Pandas中Merge、Join与Concat合并数据的性能差异分析

江岸

发布时间：2026-10-16 | 15人浏览过

原创

本文深入对比Pandas中Merge、Join和Concat三种数据合并方法的执行效率。通过从100万至1000万行的实验数据发现，Join操作在处理大规模数据时耗时显著低于Merge，且随数据量增长线性增加的速度更慢。建议处理海量数据时优先使用Join以提升性能。

在Pandas库中，存在多种对DataFrame（数据框）进行合并处理的方式。

本文将深入探讨这些不同的合并方法，并对它们的执行速度进行对比分析。

数据合并操作

Pandas提供了.merge()方法用于执行数据合并。

import pandas as pd  
# a dictionary to convert to a dataframe
data1 = {'identification': ['a', 'b', 'c', 'd'],
      'Customer_Name':['King', 'West', 'Adams', 'Mercy'],         'Category':['furniture', 'Office Supplies', 'Technology', 'R_materials'],}  
# our second dictionary to convert to a dataframe  
data2 = {'identification': ['a', 'b', 'c', 'd'],
      'Class':['First_Class', 'Second_Class', 'Same_day', 'Standard Class'],  
      'Age':[60, 30, 40, 50]}  
# Convert the dictionary into DataFrame  
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

执行代码后，我们得到两个初始DataFrame，结构如下。

identification Customer_Name Category 0 a King furniture 1 b West Office Supplies 2 c Adams Technology 3 d Mercy R_materials identification Class Age 0 a First_Class 60 1 b Second_Class 30 2 c Same_day 40 3 d Standard Class 50

接下来使用merge()函数进行进一步合并。

import pandas as pd
df1=...
df2=...
x= pd. merge( df1,df2,
left_on = "df1_col1",
right_on = "df2_col1" )

# using .merge() function  
new_data = pd.merge(df1, df2, on='identification')

该操作生成了如下新数据；

identification Customer_Name Category Class Age 0 a King furniture First_Class 60 1 b West Office Supplies Second_Class 30 2 c Adams Technology Same_day 40 3 d Mercy R_materials Standard Class 50

.join()方法同样可以将具有不同索引的DataFrame组合成一个新的DataFrame。用户可以通过指定'on'参数来明确指定依据哪一列进行合并。

import pandas as pd
df1 = ...
df2 = ...
df1.set_index ( "df1_col1", inplace = True)
df2.set_index ( "df2_col1", inplace = True)
x=df1.join( df2)

下面通过一个示例，展示如何将单索引DataFrame与多索引DataFrame进行连接；

import pandas as pd  
# a dictionary to convert to a dataframe
data1 = {
      'Customer_Name':['King', 'West', 'Adams'],  
    'Category':['furniture', 'Office Supplies', 'Technology'],} 7    
# our second dictionary to convert to a dataframe  
data2 = {
      'Class':['First_Class', 'Second_Class', 'Same_day', 'Standard Class'],  
    'Age':[60, 30, 40, 50]}  
# Convert the dictionary into DataFrame  
Ndata = pd.DataFrame(data1, index=pd.Index(['a', 'b', 'c'], name='identification'))
index = pd.MultiIndex.from_tuples([('a', 'x0'), ('b', 'x1'),
                                ('c', 'x2'), ('c', 'x3')],
                                names=['identification', 'x']) 19  
# Convert the dictionary into DataFrame  
Ndata2 = pd.DataFrame(data2, index= index)
print(Ndata, "\n\n", Ndata2)
# joining singly indexed with
# multi indexed
result = Ndata.join(Ndata2, how='inner')

最终结果如下所示；

Customer_Name Category Class Age identification x 3 a x0 King furniture First_Class 60 b x1 West Office Supplies Second_Class 30 c x2 Adams Technology Same_day 40 x3 Adams Technology Standard Class 50

数据连接操作

Pandas中的concat()方法支持在垂直方向（axis=0）和水平方向（axis=1）上连接DataFrame。此外，该方法还支持一次性连接两个以上的DataFrame或Series对象。

下面通过一个示例，演示如何在Pandas中执行连接操作；

import pandas as pd  
# a dictionary to convert to a dataframe
data1 = {'identification': ['a', 'b', 'c', 'd'],
      'Customer_Name':['King', 'West', 'Adams', 'Mercy'],  
      'Category':['furniture', 'Office Supplies', 'Technology', 'R_materials'],}  
# our second dictionary to convert to a dataframe  
data2 = {'identification': ['a', 'b', 'c', 'd'],
      'Class':['First_Class', 'Second_Class', 'Same_day', 'Standard Class'],  
      'Age':[60, 30, 40, 50]}  
# Convert the dictionary into DataFrame  
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)  
#perform concatenation here based on horizontal axis
new_data = pd.concat([df1, df2], axis=1)
print(new_data)

由此获得新的DataFrame：

identification Customer_Name Category identification \ 0 a King furniture a 3 1 b West Office Supplies b 4 2 c Adams Technology c 5 3 d Mercy R_materials d Class Age 0 First_Class 60 1 Second_Class 30 2 Same_day 40 3 Standard Class 50