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* fixed typos in foundations/jupyter.md
* fixed typos in foundations/conda.md
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## What are Packages?
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A Python package is a collection of modules, which in turn, are essentially Python scripts that contain published functionality. There are Python packages for data input, data analysis, data visualization, etc. Each package offers a unique toolset and may have its own unique syntax rules.
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A Python package is a collection of modules, which, in turn, are essentially Python scripts that contain published functionality. There are Python packages for data input, data analysis, data visualization, etc. Each package offers a unique toolset and may have its own unique syntax rules.
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Package management is useful because you may want to update a package for one of your projects, but keep it at the same version in other projects to ensure that they continue to run as expected.
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## Installing Conda
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We recommend you install Miniconda. You can do that by following the [instructions for you machine](https://docs.conda.io/en/latest/miniconda.html).
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We recommend you install Miniconda. You can do that by following the [instructions for your machine](https://docs.conda.io/en/latest/miniconda.html).
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Miniconda only comes with the `conda` package management system; it is a pared-down version of the full Anaconda Python distribution.
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[Installing Anaconda](https://docs.anaconda.com/anaconda/install/) takes longer and takes up more disk space, but provides you with more functionality: Jupyter, Spyder (a Python-specific integrated development platform or IDE), as well as other immediately installed packages. The interface of Anaconda is great if you are uncomfortable with the terminal.
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[Installing Anaconda](https://docs.anaconda.com/anaconda/install/) takes longer and uses up more disk space, but provides you with more functionality, including Spyder (a Python-specific integrated development environment or IDE) and Jupyter, in addition to other immediately installed packages. Also, the interface of Anaconda is great if you are uncomfortable with the terminal.
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We recommend Miniconda for two reasons:
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## Creating a Conda Environment
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A conda environment is an interoperable collection of specific versions of packages or libraries that you install and use for a specific workflow. The conda package manager takes care of dependencies so everything works together in a predictable way. One huge advantage of using environments is that any changes you make to one environment will not affect your other environments at all, so you are much less likely to "break" something!
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A Conda environment is an interoperable collection of specific versions of packages or libraries that you install and use for a specific workflow. The Conda package manager takes care of dependencies, so everything works together in a predictable way. One huge advantage of using environments is that any changes you make to one environment will not affect your other environments at all, so you are much less likely to "break" something!
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To create a new Conda environment, type `conda create --name` and the name of your environment in your terminal, and then specify any packages that you would like to have installed. For example, to install a Jupyter-ready environment called `sample_environment`, type
Once the environment is created, you need to _activate_ it in the current terminal session (see below)
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Once the environment is created, you need to _activate_ it in the current terminal session (see below).
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It is a good idea to create new environments for different projects because since Python is open source, new versions of the tools are released very frequently. Isolated environments help guarantee that your script will use the same versions of packages and libraries and should run the same as you expect it to. Similarly, it is best practice to NOT modify your `base` environment.
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It is a good idea to create a new environment for every project. Because Python is open source, new versions of the tools are released frequently. Isolated environments help guarantee that your scripts use the same versions of packages and libraries to ensure they run as expected. Similarly, it is best practice to NOT modify your `base` environment.
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## Useful Conda commands
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conda list
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```
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- Installing a new package into current environment
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- Installing a new package into the current environment
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```
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conda install somepackage
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```
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- Installing a specific version of a package into current environment
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- Installing a specific version of a package into the current environment
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```
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conda install somepackage=0.17
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conda env remove --name sample_environment
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```
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Lots more information is in the [conda documentation](https://docs.conda.io/) or this handy [conda cheat sheet](https://docs.conda.io/projects/conda/en/latest/_downloads/843d9e0198f2a193a3484886fa28163c/conda-cheatsheet.pdf)
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You can find lots more information in the [Conda documentation](https://docs.conda.io/en/latest/) or this handy [Conda cheat sheet](https://docs.conda.io/projects/conda/en/latest/_downloads/843d9e0198f2a193a3484886fa28163c/conda-cheatsheet.pdf).
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If you're not a command line user, the Anaconda navigator offers GUI functionality for selecting environments and installing packages.
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---
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## Summary
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Conda is a package and environment management system that allows you to quickly install, run, and update packages within your work environment(s). This is important for gathering all of the tools necessary for your workflow. Conda can be managed in the command line or in the Anacond GUI.
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Conda is a package and environment management system that allows you to quickly install, run, and update packages within your work environment(s). This is important for gathering all of the tools necessary for your workflow. Conda can be managed in the command line or in the Anaconda GUI.
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### What's Next?
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"## Jupyter Notebooks\n",
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"\n",
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"The Jupyter Notebook is an open-source web application that allows you to create and share Jupyter notebooks (*ipynb files). Jupyter Notebooks contain executable code, LaTeX equations, visualizations (e.g., plots, pictures), and narrative text. The code does not have to just be Python, other languages such as Julia or R are supported as well. \n",
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"The Jupyter Notebook software is an open-source web application that allows you to create and share Jupyter Notebooks (*.ipynb files). Jupyter Notebooks contain executable code, LaTeX equations, visualizations (e.g., plots, pictures), and narrative text. The code does not have to just be Python, other languages such as Julia or R are supported as well. \n",
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"\n",
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"Jupyter Notebooks are celebrated for their interactive output that allows movement between code, code output, explanations, and more code - similar to how scientists think and solve problems. Jupyter Notebooks can be thought of as a living, runnable publication and make for a great presentation platform."
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"\n",
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"A popular web application on which users can create and write their Jupyter Notebooks, as well as explore data, install software, etc.\n",
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"\n",
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"More information on running Jupyter Lab [here](jupyterlab)."
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"You can find more information on running Jupyter Lab [here](jupyterlab)."
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{
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"### Local Execution Model\n",
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"\n",
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"You can launch JupyterLab from a terminal, it will open up in a web browser. The application is running on the browser. When you open a notebook, Jupyter opens a kernel which can be tied to a specific coding language.\n",
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"You can launch JupyterLab from a terminal; it will open up in a web browser. The application will then be running in that web browser. When you open a notebook, Jupyter opens a kernel which can be tied to a specific coding language.\n",
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"\n",
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"To launch the JupyterLab interface in your browser follow the instructions in [Installing and Running Python: Python in Jupyter](https://foundations.projectpythia.org/foundations/jupyter.html).\n",
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"To launch the JupyterLab interface in your browser, follow the instructions in [Installing and Running Python: Python in Jupyter](https://foundations.projectpythia.org/foundations/jupyter.html).\n",
"In the remote execution model you start out in the browser, navigate to a specific URL that points to a JupyterHub. On JupyterHub you authenticate on the remote system, and then JupyterLab is launched and redirected back to your browser. The interface appears the same as if you were running Jupyter locally.\n",
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"In the remote execution model, you start out in the browser, then navigate to a specific URL that points to a JupyterHub. On JupyterHub, you authenticate on the remote system, and then JupyterLab is launched and redirected back to your browser. The interface appears the same as if you were running Jupyter locally.\n",
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