I just completed the filling missing data challenge and I had a question:

the proposed solution suggested doing “ffill” replacement for two values and then using the mean to impute the last NaN value. I’m wondering if this is the best approach or if should we get the mean before the “ffill” so that we get the average or numbers without being biased by the imputation. then run “ffill” and lastly apply the saved pre-ffill mean

Hello @Mario_Hinojosa

In filling the missing values in a DataFrame, the order of operations can affect the imputed values, and your concern is valid. Whether to calculate the mean before or after using forward-fill (“ffill”) depends on the specific requirements of your analysis and the characteristics of your data.

**Calculate mean before “ffill” (Forward fill) advantages**

- Imputing missing values with the mean before “ffill” ensures that the imputed values are consistent with the mean of the original data.
- If the missing values are clustered, this approach reduces the impact of bias introduced by “ffill” on consecutive values.

**Calculate mean after “ffill” advantages:**

- Imputing the mean after “ffill” allows you to capture the true nature of the data before imputing it, especially if there are abrupt changes in the measurements.

Ultimately, the choice between these approaches should consider the characteristics of your data and the impact of the missing values on your analysis. In practice, it’s a good idea to try both approaches and assess the impact on your analysis to determine which one better suits your specific use case.

I hope this will help.

Happy learning