#DuboisChallenge2026 Plate 37 #matplotlib #geopandas #shapely #dataviz

#DuboisChallenge2026 Plate 20 #matplotlib #geopandas #dataviz

#DuboisChallenge2026 Plate 8 #matplotlib #geopandas #shapely #dataviz

#DuboisChallenge2026 Plate 6 #matplotlib #geopandas #dataviz

A montage of my #pydytuesday submissions. #matplotlib #dataviz

This week’s #TidyTuesday explores Science Foundation Ireland grant commitments. I built 100% stacked bar charts to show how yearly commitments are divided among the top research bodies. #pydytuesday #matplotlib #dataviz

#DuboisChallenge2026 Plate 5 #matplotlib #geopandas #dataviz

#DuboisChallenge2026 Plate 1 #matplotlib #cartopy #geopandas #shapely #dataviz

#DuboisChallenge2026 Plate 2 #matplotlib #geopandas #pyfonts #dataviz

This week #TidyTuesday explores the agricultural production statistics in New Zealand. I created slope charts to show how the cultivated area of specific crops changed between two decades. #pydytuesday #matplotlib #dataviz

I created a map of Corvo Island using osmnx library, showing natural features, land use, waterways and main roads, along with some key facts about the island. #osmnx #matplotlib #pyfonts #dataviz #geospatial

This week #TidyTuesday explores the 2026 Winter Olympics. I divided the discipline names into groups and created 2D kernel density estimate subplots for each, showing how event day and duration are distributed together. #pydytuesday #matplotlib #seaborn #dataviz

This week #TidyTuesday explores the Edible Plants Database. I converted the categorical measurements into numerical values, computed the average for each category, and plotted them as triangles inside circles with circle angles corresponding to the measurements. #pydytuesday #matplotlib #dataviz

This week #TidyTuesday explores Brazilian Companies. I created a connected scatter plot of high and low capital stock pairs by legal nature. Axes are logarithmic to handle skewness, but labels display the original values. #pydytuesday #matplotlib #dataviz

This week #TidyTuesday explores the Astronomy Picture of the Day (APOD) archive. I filtered the data to focus on Portugal related images and visualized them using circle packing. #pydytuesday #matplotlib #dataviz

This week #TidyTuesday explores the Languages of Africa. I created radial bar charts for the top 3 language families. #pydytuesday #matplotlib #dataviz

This week #TidyTuesday is about bringing our own data. Using the 2021 Census geopackage data from Portugal’s national statistics agency, I created a bivariate map of Lisbon’s residences categorized by low and high numbers of divisions. #pydytuesday #matplotlib #geopandas #dataviz

This week #TidyTuesday explores Christmas novels from Project Gutenberg. I collected adjectives that directly precede the word “christmas” using part of speech tagging. Then I created an edge colormap graph to symbolize a sparkler. #pydytuesday #matplotlib #spacy #networkx #dataviz

Happy holidays to you all! #matplotlib #dataviz

This week #TidyTuesday explores the Languages of the World. I made some chord diagram subplots where each subplot highlights a different macro area. #pydytuesday #matplotlib #pycirclize #dataviz

This week #TidyTuesday explores roundabouts in the world. I filtered the data for Portugal, added extra info, and made a grid of circles that looks like the Portugal map. The colors show how many roundabouts are in each district or autonomous region. #pydytuesday #matplotlib #dataviz

This week #TidyTuesday explores data about cars in Qatar. I visualized the distribution of car performance by manufacturing country using raincloud plots. #pydytuesday #matplotlib #seaborn #dataviz

This week #TidyTuesday explores the weather prediction of Zurich's exploding snowman. I gathered additional data and created a timeline plot with some fun facts and weather prediction. #pydytuesday #matplotlib #dataviz

Although I didn’t join the 30DayMapChallenge, but it still got me in the mood to plot some city grids using the osmnx library. #osmnx #matplotlib #pyfonts #dataviz

This week #TidyTuesday explores the statistical performance indicators (SPI). I created gauge charts to show the overall statistical performance score per region. #pydytuesday #dataviz #matplotlib

This week #TidyTuesday explores the Complete Sherlock Holmes. I selected a novel to compare how often Holmes and Watson are mentioned. After that I created square heatmaps to show the frequency of each mentioned name. #pydytuesday #dataviz

This week #TidyTuesday explores the global tuberculosis (TB) burden estimates. I used the great_tables library again, but this time I tried it with nanoplots. #pydytuesday #dataviz

This week #TidyTuesday explores the lead levels in water samples collected in Flint, Michigan in 2015. I created waffle charts to show the % of samples from MDEQ and Virginia Tech that are above or below the 15 ppb threshold. The 90th percentiles are also highlighted. #pydytuesday #dataviz

This week #TidyTuesday explores the selected British literary prizes data. I created area and line charts by selected genders to show how the number of writers changed over time and added some summary metrics. #pydytuesday #dataviz

This week #TidyTuesday explores meteorological data from the UK Met Office. I calculated yearly averages and the overall average for each variable, identified anomalies and created barcode charts to visualize the anomalies, where each bar represents a year." #pydytuesday #dataviz

This week #TidyTuesday explores the World Food Day. I created proportional half circles to represent the percentage of stunted and overweight children under 5 for each available country, and placed them on a map. #pydytuesday #dataviz

I recreated my #TidyTuesday wk36 plot in #Tableau. public.tableau.com/app/profile/...

László Krasznahorkai wins the Nobel prize in literature 2025

This week #TidyTuesday explores EuroLeague Basketball, the premier men’s club basketball competition in Europe. I used a circle packing chart to show each team’s final four appearances and success rate, using size and color to represent these values. #pydytuesday #dataviz

This week #TidyTuesday explores the crane observations at Lake Hornborgasjön, Sweden. I created a dumbbell chart to show the differences between the seasonal counts. #pydytuesday #dataviz

This week #TidyTuesday explores the FIDE Chess Player Ratings. I selected the top 10 male and top 10 female players who saw the biggest jump in ratings and rankings from August to September and then presented them in a table with the great_tables library. #pydytuesday #dataviz

This week #TidyTuesday exploring a curated collection of recipes collected from Allrecipes website. I created donut chart subplots to look like plates and added a fork and knife image to each. #pydytuesday #dataviz

This week #TidyTuesday explores the Henley Passport Index Data. I created subplots of scatters for each EU member country to visually compare the number of free visa destinations over the period of 12 years. #pydytuesday #dataviz

Week 36 #TidyTuesday explored the FrogID data. I created a cartogram to visualize the frog call coverage by season and region. #pydytuesday #dataviz

Week 35 #TidyTuesday explored the Billboard Hot 100 Number Ones. I created a dot plot to visualize the distribution of hits and their BPM. #pydytuesday #dataviz

This week #TidyTuesday explores the Scottish Munros. I combined a KDE plot to visualize Munro spread with a scatter plot which is color coded by distance to the nearest Munro, and put it all on a map. #pydytuesday #dataviz

This week #TidyTuesday explores extreme weather attribution studies. I looked at studies about Africa with ISO country codes, then made spike maps to show the number of studies and their event or trend type using height and color. #pydytuesday #dataviz

This week #TidyTuesday explores the income inequality before and after taxes. I created line charts with a shaded area and small multiples within a mosaic layout. I used only a subset of the processed data where both coefficients were available. #pydytuesday #dataviz

I've recreated my week 24 plot, originally created by Matplotlib (left), using Flourish (right). This is my first time using Flourish, so I’m not sure if the height issue is something I missed when adjusting, or if it might be a limitation of this graph. #TidyTuesday #DataViz #MadeWithFlourish

This week #TidyTuesday is looking into what we have been watching on Netflix. Generally speaking, shows tend to get tired after 2-3 seasons, though there are exceptions. #pydytuesday #dataviz

This week #TidyTuesday explores the New York MTA Permanent Art Catalogue. My bee swarm plot is designed to resemble the layout of a metro station billboard banner. #pydytuesday #dataviz

This week #TidyTuesday looks into the British Library Funding. This time, I opted for a part-to-whole approach, but I wanted to experiment with something a bit different. My goal was to draw four proportional rectangles from the same starting point for each year. #pydytuesday #dataviz

I worked with the Spiritual & Religious Beliefs dataset from #MakeoverMonday and experimented with patches from #matplotlib. #python #matplotlib

My first post here 🙂 This week #TidyTuesday explores the xkcd color survey results. I joined the answers and color ranks tables to identify the most common color names associated with the top five color ranks and created a text-based plot. #pydytuesday