Deep tech. Hard tech. Or, as The Engine dubs it, Tough Tech. Venture investing today is essentially identical to what happens on Wall Street, focused on data rooms, spreadsheets, SaaS churn models and cohort analysis. Yet, the history of venture capital firms is heavily interwoven with universities and their research. Some of the most famous […]

Milan-headquartered scale-up Boom, which offers a two-sided tech platform to let companies book and manage commercial photo shoots, has raised $7 million in Series A funding. The round is led by Italy’s United Ventures, with the participation from Wellness Holding. It will be used to support the photo tech company’s next stage of growth as […]

Two automotive giants are teaming up to bring self-driving semi-trucks to market. Under this deal, Waymo and Daimler Trucks are partnering to build an autonomous version of the Freightliner Cascadia truck. This is Waymo’s first deal in the freight business. The truck would be equipped with level 4 autonomous technology, meaning it could drive itself […]

Months after its $80 million Series B fundraise, Course Hero has acquired Symbolab, an artificial intelligence-powered calculator that helps students answer and understand complex math questions. The price of the deal was undisclosed. The 9-person Symbolab team, based in Tel Aviv, will join Course Hero . The platforms will live under independent branding for the […]

“We’re building a decentralized ghost kitchen,” is a sentence that could launch a thousand investor calls, and Alex Canter, the chief executive officer behind Ordermark, knows it. The 29 year-old CEO has, indeed, built a decentralized ghost kitchen — and managed to convince Softbank’s latest Vision Fund to invest in a $120 million round for […]

The first mission to officially carry astronauts to the International Space Station for a standard crew rotation is now tentatively set for November 14. NASA provided an updated date for the mission this week, after it shifted from an original planned timeframe of sometime in October. This is the first time that Crew Dragon, SpaceX’s […]

Zoom, the video calling company that millions turned to during the pandemic, has finally launched end-to-end encrypted video calls for free accounts. The company said last week that it was readying the feature, months after it drew criticism for denying end-to-end encrypted calls to free users, effectively drawing a line between paid users whose conversations […]

Though python has been created by Guido van Rossum as a hobby project in the 90’s decade, it has become the most popular language now. Due to its ability to work in various fields and easy syntax...

Python and Java are two of the most popular programming languages in the world, and have both been around for over 20 years. In that time there have been numerous attempts to provide interoperability between them, with varying methods and levels of success. One such project is JPype, which allows you to use Java classes in your Python code. In this episode the current maintainer, Karl Nelson, explains why he chose it as his preferred tool for combining these ecosystems, how he and his team are using it, and when and how you might want to use it for your own projects. He also discusses the work he has done to enable use of JPype on Android, and what is in store for the future of the project. If you have ever wanted to use a library or module from Java, but the rest of your project is already in Python, then this episode is definitely worth a listen.Summary Python and Java are two of the most popular programming languages in the world, and have both been around for over 20 years. In that time there have been numerous attempts to provide interoperability between them, with varying methods and levels of success. One such project is JPype, which allows you to use Java classes in your Python code. In this episode the current maintainer, Karl Nelson, explains why he chose it as his preferred tool for combining these ecosystems, how he and his team are using it, and when and how you might want to use it for your own projects. He also discusses the work he has done to enable use of JPype on Android, and what is in store for the future of the project. If you have ever wanted to use a library or module from Java, but the rest of your project is already in Python, then this episode is definitely worth a listen. Announcements Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today! Your host as usual is Tobias Macey and today I’m interviewing Karl Nelson about JPype, a language bridge that lets you use Java classes in your Python programs Interview Introductions How did you get introduced to Python? Can you start by giving an overview of what JPype is? What was your motivation for becoming such a regular contributor to the project? Why might someone want to be able to call into the Java ecosystem from a Python program? There have been a number of other projects aiming to combine the capabilities of Java and Python, such as Jython and PyJNIus. What are the relative tradeoffs between the different options? Many of those other projects have stalled or stopped altogether. What about JPype has allowed it to survive for so long? Can you explain how JPype is implemented? How has the design and implementation of the project evolved since it was first implemented? How do the relative language versions influence the compatibility of programs on either side of the bridge? What is involved in creating a project that uses JPype? How are dependencies, packaging, distribution, etc. handled across the Java and Python portions of the code? What are some of the ways that JPype can be used for Android applications? What are some of the sharp edges or pitfalls that users of JPype should be aware of? What are some of the most interesting, innovative, or unexpected ways that you have seen JPype used? What have you found to be the most interesting or challenging aspects of building JPype? When is JPype the wrong choice? What is in store for the future of the project? Keep In Touch Thrameos on GitHub LinkedIn Picks Tobias Hiking All Trails The Hiking Project Karl Summoner’s Rift Links JPype Java Overview of Python to Java bridges Laurence Livermore National Lab GTK– Gnome Perl C++ Matlab Java Native Interface (JNI) SciPy NumPy Matplotlib Jython PyJNIus Py4J Jep Ruby Reflection Ivy Maven JDBC Kivy Android Python Slots PyPy Java ASM Arrow Columnar Memory Format Protocol Buffers GraalVM The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

NumFOCUS is pleased to announce the newest addition to our fiscally sponsored projects: TARDIS TARDIS is an open-source, Monte Carlo based radiation transport simulator for supernovae ejecta. TARDIS simulates photons traveling through the outer layers of an exploded star including relevant physics like atomic interactions between the photons and the expanding gas. The TARDIS collaboration […] The post TARDIS Joins NumFOCUS as a Sponsored Project appeared first on NumFOCUS.

Want to improve your Python skills? Looking for a way to practice on a regular basis, backed up by a community of learners? Look no more: A new advanced-level cohort of Weekly Python Exercise is starting tomorrow! If you’ve been using Python for at least a year, then this course will open your eyes to new techniques, and help to strengthen existing ones. Here’s how it works: Every Tuesday, you’re sent a new problem via e-mail, along with “pytest” testsOn the following Monday, you get the solution, with a detailed explanation.In between, you can chat in our private forum about your solution (and theirs).Once a month, I do free, live office hours, answering your Python questions. But wait, there’s more: As of this cohort (B3), every solution will not only be written up in e-mail, but will also be answered in a screencast! I hope that this will help you to understand the solutions better than in pure text. Questions? Comments? Wondering about discounts? Just contact me at @reuvenmlerner on Twitter, or send me e-mail at reuven@lerner.co.il. But don’t hesitate; I won’t be offering this cohort again until 2021… Click here for more info about Weekly Python Exercise The post Last chance: Weekly Python Exercise B3 starts tomorrow! appeared first on Reuven Lerner.

Wearable technology is not just smart consumer devices like watches and activity trackers. Wearable tech also includes one off projects by designers, makers, and hackers and there are more and more people producing tutorials on how to get started. Wearable tech is also a great way to get both kids and adults excited about coding, electronics, and in general, engineering skills. Sophy Wong is a designer who makes really cool stuff using code, technology, costuming, soldering, and even jewelry techniques to get tech onto the human body. Sophy joins the show to answer my many questions about getting started safely with wearable tech. Some of the questions and topics: Can I wash my clothing if I've added tech to it? Is there any danger in wearing technology or building wearable tech? Are there actual wires and cables conductive thread in the fabric and textiles of some wearable tech projects? What's a good starter project? Especially if I want to do a wearable tech project with my kids? Dealing with stretch with clothing and non-bendy electronics. Some questions around the Sophy Wong and HackSpace "Wearable Tech Projects" book. How did you get into wearable tech? Do you have a favorite project? Can I get into wearable tech if I don't know how to code or solder? Are these projects accessible to people with limited budgets? Making projects so you can reuse the expensive bits on multiple projects. Special Guest: Sophy Wong.Sponsored By:monday.com: Creating a monday.com app can help thousands of people and win you prizes. Maybe even a Tesla or a MacBook.PyCharm Professional: Try PyCharm Pro for 4 months and learn how PyCharm will save you time. Promo Code: TESTANDCODE20Support Test & Code : Python Testing for Software EngineeringLinks:sophywong.comWearable Tech Projects book — The wearable technology bookcostumes — The dress is on this page, as well as the Ghostbuster pack and costume.spacesuitMusic video with Sophy's space suitKobakant tutorials<p>Wearable technology is not just smart consumer devices like watches and activity trackers. </p> <p>Wearable tech also includes one off projects by designers, makers, and hackers and there are more and more people producing tutorials on how to get started. Wearable tech is also a great way to get both kids and adults excited about coding, electronics, and in general, engineering skills. </p> <p>Sophy Wong is a designer who makes really cool stuff using code, technology, costuming, soldering, and even jewelry techniques to get tech onto the human body. </p> <p>Sophy joins the show to answer my many questions about getting started safely with wearable tech.</p> <p>Some of the questions and topics:</p> <ul> <li>Can I wash my clothing if I&#39;ve added tech to it?</li> <li>Is there any danger in wearing technology or building wearable tech?</li> <li>Are there actual wires and cables conductive thread in the fabric and textiles of some wearable tech projects?</li> <li>What&#39;s a good starter project? Especially if I want to do a wearable tech project with my kids?</li> <li>Dealing with stretch with clothing and non-bendy electronics.</li> <li>Some questions around the Sophy Wong and HackSpace &quot;Wearable Tech Projects&quot; book.</li> <li>How did you get into wearable tech?</li> <li>Do you have a favorite project?</li> <li>Can I get into wearable tech if I don&#39;t know how to code or solder?</li> <li>Are these projects accessible to people with limited budgets?</li> <li>Making projects so you can reuse the expensive bits on multiple projects.</li> </ul><p>Special Guest: Sophy Wong.</p><p>Sponsored By:</p><ul><li><a href="https://monday.com/testandcode" rel="nofollow">monday.com</a>: <a href="https://monday.com/testandcode" rel="nofollow">Creating a monday.com app can help thousands of people and win you prizes. Maybe even a Tesla or a MacBook.</a></li><li><a href="https://testandcode.com/pycharm" rel="nofollow">PyCharm Professional</a>: <a href="https://testandcode.com/pycharm" rel="nofollow">Try PyCharm Pro for 4 months and learn how PyCharm will save you time.</a> Promo Code: TESTANDCODE20</li></ul><p><a href="https://www.patreon.com/testpodcast" rel="payment">Support Test & Code : Python Testing for Software Engineering</a></p><p>Links:</p><ul><li><a href="https://sophywong.com/" title="sophywong.com" rel="nofollow">sophywong.com</a></li><li><a href="https://hackspace.raspberrypi.org/articles/wearable-tech-projects" title="Wearable Tech Projects book" rel="nofollow">Wearable Tech Projects book</a> &mdash; The wearable technology book</li><li><a href="https://sophywong.com/costumes" title="costumes" rel="nofollow">costumes</a> &mdash; The dress is on this page, as well as the Ghostbuster pack and costume.</li><li><a href="https://sophywong.com/spacesuit" title="spacesuit" rel="nofollow">spacesuit</a></li><li><a href="https://www.youtube.com/watch?v=T3iNIylOZF0&t=4s" title="Music video with Sophy's space suit" rel="nofollow">Music video with Sophy's space suit</a></li><li><a href="https://www.kobakant.at/DIY/" title="Kobakant tutorials" rel="nofollow">Kobakant tutorials</a></li></ul>

Python supports a wide range of arithmetic operators that you can use when working with numbers in your code. One of these operators is the modulo operator (%), which returns the remainder of dividing two numbers. In this tutorial, you’ll learn: How modulo works in mathematics How to use the Python modulo operator with different numeric types How Python calculates the results of a modulo operation How to override .__mod__() in your classes to use them with the modulo operator How to use the Python modulo operator to solve real-world problems The Python modulo operator can sometimes be overlooked. But having a good understanding of this operator will give you an invaluable tool in your Python tool belt. Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions. Modulo in Mathematics# The term modulo comes from a branch of mathematics called modular arithmetic. Modular arithmetic deals with integer arithmetic on a circular number line that has a fixed set of numbers. All arithmetic operations performed on this number line will wrap around when they reach a certain number called the modulus. A classic example of modulo in modular arithmetic is the twelve-hour clock. A twelve-hour clock has a fixed set of values, from 1 to 12. When counting on a twelve-hour clock, you count up to the modulus 12 and then wrap back to 1. A twelve-hour clock can be classified as “modulo 12,” sometimes shortened to “mod 12.” The modulo operator is used when you want to compare a number with the modulus and get the equivalent number constrained to the range of the modulus. For example, say you want to determine what time it would be nine hours after 8:00 a.m. On a twelve-hour clock, you can’t simply add 9 to 8 because you would get 17. You need to take the result, 17, and use mod to get its equivalent value in a twelve-hour context: 8 o'clock + 9 = 17 o'clock 17 mod 12 = 5 17 mod 12 returns 5. This means that nine hours past 8:00 a.m. is 5:00 p.m. You determined this by taking the number 17 and applying it to a mod 12 context. Now, if you think about it, 17 and 5 are equivalent in a mod 12 context. If you were to look at the hour hand at 5:00 and 17:00, it would be in the same position. Modular arithmetic has an equation to describe this relationship: a ≡ b (mod n) This equation reads “a and b are congruent modulo n.” This means that a and b are equivalent in mod n as they have the same remainder when divided by n. In the above equation, n is the modulus for both a and b. Using the values 17 and 5 from before, the equation would look like this: 17 ≡ 5 (mod 12) This reads “17 and 5 are congruent modulo 12.” 17 and 5 have the same remainder, 5, when divided by 12. So in mod 12, the numbers 17 and 5 are equivalent. You can confirm this using division: 17 / 12 = 1 R 5 5 / 12 = 0 R 5 Both of the operations have the same remainder, 5, so they’re equivalent modulo 12. Now, this may seem like a lot of math for a Python operator, but having this knowledge will prepare you to use the modulo operator in the examples later in this tutorial. In the next section, you’ll look at the basics of using the Python modulo operator with the numeric types int and float. Python Modulo Operator Basics# The modulo operator, like the other arithmetic operators, can be used with the numeric types int and float. As you’ll see later on, it can also be used with other types like math.fmod(), decimal.Decimal, and your own classes. Modulo Operator With int# Most of the time you’ll use the modulo operator with integers. The modulo operator, when used with two positive integers, will return the remainder of standard Euclidean division: Read the full article at https://realpython.com/python-modulo-operator/ » [ Improve Your Python With ? Python Tricks ? – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Introduction Seaborn is one of the most widely used data visualization libraries in Python, as an extension to Matplotlib. It offers a simple, intuitive, yet highly customizable API for data visualization. In this tutorial, we'll take a look at how to plot a scatter plot in Seaborn. We'll cover simple scatter plots, multiple scatter plots with FacetGrid as well as 3D scatter plots. Import Data We'll use the World Happiness dataset, and compare the Happiness Score against varying features to see what influences perceived happiness in the world: import pandas as pd df = pd.read_csv('worldHappiness2016.csv') Plot a Scatter Plot in Seaborn Now, with the dataset loaded, let's import PyPlot, which we'll use to show the graph, as well as Seaborn. We'll plot the Happiness Score against the country's Economy (GDP per Capita): import matplotlib.pyplot as plt import seaborn as sns import pandas as pd df = pd.read_csv('worldHappiness2016.csv') sns.scatterplot(data = df, x = "Economy (GDP per Capita)", y = "Happiness Score") plt.show() Seaborn makes it really easy to plot basic graphs like scatter plots. We don't need to fiddle with the Figure object, Axes instances or set anything up, although, we can if we want to. Here, we've supplied the df as the data argument, and provided the features we want to visualize as the x and y arguments. These have to match the data present in the dataset and the default labels will be their names. We'll customize this in a later section. Now, if we run this code, we're greeted with: Here, there's a strong positive correlation between the economy (GDP per capita) and the perceived happiness of the inhabitants of a country/region. Plotting Multiple Scatter Plots in Seaborn with FacetGrid If you'd like to compare more than one variable against another, such as - the average life expectancy, as well as the happiness score against the economy, or any variation of this, there's no need to create a 3D plot for this. While 2D plots that visualize correlations between more than two variables exist, some of them aren't fully beginner friendly. Seaborn allows us to construct a FacetGrid object, which we can use to facet the data and construct multiple, related plots, one next to the other. Let's take a look at how to do that: import matplotlib.pyplot as plt import pandas as pd import seaborn as sns df = pd.read_csv('worldHappiness2016.csv') grid = sns.FacetGrid(df, col = "Region", hue = "Region", col_wrap=5) grid.map(sns.scatterplot, "Economy (GDP per Capita)", "Health (Life Expectancy)") grid.add_legend() plt.show() Here, we've created a FacetGrid, passing our data (df) to it. By specifying the col argument as "Region", we've told Seaborn that we'd like to facet the data into regions and plot a scatter plot for each region in the dataset. We've also assigned the hue to depend on the region, so each region has a different color. Finally, we've set the col_wrap argument to 5 so that the entire figure isn't too wide - it breaks on every 5 columns into a new row. To this grid object, we map() our arguments. Specifically, we specified a sns.scatterplot as the type of plot we'd like, as well as the x and y variables we want to plot in these scatter plots. This results in 10 different scatter plots, each with the related x and y data, separated by region. We've also added a legend in the end, to help identify the colors. Plotting a 3D Scatter Plot in Seaborn Seaborn doesn't come with any built-in 3D functionality, unfortunately. It's an extension of Matplotlib and relies on it for the heavy lifting in 3D. Though, we can style the 3D Matplotlib plot, using Seaborn. Let's set the style using Seaborn, and visualize a 3D scatter plot between happiness, economy and health: import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from mpl_toolkits.mplot3d import Axes3D df = pd.read_csv('2016.csv') sns.set(style = "darkgrid") fig = plt.figure() ax = fig.add_subplot(111, projection = '3d') x = df['Happiness Score'] y = df['Economy (GDP per Capita)'] z = df['Health (Life Expectancy)'] ax.set_xlabel("Happiness") ax.set_ylabel("Economy") ax.set_zlabel("Health") ax.scatter(x, y, z) plt.show() Running this code results in an interactive 3D visualization that we can pan and inspect in three-dimensional space, styled as a Seaborn plot: Customizing Scatter Plots in Seaborn Using Seaborn, it's easy to customize various elements of the plots you make. For example, you can set the hue and size of each marker on a scatter plot. Let's change some of the options and see how the plot looks like when altered: import matplotlib.pyplot as plt import seaborn as sns import pandas as pd df = pd.read_csv('2016.csv') sns.scatterplot(data = df, x = "Economy (GDP per Capita)", y = "Happiness Score", hue = "Region", size = "Freedom") plt.show() Here, we've set the hue to Region which means that data from different regions will have different colors. Also, we've set the size to be proportional to the Freedom feature. The higher the freedom factor is, the larger the dots are: Or you can set a fixed size for all markers, as well as a color: sns.scatterplot(data = df, x = "Economy (GDP per Capita)", y = "Happiness Score", hue = "red", size = 5) Conclusion In this tutorial, we've gone over several ways to plot a scatter plot using Seaborn and Python. If you're interested in Data Visualization and don't know where to start, make sure to check out our book on Data Visualization in Python. Data Visualization in Python, a book for beginner to intermediate Python developers, will guide you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, and show you how to take advantage of declarative and experimental libraries like Altair. Data Visualization in Python Understand your data better with visualizations! With over 275+ pages, you'll learn the ins and outs of visualizing data in Python with popular libraries like Matplotlib, Seaborn, Bokeh, and more.

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