pandas：逐行比较列并删除与第一列相比的重复项答案

【问题标题】：pandas: compare columns row-wise and remove duplicates compred to the first columnpandas：逐行比较列并删除与第一列相比的重复项
【发布时间】：2022-01-16 06:22:26
【问题描述】：

我有一个如下的数据框：

import pandas as pd
data = {'name': ['the weather is good', ' we need fresh air','today is sunny', 'we are lucky'],
        'name_1': ['we are lucky','the weather is good', ' we need fresh air','today is sunny'],
        'name_2': ['the weather is good', 'today is sunny', 'we are lucky',' we need fresh air'],
        'name_3': [ 'today is sunny','the weather is good',' we need fresh air', 'we are lucky']}
df = pd.DataFrame(data)

我想逐行比较列（意味着要比较具有相同索引的行），如果重复项与第一列具有相同的值，则用“相同”一词替换它们。我想要的输出是：

                  name               name_1               name_2  \
0  the weather is good         we are lucky               same   
1    we need fresh air  the weather is good       today is sunny   
2       today is sunny    we need fresh air         we are lucky   
3         we are lucky       today is sunny    we need fresh air   

                name_3  
0       today is sunny  
1  the weather is good  
2    we need fresh air  
3           same

为了找到这些值，我尝试了以下方法：

import numpy as np
np.where(df['name'].eq(df['name_1'])|df['name'].eq(df['name_2'])|df['name'].eq(df['name_3']))

但要替换它们，我不知道如何为 np.where() 制定（条件，x，y）。以下对于列 'name' 和 'name_3' 的返回相同：

np.where(df['name'].eq(df['name_1'])|df['name'].eq(df['name_2'])|df['name'].eq(df['name_3']),'same',df)

【问题讨论】：

ch1、ch2 和 ch3 是什么？

标签： python pandas replace compare rowwise

【解决方案1】：

IIUC，您要检查列 'name_1'、'name_2'、'name_3' 中的哪些值在列 name 中具有相同的值，如果是，请将这些值替换为 'same'，否则保留它们是。您使用numpy.where 是正确的，但请尝试将您的语句重写为：

import numpy as np

cols = ['name_1','name_2','name_3']
for c in cols:
    df[c] = np.where(df['name'].eq(df[c]),'same',df[c])

这给了你：

                  name               name_1              name_2  \
0  the weather is good         we are lucky                same   
1    we need fresh air  the weather is good      today is sunny   
2       today is sunny    we need fresh air        we are lucky   
3         we are lucky       today is sunny   we need fresh air   

                name_3  
0       today is sunny  
1  the weather is good  
2    we need fresh air  
3                 same

【讨论】：