Television Genres Over Time
Posted by Armando Brito Mendes | Filed under Data Science, visualização
Bom gráfico de áreas acumuladas para os diferentes géneros de programas televisivos
By Nathan Yau
IMDb catalogs television episodes with up to three genres. Here’s how the distribution of genres has changed since 1945, when there were only a few shows available, up to present day, when there are more shows any human could ever need.
Tags: belo, Estat Descritiva, gráfico de áreas
How the Longest Running Shows Rated Over Episodes
Posted by Armando Brito Mendes | Filed under Data Science, visualização
Um bom gráfico de barras com muita informação
By Nathan Yau
Most television shows don’t get past the first season, but there are some that manage to stick around. These are the 175 longest running shows on IMDb that have ratings.
Episodes are colored by average rating. Some shows are consistently good, some shows people seem to love to hate, and then there are shows that are good at some point but eventually drop off.
Tags: análise de dados, belo, Estat Descritiva, gráfico de barras
Simulating how just a little gender bias in the workplace can lead to big effects up the chain
Posted by Armando Brito Mendes | Filed under Data Science, visualização
Uma mistura de gráfico de dispersão com cronograma
Yuhao Du, Jessica Nordell, and Kenneth Joseph used simulations to study the effects of small gender biases at entry level up to executive level. It doesn’t take much to skew the distribution. For NYT Opinion, Yaryna Serkez shows the simulation in action with moving bubbles and stacked area charts for each work level.
The simulation imagines a company where female performance is undervalued by 3 percent. Each dot represents an employee, and they either move up with promotions or stay still. The distribution of men and women start even but end very uneven.
Tags: charts, género feminino
Unstable Ground
Posted by Armando Brito Mendes | Filed under Data Science, infogramas \ dashboards, relatórios, visualização
Um excelente relatório cheio de mapas interativos muito bem conseguidos
The Arctic is changing, but what does that mean for the north and the rest of the planet?
The Arctic is warming more than twice as fast as the global average.
Climate change is transforming the Arctic, impacting people and ecosystems across this vast region. But because our climate system is connected globally, what happens in the Arctic doesn’t stay in the Arctic.
Discover how Arctic landscapes are changing and learn about the consequences for communities across the globe.
Tags: alterações climáticas, ártico, belo, Estat Descritiva, mapas
U.S. CENSUS DATA FOR SOCIAL, ECONOMIC, AND HEALTH RESEARCH
Posted by Armando Brito Mendes | Filed under Data Science, data sets, estatística
Uma boa fonte de dados norte-americanos sobre census e saúde
IPUMS USA collects, preserves and harmonizes U.S. census microdata and provides easy access to this data with enhanced documentation. Data includes decennial censuses from 1790 to 2010 and American Community Surveys (ACS) from 2000 to the present.
Use it for GOOD — never for EVIL
Tags: census data, dados, saúde
Age and Occupation
Posted by Armando Brito Mendes | Filed under Data Science, estatística, visualização
Um bom gráfico interativo de intervalos de confiança de idades, um para cada emprego
By Nathan Yau
Whether it’s because of experience, physical ability, or education level, some jobs tend towards a certain age of worker more than others. For example, fast food counter workers tend to be younger, whereas school bus drivers tend to be older.
These are the age ranges for 529 jobs. Search for your job or look at others.
Tags: análise de dados, belo, empregos, Estat Descritiva, idades
How Men and Women Spend Their Days
Posted by Armando Brito Mendes | Filed under Data Science, estatística, relatórios, visualização
Um bom exemplo de gráficos de linhas acumuladas ou gráfico de diferenças
By Nathan Yau
For the employed, unemployed, and those not in the labor force, the charts below show the percentage of people doing an activity over a day in 2020. Switch between a weekday or a weekend day. Select activities to see individually.
Tags: análise de dados, belo, Estat Descritiva, homens e mulheres, ocupação
Olympians are probably older — and younger — than you think
Posted by Armando Brito Mendes | Filed under Data Science, estatística, relatórios, visualização
Um relatório com vários gráficos tipos histogramas unidimensionais (gráficos de pontos)
There were some outliers in the old days, but even in Tokyo, athletes range from boomers through Gen Z
By Bonnie Berkowitz andArtur Galocha July 31
As you would expect, two-thirds of the roughly 11,700 Olympians competing in Tokyo are in their 20s. Prime of life, blah blah blah.
But the rest of the athletes range from two preteens to four 60-somethings, and the older competitors nudged up the average age to 27 for the first time since 1948.
Since the first modern Olympics in 1896, the wide range of sports has allowed for a wide range of sportsmen — and sportswomen, beginning in 1900.
A 10-year-old boy competed in that first Games, and several septuagenarians have suited up. (A 98-year-old sort of competed in 1928, but whether he counts is debatable because he was entered in the art competition and also was dead.)
Tags: gráfico de pontos, idades, olimpiadas
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
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
