Pandas中DataFrame数据删除详情 Pandas中DataFrame数据删除详情

1.根据默认的行列索引操作

1.1行删除
1.2列删除

2.根据自定义的行列索引操作

2.1行删除
2.2列删除

本文介绍Pandas中DataFrame数据删除，主要使用drop、del方式。

# drop函数的参数解释drop(self,labels=None, # 就是要删除的行列的标签，用列表给定; axis=0, # axis是指处哪一个轴，0为行（默认），1为列; index=None, # index是指某一行或者多行columns=None, # columns是指某一列或者多列level=None, # level是指等级，针对多重索引的情况; inplace=False, # inplaces是否替换原来的dataframe; errors="raise",)axis=0或者和 index或columns 指定行列只需要使用一组就行

1.根据默认的行列索引操作示例数据

import numpy as npimport pandas as pd# 生成随机数组-5行5列df = pd.DataFrame(np.random.rand(5,5))print(df)

数据展示

0123400.7604890.0746330.7884160.0876120.56053910.7584500.5997770.3840750.5254830.62891020.3868080.1481060.7422070.4526270.77596330.6629090.1346400.1861860.7354290.45955640.3286940.2690880.3314040.8353880.899107

1.1行删除
[1]删除单行

# 删除单行，删除第2行df.drop(df.index[1],inplace=True) # inplace=True 原地修改print(df)

执行结果:

01234
00.6057640.2349730.5663460.5981050.478153
20.3832300.8221740.2288550.7432580.076701
30.8752870.5766680.1769820.3418270.112582
40.2054250.8985440.7991740.0009050.377990

[2]删除不连续多行

# 删除不连续多行,删除第2和第4行df.drop(df.index[[1,3]],inplace=True)print(df)

执行结果:

01234
00.9786120.5565390.7813620.5475270.706686
20.8458220.3217160.4441760.0539150.296631
40.6177350.0408590.1292350.5251160.005357

[3]删除连续多行

# 删除连续多行df.drop(df.index[1:3],inplace=True) # 开区间，最后一个索引号不计算在内print(df)

执行结果:

01234
00.0728910.9262970.8822650.9713680.567840
30.1632120.5460690.3609900.4942740.065744
40.7529170.2421120.5266750.9187130.320725

1.2列删除

列的删除可以使用del和drop两种方式，del df[1] # 删除第2列，该种方式为原地删除，本文具体讲解drop函数删除。

[1]删除指定列

df.drop([1,3],axis=1,inplace=True) # 指定轴为列# df.drop(columns=[1,3],inplace=True) # 直接指定列

执行结果:

024
00.5928690.1233690.815126
10.1270640.0939940.332790
20.4115600.1187530.143854
30.9653170.2677400.349927
40.6886040.6996580.932645

[2]删除连续列

df.drop(df.columns[1:3],axis=1,inplace=True) #指定轴# df.drop(columns=df.columns[1:3],inplace = True) # 指定列print(df)

执行结果:

034
00.3096740.9746940.660285
10.6773280.9694400.953452
20.9541140.9535690.959771
30.3656430.4170650.951372
40.7330810.8809140.804032

2.根据自定义的行列索引操作
示例数据

df = pd.DataFrame(data=https://www.it610.com/article/np.random.rand(5,5))df.index = list('abcde')df.columns = list('一二三四五')print(df)

数据展示

一二三四五a0.1884950.5744220.5303260.8424890.474946b0.9125220.9820930.9640310.4986380.826693c0.5807890.0139570.5152290.7950520.859267d0.5406410.8656020.3052560.5525660.754791e0.3754070.2361180.1292100.7117440.067356

2.1行删除
[1]删除单行

df.drop(['b'],inplace=True)print(df)

执行结果:

一二三四五
a0.3063500.6220670.0305730.4905630.009987
c0.6724230.0716610.2745290.4000860.263024
d0.6542040.8090870.0660990.1672900.534452
e0.6289170.2326290.0701670.4699620.957898

[2]删除多行

df.drop(['b','d'],inplace=True)print(df)

执行结果:

一二三四五
a0.3915830.5098620.9246340.4665630.058414
c0.8020160.6213470.6592150.5757280.935811
e0.2233720.2861160.1305870.1135440.910859

2.2列删除
[1]删除单列

df.drop(['二'],axis=1,inplace=True)# 删除单列print(df)

执行结果：

一三四五
a0.2761470.7974040.1844720.081162
b0.6301900.3280550.4286680.168491
c0.9799580.0290320.9346260.106805
d0.7629950.0031340.1362520.317423
e0.1372110.1166070.3677420.840080

[2]删除多列