Let's take a quick detour to the Data Refinery tool. Data Refinery can quickly filter and mutate data, create quick visualizations, and do other data cleansing tasks from an easy to use user interface.
This section is broken up into the following steps:
From the Project home, under the Assets tab, click on the Data assets arrow to toggle it and open up the list of data assets. Click the box next to USERxxxx.BILLING (where
USERxxxx is your username or the username of the person who granted you data access) to check it, and click the 3 dots to the right, and then Refine :
Data Refinery should launch and open the data like the image below:
X by the Details button to close it.
We'll start out in the Data tab.
For example, type filter on the Command line and observe that autocomplete will give hints on the syntax and how to use the command:
When you have completed a command, click Apply to apply the operation to your data set.
Operation + button:
We want to make sure that there are no empty values, and there may be some for the TotalCharges column, so let's fix that. Click on
Filter and choose the TotalCharges column from the drop down, then the Operator Is empty, then
We can see that there is only 1 row with an empty value for TotalCharges:
It should be safe to just drop these rows from the data set, so let's do that.
First, remove the filter that you just added. You can delete it from the "Steps" section of clicking the undo arrow on top of the page.
Next, choose the Operation Remove empty rows, select the TotalCharges column, click
Next and then click
Finally, we can remove the CustomerID column, since that won't be useful for training a machine learning model in the next exercise. Choose the Remove operator, then choose
Change column selection. Under
Select column pick customerID, click
Next and then click
What if you need to show a non-technical person the steps you took? What if we do something we don't want?
Within Data Refinery, we keep track of the steps and we can even undo (or redo) an action using the circular arrows:
As you refine your data, IBM Data Refinery keeps track of the steps in your data flow. You can modify them and even select a step to return to a particular moment in your data’s transformation.
To see the steps in the data flow that you have performed, click the Steps button. The operations that you have performed on the data will be shown:
You can modify these steps in real time and save for future use.
Clicking on the Profile tab will bring up a quick view of several histograms about the data.
You can get insight into the data from the histograms:
Twice as many customers are month-to-month as either 2-year or 1-year contract.
More choose paperless billing, but around 40% still prefer a paper bill mailed out to them.
You can see the distribution of MonthlyCharges and TotalCharges.
From the Churn column, you can see that a significant number of customers will cancel their service.
Choose the Visualizations tab to bring up an option to choose which columns to visualize. Under Columns to Visualize choose TotalCharges and click
We first see the data in a histogram by default. You can choose other chart types. We'll pick
Scatter plot next by clicking on it:
In the scatter plot, choose TotalCharges for the x-axis, MonthlyCharges for the y-axis, and Churn for the Color map. Drag the bottom TotalCharges filter to show all the data:
Scroll down and give the scatter plot a title and sub-title if you wish. Under the
Actions panel, notice that you can perform tasks such as Start over, Download chart details, Download chart image, or set Global visualization preferences (Note: Hover over the icons to see the names). Click on the "gear" icon in the
We see that we can do things in the Global visualization preferences for Titles, Tools, Theme, and Notification. Click on the
Theme tab and update the color scheme to Vivid. Then click the
Apply button :
Now the colors for all of our charts will reflect this:
We've seen a small sampling of the power of Data Refinery on IBM Cloud Pak for Data. We saw how we can transform data using R code, at the command line, or using various Operations on the columns such as filtering the data, removing empty rows, or deleting a column altogether. We next saw that all the steps in our Data Flow are recorded, so we can remove steps, repeat them, or edit an individual step. We were able to quickly profile the data, to see histograms and statistics for each column. And finally we created more in-depth visualizations, creating a scatter plot mapping TotalCharges vs. MonthlyCharges, with the Churn results highlighted in color.