No data visualization is possible without the underlying data to be represented. In this section, the various ways of providing data for plots are explained, from passing data values directly to creating a ColumnDataSource and filtering using a CDSView. In Bokeh, it is possible to pass lists of values directly into plotting functions.
When you pass in data like this, Bokeh works behind the scenes to make a ColumnDataSource for you. But learning to create and use the ColumnDataSource will enable you access more advanced capabilities, such as streaming data, sharing data between plots, and filtering data. The ColumnDataSource is the core of most Bokeh plots, providing the data that is visualized by the glyphs of the plot.
With the ColumnDataSourceit is easy to share data between multiple plots and widgets, such as the DataTable. When the same ColumnDataSource is used to drive multiple renderers, selections of the data source are also shared. Thus, it is possible to use a select tool to choose data points from one plot and have them automatically highlighted in a second plot Linked selection. At the most basic level, a ColumnDataSource is simply a mapping between column names and lists of data.
The ColumnDataSource takes a data parameter which is a dict, with string column names as keys and lists or arrays of data values as values. If one positional argument is passed in to the ColumnDataSource initializer, it will be taken as data.
There is an implicit assumption that all the columns in a given ColumnDataSource all have the same length at all times.
For this reason, it is usually preferable to update the. The index of the DataFrame will be reset, so if the DataFrame has a named index column, then CDS will also have a column with this name. However, if the index name is Nonethen the CDS will be assigned a generic name. For the index, an index of tuples will be created, and the names of the MultiIndex joined with an underscore. The column names will also be joined with an underscore. For example:. This process will fail for non-string column names, so flatten the DataFrame manually in that case.
The describe method generates columns for statistical measures such as mean and count for all the non-grouped original columns. The resulting DataFrame has MultiIndex columns with the original column name and the computed measure, so it will be flattened using the aforementioned scheme. For example, if a DataFrame has columns 'year' and 'mpg'. Then passing df. By using the stream method, Bokeh only sends new data to the browser instead of the entire dataset.
It additionally takes an optional argument rolloverwhich is the maximum length of data to keep data from the beginning of the column will be discarded. The default rollover value of None allows data to grow unbounded. ColumnDataSource patching is an efficient way to update slices of a data source.
By using the patch method, Bokeh only needs to send new data to the browser instead of the entire dataset. The patch method should be passed a dict mapping column names to list of tuples that represent a patch change to apply. We have seen above how data can be added to a ColumnDataSource to drive Bokeh plots.
This can include raw data or data that we explicitly transform ourselves, for example a column of colors created to control how the Markers in a scatter plot should be shaded. It is also possible to specify transforms that only occur in the browser. This can be useful to reduce both code i. The result can be passed to a color property on glyphs:. It is also possible to map categorical data to marker types.
Steps to be shown in this document:. Define input parameters in a CDS View. Use input parameters in a CDS View. With parameters matnr:abap. Lang as language. Call a parameterized view with open SQL. This explains the reason for your error. CDS with input parameter is supported only from 7. It can be used. Former Member. Posted on June 8, 4 minute read. Follow RSS feed Like. This key is a technical key of the CDS View. Define a View as a parameterized view. Define a Parameterized view. Shown the material description as per the language specified on input parameter.
Alert Moderator. Assigned tags. Related Blog Posts. Related Questions. You must be Logged on to comment or reply to a post. June 16, at pm. Like 0. Thomas Gauweiler. Former Member Post author. June 17, at am. Panneer Selvam. June 28, at am. What is the 7. June 30, at am.
Regards, Panneer. July 1, at am.They are somewhat similar to variables but are available at every node in the view. Input parameters are of great importance and have multiple applications but here we learn how to use it as a dynamic filter at the lowest node. This is the requirement. The below window appears which is similar to our variable creation screen. Give it a name and a description.
Notice that there is no attribute to bind this value to here. Input parameters can be created independent of fields unlike variables. Picture them as a value that can be used anywhere and this value being dynamic, can be entered at run-time by the user. Provide a datatype and length of this input parameter. Press OK. Notice that an input parameter has been created and appears in the folder as shown below. This opens up the expression editor. You can see here that the static filter that we applied earlier also appears here.
Notice that it is greyed out because there was no manual coding of filters yet. But with curious developers like us, we have to explore the options that lie ahead! Press the Edit button marked below. HANA throws us a warning that from now on, filters for this projection can only be maintained as an expression code.
Every time you need a filter even a static one you would have to add a small bit of code here. We do this because Input Parameters can only be added as filter via the expression editor.
View Range Properties
Press OK to move ahead. This opens up the expression editor for editing. Place an equal sign and double click on the parameter name as shown below. Close the bracket after the expression has been written as shown. Notice that the input parameter is always represented as covered by single quotes and double dollar signs on either side whenever used in a code. Press OK, save and activate the view. In this case, I provide it the value Thus, we successfully used input parameters as filters.
There are also further usages of Input parameters which we would learn as we progress.
ABAP CDS Table Function Implemented by AMDP
One step at a time. The differences are:. Thank you for reading this tutorial. Please help this website grow by sharing the document on social media using the share buttons below and subscribe to our newsletter for the latest updates.
Hi, We see that there are 4 types of input parameters. But as the value help i am not able to select a list of values. Can you help please.Specifies that a procedure parameter takes an optional array of elements of the specified type. ParamArray can be used only on the last parameter of a parameter list.
ParamArray allows you to pass an arbitrary number of arguments to the procedure. A ParamArray parameter is always declared using ByVal. You can supply one or more arguments to a ParamArray parameter by passing an array of the appropriate data type, a comma-separated list of values, or nothing at all. Whenever you deal with an array which can be indefinitely large, there is a risk of overrunning some internal capacity of your application.
If you accept a parameter array from the calling code, you should test its length and take appropriate steps if it is too large for your application. You may also leave feedback directly on GitHub. Skip to main content.
Table Function with Parameters
Exit focus mode. Remarks ParamArray allows you to pass an arbitrary number of arguments to the procedure. Important Whenever you deal with an array which can be indefinitely large, there is a risk of overrunning some internal capacity of your application. Is this page helpful? Yes No. Any additional feedback?
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View on GitHub.Parameters are dynamic values that can be used to change the measures or dimensions in a view. All this does is show the different values of the parameter in a drop down box you can change the view of the parameter to be, for example, a single select list.
Say for example, you have three views that you want on the same dashboard — a scatter plot, a bar chart, and a map — but you only want one to show at a time. This is one example where parameters are incredibly useful. First things first, create your parameter. The values you input here will be the values shown on the parameter drop down. Here I have named them after my worksheets.
Secondly, create your calculated field. Now, on one of your sheets, show the parameter control and select the option which corresponds to the view of the worksheet you are currently on, i. Then, drag the calculated field corresponding to the parameter on to the filter shelf. As you have only selected one option in the parameter, there will only be the same option available to select in the filter pop up; tick this option and click okay.
Finally, select the other options in the parameter in turn, and repeat the same steps for each corresponding worksheet. After all this, you are finally able to create your dashboard! Drag a vertical container on to the sheet, and drag on each of your worksheets in to the container. Watch the video below to see the above steps in action:.
I will be looking at how to include a colour legend that also changes within the view depending on the current parameter selection. Your email address will not be published. Tableau Tip 2: Using a parameter to change the view in a dashboard part 1 by Bethany Fox Nov 25, What are parameters?
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Low and High are typed differently in those data types compared to your select option. If you are lazy and have a lot select options to pass and you onyl need to pass them to a select statement within your FORM, you can skip the type definition and define you form with. Learn more. Ask Question. Asked 7 years, 8 months ago. Active 7 years, 5 months ago.
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For SAP this presented several challenges. High-quality data models should provide a single definition and format for the data. They should be clear and unambiguous, reusable and flexible, even extensible. So how can you capture the semantics of the data model in the database so that the model can be easily reused by different consumers, e.
How can you extend the meta-model to service your applications? What is the solution…? Once transported, an ABAP CDS View will create and deploy the corresponding database view on the target database automatically requiring no additional steps for the developer or transport manager.
Next, we will go through a brief step-by-step guide on how to create a CDS view, as well as features of the DDL source file. Step 4: Select the transport to which you want the DDL source file attached.
Step 5: Choose the template you would like to begin with. Table functions are an option as well, but that will be covered in the future AMDP blog. This view will also illustrate the basic structure used for DDL source files. Take a look at the screen-shot below. We will go into each of the red boxed subsections in detail. When Eclipse generates the CDS View template to work with, it also creates a skeleton for the initial annotations you see in this example. There are more Annotations available to CDS beyond the ones listed here, and fully covering all the Annotations would require an entire blog itself.
This section will usually come after the opening Annotations but before the first curly bracket. Within this section, the developer specifies:. After the first curly bracket comes the desired fields from the table, as well as any fields that are to be computed. This is the section that a developer will primarily utilize to take advantage of code-pushdown to allow the database to perform calculations. In our example, we are converting the billing status indicator into its real-world English meaning.
We will be covering both of these options in later examples. CDS Views can be previewed right within the Eclipse editor. There you have it! Can we extend the view if new z-fields are needed? Funny you should ask…. Another nice feature of CDS views is the ability to enhance them. This feature is beneficial when you want to augment an existing view, such as those that come with the standard SAP solution.
We often need to modify SAP objects with custom Z fields, and view extension present a simple and transportable way to do this.