differenc between using panda.drop_duplicate or value_count on whole frame or one column

Question

I am a new python user just for finish the homework. But I am willing to dig deeper when I meet questions. Ok the problem is from professor's sample code for data cleaning. He use drop.duplicates() and value_counts to check unique value of a frame, here are his codes:

spyq['sym_root'].value_counts() #method1
 spyq['date'].drop_duplicates() #method2

Here is the output:

SPY    7762857 #method1
0    20100506  #method2

I use spyq.shape() to help you understand the spyq dataframe :

spyq.shape #out put is (7762857, 9)

the spqy is dataframe contains trading history for spy500 in one day when is 05/06/2010. Ok after I see this, I wonder why he specify a column'date" or :'sym_root"; why he dont just use the whole spyq.drop_dupilicates() or spyq.value_counts(), so I have a try:

spyq.value_counts()
spyq.drop_duplicates()

Both output is (6993487, 9) The row has decreased! but from professor's sample code, there is no duplicated row existed because the row number from method 1 's output is exactly the same as the row number from spyq.shape! I am so confused why output of whole dataframe:spyq.drop_duplicates() is not same as spyq['column'].drop_duplicated() when there is no repeat value! I try to use

spyq.loc[spyq.drop_duplicates()] 

to see what have dropped but it is error. Can any one kindly help me? I know my question is kind of stupid but I just want to figure it out and I want to learn python from most fundmental part not just learn some code to finish homework. Thanks!