td>

  <td>121394024</td>

  <td>50008168</td>

  <td>21458:3408353;13023209:727117752;22009:2741771...</td>

  <td>1</td>

  <td>20141103</td>

</tr>

<tr>

  <th>6</th>

  <td>513441334</td>

  <td>19909384116</td>

  <td>50010557</td>

  <td>50008168</td>

  <td>25935:21991;1628665:29784;22019:34731;22019:20...</td>

  <td>1</td>

  <td>20121212</td>

</tr>

<tr>

  <th>7</th>

  <td>297411659</td>

  <td>13540124907</td>

  <td>50010542</td>

  <td>50008168</td>

  <td>21458:60020529;25935:31381;1633959:27247291;16...</td>

  <td>1</td>

  <td>20121212</td>

</tr>

<tr>

  <th>8</th>

  <td>82830661</td>

  <td>19948600790</td>

  <td>50013874</td>

  <td>28</td>

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

  <td>1</td>

  <td>20121101</td>

</tr>

<tr>

  <th>9</th>

  <td>475046636</td>

  <td>10368360710</td>

  <td>203527</td>

  <td>28</td>

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

  <td>1</td>

  <td>20121101</td>

</tr>

<tr>

  <th>10</th>

  <td>734147966</td>

  <td>15307958346</td>

  <td>50018202</td>

  <td>38</td>

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

  <td>2</td>

  <td>20121101</td>

</tr>

<tr>

  <th>11</th>

  <td>68547330</td>

  <td>21162876126</td>

  <td>50012365</td>

  <td>122650008</td>

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

  <td>1</td>

  <td>20121123</td>

</tr>

<tr>

  <th>12</th>

  <td>697081418</td>

  <td>15898050723</td>

  <td>50013636</td>

  <td>50008168</td>

  <td>21458:19726868;1633959:179425852;13836282:1290...</td>

  <td>1</td>

  <td>20121123</td>

</tr>

<tr>

  <th>13</th>

  <td>377550424</td>

  <td>15771663914</td>

  <td>50015841</td>

  <td>28</td>

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

  <td>1</td>

  <td>20121123</td>

</tr>

<tr>

  <th>14</th>

  <td>88313935</td>

  <td>22532727492</td>

  <td>50013711</td>

  <td>50008168</td>

  <td>1628665:3233941;1628665:3233942;22019:3340598;...</td>

  <td>1</td>

  <td>20131005</td>

</tr>

<tr>

  <th>15</th>

  <td>25918750</td>

  <td>16078389250</td>

  <td>50012359</td>

  <td>122650008</td>

  <td>21458:3405407;1633959:6186201;1628366:32799;81...</td>

  <td>1</td>

  <td>20131005</td>

</tr>

<tr>

  <th>16</th>

  <td>350288528</td>

  <td>35086271572</td>

  <td>50010544</td>

  <td>50008168</td>

  <td>21458:61813;25935:21991;1628665:3233938;162866...</td>

  <td>1</td>

  <td>20131129</td>

</tr>

<tr>

  <th>17</th>

  <td>348090113</td>

  <td>17436967558</td>

  <td>50009540</td>

  <td>50014815</td>

  <td>21458:21910;3110425:30696849;2191928:75373546;...</td>

  <td>1</td>

  <td>20131129</td>

</tr>

<tr>

  <th>18</th>

  <td>1635282280</td>

  <td>36153356431</td>

  <td>50013207</td>

  <td>50008168</td>

  <td>1628665:29784;1628665:29799;2904342:31004;2201...</td>

  <td>1</td>

  <td>20131129</td>

</tr>

<tr>

  <th>19</th>

  <td>530850018</td>

  <td>22058239899</td>

  <td>50024147</td>

  <td>28</td>

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

  <td>1</td>

  <td>20140210</td>

</tr>

<tr>

  <th>20</th>

  <td>749507708</td>

  <td>19171641742</td>

  <td>50018860</td>

  <td>28</td>

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

  <td>1</td>

  <td>20140210</td>

</tr>

<tr>

  <th>21</th>

  <td>201088567</td>

  <td>38564176352</td>

  <td>50013207</td>

  <td>50008168</td>

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

  <td>1</td>

  <td>20140502</td>

</tr>

<tr>

  <th>22</th>

  <td>469517728</td>

  <td>8232924597</td>

  <td>211122</td>

  <td>38</td>

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

  <td>6</td>

  <td>20140502</td>

</tr>

<tr>

  <th>23</th>

  <td>691367866</td>

  <td>17712372914</td>

  <td>121434042</td>

  <td>50014815</td>

  <td>21458:49341152;8021059:5525523;6851452:1398669...</td>

  <td>1</td>

  <td>20140804</td>

</tr>

<tr>

  <th>24</th>

  <td>77193822</td>

  <td>35537441586</td>

  <td>50006520</td>

  <td>50014815</td>

  <td>22277:6262384;21458:30992;1628665:3233941;1628...</td>

  <td>2</td>

  <td>20140804</td>

</tr>

<tr>

  <th>25</th>

  <td>605678021</td>

  <td>15502618744</td>

  <td>50010555</td>

  <td>50008168</td>

  <td>25935:31381;1628665:3233941;1628665:3233942;16...</td>

  <td>1</td>

  <td>20130226</td>

</tr>

<tr>

  <th>26</th>

  <td>47702620</td>

  <td>26481508332</td>

  <td>121412034</td>

  <td>50014815</td>

  <td>21458:49341152;11057903:4036007;130475532:7537...</td>

  <td>1</td>

  <td>20140918</td>

</tr>

<tr>

  <th>27</th>

  <td>763560371</td>

  <td>40945285800</td>

  <td>50012365</td>

  <td>122650008</td>

  <td>21458:30992;1628665:3233939;22007:30338;22007:...</td>

  <td>1</td>

  <td>20150201</td>

</tr>

<tr>

  <th>28</th>

  <td>408028533</td>

  <td>35838498718</td>

  <td>50012442</td>

  <td>50008168</td>

  <td>21458:3596449;6811831:3446999;13023209:3446999...</td>

  <td>1</td>

  <td>20141009</td>

</tr>

<tr>

  <th>29</th>

  <td>53566371</td>

  <td>27177784760</td>

  <td>121394024</td>

  <td>50008168</td>

  <td>21458:42090508;1628665:3233941;1628665:3233942...</td>

  <td>1</td>

  <td>20141009</td>

</tr>

<tr>

  <th>...</th>

  <td>...</td>

  <td>...</td>

  <td>...</td>

  <td>...</td>

  <td>...</td>

  <td>...</td>

  <td>...</td>

</tr>

<tr>

  <th>29941</th>

  <td>413188001</td>

  <td>16521677358</td>

  <td>50012478</td>

  <td>50014815</td>

  <td>21458:28155;5434803:3636603;2815901:22583732;1...</td>

  <td>1</td>

  <td>20130107</td>

</tr>

<tr>

  <th>29942</th>

  <td>474062095</td>

  <td>21129724585</td>

  <td>50013207</td>

  <td>50008168</td>

  <td>21458:21599;1628665:29798;1628665:3233938;1628...</td>

  <td>1</td>

  <td>20130107</td>

</tr>

<tr>

  <th>29943</th>

  <td>797710454</td>

  <td>18176728510</td>

  <td>50013177</td>

  <td>28</td>

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

  <td>1</td>

  <td>20130107</td>

</tr>

<tr>

  <th>29944</th>

  <td>1716505453</td>

  <td>37844041565</td>

  <td>50010555</td>

  <td>50008168</td>

  <td>21458:30992;25935:31381;1628665:3233941;162866...</td>

  <td>1</td>

  <td>20141231</td>

</tr>

<tr>

  <th>29945</th>

  <td>1966692323</td>

  <td>42504930457</td>

  <td>50012359</td>

  <td>122650008</td>

  <td>21458:3379652;1628665:3233940;1628665:3233938;...</td>

  <td>1</td>

  <td>20141231</td>

</tr>

<tr>

  <th>29946</th>

  <td>641734831</td>

  <td>22105131076</td>

  <td>50014277</td>

  <td>50014815</td>

  <td>21458:21906;13227811:51479;13230966:75369014;3...</td>

  <td>2</td>

  <td>20141016</td>

</tr>

<tr>

  <th>29947</th>

  <td>731030177</td>

  <td>41666438142</td>

  <td>121394024</td>

  <td>50008168</td>

  <td>21458:3443560;1628665:3233942;1628665:3233938;...</td>

  <td>1</td>

  <td>20141016</td>

</tr>

<tr>

  <th>29948</th>

  <td>68515755</td>

  <td>13953276547</td>

  <td>50012788</td>

  <td>28</td>

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

  <td>1</td>

  <td>20130729</td>

</tr>

<tr>

  <th>29949</th>

  <td>180436843</td>

  <td>23375100402</td>

  <td>50012451</td>

  <td>50008168</td>

  <td>21458:33514;1633959:13343071;33030:29800;33162...</td>

  <td>1</td>

  <td>20130729</td>

</tr>

<tr>

  <th>29950</th>

  <td>801784345</td>

  <td>17629938386</td>

  <td>50023670</td>

  <td>28</td>

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

  <td>1</td>

  <td>20130729</td>

</tr>

<tr>

  <th>29951</th>

  <td>124458824</td>

  <td>19739113764</td>

  <td>50013636</td>

  <td>50008168</td>

  <td>21458:30992;13658074:9306734;1628665:3233941;1...</td>

  <td>1</td>

  <td>20140322</td>

</tr>

<tr>

  <th>29952</th>

  <td>602141957</td>

  <td>37251457564</td>

  <td>50012360</td>

  <td>122650008</td>

  <td>21458:21599;1628665:29798;1628665:82340;162866...</td>

  <td>1</td>

  <td>20140322</td>

</tr>

<tr>

  <th>29953</th>

  <td>595095853</td>

  <td>41160643364</td>

  <td>121364022</td>

  <td>50008168</td>

  <td>21458:80090256;1628665:29784;1628665:29796;162...</td>

  <td>1</td>

  <td>20150111</td>

</tr>

<tr>

  <th>29954</th>

  <td>1905258237</td>

  <td>42298652641</td>

  <td>121452056</td>

  <td>50008168</td>

  <td>21458:30992;1628665:3233942;1628665:31614;1628...</td>

  <td>1</td>

  <td>20150111</td>

</tr>

<tr>

  <th>29955</th>

  <td>1957645413</td>

  <td>36768778465</td>

  <td>121448033</td>

  <td>38</td>

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

  <td>1</td>

  <td>20140815</td>

</tr>

<tr>

  <th>29956</th>

  <td>1854778218</td>

  <td>37200665444</td>

  <td>50012361</td>

  <td>122650008</td>

  <td>21458:3645338;13023209:544768204;122217803:309...</td>

  <td>1</td>

  <td>20140815</td>

</tr>

<tr>

  <th>29957</th>

  <td>268356658</td>

  <td>36932456353</td>

  <td>50010236</td>

  <td>50014815</td>

  <td>21458:10513072;12474507:706291650;3091143:9208...</td>

  <td>1</td>

  <td>20141027</td>

</tr>

<tr>

  <th>29958</th>

  <td>196272909</td>

  <td>10066997901</td>

  <td>50009540</td>

  <td>50014815</td>

  <td>21458:21906;13229910:32056435;2191928:73664723...</td>

  <td>1</td>

  <td>20141104</td>

</tr>

<tr>

  <th>29959</th>

  <td>23473499</td>

  <td>38019470815</td>

  <td>50010236</td>

  <td>50014815</td>

  <td>1628665:61550;1628665:3233940;1628665:3233936;...</td>

  <td>1</td>

  <td>20141104</td>

</tr>

<tr>

  <th>29960</th>

  <td>816394377</td>

  <td>19835118833</td>

  <td>50003700</td>

  <td>28</td>

  <td>24448:73774385;6725953:48332;22044:30715;80047...</td>

  <td>1</td>

  <td>20130912</td>

</tr>

<tr>

  <th>29961</th>

  <td>164859586</td>

  <td>15842319049</td>

  <td>50012479</td>

  <td>28</td>

  <td>NaN</td>

  <td>1</td>

  <td>20130912</td>

</tr>

<tr>

  <th>29962</th>

  <td>119149466</td>

  <td>26396292642</td>

  <td>50008875</td>

  <td>28</td>

  <td>21458:30992;11684888:104528258;21475:11488282;...</td>

  <td>1</td>

  <td>20130912</td>

</tr>

<tr>

  <th>29963</th>

  <td>704655047</td>

  <td>10506866020</td>

  <td>50007011</td>

  <td>50008168</td>

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

  <td>1</td>

  <td>20121206</td>

</tr>

<tr>

  <th>29964</th>

  <td>45662429</td>

  <td>20745380642</td>

  <td>50010555</td>

  <td>50008168</td>

  <td>25935:31381;1628665:3233941;1628665:3233942;16...</td>

  <td>1</td>

  <td>20121206</td>

</tr>

<tr>

  <th>29965</th>

  <td>35711492</td>

  <td>16563353438</td>

  <td>50010544</td>

  <td>50008168</td>

  <td>21458:11580;25935:21991;1628665:92012;1628665:...</td>

  <td>1</td>

  <td>20121206</td>

</tr>

<tr>

  <th>29966</th>

  <td>57747284</td>

  <td>35169635909</td>

  <td>50010549</td>

  <td>50008168</td>

  <td>21458:125202070;22019:3228688;22019:3248884;22...</td>

  <td>1</td>

  <td>20140109</td>

</tr>

<tr>

  <th>29967</th>

  <td>287541325</td>

  <td>19778523000</td>

  <td>50007011</td>

  <td>50008168</td>

  <td>21458:112788583;1633959:3523439;3130834:209537...</td>

  <td>2</td>

  <td>20140109</td>

</tr>

<tr>

  <th>29968</th>

  <td>82915321</td>

  <td>12766532512</td>

  <td>50011993</td>

  <td>28</td>

  <td>21475:137325;1628665:3233937;1628665:29798;162...</td>

  <td>1</td>

  <td>20131008</td>

</tr>

<tr>

  <th>29969</th>

  <td>78259523</td>

  <td>18309305134</td>

  <td>50013711</td>

  <td>50008168</td>

  <td>21458:30992;1628665:29778;1628665:29793;163395...</td>

  <td>1</td>

  <td>20131008</td>

</tr>

<tr>

  <th>29970</th>

  <td>758305789</td>

  <td>20177445814</td>

  <td>50018860</td>

  <td>28</td>

  <td>21458:3602856;1628665:29784;1628665:3233941;73...</td>

  <td>1</td>

  <td>20131008</td>

</tr>

</tbody>

</table>

<p>29971 rows × 7 columns</p>

</div>

df.info()

<class 'pandas.core.frame.DataFrame'>

RangeIndex: 29971 entries, 0 to 29970

Data columns (total 7 columns):

user_id       29971 non-null object

auction_id    29971 non-null int64

cat_id        29971 non-null int64

cat1          29971 non-null int64

property      29827 non-null object

buy_mount     29971 non-null int64

day           29971 non-null int64

dtypes: int64(5), object(2)

memory usage: 1.6+ MB

df.head(10)

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

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<thead>

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

  <th></th>

  <th>user_id</th>

  <th>auction_id</th>

  <th>cat_id</th>

  <th>cat1</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>786295544</td>

  <td>41098319944</td>

  <td>50014866</td>

  <td>50022520</td>

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

  <td>2</td>

  <td>20140919</td>

</tr>

<tr>

  <th>1</th>

  <td>532110457</td>

  <td>17916191097</td>

  <td>50011993</td>

  <td>28</td>

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

  <td>1</td>

  <td>20131011</td>

</tr>

<tr>

  <th>2</th>

  <td>249013725</td>

  <td>21896936223</td>

  <td>50012461</td>

  <td>50014815</td>

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

  <td>1</td>

  <td>20131011</td>

</tr>

<tr>

  <th>3</th>

  <td>917056007</td>

  <td>12515996043</td>

  <td>50018831</td>

  <td>50014815</td>

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

  <td>2</td>

  <td>20141023</td>

</tr>

<tr>

  <th>4</th>

  <td>444069173</td>

  <td>20487688075</td>

  <td>50013636</td>

  <td>50008168</td>

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

  <td>1</td>

  <td>20141103</td>

</tr>

<tr>

  <th>5</th>

  <td>152298847</td>

  <td>41840167463</td>

  <td>121394024</td>

  <td>50008168</td>

  <td>21458:3408353;13023209:727117752;22009:2741771...</td>

  <td>1</td>

  <td>20141103</td>

</tr>

<tr>

  <th>6</th>

  <td>513441334</td>

  <td>19909384116</td>

  <td>50010557</td>

  <td>50008168</td>

  <td>25935:21991;1628665:29784;22019:34731;22019:20...</td>

  <td>1</td>

  <td>20121212</td>

</tr>

<tr>

  <th>7</th>

  <td>297411659</td>

  <td>13540124907</td>

  <td>50010542</td>

  <td>50008168</td>

  <td>21458:60020529;25935:31381;1633959:27247291;16...</td>

  <td>1</td>

  <td>20121212</td>

</tr>

<tr>

  <th>8</th>

  <td>82830661</td>

  <td>19948600790</td>

  <td>50013874</td>

  <td>28</td>

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

  <td>1</td>

  <td>20121101</td>

</tr>

<tr>

  <th>9</th>

  <td>475046636</td>

  <td>10368360710</td>

  <td>203527</td>

  <td>28</td>

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

  <td>1</td>

  <td>20121101</td>

</tr>

</tbody>

</table>

</div>

df.columns  # 查看数据字段

Index(['user_id', 'auction_id', 'cat_id', 'cat1', 'property', 'buy_mount',

       'day'],

      dtype='object')

df['user_id'].head(5)  # 相当于嵌套列表

0    786295544

1    532110457

2    249013725

3    917056007

4    444069173

Name: user_id, dtype: object

df[['user_id', 'cat1']].head(5)

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>user_id</th>

  <th>cat1</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>786295544</td>

  <td>50022520</td>

</tr>

<tr>

  <th>1</th>

  <td>532110457</td>

  <td>28</td>

</tr>

<tr>

  <th>2</th>

  <td>249013725</td>

  <td>50014815</td>

</tr>

<tr>

  <th>3</th>

  <td>917056007</td>

  <td>50014815</td>

</tr>

<tr>

  <th>4</th>

  <td>444069173</td>

  <td>50008168</td>

</tr>

</tbody>

</table>

</div>

df[['user_id', 'cat1']][1:5]  # 分片选择

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>user_id</th>

  <th>cat1</th>

</tr>

</thead>

<tbody>

<tr>

  <th>1</th>

  <td>532110457</td>

  <td>28</td>

</tr>

<tr>

  <th>2</th>

  <td>249013725</td>

  <td>50014815</td>

</tr>

<tr>

  <th>3</th>

  <td>917056007</td>

  <td>50014815</td>

</tr>

<tr>

  <th>4</th>

  <td>444069173</td>

  <td>50008168</td>

</tr>

</tbody>

</table>

</div>

df.loc[3:4]  # 定位标签，不是位置

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>user_id</th>

  <th>auction_id</th>

  <th>cat_id</th>

  <th>cat1</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

</tr>

</thead>

<tbody>

<tr>

  <th>3</th>

  <td>917056007</td>

  <td>12515996043</td>

  <td>50018831</td>

  <td>50014815</td>

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

  <td>2</td>

  <td>20141023</td>

</tr>

<tr>

  <th>4</th>

  <td>444069173</td>

  <td>20487688075</td>

  <td>50013636</td>

  <td>50008168</td>

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

  <td>1</td>

  <td>20141103</td>

</tr>

</tbody>

</table>

</div>

# 行不做限制
# 列标签为user_id 和 buiy_mount 字段
df.loc[:,['user_id','buy_mount']].head(5)

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>user_id</th>

  <th>buy_mount</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>786295544</td>

  <td>2</td>

</tr>

<tr>

  <th>1</th>

  <td>532110457</td>

  <td>1</td>

</tr>

<tr>

  <th>2</th>

  <td>249013725</td>

  <td>1</td>

</tr>

<tr>

  <th>3</th>

  <td>917056007</td>

  <td>2</td>

</tr>

<tr>

  <th>4</th>

  <td>444069173</td>

  <td>1</td>

</tr>

</tbody>

</table>

</div>

# 行标签为1到3，列标签为user_id 和 buy_mount 字段
df.loc[1:3, ['user_id','buy_mount']]

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>user_id</th>

  <th>buy_mount</th>

</tr>

</thead>

<tbody>

<tr>

  <th>1</th>

  <td>532110457</td>

  <td>1</td>

</tr>

<tr>

  <th>2</th>

  <td>249013725</td>

  <td>1</td>

</tr>

<tr>

  <th>3</th>

  <td>917056007</td>

  <td>2</td>

</tr>

</tbody>

</table>

</div>

df.loc[df.user_id=='249013725', ['user_id', 'buy_mount']]  # 条件筛选

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>user_id</th>

  <th>buy_mount</th>

</tr>

</thead>

<tbody>

<tr>

  <th>2</th>

  <td>249013725</td>

  <td>1</td>

</tr>

</tbody>

</table>

</div>

df.loc[(df.user_id=='249013725') | (df.buy_mount>=1000), ['user_id', 'buy_mount']]  # 条件取值

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

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.dataframe thead th {

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</style>

<thead>

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

  <th></th>

  <th>user_id</th>

  <th>buy_mount</th>

</tr>

</thead>

<tbody>

<tr>

  <th>2</th>

  <td>249013725</td>

  <td>1</td>

</tr>

<tr>

  <th>1164</th>

  <td>1945590674</td>

  <td>1500</td>

</tr>

<tr>

  <th>5536</th>

  <td>2288344467</td>

  <td>10000</td>

</tr>

<tr>

  <th>6627</th>

  <td>117730165</td>

  <td>2800</td>

</tr>

<tr>

  <th>10402</th>

  <td>32141414</td>

  <td>1000</td>

</tr>

<tr>

  <th>25675</th>

  <td>173701616</td>

  <td>2748</td>

</tr>

</tbody>

</table>

</div>

# loc选择的是标签，iloc选择的是位置
df.iloc[1:3]

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>user_id</th>

  <th>auction_id</th>

  <th>cat_id</th>

  <th>cat1</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

</tr>

</thead>

<tbody>

<tr>

  <th>1</th>

  <td>532110457</td>

  <td>17916191097</td>

  <td>50011993</td>

  <td>28</td>

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

  <td>1</td>

  <td>20131011</td>

</tr>

<tr>

  <th>2</th>

  <td>249013725</td>

  <td>21896936223</td>

  <td>50012461</td>

  <td>50014815</td>

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

  <td>1</td>

  <td>20131011</td>

</tr>

</tbody>

</table>

</div>

df.iloc[1:3, 1:4]

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>auction_id</th>

  <th>cat_id</th>

  <th>cat1</th>

</tr>

</thead>

<tbody>

<tr>

  <th>1</th>

  <td>17916191097</td>

  <td>50011993</td>

  <td>28</td>

</tr>

<tr>

  <th>2</th>

  <td>21896936223</td>

  <td>50012461</td>

  <td>50014815</td>

</tr>

</tbody>

</table>

</div>

df.iloc[:, [0, 2]].head(5)

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>user_id</th>

  <th>cat_id</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>786295544</td>

  <td>50014866</td>

</tr>

<tr>

  <th>1</th>

  <td>532110457</td>

  <td>50011993</td>

</tr>

<tr>

  <th>2</th>

  <td>249013725</td>

  <td>50012461</td>

</tr>

<tr>

  <th>3</th>

  <td>917056007</td>

  <td>50018831</td>

</tr>

<tr>

  <th>4</th>

  <td>444069173</td>

  <td>50013636</td>

</tr>

</tbody>

</table>

</div>

# 选择第二行和第十一行，第1列和第三列
df.iloc[[1,10], [0,2]]

<div>

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

    vertical-align: middle;

.dataframe tbody tr th {

    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>user_id</th>

  <th>cat_id</th>

</tr>

</thead>

<tbody>

<tr>

  <th>1</th>

  <td>532110457</td>

  <td>50011993</td>

</tr>

<tr>

  <th>10</th>

  <td>734147966</td>

  <td>50018202</td>

</tr>

</tbody>

</table>

</div>

$\color{red}loc按标签选择，iloc按顺序选择$

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

python

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

python