Views¶

A View is a space where you work on your Dataset in Mammoth. This is where you explore your data and transform it to generate meaningful insights.

When you add a Dataset and open it, Mammoth creates the first View by default. You start analysis in the View with the original data of the Dataset. You can create as many Views to get varied perspectives on your Data.

Organizing your View¶

When you open a new View, you see your original data as a grid.

Fig. 86 View¶

1. Hiding columns¶

Your View may contain lot more columns than what you need for your immediate analysis. You can hide such columns using the Column Browser.

Alternatively, hide a column from the column menu.

Fig. 87 Hiding columns¶

2. Renaming columns¶

If you want to give a more relevant or meaningful name to any column you can change the name from Column Browser or clicking on name in column header.

Alternatively, double click on the column name in the grid to enter a rename a column.

The original name of a column can be viewed on hovering over the column name.

Fig. 88 Viewing original column name¶

3. Reordering columns¶

You can change the order of displayed columns using the Column Browser or by dragging and dropping column headers in the grid.

Dragging and dropping columns in the grid to reorder them.

4. Changing columns’ datatype and formatting¶

Mammoth tries to give a reasonable default format to numeric and data columns. This may not be to your liking. You can change the format by clicking on column menu in header row of any column.

You can also convert a column’s datatype as you see fit.

Exploring your data¶

You can explore the data in View to get a quick summary and detect patterns using an Explore Card. It can be created on a column for a quick visualization of data, show statistics like sum, average, standard deviation, etc. and reveal how they connect with other columns. For a detailed guide describing the full power of explore cards - Click here.

Cleaning and transforming¶

More information may be hidden in your data which can potentially be arrived through filtration, extraction, labelling, categorization, ranking or correlating with another piece of data. Mammoth provides you a whole catalogue of powerful data operations to do such activities. These actions or calculations on the data are done using Tasks in Mammoth. A Task adds a layer of change over the original Dataset and each new Task then adds a fresh layer over the previous state, and so on. For more information about Tasks, Click here.

Fig. 89 Data preparation functions¶

A number of Tasks can be used on a Dataset to get desired results. These Tasks are stored as a list in a sequential order in the Pipeline. Once a Pipeline of Tasks is created, any new data automatically follows the same path.

Fig. 90 A Pipeline¶

Joining with another View¶

You may want to correlate the data in your View with data elsewhere. This could be typically organized as facts and dimensions. You can join your data in your current View with data in another View to build a consolidated picture of the information. For more information, check Join.

To manage the dataflow via such Tasks, use the Data Sync feature in Views.

Building multiple perspectives¶

A Dataset can give you multiple information from different sets of calculations. Thus, you can add a new View and perform a different set of calculations on the raw data. You can also duplicate a View with an existing Pipeline to further build a different perspective.

Fig. 91 Multiple Views¶

Saving your work for later¶

When you work on a View, you create Pipeline, Explore Cards, Metrics etc. These assets can be saved for later use on another Dataset. It can be downloaded as a template. The template gets downloaded with the .mammoth extension.

A saved template can be applied to a View. The View on which you want to apply the template should have the same column types and names as the View whose template was saved.

Note

Some Exports like Publish to database and Publish View are not saved in templates.