From 6de0db27d2521b9e21dde542474da7c318faa34e Mon Sep 17 00:00:00 2001 From: Christian Kolset Date: Tue, 28 Jan 2025 19:55:50 -0700 Subject: changed formatting --- tutorials/02_intro_to_anaconda.md | 43 ++++++++++++++++++++++++--------------- 1 file changed, 27 insertions(+), 16 deletions(-) (limited to 'tutorials/02_intro_to_anaconda.md') diff --git a/tutorials/02_intro_to_anaconda.md b/tutorials/02_intro_to_anaconda.md index 75cea67..464be83 100644 --- a/tutorials/02_intro_to_anaconda.md +++ b/tutorials/02_intro_to_anaconda.md @@ -14,32 +14,43 @@ Note to windows 10 users: Some installation instances do not allow users to sear Once Navigator starts, under *Home*, you'll see tiles of programs that come with anaconda. The tab allows you to launch the programs we will be using in this course. Before jumping straight into the programs we will first need to configure our Python instance. -The *Environment* page allows us to install a variety of libraries and configure our environements for different project, more on this in the next section. +The *Environment* page allows us to install a variety of libraries and configure our environments for different project, more on this in the next section. ## Environments -A Python environment is like a "container" where you can have all the tools, libraries, and dependencies your Python project needs without interfering with other projects. Think of it as a toolbox dedicated to a specific task. Although the base enviroment comes with many libraries and programs pre-installed, it's recommended to create a dedicated environment for your projects. This protects the base environment from breaking due to complex dependency conflicts. +A Python environment is like a "container" where you can have all the tools, libraries, and dependencies your Python project needs without interfering with other projects. Think of it as a toolbox dedicated to a specific task. + +Although the base environment comes with many libraries and programs pre-installed, it's recommended to create a dedicated environment for your projects. This protects the base environment from breaking due to complex dependency conflicts. Let us go ahead and create a new environment for us to use. + +1. Click on the *Environments* page located on the left hand side. -Let us go ahead and create a new environment for us to use. Click on the *Environments* page located on the left hand side. ![Environment page](https://docs.anaconda.com/_images/nav-env-labeled1.png) -At the bottom of the environments list, click *Create*. +2. At the bottom of the environments list, click *Create*. + ![Create new environment](https://docs.anaconda.com/_images/nav-env-create1.png) -Select the python checkbox. -Select versions of python. For this tutorial we will be using version X.XX.X. -Choose an apropriate name for your project. We will be creating an environment for Spyder IDE so we'll call it "spyder-dev". -Click *Create*. +3. Select the python checkbox. +4. Select versions of python. For this tutorial we will be using version 3.XX.X. +5. Choose an appropriate name for your project. We will be creating an environment for Spyder IDE so we'll call it "spyder-dev". +6. Click *Create*. For more information see [Anaconda Environments](https://docs.anaconda.com/working-with-conda/environments/) and [Managing environment](https://docs.anaconda.com/navigator/tutorials/manage-environments/). ## Package Management -Now that we have a clean environment let us install some library we will be using for this class. Navigate to the environment page and select the environment we just created in the previous section. +Now that we have a clean environment let us install some library we will be using for this class. + +1. Navigate to the environment page and select the environment we just created in the previous section. ![Select environment to manage](https://docs.anaconda.com/_images/nav-pkg-list1.png) -Use the search bar in the top right corner to search for the following packages: -- **numpy**: Numerical Computation library. -- **scipy**: Scientific and techical computing. -- **pandas**: data manipulation and analysis. -- **matplotlib**: creating plots and visualizations. -- **sympy**: symbolic mathematics. -- **cython**: optimizing Python code and interfacing with C++ code. +2. Use the search bar in the top right corner to search for the following packages: + +| Library | Usage | +| ---------- | ---------------------------------------------------- | +| numpy | Numerical computation | +| scipy | Scientific and techical computing | +| pandas | Data manipulation and analysis | +| matplotlib | Plots and visualizations | +| sympy | Symbolic mathematics | +| cython | Optimizing Python code and interfacing with C++ code | + + -- cgit v1.2.3