使用DataFrame删除行和列的实例讲解

2019-11-25 14:58:46

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来源：转载

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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 npimport pandas as pdodata = pd.read_csv('example.csv')odataOut[1]:date  spring  summer  autumn  winter0  2000  12.2338809  16.9073011333  15.6923831333  14.08596223331  2001  12.8474805667  16.7504687333  14.5140663667  13.50374562  2002  13.558175  17.2033926  15.6999475  13.23365246673  2003  12.6547247  16.8949153333  15.6614647  12.84347866674  2004  13.2537298  17.0469665667  15.2090537667  14.36479125  2005  13.4443049  16.7459822  16.6221879667  11.61082256676  2006  13.5056956667  16.8335785667  15.4979282  12.19934363337  2007  13.4885262333  16.6677328333  15.8170143667  13.74382168  2008  13.1515319  16.4865069333  15.7295728667  12.93233586679  2009  13.4577154333  16.6392378333  18.2601799667  12.653159433310  2010  13.1945485  16.7286889  15.4263526667  13.883358311  2011  14.3477941667  16.6894210333  14.1765804333  12.366541966712  2012  13.6050867  17.1305677333  14.7179677667  13.292552433313  2013  13.0279078667  17.3861934333  16.2034549667  13.186121333314  2014  12.7466816333  16.5442868667  14.7367682  12.870651246715  2015  13.465904  16.5061231667  12.4424366333  11.018138416  season  spring  summer  autumn  winter17  slope  0.037969137402  -0.0116468916667  -0.0791384411275  -0.0776527455294

.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])odataOut[2]:date  spring  summer  autumn  winter0  2000  12.2338809  16.9073011333  15.6923831333  14.08596223331  2001  12.8474805667  16.7504687333  14.5140663667  13.50374562  2002  13.558175  17.2033926  15.6999475  13.23365246673  2003  12.6547247  16.8949153333  15.6614647  12.84347866674  2004  13.2537298  17.0469665667  15.2090537667  14.36479125  2005  13.4443049  16.7459822  16.6221879667  11.61082256676  2006  13.5056956667  16.8335785667  15.4979282  12.19934363337  2007  13.4885262333  16.6677328333  15.8170143667  13.74382168  2008  13.1515319  16.4865069333  15.7295728667  12.93233586679  2009  13.4577154333  16.6392378333  18.2601799667  12.653159433310  2010  13.1945485  16.7286889  15.4263526667  13.883358311  2011  14.3477941667  16.6894210333  14.1765804333  12.366541966712  2012  13.6050867  17.1305677333  14.7179677667  13.292552433313  2013  13.0279078667  17.3861934333  16.2034549667  13.186121333314  2014  12.7466816333  16.5442868667  14.7367682  12.870651246715  2015  13.465904  16.5061231667  12.4424366333  11.018138416  season  spring  summer  autumn  winter17  slope  0.037969137402  -0.0116468916667  -0.0791384411275  -0.0776527455294In [3]:dataOut[3]:date  spring  summer  autumn  winter0  2000  12.2338809  16.9073011333  15.6923831333  14.08596223331  2001  12.8474805667  16.7504687333  14.5140663667  13.50374562  2002  13.558175  17.2033926  15.6999475  13.23365246673  2003  12.6547247  16.8949153333  15.6614647  12.84347866674  2004  13.2537298  17.0469665667  15.2090537667  14.36479125  2005  13.4443049  16.7459822  16.6221879667  11.61082256676  2006  13.5056956667  16.8335785667  15.4979282  12.19934363337  2007  13.4885262333  16.6677328333  15.8170143667  13.74382168  2008  13.1515319  16.4865069333  15.7295728667  12.93233586679  2009  13.4577154333  16.6392378333  18.2601799667  12.653159433310  2010  13.1945485  16.7286889  15.4263526667  13.883358311  2011  14.3477941667  16.6894210333  14.1765804333  12.366541966712  2012  13.6050867  17.1305677333  14.7179677667  13.292552433313  2013  13.0279078667  17.3861934333  16.2034549667  13.186121333314  2014  12.7466816333  16.5442868667  14.7367682  12.870651246715  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)odataOut[4]:date  spring  summer  autumn  winter0  2000  12.2338809  16.9073011333  15.6923831333  14.08596223331  2001  12.8474805667  16.7504687333  14.5140663667  13.50374562  2002  13.558175  17.2033926  15.6999475  13.23365246673  2003  12.6547247  16.8949153333  15.6614647  12.84347866674  2004  13.2537298  17.0469665667  15.2090537667  14.36479125  2005  13.4443049  16.7459822  16.6221879667  11.61082256676  2006  13.5056956667  16.8335785667  15.4979282  12.19934363337  2007  13.4885262333  16.6677328333  15.8170143667  13.74382168  2008  13.1515319  16.4865069333  15.7295728667  12.93233586679  2009  13.4577154333  16.6392378333  18.2601799667  12.653159433310  2010  13.1945485  16.7286889  15.4263526667  13.883358311  2011  14.3477941667  16.6894210333  14.1765804333  12.366541966712  2012  13.6050867  17.1305677333  14.7179677667  13.292552433313  2013  13.0279078667  17.3861934333  16.2034549667  13.186121333314  2014  12.7466816333  16.5442868667  14.7367682  12.870651246715  2015  13.465904  16.5061231667  12.4424366333  11.0181384

删除列

del方法

In [5]:del odata['date']odataOut[5]:spring  summer  autumn  winter0  12.2338809  16.9073011333  15.6923831333  14.08596223331  12.8474805667  16.7504687333  14.5140663667  13.50374562  13.558175  17.2033926  15.6999475  13.23365246673  12.6547247  16.8949153333  15.6614647  12.84347866674  13.2537298  17.0469665667  15.2090537667  14.36479125  13.4443049  16.7459822  16.6221879667  11.61082256676  13.5056956667  16.8335785667  15.4979282  12.19934363337  13.4885262333  16.6677328333  15.8170143667  13.74382168  13.1515319  16.4865069333  15.7295728667  12.93233586679  13.4577154333  16.6392378333  18.2601799667  12.653159433310  13.1945485  16.7286889  15.4263526667  13.883358311  14.3477941667  16.6894210333  14.1765804333  12.366541966712  13.6050867  17.1305677333  14.7179677667  13.292552433313  13.0279078667  17.3861934333  16.2034549667  13.186121333314  12.7466816333  16.5442868667  14.7367682  12.870651246715  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')springOut[6]:0    12.23388091   12.84748056672     13.5581753    12.65472474    13.25372985    13.44430496   13.50569566677   13.48852623338    13.15153199   13.457715433310    13.194548511  14.347794166712    13.605086713  13.027907866714  12.746681633315    13.465904Name: spring, dtype: objectIn [7]:odataOut[7]:summer  autumn  winter0  16.9073011333  15.6923831333  14.08596223331  16.7504687333  14.5140663667  13.50374562  17.2033926  15.6999475  13.23365246673  16.8949153333  15.6614647  12.84347866674  17.0469665667  15.2090537667  14.36479125  16.7459822  16.6221879667  11.61082256676  16.8335785667  15.4979282  12.19934363337  16.6677328333  15.8170143667  13.74382168  16.4865069333  15.7295728667  12.93233586679  16.6392378333  18.2601799667  12.653159433310  16.7286889  15.4263526667  13.883358311  16.6894210333  14.1765804333  12.366541966712  17.1305677333  14.7179677667  13.292552433313  17.3861934333  16.2034549667  13.186121333314  16.5442868667  14.7367682  12.870651246715  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)withoutSummerOut[8]:autumn  winter0  15.6923831333  14.08596223331  14.5140663667  13.50374562  15.6999475  13.23365246673  15.6614647  12.84347866674  15.2090537667  14.36479125  16.6221879667  11.61082256676  15.4979282  12.19934363337  15.8170143667  13.74382168  15.7295728667  12.93233586679  18.2601799667  12.653159433310  15.4263526667  13.883358311  14.1765804333  12.366541966712  14.7179677667  13.292552433313  16.2034549667  13.186121333314  14.7367682  12.870651246715  12.4424366333  11.0181384In [9]:odataOut[9]:summer  autumn  winter0  16.9073011333  15.6923831333  14.08596223331  16.7504687333  14.5140663667  13.50374562  17.2033926  15.6999475  13.23365246673  16.8949153333  15.6614647  12.84347866674  17.0469665667  15.2090537667  14.36479125  16.7459822  16.6221879667  11.61082256676  16.8335785667  15.4979282  12.19934363337  16.6677328333  15.8170143667  13.74382168  16.4865069333  15.7295728667  12.93233586679  16.6392378333  18.2601799667  12.653159433310  16.7286889  15.4263526667  13.883358311  16.6894210333  14.1765804333  12.366541966712  17.1305677333  14.7179677667  13.292552433313  17.3861934333  16.2034549667  13.186121333314  16.5442868667  14.7367682  12.870651246715  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]:NoneTypeIn [11]:odataOut[11]:summer  autumne0  16.9073011333  15.69238313331  16.7504687333  14.51406636672  17.2033926  15.69994753  16.8949153333  15.66146474  17.0469665667  15.20905376675  16.7459822  16.62218796676  16.8335785667  15.49792827  16.6677328333  15.81701436678  16.4865069333  15.72957286679  16.6392378333  18.260179966710  16.7286889  15.426352666711  16.6894210333  14.176580433312  17.1305677333  14.717967766713  17.3861934333  16.203454966714  16.5442868667  14.736768215  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

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