Visualizing a codebase
Posted by Armando Brito Mendes | Filed under software, visualização
Uma forma alternativa de visualizar uma árvore de pastas, com círculos
How can we “fingerprint” a codebase to see its structure at a glance? Let’s explore ways to automatically visualize a GitHub repo, and how that could be useful.WHAT’S IT FOR?“Fingerprint” the structure of a GitHub repoSHARE
Imagine this: you’re looking at a new codebase, and you want to find the code for a specific function.
For example, in the create-react-app codebase,How quickly can you find a test for react-dev-utils?
Tags: círculos, GitHub, visualização
See how California changed in the last decade, right down to your neighborhood
Posted by Armando Brito Mendes | Filed under Data Science, relatórios, visualização
Um relatório com vários tipos de gráficos de comparação das etnias em duas datas distintas
The demographics of California’s communities are changing quickly. Take Alameda County. Today, the share of people who identify as Asian make up 32% of Alameda County — the county’s largest single demographic group. In 2010, that figure was 27%. White people made up 34% of the county back then. Now they are just 28%.
Scroll down to plug in your address to see how your community has changed
Tags: comparação, demografia, gráficos variação
Repulsive Curves
Posted by Armando Brito Mendes | Filed under materiais para profissionais, refs bibliográficas, visualização
Um trabalho muito interessante e com muito código sobre os melhores algoritmos para construir curvas que não se sobrepõem
Curves play a fundamental role across computer graphics, physical simulation, and mathematical visualization, yet most tools for curve design do nothing to prevent crossings or self-intersections. This paper develops efficient algorithms for (self-)repulsion of plane and space curves that are well-suited to problems in computational design. Our starting point is the so-called tangent-point energy, which provides an infinite barrier to self-intersection. In contrast to local collision detection strategies used in, e.g., physical simulation, this energy considers interactions between all pairs of points, and is hence useful for global shape optimization: local minima tend to be aesthetically pleasing, physically valid, and nicely distributed in space. A reformulation of gradient descent, based on a Sobolev-Slobodeckij inner product enables us to make rapid progress toward local minima—independent of curve resolution. We also develop a hierarchical multigrid scheme that significantly reduces the per-step cost of optimization. The energy is easily integrated with a variety of constraints and penalties (e.g., inextensibility, or obstacle avoidance), which we use for applications including curve packing, knot untangling, graph embedding, non-crossing spline interpolation, flow visualization, and robotic path planning.
Data Visualization In Excel Using Python
Posted by Armando Brito Mendes | Filed under estatística, lições, materiais ensino, materiais para profissionais, SAD - DSS, software
Um pequeno tutorial para utilizar ExcelWriter em Python
Using ExcelWriter for Creating Visualizations in Excel by Python Code
Himanshu Sharma4 days ago·3 min read
Excel is widely used for data analysis and has a lot of functionalities for analyzing, manipulating, visualizing, etc. Using excel should be one of the main skills required for a Data Analyst, Product Analyst, and Business Analyst. It helps in understanding the data and how we can use it for generating useful insights.
Python is also widely used for Data Analysis purposes and also overcomes the drawbacks of Excel. With a little knowledge of Python, we can enhance our Data Analysis skills and also generate more useful insights.
Combining both Excel and Python can be fruitful and makes Data Analysis more interesting. ExcelWriter is used for performing different Excel operations using Python. It provides a wide variety of functionalities like creating an Excel, writing data into an Excel sheet, creating visualizations in Excel, etc.
Tags: dataviz, Excel, Python, visualizações
A Better Way for Data Preprocessing: Pandas Pipe
Posted by Armando Brito Mendes | Filed under Data Science, lições, linguagens de programação, materiais ensino, materiais para profissionais
Um pequeno tutorial sobre como começar a usar o pipe
Efficient, organized, and elegant.
Soner YıldırımJul 31·4 min read
Real-life data is usually messy. It requires a lot of preprocessing to be ready for use. Pandas being one of the most-widely used data analysis and manipulation libraries offers several functions to preprocess the raw data.
In this article, we will focus on one particular function that organizes multiple preprocessing operations into a single one: the pipe function.
When it comes to software tools and packages, I learn best by working through examples. I keep this in mind when creating content. I will do the same in this article.
Tags: pipe, préprocessamento, Python
Cycle of Many
Posted by Armando Brito Mendes | Filed under relatórios, visualização
uma representação original, circular, de um dia na vida dos americanos
By Nathan Yau
This is a 24-hour snapshot for a day in the life of Americans. Each ring represents an activity with a color. More dots means a greater percentage of people doing the respective activity during a certain time of day.
If you start at the top of the circle, you’ll be at 9:00am when most people who work are already working. Move clockwise, and you see the flows of the day. People break for lunch at noon, get off work around 5:00pm, shift to dinner and then relax. Most people are sleeping by midnight but a small percentage of people are work at night.
Focus on the inside rings versus the outer rings for a rough comparison between work life and home life, each with its own responsibilities.
Source
The data comes from the American Time Use Survey 2020, which is run by the Bureau of Labor Statistics. I downloaded microdata via IPUMS.
Tags: 24h, gráficos, time, uso do tempo
THREE RESOURCES TO STUDY TIME USE
Posted by Armando Brito Mendes | Filed under data sets
Boa fonte de dados sem custo.
IPUMS provides census and survey data from around the world integrated across time and space. IPUMS integration and documentation makes it easy to study change, conduct comparative research, merge information across data types, and analyze individuals within family and community contexts. Data and services available free of charge.
Tags: census, mapas, microdata, saúde, time, vários países
179 Reasons You Probably Don’t Need to Panic About Inflation
Posted by Armando Brito Mendes | Filed under Data Science, estatística, relatórios, visualização
relatório dinâmico com gráficos alto \ baixo
By Josh Bivens and Stuart A. ThompsonAug. 18, 2021Gasoline (all types)March 2019March 2020March 2021Used cars and trucksMarch 2019March 2020March 2021Airline faresMarch 2019March 2020March 2021
Here’s how the price of gasoline changed each month throughout 2019, compared with the previous year. ↑ Red means the price went higher, while ↓ blue means it fell.
The coronavirus gripped the United States by March 2020. The economy halted. Gas prices ↓ plummeted.
This year, things have taken another turn. Gasoline prices have increased sharply, ↑ up 41.8% by July compared with a year earlier.
It’s a similar story for used cars and trucks, with prices ↓ falling during the pandemic shutdown, then ↑ soaring as things reopened.
Tags: finanças, gráficos altor\baixo, gráficos variação, inflação
fishdraw
Posted by Armando Brito Mendes | Filed under Data Science, visualização
Estes peixes não existem, são gerado por algoritmos
procedurally generated fish drawings. Lingdong Huang 2021
Tags: desenhos, gerador, peixe
2020 Census Results
Posted by Armando Brito Mendes | Filed under Data Science, data sets, estatística
Fonte de dados do census dos EUA
Decennial Census P.L. 94-171 Redistricting Data Summary Files
Includes the official data, documentation, and support materials to assist in accessing P.L. 94-171 Redistricting Data.
2020 Census Apportionment Results
On August 12, we released the redistricting data to the states and the public. States may use these data in redrawing congressional, legislative, and local district boundaries. The Census Bureau will also deliver the final redistricting data toolkit to all states and the public by September 30. COVID-19-related delays and prioritizing the delivery of these apportionment results delayed our original plan.
More 2020 Census population results will be available later including statistics on age, sex, race and ethnicity, and details about the center of population. The results for the U.S. Island Areas will also be provided in a separate release at a later date.
