![]() Press CTRL-C to abort.Īlso, using conda update python only changed the micro version number (I think it upgraded from Python 3.8.10 to 3.8. Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.Ĭollecting package metadata (repodata.json): doneįound conflicts! Looking for incompatible packages. Solving environment: failed with initial frozen solve. ![]() I tried using conda install python=3.9 and conda install python=3.10, which is recommended by a few posts a few years ago, but they didn't work and I ended up with the following error Collecting package metadata (current_repodata.json): done ![]() If you any questions or thoughts on the tutorial, feel free to reach out in the comments below or through Twitter.Is there any way to update the current conda base (root) environment, which has Python 3.8.11 currently to Python 3.9 or 3.10? I know using a new virtual environment is the recommended way to go, but I still want to learn how to do it. If you want to learn about Python for Data Science, I suggest you check out the DataCamp course Intro to Python for Data Science. If you aren't sure what to do to start coding on your computer, I recommend you check out the the Jupyter Notebook Definitive Guide to learn how to code using Jupyter Notebooks. If you would like to learn more about Anaconda, you can learn more about it here. This tutorial provided a quick guide on how to install Anaconda on Windows as well as how to deal with a common installation issue. Try typing conda -version and python -version into the Command Prompt to check to see if everything went well. If you are having issues, here is a short video on adding conda and python to your PATH.ĥ. You can do this by going to your Environment Variables and adding the output of step 3 (enclosed in the red rectangle) to your path. This is telling you where conda and python are located on your computer. If you don't know where your conda and/or python is, open an Anaconda Prompt and type in the following commands. If you get an output similar to the right side of the image below, you have already added Anaconda to your path. If you get a command not recognized error like in the left side of the image below, proceed to step 3. This is checking if you already have Anaconda added to your path. Enter the commands below into your Command Prompt. Check if you already have Anaconda added to your path. The advantage of this is that you will be able to use Anaconda in your Command Prompt, Git Bash, cmder etc.Ģ. This is for the case where you didn't check the box in step 6 and now want to add Anaconda to your Path. You can install Microsoft VSCode if you wish, but it is optional.ĩ. If you want to be able to use Anaconda in your command prompt (or git bash, cmder, powershell etc), please use the alternative approach and check the box. This means you will have to use Anaconda Navigator or the Anaconda Command Prompt (located in the Start Menu under "Anaconda") when you wish to use Anaconda (you can always add Anaconda to your PATH later if you don't check the box). The recommended approach is to not check the box to add Anaconda to your path. This one turns out to be stable and the installation was finished in 3 minutes. This is an important part of the installation process. My solution was to go to the anaconda archive page: and download the second newest version, i.e. Note your installation location and then click Next.Ħ. Read the license agreement and click on I Agree.ĥ. When the screen below appears, click on Next.ģ. Locate your download and double click it. If you aren't sure which Python version you want to install, choose Python 3. Go to the Anaconda Website and choose a Python 3.x graphical installer (A) or a Python 2.x graphical installer (B). With that, let's get started! Download and Install Anacondaġ. How to test your installation and fix common installation issues In fact, an installation of Anaconda is also the recommended way to install Jupyter Notebooks which you can learn more about here on the DataCamp community. ![]() Conda even makes it easy to switch between Python 2 and 3 (you can learn more here). This is highly advantageous as you don't have to manage dependencies between multiple packages yourself. If you need additional packages after installing Anaconda, you can use Anaconda's package manager, conda, or pip to install those packages. This is advantageous as when you are working on a data science project, you will find that you need many different packages (numpy, scikit-learn, scipy, pandas to name a few), which an installation of Anaconda comes preinstalled with. Anaconda is a package manager, an environment manager, and Python distribution that contains a collection of many open source packages.
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