How To Identify Whats NOT In The Inner Join While Merging 3 Data Frames

I have got 3 data frames: energy, GDP & ScimEn. All the data frames have a column 'Country' and I merged all 3 data frames while using inner join: a = pd.merge(energy,GDP,left_

Solution 1:

ValueError is due to the indicator=True twice in the merging, by default when indicator is set as True then _merge column will be added to the dataframe.

>>> z.columns[z.columns.str.contains('_merge')]
Index(['_merge'], dtype='object')

Since the _merge is already present in z dataframe hence the ValueError for creating the next f dataframe.

z = pd.merge(energy,GDP,left_on='Country',right_on='Country',how='outer', indicator=True)
f = pd.merge(z,ScimEn,left_on='Country',right_on='Country',how='outer',indicator = 'merge1')
j = pd.merge(f,energy,left_on='Country',right_on='Country',how='outer',indicator = 'merge2')

j[(j['_merge'] != 'both') | (j['merge1']!='both')  | (j['merge2']!='both') ].shape[0]

or

j.shape[0] - b.shape[0]