better way to drop nan rows in pandas

Question

On my own I found a way to drop nan rows from a pandas dataframe. Given a dataframe dat with column x which contains nan values,is there a more elegant way to do drop each row of dat which has a nan value in the x column?

dat = dat[np.logical_not(np.isnan(dat.x))] dat = dat.reset_index(drop=True) 

Answer 1

Use dropna:

dat.dropna() 

You can pass param how to drop if all labels are nan or any of the labels are nan

dat.dropna(how='any')    #to drop if any value in the row has a nan dat.dropna(how='all')    #to drop if all values in the row are nan 

Hope that answers your question!

Edit 1: In case you want to drop rows containing nan values only from particular column(s), as suggested by J. Doe in his answer below, you can use the following:

dat.dropna(subset=[col_list])  # col_list is a list of column names to consider for nan values.