join two dataframes on common column

Question

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

orders is an SQL Server table:

orderid| customerid | orderdate | ordercost
------ | ----------         


        

Answer 1

empty dataframe result for pd.merge means you don't have any matching values across the two frames. Have you checked the type of the the data? use

df1['customerid'].dtype

to check.

as well as converting after importing (as suggested in the other answer), you can also tell pandas what dtype you want when you read the csv

df2=pd.read_csv(customer_csv, dtype={'customerid': str))

Answer 2

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

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

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

Or:

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

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

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