在 Pandas 中使用 Merge、Join 、Concat合并数据的效率对比

excel05 2022-05-23 发布于福建

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在 Pandas 中有很多种方法可以进行DF的合并。本文将研究这些不同的方法，以及如何将它们应用到我们的数据中。

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。

# 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。

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 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

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', '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)

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