inner join/merge in pandas dataframe give more rows than left dataframe

Question

Here are how the dataframes columns look like.

df1='device number', 'date', ....<<10 other columns>> 3500 records

df2='device number', 'date', ....<<9 other columns>> 14,000 records

In each data frame, neither 'device number', nor 'date' are unique. However, their combination is unique to identify a row.

I am trying to form a new data frame which matches the rows from df1 and df2 where both device number and date are equal, and have all the columns from these df1 and df2. The pandas command I am trying is

df3=pd.merge(df1, df2, how='inner', on=['device number', 'date'])

However, df3 gives me a dataframe of shape (14,000, 21). The column number makes sense, but how can the inner join has more rows than any of the left dataframes? Does it mean I have a flaw in my understanding of inner join? Also, how can I achieve the result I described?

Answer 1

Only way I can see this happening... particularly with the 14,000 being the same exact number as the number of records in df2 is if the column combination in df2 are not unique.

You can verify that they are not unique with the following (True if unique)

df2.duplicated(['device number', 'date']).sum() == 0

Or

df.set_index(['device number', 'date']).index.is_unique