Imputing Missing Values with Group Mean in Pandas DataFrames
In data manipulation tasks, it's common to encounter missing values denoted as NaN. To address this issue, one approach is to fill in these missing values with the mean value computed within specific groups.
Consider the example dataframe:
| name | value |
|---|---|
| A | 1 |
| A | NaN |
| B | NaN |
| B | 2 |
| B | 3 |
| B | 1 |
| C | 3 |
| C | NaN |
| C | 3 |
Our goal is to replace the NaN values with the corresponding group mean of 'value'. To achieve this, we can leverage the transform() method:
mean_values = df.groupby('name').transform(lambda x: x.fillna(x.mean()))
df["value"] = mean_valuesAfter execution, the dataframe is updated:
| name | value |
|---|---|
| A | 1 |
| A | 1 |
| B | 2 |
| B | 2 |
| B | 3 |
| B | 1 |
| C | 3 |
| C | 3 |
| C | 3 |
Each NaN value has been substituted with its respective group mean, preserving the integrity of the data for further analysis.
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