fabiochiu/medium-articles huggingface dataset:
Flutter charting libraries: an evaluation
By: Jim Anster
Tags: Flutter, Charts

Flutter is growing fast, getting more and more mature. But what about the charting libraries? How powerful and usable are they today? We will compare five libraries:

The code used to test is available on Github:

Note before to start: this comparison is totally subjective, it is just my opinion and feeling. The comparison is made only for a timeline chart so it is not covering all the features of the libraries presented here. Note for the authors of the libraries if they read this: I might not have understood everything right so forgive me if I say something wrong and correct me please.

Let’s start by checking the general results and then we will review the details library by library. The criteria that we use are: general api power, api comfort, customization, maintenance, documentation and beauty.

Api

The api power represents the possibilities offered by the library and the api comfort is the ease of use and understanding.

Maintenance and documentation

The maintenance represents the frequency and recentness of the commits in the repository.

Customization and beauty

The customization is the amount of options and visual changes possible for the chart. The beauty is really subjective, make your own opinion.

Now let’s check the details for each library. The test was made with some stock price data: a date and a double. All the libraries have been tested with 3 dataset sizes: 15 records, 500 records and 3000 records. Note that the speed of rendering has never been a problem: all the libraries render the chart almost instantly in release mode even with 3000 datapoints on an old device (Flutter rocks!), so the speed of rendering has not been included in the comparison.

Flutter charts

The good old chart library from Google (non official).

This library is very powerful: many charts are available: check the gallery. There are a lot of customization options but the api is really complicated and unfriendly. Furthermore the IDE popups help when you hover a function are not helpful at all (in Vscode).

Two more criteria will be used to evaluate the libraries: the ability to use directly some model data from a class without transforming it and the automatic labeling from a DateTime serie on the x axis, which is very helpful for charting time series.

Use model data directly: yes

Automatic labeling from datetimes: yes

Some very important criteria for me are the documentation and the maintenance. For Flutter Charts the documentation is good with a lot of examples with code in the gallery. Where it hurts is the maintenance: the repository has not been updated since 4 months. Here is the repository activity chart:

To sum up this library is powerful but hard to get a grip on. It can still be very useful and produces good looking animated charts. The problem is that it seems pretty abandoned today. Strengths and weaknesses of this library in a chart:
Creating a chart assembly line with react-chartjs-2
By: Nicolás Lobruno
Tags: React Charts, React, JavaScript, Charts, Software Development

Our Challenge

The challenge was to create a generic component that could manage any kind of graphic and that would be simple enough to be maintained and reused over time. In addition to this, we needed a graphics library that could mount different types of graphics with the same data structure received from the backend. This condition was of the utmost importance and was the determining factor when choosing the library for the task.

Just follow these steps and you’ll become a chart ninja!

Select the best library

After a long search, I finally chose the react-chartjs-2 library because it fits my needs almost perfectly. This library is a wrapper of chartjs that has a complete documentation and a large community behind it. It generates easy-to-understand graphics with nice animations without being overwhelming, and in general it is quite customizable.

If you want to check out the basic ways to use this library I recommend that you read this post. Here I will talk about the more complex features of the library.

To mount a graph with react-chartjs-2 we will need a structure similar to this one.

Create a generic Component

After choosing the library it was time to create the generic component. This component receives the props `type` for the type of graphic, `data` that has the graphic information, `width` and `height`. The `options` key includes other customizable stuff like tooltips, labels, axes, and more.

Next, I will specify the GRAPH_TYPES constant and optionsByGraphic function because they are important in the component.

Graph Types

In this constant I defined the types of graphs that we use. Here we match the type sent from the backend with the type that we want to show.

In the future if we want to add a new type of graphic we can just add it here.

Customize the options for Graphic

The react-chartjs-2’s components receive the `options` prop, where we can specify different configuration options like axes, tooltips, labels and legends on the graphics. For this reason we create a optionsForGraphic function to return the configurations according to the type of each graphic.

Well, now let’s take a look at the horizontalGraphicOptions function which returns the key ‘options’ for each graphic. For the ‘tooltips’ key we need to create a different one for each graphic type. For this we create a graphicTooltips function that receives the graphic id and returns the proper tooltip object . We do this because we have two graphics with the same type that need different ‘tooltips’. This is why we use the id of each graphic and not just the type.
Why Visual Paradigm is a better choice than Microsoft Visio
By: Warren Lynch
Tags: Visio, Charts, Dashboard, Diagramming, Data Visualization

Why is Visual Paradigm a better choice than Microsoft Visio®?

If you’ve ever tried to create MS’s Visio Flowchart®, you know how hard it is. You can start with a few basic tools, if you need to change anything — move shapes, delete shapes or add new shapes — it’s too hard! You know what I mean.

Visual Paradigm Online automates all your complex tasks with just a few clicks.

Need a free alternative to Visio?

You can create an unlimited number of drawings and output charts in an image without a watermark. There are no ads, no time limit, you don’t need to provide any payment information. Just free*! Thousands of companies have moved from Visio to Visual Paradigm. Visual Paradigm Online is the best choice for Visio, no need to pay a penny.

Easier to use than Visio

Visual Paradigm Online is easier to use than Visio. Create charts, charts, infographics, flowcharts, etc., spend less time, fewer clicks, no hassle . Start your thoughts with hundreds of templates and examples. There are no online learning software for learning curves and visualization paradigms. It can be accessed from anywhere in the browser without any configuration .

Publish your work with Easy Dashboard

Even the dashboard is free? incredible? Yes! You are right, the free dashboard tool is included in the Visual Paradigm online Express.

Dashboards help you visualize your data into valuable insights. Dashboards and perform temporary data analysis in just a few steps. A variety of charts and charts are available to meet different business needs. Together with the intuitive spreadsheet editor, anyone can visualize data in minutes. No coding, just insight.

Why is Visual Paradigm Online a better choice for you?

Not installed. Zero configuration

Access anytime, anywhere with a browser

Hundreds of startup diagram examples and chart templates

Compatible with all web browsers (Chrome, Firefox, Edge, IE Safari, etc.)

Save your graphic in Google Drive

First import Visio drawing

Visual Paradigm is the international award winner for modeling tools in many of the top IT competitions

Top user experience compatible with Visual Paradigm Community Edition (free cross-platform modeling software in the desktop version) and other commercial versions, with over 1 million users

VP Online Express Edition is part of other commercial versions that support a variety of charts

Try it for yourself. Just browse through the chart example below and click Open Chart to open and edit. You will see for yourself that it is everything we say, or more!

# Open and Edit

Click and Edit
How to override ngx-charts tooltip styles with angular-material theme?
By: Gaurav Soni
Tags: Material Design, Angular, Charts, Ngx Charts, Angular Material

Photo by Markus Winkler on Unsplash

In this article, we will see how we can use angular material with ngx-charts.

What is ngx-charts?

ngx-charts is the charting library built with angular and D3. It is used for data visualizations. It is using Angular to render and animate the SVG elements with all of its binding and speed goodness and uses d3 for the excellent math functions, scales, axis, and shape generators.

ngx-charts come with some default styles for the tooltip, labels, legends, etc.

In this article, we will override the tooltip styles to make it look like as in the material data-visualization guidelines.

Let’s start by creating a new material theme. We are assuming that angular material is already installed. We can create our custom angular theme as follow:-

style.scss

As a next step, we will create scss mixin which will accept the angular material theme and override the tooltip style for us.

The mixin is like below:-

style.scss

We are assuming here the tooltip is consistent throughout the application. So we are good to override the style with !important.

As you can see, we have access to angular material typography, mixins variables, etc. A couple of points to notice:-

We are targetting the ```.ngx-charts-tooltip``` directly. But we can wrap it in our custom class wrapper to avoid collision with some other components in case we are writing a library.

As the tooltip caret is separate from the tooltip box so we need to override the background color for carets separately(.tooltip-caret.position-right etc).

We are using ```mat-elevation``` mixin from angular material to provide the box-shadow to the tooltip. You can read more about it here.

We are also using the ```secondary-text``` color from the material theme to make the sub-label looks like the secondary.

to make the sub-label looks like the secondary. We are also getting the font-size from the material typography and making the sub-label as per the material data-visualization guidelines. Although we can also apply the mat-body-2 class in HTML(on span tag in below HTML file) as well I believe it's better to put the related code in the same class as have access to it.

We can also override the other things axis labels, legend labels, etc with the material typography and theme.

The basic idea is to have the charts consistent with the material theme. So that if we implement multiple themes, then we can have our ngx-charts styles according to the other themes as well.

Our HTML file will look like:-

app.component.html

The end result will look like below:-

chart example

Stackblitz example:-
New Economic Model & Subscription Updates
Tags: Technical Analysis, Charts, Defi

With the CHART BSC Liquidity Mining event kicking off on 0.Exchange in 3 short days we felt it a perfect time to announce our new CHART economic model.

As we prepare to be the first full candlestick charting service to offer support for Ethereum, Avalanche, and Binance Smart Chain we are getting close to exiting beta and beginning our subscription service. Our subscription service has always been a top priority to bring value back to CHART holders, but gas fees on Ethereum have always made it a very difficult value proposition. The Avalanche and Binance Smart Chain blockchains have changed that equation dramatically.

The CHART subscription service will start later this month — the focus of which is intended to drive maximum value to the CHART token and the ChartEx platform.

Tier 1: Holding 2000 CHART or 500 ZERO

secures your ability to save and load charts and indicator layouts

Live candles from Binance Smart Chain and Avalanche blockchains

Trade on-the-page for supported DEX platforms

Tier 2: Holding 3000 CHART or 750 ZERO

Everything from Tier 1 plus:

create up to 3 watchlists

Price Alerts via Telegram*

Tier 3: Holding 4000 CHART or 1000 ZERO + $10 Subscription paid in CHART

Everything from Tiers 1 & 2 plus:

create up to 10 watchlists

Whale trades sidebar

Whale tracking telegram bot*

Whale alert telegram bot*

Tier 4: Holding 5000 CHART or 1250 ZERO + $20 Subscription paid in CHART

Everything from Tiers 1 & 2 & 3 plus:

create up to 20 watchlists

Highlight recent trades by the same address

Overview of buy/sell volume by wallets

Trade analytics sidebar

Technical Analysis screening Telegram bot

This new subscription model becomes the new economic model for CHART. 70% of all subscription fees collected will be burnt making the CHART token deflationary. The more partnerships and subscribers we are able to accrue, the faster the burn will occur. The entire subscription model becomes self sustaining in terms of returning value to CHART holders.

Better yet, since subscriptions are rolling in 30 day windows, and the burn occurs at the end of the users’ 30 day window, the burn is both unpredictable and constant — thus making it unable to be gamed.

We at ChartEx have always felt we had superior charts, and now we think we have an economic model to reflect our charting product.

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
Novel method of 3D geometry visualization
By: Niilo Keinänen
Tags: Data Visualization Tools, 3d Modeling, Charts, Sonar, Data Visualization

This week me and one colleague were playing with the idea of visualizing sonar data (we both work in development of data visualization tools).

At first, it sounded like a really simple application for 2D heatmaps, where X coordinate represent time, Y coordinate represents sonar angle, or the horizontal axis relative to the boat, for example and the color represents depth.

I initially wanted to make some kind of data generator that simulates a real life sonar scenario but it struck me real fast that there are a lot of ways to complicate the matter. I really didn’t have the motivation or need to create any proper solution, but a spark of inspiration hit us which led to me creating a novel solution which enabled simple sonar visualization simulation.

Me, a software developer with no prior experience in 3D modeling or design, downloaded Blender, a free 3D modeling software and within half an hour I had in front of me a 3D model of a waterbed.

Next, I exported the model to OBJ format, which I had general understanding of from my previous life. What I was particularly interested with the OBJ format was that it contained a list of all geometry coordinates and it was easy to parse. I then wrote a Node.js script that

1. Loads the OBJ file.

2. Parses all geometry coordinates of the model.

3. Scans the Y coordinates of the model to a XZ grid, effectively forming a height map (matrix that contains a number “height” value for each column/grid).

What followed was a glorious moment (at least for good old me, who was having a quality Friday evening after work)— I input the height map into our web data visualization software and was met with this:

It actually worked! Ultimately, I had to also try to simulate the data being recorded in real-time to really get the feeling of sonar scanning — this end result, I was really happy with even if its far from a real sonar application.

During this hour long adventure I also made some happy accidents which I found looked really cool (some were intentional, some not).

Here’s the result with some vertices flipped in the height map scanning stage (side projection):

And here’s a 3D version (instead of using color for depicting height, actual 3D geometry is used for visualization):

Find the code, application and tools at the GitHub project, here.
Less is more — best data visualization principle
By: Weronika Gawarska-Tywonek
Tags: Dataviz, Incremental Improvement, Charts, Data, Data Visualization

Less is more — best data visualization principle

This week I came across Eurostat’s Instagram account, which is a resourceful place when you are looking for European statistics. The one visualization particularly caught my eye because of the usage of quite an uncommon chart — a combined chart consisted of a column chart and some variation of a dot plot. The chart itself is about changes in the employment rate in the EU during the pandemic and it shows three different levels of data — a total change for each of the countries, change for each gender within the country, and an aggregated data for the EU.

When I started analyzing the chart I came to the conclusion that there is a reason why you don’t see a chart like this very often. It is simply confusing and showing the total number as the column (or bar if it’s rotated) doesn’t bring additional value. Even if we would like to show more than two subgroups using this method it would be more confusing than the boxplot (I found an interesting post about that).

That’s why in my makeover I decided to focus on simplification. Check how it went.

Original dataviz by European Commission
PolyMoon Use Case Update
By: Polymoon Team
Tags: Dex, Charts, Matic Network

You all asked, and we’re delivering: PolyMoon is bringing charts and other DEX tools to Matic! We will be creating high-performant cross-chain charting tools to provide unparalleled coverage into DeFi markets. We will be rolling out a smooth user interface, complete with DEX tools and a never-before-seen feature that pays our holders increased passive income through our platform’s revenue structure.

Over this past week, we’ve been working behind the scenes to provide our community with the most value possible. We have created buying guides, fixed errors, and tested many ways for swapping tokens, and we appreciate your patience as we overcome challenges. The Matic network has a ton of potential, but due to its newness, usability remains challenging to most investors. We hope to develop a platform that makes using Matic easier. These tools will benefit all Matic users and Lightning IDO holders by building the foundational features and tools needed for widespread adoption.

Our platform will provide access to current and historical charts, free of charge. PolyMoon token holders will benefit from exclusive access to many premium features beyond the charts. We have expanded our dev team to handle the additional workload, and they have already started working on it. The private round of beta testing will start very soon, and following that, we will open beta testing for a group of PolyMoon holders. We will provide additional details regarding the amounts that users will need to store on their wallets in future announcements.
How to read crypto charts?
Tags: Cryptocurrency, Charts

Cryptocurrencies are taking the world via a whirlwind. The oldest and most popular one is the Bitcoin. Moreover, it’s highest ever market value almost touched $20,000 in late 2017. Other players such as Ethereum, TRON, Ripple, Litecoin, etc. have also gained a considerable market share and present investors with an opportunity to trade and earn with this emerging technology.

Common mistake rookie traders make is trading with intuition, without knowing how to use crypto charts. They buy when everyone is buying, the demand surges while the supply remains stagnant and, hence, the price goes up. Later, they sell when everyone else is selling, leading to over-supply and bringing down the price. This exercise makes an investor buy at a high price and sell at a lower price, and they lose a lot of money in the process.

Markets are essentially about timing. You might have the right idea, intuition, or even knowledge about some fundamental analysis by reading crypto charts to back a decision of purchasing a cryptocurrency. But if it’s at the wrong time, you are destined to lose money. At the same time, you need to be prudent about when you exit the market. Otherwise, you lose money, which otherwise you could earn with just a bit of patience.

A major risk in investing in cryptocurrency is its volatility. However, risks also present us with opportunities, such as making substantial short-term gains. Bitcoin traded anywhere between $5000 and $12000 from August’19 to July’20, and it grew about 2,000% in the year 2017 alone when it touched its all-time peak.

There are a lot of cryptocurrency charts with indicators available freely on the internet. However, the task is to understand how to read them. For better clarity, we will limit the scope of this article to Bitcoin for fundamental analysis and present some basic techniques on reading Bitcoin charts.

Continue reading on ChangeNOW
Slope Charts, Why & How
By: Darío Weitz
Tags: Data Science, Python, Matplotlib, Charts, Data Visualization

AKA: Slope Graphs, Slopegraphs, Bump Charts

WHY: it is an appropriate chart when the nature of the message is to compare between two instances or to show trends over time. The two instances can be: a time interval; two countries, regions, or continents; location of teams in the table between two seasons in a soccer league; temporary location in the FIFA ranking; ranking of billionaires; men versus women, and the like.

Slope charts are simple graphs that quickly and directly show transitions, changes over time, absolute values, and even rankings. Their main use is in before and after storytelling since they show very easily what changes have occurred in the analyzed time interval. They allow you to visualize the rate of change in sales, costs, prices, revenues, profits, losses, and other crucial aspects of any business. It is essential for management to know what has gone up, what has gone down, what has remained the same, what the magnitude or rate of the ups and downs was. Like most management, they prefer to know it through simple graphs.

HOW: Edward Tufte first introduced them in his 1983 book The Visual Display of Quantitative Information under the name Table-Graphic, although he later changed the name to Slopegraph. The conceptual idea was to develop a simple diagram, with an abundance of text, showing “a progression of univariate data among multiple actors over time” [1].

The diagram has two parallel vertical axes that indicate the same type of categorical variable with the same units of measurement. While there is no horizontal axis, the distance between the vertical axes indicates an ordinal scale or a time interval. The numerical values of each category are indicated with points on each vertical axis and linked by a straight line. The slopes of these lines reveal the changes in absolute or relative values over time for each category. The information can also be coded using lines of different colors or different thicknesses.

Fig.1: a slope chart. The figure was developed with Matplotlib.

The figure above shows the evolution in the male obesity rate for different Latin American countries during the period 1985–2016. Clearly, in countries with lower per capita income (Bolivia, Haiti) a similar slope to that of the countries with higher GDP (Argentina, Uruguay) can be observed.
Paint by Numbers: How to Color Your Data Visualizations
By: Therese Moriarty
Tags: Dashboard Design, Business Intelligence, Eyeful How To, Charts, Data Visualization

All of these circles have the same hue, but note how different levels of saturation or brightness can alter their appearance.

HOW WE PERCEIVE COLOR

Color is an attribute that sighted individuals process preattentively, meaning that they recognize it without any conscious thought. To observe this principle in practice, try scanning this sequence of numbers and count every instance of three. How many are there?

The correct answer is that there are nine instances of three. Now repeat the same exercise with this sequence of numbers.

Whether you got the answer right or wrong the first time, surely it was easier to count every instance of three in red. As you can see, the shape of each number is too elaborate to process preattentively — but not the color. Humans subconsciously group things together based on preattentive attributes like color; understanding this instinct is key to producing effective data visualizations.

6 TIPS FOR USING COLOR

There isn’t a universal approach to coloring data visualizations; various metrics, charts, and audiences all require their own considerations. In any case, a lot of the best practices are easy to remember because they appeal to our basic impulses. Armed with the right techniques, you can turn any blank canvas into a visually compelling chart.

1. Lighten Up Your Axes & Gridlines

Axes and gridlines provide the structure for data visualizations like bar charts and line charts. Axes define the area of the graph and gridlines mark the intervals where you plot the data. Although they’re essential components, they should always be muted.
react-dynamic-charts — A React Library for Visualizing Dynamic Data
By: Daniel Sternlicht
Tags: Dynamic Data, React, Npm Module, Charts, JavaScript

react-dynamic-charts in action!

If you don’t have time to read the whole story, just browse to the GitHub repository below👇🏻

Background

A few months ago, we had a global company summit at the beautiful island of Tenerife. It was a great opportunity for all of us to meet and talk to our colleagues from around the world.

Another advantage of this event was that we had all of Outbrain’s R&D group in one place, meaning only one thing: A global CODE CHALLENGE!(!!)

In this code challenge, our engineers wrote code that competed each other in (probably) the biggest bidder coding competition in the world. It was a blast, but it’s a subject for a whole different blog post. In the following blog post I want to focus on a React library I created for this challenge in order to visualize the competition, and present who won.

Here’s how it looked like:

Surprisingly, it worked well! In fact, it worked so well that I realized other developers might enjoy it as well. So I open-sourced it 😎

react-dynamic-charts

react-dynamic-charts is a React library for creating dynamic charts visualizations based on dynamic data.

It was designed to be super flexible, and allows you as a developer to control each and every element and event inside.

react-dynamic-charts is a node module so in order to add it to your project just run:

npm install react-dynamic-charts --save

There are a few demos I created in order to show the various options and configurations to customize the design and behaviour of the charts, but the most basic example will look like as follows:

The example is pretty straight forward. Importing the library to your project (line 3), using the LiveBarChart component (line 15), passing data as a property (line 16) to the component.

Note: in order to use the library, you’ll need to import both component, and its CSS styles (line 4).

There are many properties you may use in order to customize the speed of transitions between each iteration, and the way the chart looks and behaves — all is well documented on GitHub

The “data” Prop

This is probably the most important property since all of the rest have defaults.

The data property is expected to be an array of objects. Each object will present an iteration and will include the following fields:

name — the name of the iteration (which will be presented at the top of the chart).

values — an array of data objects (see below).

Each value in the values array will contain the following properties:

id — A unique identifier for the item. Note that it should be consistent across all iterations.

label — The label of the item. Could be either a string, or a React component.

value — A numeric value of the item.

color (optional)— Set a fixed color for the item. Could be also an array of colors that will generate a gradient effect. By default, if not set, each item will get a random color.

Here’s an example of a data object:

And that’s basically it.
Bored of using Bar charts?
By: Isha Garg
Tags: Data Science, Tableau, Visualization, Charts, Data Visualization

Bored of using Bar charts?

Bar charts are one of the most used chart forms I use when presenting data insights at work. But I often feel like it gets a bit monotonous and repetitive especially when you need to present a lot of KPI’s on the same dashboard.

Photo by Priscilla Du Preez on Unsplash

Here are five different variations you can use to show data using bar charts.

1. Using rounded bars

I used this technique in one of my recent viz about F.R.I.E.N.D.S. tv series. The way it works is basically you provide two measure names to tableau and draw a line using those. It’s very similar to how a comet chart is drawn.

a. You start by drawing a normal bar chart by dragging dimensions to the columns shelf and measure to the rows shelf.

A normal Bar chart in Tableau(Image by Author)

b. Next add another measure on the rows shelf as min(0). It is basically a point at 0 and will act as the second point for drawing the line.

c. Now we need to change the chart type to Line.

Step 1c. Image by Author

d. Add Measure Names to path shelf, Measure Values to Rows shelf and move both measures to Measure Values Shelf as shown below.

e. Now, just adjust the size of the line and make it a little thicker. Adjust the axis if needed.

You are done!
One chart to rule them all
By: Ariel Viñas
Tags: Helm, Dependencies, Chart, Kubernetes

How to implement Helm subcharts

As part of my work as a DevOps Engineer in my company, I was in charge of building services Helm charts for deployments. One of the things I personally hated the most was working with multiple microservices with their own Helm Chart. As the company began its journey to Kubernetes, we decided to include the microservice chart inside its application git repo. So the repo tree would look something like this:

├── apps

├── deploy

│ └── chart

│ ├── templates

│ └── values.yml

├── docs

├── ...

.

.

The problem is that, when you decide to upgrade a microservice chart(for example, including a way to mount secrets and configmaps dynamically), it is almost certain that the other services will need that same feature. At first, we started adding these features to the other services, but of course, this isn’t very efficient (or fun, for what matters…)

At first, we developed a common chart based on the microservice framework. We had a separate group in GitLab that hosted the Helm Charts for apps based on Django, React, AioHTTP, and so on. He had our own Chartmuseum as a chart repository. This wasn’t all bad, but when you wanted to install an application in a different namespace or locally, that meant that you had to customize the values from the chart completely (even the docker registry).

That’s how we came across Helm Subcharts. It’s Helm’s approach to Inheritance. Charts can have dependencies, called subcharts, that also have their own values and templates.
Visualize Your Team’s Projects Using Python Gantt Chart
By: Khuyen Tran
Tags: Data Science, Python, Charts, Gantt Chart, Data Visualization

Yes, you can with python-gantt.

What is a Gantt Chart?

A Gantt chart is a bar chart that provides a visual view of tasks scheduled over time. The x-axis represents the timeline, and the y-axis presents the tasks.

There are multiple libraries in Python that allow you to create a Gantt chart, but the one with the most beautiful graphic I found is python-gantt.

To install python-gantt, use

pip install python-gantt

Getting Started

We will learn how to use python-gantt from a simple example. Imagine your company has just 2 employees: Ben and Alex. You want to assign different employees to different tasks within 1 project.

Create Employees

Start with creating the employees using the gantt.Resource() method.

Create Some Tasks

Next, we create some tasks for your employees to do by using gantt.Tasks method

Explanations of the parameters inside gantt.Tasks method:

start : start date

: start date duration : duration of the task

: duration of the task resources (optional): who will the task be assigned to

(optional): who will the task be assigned to color (optional): the color of the taskbar

Create a Project

One project might have different tasks. For example, the project “Clean data” can include smaller tasks such as “Research about the data”, “Choose the best features”, etc.

There might be multiple projects such as “Clean data”, “Build models”, and “Test model performance”. In this example, we will create only one project that contains all tasks created above.

Create Vacations (Optional)

You can also add custom vacations to your Gantt chart using add_vacation method

Now we are ready to visualize our Gantt chart!

Visualize with Gantt chart

To visualize the Gantt chart, use make_svg_for_tasks method

This will create an SVG image as shown below
Coronavisual
By: Matija Dukic
Tags: Covid 19, Statistics, Coronavirus, Charts

Coronavisual

There are two key statistics about the COVID-19 pandemic that I find particularly useful: currently active cases and average daily death toll.

Tracked over time, they provide a clear picture of the progression and current state of this outbreak, both globally and especially when comparing different countries.

Here’s my attempt at visually presenting this (including a bit on Croatia and our neighboring countries), using Johns Hopkins University data with a few manual corrections where data is missing. I will update these charts and comments daily (last updated on May 5th, 2020).

Active cases

Tracking daily active cases (confirmed cases minus deaths minus recovered cases) instead of total (or only new) cases provides a better insight into the current spread of the disease and the burden on the healthcare system of a country on a particular day.

Comparing some of the most affected countries in this way provides a quick snapshot of the current global hotspots. I’ve also noted dates when countries officially went into quarantine (according to Wikipedia), although in most cases lockdowns were introduced gradually (by region and scope).
Flutter Web with amazing charts! (ChartJS)
By: Victor Ramos
Tags: Chartjs, Flutter Web, Charts, Flutter Widget, Flutter

Flutter is an amazing technology. You can build almost everything for mobile, web and now for desktop too. Besides that, we already can see big companies — such as Alibaba, Google and Tencent — using the technology in their main products.

In the company I work at(Fliper ❤️), we have been using Flutter for mobile and web, and we have been facing some issues by using Flutter for the second use. One of the most important features from our software depends on charts, and we haven’t seen much from “ready” and nice libraries for that.

So… I’ve create one solution! I’ve integrated the ChartJS library to Flutter Web by using the powerful HtmlElementView, an widget that allows you to use any HTML element in Flutter Web.

By using this element, you can render a HTML canvas! Combining this capacity with JS context (dart:js), that enables you to call Javascript, you can present amazing and full-speed charts in Flutter Web.

Check it now:

First, you will need to install flutter_web_chartjs package:

Include in your pubspec.yaml : (check flutter_web_chartjs package for the latest version)

dependencies:

flutter_web_chartjs: ^0.2.5

Next, you will need to include a Javascript to your HEAD tag in your web/index.html file, the ChartJS javascript file and another javascript file to communicate with Flutter(chartjs.wrapper.utils.js):

Now, you can use the ChartJS charts with Flutter!
Ionic 5 Charts & Graphs using Chart.js Library
By: Ankit Maheshwari
Tags: Ionic, Ionic Framework, Angular, Charts, JavaScript

Photo by Slidebean on Unsplash

Chart.js is an open-source JavaScript library, which is simple and clean, and engages HTML5-based JavaScript charts. Chart.js is an easy way to include animated, interactive graphs on your website for free. In this tutorial, we will add a chart from Chart.js in an Ionic (Angular) application.

1. Install Chart.js

I am assuming that you already have an Ionic project created in which you want to set up charts — we will be walking through an example of adding charts that can be added to any page you like.

To begin, first, we are going to install the Chart.js library using npm. Just run the following command:

npm install chart.js --save

Now we can use Chat.js anywhere we want — add the following code in any page.ts or any component.ts

In your project import like this:

import { Chart } from 'chart.js';

We’ll learn to create doughnut, bar, and a line chart, in order to display the chart we need a canvas element. We have 3 cards, each card has an element canvas element, and we add a local variable like #barCanvas for each canvas to get a reference to it in our TypeScript file.

Your typescript file should have the following code:

2. Set up the Template

Chart.js uses an HTML5 canvas element to display the charts, so we will need to set that up in our template.

Your template (HTML) file should have the following code:

The charts in your application should be displayed like this:
Yield farming options compared
By: Waves Tech
Tags: Wavesplatform, Farming, Waves, Charts, Apps

In this post, Inal Kardanov, Waves development advocate, offers a comparative table of most popular yield farming options.

Over the past year, yield farming has become a hot topic, introducing many new users to decentralized finance (DeFi) and offering them new earning options.

During a recent YouTube stream, me and a few colleagues and experts discussed various aspects of yield farming. Now, it’s time to share our findings with you in the form of a comparative table, featuring information on yield farming options in various blockchains, including Waves, Binance Smart Chain and Curve.

What is yield farming?

Yield farming is the practice of staking or lending crypto assets in order to generate high returns or rewards in the form of additional cryptocurrency.

Yield farming was originally introduced in Ethereum apps. One of the first and most popular apps offering yield farming is Uniswap. Over the past year, the number of farming options has seen a many-fold increase. Our livestream discussion mostly focused on the best known solutions, such as SushiSwap and Curve on Ethereum, PancakeSwap on Binance Smart Chain и Swop.fi on Waves.

The comparative table features the most vital metrics.

Yield farming comparison table

\* — no impermanent loss in pools with same pegged assets and stable prices.

\** — will be implemented in the future, not available yet.
Difference Between Digital Maps And Navigation Versus Paper Maps
By: Pro Charts
Tags: Marine, Navigation, Maps, Charts, Procharts

Earlier, when anyone has to travel from one place to another, they used to track their way with the help of paper maps. Similarly, for marine navigation, they had some paper maps that give direction to them in the water. But, as time passes and technology started increasing, the place of paper charts gets occupied by the digital boating navigation app as well as road map apps. People can use this app using their mobile phones. They have to download these online maps and navigation systems on their devices, and then they are ready to use the app. But the question is which map system was better, a paper map or the digital one?

So, let us find the answer to the question by differentiating between both map systems.

DIFFERENCE 1:

· You have to buy it:

You have to buy the paper chart from the market. You have to put extra effort into visiting the market or to the nearby libraries to find out the route map for your journey, and then you see there are a lot of marine navigational-maps for one route. After that, you have to select one out of that whole pile of papers. So, who wants to put extra efforts? No one, right! So, for that, the best boat navigation app is a fantastic solution.

· You have to download an app for navigation:

There is no need to go anywhere, connect your phone with the internet, and download the best app for navigation. That is all! After that, download the charts for offline availability. No extra efforts and free to use also.

DIFFERENCE 2:

· Not updated easily:

These maps need the hard work of several hours to print, as it has many points to consider. Once these maps are ready, it cannot get updated easily.

· Easily updated:

Digital navigation-maps are easy to update. These maps show us the real-time representations of an area. In digital maps and navigation systems, the changes get automatically updated in the charts. They did not need any manual updations.

DIFFERENCE 3:

· It requires skills to read the paper maps:

Due to a lot of symbols and land-marks printed on the paper maps, you may need some special skills to read your paper maps. In paper maps, you will not get the view of real-time objects so, it is not easy to analyze these maps.

· Not require special skills:

A digital app for navigation does not require any extra skill because it shows us the real-life object. When you get the directions using by representing real-world objects, then you can safely travel anywhere in the sea, and also you may not require special skills for using the maps.
Data Lab
By: Dragon Den Sports
Tags: Charts, Data Visualization

Google Trends

The chart says that as YouTube and Facebook were growing media platforms from 2004–2019, MySpace was on a rapid decline. It explains the versatility of Facebook and why media consumers indulged in Facebook more than MySpace. YouTube, also experiencing an incline in media consumption was growth due to their ability to deliver video content.

2. Pie Chart

The data is the same for both tests. Students typically prefer one test score over the other because of the bigger number.

3. Bar Chart or Range Chart

This range chart explains the differences between Josh Rosen and Drew Brees during the regular season of 2018. The date indicates that Brees was amongst the top quarterbacks in the league statistically in interceptions, touchdowns, sacks and completion percentage.

4. Annotating Graph

The graph indicates three recession periods in the United States from information obtained by the Bureau Labor of Statistics. The graph also explains the increase of jobs following those recessions for an extended period of time.
Need an eye-catching infographic chart but stuck with Excel? No problems!
By: Tessica Dall
Tags: Data Science, Charts, Excel, Data, Data Visualization

Need an eye-catching infographic chart but stuck with Excel? No problems!

Sometimes you need something eye-catching for a data presentation, annual report or an infographic. You don’t want to go overboard, but are sick of bar chart after bar chart. Maybe you just want to impress your boss and make them think — how did you do that?!!

Few of us have graphic designers on hand, know how to program JavaScript, or even have some fancy data viz software. But all is not lost. Excel to the rescue! Yes, I said Excel. Microsoft Excel. And not some fancy add-in that you need to pay for or entices you in but then leaves you wondering whether they had a 3 year old design their color scheme! No, just plain old vanilla Excel!

A challenge

A few months back I saw this cool animated chart on the SEQWater website. Simple, but effective. It attracts your attention to the most important statistic on their website — dam levels. Too low means drought and the introduction of restrictions on water use. Too high and water releases from the dams cause river levels to rise and perhaps even flooding.

Some of the animated charts on the SEQWater website

So I challenged myself to see if I could recreate this in Excel. Minus the animation, though I have some ideas for how I could use Powerpoint to do that. I want it to be updateable so I can update it and use it in an Excel dashboard as well — so no cheating by just using shapes.

Here’s how I did, step by step, so you can create your own version!

Whether you use this example as is, or borrow elements for your own design, just remember to not limit yourself to the defaults in Excel. If all you have is Excel you can still create some cool stuff!
Explore Music Festivals with Viberate’s Festival Charts & Analytics
By: Miha Vidmajer
Tags: Data Analytics, Music Festivals, Music Business, Charts, Product Updates

Explore Music Festivals with Viberate’s Festival Charts & Analytics

Check out any festival’s popularity, lineup strength and other highlights in this handy tool. Miha Vidmajer Follow Feb 23 · 3 min read

If you’re reading this, you’re most likely a music festival enthusiast. Hello, friend! 👋 Whether you’re a festival organizer, promotor, or an artist creating their “bucket list” of future gigs, you’ll find our latest update very handy.

Festival charts ranking thousands of music festivals

Only on Viberate can you easily overview thousands of festivals in one place.

The three main parameters for ranking are a festival’s overall online popularity, how strong its lineup is (which artists have been included in each festival edition and how influential they are), and the quality of their festival network (who they are connected to and get mentioned by on social media). Simply switch to each parameter to see how the rankings change.

Use filters to get even more specific results. Festival charts can also be sorted by country, prevailing music genres, festival size, month, and various highlights, such as their unique location (mountains, forests, cities …), family friendliness, etc.

The data examined goes back to 2018, so you’ll naturally see more details in the “pre-Covid” timeframe.

Get to know a festival through its analytics

For a more detailed look into a festival, click on it to check out its analytics.

These offer a visual overview of the festival, using various helpful metrics:

💪 “Festival Health” according to the festival’s lineup strength and industry connections, compared to other similarly ranked festivals in that genre

🎸 Genre distribution through time, showing how represented different music genres have been and how the festival’s focus has evolved.

🙌 Audience insights, showing where fans of the latest lineup artists are coming from, with gender and age insights for potential marketing use. The data includes fans and listeners from Instagram, YouTube, and TikTok.

🕸️ Network insights into which artists and other festivals follow the analyzed festival on social media and music channels, and who has mentioned the festival in the last 12 months.

💖 Instagram content performance, examining the engagement and changes in following the festival’s Instagram page has experienced in the last 3 months.

All these features are available in the paid subscription packages. Use the free trial to see which one works best for you.

Have a good time exploring!

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