本文通过一个csv实例文件来展示如何删除Pandas.DataFrame的行和列

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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

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