-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA512

Ignoring `NaN` values when calculating means or other statistical measures can be appropriate in many situations, but it's important to understand the context and potential implications:

Pros of Ignoring `NaN` Values:
1. **Prevents Bias from Missing Data**: Calculating statistics by ignoring `NaN` values can give a more accurate representation of the data that is present, avoiding the bias that missing values might introduce.
2. **Simplicity**: It allows you to proceed with your analysis without needing to impute or otherwise handle missing data.
3. **Common Practice**: In many fields, particularly those dealing with large datasets (like bioinformatics, finance, etc.), it's common to ignore `NaN` values as a first step.

Cons of Ignoring `NaN` Values:
1. **Data Integrity**: If `NaN` values are numerous, ignoring them can lead to significant data loss, potentially skewing the results.
2. **Bias**: The remaining data might not be representative of the whole dataset if `NaN` values are not randomly distributed.
3. **Context Loss**: Ignoring `NaN` values can sometimes mean ignoring important context or patterns related to why the data is missing.

Considerations:
1. **Proportion of Missing Data**: If only a small percentage of your data is `NaN`, ignoring these values is less likely to affect your results significantly. If a large percentage is `NaN`, consider other approaches.
2. **Pattern of Missingness**: Investigate if there is a pattern to the missing data. If `NaN` values are randomly distributed, the impact is different than if they are systematically related to certain conditions.
3. **Imputation**: Depending on the data and the analysis, imputing `NaN` values with mean, median, or using more sophisticated methods (like KNN imputation, regression imputation) might be a better approach.
4. **Domain Knowledge**: Use domain knowledge to decide if ignoring `NaN` values makes sense for your specific case. 

Example Decision-Making:

**Scenario 1**: You have a large dataset with less than 5% missing values, and initial analyses show no clear pattern to the missingness.
- - **Approach**: Ignoring `NaN` values might be reasonable.

**Scenario 2**: You have a dataset where missing values are concentrated in specific variables or under certain conditions.
- - **Approach**: Investigate the pattern of missingness. Consider imputation or more complex modeling techniques to handle `NaN` values.

**Scenario 3**: Your dataset is relatively small, and the proportion of missing values is high.
- - **Approach**: Ignoring `NaN` values might not be appropriate. Consider imputation or other methods to address the missing data.

Summary:
Ignoring `NaN` values can be a valid approach, especially for initial analyses, but always consider the context and potential implications. Balancing simplicity with the need for accuracy and representativeness in your data is key.


My take: the nan count and non-nan count will be determined, the percentage of missing data will be shown. Users can decided if the final analysis would suitable for their specific case. After all our focus is on automation, users must ensure if this is applicable to their or not.

- --msb

-----BEGIN PGP SIGNATURE-----
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=+yCt
-----END PGP SIGNATURE-----