This is the Windows app named missingno whose latest release can be downloaded as 0.5.2maintenancerelease.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named missingno with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS
Ad
missingno
DESCRIPTION
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. Just pip install missingno to get started. This quickstart uses a sample of the NYPD Motor Vehicle Collisions Dataset dataset. The msno.matrix nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion. 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 rows with the maximum and minimum nullity in the dataset. This visualization will comfortably accommodate up to 50 labelled variables.
Features
- Visualization will comfortably accommodate up to 50 labelled variables
- msno.bar is a simple visualization of nullity by column
- You can switch to a logarithmic scale
- The missingno correlation heatmap measures nullity correlation
- Variables that are always full or always empty have no meaningful correlation
- Variables that are always full or always empty have no meaningful correlation,
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/missingno.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
