Python – lambda on pandas dataframe with nan rows

I want to apply an alteration to a column of my dataframe where the cells are not empty. This is the dataframe that I am using:

df = pd.DataFrame ([{'name':None, 'client':None, 'fruit':'orange'},
                    {'name':'halley','client':'abana', 'fruit':'pear'},
                    {'name':'josh','client':'a', 'fruit':'apple'},
                    {'name':'kim','client':'b', 'fruit':'apple'}])

output:

   name    client fruit
0  nan     nan    orange
1  halley  abana  pear
2  josh    a      apple
3  kim     b      apple

I want to rename clients with string shorter than 5 characters to be ‘client_x’ instead and this is what I did:

df['client'] =df['client'].apply(lambda x: x if len(x)>5 else "client_"+x)

but I have witnessed the following two possible errors:

TypeError: object of type 'float' has no len()
TypeError: object of type 'NoneType' has no len()

I don’t understand how nan can be assumed as a float, but I would really like a smart way to get through this.

Any help would be greatly appreciated!!

>Solution :

Use Series.str.len for working with missing values NaNs with numpy.where:

df['client'] = np.where(df['client'].str.len()>=5, df['client'], "client_"+df['client'])