数据清洗之数据修改和查找

原创

ruochen

修改于 2021-05-13 14:33:16

6780

修改于 2021-05-13 14:33:16

文章被收录于专栏：若尘的技术专栏

数据修改和查找

在数据中，可以使用rename修改列名称或者行索引名称
使用loc方法修改数据
使用loc方法查找符合条件的数据
条件于条件之间用&或者|连接，分别代表 '且' 和 '或'
使用between和isin选择满足条件的行

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')

df1 = pd.read_csv('sam_tianchi_mum_baby.csv', encoding='utf-8', dtype=str)

df1.head(5)

<div>

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<thead>

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

  <th></th>

  <th>user_id</th>

  <th>birthday</th>

  <th>gender</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>2757</td>

  <td>20130311</td>

  <td>1</td>

</tr>

<tr>

  <th>1</th>

  <td>415971</td>

  <td>20121111</td>

  <td>0</td>

</tr>

<tr>

  <th>2</th>

  <td>1372572</td>

  <td>20120130</td>

  <td>1</td>

</tr>

<tr>

  <th>3</th>

  <td>10339332</td>

  <td>20110910</td>

  <td>0</td>

</tr>

<tr>

  <th>4</th>

  <td>10642245</td>

  <td>20130213</td>

  <td>0</td>

</tr>

</tbody>

</table>

</div>

df1.info()

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

RangeIndex: 953 entries, 0 to 952

Data columns (total 3 columns):

user_id     953 non-null object

birthday    953 non-null object

gender      953 non-null object

dtypes: object(3)

memory usage: 22.4+ KB

# 修改0为女性
df1.loc[df1['gender']=='0', 'gender'] = '女性'

df1.loc[df1['gender']=='1', 'gender'] = '男性'
df1.loc[df1['gender']=='2', 'gender'] = '未知'

df1.head(5)

<div>

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<thead>

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

  <th></th>

  <th>user_id</th>

  <th>birthday</th>

  <th>gender</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>2757</td>

  <td>20130311</td>

  <td>男性</td>

</tr>

<tr>

  <th>1</th>

  <td>415971</td>

  <td>20121111</td>

  <td>女性</td>

</tr>

<tr>

  <th>2</th>

  <td>1372572</td>

  <td>20120130</td>

  <td>男性</td>

</tr>

<tr>

  <th>3</th>

  <td>10339332</td>

  <td>20110910</td>

  <td>女性</td>

</tr>

<tr>

  <th>4</th>

  <td>10642245</td>

  <td>20130213</td>

  <td>女性</td>

</tr>

</tbody>

</table>

</div>

# 对列标签进行修改，传入参数为字典形式
# 加 inplace=True，会对原数据进行修改
df1.rename(columns={'user_id': '用户ID', 'birthday': '出生日期', 'gender': '性别'}, inplace=True)

df1.head(5)

<div>

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<thead>

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

  <th></th>

  <th>用户ID</th>

  <th>出生日期</th>

  <th>性别</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>2757</td>

  <td>20130311</td>

  <td>男性</td>

</tr>

<tr>

  <th>1</th>

  <td>415971</td>

  <td>20121111</td>

  <td>女性</td>

</tr>

<tr>

  <th>2</th>

  <td>1372572</td>

  <td>20120130</td>

  <td>男性</td>

</tr>

<tr>

  <th>3</th>

  <td>10339332</td>

  <td>20110910</td>

  <td>女性</td>

</tr>

<tr>

  <th>4</th>

  <td>10642245</td>

  <td>20130213</td>

  <td>女性</td>

</tr>

</tbody>

</table>

</div>

# 修改行标签
df1.rename(index={3: 333, 4:444}, inplace=True)

df1.head(5)

<div>

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

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<thead>

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

  <th></th>

  <th>用户ID</th>

  <th>出生日期</th>

  <th>性别</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>2757</td>

  <td>20130311</td>

  <td>男性</td>

</tr>

<tr>

  <th>1</th>

  <td>415971</td>

  <td>20121111</td>

  <td>女性</td>

</tr>

<tr>

  <th>2</th>

  <td>1372572</td>

  <td>20120130</td>

  <td>男性</td>

</tr>

<tr>

  <th>333</th>

  <td>10339332</td>

  <td>20110910</td>

  <td>女性</td>

</tr>

<tr>

  <th>444</th>

  <td>10642245</td>

  <td>20130213</td>

  <td>女性</td>

</tr>

</tbody>

</table>

</div>

# iloc按照位置取值，与标签无关
df1.iloc[:5]

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<thead>

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

  <th></th>

  <th>用户ID</th>

  <th>出生日期</th>

  <th>性别</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>2757</td>

  <td>20130311</td>

  <td>男性</td>

</tr>

<tr>

  <th>1</th>

  <td>415971</td>

  <td>20121111</td>

  <td>女性</td>

</tr>

<tr>

  <th>2</th>

  <td>1372572</td>

  <td>20120130</td>

  <td>男性</td>

</tr>

<tr>

  <th>333</th>

  <td>10339332</td>

  <td>20110910</td>

  <td>女性</td>

</tr>

<tr>

  <th>444</th>

  <td>10642245</td>

  <td>20130213</td>

  <td>女性</td>

</tr>

</tbody>

</table>

</div>

# 重置索引
df1.reset_index(drop=True, inplace=True)

df1.head(5)

<div>

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.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>用户ID</th>

  <th>出生日期</th>

  <th>性别</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>2757</td>

  <td>20130311</td>

  <td>男性</td>

</tr>

<tr>

  <th>1</th>

  <td>415971</td>

  <td>20121111</td>

  <td>女性</td>

</tr>

<tr>

  <th>2</th>

  <td>1372572</td>

  <td>20120130</td>

  <td>男性</td>

</tr>

<tr>

  <th>3</th>

  <td>10339332</td>

  <td>20110910</td>

  <td>女性</td>

</tr>

<tr>

  <th>4</th>

  <td>10642245</td>

  <td>20130213</td>

  <td>女性</td>

</tr>

</tbody>

</table>

</div>

查询

df = pd.read_csv('baby_trade_history.csv', encoding='utf-8', dtype={'user_id':str})

df.head(2)

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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>

</tbody>

</table>

</div>

# 选择buy_mount 大于10 的数据
# 返回的是布尔索引值
# 放入pandas dataframe 中，会自动筛选
# 数据太多，使用分片打印前5个
df[df['buy_mount'] > 10][:5]

<div>

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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>49</th>

  <td>103125167</td>

  <td>18426669796</td>

  <td>50018438</td>

  <td>50014815</td>

  <td>21458:46896;1628665:3233941;1628665:3233942;21...</td>

  <td>16</td>

  <td>20140220</td>

</tr>

<tr>

  <th>65</th>

  <td>605724983</td>

  <td>19747694834</td>

  <td>50006520</td>

  <td>50014815</td>

  <td>21458:30992</td>

  <td>12</td>

  <td>20141017</td>

</tr>

<tr>

  <th>89</th>

  <td>277279277</td>

  <td>18024521052</td>

  <td>211122</td>

  <td>38</td>

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

  <td>12</td>

  <td>20130513</td>

</tr>

<tr>

  <th>247</th>

  <td>392530596</td>

  <td>17001611735</td>

  <td>50011993</td>

  <td>28</td>

  <td>122218042:50276;21475:135183931;1628861:45151;...</td>

  <td>101</td>

  <td>20140301</td>

</tr>

<tr>

  <th>409</th>

  <td>1968453717</td>

  <td>12567034563</td>

  <td>122616024</td>

  <td>28</td>

  <td>135925585:42825;138052423:142000990;18822961:2...</td>

  <td>14</td>

  <td>20150127</td>

</tr>

</tbody>

</table>

</div>

# 选择buy_mount 小于等于10 的数据
df[~(df['buy_mount'] > 10)][:5]

<div>

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

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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>

</tbody>

</table>

</div>

# 多条件查询
# 每个条件用括号括起来
df[(df['buy_mount'] > 10) & (df['day'] > 20140101)][:5]

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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>49</th>

  <td>103125167</td>

  <td>18426669796</td>

  <td>50018438</td>

  <td>50014815</td>

  <td>21458:46896;1628665:3233941;1628665:3233942;21...</td>

  <td>16</td>

  <td>20140220</td>

</tr>

<tr>

  <th>65</th>

  <td>605724983</td>

  <td>19747694834</td>

  <td>50006520</td>

  <td>50014815</td>

  <td>21458:30992</td>

  <td>12</td>

  <td>20141017</td>

</tr>

<tr>

  <th>247</th>

  <td>392530596</td>

  <td>17001611735</td>

  <td>50011993</td>

  <td>28</td>

  <td>122218042:50276;21475:135183931;1628861:45151;...</td>

  <td>101</td>

  <td>20140301</td>

</tr>

<tr>

  <th>409</th>

  <td>1968453717</td>

  <td>12567034563</td>

  <td>122616024</td>

  <td>28</td>

  <td>135925585:42825;138052423:142000990;18822961:2...</td>

  <td>14</td>

  <td>20150127</td>

</tr>

<tr>

  <th>462</th>

  <td>1802549062</td>

  <td>17383345857</td>

  <td>50006520</td>

  <td>50014815</td>

  <td>22277:6262384;21458:30992;1628665:61550;162866...</td>

  <td>11</td>

  <td>20141120</td>

</tr>

</tbody>

</table>

</div>

# between方法不适用于字符串数据
df[df['buy_mount'].between(4, 10, inclusive=True)][:5]

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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>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>52</th>

  <td>55544814</td>

  <td>4917672059</td>

  <td>50015727</td>

  <td>50014815</td>

  <td>21458:4540492;1633959:58840623;7107736:3227806...</td>

  <td>4</td>

  <td>20131106</td>

</tr>

<tr>

  <th>117</th>

  <td>296448405</td>

  <td>18524578446</td>

  <td>50016030</td>

  <td>50008168</td>

  <td>21458:247918101;1628665:29782;1628665:29784;16...</td>

  <td>7</td>

  <td>20131202</td>

</tr>

<tr>

  <th>134</th>

  <td>97481514</td>

  <td>41161316434</td>

  <td>211122</td>

  <td>38</td>

  <td>6940834:29865;21458:4331527;1804977:606613769;...</td>

  <td>6</td>

  <td>20141126</td>

</tr>

<tr>

  <th>148</th>

  <td>662134541</td>

  <td>7594318922</td>

  <td>211122</td>

  <td>38</td>

  <td>21458:21776;36780:15333590;2675455:19653564;69...</td>

  <td>6</td>

  <td>20131211</td>

</tr>

</tbody>

</table>

</div>

df.dtypes

user_id       object

auction_id     int64

cat_id         int64

cat1           int64

property      object

buy_mount      int64

day            int64

dtype: object

# 多个值放入列表中
df[df['cat1'].isin([38,28])][:5]

<div>

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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>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>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>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>

</tbody>

</table>

</div>

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