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Hi Maveryx,
A solution to last week’s challenge can be found here.
This week, we are diving deep into the realms of Math and Spatial tools by tackling the creation of Sierpinski’s triangle fractal. This challenge, designed by Roland van Leeuwen @RWvanLeeuwen, is an Expert-level task. If you are preparing for certification and plan to attempt an exam during Inspire, it is an excellent opportunity to hone your skills. Thank you, Roland, for crafting this challenge!
What is a Sierpinski triangle?
A Sierpinski triangle is a fractal shape composed of smaller triangles, each a scaled-down replica of the whole. It is created by repeatedly dividing an equilateral triangle into smaller triangles and removing the middle triangle at each iteration. This process results in a geometric pattern that exhibits self-similarity at different scales, forming a visually striking and intricate triangle-based fractal.
(This definition is sourced from https://en.wikipedia.org/wiki/Sierpi%C5%84ski_triangle.)
The provided input consists of the of latitude, longitude, and corners a, b, and c. (The corners are used to determine each point of the triangle.) Your output triangle will look like this:
For this challenge, we are providing additional guidance to simplify the tasks and help you build your Sierpinski’s triangle.
To construct a fractal triangle, follow these steps:
Find the corners (points a, b, and c), and any random point within the triangle as a starting point.
Select one of the corners of the triangle and form a line from the point to the corner.
The center point of the line created is the one we will iterate with.
Using the center point of the created line, pick another random corner, draw a line, and find its center.
Repeat steps 2 to 4 for 100 iterations.
Map all points as green diamonds and the first random centroid as black.
By repeating these steps, a fractal should appear in the shape of Sierpinski's triangle!
If you need a refresher on how to build an iterative macro or create spatial objects, review these lessons in Academy to gear up:
Creating Spatial Objects
Creating an Iterative Macro
Good luck!
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Hi Maveryx,
A solution to last week’s challenge can be found here.
In April, we celebrate Earth Month, a time dedicated to raising awareness and taking action for environmental conservation and sustainability.
This weekly challenge delves into temperatures, highlighting their crucial role in our planet's health. The dataset presents comprehensive information on global temperature records, covering various countries worldwide. It includes average temperature records in Celsius for major cities from 1743 to 2013.
To solve this challenge, we will be concentrating on the data from 1950 onwards.
Your tasks are as follows:
Determine which cities have average temperatures greater than or equal to 25 degrees.
Among the cities identified in the previous task, identify the country with the highest number of such cities.
Examining all countries within the dataset, pinpoint the year with the highest average temperature and the year with the lowest average temperature across the globe.
Need a refresher? Review these lessons in Academy to gear up:
Sorting Data
Separating Data into Columns and Rows
Summarizing Data
Source: https://www.kaggle.com/datasets/maso0dahmed/global-temperature-records-1850-2022
Good luck!
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We hope you enjoyed last week's challenge. For the third challenge, let’s look at creating 3 and 6 month running averages.
The goal is to create 3 and 6 month running averages for the values contained in columns: c.LK98, p.LK98, c.1K, p.1K, c.NLP3, and p.NLP3. Create the averages by RM Category.
Update: As of 9/20/19, the start and solution files were updated. Your solutions may not look like those posted by Community members prior to this date.
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A solution to last week’s challenge can be found here.
The Eurovision Song contest or Eurovision is an annual song competition in Europe.
During a live show, contestants representing European countries, sing a song and receive points. The contestant with the most points wins. In 1974, the Swedish group ABBA became a household name in Europe by winning this contest with the song "Waterloo".
The dataset for this challenge provides information about the Eurovision from 1975 to 2019. The column “Edition” contains a date followed by one or two initials “f” or “fs”. For this challenge, use the values followed by “f”.
Use the dataset to: - Find out which country has won the contest from 1975 to 2019 - List by descending order the countries who have won the most time between these dates
Hint A country cannot give points to its representative. Make sure to take this rule into consideration. Mistakes happen in datasets.
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We hope you enjoyed last week's challenge. The solution has been posted here. For the second challenge lets look at removing characters and splitting data into columns based on delimiters.
Many products will export textual data with delimiters such as quotes. This is done so that strings can contain delimiters or control characters within them. Having more than one type of delimiter can be hard for ETL programs to interpret. In the input text file, there are two different delimiters (double quotes, single quotes) and they surround different data types.
Use Alteryx to strip out the delimiters as superfluous and format the data as represented in the output.
You may notice that we have started classifying the exercises into beginner, Intermediate and advanced. This classification is used by Alteryx internally to sequence exercises as users advance.
Update 11/23/2015:
The solution has been uploaded.
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