Messy datasets? Missing values? missingno provides a small toolset of flexible and easy-to-use missing data visualizations and utilities that allows you to get a quick visual summary of the completeness (or lack thereof) of your dataset. It's built using matplotlib, so it's fast, and takes any pandas DataFrame input that you throw at it, so it's flexible. Just pip install missingno to get started.

Examples use the NYPD Motor Vehicle Collisions Dataset (cleaned up) and the PLUTO Housing Sales Dataset (cleaned up).

In the following walkthrough I take nullity to mean whether a particular variable is filled in or not.

The msno.matrix nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

>>> import missingno as msno
>>> %matplotlib inline
>>> msno.matrix(collisions.sample(250))

At a glance, date, time, the distribution of injuries, and the contribution factor of the first vehicle appear to be completely populated, while geographic information seems mostly complete, but spottier.

The sparkline at right summarizes the general shape of the data completeness and points out the maximum and minimum rows.

This visualization will comfortably accommodate up to 50 labelled variables. Past that range labels begin to overlap or become unreadable, and by default large displays omit them.

>>> msno.matrix(housing.sample(250))

If you are working with time-series data, you can specify a periodicity using the freq keyword parameter:

>>> null_pattern = (np.random.random(1000).reshape((50, 20)) > 0.5).astype(bool)
>>> null_pattern = pd.DataFrame(null_pattern).replace({False: None})
>>> msno.matrix(null_pattern.set_index(pd.period_range('1/1/2011', '2/1/2015', freq='M')) , freq='BQ')

msno.bar is a simple visualization of nullity by column:

>>> msno.bar(collisions.sample(500))

You can switch to a logarithmic scale by specifying log=True:

bar provides the same information as matrix, but in a simpler format.

The missingno correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another:

>>> msno.heatmap(collisions)

In this example, it seems that reports which are filed with an OFF STREET NAME variable are less likely to have complete geographic data.

Nullity correlation ranges from -1 (if one variable appears the other definitely does not) to 0 (variables appearing or not appearing have no effect on one another) to 1 (if one variable appears the other definitely also does).

Variables that are always full or always empty have no meaningful correlation, and so are silently removed from the visualization—in this case for instance the datetime and injury number columns, which are completely filled, are not included.

Entries marked <1 or >-1 are have a correlation that is close to being exactingly negative or positive, but is still not quite perfectly so. This points to a small number of records in the dataset which are erroneous. For example, in this dataset the correlation between VEHICLE CODE TYPE 3 and CONTRIBUTING FACTOR VEHICLE 3 is <1, indicating that, contrary to our expectation, there are a few records which have one or the other, but not both. These cases will require special attention.

The heatmap works great for picking out data completeness relationships between variable pairs, but its explanatory power is limited when it comes to larger relationships and it has no particular support for extremely large datasets.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap:

>>> msno.dendrogram(collisions)

The dendrogram uses a hierarchical clustering algorithm (courtesy of scipy) to bin variables against one another by their nullity correlation (measured in terms of binary distance). At each step of the tree the variables are split up based on which combination minimizes the distance of the remaining clusters. The more monotone the set of variables, the closer their total distance is to zero, and the closer their average distance (the y-axis) is to zero.

To interpret this graph, read it from a top-down perspective. Cluster leaves which linked together at a distance of zero fully predict one another's presence—one variable might always be empty when another is filled, or they might always both be filled or both empty, and so on. In this specific example the dendrogram glues together the variables which are required and therefore present in every record.

Cluster leaves which split close to zero, but not at it, predict one another very well, but still imperfectly. If your own interpretation of the dataset is that these columns actually are or ought to be match each other in nullity (for example, as CONTRIBUTING FACTOR VEHICLE 2 and VEHICLE TYPE CODE 2 ought to), then the height of the cluster leaf tells you, in absolute terms, how often the records are "mismatched" or incorrectly filed—that is, how many values you would have to fill in or drop, if you are so inclined.

As with matrix, only up to 50 labeled columns will comfortably display in this configuration. However the dendrogram more elegantly handles extremely large datasets by simply flipping to a horizontal configuration.

>>> msno.dendrogram(housing)

One kind of pattern that's particularly difficult to check, where it appears, is geographic distribution. The geoplot makes this easy:

>>> msno.geoplot(collisions.sample(100000), x='LONGITUDE', y='LATITUDE')

If no geographical context can be provided, geoplot can be used to compute a quadtree nullity distribution, as above, which splits the dataset into statistically significant chunks and colorizes them based on the average nullity of data points within them. In this case (fortunately for analysis, but unfortunately for the purposes of demonstration) it appears that our dataset's data nullity is unaffected by geography.

A quadtree analysis works remarkably well in most cases, but will not always be what you want. If you can specify a geographic grouping within the dataset (using the by keyword argument), you can plot your data as a set of minimum-enclosure convex hulls instead (the following example also demonstrates adding a histogram to the display, using the histogram=True argument):

>>> msno.geoplot(collisions.sample(100000), x='LONGITUDE', y='LATITUDE', by='ZIP CODE', histogram=True)

Finally, if you have the actual geometries of your grouping (in the form of a dict or pandas Series of shapely.Geometry or shapely.MultiPolygon objects), you can dispense with all of this approximation and just plot exactly what you mean:

>>> msno.geoplot(collisions.sample(1000), x='LONGITUDE', y='LATITUDE', by='BOROUGH', geometry=geom)

In this case this is the least interesting result of all.

Two technical notes:

• For the geographically inclined, this a plat carre projection—that is, none at all. Not pretty, but functional.
• geoplot requires the shapely and descartes libraries, which are ancillary to the rest of this package and are thus optional dependencies.

missingno also provides utility functions for filtering records in your dataset based on completion. These are useful in particular for filtering through and drilling down into particularly large datasets whose data nullity issues might otherwise be very hard to visualize or understand.

Let's first apply a nullity_filter() to the data. The filter parameter controls which result set we want: either filter=top or filter=bottom. The n parameter controls the maximum number of columns that you want: so for example n=5 makes sure we get at most five results. Finally, p controls the percentage cutoff. If filter=bottom, then p=0.9 makes sure that our columns are at most 90% complete; if filter=top we get columns which are at least 90% complete.

For example, the following query filtered down to only at most 15 columns which are not completely filled.

>>> filtered_data = msno.nullity_filter(data, filter='bottom', n=15, p=0.999) # or filter='top'
>>> msno.matrix(filtered_data.sample(250))

nullity_sort() simply reshuffles your rows by completeness, in either ascending or descending order. Since it doesn't affect the underlying data it's mainly useful for matrix visualization:

>>> sorted_data = msno.nullity_sort(data, sort='descending') # or sort='ascending'
>>> msno.matrix(sorted_data.sample(250))

These methods work inline within the visualization methods themselves. For instance, the following is perfectly valid:

>>> msno.matrix(data.sample(250), filter='top', n=5, p=0.9, sort='ascending')

Each of the visualizations provides a further set of lesser configuration parameters for visually tweaking the display.

matrix, bar, heatmap, dendrogram, and geoplot all provide:

• figsize: The size of the figure to display. This is a matplotlib parameter which defaults to (20, 12), except for large dendrogram visualizations, which compute a height on the fly based on the number of variables to display.
• fontsize: The figure's font size. The default is 16.
• labels: Whether or not to display the column names. For matrix this defaults to True for <=50 variables and False for >50. It always defaults to True for dendrogram and heatmap.
• inline: Defaults to True, in which case the chart is plotted and nothing is returned. If this is set to False the methods omit plotting and return their visualizations instead.

matrix also provides:

• sparkline: Set this to False to not draw the sparkline.
• freq: If you are working with timeseries data (a pandas DataFrame with a PeriodIndex or DatetimeIndex) you can specify and display a choice of offset.
• width_ratios: The ratio of the width of the matrix to the width of the sparkline. Defaults to (15, 1). Does nothing if sparkline=False.
• color: The color of the filled columns. Defaults to (0.25, 0.25, 0.25).

bar also provides:

• log: Set this to True to use a logarithmic scale.
• color: The color of the filled columns. Defaults to (0.25, 0.25, 0.25).

heatmap also provides:

• cmap: What matplotlib colormap to use. Defaults to RdBu.

dendrogram also provides:

• orientation: The orientation of the dendrogram. Defaults to top if <=50 columns and left if there are more.
• method: The linkage method scipy.hierarchy uses for clustering. average is the default argument.

geoplot also provides:

• x AND y OR coordinates: A column of points (in either two columns or one) to plot. These are required.
• by: A column of values to group points by.
• geometry: A hash table (dict or pd.Series generally) geometries of the groups being aggregated, if available.
• cutoff: The minimum number of observations per rectangle in the quadtree display. No effect if a different display is used. Defaults to min([50, 0.05*len(df)]).
• histogram: Whether or not to plot the histogram. Defaults to False.

If you are not satisfied with these admittedly basic configuration parameters, the display can be further manipulated in any way you like using matplotlib post-facto.

The best way to do this is to specify inline=False, which will cause missingno to return the underlying matplotlib.figure.Figure object. Anyone with sufficient knowledge of matplotlib operations and the missingno source code can then tweak the display to their liking. For example, the following code will bump the size of the dendrogram visualization's y-axis labels up from 20 to 30:

>>> mat = msno.dendrogram(collisions, inline=False)
>>> mat.axes[0].tick_params(axis='y', labelsize=30)

• The way that numpy and pandas represent null data informs how one goes about working with such data in Python. It's an interesting subject and an important design limitation that's good keep in mind in your work higher up the stack, so I wrote an exploratory blog post on the subject that's well worth reading if you're into this sort of thing.
• For public reviews and third-party test drives of this library's capacities check out this post and this one.
• For slightly more details on this module's ideation check out this post on my personal blog.

Bugs? Thoughts? Feature requests? Throw them at the bug tracker and I'll take a look.

As always I'm very interested in hearing feedback—reach out to me at [email protected].