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?](https://stackoverflow.com/questions/27905295/how-to-replace-nans-by-preceding-values-in-pandas-dataframe) — Bharath M Shetty, Dec 02 '17 at 10:31

score 1 · Accepted Answer · answered Dec 02 '17 at 10:22

1

What about

df.fillna(method='ffill')

answered Dec 02 '17 at 10:22

rpanai

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Perhaps `df.ffill()` – Bharath M Shetty Dec 02 '17 at 10:29
Okay, I will do some investigations! Thank you! – xxyy Dec 02 '17 at 10:29
@Bharath it is exactly the same see [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.ffill.html) – rpanai Dec 02 '17 at 10:33
1

Yes, I know I meant you to add the short form as well – Bharath M Shetty Dec 02 '17 at 10:34
@xxyy are you trying to do on all the dataframe or on a given column? – rpanai Dec 02 '17 at 10:40
On a specific column. It works now. Thank you very much! – xxyy Dec 02 '17 at 10:41

slow loop using panda

1 Answers1