数据清洗之数据增加和删除

原创

ruochen

修改于 2021-05-13 14:33:12

1.8K0

修改于 2021-05-13 14:33:12

文章被收录于专栏：若尘的技术专栏若尘的技术专栏

数据增加和删除

在数据中，直接添加列
使用df.insert方法在数据中添加一列
drop(labels, axis, inplace=True)方法（删除）
- labels表示删除的数据，axis表示作用轴，inplace=True表示是否对原数据生效
- axis=0按行操作，axis=1按列操作
使用del函数直接删除其中一列

import pandas as pd
import os
import numpy as np

os.getcwd()

'D:\\Jupyter\\notebook\\Python数据清洗实战\\数据'

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['购买量'] = np.where(df['buy_mount']>3, '高', '低')

df.head(5)

<div>

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

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</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>

  <th>购买量</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>

  <td>低</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>

  <td>低</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>

  <td>低</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>

  <td>低</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>

  <td>低</td>

</tr>

</tbody>

</table>

</div>

# 将第二列放在第一列
auction_id = df['auction_id']

del df['auction_id']

df.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>

  <th>cat1</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

  <th>购买量</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>786295544</td>

  <td>50014866</td>

  <td>50022520</td>

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

  <td>2</td>

  <td>20140919</td>

  <td>低</td>

</tr>

<tr>

  <th>1</th>

  <td>532110457</td>

  <td>50011993</td>

  <td>28</td>

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

  <td>1</td>

  <td>20131011</td>

  <td>低</td>

</tr>

<tr>

  <th>2</th>

  <td>249013725</td>

  <td>50012461</td>

  <td>50014815</td>

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

  <td>1</td>

  <td>20131011</td>

  <td>低</td>

</tr>

<tr>

  <th>3</th>

  <td>917056007</td>

  <td>50018831</td>

  <td>50014815</td>

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

  <td>2</td>

  <td>20141023</td>

  <td>低</td>

</tr>

<tr>

  <th>4</th>

  <td>444069173</td>

  <td>50013636</td>

  <td>50008168</td>

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

  <td>1</td>

  <td>20141103</td>

  <td>低</td>

</tr>

</tbody>

</table>

</div>

# 第一个参数：插入位置
# 第二个参数：标签名称
# 第三个参数：数据
df.insert(0, 'auction_id_new', auction_id)

df.head(5)

<div>

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

    vertical-align: middle;

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    vertical-align: top;

.dataframe thead th {

    text-align: right;

</style>

<thead>

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

  <th></th>

  <th>auction_id_new</th>

  <th>user_id</th>

  <th>cat_id</th>

  <th>cat1</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

  <th>购买量</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>41098319944</td>

  <td>786295544</td>

  <td>50014866</td>

  <td>50022520</td>

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

  <td>2</td>

  <td>20140919</td>

  <td>低</td>

</tr>

<tr>

  <th>1</th>

  <td>17916191097</td>

  <td>532110457</td>

  <td>50011993</td>

  <td>28</td>

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

  <td>1</td>

  <td>20131011</td>

  <td>低</td>

</tr>

<tr>

  <th>2</th>

  <td>21896936223</td>

  <td>249013725</td>

  <td>50012461</td>

  <td>50014815</td>

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

  <td>1</td>

  <td>20131011</td>

  <td>低</td>

</tr>

<tr>

  <th>3</th>

  <td>12515996043</td>

  <td>917056007</td>

  <td>50018831</td>

  <td>50014815</td>

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

  <td>2</td>

  <td>20141023</td>

  <td>低</td>

</tr>

<tr>

  <th>4</th>

  <td>20487688075</td>

  <td>444069173</td>

  <td>50013636</td>

  <td>50008168</td>

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

  <td>1</td>

  <td>20141103</td>

  <td>低</td>

</tr>

</tbody>

</table>

</div>

# 删除两列数据
df.drop(labels=['auction_id_new', '购买量'], axis=1).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>

  <th>cat1</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>786295544</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>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>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>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>50013636</td>

  <td>50008168</td>

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

  <td>1</td>

  <td>20141103</td>

</tr>

</tbody>

</table>

</div>

# 再次查看df，发现刚才删除数据仍然存在
# 因为没有对原数据生效 
df.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>auction_id_new</th>

  <th>user_id</th>

  <th>cat_id</th>

  <th>cat1</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

  <th>购买量</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>41098319944</td>

  <td>786295544</td>

  <td>50014866</td>

  <td>50022520</td>

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

  <td>2</td>

  <td>20140919</td>

  <td>低</td>

</tr>

<tr>

  <th>1</th>

  <td>17916191097</td>

  <td>532110457</td>

  <td>50011993</td>

  <td>28</td>

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

  <td>1</td>

  <td>20131011</td>

  <td>低</td>

</tr>

<tr>

  <th>2</th>

  <td>21896936223</td>

  <td>249013725</td>

  <td>50012461</td>

  <td>50014815</td>

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

  <td>1</td>

  <td>20131011</td>

  <td>低</td>

</tr>

<tr>

  <th>3</th>

  <td>12515996043</td>

  <td>917056007</td>

  <td>50018831</td>

  <td>50014815</td>

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

  <td>2</td>

  <td>20141023</td>

  <td>低</td>

</tr>

<tr>

  <th>4</th>

  <td>20487688075</td>

  <td>444069173</td>

  <td>50013636</td>

  <td>50008168</td>

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

  <td>1</td>

  <td>20141103</td>

  <td>低</td>

</tr>

</tbody>

</table>

</div>

df.drop(labels=['auction_id_new', '购买量'], axis=1, inplace=True)

---------------------------------------------------------------------------

KeyError                                  Traceback (most recent call last)

<ipython-input-30-acf2a75acaf3> in <module>

----> 1 df.drop(labels=['auction_id_new', '购买量'], axis=1, inplace=True)

D:\Anaconda3\lib\site-packages\pandas\core\frame.py in drop(self, labels, axis, index, columns, level, inplace, errors)

   3938                                            index=index, columns=columns,

   3939                                            level=level, inplace=inplace,

-> 3940                                            errors=errors)

   3942     @rewrite_axis_style_signature('mapper', [('copy', True),

D:\Anaconda3\lib\site-packages\pandas\core\generic.py in drop(self, labels, axis, index, columns, level, inplace, errors)

   3778         for axis, labels in axes.items():

   3779             if labels is not None:

-> 3780                 obj = obj._drop_axis(labels, axis, level=level, errors=errors)

   3782         if inplace:

D:\Anaconda3\lib\site-packages\pandas\core\generic.py in _drop_axis(self, labels, axis, level, errors)

   3810                 new_axis = axis.drop(labels, level=level, errors=errors)

   3811             else:

-> 3812                 new_axis = axis.drop(labels, errors=errors)

   3813             result = self.reindex(**{axis_name: new_axis})

D:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in drop(self, labels, errors)

   4963             if errors != 'ignore':

   4964                 raise KeyError(

-> 4965                     '{} not found in axis'.format(labels[mask]))

   4966             indexer = indexer[~mask]

   4967         return self.delete(indexer)

KeyError: "['auction_id_new' '购买量'] not found in axis"

df.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>

  <th>cat1</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>786295544</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>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>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>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>50013636</td>

  <td>50008168</td>

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

  <td>1</td>

  <td>20141103</td>

</tr>

</tbody>

</table>

</div>

# 删除标签为3，4数据
df.drop(labels=[3,4], axis=0, inplace=True)

df.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>

  <th>cat1</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>786295544</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>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>50012461</td>

  <td>50014815</td>

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

  <td>1</td>

  <td>20131011</td>

</tr>

<tr>

  <th>5</th>

  <td>152298847</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>50010557</td>

  <td>50008168</td>

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

  <td>1</td>

  <td>20121212</td>

</tr>

</tbody>

</table>

</div>

df.drop(labels=range(0,3), axis=0, inplace=True)

df.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>

  <th>cat1</th>

  <th>property</th>

  <th>buy_mount</th>

  <th>day</th>

</tr>

</thead>

<tbody>

<tr>

  <th>5</th>

  <td>152298847</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>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>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>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>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>

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