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Badfish - A missing data analysis and wrangling library in Python

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Quick method to get the density of frame. Can be built over `counts` as a separate method or as a parameter inside the `counts` method itself.

Add a plot that shows recovery shares, i.e. percentage of actual vs. expected values depending on the frequency: ![missing-data_recovery_actual-vs-expected](https://cloud.githubusercontent.com/assets/8660305/17853700/80147532-686e-11e6-9105-fcf1bd3e511a.png)

Currently, the focus is on columns but there should be an option to analysis frames based on pandas index names or based on a particular column with unique values (horizontally...

enhancement

Code run: mf.plot(kind='pattern', norm = False, threshold=0.0) ![issue](https://cloud.githubusercontent.com/assets/4502531/15929610/9c15aee6-2e6b-11e6-8622-10b91c02013e.png) This is difficult to interpret. An ideal arrangement of the rows would be as shown here: ![rplot-490x267](https://cloud.githubusercontent.com/assets/4502531/15930095/99652ec6-2e6e-11e6-8f2b-8a45e675eaab.png) .

enhancement

![correlplot](https://cloud.githubusercontent.com/assets/4502531/16176384/ff359b14-362a-11e6-8995-9d57f17c161b.png) "The red box plot on the left shows the distribution of Solar.R with Ozone missing while the blue box plot shows the distribution of the remaining datapoints. Likewhise for...

enhancement

I want to see the layout of the missing data - whether it is in chunks/spikes/one big chunk/at intervals etc? I am thinking a heatmap would do the job. So...