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Beginner trying to ignore NaN type missing data in the pipeline
Rob F
posted Jan 23, 2017

I've been trying to create some filters in the pipeline with fundamental data ratios. However, I want to use adjusted ratios. When I apply a filter, the data frame automatically invalidates all calculated fields if there is at least one NaN in the calculation. I was wondering whether there is a way to replace NaN fields with zero so that the calculated fields are no longer invalid? This would allow me to use them later in the pipeline as a filter.

As an example I attach a notebook where the calculated field NWC is invalidated in cases when unearned revenue is missing.

Any suggestions are very appreciated!
Rob

5 responses
Stephen Kearney
Jan 23, 2017

df.fillna(0)

See:
http://chrisalbon.com/python/pandas_missing_data.html

Rob F
Jan 23, 2017

Thanks Stephen for the tip, however I can do this only once I run the pipeline, suggesting that I would have to calculate NWC again after I run the pipeline. What if I wanted to do additional calculations with NWC before I run the pipeline? If I used .fillna(0) I would have to calculate NWC again after I run the pipeline and filter multiple times. It would be easier if I could just do all this before I run the pipeline.

Stephen Kearney
Jan 24, 2017

Hi Rob,

Try changing the next to last line to this:

result_df = run_pipeline(mypipeline(), '2016-05-05', '2016-05-05').fillna(0)

Rob F
Jan 24, 2017

good idea but unfortunately I'm running into the same issue. So far I had the most success with attaching .isnan() to the column. This however returns boolean and I'm having trouble specifying that I want 0 when true.

I'd appreciate a little more guidance.

Thanks
Rob

Stephen Kearney
Jan 24, 2017

Here's my copy of your notebook with the fillna(0) change. It's returning zeros for the cells that were NaN in your first post.

The return object of run_pipeline() is a DataFrame. So the .fillna(0) I added changes the NaNs to zero in that returned DataFrame.

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