[python] better way to drop nan rows in pandas

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)

To expand Hitesh's answer if you want to drop rows where 'x' specifically is nan, you can use the subset parameter. His answer will drop rows where other columns have nans as well

dat.dropna(subset=['x'])

Just in case commands in previous answers doesn't work, Try this: dat.dropna(subset=['x'], inplace = True)

bool_series=pd.notnull(dat["x"])
dat=dat[bool_series]

To remove rows based on Nan value of particular column:

d= pd.DataFrame([[2,3],[4,None]])   #creating data frame
d
Output:
    0   1
0   2   3.0
1   4   NaN

d = d[np.isfinite(d[1])]  #Select rows where value of 1st column is not nan
d

Output:
    0   1
0   2   3.0