fabiochiu/medium-articles huggingface dataset:
Funding Rates: January 16–31, 2021
Tags: Rate, English, Funding, Historical Data, Charts

In the spirit of complete transparency we continue to publish historical hourly funding data. Here is the 14th data set in our series of funding rate reports.

ETHUSD (DAI) Perpetual

Jan 16–Jan 31, 2021 (time in UTC)

Jan 16–Jan 31, 2021 (time in UTC)

The funding rate is in floating point numeric format. A value at 0.00060000 implies a rate of 0.06% or 6 basis points.

The data is available in csv format in the funding-report github repository.

ETHUSD (USDT) Perpetual

Jan 16–Jan 31, 2021 (time in UTC)

Jan 16–Jan 31, 2021 (time in UTC)

The data is available in csv format in the funding-report github repository.

BTCUSD (DAI) Perpetual

Jan 16–Jan 31, 2021 (time in UTC)

Jan 16–Jan 31, 2021 (time in UTC)

The data is available in csv format in the funding-report github repository.

BTCUSD (USDT) Perpetual

Jan 16–Jan 31, 2021 (time in UTC)

Jan 16–Jan 31, 2021 (time in UTC)

The data is available in csv format in the funding-report github repository.

Don’t hesitate to drop us a note in our Telegram channel if you have any questions.

Please keep in mind

US Persons are not allowed to trade on Leverj. Users from sanctioned country or Specially Designated National (SDN) as per OFAC are also not allowed to use the system.

Before you trade, please make sure you are legally permitted to trade cryptocurrencies, derivatives, and any other instruments offered on this platform from your home jurisdiction.

Nothing in this article constitutes an offer, solicitation, or investment advise. The content is for educational purposes only. Images and screenshots may be from our test environments and do not represent data on the live system.

Follow us:

Website | Twitter | Telegram | Reddit
How to make a Comet chart in Tableau
By: Isha Garg
Tags: Tableau, Data Analysis, Charts, Comet, Data Visualization

1. Get Dataset

The comet chart works best when you want to compare some metric between two dimensions. So, I decided to use the Covid-19 Vaccine Willingness data set. You can get it from here.

2. Connect to dataset

The dataset downloaded is an xls format file. It threw error for me when I tried to connect it as a data source. So, you might want to open in excel and save as .xlsx and then try reconnecting.

3. Data Manipulation

The dataset is in wide format as shown below. We need to convert it into long format.

So, once you connect the file as a data source, click on the top of all columns except DateTime, right click and select Pivot.

Image by Author

Your data should look like this now. Rename the Pivot Field Names as Country and Values as Willingness.

Image by Author

Next, we need to create few fields-

Willingness in 2020

Image by Author

Willingness in 2021

Image by Author

Difference between the two

Image by Author

Sign function gives the sign of the calculation. Positive is represented by 1, negative by -1 and zero is represent by 0. This will be used to highlight the positive and negative change in the metric.

4. Drag and drop!

Now, just drag Country to Rows shelf and measure values to column shelf like you would do for a horizontal bar chart.

Add Avg Willingness 2021 and Avg Willingness 2020 to Measure Values card.

Drag Measure Names to Path and Size.

Drag the Sign field(diff in my case) to colour.

Voila! The comet chart is ready. Your output would look something like this.

Image by Author

You can customise it further like add labels, change sheet formatting add more information in tooltips etc.

Here’s my final version. I have added detailed trends for each country in the tooltip along with the %change.

Image by Author

You can find the complete viz here.

I hope you find this information useful! Please feel free to leave any questions in the comments.

Happy Learning!
Make Your Dashboard Stand Out- Dotted Progress Chart
By: Memphis Meng
Tags: Tableau, Ideas, Creativity, Charts, Data Visualization

Bar Chart

It is not difficult to display a bar chart, all you should be aware of here is that you need to make sure the colored dots have your desirable colors here by selecting a certain color now. And note that you should put the measure on the “Columns” shelter to make it horizontal.

Image by Author

Overlay

Imagine that we are going to overlap something onto the bar chart shown in the figure above to make it look like a dotted chart. The overlay is supposed to, first of all, have the transparent circles that make the dots appear. Besides, the color of the overlay should be the same as the background. In Tableau, you can always click on “More colors” on the color card, copy the HTML code of a certain color, and past it to your photo modification software to make sure the two look no different.

“More colors” option on Color Card (Image by Author)

Mask that I used to make a bar chart look like dots. I also made them footballs because my latest project is related to football analytics. (Image by Author)

Final Work

Image by Author

It looks cool, right? Now you can show your bar chart result more brilliantly. Hopefully, you can impress your viewers!

For details, you can check out my public gallery.

Enjoy Data Visualization!
Why Tradier powered Tradehawk has grown to stand apart from other legacy options trading platforms
By: Tradier Content
Tags: Trade Commission, Advanced Trading Tool, Options Trading, Charts, Td Ameritrade

TradeHawk is one of the more widely used options trading platforms today and used by some of the most active options traders. Built on innovative cloud-based technology, Tradehawk offers an intuitive stock and options trading experience that was designed by professional traders. TradeHawk stands as a robust alternative to TD Ameritrade’s Thinkorswim, Interactive Brokers, Tastyworks and Fidelity. Its efficient, user friendly front-end provides retail options traders with an edge, allowing them to stay on top of current market trends and investing opportunities.

TradeHawk is an dynamic, customizable platform with an engaging educational interface for options traders. Through Tradier Brokerage TradeHawk users get unlimited, commission-free equity and options trading. *

This powerful combination of comprehensive trading services combined with unlimited, commission-free trading* has accelerated the growth and adoption of the platform. TradeHawk subscribers have saved thousands of dollars in monthly commissions while enjoying a holistic trading experience. TradeHawk offers order entry, order management, risk analysis, simulation, alerts, advanced and conditional orders, charting, portfolio management, spread logs and many other robust features.

The inspiration behind the platform, Kevin “Lex” Luthringshausen says, “We at TradeHawk have a simple mission to provide the best technology, trading experience and value for our traders. We have a very compelling product in the market and our growth proves that.”

Here are some of the reasons why traders are leaving other platforms and moving to Tradehawk

1) Unlimited commission-free stock and options trading*. No more per contract fees, minimums, or ticket charges.

2) Paper Trading to learn and explore the functionality of the system.

3) Room Chat to follow your favorite Educator.

4) Easy one-click trading from multiple templates including signals posted in Room Chat.

5) Create simple and complex exit strategies with conditional triggers.

6) Track your strategies effectively. Our Strategy Tracker separates your spreads into custom, user-defined “buckets” for analysis.

7) Implied and Historical Volatility studies.

8) Large ecosystem of Traders, Educators and Mentors.

9) Intuitive implementation of advanced and conditional orders.

10) Perform real-time risk analysis on options, spreads, strategies, and entire positions.

*Single Listed Index Options are subject to a $0.35/contract fee in addition to any other charges for exchange, OCC, and regulatory fees. See Fee Schedule for more details.

Brokerage services provided by Tradier Brokerage Member FINRA/SIPC

Download Tradehawk at https://tradier.com/products/tradehawk. Start your FREE trial today!
Cryptowatch Contest: Create a Custom Theme
By: Cryptowatch Team
Tags: Web Development, Bitcoin, Web Design, Charts, User Interface

This post is now on blog.cryptowat.ch

Impress our designers with your custom theme and win a year of Cryptowatch Pro — the top trading terminal.

EDIT: The contest is now closed. Read on for how to create custom themes.

We just launched Custom Themes for Cryptowatch, allowing our clients to change the look and feel of their charts by modifying 7 different color values.

Win a Year of Cryptowatch Pro

Our designers want to reward your creativity — so we’ll be sharing a free year of our Cryptowatch Pro plan ($900 value) with 3 traders who create the best custom themes.

Cryptowatch Pro is the top terminal for traders to monitor, trade, and automate strategies across the crypto market.

Everything you get with Pro.

How to Enter

Create a free trial account, then start playing with the theme colors accessible from the cog wheel in the upper right corner of most pages on the site. The video below explains how.

Once you’ve created a theme you’re happy with, tweet @cryptowat_ch a picture with the “theme string” (hit Copy) so others can import it too. Extra points for recreating our Halloween theme.

You may enter as many themes as you’d like.

Deadline

Entries are open until 11:59pm US Pacific time, Sunday, September 1st.

Questions?

Watch the video we tweeted out, or tweet at us @cryptowat_ch.

Here are a few early entrants:

And here are a few themes from our team:

The Bitcoin theme

The Bitcoin theme: “Bitcoin,#4d4d4e,#fcfcfc,#7f7f80,#f2a900,#f2a900,#f2a900,#fcfcfc”

Cryptowatch Team Themes

Clockwise from top left:

jespow’s Purpatrader: “Purpatrader,#2e0d73,#d9acfc,#5b0cb1,#03fcf8,#feef39,#22f7b2,#ef1fd3”

captainsidd’s Work Mode (Bloomberg colors): “Work-Mode,#000000,#Fc9d31,#4b4d51,#C8000c,#2800D7,#00a308,#D62834”

artur’s 80s Terminal: “Terminal,#000000,#41ff00,#000000,#218000,#41ff00,#41ff00,#218000”

bill’s Hand-Drawn Charts: “Bills-Hand-Drawn-Charts,#fbf9f4,#3a354b,#f5fc2b,#ad0002,#3759a6,#212121,#Cb0b12”
Bitcoin breaks $10,000 Margin
By: Altcoin Ninjas
Tags: Trading, Bitcoin, Charts

Hey Ninjas, how’s the trading going? LedgerX has officially launched physical bitcoin futures trading, and have become the first to do so in the domestic market.

The largest cryptocurrency Bitcoin (BTC) has once again broken through the $10,000 margin and is currently trading at $10,030 at press time. Bitcoin has had quite an unstable week as its price fluctuated between $9,200 to $9,900. BTC is reportedly seeing moderate gains on the day, as its value has increased by 5.31% in the last 24 hours. BTC is however still down 0.03% on the weekly chart.

The largest altcoin by market Ethereum is also reporting moderate gains on the day. Ether is has seen its value increase over 3.03% in the last 24 hours and is currently trading around $215 at press time, the largest altcoin reached its highest point of the day trading at $218. Ether is also up 1.04% on the weekly chart.

Ripples XRP the second-largest altcoin by market cap reported moderate losses over the last 24 hours. XRP is currently trading at 0.03% on the day and is currently trading at $0.32. XRP is up 1.80% on the weekly chart.

Bitcoin cash (BCH) is also reporting gains on the day. BCH is up 2.25% in the last 24 hours and is also up 7.23% on the weekly chart. It achieved its highest point of the day trading at $337.

EOS and Litecoin both reported moderate gains on the day. EOS is up 3.39% on the daily chart, and Litcoin is also up by 8.77% in the last 24 hours. EOS and Litecoin are both up 5.08% and 7.97% on the weekly chart respectively.
Why do I not use Pie or Donut Charts?
By: Priyanka Dobhal
Tags: Donut Chart, Tableau, Pie Charts, Charts

When I started working on visualization as my day job, I started using pie charts initially but would always end up replacing them later.

Why? It’s too distracting to me!

Fig1. Sales by Ship Mode

Looking at this pie chart makes me wonder if these many colors are really needed. Fine, I can see the big chunk of the pie for Standard Class but how easy is it to compare First Class with Second Class? If you took a look at the label, then I guess the issue is pretty clear.

Now let’s do the same using a bar chart. Clearly Second Class has more sales than using First Class Ship Mode. And most importantly this was answered without looking at the label (which I have hidden intentionally).

Fig2. Sales by Ship Mode

So does that mean no Pie or Donut Chart at all?

Nah, I wouldn’t go that far. I mean it has a use case for me when I’m just showing two values for a dimension.

Fig3. Sale by Same Day ship mode

Having the item be focused in a dark color with Others in a lighter shade of grey helps me focus on the Same Day easily.

Or maybe as Donut showing the Percentage of Sales using Same Day shipping

Fig4. % of Sales by Same Day ship mode

As much as I like these, I still tend to avoid them since they feel a bit redundant to me.

What is my replacement?

My number one choice is a bar chart showing the measures by the dimension value.

Fig5. Sale by ship mode (bar)

If I want to focus on something in particular, just go with a BAN showing the number you want to focus on. For example, taking the above case

Fig6. % of Sales by Same Day ship mode

Or if I want to highlight the percentage in some way, I would prefer a stack bar chart. They look quite trendy to show the percentage of total and also take a lot less space 😄
Some Quick Updates. Over the last couple of weeks the…
Tags: Avalanche, Avax, Polygon Network, Polygon, Charts

Over the last couple of weeks the ChartEx team has been extremely hard at work.

Polygon (MATIC) Launch

We’ve launched full candlestick charting for Polygon (MATIC) for the following AMMs:

@QuickswapDEX, @SushiSwap, @MUSTCometh, @PolyZapFinance, @AI_smartdex, @elk_finance, @_DFyn, and @PolyDexFi

Liquidity Mining Updates

Avalanche

Our Avalanche Liquidity Mining Event with 0.Exchange is coming to a close.

We averaged nearly 500% APR throughout the entire event and it was a great opportunity to earn ARC20 CHART tokens for the Avalanche community!

As a reminder, both ERC20 and ARC20 CHART tokens count towards Metamorphosis platform access!

We are currently exploring additional liquidity incentive options on the Avalanche blockchain to launch over the coming weeks.

Polygon

We will be launching a liquidity mining campaign on POLYGON (MATIC) in the near future. We’ll share details of what that program will look like over the coming week!

0.Exchange Integrations

ZERO token balances can now be read for ChartEx tiers:

And coming soon will be ChartEx integrations directly into 0.Exchange:

ChainStack

We were spotlighted by Chainstack who is helping run our Ethereum, BSC, and Polygon nodes. They are the best in the business and we’re excited to be working with them!

Finally…

We have some huge platform updates coming out in the next couple of weeks and hopefully some more chain integrations as well. We aim to be the best charting platform out there and will continue to ensure our charts exist on the most chains possible — while having the stability, accuracy, and transparency that you’ve come to expect from us!

About ChartEx

ChartEx is a leading provider of full Candlestick charting for markets on AMM platforms on both Ethereum and Binance Smart Chain. It was the first platform to offer mainstream charting for Uniswap, PancakeSwap, Sushiswap and many other AMMs not covered by typical charting providers. It continues to pioneer in development of tools to benefit Defi trades with it’s user-centric ethos to product development.

Website | Telegram | Medium | Twitter
Bar Chart vs Column Chart — What is the difference?
By: The Big Crunch
Tags: Data Science, Charts, Data, Data Visualization, Charts And Graphs

Both bar and column charts display discrete categorical data and answer the question of ‘how many?’ or ‘how much?’ in each category. The categories are usually qualitative data such as movie titles, types of houseplants or geographical locations. Bar and column charts are different to histograms as they do not show continuous developments over any interval. Different types of data may suit either a bar chart or a column chart better.

Bar Chart

Bar charts use horizontal bars to display data and are used to compare values across categories. The lengths of the bars are proportional to the values they represent. For a bar chart the Y axis typically displays a category such as top grossing movies of 2019 in the example below, whilst the X axis displays a discrete value.

Create your own

Column Chart

A simple column chart uses vertical bars to display data. Column charts are used to compare values across categories and can be used to show change over a period of time. In the case of showing change over a period of time, a column chart can also be displayed as a line chart. In a column chart the Y axis typically displays a discrete value whilst the X axis displays the category.

Create your own

Some best practice tips:
Exploring Recharts: Line Chart with different colored segments for a single Line
By: Gaurav Gupta
Tags: D3js, React, Recharts, Charts, JavaScript

Like any other charting library, Recharts allows you to work with line series data. A line chart has a lot of varied use cases. A very basic example is using a line series chart to show progress of something over time, like, how sales of smartphones has increased over the years. It is very easy to create a simple line chart out of the box in Recharts.

A simple static line chart is usually never enough for our data visualization needs. You would probably need to show some extra information like reference lines on the chart to mark important events, reference areas to mark important periods, adding extra information to the chart with Tooltips, etc. One such usage is to show the line chart in different colors before and after an important event. For example, we might want to show a different color for the smartphone data before iPhone was launched, and a different color after iPhone was launched.

One naive approach to this in any charting library would be to have 2 different line series, one which renders the data before the reference event and another which renders the data after the event,

OR

one line which displays the full data, and another line which only displays data before the event and renders on top of the other line with a different color.

Both of these approaches have some drawbacks:

If you are drawing two different lines which have different starting points, both of them would be animated separately in Recharts, and the effect would not seem like a single line chart with different colors.

If you draw two different lines with different end points, then based on the type of interpolation, your charts might not overlap appropriately towards the end of the shorter line.

Both of the above cases can be fixed by either choosing the appropriate interpolation type, or may be overriding animations, but this seems like an overkill to achieve what we want.

There might be an easier and less hack-y way to do this. Since these lines are svg elements and they accept a stroke , what if we could somehow change the color of stroke based on the data? What if we could use a syntax like this:

<Line dataKey="value" stroke={

d => d > 50 ? "red" : "green"

} />

Sadly, this is not yet allowed by Recharts.

Currently Recharts allows to only provide stroke for the whole line. This means we can change the color of the whole line based on some data, but we cannot do that for different parts of the line chart.

But, we have a workaround. Since these line charts are svg elements, we can pass in a LinearGradient as the stroke .

This opens up a whole new set of possibilities, you can, for instance change the stroke for different parts of the chart on user interaction.

This capability to provide a stroke based on gradient is not specific to Recharts, but is applicable across all svg based charting libraries which allow you to pass in a stroke same as you pass stroke to any other svg element.

References:

A list of all other posts in this series:
Exploring Recharts: different ways of accessing data
By: Gaurav Gupta
Tags: D3js, React, Recharts, Charts, JavaScript

In Recharts, you provide data to the top level container, and on appropriate children you specify the key from which that particular child should get its data, using the prop named dataKey . This is only applicable for components that actually make use of this data, like Line or XAxis , YAxis , but does not make sense for components that do not need this whole data. For example, ReferenceArea , ReferenceLine etc only need some slice of this data, which does not require the same mechanism as dataKey . For these components that data is passed on or handled by Recharts internally.

Accessing data using dataKey:

Passing data in dataKey helps with some level of customization to allow accessing data or nested data directly using object keys.

Accessing nested data using dataKey

Accessing data using accessor function

You can also access the data using an accessor function, in case you want to run any transformations on the data, just for displaying in the chart.

(As an aside, you can also transform how data is displayed on the axes by providing custom ticks).

Other hack-ish workarounds

The accessor function might not look helpful on its own, but in some situations it can make consuming data more convenient. These usages come with their own set of caveats though.

Let’s consider a scenario where you need to show multiple line series. If you go by the default method of property access on object using dataKey it would mean that you need to create a single object and pass it on to the top level container and access this data using different keys on the child. Semantically, this is usually alright, because all the children share the same axes and hence all their data could come from the same place, in this case the same object. You might find it easier to keep this data separate in different objects and bind those objects somehow to the different children, while still expecting Recharts to handle everything else correctly. Accessor functions provide the capability to do so.

The above example is a bit fragile. The XAxis and YAxis are calculated based on the data passed to the top level container, and if the second line series has values outside of this already calculated values, the axes and other components may behave erratically, any automatic calculations might break as they would only happen based on the data passed in to the top level container. But if you can make sure that everything else remains the same, and the data that you are passing externally is derived from the data that you pass to the top level container, you can get some benefits from this approach, mostly in terms of developer convenience. In my usages, i haven’t seen a use case where this breaks, but do keep this in mind.

A similar workaround is discussed here, although it suffers from the same issues as our workaround.

References:

A list of all other posts in this series:
Exploring Recharts: using foreignObject to render custom HTML
By: Gaurav Gupta
Tags: D3js, React, Recharts, Charts, JavaScript

As we have already seen here, Recharts allows you to render any svg element as a child element of a chart container and only accepts a svg element when customizing Labels or ticks and other customizable elements (with the exception of Tooltip , as Tooltip is rendered outside the svg chart, so it can accept regular html), but there are a lot of things that are just way easier to render with html. svg allows this using the foreignObject svg element and since Recharts can render any svg elements, Recharts allows this as well. This can help you have custom labels which are not restricted to only svg elements.

See the below example:

This renders a custom label for a ReferenceLine using svg elements. So, we render a g , and a rect and a text inside the group. We have to explicitly set the coordinates for the rect and the text , and we have to take care of all the alignments for the text as well. To center the text inside the rect , we would need to set a width for the rect , then calculate the text position, and what not.

Enter foreignObject !

See the same example implemented using a foreignObject .

We still have to take care of a few things, but, and this is a big BUT, we get the capability to render html elements, and style them using our usual css. No more figuring out the text alignment, as easy as setting the text-align css property on the html element.

Cool, now that you are on onboard with the idea, let’s list out how to make it work:

when using a foreignObject inside svg , we need to explicitly specify the namespace that the foreign object is from. In this case we do that here: <div xmlns=”http://www.w3.org/1999/xhtml" … >

inside , we need to explicitly specify the namespace that the foreign object is from. In this case we do that here: we still need to position the foreignObject container itself, similar to how we needed to do it for svg elements, but here we only need to align the container and all nested html elements would be aligned accordingly.

container itself, similar to how we needed to do it for elements, but here we only need to align the container and all nested html elements would be aligned accordingly. we need to set a width, height on the foreignObject container to set constraints, otherwise the children would not show up at all.

OR

we can set overflow: visible on foreignObject and let the height , width be default to zero. This way the html elements can decide their own width and everything. This is much better so that the HTML elements are not constrained by the width of the foreignObject container itself. This also helps with complex positioning and alignment.

This was just an example, you can of course use foreignObject anywhere you are customizing any Recharts elements.

References:

A list of all other posts in this series:
How We Visualized the 43 Indicators of OECD’s Going Digital Toolkit
By: Luc Guillemot
Tags: Making Of, Charts, Design, Digital, Data Visualization

The indicator titled “Women as a share of all 16–24 year-olds who can program” depicted above is a good example of the level of curation that we and OECD wanted to achieve. It’s an indicator about gender disparities in software education among young adults. To establish a comparison, we visualized the benchmark for each country (the ability to code among all young adults) as a bar, and the values that convey the message (gender disparities) as dots. We added a high-low line that depicts, for each country, the difference between males and females; the longer the line, the more important the disparity.

We decided to order the countries by the breakdown dedicated to women, instead of using the expected benchmark value of all individuals (the bars) as sorting value, in order to create a slight disruption, a visual nudge that points the eyes of the visitor to the fact that we want to emphasize: the share of young women who can code, which is always lower than the share of young males who can code.

To add more depth to this indicator, users can select among a subset of other breakdowns, other “dots”, to add to the chart. In most countries, the share of young women who can code is for instance higher than the share of all adults aged 25–54, gender plays a role in programming skills, but age also does.

Building a scalable chart system

Given the challenge of bridging the gap between a data story and an exploration tool, we followed an incremental strategy to fulfill prioritized goals that also fit the tight timeframe we had:

The primary goal was to have a basic chart for all 43 indicators and breakdowns. The secondary goal was to customize the charts individually, in close collaboration with OECD. The tertiary goal was to add additional chart types to add more diversity and relevance to the visualizations.

We endeavored to visualize as much information as possible within the given timeframe by building a chart system that has the potential to scale in the future, and that can be enriched with more indicators on demand, without extra work.

Every small decision

When designing and developing an application, every improvement is the result of small decisions made along the way. This is how we gradually enriched the visualizations and how the Going Digital Toolkit’s indicators came to life:
Line Chart or Pie Chart?
Tags: Charts, Data Visualization, Business

Charts are an opportunity to tell a story or provide information to your audience. It’s easy to pick a common chart that is straight forward to read however, if you are using more than one variable it can be confusing. Using bar charts are a common use to show to others. Bar charts are great when you need to track one or two variables at a time. But it gets hairy when you compare the development of more than two variables over a long period of time. Have you seen a bar chart with many different colors and lines? It usually looks terrible and confusing. When you are dealing with many different variables and percentages its wise to use a different type of chart. If you would like to portray the main portion of an online business revenue a pie chart can come in handy. In light of this it’s important that all the variables must add up to 100%. The purpose for pie chart is to highlight the split between each variable. For example, if the online business revenue was accumulated by 5 different sources, it would be ideal to state the name to each slice and percentage of the contribution to the online business revenue. This will give your audience a greater visual understanding of the different streams of income. For example, the online business can get its revenue from various online platforms such as: Facebook at 10%, 45% from amazon, 20% from eBay, 15% company website, and 15% from adds. All these variable contribute to the revenue growth. In closing when using pie charts verse bar charts its important to know when to use one over the other. Bar charts are helpful when presenting a single break down which contain less than 3 variables at a time. If you are planning to show the different types of: car models sold, race of audience, revenue, income distribution, statistics, brands of clothing sold, dieting, daily production or the difference between multiple variables, pie are ideal over bar charts. The problem with pie charts is the limited amount of variable you would want to use for that sake of your audience. Pie charts can easy look like a painting rather something that is appealing and simple. Be aware of the number of variables in any chart type. Here is a link that can provide further information on charting. https://www.youtube.com/watch?time_continue=139&v=8iSHYg8R2W8&feature=emb_logo
Funding Rates: July 1–15, 2021
Tags: Rate, English, Funding, Charts, Historical

Decentralization and complete transparency remain top priorities. Therefore, we continue to publish historical hourly funding data in line with making all relevant transaction data available.

Details of every transaction on the Gluon layer 2 is available and verifiable via the Gluon Explorer.

Here is the 24th data set in our series of funding rate reports.

ETHUSD (DAI) Perpetual

July 1–July 15, 2021 (time in UTC)

July 1–July 15, 2021 (time in UTC)

The funding rate is in floating point numeric format. A value at 0.00060000 implies a rate of 0.06% or 6 basis points.

The data is available in csv format in the funding-report github repository.

ETHUSD (USDT) Perpetual

July 1–July 15, 2021 (time in UTC)

July 1–July 15, 2021 (time in UTC)

The data is available in csv format in the funding-report github repository.

BTCUSD (DAI) Perpetual

July 1–July 15, 2021 (time in UTC)

July 1–July 15, 2021 (time in UTC)

The data is available in csv format in the funding-report github repository.

BTCUSD (USDT) Perpetual

July 1–July 15, 2021 (time in UTC)

July 1–July 15, 2021 (time in UTC)

The data is available in csv format in the funding-report github repository.

Don’t hesitate to drop us a note in our Telegram channel if you have any questions.

Please keep in mind

US Persons are not allowed to trade on Leverj. Residents of UK are not allowed to trade futures on the platform. Users from sanctioned country or Specially Designated National (SDN) as per OFAC are also not allowed to use the system.

Before you trade, please make sure you are legally permitted to trade cryptocurrencies, derivatives, and any other instruments offered on this platform from your home jurisdiction.

Nothing in this article constitutes an offer, solicitation, or investment advise. The content is for educational purposes only. Images and screenshots may be from our test environments and do not represent data on the live system.

Follow us:

Website | Twitter | Telegram | Reddit | Discord | Youtube
Chart Visualization Examples Showing Data About Snow, Prejudice, Names, and Prosperity — DataViz Weekly
Tags: Data Science, Charts, Data Journalism, Infographics, Data Visualization

It’s Friday, and here’s another selection of cool chart visualization projects we’ve come across these days. Look at them for fun and inspiration!

Today on DataViz Weekly:

winter weather and snow in Canada;

partisan prejudice across the United States of America;

names in movies and TV shows vs real life;

prosperity in the United States, by county.

New Chart Visualization Examples: March 1, 2019 — March 8, 2019

Winter Weather and Snow in Canada

Naël Shiab, a data journalist at CBC/Radio-Canada, together with his colleagues made an awesome visual story telling about the reduction in the amount of snow in the Canadian winter. The underlying research is based on the snowfall data from Environment Canada, a government department responsible for coordinating environmental and climate change programs. Take a look to understand how winter temperatures and snow depth have changed for decades, as well as why, and what to expect in the future.

Partisan Prejudice Across America

The Atlantic, in collaboration with experts in polling and data analysis from PredictWise, conducted U.S.-wide research into what political scientists and statisticians call “affective partisan polarization,” indicating the degree of the mutual dislike between Democrats and Republicans across the country. The interactive maps published on Monday along with the analytics nicely display the geography of such a partisan prejudice in every U.S. county. If you are an American, find out whether your area appears to be more (or less) politically tolerant than the others and learn more on The Atlantic.

Names in Movies and TV Shows vs Real Life

Mary Zam, a data visualization enthusiast and software developer, looked into cinematic names to see how naming in movies and TV shows is different from real life. For this research, she took data from the U.S. Social Security Administration (for real baby names) and IMDb (for the names of cinema characters). Check it out. There are multiple cool charts in the story, and the resulting data sets and the full source code are accessible on GitHub.

Prosperity Across United States

The Hamilton Project, an economic policy initiative within the Brookings Institution, published its Vitality Index, measuring well-being in each U.S. country across six specific indicators. Those are median household income, poverty rate, life expectancy, prime-age employment-to-population ratio, housing vacancy rate, and unemployment rate. Explore the dedicated interactive map to take a closer look at the results of their research.
Mackenzie Bezos and the Giving Pledge
By: Chart Westcott
Tags: Philanthropy, Charts, Charity, Business

After a quarter of a century of marriage, founder and CEO of Amazon Jeff Bezos and his wife Mackenzie have divorced. Currently holding the titles of the richest man and third richest woman in the world, the two have agreed to an amicable split. They plan to continue to not only parent their 4 children together but remain business partners as well, each holding stock in Amazon. While the situation is not one to be taken lightly, Mackenzie has made it her mantra to use her 25% stake worth in Amazon to continue to give back. She has since signed the Giving Pledge — an international agreement signed by over 200 of the world’s richest who have vowed to donate over half of their wealth to philanthropic causes.

An experienced philanthropist, Mackenzie Bezos carries with her a strong resume of charitable work and attributions. In 2013, Bezos started the Bystander Revolution, an anti-bullying website aimed to educate youth on the effects of bullying as well as providing advice and an outlet to learn about ways to diminish the widespread issue. The organization accepts contributions from children all over the country to come up with different bullying scenarios and issues, and the resolutions to them. It also approaches the solutions from a parent or teachers point of view as well. Mackenzie has also devoted much of her time and funding into cancer research, Alzheimer’s Disease, marriage equality, and education for young immigrants.

The Giving Pledge, founded by Bill & Melinda Gates with longtime friend and fellow billionaire Warren Buffet, was coined to inspire those with the financial means to do so to contribute to making the world a better place to live. The pledge gives philanthropists the freedom to choose the charitable organizations in which they will donate to, at their own discretion. The common goal of the group is to share ideas and organizations that can benefit directly from their contributions. The organization also orchestrates annual learning events in which the signatories can all convene together to discuss their philanthropic ventures.

While the Giving Pledge’s intent is to donate half of an individual or couples wealth, Mackenzie has taken it upon herself to promise to exceed this requirement. She believes that she has come across her wealth by luck, among other reasons, and that it is her duty to share her wealth with those who have not been given such opportunities for success. However, she does intend to take careful consideration into choosing the organizations that she will bestow this wealth to. She fully plans to continue her philanthropic dedication throughout her life until “the safe is empty” as she referenced from Annie Dillard’s The Writing Life in her Giving Pledge Letter.

This article was originally published on ChartWestcott.net
Lies, Damn Lies, and Data Visualisation
By: Callum Ballard
Tags: Data Science, Mathematics, Charts, Media, Data Visualization

Lies, Damn Lies, and Data Visualisation

The 3D stacked pie chart shows 8 statistics simultaneously, and none of them effectively

As an analyst or data scientist, it might feel like creating insight from a million-row dataframe is the hard part of the process.

You spend hours scrubbing null-values, transforming data, testing hypotheses, recording R²s, and scrutinising p-values. At some point, you reach a conclusion. You sit back, smug in the knowledge that you’ve discovered some truly groundbreaking insight.

And now you need to tell the world all about it.

If mathematics, data science, and statistics are dialects of a single foreign language, then data visualisation might be thought of as a Rosetta Stone, allowing number crunchers to convey insight to kind of person who might, quite reasonably, assume that ‘y-hat’ is a question about headwear.

Thinking about how to show insight is, in and of itself, a fine art. If one doesn’t visualise data appropriately, then, frankly, it will not be understood. And this is true regardless of who the insight is being shared with. In my previous life as a management consultant, I saw perfectly sound, evidence-driven recommendations dismissed by clients — even CEOs — due to bad presentation of that evidence. And one could hardly accuse a CEO of innumeracy, or of generally ignoring sound advice.

I also know from bitter personal experience that a bafflingly formatted chart can even confuse a colleague who’s worked on the same dataset that the very insight was derived from. Thus, whenever I was training new recruits on this matter, I’d encourage them to follow a short list of simple data visualisation rules:

Keep it simple. Always assume that a bar chart will do, and only deviate if the nature of the insight genuinely demands it (e.g. a line chart to show a time series, or a scatter to show a correlation).

A chart should be fully understandable without accompanying text.

Show one insight, and one insight alone, per chart.

Show any data using a 3D chart and I will make it a personal goal to end your career.

The key to achieving the above (give or take the last point) is effective use of Preattentive Attributes — the fundamental visual properties of any graphic.

How much longer did it take you to spot the odd one out in ‘Curvature’, compared to ‘Position’? How much longer would it take you if the dots were 3-dimensional?

Preattentive processing, i.e. the act of extracting ‘obvious’ visual information, as in the above examples, takes place in sensory memory. It requires no conscious effort from the viewer, and it takes less than 500 milliseconds to complete. Effective data visualisation, then, is not just about allowing a viewer to read numbers off an axis, it’s about appealing to their very subconscious.
Periodic Transformation
By: Sameh Zeid
Tags: Transformation, Agile, Framework, Leadership, Coaching

The premise of Agile Frameworks(models) is that when adopting them as designed, organizations would develop capabilities to innovate and cope with ever changing business challenges.

Adopting Frameworks as designed usually requires:

- Organizational restructuring.

- Framework consultants having executives support.

As the transformation program progresses, it creates various decisions that managers action them within their teams.

Over decades, organizations have embarked in periodic transformation programs that had different flavors with little improvement in behaviors. Every transformation program has employed different frameworks with underlying philosophies that are often not incorporated in the daily work.

After adopting the Framework, as ‘designed’, usually organizations return to work as usual. Structures, roles, verbiage, and processes might have changed while the underlying behaviors remain unchanged.

Over the years, these periodic programs formed organizational culture that separated “the real work” from “improving our way of work”. In other words, without transformation programs we mostly focus on the real work, while improvements are second-class citizen at best.

What we suggest

We rather employ a behavioral model for managers to daily coach their teams on experimenting as the way we work.

According to a book released by Gallup in May, 2019 here ; managers are responsible for more than 70% of employees behaviors. Meaning, if we like to introduce new behaviors of experimentation to exploit the benefits from Frameworks, managers should become experimentation coaches for their people.

In fact, managers are the real coaches for their people rather than external coaches. Managers can be trained on coaching behaviors and practices that would redefine how they interact with their people. These coaching interactions empower people to experiment so that organizations can realize the benefits from adopting Frameworks.

To summarize

Periodic transformation programs once ended, usually organizations return to “work as usual”. Instead, organizations can transcend their managers to be coaches for their people/teams guided by a model/Framework. For this purpose, managers hold daily coaching sessions with their people/teams to empower them to experiment as the way of work.
My personal javascript library
By: Yeremi Loli
Tags: Ecmascript, Isomorphic Javascript, Framework, JavaScript, Development

My personal javascript library

In 2013 I worked on a project where it was necessary to have a javascript library with specific functionalities (neither more nor less), it needed to be cross-browser and also work well in different browser versions. There were other requirements the library needed to keep, such as being light and fast. Obviously jQuery was my first choice.

Currently jQuery is used by 73.8% of all websites, that is the JavaScript library market share of 97.4%. By 2013 the percentage was not much different and jQuery 2.0 beta was being tested. You can read about that year’s jQuery status at this link: https://blog.jquery.com/2013/01/14/the-state-of-jquery-2013/

After testing some javascript libraries, I decided to write my own little javascript library to work with along with Sizzle Js , which also belongs to jQuery. At the time Sizzle was working on v1.10.6-pre version and I developed my my library to work along with that version of Sizzle.

jQuery helped me a lot to understand how ECMA standardization worked and also about Isomorphic JavaScript that in that time was considerate as a future of web apps by Airbnb Engineering. So I started to develop Xodo, my personal javascript library, which was used in the project I was working on at the time.

This is the short list of features that have been implemented. You can use Xodo as a case study or apprenticeship, or if you want you can also use it in some personal project but under your responsibility since I have recovered the library from my backup and have not made adjustments or improvements.

Methods

Element Methods

width()

height()

remove()

Style Methods

hasClass()

addClass()

removeClass()

css()

style()

DOM Manipulation methods

html()

append()

prepend()

Events handler

onload()

ready()

bindEvent()

unbindEvent()

resize()

Usage example
Pytest — a beginner guide. 📄. Getting started with pytest framework…
By: Lalitha Gunturu
Tags: Python, Test Automation, Pytest, Framework, Sdet

Substring matching

Suppose, if we want to run only a specific set of tests, this can be done by marking the tests and run tests based on substring matching.

To execute the tests containing a string in its name we can use the following syntax −

pytest -k <substring> -v

for following tests, lets execute with substing “str”

# Replacing String with another string

def test_strReplace():

string = "Hello, World!"

assert string.replace("H", "J") == "Jello, World!" # String Split - Splits a string to two substrings

def test_strSplit():

string = "Hello,World"

assert string.split(",") == ["Hello", "World"] # String Strip

def test_strStrip():

string = " Hello, World! "

assert string.strip() == "Hello, World!" # String Concatenate

def test_strConcat():

string1 = "Hello"

string2 = "World"

assert string1 + string2 == "HelloWorld"

to execute tests based on matching substring, execute with following command,

pytest -k str -v

This will execute all the test names having the word ‘str’ in its name. In this case, they are test_strReplace() ,test_strSplit() , test_strStrip() and test_strConcat().

test execution process,

test_stringOps.py::test_strReplace PASSED [ 25%]

test_stringOps.py::test_strSplit PASSED [ 50%]

test_stringOps.py::test_strStrip PASSED [ 75%]

test_stringOps.py::test_strConcat PASSED [100%] ============================= 4 passed, 10 deselected in 0.07s =======================================================================

Here comes another important concept of pytest. In the below code str variable is declared twice and what if we have more functions which uses this variable.

# Uppercase

def test_upper():

str ="python"

assert str.upper() == "PYTHON" # Is Alpha

def test_isalpha():

str = "python"

assert str.isalpha() == True

In order to decrease the length of code, we can keep this input data in a fixture and use it when ever we need. This can be done using Fixtures.