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
重庆分公司
重庆分公司
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
