Pandas使用Merge與Join和Concat分別進行合并數(shù)據(jù)效率對比分析

更新時間：2022年12月08日 09:00:21 作者：宋宋講編程

這篇文章主要給大家介紹了關于pandas中DataFrame數(shù)據(jù)合并連接（merge、join、concat)的相關資料，文中介紹的非常詳細，需要的朋友可以參考下

合并DF

Pandas 使用 .merge() 方法來執(zhí)行合并。

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)

運行我們的代碼后，有兩個 DataFrame，如下所示。

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

使用 merge() 函數(shù)進一步合并。

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')

這產(chǎn)生了下面的新數(shù)據(jù)；

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() 方法也可以將不同索引的 DataFrame 組合成一個新的 DataFrame。我們可以使用參數(shù)‘on’參數(shù)指定根據(jù)哪列進行合并。

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

讓我們看看下面的例子，我們如何將單索引 DataFrame 與多索引 DataFrame 連接起來；

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')

我們的結果如下所示；

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

連接DF

Pandas 中concat() 方法在可以在垂直方向（axis=0）和水平方向（axis=1）上連接 DataFrame。我們還可以一次連接兩個以上的 DataFrame 或 Series。

讓我們看一個如何在 Pandas 中執(zhí)行連接的示例；

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)

這樣就獲得了新的 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