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使用DataFrame刪除行和列的實(shí)例講解

發(fā)布時(shí)間：2020-08-21 12:13:38 來源：腳本之家 閱讀：218 作者：Clarmy 欄目：開發(fā)技術(shù)

本文通過一個(gè)csv實(shí)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(shí)現(xiàn)刪除效果，而不能刪除原有數(shù)據(jù)塊的相應(yīng)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(yīng)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(dāng)inplace=True時(shí).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(dá)到目的，為了方便好記，熟練操作，所以應(yīng)該盡量多使用.drop()方法

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