Anaconda with Python 2 (x86_64)
Anaconda, Inc. | Anaconda2 2019.07 20190724Linux/Unix, Amazon Linux 2018.03 - 64-bit Amazon Machine Image (AMI)
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Reviewing Anaconda
What do you like best about the product?
Support from the community: Anaconda has a sizable and vibrant user base that actively contributes to its growth and helps those just starting.
Fast prototyping: Anaconda's interactive computing environment makes it possible to iterate and test concepts in data science projects swiftly.
The Anaconda data science platform offers many tools for data scientists, academics, and developers. It is a flexible and robust data science platform. Thanks to its distinctive features and capabilities, it is a fantastic option for anyone wishing to deal with data, regardless of their degree of experience or specific demands.
Fast prototyping: Anaconda's interactive computing environment makes it possible to iterate and test concepts in data science projects swiftly.
The Anaconda data science platform offers many tools for data scientists, academics, and developers. It is a flexible and robust data science platform. Thanks to its distinctive features and capabilities, it is a fantastic option for anyone wishing to deal with data, regardless of their degree of experience or specific demands.
What do you dislike about the product?
Although Conda, Anaconda's package manager, is robust and useful, it may also be difficult and confusing for beginning users. To manage dependencies and handle package conflicts, a lot of time and knowledge may be required.
Anaconda can require a lot of resources, particularly when managing large or challenging data science projects. This may lead to a slower performance and necessitate the use of additional resources or a more powerful machine.
Overall, Anaconda is a reliable and useful platform for data exploration, but it is essential to consider these potential drawbacks before using it. Anaconda users should weigh its benefits and drawbacks to determine if it is the best choice for their needs and available resources.
Anaconda can require a lot of resources, particularly when managing large or challenging data science projects. This may lead to a slower performance and necessitate the use of additional resources or a more powerful machine.
Overall, Anaconda is a reliable and useful platform for data exploration, but it is essential to consider these potential drawbacks before using it. Anaconda users should weigh its benefits and drawbacks to determine if it is the best choice for their needs and available resources.
What problems is the product solving and how is that benefiting you?
Anaconda provides answers to a variety of problems that come up regularly in data science, including:
Package management: Software libraries and packages used by data scientists are usually large, complex, and dependent. This problem is resolved by Conda, a competent package manager provided by Anaconda, which makes it straightforward to install, manage, and update packages.
Environment management: Data scientists frequently need to manage their environment while working on multiple projects at once, each with its own dependencies and configuration settings. Anaconda provides a solution to this problem by providing an environment management system that enables users to set up and manage separate environments for each project.
Compatibility issues: Data scientists typically have compatibility issues while using multiple operating systems or software versions. Cross-platform compatibility is provided by Anaconda to address
Package management: Software libraries and packages used by data scientists are usually large, complex, and dependent. This problem is resolved by Conda, a competent package manager provided by Anaconda, which makes it straightforward to install, manage, and update packages.
Environment management: Data scientists frequently need to manage their environment while working on multiple projects at once, each with its own dependencies and configuration settings. Anaconda provides a solution to this problem by providing an environment management system that enables users to set up and manage separate environments for each project.
Compatibility issues: Data scientists typically have compatibility issues while using multiple operating systems or software versions. Cross-platform compatibility is provided by Anaconda to address
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The packages it provides is really great and make like of a developer easy
What do you like best about the product?
I really like anaconda because so many pre-installed packages are available there that make it simple for developers and data scientists to get started on their projects without having to spend time installing and configuring dependencies. I prefer using the Anaconda terminal. Moreover, Anaconda is accessible to a large number of consumers thanks to its availability across several platforms, including Windows, macOS, and Linux.
What do you dislike about the product?
The only thing I dislike about anaconda is that it takes a long time to open. Because of that, I prefer using the anaconda terminal rather than using navigator app.
What problems is the product solving and how is that benefiting you?
It provides a lot of extensions that help to make day to day life of a developer easy, and I think it is a perfect tool for a Data Scientist. It helps by providing all the tools for Data Science in one place.
Reviewing Anaconda
What do you like best about the product?
Package administration: Anaconda software offers a thorough package administration system that enables users to quickly install, manage, and update packages and dependencies for several programming languages, including Python and R.
Environment Management: With the Anaconda software, users may build and oversee separate environments for several projects or applications, each with their own collection of packages and dependencies. The code is made reproducible and conflicts between various packages are reduced as a result.
Data Science Libraries: A number of well-known data science libraries, including NumPy, Pandas, and Scikit-learn, are pre-installed in Anaconda software, making it simpler for data scientists to get started on their projects without having to manually install and set up these libraries.
Environment Management: With the Anaconda software, users may build and oversee separate environments for several projects or applications, each with their own collection of packages and dependencies. The code is made reproducible and conflicts between various packages are reduced as a result.
Data Science Libraries: A number of well-known data science libraries, including NumPy, Pandas, and Scikit-learn, are pre-installed in Anaconda software, making it simpler for data scientists to get started on their projects without having to manually install and set up these libraries.
What do you dislike about the product?
Huge Installation Size: As compared to other tools of a similar nature, Anaconda software has a rather high installation size. This can consume a lot of disc space, especially on devices with little storage.
Performance Problems: Anaconda software occasionally experiences performance problems, especially when handling huge data sets or sophisticated machine learning models. Longer waiting times and delayed execution times may result from this.
Versioning difficulties can occasionally occur with Anaconda software, especially when changing packages or dependencies. This may result in conflicts between various package versions and errors or unexpected behaviour.
Performance Problems: Anaconda software occasionally experiences performance problems, especially when handling huge data sets or sophisticated machine learning models. Longer waiting times and delayed execution times may result from this.
Versioning difficulties can occasionally occur with Anaconda software, especially when changing packages or dependencies. This may result in conflicts between various package versions and errors or unexpected behaviour.
What problems is the product solving and how is that benefiting you?
Data scientists and developers may more easily manage and install the various packages and dependencies needed for their projects thanks to Anaconda's extensive package management system. Time is saved, and production is increased.
Environment Management: Anaconda enables users to build and maintain isolated environments, preventing conflicts between various packages' and dependencies' versions and promoting code reproducibility.
Data Science Libraries: A number of well-known data science libraries, including NumPy, Pandas, and Scikit-learn, are pre-installed in Anaconda, making it simpler for data scientists to get started on their projects without having to manually install and set up these libraries.
Environment Management: Anaconda enables users to build and maintain isolated environments, preventing conflicts between various packages' and dependencies' versions and promoting code reproducibility.
Data Science Libraries: A number of well-known data science libraries, including NumPy, Pandas, and Scikit-learn, are pre-installed in Anaconda, making it simpler for data scientists to get started on their projects without having to manually install and set up these libraries.
Using Anaconda for managing python environments
What do you like best about the product?
I the following about Anaconda.
1. Creating python environments - creating new environments is quick and easy
2. Adding packages - conda packages contains most of the packages that are needed
3. Jupiter Notebook is very user friendly and easy to learn
1. Creating python environments - creating new environments is quick and easy
2. Adding packages - conda packages contains most of the packages that are needed
3. Jupiter Notebook is very user friendly and easy to learn
What do you dislike about the product?
- Few packages cannot be found on conda install
What problems is the product solving and how is that benefiting you?
Managing python environments with good GUI is easy to read.
Also, sharing environment files for other users is easy
Also, sharing environment files for other users is easy
One Stop solution for a python developer need
What do you like best about the product?
Anaconda gives us a place to access different tools like jupyter notebook, and machine learning tools like libraries like TensorFlow, scikit-learn and Theano. Data science libraries like pandas, NumPy and Dask so we can work on one interface instead of installing many applications.
What do you dislike about the product?
It is on the heavy side for processor usage as it contains many applications that might not be needed; it takes some time to load and slows down the computer eventually, but considering it also helps incorporate several API in one makes it manageable.
What problems is the product solving and how is that benefiting you?
Direct access to jupyter notebook is a boon as we don't have to use the cloud version, which makes us dependent on network parameters. It also has access to the solid debugging platform Spyder which I don't use often but need during production.
Environ-mental friendly ANACONDA
What do you like best about the product?
The most helpful thing about anaconda are its environments, you can isolate any project you want without interfering with the rest of your projects, helping enormously along the way.
What do you dislike about the product?
The least helpful thing about Anaconda its probably the lack of a beginner friendly experience, resulting in a steep learning curve at the start, but after that its fantastic. They are doing a lot with the tutorials but it still is a hard to approach at first system.
What problems is the product solving and how is that benefiting you?
Anaconda is solving the isolation on projects and making sure every step of the way is secure and doesn't involve an expert skill tree to perform at senior levels.
Open Source distribution for python.
What do you like best about the product?
*Anaconda is one of the most used and free open source available on the internet for python and R languages.
*Best suited for data sciences and Machine learning projects. It provides no of the free package to install and work in a python environment on our desired settings.
*Numpy, Matplolib, pandas and many more are very fast and effective on it.
*We can use jupyter notebook also here.
*Best suited for data sciences and Machine learning projects. It provides no of the free package to install and work in a python environment on our desired settings.
*Numpy, Matplolib, pandas and many more are very fast and effective on it.
*We can use jupyter notebook also here.
What do you dislike about the product?
*Anaconda is a heavy tool; that's why it sometimes lags to get opened.
*Sometimes launch issues but overall good experience.
*Sometimes launch issues but overall good experience.
What problems is the product solving and how is that benefiting you?
*Its range of community users is high. Easy access to files and folders.
*It gives us a very great user interface experience. I have used Jupyter Notebook for a project based on data extraction.
*It gives us a very great user interface experience. I have used Jupyter Notebook for a project based on data extraction.
it's a user friendly platform help beginners to code in an easy way in python
What do you like best about the product?
it's a user-friendly platform that makes it easy to interact with the new user entering to python code world. We don't need to write driver code and main code together. It's totally a beginner's platform
What do you dislike about the product?
Sometimes we need to refresh the kernel again and again which sometimes becomes irritating. The application takes time to open the Jupiter notebook and sometimes lag's
What problems is the product solving and how is that benefiting you?
Coming from a data analyst background, the anaconda is very beneficial for me as most of the data visualization tasks are done there, and compilation of the code is easy, fast and effective. It contains several machine learning functions, which help in proper visuals for analysis
Is anaconda any good? Definitely YES!!
What do you like best about the product?
Yes! It is. Anaconda is an open source tool. We can find many integrated tools as pandas, matplotlib, Numpy, spyder, R studio, Jupyter notebook, Jupyter lab and more. Users who work on Data science can use this tool without installing any packages using pip.
It supports python and R programming. And the UI is very simple that anyone can easy use it.
It supports python and R programming. And the UI is very simple that anyone can easy use it.
What do you dislike about the product?
Only thing is we need to install the software once and the startup time is slow. It takes time to open the software and load the tools. Other than this Anaconda can be a pleasant experience for machine learning and data scientists.
What problems is the product solving and how is that benefiting you?
Machine learning projects requires many packages and tools to perform complex tasks which we need to install using pip command or wheel tool. But anaconda provides everything in a finger tip, which is very convenient.
Anaconda
What do you like best about the product?
This software is like a combination of the all the software like it's included all the type of software which you need for the coding like Spyder , Jupyter notebook, Vs code
What do you dislike about the product?
This software consume more space and if you need to working on perticular software like Spyder then you can also download from here but also it consumes also it's different space second if you download one library in one software then you also need to download that library for other software.
What problems is the product solving and how is that benefiting you?
If it solve module or library download problem then this software is really best software for user if they work on the machine learning and artificial intelligence.and if it provides some more features then that's great.
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