Remove NaN values from string and numeric column in pandas data frame

In [1]:

import pandas as pd
import numpy as np

In [14]:

df=pd.DataFrame(np.array(["A",None,"C"]))
df.columns=['Name']
df

Out[14]:

	Name
0	A
1	None
2	C

Removing rows where Name is NA¶

In [18]:

df[df['Name'].notna()]

Out[18]:

	Name
0	A
2	C

When we are introducing NaN values in a string, we need to use None. None is recognized as valid NaN values within a string columns.¶

Lets create a dataframe with a numeric column and add NaN values to it¶

In [19]:

df2=pd.DataFrame(np.array([1,2,np.nan]))
df2.columns=['Val']
df2

Out[19]:

	Val
0	1.0
1	2.0
2	NaN

Removing NaN values¶

In [20]:

df2[df2['Val'].notna()]

Out[20]:

	Val
0	1.0
1	2.0