Pandas.DataFrame删除行和列

本文通过一个csv实例文件来展示如何删除Pandas.DataFrame的行和列
数据文件名为:example.csv
内容为：

|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

删除行
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
slope0.037969137402 -0.0116468916667 -0.0791384411275 -0.0776527455294
想要删除最后两行
.drop()方法如果不设置参数inplace=True，则只能在生成的新数据块中实现删除效果，而不能删除原有数据块的相应行。
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则原有数据块的相应行被删除
In [4]: odata.drop(odata.index[[16,17]],inplace=True) odata Out[4]: date
spring summer autumn winter0 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
删除列

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方法可以将所选列从原数据块中弹出，原数据块不再保留该列
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方法既可以保留原数据块中的所选列，也可以删除，这取决于参数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
当inplace=True时.drop()执行内部删除，不返回任何值，原数据发生改变
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

总结，不论是行删除还是列删除，也不论是原数据删除，还是输出新变量删除，.drop()的方法都能达到目的，为了方便好记，熟练操作，所以应该尽量多使用.drop()方法


作者：Clarmy
链接：https://www.jianshu.com/p/67e67c7034f6
來源：简书
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