Conda VS pipenv

Compare Conda VS pipenv and see what are their differences

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Conda

Binary package manager with support for environments.

pipenv

Python Development Workflow for Humans. Contribute to pypa/pipenv development by creating an account on GitHub.

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2023-08-26

Conda features and specs

Cross-Platform Package Manager
Conda is a versatile package manager that works across multiple operating systems including Windows, macOS, and Linux, making it a universal solution for environment management.
Environment Management
Conda can create, export, list, remove, and manage environments that contain different versions of Python and/or various packages, enhancing reproducibility and isolation.
Wide Range of Packages
Conda supports a broad spectrum of packages not limited to Python, which means it can install software and their dependencies from the C, C++, FORTRAN, and other ecosystems.
Binary Package Delivery
Packages are delivered as binaries, meaning you don't have to compile anything. This speeds up the installation process and reduces the possibility of errors.
Easy Dependency Resolution
Conda automatically manages dependencies, ensuring that the required packages are installed in the correct versions and reducing compatibility issues.
Version Control
It allows you to manage different versions of software and switch between them seamlessly without conflict, which is crucial for development, testing, and deployment.

Possible disadvantages of Conda

Large Disk Space Requirement
Conda environments can take up a significant amount of disk space due to the inclusion of multiple versions of Python and other binaries.
Complexity
While Conda is powerful, its comprehensive set of features may be overwhelming for beginners who only need simpler package management.
Performance Overhead
The convenience of automated dependency resolution and environment management can sometimes come at the cost of performance, particularly during the first setup.
Slower Package Availability
Newer versions of some packages may take longer to become available on Conda compared to other package managers like pip, leading to potential delays in adopting the latest features.
Third-Party Channels
While Conda has its main channel, many packages are hosted on third-party channels, which can lead to inconsistencies or reliability issues.
Not Limited to Python
Although this is also a strength, for users who are primarily working with Python, Conda might feel over-engineered for their needs.

pipenv features and specs

Integrated Workflow
Pipenv combines the functionalities of pip and virtualenv, providing a seamless environment for package installation and management, making the development workflow more efficient and organized.
Automatic Virtual Environment Management
Automatically creates and manages a virtual environment for projects, ensuring that dependencies are maintained separately and do not interfere with the system Python or other projects.
Lock File Generation
Generates a Pipfile.lock to ensure deterministic builds, making sure that installations are consistent across different environments or deployments.
User-Friendly Package Installation
Simplifies package installation with a straightforward and intuitive interface. Pipenv handles both direct package specification and environment management in a unified manner.
Environment Consistency
By using the Pipfile and Pipfile.lock, Pipenv ensures that all developers working on a project have a consistent set of dependencies, reducing 'it works on my machine' issues.
Dependency Resolution
Pipenv uses an advanced dependency resolver, helping to avoid dependency conflicts that can occur with complex package requirements.

Possible disadvantages of pipenv

Performance Overhead
The dependency resolution process can sometimes be slow, which might be noticeable in larger projects or when installing multiple packages at once.
Limited Flexibility
Pipenv abstracts away some of pip and virtualenv’s flexibility, which might limit advanced configurations or setups required by more complex projects.
Complexity for Simple Projects
May add unnecessary complexity for simple or small projects where virtualenv and pip would suffice without additional layers.
Slower Updates
Pipenv may lag behind updates compared to pip and virtualenv due to its additional integration layer, meaning it might not always provide immediate support for the latest Python packaging developments.
Learning Curve
Requires initial learning and adjustment for developers who are accustomed to using pip and virtualenv separately, potentially slowing down onboarding for new team members.

Analysis of Conda

Overall verdict

Yes, Conda is generally regarded as a good tool due to its versatility, efficiency in managing dependencies, and user-friendly features.

Why this product is good

Conda is considered good because it is a powerful package manager and environment manager that is language agnostic. It simplifies the installation of packages and dependencies across different programming languages, particularly beneficial for data science and machine learning tasks. It also handles library conflicts with ease, making it a preferred choice for managing complex software environments.

Recommended for

Data scientists
Machine learning engineers
Software developers using Python, R, or any other language needing isolated environments
Researchers requiring reproducible scientific environments
Anyone who frequently works with packages that have complex dependencies

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Category Popularity

0-100% (relative to Conda and pipenv)

pipenv

Front End Package Manager

83 83%

Front End Package Manager

17% 17

Package Manager

84 84%

Package Manager

16% 16

Developer Tools

79 79%

Developer Tools

21% 21

Kids

63 63%

Kids

37% 37

User comments

Share your experience with using Conda and pipenv. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, Conda should be more popular than pipenv. It has been mentiond 32 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Conda mentions (32)

Say Hello to UV: A Fast Python Package & Project Manager Written in Rust
If you’ve been managing Python projects long enough, you’ve probably dealt with a mess of tools: pip, pip-tools, poetry, virtualenv, conda, maybe even pdm. - Source: dev.to / about 2 months ago
The Simplest Data Architecture
You can use isolated Python environments like venv or conda. If you do this, you'll have to manage your environments yourself, and also constantly switch between them to run your data engineering code vs dbt. - Source: dev.to / 8 months ago
Python's virtual environments
Conda is an open-source package management system and environment management system that runs on Windows, macOS, and Linux. It is a powerful tool that allows you to create and manage virtual environments, install and update packages, and manage dependencies. Conda is particularly popular in the scientific computing community, as it provides access to a wide range of scientific computing libraries and tools. I... - Source: dev.to / 11 months ago
Introducing Flama for Robust Machine Learning APIs
When dealing with software development, reproducibility is key. This is why we encourage you to use Python virtual environments to set up an isolated environment for your project. Virtual environments allow the isolation of dependencies, which plays a crucial role to avoid breaking compatibility between different projects. We cannot cover all the details about virtual environments in this post, but we encourage... - Source: dev.to / over 1 year ago
Ask HN: Package management for multiple modules in C++, Python, Java project?
Conda https://docs.conda.io/en/latest/ ?? I'm not sure, but I used it to download some Python packages. It's an alternative to pip, but I'm not sure about the details. - Source: Hacker News / over 1 year ago

pipenv mentions (6)

Generate pip requirements.txt file based on imports of any project
https://github.com/pypa/pipenv Pipenv was last updated 10 hours ago. Looks like it's still an active project to me. - Source: Hacker News / 8 months ago
Adding Virtual Environments to Git Repo
Pipenv solves this by having both kinds of requirement files: Pipfile lists package names and known constraints on which versions can be used, while Pipfile.lock gives specific package versions with hashes. Theoretically the Pipfile (and its lockfile) format were supposed to be a standard that many different tools could use, but I haven't seen it get adopted much outside of pipenv itself, so I'm not sure if it's... Source: about 2 years ago
Top 10 Python security best practices
Alternatively, you can look into Pipenv, which has a lot more tools to develop secure applications with. - Source: dev.to / almost 3 years ago
Why and how to use conda?
I’m partial to pipenv but it does depend on pyenv (which works on Windows albeit via WSL, no?). Source: about 3 years ago
How to make a Python package in 2021
I think I went through the same progression — thinking pipenv was the official solution before deciding it isn’t. To add to the confusion, I just realized that pipenv [1] is currently owned by the Python Packaging Authority (PyPA) which also owns the official pip [2] and virtualenv [3]. [1]: https://github.com/pypa/pipenv [2]: https://github.com/pypa/pip [3]: https://github.com/pypa/virtualenv. - Source: Hacker News / about 4 years ago