Pandas使用Merge与Join和Concat分别进行合并数据效率对比分析_开发_开发者

合并DF

Pandas 使用 .merge() 方法来执行合并。

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)

运行我们的代码后，有两个 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() 函数进一步合并。

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')

这产生了下面的新数据；

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() 方法也可以将不同索引的 DataFrame 组合成一个新的 DataFrame。我们可以使用参数‘on’参数指定根据哪列进行合并。

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

让我们看看下面的例子，我们如何将单索引 DataFrame 与多索引 DataFrame 连接起来；

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')

我们的结果如下所示；

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

连接DF

Pandas 中concat() 方法在可以在垂直方向（axis=0）和水平方向（axis=1）上连接 DataFrame。我们还可以一次连接两个以上的 DataFrame 或 Series。

让我们看一个如何在 Pandas 中执行连接的示例；

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', 'S开发者_C教程econd_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 horipythonzontal axis
new_data = pd.concat([df1, df2], axis=1)
print(new_data)

这样就获得了新的 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