#software-engineering ## What is the Python Dependency Hell? why there is no such Problem in Node Js? Anyone Try to Fix It? Dependency hell is a common problem in software development that occurs when a project has many dependencies, and those dependencies have their own dependencies, leading to a complex and tangled web of dependencies that can be difficult to manage. In Python, dependency hell can occur when different packages or libraries require different versions of the same dependency, leading to conflicts and errors. Node.js also has a similar problem with dependency management, known as "dependency hell". It occurs when a project has many dependencies, and those dependencies have their own dependencies, leading to a complex and tangled web of dependencies that can be difficult to manage. However, Node.js has a built-in package manager called npm (Node Package Manager) that makes it easier to manage dependencies and avoid dependency hell. npm allows developers to specify the exact version of a dependency that their project requires, ensuring that the correct version is installed and avoiding conflicts with other dependencies. In Python, there are several tools and techniques that can be used to manage dependencies and avoid dependency hell, including virtual environments, pip, and pip-tools. Virtual environments allow you to create isolated Python environments for each project, ensuring that dependencies are kept separate and avoiding conflicts. pip is a package manager for Python that allows you to install and manage packages and their dependencies. pip-tools is a set of command-line tools that can be used to manage dependencies and generate a requirements.txt file that specifies the exact versions of all dependencies required by a project. In conclusion, dependency hell is a common problem in software development that can occur in both Python and Node.js projects. While Node.js has a built-in package manager that makes it easier to manage dependencies, Python has several tools and techniques that can be used to manage dependencies and avoid dependency hell. ## When We Use Npm Command, it is Local Env by Default. Is it the Same for Python? No, when we use the `pip` command in Python, it installs packages globally by default. This means that the packages are installed in the system-wide Python environment and can be accessed by any Python script or application running on the system. This can lead to conflicts between different versions of the same package and make it difficult to manage dependencies. However, Python provides a built-in tool called `virtualenv` that allows you to create isolated Python environments for each project. A virtual environment is a self-contained Python environment that can have its own set of packages and dependencies, separate from the system-wide Python environment. This makes it easier to manage dependencies and avoid conflicts between different versions of the same package. To create a virtual environment in Python, you can use the following command: ```bash python3 -m venv myenv ``` This will create a new virtual environment named `myenv` in the current directory. You can activate the virtual environment using the following command: ```bash source myenv/bin/activate ``` This will activate the virtual environment and allow you to install packages using `pip` without affecting the system-wide Python environment. When you are done working in the virtual environment, you can deactivate it using the following command: ```bash deactivate ``` In summary, while `npm` installs packages locally by default, `pip` installs packages globally by default in Python. However, Python provides a built-in tool called `virtualenv` that allows you to create isolated Python environments for each project, making it easier to manage dependencies and avoid conflicts. ## Fix This with Conda Conda is a solution to the dependency hell problem in Python, as it allows you to create isolated environments for each project and manage dependencies within those environments. First go to  [https://docs.conda.io/en/latest/miniconda.html](https://docs.conda.io/en/latest/miniconda.html) and download miniconda. And then run this. ``` conda create -n stenv python=3.9 ``` when you create a conda env, the env will be created in the miniconda folder. And there will be a `environments.txt` in `.conda` pointing to this `stenv`.