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

df.dropna(subset=[columns],inplace=True)

a = a[~(np.isnan(a).any(axis=1))] # removes rows containing at least one nan

a = a[~(np.isnan(a).all(axis=1))] # removes rows containing all nan

​

df.dropna()     #drop all rows that have any NaN values

df.dropna(how='all')

import pandas as pd

​

df = pd.DataFrame({'values_1': ['700','ABC','500','XYZ','1200'],

                   'values_2': ['DDD','150','350','400','5000'] 

                   })

​

df = df.apply (pd.to_numeric, errors='coerce')

df = df.dropna()

df = df.reset_index(drop=True)

​

print (df)

​

df.dropna(subset=['EPS'], how='all', inplace=True)

df = df.dropna(axis = 0)

x = x[~numpy.isnan(x)]

df = df[df.index.notnull()]

a[ ~np.isnan(a).any(axis=1),:]