Dropping rows in a dataframe

[sobrio1]

Hi,
I'm trying to delete a couple rows from a dataframe so I don't have unwanted text/values

import numpy as np
import pandas as pd
#Saving a copy of the original values
temp['Sales_Orig']=temp['Sales']
#Log-transform Sales
temp['Sales']=np.log(temp['Sales'])
temp.tail()

Output:
 	  Month 	  Sales 	  Sales_Orig
102 	1972-07 	2.124165 	8.365905
103 	1972-08 	1.981480 	7.253470
104 	1972-09 	2.160883 	8.678802
105 	NaN 	NaN 	NaN
106 	Perrin Freres monthly champagne sales millions... 	NaN 	NaN

I couldn't figure out how to directly delete those last 2 rows, so I tried copying to a new dataframe and leaving off the last couple rows:

new_df = temp[:105]
new_df['Sales']=np.log(new_df['Sales'])
new_df.tail()

...however here I got the following message

Output:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

I don't really care how they get deleted - I just want to know how to do it cleanly and consistently.
Any help is appreciated. Thanks!