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Dec 8, 2019 · Python 64-bit can't load 32-bit libraries without some heavy hacks running another Python, this time in 32-bit, and using IPC. If you have to load DLLs that you compile yourself, you'll have to compile them to 64-bit, which is usually harder to do (specially if using MinGW on Windows).
Python comes in both 32- and 64-bit editions. Which one should you choose, and what difference will it make?
- 4 min
- 6.1K
- TECHtalk
- Why You Need Multiple Python Environments
- Package and Environment Managers
- How to Choose An Appropriate Conda Download Option
- Python Environments: Root and Additional
- Directory Structure
- Gui vs. Command Line
- Managing Environments
- Managing Packages
- Summary
- Using Docker
When you start learning Python, it is a good starting point to install the newest Python version with the latest versions of the packages you need or want to play around with. Then, most likely, you immerse yourself in this world, and download Python applications from GitHub, Kaggleor other sources. These applications may need other versions of Pyt...
The two most popular tools for setting up environments are: 1. PIP (a Python package manager; funnily enough, it stands for “Pip Installs Packages”) with virtualenv(a tool for creating isolated environments) 2. Conda(a package and environment manager) In this article, I cover how to use Conda. I prefer it because: 1. Clear Structure: It is easy to ...
Installing your Conda systemis a bit more complicated than downloading a nice picture from Unsplash or buying a new ebook. Why is that?
So now you’ve picked an appropriate installer for yourself, well done! Now let’s take a look at the different types of environments and how they are created. Miniconda sets up two things for you: Conda and the root environment. The process looks like this: the installer installs Conda first, which is — as I already mentioned — the package and envir...
As I mentioned above, the Conda system is installed into a single directory. In my example this directory is: D:\Miniconda3-64\. It contains the root environment and two important directories (the other directories are irrelevant for now): 1. \pkgs(it contains the cached packages in compressed and uncompressed formats) 2. \envs(it contains the envi...
As I mentioned above, the Anaconda installer also installs a graphical user interface (GUI) tool called Anaconda Navigator. I also pointed out that I prefer using Miniconda, and that does not install a GUI for you, so you need to use text-based interfaces (for example command line tools or the Terminal). In this article, I focus on the command line...
Adding a new environment
To create a new environment named, for instance mynewenv(you can name it what ever you like), that includes, let’s say, a Python version 3.4., run: You can change an environment’s Python version by using the package management commands I describe in the next section.
Activating and leaving (deactivating) an environment
Inside a new Conda installation, the root environment is activated by default, so you can use it without activation. In other cases, if you want to use an environment (for instance manage packages, or run Python scripts inside it) you need to first **activate**it. Here is a step by step guideof the activation process: First, open the command line (or the Terminal on Linux/Mac OS X). To activate the mynewenvenvironment, use the following commands depending on the operating system you have: 1....
How do you learn the version of your Conda?
It can be useful to check what version of Conda you are using, and also what are the other parameters of your environment. I’m going to show you below how to easily list out this information. To get the Conda versionof the currently active environment, run this command: Example result: To get a detailed list of informationabout the environment, for instance: 1. Conda version, 2. platform (operating system and bit count — 32- or 64-bit), 3. Python version, 4. environment directories, run this...
Depending on the installer you chose, you’re going to end up with some basic (in case of using Miniconda) or a lot of (in case of using Anaconda) packages to start with. But what happens if you need 1. a new packageor 2. another versionof an already installed package? Conda — your environment and package management tool — will come to the rescue. L...
So let’s wrap this up. I know that it seems quite complicated — and it is, in fact, complicated. However, utilizing environments will save you a lot of trouble. In this article, I’ve summarized how you can: 1. choose an appropriate Conda installerfor yourself 2. create additional environments(next to the root environment) 3. add or replace packages...
A little side note based on one of my reader’s question (thanks for bringing this up Vikram Durai!): If your application 1. uses a server(for example a database server with preloaded data), AND 2. you want to distributethis server and its data together with your application and its Python environment to others (for instance to a fellow developer or...
Oct 22, 2024 · When to Use Python 64-bit. Data Science and Analytics: If you’re working with extensive datasets or performing large-scale computations. Machine Learning: Utilizing 64-bit Python allows you to leverage vast amounts of data and complex models without running into memory issues.
2 days ago · Four Python 3.13 installers are available for download - two each for the 32-bit and 64-bit versions of the interpreter. The web installer is a small initial download, and it will automatically download the required components as necessary.
The most likely answer is 64-bit, for the following reasons: Most modern operating systems use a 64-bit edition of Python by default. Windows users can run 32-bit editions of Python on...
People also ask
Why should I use 64-bit Python?
Can Python 64-bit run a 32-bit library?
Do I need a 64-bit python installer?
How do I know if Python is 64-bit or 32-bit?
What if Py_Python is 32-bit?
Can Python run in 64-bit mode?
Oct 2, 2024 · In general, 64-bit Python can offer better performance for CPU-bound tasks due to its ability to handle larger data types and more memory. However, the performance difference may not be noticeable for smaller scripts or applications that do not require extensive memory usage. Example: Memory Allocation. import numpy as np. # Create a large array.
