Python Pandas中DataFrame.drop_duplicates()删除重复值详解

2024-04-02 19:55

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语法

df.drop_duplicates(subset = None,
                   keep = 'first', 
                   inplace = False, 
                   ignore_index = False)

参数

1.subset:指定的标签或标签序列，仅删除这些列重复值，默认情况为所有列

2.keep:确定要保留的重复值，有以下可选项:

first:保留第一次出现的重复值，默认

last:保留最后一次出现的重复值

False:删除所有重复值

3.inplace:是否生效

4.ignore_index:如果为True，则重新分配自然索引(0,1,…,n - 1)

# 删除重复值 DataFrame.drop_duplicates()
import pandas as pd
 
df = pd.DataFrame([['x','x',1],['x','x',1],['z','x',2]], columns = ['A','B','C'])
 
# 删除重复行
res1 = df.drop_duplicates()
 
# 删除指定列
res2 = df.drop_duplicates(subset = ['A'])
 
# 保留最后一个
res3 = df.drop_duplicates(subset = ['A'], keep = 'last')

结果展示

res1

res2

res3

扩展:识别重复值

import pandas as pd
 
df = pd.DataFrame({
    'studentID':['A001','A002','A003','A004','A005','A006','A006'],
    'score':[100,93,94,96,93,95,95]})
 
# 识别重复值
duplicate_value = df[df.duplicated()]