The Nan example above misses one piece, which makes it less generic. To do this more "generically" use df['column_name'].value_counts()
This will give you the counts of each value in that column.
d=['A','A','A','B','C','C'," " ," "," "," "," ","-1"] # for simplicity
df=pd.DataFrame(d)
df.columns=["col1"]
df["col1"].value_counts()
5
A 3
C 2
-1 1
B 1
dtype: int64
"""len(df) give you 12, so we know the rest must be Nan's of some form, while also having a peek into other invalid entries, especially when you might want to ignore them like -1, 0 , "", also"""