Is there any way we can restructure the whole numbers in Power BI to distinguish the thousands, millions and billions using normal comma operator.
For example: 1,047,890 is represented as 1047890 or 1.04M in Power BI where as I would like it to be represented as 1,047,890. Is there any way we can do that?
Those features are available in the Power BI Desktop tool, a free download from www.powerbi.com.
On the Data view you can set the default numeric format shown in tables, cards, tool-tips etc. On the Report view you can set the numeric format for Chart Axes etc (those are dynamic by default based on the aggregated results).
Related
Hello everyone I have prometheus as a label returns the amount. The metric value is the number of payments. How do I withdraw the total amount a day to the dashboard? i.e. value_metric*sum
As far as I know, there is no way to do that because labels aren't meant to be used in calculations. Labels and their values are essentially the index of Prometheus' NoSQL TSDB, they're used to create relations and join pieces of data together. You wouldn't store values and do math with column names of a relational database, would you?
Another problem is that labels with high cardinality greatly increase database size. Here is an extraction from Prometheus best practices:
CAUTION: Remember that every unique combination of key-value label pairs represents a new time series, which can dramatically increase the amount of data stored. Do not use labels to store dimensions with high cardinality (many different label values), such as user IDs, email addresses, or other unbounded sets of values.
Though I see that you use somewhat fixed values in labels, maybe a histogram would fit your needs.
We are using Google AutoML with Tables using input as CSV files. We have imported data , linked all schema with nullable columns and train model and then deployed and used the online prediction to predict value of one column.
Column we targeted has values min-max ( 44 - 263).
When we deployed and ran online-prediction it return values like this
Prediction result
0.49457597732543945
95% prediction interval
[-8.209495544433594, 0.9892584085464478]
Most of the resultset is in above format. How can we convert it to values in range of (44-263). Didn't find much documentation online on the same.
Looking for documentation reference and interpretation along with interpretation of 95% prediction.
Actually to clarify (I'm the PM of AutoML Tables)--
AutoML Tables does not do any normalization of the predicted values for your label data, so if you expect your label data to have a distribution of min/max 44-263, then the output predictions should also be in that range. Two possibilities would make it significantly different:
1) You selected the wrong label column
2) Your input features for this prediction are dramatically different than what was seen in the training data used.
Please feel free to reach out to cloud-automl-tables-discuss#googlegroups.com if you'd like us to help debug further
I am working on a development where we need to dynamically generate visuals based on data. Structure of data changes based on query fired on data.
I have seen this feature in many BI tools where it suggests the type of suitable visualization based on data structure.
I have tried creating my own algorithm based on rules which we generally use to create a chart.
I want to know if there are any such algorithms or rules which can help us build this
I'm just trying to give you a head start here with what I could think. Since you have mentioned you have already tried writing your own algorithm with rules, please show your work. As far as I know the chart types can be determined based on the nature of (x, y) points you're trying to plot.
For a single x, if there are many corresponding y values, go with scatter plot.
For a single x, if there is only one corresponding y value,
If x takes integer values, go with line charts
If x takes rather string values, go with bar charts
I am seeking guidance on a machine learning problem involving the tagging of data columns. Currently, I have a system where users can add multiple tags to a columns in a table. However, I want to automate the tagging of new columns by using Multi Label Classification. I have extracted 21 features from each column by doing a column analysis on the column values. The features obtained would include statistical values such standard deviation, max,min, kurtosis and etc. Am I on the right path in using these features as inputs for a Multi Label Classification model ? Right now I am focusing on numeric values in columns.
I'm trying to represent numbers larger than 100,000,000,000 (the maximum of the Decimal data type) in Dynamics CRM 2011. It seems like the only way to do this is to either use the Currency field or use a text field. Neither of these options are very appealing.
What is the best way to represent large numbers in Dynamics CRM?
The only way is indeed in a currency field or use a text field. You can't increase the maximum value of a decimal field.
However the Float-Field covers until 100,000,000,000 if that's any comfort.
One possible answer is to just store in billions (2.15 = 2,150,000,000) - though this won't work if you're adding small numbers to a large number.
Another approach is to add a 'multiplier' field that contains a thousand, million or billion - and multiply the numbers together when required for reporting.
Finally, you could store the number in a string and validate/copy it to/from the number property in RetrieveMultple and PreSave plugins. As long as you're not saving the large value in the number field, you can use it to hold transient large values. Use a string as a persistent holder. Hacky, yes - but at least you can then use the value in charts etc.
The 'currency' type is unstable to use