Lindsay Betzendahl’s Post

In my new #tableau visualization 'Global Flags: A Colorful World,' I present 250 flags of countries and territories from around the world. I was keen to delve into the history of flag creation and understand the meanings behind the colors. From the perspective of image analysis algorithm development, it was quite fascinating to write a script that analyzes all pixels in high-resolution flag images, categorizing them into primary colors. I then represented the percentage composition of colors for each flag using diagrams. The script was written in #python (pillow lib): it parses a zip archive of images and generates array of colors. If the content of a shade on a flag covers less than 0.5% of its area, the shade is merged with the nearest color on the flag. Following this, the script generates a dataset with the country or territory code, along with the colors in hex format. AI processes the hex colors and associates each shade with a base color, for example, '#ffffff' is white. This dataset is then connected to Tableau. The #dataviz in Tableau contains two types of diagrams: donuts and bar charts. Flags can be filtered by part of the world, countries, base colors, and the number of colors on the flag. Additionally, flags can be sorted by country name, population, territory area, as well as base colors. Working with filters and sorting by base colors is quite interesting. For instance, one can filter all flags with red colors and arrange them in descending order of the amount of green color on them. One intriguing feature is the dynamic adjustment of the number of columns on the bar charts tab, which is based on rounding, calculating the remainder of a number, and operates on table calculations. You can view and download the interactive visualization here. https://lnkd.in/dBWXzcGW Feedback is welcome. #datafam

𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐢𝐧 𝐓𝐡𝐫𝐞𝐞 𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐬: 𝐀 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 #3𝐃 #𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 Performing 3D exploratory analysis involves using various tools and techniques to interactively visualize and analyze data in a three-dimensional space. There are several software and programming libraries available that provide functionalities for 3D data exploration and visualization. Some popular tools include: 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Libraries like #Matplotlib, #Plotly, and #Mayavi offer #3D #visualization capabilities. For instance, Plotly's plotly.graph_objs module allows you to create interactive 3D plots that can be customized and manipulated in real-time. 𝐑 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠: R provides packages such as rgl and plot3D that enable the creation of interactive 3D plots for data exploration. Tableau: Tableau, a data visualization tool, offers 3D visualization features for exploring data interactively and creating dynamic visualizations. 𝐔𝐧𝐢𝐭𝐲 𝐨𝐫 𝐔𝐧𝐫𝐞𝐚𝐥 𝐄𝐧𝐠𝐢𝐧𝐞: These game development engines can be repurposed for 3D data visualization and exploration, allowing for interactive manipulation of data within a 3D environment. 𝐓𝐡𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐭𝐲𝐩𝐢𝐜𝐚𝐥𝐥𝐲 𝐢𝐧𝐯𝐨𝐥𝐯𝐞𝐬: 𝐃𝐚𝐭𝐚 𝐏𝐫𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧: Organizing and formatting data in a way that suits the visualization tool or library. 𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 3𝐃 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬: Using the selected tool to create 3D plots or visual representations of the data. 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐚𝐧𝐝 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧: Utilizing features provided by the tool to interactively explore the data. This may involve adjusting parameters, clicking on elements for more details, or even allowing users to modify the visualization. For instance, in Python using Plotly, you can create a 3D scatter plot and interactively explore it: This code generates a 3D scatter plot using random data points. With Plotly's interactivity, you can rotate the plot, zoom in/out, and inspect individual data points. For real data, you'd replace the random data generation with your dataset. The specifics of interaction and manipulation depend on the capabilities of the chosen tool or library.

🎨 Ready to turn your data into stunning visualizations that tell compelling stories? Let's embark on a journey to explore the basics of Matplotlib and unleash your creative power. 📊 Basic Plots: Let's Dive In! 1. **Line Plot:** 📈 Visualize trends and changes over time with ease using the 'plot()' function. Check out the code below to create a simple line plot: #code import matplotlib.pyplot as plt x = [1, 2, 3, 4, 5] y = [10, 25, 18, 30, 12] plt.plot(x, y, marker='o') plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.title('Simple Line Plot') plt.show() 2. **Bar Plot:** 📊 Compare values across categories effortlessly. Use the 'bar()' function to craft a basic bar plot: #code categories = ['A', 'B', 'C', 'D'] values = [15, 30, 10, 25] plt.bar(categories, values) plt.xlabel('Categories') plt.ylabel('Values') plt.title('Basic Bar Plot') plt.show() 3. **Scatter Plot:** 🌟 Explore relationships between continuous variables using scatter plots: #code x = [1, 2, 3, 4, 5] y = [10, 25, 18, 30, 12] plt.scatter(x, y, color='red', marker='o') plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.title('Simple Scatter Plot') plt.show() 🌈 **Customization and Advanced Visualizations: Unleash Your Creativity!** Matplotlib empowers you to create visual masterpieces with customization options like adjusting colors, markers, and line styles, adding legends, annotations, and text, crafting subplots for 📊 **Seaborn Integration: Elevate Your Aesthetics!**   Boost your visual aesthetics with Seaborn, a powerful data visualization library built on Matplotlib: #code import seaborn as sns sns.set(style='whitegrid') # Choose a style you prefer

A recent #WorkoutWednesday challenge made use of annotations to ‘label’ a mark that had been selected. But that selected mark could change based on some interactivity. Unable to keep the annotation showing, and to find any other community blogs or knowledge articles to explain how to make it work, our #TableauVisionary Donna Coles ended up seeking the help from colleagues to get to the bottom of it. Discover how more about how to annotate a mark that changes in @tableau here: https://hubs.ly/Q02b7ZNL0 #Tableau #Dashboarding #DataVisualization #TableauTips

I have had some success with incorporating DAX code for IBCS-styled SVG charts into a calculation group. 🎇 Without a calculation group, you would have to create multiple instances of a large DAX measure (for generating SVG code) - one measure for each chart. This becomes cumbersome, especially when multiple different charts are housed in a single Table visual, multiple visuals are on a report page, and there are multiple pages in a report. A calculation group effectively solves this issue. Although it's a bit tricky—since each chart requires multiple measures (main value, baseline value, min and max values for proper scaling etc.) and a calculation item processes only one SELECTEDMEASURE(). Utilizing multiple calculation items accessed from the main calculation item has simplified the problem to an almost code-free solution. The dimensions and measures required for a new chart need to be declared in a few calculation items, for example: SWITCH (   SELECTEDMEASURENAME (),   "SVG Chart Name 1", [Sales Amount],   "SVG Chart Name 2", [Sales Quantity]   SELECTEDMEASURE () ) means [Sales Amount] will be used for a chart based on [SVG Chart Name 1] measure and [Sales Quantity] will be used for a chart based on [SVG Chart Name 2]. Another calculation item defines chart type: VAR _ChartType =   SWITCH (     SELECTEDMEASURENAME (),     "SVG Chart Name 1", "SVG Absolute Variance",     "SVG Chart Name 2", "SVG Absolute and Variance Monthly",     "N/A"   ) All the SVG-generating code now exists in a single instance, regardless of the number of charts in the report. To create a new instance of SVG chart you need: 0) import the calculation group from a file [Tabular Editor required] - only once per report 1) create an empty measure, e.g. Chart Name 1 := "", change its type to "Image URL" 2) drag & drop it into a data field of a visual (e.g. Table or Matrix) 3) apply Calculation Group filter to the visual (Name="CHART TYPE") 4) defined required chart type, dimensions and measures in the calculation items (see the above SWITCH examples) [Tabular Editor required] 5) use measures stored in the calculation group to edit formatting settings (Font Size, color etc.) if required 🎇 🎇 🎇 What is next? C# script for Tabular Editor to import (and update) the calculation group directly from a GitHub repository??? P.S. There is a delay with my next YouTube video. My schedule has been disrupted due to health issues. While I'm still recovering, I need to prioritize my clients' projects first. #PowerBI #datavisualization #SVG #IBCS

Power of data visualization Data is the lifeblood of software development, and understanding how to harness its power is key for software engineers and developers. Data visualization, a crucial skill, transforms raw data into actionable insights, improving user experiences and driving decision-making. Types of data visualizations include bar charts, line charts, pie charts, scatter plots, heatmaps, and tree maps. Choosing the right visualization depends on your data and the story you wish to convey, considering your audience. Various tools can help, such as Python libraries (Matplotlib, Seaborn, Plotly), JavaScript libraries (D3.js, Chart.js, Three.js), and business intelligence tools (Tableau, Power BI). Real-world examples demonstrate the impact of data visualization: analyzing website traffic, tracking bugs, and conducting A/B testing. Key best practices include keeping visualizations simple, labeling elements for clarity, maintaining consistency, and adding interactivity for a user-friendly experience. Mastering data visualization is invaluable for software engineers and developers. It simplifies complex data, enhances decision-making, and elevates user experiences. Whether you're a seasoned software engineer or a budding developer, this skill is essential in the world of software development. Follow TheAlpha.Dev for more such content. #engineering #softwareengineer #softwaredeveloper #datavisualization #softwareengineering #dataanalytics

Creating Dynamic Shapes by Dimension with Multiple Axes in Tableau To add dynamic shapes against a dimension in Tableau using multiple axes, allowing for a variety of visualization options, follow these steps. This technique is useful for creating more engaging and informative visualizations, particularly when you want to highlight different categories or values with distinct shapes. Prepare Your Data: Ensure your data is structured in a way that supports the visualization you're aiming to build. You'll need a dimension to base your shapes on and measures for the axes. Create the Initial View: Drag the dimension you want to use for your shapes to the Columns shelf. Drag a measure to the Rows shelf. This measure will determine the position of the shapes on one axis. Add the Shape Encoding: Drag the dimension you want to encode with shapes onto the Marks card, specifically to the Shape shelf. This will allow you to assign different shapes to different values within the dimension. Go to the Shape shelf on the Marks card and select the shapes you want to associate with each dimension value. Add a Second Axis for Additional Detail: If you want to incorporate another measure, drag it to the opposite side of the Rows shelf from the first measure. This will create a dual-axis chart. Right-click on the new axis that appears and select "Dual Axis" to align the two measures on top of each other. Synchronize the axes by right-clicking on one of the axes and selecting "Synchronize Axis" if necessary. Refine Your Visualization: Adjust the size, color, and shape options to make your visualization more informative and visually appealing. You might need to adjust the transparency of shapes or the scale of the axes to ensure that all data points are clearly visible and do not overlap in a way that obscures information. Ensure Interactivity (Optional): To make your visualization dynamic and interactive, consider using parameters or filters that allow users to select different dimensions or measures to display. You can also use dashboard actions to create interactions between this and other visualizations in a dashboard. Finalize and Share: Once you're satisfied with your visualization, ensure that the axes are correctly titled and that any legends or labels are clear and informative. This approach allows you to dynamically represent data with shapes based on dimension values, making your visualizations more engaging and allowing viewers to easily distinguish between different categories or values To gain a deeper understanding, please refer to the attached link featuring Beniamin Bataneant, Data Analyst @btProvider. He has provided an excellent explanation on this topic https://lnkd.in/gnJ8GW7c

HOW TO CHOOSE THE RIGHT DATA VISUALISATION follow KUSUM SINHA for more 🤗 credit: respective owner 🙂 Choosing the right data visualization involves considering the nature of your data and the message you want to convey. Here's a step-by-step guide: 1.Understand Your Data: - Identify the type of data you have: Is it numerical, categorical, time-series, etc.? - Determine the relationships within the data and any patterns or trends. 2.Define Your Objective: - Clearly articulate the message or insights you want to convey with the visualization. - Consider your audience and what will be most effective for them. 3.Select the Right Chart Type: - Bar Charts:Suitable for comparing quantities across categories. - Line Charts: Useful for showing trends over time. - Pie Charts:Good for displaying parts of a whole. - Scatter Plots: Ideal for visualizing relationships between two variables. - Heatmaps: Effective for displaying the intensity of values in a matrix. 4.Consider Data Distribution: - If your data is skewed or has outliers, choose visualizations that handle these well. - Histograms or box plots can help show the distribution of numerical data. 5.Choose Color Wisely: - Use colors purposefully to highlight important information. - Consider color-blindness and ensure accessibility. 6.Simplify and Declutter: - Avoid unnecessary elements that can distract from the main message. - Use clear labels and a simple legend. 7.Scale Appropriately: - Ensure that the scale of your visualization is appropriate for the data. - Avoid distorting the data to mislead the viewer. 8.Interactivity (if needed): - Interactive elements can enhance understanding. - But be cautious not to overcomplicate or distract with too much interactivity. 9.Consistency Across Visuals: - If using multiple visualizations, maintain consistency in style and units. 10.Test and Iterate: - Get feedback from others to ensure your visualization effectively communicates the intended message. - Iterate based on feedback and make improvements. Remember, the "right" visualization depends on the context, data, and your communication goals. Always align your choice with what best conveys your message to the target audience. #programming #programmer #code #coder #coding #web #webdevelopment #webdesigning #website #design #designer #designing #developer #webdevelopment #frontend #backend #html #html5 #css #javascript #java #python #dsa #dsainc #oops #oopsinpython #oopsinc++ #js #reactjs #engineer #internship #intern #stack #sql #job #cse #computerscience

Introducing Morph 1.0 ✨ We're thrilled to announce the release of Morph 1.0. With this version, we provide an all-in-one tool that can handle all of the data tasks in a business, without the need for coding, database knowledge, or data analysis expertise. What's new in Morph 1.0? ▫ Notebook Evolved into Canvas: The evolution from Notebook to Canvas provides a two-dimensional, non-linear data analysis space. This upgrade is aimed at enhancing accessibility for non-developers. ▫ Collaborative Whiteboarding: Work on data collaboratively with your team on the Canvas Playground. Leave comments, add intermediate steps, and collectively contribute to the data journey. ▫ Flexible BI: The introduction of "@" variables and Dashboard functionalities brings a new level of flexibility to Business Intelligence. You can dynamically change analysis results, providing adaptability without complexity. ▫ Share and Embed: Share your data insights via URLs or embed them in tools like Notion. Collaborate seamlessly with your team. ▫ Semantic Search: Query your data based on meaning not just content matches. https://lnkd.in/gTRNtUNN

🎨 𝐂𝐫𝐚𝐟𝐭𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐢𝐞𝐬: 𝐓𝐚𝐛𝐥𝐞𝐚𝐮/𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐯𝐬. 𝐌𝐚𝐭𝐩𝐥𝐨𝐭𝐥𝐢𝐛 & 𝐠𝐠𝐩𝐥𝐨𝐭𝟐 🎨 In the realm of data storytelling, visualization is not just a skill, it's an art form. As decision-making increasingly relies on data narratives, let's explore which tools illuminate insights most effectively. 𝐓𝐚𝐛𝐥𝐞𝐚𝐮/𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 - 𝐓𝐡𝐞 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐃𝐮𝐨: 𝙄𝙣𝙩𝙚𝙧𝙖𝙘𝙩𝙞𝙫𝙚 𝙎𝙩𝙤𝙧𝙮𝙩𝙚𝙡𝙡𝙞𝙣𝙜: These platforms are the narrators of the business world, turning complex data into interactive, user-friendly stories. Best for stakeholder engagement and real-time business intelligence with their intuitive dashboards and advanced visualization features. 𝐌𝐚𝐭𝐩𝐥𝐨𝐭𝐥𝐢𝐛 & 𝐠𝐠𝐩𝐥𝐨𝐭𝟐 - 𝐓𝐡𝐞 𝐒𝐜𝐫𝐢𝐩𝐭𝐰𝐫𝐢𝐭𝐞𝐫𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚: 𝙈𝙖𝙩𝙥𝙡𝙤𝙩𝙡𝙞𝙗 (𝙋𝙮𝙩𝙝𝙤𝙣): The artist's toolkit for precision and customization. It's my go-to for creating detailed, publication-quality figures within the Python ecosystem. 𝙜𝙜𝙥𝙡𝙤𝙩2 (𝙍): Known for its elegant and sophisticated visualizations, ggplot2 follows a 'grammar of graphics' approach, making complex multi-layered plots more intuitive. Together with Matplotlib, they offer a powerful suite for in-depth data exploration and academic rigor. 👉 In Practice, my journey with visualization tools is one of discovery and mastery. With Tableau and Power BI, I've navigated the corporate landscape where speed and interactivity are paramount. During a pivotal project, Tableau’s dynamic dashboards became my guiding light. They revealed hidden trends and insights, leading to a significant uptick in user engagement—an exhilarating 12% boost in user retention. Transitioning to academia, where precision paints the bigger picture, Matplotlib, and ggplot2 have been my loyal scribes. Crafting visuals for peer-reviewed journals, I've illustrated nuanced data stories with precision and clarity. 𝐒𝐤𝐢𝐥𝐥𝐬 𝐚𝐧𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐚𝐭𝐡: As we continue to script our data-driven sagas, the quest for knowledge is never-ending. For those starting with 𝐓𝐚𝐛𝐥𝐞𝐚𝐮, explore 𝐃𝐚𝐭𝐚𝐂𝐚𝐦𝐩’𝐬 structured learning paths and 𝐂𝐨𝐮𝐫𝐬𝐞𝐫𝐚’𝐬 certifications. Dive into the community and resources at Tableau Public and Tableau eLearning for practical experience. In the guild of Python and R, the scriptwriters thrive on the lore found in 𝐊𝐢𝐞𝐫𝐚𝐧 𝐇𝐞𝐚𝐥𝐲’𝐬 books and Sharp Sight’s tutorials. Discover the essentials of ggplot2 with Tidyverse's comprehensive guide and Udemy's in-depth courses. Now, I turn the page to you, fellow narrators of numbers. Which visualization tools have starred in your data epics? Are you a Tableau /Power BI artist, or do you prefer the detailed control of Matplotlib/ggplot2? Share your experiences, seek advice, or discuss your favorite visualization victories! As we craft the future of data storytelling, let's unite as chronicles of data-driven tales. #datavisualization #storytelling #Tableau #PowerBI #Matplotlib #ggplot2