Why does Pandas inner join give ValueError: len(left_on) must equal the number of levels in the index of "right"?

I'm trying to inner join DataFrame A to DataFrame B and am running into an error.

Here's my join statement:

merged = DataFrameA.join(DataFrameB, on=['Code','Date'])

And here's the error:

ValueError: len(left_on) must equal the number of levels in the index of "right"

I'm not sure the column order matters (they aren't truly "ordered" are they?), but just in case, the DataFrames are organized like this:

DataFrameA:  Code, Date, ColA, ColB, ColC, ..., ColG, ColH (shape: 80514, 8 - no index)
DataFrameB:  Date, Code, Col1, Col2, Col3, ..., Col15, Col16 (shape: 859, 16 - no index)

Do I need to correct my join statement? Or is there another, better way to get the intersection (or inner join) of these two DataFrames?

use merge if you are not joining on the index:

merged = pd.merge(DataFrameA,DataFrameB, on=['Code','Date'])

Follow up to question below:

Here is a reproducible example:

import pandas as pd
# create some timestamps for date column
i = pd.to_datetime(pd.date_range('20140601',periods=2))

#create two dataframes to merge
df = pd.DataFrame({'code': ['ABC','EFG'], 'date':i,'col1': [10,100]})
df2 = pd.DataFrame({'code': ['ABC','EFG'], 'date':i,'col2': [10,200]})

#merge on columns (default join is inner)
pd.merge(df, df2, on =['code','date'])

This results is:

 	code 	col1 	date 	col2
0 	ABC 	10 	    2014-06-01 	10
1 	EFG 	100 	2014-06-02 	200

What happens when you run this code?

Here is another way of performing join. Unlike the answer verified, this is a more general answer applicable to all other types of join.

Inner Join

inner join can also be performed by explicitly mentioning it as follows in how:

pd.merge(df1, df2, on='filename', how='inner')

The same methodology aplies for the other types of join:

OuterJoin

pd.merge(df1, df2, on='filename', how='outer')

Left Join

pd.merge(df1, df2, on='filename', how='left')

Right Join

pd.merge(df1, df2, on='filename', how='right')