使用DataFrame刪除行和列的實例講解
更新時間：2018年04月08日 10:13:15 作者：Clarmy
下面小編就為大家分享一篇使用DataFrame刪除行和列的實例講解，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧
本文通過一個csv實例文件來展示如何刪除Pandas.DataFrame的行和列
數(shù)據(jù)文件名為:example.csv
內(nèi)容為：
| date | spring | summer | autumn | winter |
|---|---|---|---|---|
| 2000 | 12.2338809 | 16.90730113 | 15.69238313 | 14.08596223 |
| 2001 | 12.84748057 | 16.75046873 | 14.51406637 | 13.5037456 |
| 2002 | 13.558175 | 17.2033926 | 15.6999475 | 13.23365247 |
| 2003 | 12.6547247 | 16.89491533 | 15.6614647 | 12.84347867 |
| 2004 | 13.2537298 | 17.04696657 | 15.20905377 | 14.3647912 |
| 2005 | 13.4443049 | 16.7459822 | 16.62218797 | 11.61082257 |
| 2006 | 13.50569567 | 16.83357857 | 15.4979282 | 12.19934363 |
| 2007 | 13.48852623 | 16.66773283 | 15.81701437 | 13.7438216 |
| 2008 | 13.1515319 | 16.48650693 | 15.72957287 | 12.93233587 |
| 2009 | 13.45771543 | 16.63923783 | 18.26017997 | 12.65315943 |
| 2010 | 13.1945485 | 16.7286889 | 15.42635267 | 13.8833583 |
| 2011 | 14.34779417 | 16.68942103 | 14.17658043 | 12.36654197 |
| 2012 | 13.6050867 | 17.13056773 | 14.71796777 | 13.29255243 |
| 2013 | 13.02790787 | 17.38619343 | 16.20345497 | 13.18612133 |
| 2014 | 12.74668163 | 16.54428687 | 14.7367682 | 12.87065125 |
| 2015 | 13.465904 | 16.50612317 | 12.44243663 | 11.0181384 |
| season | spring | summer | autumn | winter |
| slope | 0.0379691374 | -0.01164689167 | -0.07913844113 | -0.07765274553 |
刪除行
In [1]:
import numpy as np
import pandas as pd
odata = pd.read_csv('example.csv')
odata
Out[1]:
date spring summer autumn winter
0 2000 12.2338809 16.9073011333 15.6923831333 14.0859622333
1 2001 12.8474805667 16.7504687333 14.5140663667 13.5037456
2 2002 13.558175 17.2033926 15.6999475 13.2336524667
3 2003 12.6547247 16.8949153333 15.6614647 12.8434786667
4 2004 13.2537298 17.0469665667 15.2090537667 14.3647912
5 2005 13.4443049 16.7459822 16.6221879667 11.6108225667
6 2006 13.5056956667 16.8335785667 15.4979282 12.1993436333
7 2007 13.4885262333 16.6677328333 15.8170143667 13.7438216
8 2008 13.1515319 16.4865069333 15.7295728667 12.9323358667
9 2009 13.4577154333 16.6392378333 18.2601799667 12.6531594333
10 2010 13.1945485 16.7286889 15.4263526667 13.8833583
11 2011 14.3477941667 16.6894210333 14.1765804333 12.3665419667
12 2012 13.6050867 17.1305677333 14.7179677667 13.2925524333
13 2013 13.0279078667 17.3861934333 16.2034549667 13.1861213333
14 2014 12.7466816333 16.5442868667 14.7367682 12.8706512467
15 2015 13.465904 16.5061231667 12.4424366333 11.0181384
16 season spring summer autumn winter
17 slope 0.037969137402 -0.0116468916667 -0.0791384411275 -0.0776527455294
.drop()方法如果不設(shè)置參數(shù)inplace=True，則只能在生成的新數(shù)據(jù)塊中實現(xiàn)刪除效果，而不能刪除原有數(shù)據(jù)塊的相應行。
In [2]: data = odata.drop([16,17]) odata Out[2]: date spring summer autumn winter 0 2000 12.2338809 16.9073011333 15.6923831333 14.0859622333 1 2001 12.8474805667 16.7504687333 14.5140663667 13.5037456 2 2002 13.558175 17.2033926 15.6999475 13.2336524667 3 2003 12.6547247 16.8949153333 15.6614647 12.8434786667 4 2004 13.2537298 17.0469665667 15.2090537667 14.3647912 5 2005 13.4443049 16.7459822 16.6221879667 11.6108225667 6 2006 13.5056956667 16.8335785667 15.4979282 12.1993436333 7 2007 13.4885262333 16.6677328333 15.8170143667 13.7438216 8 2008 13.1515319 16.4865069333 15.7295728667 12.9323358667 9 2009 13.4577154333 16.6392378333 18.2601799667 12.6531594333 10 2010 13.1945485 16.7286889 15.4263526667 13.8833583 11 2011 14.3477941667 16.6894210333 14.1765804333 12.3665419667 12 2012 13.6050867 17.1305677333 14.7179677667 13.2925524333 13 2013 13.0279078667 17.3861934333 16.2034549667 13.1861213333 14 2014 12.7466816333 16.5442868667 14.7367682 12.8706512467 15 2015 13.465904 16.5061231667 12.4424366333 11.0181384 16 season spring summer autumn winter 17 slope 0.037969137402 -0.0116468916667 -0.0791384411275 -0.0776527455294 In [3]: data Out[3]: date spring summer autumn winter 0 2000 12.2338809 16.9073011333 15.6923831333 14.0859622333 1 2001 12.8474805667 16.7504687333 14.5140663667 13.5037456 2 2002 13.558175 17.2033926 15.6999475 13.2336524667 3 2003 12.6547247 16.8949153333 15.6614647 12.8434786667 4 2004 13.2537298 17.0469665667 15.2090537667 14.3647912 5 2005 13.4443049 16.7459822 16.6221879667 11.6108225667 6 2006 13.5056956667 16.8335785667 15.4979282 12.1993436333 7 2007 13.4885262333 16.6677328333 15.8170143667 13.7438216 8 2008 13.1515319 16.4865069333 15.7295728667 12.9323358667 9 2009 13.4577154333 16.6392378333 18.2601799667 12.6531594333 10 2010 13.1945485 16.7286889 15.4263526667 13.8833583 11 2011 14.3477941667 16.6894210333 14.1765804333 12.3665419667 12 2012 13.6050867 17.1305677333 14.7179677667 13.2925524333 13 2013 13.0279078667 17.3861934333 16.2034549667 13.1861213333 14 2014 12.7466816333 16.5442868667 14.7367682 12.8706512467 15 2015 13.465904 16.5061231667 12.4424366333 11.0181384
如果inplace=True則原有數(shù)據(jù)塊的相應行被刪除
In [4]: odata.drop(odata.index[[16,17]],inplace=True) odata Out[4]: date spring summer autumn winter 0 2000 12.2338809 16.9073011333 15.6923831333 14.0859622333 1 2001 12.8474805667 16.7504687333 14.5140663667 13.5037456 2 2002 13.558175 17.2033926 15.6999475 13.2336524667 3 2003 12.6547247 16.8949153333 15.6614647 12.8434786667 4 2004 13.2537298 17.0469665667 15.2090537667 14.3647912 5 2005 13.4443049 16.7459822 16.6221879667 11.6108225667 6 2006 13.5056956667 16.8335785667 15.4979282 12.1993436333 7 2007 13.4885262333 16.6677328333 15.8170143667 13.7438216 8 2008 13.1515319 16.4865069333 15.7295728667 12.9323358667 9 2009 13.4577154333 16.6392378333 18.2601799667 12.6531594333 10 2010 13.1945485 16.7286889 15.4263526667 13.8833583 11 2011 14.3477941667 16.6894210333 14.1765804333 12.3665419667 12 2012 13.6050867 17.1305677333 14.7179677667 13.2925524333 13 2013 13.0279078667 17.3861934333 16.2034549667 13.1861213333 14 2014 12.7466816333 16.5442868667 14.7367682 12.8706512467 15 2015 13.465904 16.5061231667 12.4424366333 11.0181384
刪除列
del方法
In [5]: del odata['date'] odata Out[5]: spring summer autumn winter 0 12.2338809 16.9073011333 15.6923831333 14.0859622333 1 12.8474805667 16.7504687333 14.5140663667 13.5037456 2 13.558175 17.2033926 15.6999475 13.2336524667 3 12.6547247 16.8949153333 15.6614647 12.8434786667 4 13.2537298 17.0469665667 15.2090537667 14.3647912 5 13.4443049 16.7459822 16.6221879667 11.6108225667 6 13.5056956667 16.8335785667 15.4979282 12.1993436333 7 13.4885262333 16.6677328333 15.8170143667 13.7438216 8 13.1515319 16.4865069333 15.7295728667 12.9323358667 9 13.4577154333 16.6392378333 18.2601799667 12.6531594333 10 13.1945485 16.7286889 15.4263526667 13.8833583 11 14.3477941667 16.6894210333 14.1765804333 12.3665419667 12 13.6050867 17.1305677333 14.7179677667 13.2925524333 13 13.0279078667 17.3861934333 16.2034549667 13.1861213333 14 12.7466816333 16.5442868667 14.7367682 12.8706512467 15 13.465904 16.5061231667 12.4424366333 11.0181384
.pop()方法
.pop方法可以將所選列從原數(shù)據(jù)塊中彈出，原數(shù)據(jù)塊不再保留該列
In [6]:
spring = odata.pop('spring')
spring
Out[6]:
0 12.2338809
1 12.8474805667
2 13.558175
3 12.6547247
4 13.2537298
5 13.4443049
6 13.5056956667
7 13.4885262333
8 13.1515319
9 13.4577154333
10 13.1945485
11 14.3477941667
12 13.6050867
13 13.0279078667
14 12.7466816333
15 13.465904
Name: spring, dtype: object
In [7]:
odata
Out[7]:
summer autumn winter
0 16.9073011333 15.6923831333 14.0859622333
1 16.7504687333 14.5140663667 13.5037456
2 17.2033926 15.6999475 13.2336524667
3 16.8949153333 15.6614647 12.8434786667
4 17.0469665667 15.2090537667 14.3647912
5 16.7459822 16.6221879667 11.6108225667
6 16.8335785667 15.4979282 12.1993436333
7 16.6677328333 15.8170143667 13.7438216
8 16.4865069333 15.7295728667 12.9323358667
9 16.6392378333 18.2601799667 12.6531594333
10 16.7286889 15.4263526667 13.8833583
11 16.6894210333 14.1765804333 12.3665419667
12 17.1305677333 14.7179677667 13.2925524333
13 17.3861934333 16.2034549667 13.1861213333
14 16.5442868667 14.7367682 12.8706512467
15 16.5061231667 12.4424366333 11.0181384
.drop()方法
drop方法既可以保留原數(shù)據(jù)塊中的所選列，也可以刪除，這取決于參數(shù)inplace
In [8]: withoutSummer = odata.drop(['summer'],axis=1) withoutSummer Out[8]: autumn winter 0 15.6923831333 14.0859622333 1 14.5140663667 13.5037456 2 15.6999475 13.2336524667 3 15.6614647 12.8434786667 4 15.2090537667 14.3647912 5 16.6221879667 11.6108225667 6 15.4979282 12.1993436333 7 15.8170143667 13.7438216 8 15.7295728667 12.9323358667 9 18.2601799667 12.6531594333 10 15.4263526667 13.8833583 11 14.1765804333 12.3665419667 12 14.7179677667 13.2925524333 13 16.2034549667 13.1861213333 14 14.7367682 12.8706512467 15 12.4424366333 11.0181384 In [9]: odata Out[9]: summer autumn winter 0 16.9073011333 15.6923831333 14.0859622333 1 16.7504687333 14.5140663667 13.5037456 2 17.2033926 15.6999475 13.2336524667 3 16.8949153333 15.6614647 12.8434786667 4 17.0469665667 15.2090537667 14.3647912 5 16.7459822 16.6221879667 11.6108225667 6 16.8335785667 15.4979282 12.1993436333 7 16.6677328333 15.8170143667 13.7438216 8 16.4865069333 15.7295728667 12.9323358667 9 16.6392378333 18.2601799667 12.6531594333 10 16.7286889 15.4263526667 13.8833583 11 16.6894210333 14.1765804333 12.3665419667 12 17.1305677333 14.7179677667 13.2925524333 13 17.3861934333 16.2034549667 13.1861213333 14 16.5442868667 14.7367682 12.8706512467 15 16.5061231667 12.4424366333 11.0181384
當inplace=True時.drop()執(zhí)行內(nèi)部刪除，不返回任何值，原數(shù)據(jù)發(fā)生改變
In [10]: withoutWinter = odata.drop(['winter'],axis=1,inplace=True) type(withoutWinter) Out[10]: NoneType In [11]: odata Out[11]: summer autumne 0 16.9073011333 15.6923831333 1 16.7504687333 14.5140663667 2 17.2033926 15.6999475 3 16.8949153333 15.6614647 4 17.0469665667 15.2090537667 5 16.7459822 16.6221879667 6 16.8335785667 15.4979282 7 16.6677328333 15.8170143667 8 16.4865069333 15.7295728667 9 16.6392378333 18.2601799667 10 16.7286889 15.4263526667 11 16.6894210333 14.1765804333 12 17.1305677333 14.7179677667 13 17.3861934333 16.2034549667 14 16.5442868667 14.7367682 15 16.5061231667 12.4424366333
總結(jié)，不論是行刪除還是列刪除，也不論是原數(shù)據(jù)刪除，還是輸出新變量刪除，.drop()的方法都能達到目的，為了方便好記，熟練操作，所以應該盡量多使用.drop()方法
相關(guān)文章
Django model反向關(guān)聯(lián)名稱的方法
今天小編就為大家分享一篇Django model反向關(guān)聯(lián)名稱的方法，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧2018-12-12
這篇文章主要介紹了Python運行shell命令的一些方法,我們來看一下 python 中有哪些自帶模塊或者方法可以實現(xiàn)，對Python運行shell命令感興趣的朋友跟隨小編一起看看吧2023-07-07
這篇文章主要介紹了python簡單文本處理的方法,涉及Python針對文本文件及字符串操作的相關(guān)技巧,需要的朋友可以參考下2015-07-07
Python實現(xiàn)的隨機森林算法與簡單總結(jié)
這篇文章主要介紹了Python實現(xiàn)的隨機森林算法,結(jié)合實例形式詳細分析了隨機森林算法的概念、原理、實現(xiàn)技巧與相關(guān)注意事項,需要的朋友可以參考下2018-01-01
在數(shù)據(jù)分析和處理過程中,數(shù)據(jù)清洗是一個至關(guān)重要的步驟,其中字段去重是一個常見且關(guān)鍵的任務(wù),下面我們看看如何使用Python進行表格字段智能去重吧2025-03-03
Pytorch中的數(shù)據(jù)轉(zhuǎn)換Transforms與DataLoader方式
這篇文章主要介紹了Pytorch中的數(shù)據(jù)轉(zhuǎn)換Transforms與DataLoader方式，具有很好的參考價值，希望對大家有所幫助。如有錯誤或未考慮完全的地方，望不吝賜教2023-02-02
Python中使用PyHook監(jiān)聽鼠標和鍵盤事件實例
這篇文章主要介紹了Python中使用PyHook監(jiān)聽鼠標和鍵盤事件實例,這個庫依賴于另一個Python庫PyWin32,并且只能運行在Windows平臺,需要的朋友可以參考下2014-07-07
這篇文章主要介紹了python pandas生成時間列表，文中通過示例代碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值,需要的朋友可以參考下2019-06-06
這篇文章主要介紹了Python對List中的元素排序 ,需要的朋友可以參考下2018-04-04
在python中枚舉是一種類（Enum,IntEnum），存放在enum模塊中。枚舉類型可以給一組標簽賦予一組特定的值，本文給大家介紹的非常詳細，對大家的學習或工作具有一定的參考借鑒價值，需要的朋友可以參考下2022-10-10