Jupyter Notebook Eclipse

Read the Docs v: stable. Versions master latest stable 5.7.6 5.7.5 5.7.4 5.7.3 5.7.2 5.7.1 5.7.0 5.6.0 5.5.0. Using SageMaker Studio or Notebook Instances are good options for you to access an ML environment running Jupyter with compute and pre-installed popular ML libraries. SageMaker manages the creation.

Eclipse vs The Jupyter Notebook. When assessing the two solutions, reviewers found The Jupyter Notebook easier to use, set up, and administer. Reviewers also preferred doing business with The Jupyter Notebook overall. Reviewers felt that The Jupyter Notebook meets the needs of their business better than Eclipse. Planet of Bits publishes blogs related to SAP Netweaver Technology Platform, SAPUI5, Native Mobile Application Development Technologies (Android, iOS SDK), Enterprise Application Development using Java/JEE and other useful tips and tricks related to software development. All the code snippets, demo projects provided in the blogs/articles have been tested on local development systems and are.

Seeing “ImportError: No module named tensorflow” but you know you installed it? Sometimes you can import packages from the console, but not from the Jupyter notebook? !pip install tensorflow sometimes just does not work?

It’s not about you. It’s not about python being flaky.It’s actually about your machine running multiple python installations (environments).Let’s build a basic understanding of what’s happening there and what causes your packages missing even after being installed. Once you understand that, you’ll have fewer problems installing and importing packages you need.

You are running multiple environments of python. That is fine!

Not fine like this! It is actually fine and not your fault. Magic screen. Because not all python2 tools are ported to python3, it is possible that your operating system runs both versions without you touching anything.

For example, I have both python 2 and python 3 installed. When I call python in my console – python 2.7.10 gets invoked; when I call python3, python 3.7.1 is invoked. It gets even better. I have multiple environments of python 3.6.3 (yes, all the same version) and they load dynamically depending on the project I am currently working on (thanks to conda). I create and destroy python environments daily.

So how do I know what environment is currently running? Easy! By running import sys; sys.prefix in interactive console. That reveals what environment of python you are currently in.

Different python environment means different sets of packages

Unless you’ve modified $PYTHONPATH variable (it’s ok if you haven’t heard of it), each of these environments will use a completely separate set of packages. That is the reason you can’t import the package and you know you installed it. You installed it in the wrong environment!

But you’re running Jupyter and not the console and how does it all fit together? We’re getting there just now.

Jupyter kernel you use is not using the same python environment as your console.

Jupyter does not run python the same way your console does. It has a concept of a kernel (if you are not familiar with that concept, think about it as python environment registered with Jupyter). The kernel running your notebook likely uses a different python environment and definitely does not have all the environment variables set as your console does. That is fine, too. We know how to figure out which environment is running our code so we can do exactly the same in Jupyter notebook.

Aha!!! You know which environment Jupyter uses. Now you just have to:

  • make sure your console (temporarily) uses the same python environment as your Jupyter notebook.
  • install the package with conda install or pip install (if you don’t know what is the difference, quickly go to this guide).

Cool, cool, cool. But you don’t know how to make your console use the same environment? All good, we’ll do it from your Jupyter notebook, but not like the last time you did it.

You are installing packages straight from Jupyter notebook and you are doing it wrong.

Browsing through StackOverflow about similar issues made me realise people are suggesting the thing that won’t work most of the time. If you are installing packages by running

you are using very fragile commands (if run in notebook) and that’s the reason packages you installed can’t be imported. It is not fine this time. But we will fix it 🙏. If you are interested in low-level details about why it does not work, read this great blog post from Jake Vanderplas.

Instead, use these commands:

and you will not have problems with damn ImportError again.

Understanding these concepts helped me and my teammates to understand what’s happening and why we were getting these errors. If you think I made a mistake, I missed something or you just want to say thanks, please do that in comments 🗣.

By Saurabh Hooda, Hackr.io


Credit: Hitesh Choudhary via Unsplash

Created by Guido van Rossum, Python was first released back in 1991. The interpreted high-level programming language is developed for general-purpose programming. Python interpreters are available on several operating systems, including Linux, MacOS, and Windows.

Editor: Here are the most popular IDEs / Editors for Python, based on KDnuggets Poll inspired by this blog.

With a running course of almost 3 decades, Python has garnered enormous popularity among the programming community. Using IDLE or the Python Shell for writing down Python code is effectual for smaller projects, but not practical while working on full-fledged machine learning or data science projects.

Eclipse

In such a case, you need to use an IDE (Integrated Development Environment) or a dedicated code editor. As Python is one of the leading programming languages, there is a multitude of IDEs available. So the question is, “Which is the best IDE for Python?”

Apparently, there is no single IDE or code editor for Python that can be crowned with “THE BEST” label. This is because each of them has their own strengths and weaknesses. Furthermore, choosing among the vast number of IDEs might be time-consuming.

Worry not though, as we’ve got you covered. In order to help you pick the right one, we’ve sorted out some of the prominent IDEs for Python, specifically created for working with data science projects. These are:

Atom


Platform – Linux/macOS/Windows
Official Website – https://atom.io/
Type – General Text Editor

Atom is a free, open-source text and source code editor available for a number of programming languages, including Java, PHP, and Python. The text editor supports plugins written in Node.js. Although Atom is available for a number of programming language, it shows an exceptional love for Python with its interesting data science features.

Jupyter Notebook Online

One of the greatest features that Atom brings to the table is support for SQL queries. However, you need to install the Data Atom plugin first to access the feature. It provides support for Microsoft SQL Server, MySQL, and PostgreSQL. Furthermore, you can visualize results in Atom without the need of opening any other window.

Jupyter Notebook Eclipse

Yet another Atom plugin that will benefit Python data scientists is the Markdown Preview Plus. This provides support for editing as well as visualizing Markdown files, allowing you to preview, render LaTeX equations, etc.

Jupyter notebook download

Advantages:

  • Active community support
  • Awesome integration with Git
  • Provides support for managing multiple projects

Disadvantages:

  • Might experience performance issues on older CPUs
  • Suffers from migration issues

Jupyter Notebook


Platform – Linux/macOS/Windows
Official Website – https://jupyter.org/
Type – Web-based IDE

Born out of IPython in 2014, Jupyter Netbook is a web application based on the server-client structure. It allows you to create as well as manipulate notebook documents called notebooks. For Python data scientists, Jupyter Notebook is a must-have as it offers one of the most intuitive and interactive data science environments.

In addition to operating as an IDE, Jupyter Notebook also works as an education or presentation tool. Moreover, it is a perfect tool for those just starting out with data science. You can easily see and edit the code with Jupyter Notebook, allowing you to create impressive presentations.

By using visualization libraries like Matplotlib and Seaborn, you can display the graphs in the same document as the code is in. Also, you can export your entire work to a PDF, HTML or .py file. Like IPython, Project Jupyter is an umbrella term for a bunch of projects, including Notebook itself, a Console, and a Qt console.

Advantages:

  • Allows creation of blogs and presentations from notebooks
  • Ensures reproducible research
  • Edit snippets before running them

Disadvantages:

  • Complex installation process

PyCharm


Platform – Linux/macOS/Windows
Official Website – https://www.jetbrains.com/pycharm/
Type – Python-specific IDE

PyCharm is a dedicated IDE for Python. PyCharm to Python is what Eclipse is to Java. The full-featured Integrated Development Environment is available both in free and paid versions, dubbed Community and Professional editions, respectively. It is one of the quickest IDEs to install with a simplistic set up thereafter, and is preferred by data scientists.

For those with a likeness for IPython or Anaconda distribution, know that PyCharm easily integrates tools like Matplotlib and NumPy. This means you can work easily with array viewers and interactive plots while working on data science projects. Other than that, the IDE extends support for JavaScript, Angular JS, etc. This makes it opportune for web development too.

Once you finish the installation, PyCharm can be readily used for editing, running, writing, and debugging the Python code. To start with a new Python project, you need to simply open a fresh file and start writing down the code. In addition to offering direct debugging and running features, PyCharm also offers support for source control and full-sized projects.

Advantages:

  • Active community support
  • De facto of Python development, both for data science and non-data-science projects
  • Easy to use by both newcomers and veterans alike
  • Faster reindexing
  • Runs, edits, and debugs Python code without any external requirement

Disadvantages:

  • Might be slow in loading
  • The default setting may require adjustment before existing projects can be used

Rodeo


Platform – Linux/macOS/Windows
Official Website – https://rodeo.yhat.com/
Type – Python-Specific IDE

The logo with the orange hints at the fact that this Python IDE is developed especially for carrying out data analysis. If you have some experience with RStudio, then you will know that Rodeo shares many of its traits with it. For those of you unaware of RStudio, it is the most popular integrated development environment for the R language.

Like RStudio, Rodeo’s window is divided into four divisions, namely text editor, console, environment for variable visualization, and plot/libraries/files. Amazingly, both Rodeo and RStudio shares a great degree of resemblance with MATLAB.

What’s best about Rodeo is that it offers the same level of convenience to both beginners and veterans. As the Python IDE allows you to see and explore while creating simultaneously, Rodeo is undoubtedly one of the best IDEs for those starting out with data science using Python. The IDE also boasts built-in tutorials and comes with helper material.

Advantages:

  • A great deal of customization
  • See and explore what you are creating in real-time
  • Write code faster with autocomplete and syntax highlighting features, and support for IPython

Disadvantages:

  • A lot of bugs
  • Not-so-active support
  • Plagued by memory issues

Spyder


Platform – Linux/macOS/Windows
Official Website – https://www.github.com/spyder-ide/spyder
Type – Python-Specific IDE

Spyder is an open-source, dedicated IDE for Python. What’s unique about the IDE is that it is optimized for data science workflows. It comes bundled with the Anaconda package manager, which is the standard distribution of Python programming language. Spyder has all the necessary IDE features, including code completion and an integrated documentation browser.

Build especially for data science projects, Spyder flaunts a smooth learning curve allowing you to learn it in a flash. The online help option allows you to look for specific information about libraries while side-by-side developing a project. Moreover, the Python-specific IDE shares resemblance with RStudio. Hence, it is opportune to go for when switching from R to Python.

Notebook

Spyder’s integration support for Python libraries, such as Matplotlib and SciPy, further testifies to the fact that the IDE is meant especially for data scientists. Other than the appreciable IPython/Jupyter integration, Spyder has a unique “variable explorer” feature at its disposal. It allows displaying data using a table-based layout.

Jupyter Notebook Vs Eclipse

Advantages:

  • Code completion and variable exploring
  • Easy to use
  • Ideal to use for data science projects
  • Neat interface
  • Proactive community support

Disadvantages:

Jupyter Notebook Eclipse Tutorial

Jupyter notebook vs eclipse
  • Falls short in capability for non-data-science projects
  • Too basic for advanced Python developers

How to Choose the Best IDE for Python?


Well, this depends entirely on the kind of requirements you need to fulfill. Nonetheless, here is some general advice:

Jupyter Notebook Or Spyder

  • When starting fresh with Python, go for an IDE that has fewer customizations and additional features. The less distraction is there, the better it is to get started with
  • Compare the IDE features with your expectations
  • Giving a try to several IDEs will help you understand better which one will work best against specific requirements


Bio: Saurabh Hooda has worked globally for telecom and finance giants in various capacities. After working for a decade in Infosys and Sapient, he started his first startup, Leno, to solve a hyperlocal book-sharing problem. He is interested in product marketing, and analytics. His latest venture Hackr.io recommends the best Data Science tutorial and online programming courses for every programming language. All the tutorials are submitted and voted by the programming community.

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