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NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:Extract, Transform, Load: Pandas, Intake, PyJanitorExploratory analysis: Jupyter, Seaborn, Matplotlib, AltairModel and evaluate: scikit-learn, statsmodels, PyMC3, spaCyReport in a dashboard: Dash, Panel, Voila

Programming in Go makes it easy to create a zip of your app that runs across Linux, without dependencies. However, end user discovery and update management remain a challenge. Snaps fill this gap, letting you distribute a Go app in an app store experience for end users.

Distributing a Java application for Linux and reaching the widest possible audience is complicated. Typically, the user has to make sure the JRE/SDK version and their environment are configured correctly. When a Linux distribution changes the delivered JRE, this can be problematic for applications. Snapcraft ensures the correct JRE is shipped alongside the application at all times.

Distributing the Linux build of your app as a zip lets you provide one download and set of instructions for all of Linux. However, end user discovery and update management remain a challenge. Snaps fill this gap, letting you wrap your existing Linux build in an app store experience for end users.

You can distribute your apps across Linux using a musl-enabled version of Rust, with all the dependencies satisfied. However, end user discovery and update management remain a challenge. Snaps fill this gap, letting you distribute a Rust app in an app store experience for end users. 781b155fdc