使用DataFrame删除行和列的实例讲解


Posted in Python onApril 08, 2018

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

Python 相关文章推荐
编写同时兼容Python2.x与Python3.x版本的代码的几个示例
Mar 30 Python
Python使用multiprocessing创建进程的方法
Jun 04 Python
Python 中的Selenium异常处理实例代码
May 03 Python
python dlib人脸识别代码实例
Apr 04 Python
Django ORM多对多查询方法(自定义第三张表&ManyToManyField)
Aug 09 Python
pymysql 开启调试模式的实现
Sep 24 Python
在tensorflow中设置保存checkpoint的最大数量实例
Jan 21 Python
python实现猜拳游戏
Mar 04 Python
PYcharm 激活方法(推荐)
Mar 23 Python
对Matlab中共轭、转置和共轭装置的区别说明
May 11 Python
Python小白不正确的使用类变量实例
May 29 Python
基于python 取余问题(%)详解
Jun 03 Python
将字典转换为DataFrame并进行频次统计的方法
Apr 08 #Python
pandas创建新Dataframe并添加多行的实例
Apr 08 #Python
DataFrame中去除指定列为空的行方法
Apr 08 #Python
python 定时修改数据库的示例代码
Apr 08 #Python
对Python中DataFrame按照行遍历的方法
Apr 08 #Python
python2.6.6如何升级到python2.7.14
Apr 08 #Python
python解决pandas处理缺失值为空字符串的问题
Apr 08 #Python
You might like
php中将图片gif,jpg或mysql longblob或blob字段值转换成16进制字符串
2011/08/23 PHP
php制作动态随机验证码
2015/02/12 PHP
PHP从FLV文件获取视频预览图的方法
2015/03/12 PHP
php简单实现快速排序的方法
2015/04/04 PHP
Zend Framework动作助手(Zend_Controller_Action_Helper)用法详解
2016/03/05 PHP
基于php实现的php代码加密解密类完整实例
2016/10/12 PHP
PHP基于DOM创建xml文档的方法示例
2017/02/08 PHP
详解thinkphp中的volist标签
2018/01/15 PHP
JavaScript基本对象
2007/01/11 Javascript
读JavaScript DOM编程艺术笔记
2011/11/15 Javascript
js调用打印机打印网页字体总是缩小一号的解决方法
2014/01/24 Javascript
jQuery插件slides实现无缝轮播图特效
2015/04/17 Javascript
聊一聊jQuery插件uploadify使用方法
2016/08/24 Javascript
js/jquery控制页面动态加载数据 滑动滚动条自动加载事件的方法
2017/02/08 Javascript
js for循环倒序输出数组元素的实例
2017/03/01 Javascript
ActiveX控件的使用-js实现打印超市小票功能代码详解
2017/11/22 Javascript
PM2自动部署代码步骤流程总结
2018/12/10 Javascript
js实现web调用摄像头 js截取视频画面
2019/04/21 Javascript
在layui中对table中的数据进行判断(0、1)转换为提示信息的方法
2019/09/28 Javascript
nuxt静态部署打包相对路径操作
2020/11/06 Javascript
[43:03]完美世界DOTA2联赛PWL S2 PXG vs Magma 第二场 11.21
2020/11/24 DOTA
Django之使用内置函数和celery发邮件的方法示例
2019/09/16 Python
python自动化测试之异常及日志操作实例分析
2019/11/09 Python
css3实现书本翻页效果的示例代码
2021/03/08 HTML / CSS
HTML5中使用postMessage实现两个网页间传递数据
2016/06/22 HTML / CSS
线程的基本概念、线程的基本状态以及状态之间的关系
2012/10/26 面试题
如何写出高质量、高性能的MySQL查询
2014/11/17 面试题
大学运动会入场词
2014/02/22 职场文书
秸秆管理实施方案
2014/03/15 职场文书
数控专业自荐书范文
2014/03/16 职场文书
环保专项行动方案
2014/05/12 职场文书
电子信息工程专业自荐书
2014/06/24 职场文书
岗位说明书标准范本
2014/07/30 职场文书
2014年六五普法工作总结
2014/11/25 职场文书
《狮子和鹿》教学反思
2016/02/16 职场文书
Python 如何解决稀疏矩阵运算
2021/05/26 Python