使用DataFrame删除行和列的实例讲解 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 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]) 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 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 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