Python: how to fill up the mean value referencing from another dataframe column

Question

I have a housing dataframe:

where there are missing values in the Price column. I wish to fill the missing values by the mean price in the respective suburb.

This is my code for filling up the mean price by the same column:

all_housing_df['Price'].fillna(all_housing_df['Price'].mean())

How to fill in the mean price by the respective suburb?

please post your code as a text instead of image/picture so that we can reproduce your dataframe — Anurag Dabas, Apr 11 '21 at 06:05
Does this answer your question? [Pandas: filling missing values by mean in each group](https://stackoverflow.com/questions/19966018/pandas-filling-missing-values-by-mean-in-each-group) — Amit Vikram Singh, Apr 11 '21 at 06:07

Yefet · Answer 1 · 2021-04-11T06:13:39.267

1

You can use transform to fill missing values with the full list after grouping by Suburb

all_housing_df["Price"].fillna(all_housing_df.groupby("Suburb")["Price"].transform("mean"))

edited Apr 11 '21 at 06:13

answered Apr 11 '21 at 06:07

Yefet

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score 1 · Answer 2 · answered Apr 11 '21 at 06:13

You can group by Suburb, get the mean Price and save this as a dictionary to conditionally replace null values.

# Create dictionary for NaN values
nan_dict = all_housing_df.groupby('Suburb')['Price'].mean().to_dict()

# Replace NaN with dictionary
all_housing_df['Price'].fillna(all_housing_df['Suburb'].map(nan_dict))

Python: how to fill up the mean value referencing from another dataframe column

2 Answers2