数据清洗之数据修改和查找

Love The Way You Lie 2023-07-25 12:35 39阅读 0赞

# 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)

<table> 
 <thead> 
  <tr> 
   <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>

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)

<table> 
 <thead> 
  <tr> 
   <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>

# 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)

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

df1.head(5)

# iloc按照位置取值，与标签无关
    df1.iloc[:5]

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

df1.head(5)

*  查询

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

df.head(2)

# 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]

<table> 
 <thead> 
  <tr> 
   <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>

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

<table> 
 <thead> 
  <tr> 
   <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>

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

<table> 
 <thead> 
  <tr> 
   <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>

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

<table> 
 <thead> 
  <tr> 
   <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>

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]

<table> 
 <thead> 
  <tr> 
   <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>