Get began with Anaconda Python

No query about it, Python is a vital a part of trendy information science. Handy and highly effective, Python connects information scientists and builders with a complete galaxy of instruments and performance, in handy and programmatic methods.

Nonetheless, these instruments generally include a bit of—or quite a bit—of meeting required. As a result of Python is a general-purpose programming language, the way it’s packaged and delivered doesn’t communicate particularly to information scientists. However varied people have delivered Python to that viewers in a means that’s prepackaged, with little to no meeting required—a venture that common Python customers can profit from, too.

Continuum Analytics’s Anaconda distribution is a repackaging of Python aimed toward builders who use Python for information science. It supplies a administration GUI, a slew of scientifically oriented work environments, and instruments to simplify the method of utilizing Python for information crunching. It can be used as a common substitute for the usual Python distribution, however provided that you’re acutely aware of how and why it differs from the inventory model of Python.

Anaconda editions

Anaconda is available in 4 distinct editions, every meant for various use instances for various audiences.

Anaconda Particular person Version

The free-to-use Particular person Version of Anaconda comes with the core options present in all Anaconda editions — the Anaconda Navigator, Jupyter Notebooks, the Spyder IDE, and so forth. (Extra on these later.) The Particular person Version is one of the best place to start out with Anaconda, as it is going to assist you to achieve expertise with the entire main components in Anaconda and their behaviors.

Anaconda Industrial Version

The Industrial Version supplies entry to a package deal repository that has been curated for business use, with uptime ensures. Additionally it is the version you should purchase in case you plan to make use of Anaconda for business use (versus particular person or educational analysis). Every seat license begins at $14.95 per 30 days.

Anaconda Crew Version

The Crew Version supplies groups of builders with consumer administration options, high-priority updates to packages, and fine-grained package deal controls (block/enable lists). It’s licensed for business use, with costs starting at $10,000 for a staff of 5 customers for one 12 months.

Anaconda Enterprise Version

The Enterprise Version is aimed toward enterprises that need to develop machine studying fashions and deploy them into manufacturing. Thus it supplies infrastructure for all phases of the machine studying lifecycle, akin to containerization for initiatives. Pricing is obtainable on request solely.

What’s included in Anaconda

CPython, the reference model of Python, features a few issues to make life simpler—the usual library, the IDLE mini-IDE, and the Tkinter user-interface library. However every little thing you would possibly want for information science is an add-on—even probably the most fundamental instruments. Anaconda, against this, tries to incorporate a good choice of data-science instruments out of the field.

Right here’s what’s included by default in Anaconda.

The Python interpreter

Anaconda contains by default the latest launch model of the Python interpreter. This isn’t the inventory CPython construct that comes from the Python Software program Basis—it’s a customized construct, created by Anaconda Inc. particularly for the Anaconda distribution. In line with Anaconda CTO Peter Wang, the interpreter has “safer compiler flags on some platforms, higher efficiency optimizations on others.”

That mentioned, Anaconda’s Python interpreter must be drop-in suitable with CPython. C extensions written for it ought to work as is. In Microsoft Home windows, for instance, the interpreter has been compiled with Microsoft Visible C/C++ model 1928, identical because the inventory version of CPython itself.

The Anaconda Navigator

Essentially the most noticeable factor Anaconda provides to the expertise of working with Python is a GUI, the Anaconda Navigator. It isn’t an IDE, and it doesn’t attempt to be one, as a result of most Python-aware IDEs can register and use the Anaconda Python runtime themselves. As a substitute, the Navigator is an organizational system for the bigger items in Anaconda.

With the Navigator, you may add and launch high-level purposes like R Studio or Jupyterlab; handle digital environments and packages; arrange “initiatives,” a technique to handle work in Anaconda; and carry out varied administrative features.

Though the Navigator supplies the comfort of a GUI, it doesn’t exchange any command-line performance in Anaconda, or in Python typically. For instance, though you may handle packages via the GUI, it’s also possible to use the command line to take action.

CPython, against this, has no formal GUI. It does include IDLE, a mini-IDE appropriate for fast one-off duties. However something for managing Python itself has to return from third events. To that finish, some IDEs present GUI interfaces to CPython’s parts. Microsoft Visible Studio, for instance, has a GUI for Python’s Pip package-management system, akin to the UI Anaconda supplies for its personal Conda package deal supervisor.

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Anaconda Navigator provides all of the major elements of the Anaconda Python distribution via a user-configurable UI.

Conda package manager

Python comes with the Pip package manager, for installing and managing third-party Python packages. As much as Python’s developers have expanded Pip’s powers over the years, it’s still limited. It only manages packages for Python itself, not the rest of the system.

Anaconda’s developers struggled with this limitation, but eventually decided to engineer their own solution: Conda, a package management solution that handles not only Python packages but dependencies outside the Python ecosystem.

Here’s an example of what Conda helps with: If you have multiple Conda packages that rely on a compiler, like GCC or LLVM, Conda can resolve that external dependency for all those packages. It can install a single instance of a specific version of GCC for all Conda packages that need it. Pip would either have to assume you already have GCC installed somewhere on your system—or bundle a copy of GCC with each package that used it, a horribly inefficient and cumbersome solution.

Thus, Conda isn’t interchangeable with Pip. It doesn’t even use the same package format; packages created for Pip have to be re-created for Conda. But almost every package of significance used in the Python ecosystem is available through Conda.

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Python data science tools often are a rat’s nest of dependencies, and hard to install and manage. Anaconda’s package management system, Conda, shown here in its GUI version, manages both Python packages and any dependencies they have outside of Python’s ecosystem.

How Anaconda makes data work easier

A fair number of Anaconda’s improvements revolve around the workaday use of Python, things that benefit most any Python user. But the most important benefits are aimed specifically at how data science users often find themselves at odds with their Python environments.

Conda environments

Python packages, even as managed with Conda, don’t always play nice with each other. Sometimes, you need different versions of things for particular projects. Python’s virtual environments feature, aka venv, was developed to offset this problem, but Conda takes the idea a step further.

Conda environments, as they’re called, are functionally similar to venv-type virtual environments. If you want to use specific versions of packages, or specific versions of the Python interpreter as well, you can place them into a Conda environment and use them in isolation.

Venv environments can be moved around, but they don’t necessarily have detailed information about how they were created. This can be a problem if you need to have a reproducible environment for the work you’re doing. Conda environments try to address this problem, because they’re meant to be reproducible.

If you want other people to use your Conda environment, you provide them with a copy of the environments definition file, which describes how to re-create the environment on another system. There are limitations to how well this can work in a cross-platform fashion, so any differences between how packages work on different platforms (such as MacOS vs. Linux) will need to be ironed out manually.

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Three Conda environments, each with its own set of packages and Python runtimes. The env-37 environment uses Python 3.7 instead of a more recent version. The no-sqlite environment omits the sqlite package (as shown in the package list at right). Each Conda environment must have its set of packages updated separately.

Anaconda Project

One common problem with data science, and software development in general, is reproducing the exact environment used for a particular job. Even Conda environments provide only a partial solution for this problem, because CPython venv-type environments don’t and can’t reproduce things like environment variables.

Enter Anaconda Project. It lets you take a directory full of things related to something you’re doing with Anaconda—“web apps, scripts, Jupyter notebooks, data files, whatever it may be,” as Anaconda puts it—and turn it into a reproducible resource. That directory, once it’s managed by Anaconda Project, can be run in a consistent way no matter where it’s run, as long as there’s a copy of Anaconda itself handy.

Anaconda Project’s biggest issue right now is that it’s still considered a beta-level product, so it isn’t stable yet. Until it is, it shouldn’t be used for sharing work in environments where you can’t guarantee that everyone will be running the same version. In the meantime, Conda environments can provide a dependable subset of the same functionality.

Applications in Anaconda

Another way Anaconda adds convenience to using Python for analysis and scientific work is how it bundles and makes accessible several common projects for working with data interactively.

Two of the most common such projects are Jupyter Notebook and JupyterLab, which provide live environments for writing Python code, importing data, running experiments, and visualizing the results. Anaconda handles all the setup and management for running Notebook and JupyterLab instances, so working with them involves little more than clicking the Launch button next to each app in Navigator’s main menu. You can also install prior versions of each app by clicking the app’s gear icon, assuming they’re available.

Other bundled apps include:

  • Qtconsole: A GUI for Jupyter that uses the Qt interface library. It’s useful if you’d rather work with Jupyter notebooks through an interface that’s native to the platform you’re running on rather than through a web browser.
  • Spyder: The Scientific Python Development Environment, a mini-IDE written in Python geared mainly towards developers writing apps that work with IPython/Jupyter notebooks. It can also be used as a library for Python applications that need an IDE-like interface.
  • RStudio: Tools for working with the R language, used in many fields for data analysis. Python has grown in popularity with users of R, but there are still plenty of scenarios where R remains the language of choice, and RStudio provides ways to work with the two languages together.
  • Visual Studio Code: Microsoft’s editor can be as simple or as advanced as you want to make it, thanks to its enormous culture of extensions. It’s also one of the best environments for working with Python. Anaconda users can jump right into Visual Studio Code without having to install it separately.
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Anaconda bundles many auxiliary applications, such as Jupyter Notebook, an in-browser interactive work environment for Python. All the management details for Jupyter are automatically handled by Anaconda.

Miniconda, the lightweight Anaconda

If you want to use Anaconda, but don’t want to install everything at once, and don’t necessarily need the Navigator, you can take an incremental approach with Miniconda.

Miniconda installs only the absolute minimum you need to get started with Anaconda: the Python interpreter (as packaged by Anaconda), the Conda package manager, and a few other basic bits. You can add more components or create environments using Conda from the command line, much as you would for the full-blown version of Anaconda.

If you’re not a data-science user, but you want to take advantage of how Anaconda is designed and packaged, Miniconda is a good way to work with Python. Packages are generally easier to handle with Conda, and you have access to the broader ecosystem of Anaconda software if and when you need it.

A few things are worth keeping in mind. First, as hinted above, the Anaconda Navigator GUI isn’t installed by default. However, if you find that you want it, you can add it after the fact in Conda (conda install anaconda-navigator).

Second, Miniconda installs by default to a directory named Miniconda3, rather than Anaconda. This might throw off someone making assumptions about what path to use to find the Miniconda installation. The install directory can be customized as needed, though.

Third, and in some ways most important, Conda can be used only to install packages available through Conda’s own repository. It isn’t used to install packages available through the default Python package repository, PyPI. You can use the standard Python package management tool, Pip, to install Python packages from PyPI inside Miniconda—but those packages can’t be managed by Conda, only Pip, and you will need to take specific steps to allow Pip and Conda to coexist.

If you absolutely want Conda to manage everything, you can repackage PyPI packages as Conda packages via a two-step process.

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