Replacing NaN values using a defined function

Question

Year    Price
2010     60.000
2010     70.000 
2010     nan    
2009     92.000     
2008     160.000  
2008     nan  
2008     87.000

I want to replace the nan values with the average prices of the years they belong to. I want my output something like this:

    Year    Price
    2010     60.000
    2010     70.000 
    2010     65.000    
    2009     92.000     
    2008     160.000  
    2008     123.500  
    2008     87.000

How can I get it?

To get good answers to your question, please share the piece of code you tried and meet the minimum repeatability principle. To learn how to ask questions, read this article: [How do I ask a good question?](https://stackoverflow.com/help/how-to-ask) — Sercan, Jan 04 '22 at 09:43
Does this answer your question? [How to replace NaN values by Zeroes in a column of a Pandas Dataframe?](https://stackoverflow.com/questions/13295735/how-to-replace-nan-values-by-zeroes-in-a-column-of-a-pandas-dataframe) — Sercan, Jan 04 '22 at 09:45
To learn about minimum repeatability on SO, you can read this article: [How to create a Minimal, Reproducible Example](https://stackoverflow.com/help/minimal-reproducible-example) — Sercan, Jan 04 '22 at 09:51

kingkong · Answer 1 · 2022-01-03T12:34:36.780

-1

You can try like:

df["Price"] = df.groupby("Year").transform(lambda x: x.fillna(x.mean()))

Reference Pandas: filling missing values by mean in each group

edited Jan 03 '22 at 12:34

answered Jan 03 '22 at 12:32

kingkong

122
6

FYI: not my downvote but you're answering an already answered question as you link maybe thats the reason for it. – Jan 03 '22 at 12:34
@Neither Ok, my mistake, but I don't have 50 reputation to put links in comments :( – kingkong Jan 03 '22 at 12:37

Replacing NaN values using a defined function

1 Answers1