


Briefly describe the differences between Python, Anaconda, virtualenv and Miniconda
/1 Introduction/
Last week we shared two basic articles about Anaconda. Friends who didn’t have time to get on the bus can get on the bus and take a look: I will teach you step by step how to install Anaconda. Briefly describe the two ways to verify whether Anaconda is installed successfully and the Anaconda environment variable configuration process. Today we will take a look at the differences between Python, Anaconda, virtualenv and Miniconda.
/2 The difference between Anaconda and installing Python directly/
## When you start a new computer, of course you start configuring a series of environments , in fact, this time I installed Python in the conventional way, but when I thought about the various weird problems I encountered when installing Python on ubuntu, I lost a few hairs. It happened that a friend said that whether it is windows or linux, Anaconda can solve strange problems when installing Python, especially on the Linux platform, so this article will record Anaconda installation and usage tutorials, nanny-level tutorials.
##/3 The difference between Anaconda and virtualenv/
virtualenv## If I install the Python3.5 interpreter directly, virtualenv only Being able to virtualize environments based on Python3.5 is essentially copying an empty Python3.5 environment.
Anaconda
If I were using Anaconda, that would be awesome, I can still go out virtually Each virtual environment, but I can decide whether to use Python 3.6 or Python 3.8. After all, there are sometimes slight differences between versions. Just update the Python interpreter and directly virtualize one without uninstalling the original Python.
/4 The difference between Anaconda and Miniconda/
## In human terms: two It's all the same thing.
After Anaconda is installed, a bunch of things will be installed, which are very large, with more than 1,000 libraries, occupying several gigabytes.
After Miniconda is installed, it comes with nothing. You can install whatever you need. It is very small. I didn’t install it, so I won’t take a screenshot. The size of Miniconda may be around 1G.
Apart from that, there is no other difference between the two, the commands are exactly the same.
#/5 Summary/
This article mainly focuses on the differences between Anaconda and direct installation of Python, virtualenv, and Miniconda. The writing is relatively basic, mainly to popularize science for friends who are new to Anaconda. The software tools supporting Python are really There are too many, and it is indeed easy to confuse beginners. I hope that through this article, everyone can learn more about Python.The above is the detailed content of Briefly describe the differences between Python, Anaconda, virtualenv and Miniconda. For more information, please follow other related articles on the PHP Chinese website!

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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