實(shí)測，在 Pandas 中使用 Merge、Join 、Concat合并數(shù)據(jù)的效率對(duì)比！

來源：Deephub Imba
本文約1400字，建議閱讀15分鐘

在 Pandas 中有很多種方法可以進(jìn)行DF的合并。本文將研究這些不同的方法，以及如何將它們執(zhí)行速度的對(duì)比。

合并DF

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)  

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

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

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

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')

              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

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)

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

Merge和Join的效率對(duì)比