Comparing dataframe columns

[Keith Brown]

So I have a df like this.

df=pd.DataFrame([

            ['2012-02-03 13:00','honda',100,'k1',False],

            ['2012-02-03 13:21','nissan',100,'k1',False],

            ['2012-02-03 11:03','toyota',400,'d1',False],

            ['2012-02-03 10:03','bmw',300,'s1',False],

            ['2012-02-03 11:02','toyota',400,'d1',False],

            ],

            columns=['ts','manufacture','size','form','sentinel'])

df.ts=pd.to_datetime(df.ts)

df=df.sort(['ts','manufacture'])

df['verified']=False

df

The dataframe is sorted by timestamp and manufacture. 

Find all duplicate manufacture (toyota) and then compare to see if size (400 & 400) match. 

If they do, change sentinal to True for the first occurance and verified to True on the second instance

So, I am able to find the manufacture. Toyota here

vc=df.manufacture.value_counts()

vci=vc[vc>1]

But, I am not sure how to compare inside the original dataframe, df

Any ideas?