slow loop using panda

Question

I am trying to replace each nan value in a series by the previous row's value. The data looks like:

16.5

NaN

16.5

NaN

16

NaN

Here is my code:

import pandas as pd
import numpy as np

df=pd.read_csv('test.csv')
AskPrice=df.AskPrice
for i, line in enumerate(AskPrice):
if np.isnan(line):
    AskPrice[i]=AskPrice[i-1]
print(AskPrice)

I want it to be:

16.5

16

I got the result but it took ages to complete the task. Is there a faster way? Thanks in advance!

Possible duplicate of How to replace NaNs by preceding values in pandas DataFrame? — Bharath M Shetty
– Bharath M Shetty, Commented Dec 2, 2017 at 10:31

rpanai · Accepted Answer · 2017-12-02 10:22:21Z

1

What about

df.fillna(method='ffill')

answered Dec 2, 2017 at 10:22

rpanai

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6 Comments

Bharath M Shetty Over a year ago

Perhaps df.ffill()

xxyy Over a year ago

Okay, I will do some investigations! Thank you!

rpanai Over a year ago

@Bharath it is exactly the same see documentation

Bharath M Shetty Over a year ago

Yes, I know I meant you to add the short form as well

rpanai Over a year ago

@xxyy are you trying to do on all the dataframe or on a given column?

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