conda activate yourenvnameĪctivating my datascience Conda environment Installing Ipykernel Once activated you can see the environment name instead of basename inside the brackets. conda create -name yourenvname python=3.6 Activating Conda Environmentīefore you start installing packages you should first activate the environment using conda activate your_existing_environment_name. Here, I have set the python version to 3.6. Just supply the yourenvname with your preferred environment name. You can create an environment with the following code. First, search the Anaconda Prompt in the start menu and open it. The next step is to create a new virtual environment. Creating a Conda EnvironmentĪfter installing Anaconda. Let’s assume that you have downloaded and installed Anaconda Distribution in your operating system. I’m currently writing this so that one doesn’t have to go through the same search + frustration stage. The time it won’t work for you, you start to feel the frustration for sure.
#MINICONDA INSTALL IPYTHON NOTEBOOK CODE#
After experimenting with lots of available code I have come to a conclusion that sometimes it works and sometimes it doesn’t. Now, you will be wondering, thinking that I can do that by just searching the web, yes you can do that but this would take time if you are new to Anaconda. Even when you messed up some library and want to freshly create a new kernel specification and name by removing the old one. “ But problem starts when you want to link that environment to Jupyter notebook kernel name”. You can find the code just searching it on google. I know anaconda environment setting is pretty easy. The problem started when I started playing around the environment and setting up the IPython notebook.
#MINICONDA INSTALL IPYTHON NOTEBOOK DOWNLOAD#
At the beginning of my python journey, I was able to download and install the Anaconda package smoothly. I personally like the anaconda distribution because of its library/package management capabilities.
Anaconda is a free and open-source distribution of the Python and R programming languages for scientific computing (data science, machine learning applications, large-scale data processing, predictive analytics, etc.), that aims to simplify package management and deployment.