在公共列上连接两个数据框 [英] join two dataframes on common column

问题描述

我想加入两个数据源，订单和客户:

I want to join two data sources, orders and customers:

orders是一个SQL Server表:

orders is an SQL Server table:

orderid| customerid | orderdate | ordercost
------ | -----------| --------- | --------
12000  | 1500       |2008-08-09 |  38610

而客户是一个csv文件:

and customers is a csv file:

customerid,first_name,last_name,starting_date,ending_date,country
1500,Sian,Read,2008-01-07,2010-01-07,Greenland

我想在我的Python应用程序中连接这两个表，所以我编写了以下代码:

I want to join these two tables in my Python application, so I wrote the following code:

# Connect to SQL Sever with Pyodbc library

connection = pypyodbc.connect("connection string here")
cursor=connection.cursor();
cursor.execute("SELECT * from order)
result= cursor.fetchall()

# convert the result to pandas Dataframe
df1 = pd.DataFrame(result, columns= ['orderid','customerid','orderdate','ordercost'])

# Read CSV File
df2=pd.read_csv(customer_csv)

# Merge two dataframes
merged= pd.merge( df1, df2, on= 'customerid', how='inner')
print(merged[['first_name', 'country']])

我希望

first_name | country
-----------|--------
Sian       | Greenland

但是我得到的结果是空的.

But I get empty result.

当我对来自CSV文件的两个数据帧执行此代码时，它可以正常工作.有帮助吗?

When I perform this code for two data frames that are both from CSV files, it works fine. Any help?

谢谢.

推荐答案

我认为问题是列customerid在两个DataFrames中都具有不同的dtypes，因此没有匹配项.

I think problem is columns customerid has different dtypes in both DataFrames so no match.

因此需要将两列都转换为int或都转换为str.

So need convert both columns to int or both to str.

df1['customerid'] = df1['customerid'].astype(int)
df2['customerid'] = df2['customerid'].astype(int)

或者:

df1['customerid'] = df1['customerid'].astype(str)
df2['customerid'] = df2['customerid'].astype(str)

也可以省略how='inner'，因为默认值 merge :

Also is possible omit how='inner', because default value of merge:

merged= pd.merge( df1, df2, on= 'customerid')

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