Drop rows with 'NoneType' and with strings: keep only numeric values

Question

I have this dataset in which one of the columns has empty rows and some strings, whereas I only need to keep the numeric ones.

I have tried this for the strings:

df_3 = df_cor_inc[['Person ID','rt']]
df5 = df_3.to_csv('Documents/a.csv',index=False)
df5['rt'].apply(lambda x: pd.to_numeric(x, errors = 'coerce')).dropna()

But I get: AttributeError: 'NoneType' object has no attribute 'dropna'.

This doesn't work either because 'AttributeError: 'NoneType' object has no attribute 'rt':

df5[df5.rt.apply(lambda x: x.isnumeric())]

Same thing happens when I try to get rid of the empty rows, I get an error because I have 'NoneType'. How do I get rid of it so that I only keep the numeric values of that column and delete all the rows that don't have them?

This is how the data looks:

Person ID,rt
0,445   
0,445   
0,445   
1,  
1,  
1,  
1,  
1,Wait success  
1,  
1,  
1,  
1,  
1,  
1,Wait success  
1,  
1,  
1,  
1,  
1,  
1,Wait success  
1,  
1,  
1,  
1,  
1,  
1,Wait success  
1,  
1,  
1,  
1,  
1,  
1,Wait success  
1,  
1,  
1,  
1,  
1,  
1,Wait success  
1,  
1,  
1,  
1,  
1,  
1,Wait success  
1,  
1,  
1,  
1,  
1,  
1,Wait success  
1,  
1,  
1,  
1,  
1,  
1,Wait success  
1,  
1,  
1,  
1,  
1,  
1,1230  
1,1230  
1,1230  
1,1230  
1,1230  
1,1230  
1,1721  
1,1721  
1,1721  
1,1721  
1,1721  
1,1721

What is code before? Seems df5 is Nonetype, not DataFrame — jezrael
– jezrael, Commented Jul 9, 2021 at 12:50
@jezrael oh, I added it. It comes from another dataframe and then I selected only two columns and saved them into a csv — dazai
– dazai, Commented Jul 9, 2021 at 12:51

jezrael · Accepted Answer · 2021-07-09 13:06:28Z

1

Problem here is ouput variable is set to None if write df_3 by to_csv to file.

df5 = df_3.to_csv('Documents/a.csv',index=False)

Solution is working only with df_3 like:

df_3 = df_cor_inc[['Person ID','rt']]
#no assign to df5
df_3.to_csv('Documents/a.csv',index=False)

#assigned converted values to numeric
df_3['rt'] = pd.to_numeric(df_3['rt'], errors = 'coerce')
#removed NaNs rows by rt column
df5 = df_3.dropna(subset=['rt'])

edited Jul 9, 2021 at 13:06

answered Jul 9, 2021 at 12:52

jezrael

868k103 gold badges1.4k silver badges1.3k bronze badges

Sign up to request clarification or add additional context in comments.

2 Comments

dazai Over a year ago

Thank you, I understand the part about the file but I still get some errors when it comes to dropping the rows. With the first option: pandas.core.base.DataError: No numeric types to aggregate. With the second option: AttributeError: 'float' object has no attribute 'isnumeric'

jezrael Over a year ago

@AnuragDabas - I copy it from OP, you are right.

Collectives™ on Stack Overflow

Drop rows with 'NoneType' and with strings: keep only numeric values

1 Answer 1

2 Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

2 Comments

Your Answer

Sign up or log in

Post as a guest

Related