For this, we'll use the Django shell - it's similar to the Python shell but allows accessing the database and creating new entries. Let's figure out how to pass the data from your model to the data visualization tool on the front end. In my mind, the second one is the most convenient because of a number of reasons.
To be precise, it can accept an array of JSON objects as input data. Another benefit of using async requests is the better page loading speed and more maintainable code. Here we've created two div containers where the pivot grid and pivot charts will be rendered. Once the request is completed, the JSON response returned by our server is set to the data parameter, and the pivot table, filled with this data, is rendered.
The query result the instance of JSONResponse returns a string that contains an array object with extra meta information, so we should add a tiny function for data processing on the front end.
It will extract only those nested objects we need and put them into a single array. After processing the data, the component receives it in the right format and performs all the hard work of data visualization.
Flexmonster provides a special property of the data source that allows setting field data types, custom captions, and defining multi-level hierarchies. This is a nice feature to have - we can elegantly separate data and its presentation right in the report's configuration. One is for the pivot table with summarized data, and the other is for the pivot charts. Both instances share the same data source from our model. I encourage you to try making them work in sync: with the reportchange event, you can make one instance react to the changes in another one.
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Please try enabling it if you encounter problems. Search PyPI Search. Default: standard. Whether or not to show a percentage - only applicable to the funnel chart. Default: show. A boolean value either t or f indicating whether or not to sort the statistics - only applicable to the funnel chart. Default: f. Activates the metric with the specified UID, e. Deactivates the metric with the specified UID. Calculates the specified metric, e. If the --calculate command is specified, this will allow one to force a particular frequency's statistics to be calculated.
Possible values are: d daily , w weekly , m monthly and a all. Default is all. Default: show. Default: f. By default, this returns the daily statistics for the metric. Metrics Explained The django-analytics module creates Metric objects for each type of metric that needs to be tracked, such as registrations, page impressions, etc. Command Line Reference The following options are available from the command line for the metrics management command: -l , --list Lists all of the available metrics, along with some basic information about each.
Possible values are: d daily , w weekly , m monthly and a all. Default is all. Inner Workings When running the manage. BaseMetric class an abstract class. It also makes sure the class has two properties: uid and title. If the class has these two functions, the script creates a Metric instance whose unique identifier and title are set to the uid and title values of the discovered class.
Todo The following features are planned for future versions of django-analytics : Custom visualisation integrated into Django admin back-end.
Hourly statistics. Version History Version Description 0. Project details Project links Homepage. So the answer to "should I upgrade to the latest patch release? Certain feature releases will be designated as long-term support LTS releases.
These releases will get security and data loss fixes applied for a guaranteed period of time, typically three years. See the supported versions policy for detailed guidelines about what fixes will be backported. We improve Django almost every day and are pretty good about keeping the code stable. If you choose to follow the development version, keep in mind that there will occasionally be backwards-incompatible changes.
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