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Here is one way using df.pivot_table():

Replace any other party except Bharatiya Janata Party as Others using np.where() and then use pivot_table, finally get sum() across axis=1 for sum of votes.

df1=(df.assign(party=np.where(df.party.ne('Bharatiya Janata Party'),'Others',df.party)).
pivot_table(index='state',columns='party',values='votes',aggfunc='sum'))

Another method with crosstab() similar to pivot_table:

df1=pd.crosstab(df.state,np.where(df.party.ne('Bharatiya Janata Party'),'Others',df.party)
,m.votes,aggfunc='sum')

Finally, getting the Total and reset_index():

df1=df1.assign(Total=df1.sum(axis=1)).reset_index().rename_axis(None,axis=1)

Output: (Note: I had added dummy Andhra Pradesh rows for testing)

                       state  Bharatiya Janata Party  Others   Total
0  Andaman & Nicobar Islands                   90969   90954  181923
1            Andhra Pradesh                      100      85     185

You can opt to delete the Others column later : df1=df1.drop('Others',1)

Here is one way using df.pivot_table():

Replace any other party except Bharatiya Janata Party as Others using np.where() and then use pivot_table, finally get sum() across axis=1 for sum of votes.

df1=(df.assign(party=np.where(df.party.ne('Bharatiya Janata Party'),'Others',df.party)).
pivot_table(index='state',columns='party',values='votes',aggfunc='sum'))

Another method with crosstab() similar to pivot_table:

df1=pd.crosstab(df.state,np.where(df.party.ne('Bharatiya Janata Party'),'Others',df.party)
,df.votes,aggfunc='sum')

Finally, getting the Total and reset_index():

df1=df1.assign(Total=df1.sum(axis=1)).reset_index().rename_axis(None,axis=1)

Output: (Note: I had added dummy Andhra Pradesh rows for testing)

                       state  Bharatiya Janata Party  Others   Total
0  Andaman & Nicobar Islands                   90969   90954  181923
1            Andhra Pradesh                      100      85     185

You can opt to delete the Others column later : df1=df1.drop('Others',1)

Here is one way using df.pivot_table():

Replace any other party except Bharatiya Janata Party as Others using np.where() and then use pivot_table, finally get sum() across axis=1 for sum of votes.

df1=(df.assign(party=np.where(df.party.ne('Bharatiya Janata Party'),'Others',df.party)).
pivot_table(index='state',columns='party',values='votes',aggfunc='sum'))

df1=df1.assign(Total=df1.sum(axis=1)).reset_index().rename_axis(None,axis=1)

Output: (Note: I had added dummy Andhra Pradesh rows for testing)

                       state  Bharatiya Janata Party  Others   Total
0  Andaman & Nicobar Islands                   90969   90954  181923
1            Andhra Pradesh                      100      85     185

You can opt to delete the Others column later : df1=df1.drop('Others',1)

Here is one way using df.pivot_table():

Replace any other party except Bharatiya Janata Party as Others using np.where() and then use pivot_table, finally get sum() across axis=1 for sum of votes.

df1=(df.assign(party=np.where(df.party.ne('Bharatiya Janata Party'),'Others',df.party)).
pivot_table(index='state',columns='party',values='votes',aggfunc='sum'))

Another method with crosstab() similar to pivot_table:

df1=pd.crosstab(df.state,np.where(df.party.ne('Bharatiya Janata Party'),'Others',df.party)
,m.votes,aggfunc='sum')

Finally, getting the Total and reset_index():

df1=df1.assign(Total=df1.sum(axis=1)).reset_index().rename_axis(None,axis=1)

Output: (Note: I had added dummy Andhra Pradesh rows for testing)

                       state  Bharatiya Janata Party  Others   Total
0  Andaman & Nicobar Islands                   90969   90954  181923
1            Andhra Pradesh                      100      85     185

You can opt to delete the Others column later : df1=df1.drop('Others',1)

Here is one way using df.pivot_table():

Replace any other party except Bharatiya Janata Party as Others using np.where() and then use pivot_table), finally get sum() across axis=1 for sum of votes.

df1=(df.assign(party=np.where(df.party.ne('Bharatiya Janata Party'),'Others',df.party)).
pivot_table(index='state',columns='party',values='votes',aggfunc='sum'))

df1=df1.assign(Total=df1.sum(axis=1)).reset_index().rename_axis(None,axis=1)

Output: (Note: I had added dummy Andhra Pradesh rows for testing)

                       state  Bharatiya Janata Party  Others   Total
0  Andaman & Nicobar Islands                   90969   90954  181923
1            Andhra Pradesh                      100      85     185

Here is one way using df.pivot_table():

Replace any other party except Bharatiya Janata Party as Others using np.where() and then use pivot_table, finally get sum() across axis=1 for sum of votes.

df1=(df.assign(party=np.where(df.party.ne('Bharatiya Janata Party'),'Others',df.party)).
pivot_table(index='state',columns='party',values='votes',aggfunc='sum'))

df1=df1.assign(Total=df1.sum(axis=1)).reset_index().rename_axis(None,axis=1)

Output: (Note: I had added dummy Andhra Pradesh rows for testing)

                       state  Bharatiya Janata Party  Others   Total
0  Andaman & Nicobar Islands                   90969   90954  181923
1            Andhra Pradesh                      100      85     185

You can opt to delete the Others column later : df1=df1.drop('Others',1)