Leveraging Sisense Notebooks with Compose SDK In today’s developer ecosystem, delivering a tailored creator experience is essential for success, especially when integrating analytics into customer-facing applications. Many existing solutions offer a...
The .describe() function in Python is really handy for exploratory analysis! Sometimes, you would want to display this as a table on a dashboard, but how? The output is typically printed out as text, not as a dataframe.Fear not, getting this into a d...
Here is an alternate visualization of a funnel chart - instead of using bars, it depicts the different steps as concentric circles.How to use: define an array in the extract() function of the values you'd like to use. If you are pulling data from a d...
Often, users will have a large number of filter values selected in a multi-select filter, and rather than having to deselect or 'x' out every filter value, you may want to quickly check the full, unfiltered data. This is something you can do in Sisen...
Sometimes users prefer to receive alerts via Slack channel as opposed to email. While alerts are only built for email, we can receive alerts in Slack using Slack's 'Email' app. 1. Click 'Add Configuration' 2. Choose the Slack channel you want to sen...
Ever have a SQL view that won't seem to materialize? Getting that dreaded red X even when results are showing in the View editor? Well follow the below instructions to turn those red X's into green checks!1. YOUR SQL SYNTAX ISN'T REDSHIFT COMPATIBLE....
When summarizing statistics across multiple categories, analysts often have to decide between using weighted and unweighted averages. An unweighted average is essentially your familiar method of taking the mean. Let's say 0% of users logged into m...
One of the first steps in exploratory data analysis is to identify the characteristics of the data, importantly including a test for distribution patterns. In this example, learn how to check if your data is normally distributed in Python with a visu...
Often times, we want to analyze data with the date from yesterday. How do we auto-populate yesterday's date? There are a few formats that we can use! The syntax below lets us get yesterday's date with timestamp:select dateadd(day,-1,getdate()) To onl...
Here's the scenario: you just used SQL and have an output that shows the results you want, but it isn't optimally displayed. Instead of writing cumbersome case when statements, unions/union alls, we can quickly use a few short functions in Python or...
Gantt charts are a great way to show which projects and tasks different teams are working on at a given time. This chart type is heavily used by Project Managers, Task Managers, and anyone else managing multiple workflows.Using Periscope Data's Pytho...
Usually when I need to upload a CSV I will use the Sisense for Cloud Data Team's CSV functionality. It's fast, easy, allows me to join the data with all my databases, and automatically casts types. Sometimes, however, I like to interact directly with...
Pandas makes transposing tables beautifully simple! Below are a couple examples using the example SQL output below: This first Python snippet allows you to define your own column headers: # SQL output is imported as a pandas dataframe variab...
Update: Sisense for Cloud Data Teams now allows you the ability to create additional date range filters without having to go through the below workaround. Refer to our docs here under "Additional Date Range Filters"Sometimes, users would like to appl...
A common hazard when adding data labels to a chart, is that often times the data labels will overlap when there are two points located relatively close to one another. Fortunately, the flexibility of python allows us a way around overlapping data lab...
Sometimes outliers are just in the way of chart creation and we want to put them in their own section of chart. Python can help us do that! I started with a data set like "count of users created by month" where there were only values for Jan-Feb and ...
Does your team prefer Python over R? Or are you looking to brush up your on your Python skills? We'll walk through a simple example of a linear regression model using the scikit-learn library in the Python/R Integration. In this exercise, we will als...
Plotly is wonderful, but sometimes you end up with a chart that looks like it's too small, or that it's not taking up all the space. Why does it do that? In this post, you'll learn how to fix that:With Python, this is actually very easy. We just need...
The above example is taken from plot.ly's documentation here. A few updates have been made to the example to make it easier to use within the Sisense for Cloud Data Team's Python/R integration.Pie charts with a hole in it give us donut charts. Now ma...
Visually it can be preferable to display a funnel chart horizontally. The following code outlines how to do this. Note, your SQL output must have 2 columns titled as follows:phases: the names of each stage along the funnelvalues: the value associate...
