Plotly Blog

We have entered a golden age of freely available weather and climate data on the web - spanning time horizons from the next few hours or days and even into the coming weeks or months. NOAA's NCEP NOMADS is a treasure trove of such information and now ECMWF's [European Centre for Medium Range Weather Forecasts] Copernicus Climate Data Store has burst onto the scene. Before we start mapping, let's get set up with Copernicus. We'll use their API to get the climate data for our visualizations. Getting started is easy: 1. Make an account here. 2. Navigate to the 'data store' portion of the site. 3. In this post, 'seasonal forecast' datasets will be considered (access here). For further weather and climate map inspiration, check out our post 'How to Create 2D and 3D Interactive Weather Maps in Python and R'.

1. Download your first dataset

In the climate forecasting space, anomalies (difference from average) are often the most frequently examined data. While looking at raw outputs can be useful in some circumstances, understanding how much warmer, cooler, drier, or wetter than normal an area may be is the end goal. For the purposes of this post, we'll be looking at forecast anomalies. In order to download the data in NetCDF format (the file type you'll need to work with the climate data in Plotly, you will have to install the Climate Data Store Application Program Interface (CDS API). For Python users, this step should be pretty straightforward: 1. Install the CDS API key and store it in your home directory.

2. Install the CDS API client "pip install cdsapi."

Now let's download some data 👌 We'll start off with a sea surface temperature dataset from the ECMWF model, often considered the gold standard when it comes to weather and climate prediction. Follow this link to the data and navigate to the "Download Data" tab in the page. Under the "Download Data" tab, you'll find a form where you can specify the data you'd like to download through the API. Selections in this form will create an API request for the desired data. To view this request, fill out the form and click the red "Show API request" button at the bottom-left beneath the form. Fill out your form like this:

Then, click the "Show API request" to reveal your request. ("Hide API request" tucks it away again.) Next, copy the API request and paste it into a Jupyter notebook. Remember when we mentioned that it needs to be in NetCDF format? This is where we'll take change the requested file format from GRIB to NetCDF:

Click HERE for an example Jupyter Notebook to download and process the climate data.

2. Your first plot: Sea Surface Temperature Anomaly

Now you should have your dataset in your home directory! Next, we leverage the power of Python with Plotly to view and inspect climate data in a new, interactive format that enables you to zoom to low levels for greater detail. Gone are the days of worrying about whether data for your country will show, in this case we'll produce a world map with data that covers the Earth 👌 🗺 The first plot shows sea surface temperature anomalies, or a difference from the average sea surface temperature at a given location (or grid box). Sea surface temperature anomalies are particularly important in the tropical oceans, as anomalies help to modify the Walker Circulation, whose behavior play a major role in precipitation (and temperature) patterns across the global tropics (and to a lesser extent the middle latitudes). Early in 2019, we have an El Niño signature in the tropical Pacific Ocean. The warmest sea surface temperatures (and anomalies) are located across the west-central equatorial Pacific, which has been an area of consistent atmospheric lift, increased cloud cover, and precipitation. You can download the Jupyter Notebook here and use it to make all the plots below.

3. Air Temperature Anomaly

This plot looks at the forecast temperature anomaly, in Celsius, for the month of March 2019. The model data again comes from the ECMWF, although the Copernicus data store hosts four additional models from institutions across Europe: UK Met Office (UKMO), Météo France, Deutscher Wetterdienst (DWD), and Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC). Red colors show warmer-than-average temperatures while blue indicates below-average temperatures.

4. Precipitation Anomaly

This plot examines precipitation rate anomaly in mm/second for the month of March 2019. Precipitation is often considered one of the most challenging parameters to forecast. Generally speaking, precipitation outlooks have lower confidence than temperature outlooks. This is because precipitation (rain, snow) can be irregular in coverage and intensity. There may be sharp differences in amounts over short distances, depending on the lay of the land (e.g. more in the mountain ranges, less in the valleys). Compared to temperature, it is generally much less uniform and therefore a more frequent forecast challenge. In the chart below, negative values (blues) indicate below-normal monthly precipitation while positive values (yellows, oranges) indicate above-normal monthly precipitation.

"No one is dumb who is curious. The people who don't ask questions remain clueless throughout their lives." -Neil deGrasse Tyson

1. Who drives the most?

Following the low-population Republic of San Marino on the Italian Peninsula, the United States has the dubious distinction of having the most motor vehicles per 1,000 people. The number sits at 910. Of countries with more than a million inhabitants, New Zealand is next on the list. This country in the southwest Pacific, whose tagline is "clean and green," has 774 cars per 1,000 folks. Italy (679), Canada (662), and Finland (612) follow closely. On the other side of the spectrum, West African Togo (2), Bangladesh (3), Liberia (3), and the Solomon Islands (3) have the fewest cars per 1,000 individuals.

Data Source

2. Which country boasts the largest proportion of electric vehicles?

Norway. Nearly 40 percent of total new car sales between 2013–2017 were electric, meaning this Scandinavian nation has the most plug-in electric vehicles (EV) per capita in the world. In fact, Oslo, Norway, is recognized as the EV capital of the world. What's the secret? Ninety-eight percent of Norway's electricity comes by way of hydropower, making the country's fleet of electric cars one of the cleanest on the globe. All electric cars and vans in Norway are exempt from on-purchase taxes. In second place is Iceland at 14 percent electric, and in third place, Andorra at 5.6 percent. Although the United States has the most motor vehicles per 1,000 people, only 1.1 percent of them are electric.

Data Source

3. Which U.S. states are winning the EV race? 🏎️

California, Hawaii, Washington, Oregon, Vermont, and Georgia - in that order. Those states are the ones with more than two EVs registered per 1,000 people. On the flip side of the coin, Mississippi is dead last with one EV for every 6,667 people. Arkansas, West Virginia, and Louisiana aren't far behind. EV registrations within a state are influenced by a variety of factors, including state and local incentive programs, charging infrastructure, and fuel pricing.

Data Source

4. Who's who in the EV biz?

Perhaps it doesn't come as a surprise, but over 90 percent of Tesla's manufactured vehicles are electric. This number dwarfs the second place company, China's Zhidou (31 percent), and third place BYD Auto (12 percent) also of China. Other notable electric producers include BMW (0.7 percent), Nissan (0.6 percent), and General Motors (0.3 percent).

Data Source

5. Tesla's slide in European markets

Tesla's sales fell in several European countries in early 2018.

Data Source A blend of stiffer competition, like Porsche's all-electric Taycan (pictured below), and a lack of significant rebates, contributed to the downturn.

Porsche's all-electric Taycan, the first battery-powered car from the iconic German sports car maker. Jaguar is also delivering its new and (relatively) affordable I-Pace, an all-electric sport-utility vehicle, seen as direct competition to Tesla. "The data from both IHS and regional transportation departments is not reflective of actual sales data," Tesla countered (Behrmann and Kehnscherper, Bloomberg, 2018).

6. What does it cost to charge an EV? 💰

Average retail electricity prices have gradually ticked upward over the past several decades. During 2017, the average price for one kilowatt hour of electricity was about 11 cents - although there is significant regional variability and it even changes depending on the time of day. Every EV has a kWh/100 mile figure. Take the Nissan Leaf for instance: 29. Simple math tells us that it would cost $3.19 to travel 100 miles (29*11). However, the cost of electricity is based on the rates set by utility companies, and the more you use, the more you pay, typically. Therefore, you might find yourself paying much more than the national average to charge your EV. You'd be better off juicing up the battery at the mall during your lunch break!

Data Source

7. EV batteries have become much less expensive

Despite how pricey it is to charge them, lithium-ion batteries used in EVs have been dramatically cheaper. From 2009 to 2017, the average price had decreased from $1000 kWh to $209 kWh. For EVs to become cost-competitive with internal combustion engine vehicles, the price needs to reach approximately $100 kWh, which isn't far off.

Data Source

8. China is the lithium ion battery powerhouse

The Tesla Gigafactory 1, based in Sparks, Nevada, USA, will not only be the world's biggest lithium ion battery plant when construction finishes -- but possibly the biggest building in the world, full stop.

Tesla's Gigafactory 1, Sparks, Nevada, USA [image source] Despite that impressive feat, China is still expected to dominate EV battery production over the next decade.

Data Source

9. and 10. Which countries offer the most charging ports?

During 2017, France was the king of EV charge points 🥇 A whopping 11,987 additional charge points were installed in the country, which was 4,050 more than Germany, which came in second. The remainder of the top 10 included many European countries: U.K., Switzerland, Austria, Norway, Italy, Belgium, and Sweden, in that order. Canada followed in 10th place with 1,070 charge points installed during 2017. By 2020, The City of Montreal, where Plotly is based, will have a network of 1,000 charging stations of its own, with a price tag of $10 million. In our next post, we'll explore how Montreal and the province of Quebec are promoting EV use.

Data Source Tesla's "Supercharger network" accounts for 1,317 chargers globally. This particular charger gives the Tesla Model S 170 miles of range in just 30 minutes and a full charge in 75 minutes.

🌟 Before we introduce this seriously cool visualization, we’d like to announce that plotly.py has surpassed 5 million downloads! Thank you for helping us reach this milestone! 🙌

Once Upon a Time 🕰 It was the year 2008. If you wanted to create 3-D plots for interactive scientific data visualization, the Mayavi Python library was your go-to. The charts may have looked slick for their time, but it doesn't take a rocket scientist to tell you that we've come a long way. See for yourself:

Mayavi Streamtube Example, circa 2008

Plotly Streamtube Example, 2018

So what's a 3-D vector?

∙ A vector is a geometric object that has magnitude and direction. ∙ A 3-D vector is useful in any physical space where both magnitude and direction matter. ∙ In 3-D space, vectors are identified with triples of scalar components. In the case of the example image below: a = (ax, ay, az)

Scatterplot Matrix for Diabetes Dataset

This dataset, originally from the National Institute of Diabetes and Digestive and Kidney Diseases, can be used to attempt to predict whether or not a patient has diabetes based on diagnostic measurements. The dataset includes several medical predictor variables, such as glucose, blood pressure, and BMI. In this case, a SPLOM is a effective way of examining any correlations between the variables. • The existence of high blood pressure and high glucose is correlated well with diabetes. • BMI above 30 especially for those over the age of 40 appears to be an indicator for diabetes. • Have high glucose while being pregnant appears to typically be associated with diabetes.

Source | Data | Python code

Scatterplot Matrix for Heart Disease Dataset

This database contains 76 medical attributes where the goal is to identify the presence of heart disease in the patient. Published experiments refer to a subset of the attributes, in which researchers concentrated on attempting to distinguish heart disease positive (values 1, 2, 3, 4) from heart disease negative (value 0) patients. The dataset was included in the University of Toronto Deep Health Hackathon in 2017. • Having a lower maximum heart rate and a higher cholesterol value seems to be a heart disease indicator. • A larger ST depression as determined by an electrocardiogram appears to be a good indicator of heart disease. • The finding of a reversible defect during a thallium stress test appears to be a reasonable indicator for heart disease, particularly in older patients and those with a higher resting blood pressure.

Source | Data | Python code

Scatterplot Matrix for Predicting Rain

Precipitation is typically considered to be the most impactful weather parameter. While sophisticated computer models are used to forecast rain (or the lack thereof) on a daily basis, a meteorologist might glean useful climatological information from a SPLOM. In this plot, we examine the relationship between various weather parameters in an attempt to understand when rainfall may be more likely. Blue dots indicate days on which rain fell and brown dots indicate days on which no rain fell. The city used in this example is Auckland, New Zealand. • Lower amounts of sun hours and higher humidity are typical of days that have rain. • When the minimum temperature is 5.0ºC (41ºF) or lower, rain is unlikely. • When the maximum temperature is 23.0ºC (73ºF) or higher, rain is unlikely. • Two particular wind directions, in conjunction with higher wind speeds, are more associated with rain than others: 220–300º on the compass (southwest to northwest) and 010–070º (north to northeast).

Source | Data | Python code

Scatterplot Matrix for Identifying Iris Flower Species

Through the analysis of flower traits, a scatterplot matrix can help to distinguish between different Iris flower species. This dataset examines sepal length, sepal width, petal length, and petal width and then classifies the species. Light blue indicates Iris setosa, pink Iris versicolor, and purple Iris virginica. • Iris setosa has a large sepal width but small petal width. • Iris virginica has a large sepal length and petal length. • Iris versicolor sits between setosa and virginica much of the time.

FIPS codes are five-digit codes that are assigned to each U.S. county.The first two digits identify the state and the last three identify the county. Think of it like a fancy version of a ZIP Code or postal code that distinguishes a county. FIPS codes are easier to utilize in data and information systems than state and county names. This makes datasets that come packaged with FIPS codes a portal into national, state, and county-level graphical and/or statistical analyses for a variety of topics.

Data visualizations such graphs, charts, presentations, and reports represent the intersection point of your company’s data, statistics, and numbers (i.e. science) and its brand, style, and personality (i.e. art). However, this “marriage” of science and art will only be a successful one if the platform it is presented on is simple, effective, and beautiful. “Design is not just what it looks like and feels like. Design is how it works.” -Steve Jobs This is where Plotly’s dashboards, reports, and presentations come in — to bridge the gap between what your creation looks and feels like, and how it works. Plotly gives you the tools and examples to make beautiful dashboards and reports, even if you’re not trained in design. In this post, we’ll teach you how to be situationally aware when it comes to choosing graph types. Additionally, we’ll show you how easy it is to apply your brand’s style guide, from the logo to the color palette, to a Plotly product. Inspiration for this post stems from IBM’s Design Language data visualization guidelines and therefore we created some IBM-branded graphs, reports, and presentations in Plotly. We recently provided some dashboard and slide deck templates that you can use — now we’ll expand on that work, personalizing your figures and applying industry-leading techniques to ensure the best visualizations possible.

How it works

Let’s go through several different chart types. We’ll address how to best utilize each type of graph according to IBM’s data visualization language. We’ll later use these to construct our Dashboard and Report. Line chart, costs vs revenues: “Line graphs are used to track changes over short and long periods of time. When smaller changes exist, it’s better to use line graphs than bar graphs. Line graphs can also be used to compare changes over the same period of time for more than one group.”

Line chart, units sold over time: “Avoid [line charts] if not comparing values over time, as it might create confusion. Select a bar graph in this case.”

Bar chart, revenue by year: “Rectangular bars with lengths proportional to the values they represent. Bar graphs should be used to compare different values that are hierarchically equivalent. Using a colored element among gray elements makes the focus of the chart clear to the reader.”

Stacked bar chart, evaluation of manager by department: “A variant of the bar graph, where each rectangle is divided in multiple parts. Stacked bar charts are good for comparing elements across categories. In this example, a diverging palette is especially effective — the two opposing values have a positive/negative connotation.”

Pie chart, comparison of revenue subdivision: “Use only when the different values add up to a total and there is a need to highlight percentages.”

Bubble chart, students by faculty: “Used to show values among categories or groups with circles, avoiding any kind of axis. Use it as alternative to bar chart.”

Map, temperature variations across the USA: “Cartography is used to display geographical data. Use it when the focus of the analysis is geography, and when it’s important to zoom in on different places.”

What it looks like

Notice we’ve applied IBM’s color palette and logo to our dashboard and slide deck below [click here to reference the color palette]. If you’re on a Mac, you can use Digital Color Meter to ‘color pick’ off your screen. Windows users might try paint. Pro tip: simple touches like this not only make your figures look more sleek but also reputable, professional—it readies it for the big stage (i.e. a presentation for the execs) in a jiffy.

Dashboard

Slide Deck

When using Slide Deck, make your graphs as large as possible and use at least 50 size-font for slide titles; match your charts background color to the color of the slide deck (we recommend white or light gray). Your presentation should tell a story and not be a lecture — your goal should be to not only convey information to your audience, but inject some of your passion and expertise about the topic(s) into them. Consider it a journey that you are taking together and reach the finish line at the same time! A limited text-plentiful graphic combination will set you on the right path…

We’ve already demonstrated in a previous post how to apply these techniques to graphs in Plotly, but we’ve yet to demonstrate it using dashboards and slide decks. Until now. In this post, find 3 dashboard and 3 slide deck templates (that you can use!) that follow Tufte’s maxims.

1. Simple Slide Deck

Minimalist by design, our Simple Slide Deck template is optimized to clearly present interactive charts, tables, and data. We’ve eliminated all non-data ink. If you’d like to use the slide deck above and make it your own, you can download it as a.json file here. Then, if you haven’t already, download Spectacle Editor and open it. If you need more guidance, check out our previous post on using Spectacle Editor.

2. Simple Report

“Clutter and confusion are not attributes of data—they are shortcomings of design.” -Edward Tufte Edit the dashboard and save your own version here.

3. Vibrant Slide Deck

“There is no such thing as information overload. There is only bad design.” -Edward Tufte

4. Vibrant Report

“Never underestimate your audience. It’s the most common mistake made by presenters. It is not about you anymore. It’s about your audience’s relationship with your content.” -Edward Tufte

5. Minimalist Slide Deck

“Zero out your interface.” -Edward Tufte

6. Minimalist Report

1. Healthy Life Expectancy

Did you know? With an average healthy life expectancy (HLE) of nearly 74 years, Japan leads the world. On the flip side, the Kingdom of Lesotho, a landlocked country in southern Africa, holds the distinction for having the globe’s lowest average HLE at 41.19 years. Iceland has the 2nd highest average HLE at 72.95 years and the Central African Republic the 2nd lowest at 43.19 years.

2. The Prevalence of Overweight Adults

Kuwait holds the distinction of having the highest prevalence (79%) of overweight adults in the world (BMI ≥ 25 for adults aged 20+). The overweight and obesity epidemic has been investigated in this journal article. Despite this, Kuwait sports a fairly average HLE of 69.56 years. Egypt also has a near-global high prevalence of overweight adults at 70.9%. Perhaps this high value can be attributed to a high carbohydrate and calorie diet, as speculated about by The Guardian. To the contrary, Burundi in south central Africa, has the lowest prevalence of overweight adults at 6.3%.

2a. The Prevalence of Overweight Adults vs Healthy Life Expectancy

China and the U.S. have very nearly the same HLE, but being overweight is 38% more prevalent in the states. Meanwhile, the Philippines has an overweight prevalence of 26.9% but an HLE of just 62.05 years. HLE is likely more closely linked to things like local conditions, reliable access to healthcare, and nutrition than it is to being overweight.

3. Daily Smoking Prevalence

At 45.6%, Greenland has the highest prevalence of smokers in the world. 14% less prevalent than Greenland, Macedonia has the second most smokers. Meanwhile, smoking is least prevalent in Panama, with only 3.5% of the population identifying as smokers. A smoker in Panama would have to spend 13.1% the of national median income to purchase 10 of the cheapest cigarettes to smoke each day.

3a. Daily Smoking Prevalence vs Healthy Life Expectancy

India has a daily smoking prevalence of 9.8% while Greece is sky high at 31.2%. Despite this, India has an HLE of 58.12 years and Greece 71.13.

4. High Risk Alcohol Consumption

Belarus in Eastern Europe is home to the world’s heaviest drinkers. High risk alcohol consumption is pegged at 28.7%. It is speculated that 80% of murders and grave injuries in Belarus are committed under the influence of alcohol. Only 11% of Belarusians completely abstain from alcohol. Mauritania in western Africa has the distinction of having the least prevalent amount of high-risk drinkers — this is because it is a dry country.

4a. High Risk Alcohol Consumption vs Healthy Life Expectancy

It likely isn’t a coincidence that the three countries where high risk drinking is most prevalent (Russia, Ukraine, Belarus) are not setting healthy life expectancy records.

5. Deaths by State

Prevalence of all-cause death in the U.S. is highest across the mid-south, Southeast, and Appalachia. It is interesting that the region is well aligned with the CDC-identified diabetes belt. Within the diabetes belt, 11.7% of the people have diagnosed diabetes. Outside the belt, 8.5% have diagnosed diabetes.

6. Crossfilter

1. Where the robots are

Michigan had the most industrial robots in 2015, with over 27,600 in operation. In fact, the high bot concentration across the Upper Midwest and Mid-South aligns well with the automotive industry which employs about half of all industrial robots in use (see Robots by industry below). Hawaii (83) edged out Wyoming (84) for the fewest industrial robots in 2015.

2. Robotic trends

Industrial robot use is increasing most quickly where it is already prevalent. Between 2010 and 2015, Michigan amassed over 16,700 new bots. Ohio (11,900) and Indiana (11,400) are a distant 2nd and 3rd, respectively. California is an honorable mention with nearly a 10,200-bot increase. Wyoming added a paltry 30 bots between 2010 and 2015.

3. Where a robot is most likely to be your colleague

The Elkhart-Goshen Metropolitan Statistical Area was the epicenter of robotic activity in 2015, where for every 1,000 workers was 35.9 bots. Here’s a little known fact about the area: “Elkhart is best known for two industries: recreational vehicles and musical instruments. It has been referenced as the “RV Capital of the World” and the “Band Instrument Capital of the World” for decades.” 🤖 Thus, the clustering across the Upper Midwest makes a lot of sense. But what about the South? Take east Tennessee for instance, Morristown -- here, 3 of the top 4 employers fit the employ-a-robot mold: poultry processing (Koch Foods), aluminum piston manufacturing (MAHLE Engine Components), and power steering systems (JTEKT Automotive).

4. “Robot-as-a-colleague” trends

The largest annualized percentage change of total industrial robots between 2010 and 2015 was found in northwest Alabama. In Florence-Muscle Shoals, AL, specifically, where the increase was a dramatic 45% during the 5-year period. A diverse manufacturing center, this NW Alabama region calls manufacturing its 2nd largest industry by payroll. Chambersburg in central Pennsylvania is another up and coming bot-center. Here, manufacturing facilities are common and include machinery production, metal fabrication, and food processing.

5. Robots by industry

Approximately 62% of the globe’s estimated operational stock of robots were found in the automotive industry in 2015. Between 2010 and 2015, the electronics industry experienced the largest percentage gain (135%) in operational robot use.

6. Global robot stock increasing

By 2019, the world is estimated to have 2.6 million robots in operation. This would be well over two-fold decade-over-decade increase and over a three-fold increase since the turn of the century.

7. Who’s winning the race?

According to comScore’s 2017 U.S. Cross-Platform Future in Focus, as of December 2016, 69% of digital media is consumed via a smartphone or tablet, with the remaining 31% going to desktop computers. This is in stark contrast to data from late 2013, when the smartphone/tablet vs desktop media consumption battle was very nearly 50%/50%.

Learn how to add images to your charts in Python and R.

1. The Brady Bunch

Here’s what TheRichest had to say about The Brady Bunch and its rating fallout: During the fifth season, Cousin Oliver was brought in as a member of the family. It was seen as such a big shark-jumping moment that it even caused a new term to be coined. “Cousin Oliver Syndrome” is known as what happens when a show brings in a new character to re-attract fans, and the gimmick didn’t pay off: after just six episodes, Cousin Oliver was removed from the show.

2. Lost

Lost is an interesting case. The rating trend a flat line through the whole series, but still the show is considered a shark jumper by RollingStone: No one agrees when exactly Lost lost its mojo, because there is no one JTS moment that stands above the rest. Was it when the survivors met the Others and they didn’t make any sense? Or when the infamously annoying Nikki and Paulo materialized on the scene? Or when Claire got amnesia? Or when the Island traveled back in time? Or when Locke was resurrected? Or when it turned out that all the evil in the world was being held back by a literal cork? Or when… The point is, Lost disappointed us at least as many times as it bowled us over.

3. Prison Break

Prison Break exhibited a steep decline in show ratings over the course of its 4 season run. SCREENRANT declared it a shark jumper: Another high-concept show of the 2000s, Prison Break was another instant hit. The story of the two brothers, one sentenced to death for a crime he did not commit, and the other who devises an elaborate plan to help his brother escape prison and clear his name drew in big audiences. At the end of the first season, the brothers, along with several others, escape in a tunnel dug beneath the walls of the prison. The second season followed a very different format and met with mixed reactions. Some praised the change in direction, feeling that they couldn’t stay in the prison forever. Others felt that the show had become too different to the original formula and had become a show about fugitives as opposed to prisoners. At the end of season 2, several prisoners were recaptured and imprisoned in Panama. Despite running for a further 2 seasons, most viewers agreed that the show declined in quality sometime during season 2.

4. Moonlighting

Moonlighting was the definitive will-they-won’t-they show, with most of the attention being given to the chemistry between co-stars Bruce Willis and Cybill Sheppard. The show was a mixture of drama, comedy, and romance, and was considered to be one of the first successful examples of comedy-drama, or “dramedy.” Despite being a massive hit, once the two leads entered into a relationship, the show was in trouble. While the investigations of the private detective agency had been central to the show, along with successful gimmicks such as their “breaking the fourth wall,” the main audience draw was waiting for the couple to give in to the sexual chemistry between them. When the couple finally got together in season 3, there were behind the scenes complications which added to the shows problems. Cybill Sheppard was off having twins, so they had to shoot her scenes in advance, causing much fewer scenes with her and Bruce Willis. Also, Willis was making Die Hard. When that was a massive success, his interest in making a weekly show waned, due to his blooming movie career. -SCREENRANT

5. The X Files

In a way, the fact that this show named one of their episodes “Jump the Shark” says it all. If you have to name an episode of your show that, it’s a pretty big example of self-realization. You could use several episodes as examples as to how The X-Files tried desperately to regain the luster it once had, but the most obvious ones are probably through their first movie in 1998, or the episode where Mulder leaves following the seventh season. To break up one of the most distinctive pairings on TV is a bold move, and one that didn’t exactly pay off the way The X-Files’ producers had hoped. -TheRichest

6. Dexter

Au revoir, unholy Trinity (Season 4, Episode 12) Facile argument: Dexter was ruined the moment its second season revealed itself, early on, as a weak re-brew of everything those first 12 episodes had so beautifully concocted. (Kill, cover-up, stalk worse killer, rinse, repeat.) But the true shark-leap was the moment that distinguished gentleman actor John Lithgow was knocked off at the end of Season Four. Many memorable stars came and went in Showtime’s serial-murderer drama, but in his single-season run as the Trinity Killer, Lithgow repeatedly stole the show — and then was forced to vacate it (at the same time as some crucial co-stars), leaving a huge void in his wake. You knew it was coming, yet the series never really recovered from the loss, and only worsened in its damnably formulaic approach as it bled out over four more long seasons. -RollingStone

7. The Office

The plot above shows something called anomaly correlation — just a fancy phrase that implies weather model skill — for the world’s three most accurate weather models. It may seem a bit noisy, but here’s the takeaway: since the start of the time series (2010), all three models have a positive (skill) trendline. When Yale recently released its Climate Opinion Maps describing the “geographic variation in opinions on climate change at state and local scales in the USA,” I felt it would be an ideal creative outlet and also a chance to make some killer content for Plotly. At the same time, I thought it would be an excellent opportunity to share some projections for later in 2017 and how it might affect the Earth’s weather, climate and you.

What is Climate Change and Global Warming?

Climate change, according to NASA, is a change in the typical or average weather of a region or city and/or Earth. Not only is this phrase associated with a warming Earth, but also changing precipitation patterns, sea levels, sea ice, and more. One of the largest components of our changing climate is global warming, or referring to surface temperature increases from both natural (El Niño) and anthropogenic (greenhouse gases) causes. There’s no denying that we live on a warming Earth. Land temperatures are modulated by sea temperatures, which are also rising. Both are being influenced by carbon pollution, which continues to break records year after year. The monthly average carbon dioxide concentration for March 2017 was 407 parts per million (ppm).

Seven in ten Americans think global warming is occurring. One in ten does not.

Are human activities the main cause?

There’s no denying the distinctive, positive relationship between global temperatures and atmospheric carbon dioxide concentration. Some individuals that are not in favor of anthropogenic climate change refer to warming oceans as the guiding hand for increased global temperatures. Warming oceans certainly play a large role, but it begs the question: why are ocean temperatures warming? While the Earth’s climate has exhibited marked “natural” changes, with time scales varying from many millions of years down to a few years, the recent warming is most likely attributable to carbon pollution. Furthermore, El Niño events, which I’ll cover in more detail further down, are associated with semi-permanent upward steps in global temperatures, initially caused by a warming of sea surface temperatures in the eastern and central equatorial Pacific Ocean. Research has shown that El Niño events may be increasing in frequency due to human-induced climate change. States that are most convinced that global warming is caused by human activities: California (59%) Maryland (58%) New York (58%) States that are least convinced that global warming is caused by human activities: Wyoming (42%) Utah (43%) West Virginia (44%)

Is global warming harming you now or will it within the next 10 years (from 2016)?

A question posed in the Yale survey, residents of New York (57%), California (56%), and Maryland (55%) had the most bullish opinion in the “yes” direction. On the other hand, North Dakota (39%), Wyoming (39%), and West Virginia (40%) weren’t as convinced.

While no one weather event is caused by climate change, all events are influenced by climate change since the atmosphere is now warmer and moister than it was in the past. Climate change increases the likelihood of extreme rainfall, given the appropriate weather setup. Research suggests that there will be up to 8% more intense rain for every 1°C of warming. Since averages are a product of the extremes and extremes are expected to increase in the coming decades, it may involve more impactful tropical cyclones. Yale polled 996 resident’s of Connecticut’s coast who live in coastal flood zones and experienced a hurricane or tropical storm previously, asking the question, “generally speaking, when a hurricane or tropical storm is approaching your city or town, how worried do you feel (1–7)? The results were skewed toward the mid-upper part of the scale, with “5” receiving 22% of the vote. Since a warmer climate will hold more water vapor, climate change indirectly yield more concern and worry amongst coastal residents when it comes to tropical storms and hurricanes.

Natural Influences

El Niño, a climate driver associated with a band of warm ocean water that develops in the central and east-central equatorial Pacific, has been associated with semi-permanent upward step changes in global temperature during and following the “event.” An El Niño event typically takes 3–6 months to develop, reaches a peak during November to January, and then declines for the following 3–6 months. The temperature rises associated with El Niño events have been disproportionate to the temperature falls during the opposite climate driver, La Niña. The natural effects from El Niño events are likely enhanced by greenhouse gas emissions, or the anthropogenic warming that continuously works in the background. And the impact(s) from El Niño events may not be entirely natural anymore — research indicates that there may be an increasing frequency of extreme El Niño events due to greenhouse warming. Global climate modeling is now suggesting that another El Niño event is possible later in 2017, in very quick succession to the strong El Niño that occurred during 2015–16.

Such an outcome would result in yet another upward step in global temperatures, but starting from the new “baseline” that was established by the super El Niño observed during 2015–16.

Trump not fussed

During his first two months in office, U.S. President Trump rolled back key Obama-era greenhouse gas regulations. Without these rules in place, the United States will likely fall far short of its 2015 Paris Agreement pledge: to lower emissions by at least 26 percent below 2005 levels by 2025. “Mr. Trump instructed the Environmental Protection Agency to reverse course on the Obama administration’s biggest climate change policy, the Clean Power Plan, which aimed to cut emissions from existing coal- and gas-fired power plants,” writes Nadja Popovich of the New York Times.

If implemented to its fullest extent, the plan would have reduced carbon emissions by nearly 650 megatons by 2025 — just under halfway to the Paris pledge, according to an analysis by Climate Interactive.

In the meantime, as global temperatures continue to rise, so will the number of “hot days” and likelihood for heat waves. For some, winters will become milder and the growing season longer. However, it goes well beyond just warmer temperatures — but how ecosystems will cope and respond, how we grow our food, and where and when we get our water. All those things will change, some more gradually than others. With respect to Earth’s lifeforms, the words cope and adapt may have never been more relevant. For those that can do this most efficiently will best set themselves up for the future on the ever-changing planet that we call home.

Plotly's latest invention? Something akin to "invisible ink." That's right -- Plotly users can harness our new animation functionality to create charts, code-free (!), with secret messages. One of the earliest writers to mention invisible ink was Aeneas Tacticus over 2400 years ago. Since then, several others applied varying techniques to craft secret messages: Pliny the Elder and the Roman poet Ovid gave advice on the use of plant juices and milk to write secret messages, similar to what George Washington used during the American Revolution. But this is not George Washington's invisible ink -- this is the 21st century edition: 1. Keep your hands clean: no sticky substances like plant juice and milk needed to create it. 2. No ultraviolet light needed to read it. 3. All digital! Super flash.

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Bioinfo-what?! At Plotly, we're proud of our diverse and talented user base. In this post, we show off the work of our bioinformaticians. In case you didn't know: Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data.