To extract non-nan values from multiple rows in a pandas dataframe

I am working on several taxi datasets. I have used pandas to concat all the dataset into a single dataframe. My dataframe looks something like this. ``` 675 1039 #and rest 125 taxis longitude latitude longitude latitude date 2008-02-02 13:31:21 116.56359 40.06489 Nan Nan 2008-02-02 13:31:51 116.56486 40.06415 Nan Nan 2008-02-02 13:32:21 116.56855 40.06352 116.58243 39.6313 2008-02-02 13:32:51 116.57127 40.06324 Nan Nan 2008-02-02 13:33:21 116.57120 40.06328 116.55134 39.6313 2008-02-02 13:33:51 116.57121 40.06329 116.55126 39.6123 2008-02-02 13:34:21 Nan Nan 116.55134 39.5123 ``` where 675,1039 are the taxi ids. Basically there are totally 127 taxis having their corresponding latitudes and longitudes columned up. I have several ways to extract not-null values for a row. ``` df.ix[k,df.columns[np.isnan(df.irow(0))!=1]] (or) df.irow(0)[np.isnan(df.irow(0))!=1] (or) df.irow(0)[np.where(df.irow(0)[df.columns].notnull())[0]] ``` any of the above commands will return, ``` 675 longitude 116.56359 latitude 40.064890 4549 longitude 116.34642 latitude 39.96662 Name: 2008-02-02 13:31:21 ``` now i want to extract all the notnull values from first few rows(say from row 1 to row 6). how do i do that? i can probably loop it up. But i want a non-looped way of doing it. Any help, suggestions are welcome. Thanks in adv! :)