badfish
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Badfish - A missing data analysis and wrangling library in Python
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: 
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...
Code run: mf.plot(kind='pattern', norm = False, threshold=0.0)  This is difficult to interpret. An ideal arrangement of the rows would be as shown here:  .
 "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...
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...