how to join two pandas dataframe on specific column -


i have 1st pandas dataframe looks this

order_id buyer_id caterer_id item_id  qty_purchased       387      139          1       7              3        388      140          1       6              3        389      140          1       7              3        390       36          1       9              3        391       79          1       8              3        391       79          1       12              3        391       79          1       7              3        392       72          1       9              3        392       72          1       9              3        393       65          1       9              3        394       65          1      10              3        395      141          1      11              3        396      132          1      12              3        396      132          1      15              3        397       31          1      13              3        404       64          1      14              3        405      146          1      15              3

and 2nd dataframe looks this

 item_id meal_type       6       veg       7       veg       8       veg       9    nonveg      10       veg      11       veg      12       veg      13    nonveg      14       veg      15    nonveg      16    nonveg      17       veg      18       veg      19    nonveg      20       veg      21       veg

i want join 2 data frames on item_id column. final data frame should contain item_type has match item_id.

i doing following in python

pd.merge(segments_data,meal_type,how='left',on='item_id')

but gives me nan values

you have check types dtypes of both columns (names) join on.

if there different, can cast them, because need same dtypes. numeric columns string columns, looks numbers.

if there both same string types, maybe cast both of them int. problem can whitespaces:

segments_data['item_id'] = segments_data['item_id'].astype(int)  meal_type['item_id'] = meal_type['item_id'].astype(int)  pd.merge(segments_data,meal_type,how='left',on='item_id')

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