2. 程式人生 > >Pandas.DataFrame刪除行和列

Pandas.DataFrame刪除行和列

阿新 • • 發佈：2019-01-16

本文通過一個csv例項檔案來展示如何刪除Pandas.DataFrame的行和列
資料檔名為:example.csv
內容為：

|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

刪除行

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

想要刪除最後兩行
.drop()方法如果不設定引數inplace=True，則只能在生成的新資料塊中實現刪除效果，而不能刪除原有資料塊的相應行。

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則原有資料塊的相應行被刪除

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

刪除列

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方法可以將所選列從原資料塊中彈出，原資料塊不再保留該列

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方法既可以保留原資料塊中的所選列，也可以刪除，這取決於引數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

當inplace=True時.drop()執行內部刪除，不返回任何值，原資料發生改變

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

總結，不論是行刪除還是列刪除，也不論是原資料刪除，還是輸出新變數刪除，.drop()的方法都能達到目的，為了方便好記，熟練操作，所以應該儘量多使用.drop()方法

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