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 my s...
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 variable called...
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 funnel values: the value associat...
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