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?

From: stackoverflow.com/q/28228781