PYTHON

drop rows that contain null values in a pandas dataframe

df.dropna(inplace=True)
# to drop any rows that contain any null values
df.dropna(how='all', inplace=True)
# to drop the rows wich all of it's values is any

# if you want to drop the columns not the rows you just set the axis to 1 like this:
df.dropna(axis=1, inplace=True)

pandas drop rows with null in specific column

df.dropna(subset=['Column name'])

drop null rows pandas

df.dropna()

pandas remove rows with null in column

df = df[df['EPS'].notna()]

pandas drop rows with nan in a particular column

In [30]: df.dropna(subset=[1])   #Drop only if NaN in specific column (as asked in the question)
Out[30]:
          0         1         2
1  2.677677 -1.466923 -0.750366
2       NaN  0.798002 -0.906038
3  0.672201  0.964789       NaN
5 -1.250970  0.030561 -2.678622
6       NaN  1.036043       NaN
7  0.049896 -0.308003  0.823295
9 -0.310130  0.078891       NaN

remove or drop rows and columns with null values

DataFrame.dropna() method 

drop null values in dataframe

df=df.interpolate(method='pad', limit=3)