问题
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?
回答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
来源：https://stackoverflow.com/questions/45262134/inner-join-merge-in-pandas-dataframe-give-more-rows-than-left-dataframe