Advance mathematics operation is easily done …
Data Transformations (25)
Extend Existing Data Sources (23)
Interactive Data Analysis (23)
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Entry-level set up fee?
- No setup fee
- Free Trial
- Free/Freemium Version
- Premium Consulting / Integration Services
- Several IDEs
- Using better graphics for spyder
- Almost all required libraries are available in it.
- Easy to create a notebook for a data science project.
- [It is] flexible to work on multiple Python environments based on your requirements.
- In [the] community, [it is] easy to find the forum [and] events.
- [The] application [takes a lot of] time to load the first time.
- Sometimes, it [stops working because it] consumes more ram.
- [I would like it to] add some ready-made use case environments.
It is being used by Engineering and Geoscience teams to prototype custom algorithms for use in solving use cases in the oil and gas industry, including subsurface, operations and other relevant functional area such as health, safety and environment.
- Open-source - free!
- Supports multiple popular data analytics languages.
- Easy to create reproducible projects via environments.
- Getting Spyder IDE to work consistently across environment.
- Platform speed.
- Make it available in cloud marketplace (e.g., Azure) for ease of deployment.
Advance mathematics operation is easily done by Anaconda.
I mostly used Jupyter Notebook and Spyder.
It makes it easy to script in python through the user interface of Anaconda software.
Accessing libraries of python through Anaconda is easy and efficient.
- User interface is simple and easy to use.
- Making the Jupyter notebook is great because that is a very great tool to run python script line by line for learning purposes.
- We can easily access files and folder through it.
- Auto suggesting in code is great of Spyder.
- Anaconda is taking much RAM of device which needs improvements.
- Spyder is sometime crashing while running the application.
- Git integration is not there which is require in Anaconda.
I have used Anaconda for image processing application making.
In which I have used Spyder and include many libraries of image processing.
Debugging of code made easy through it.
Auto suggestions in spyder are great to write code fast and efficiently.
You can observe the memory space required for your file through it.
- Set environment for particular use cases.
- Comes with all the libraries that we require.
- One stop solution for data scientist.
- Best in all the tools.
- Built In data analysis tool.
- Students should have some extra benefits to exploring the advanced options that can be beneficial for them to have some real-world experience.
- Automation tool.
- Some predefined environment according to use case.
- Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
- Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
- Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.
- It'd be great to see some good data visualization tools on Anaconda Navigator.
- Its ability to handle large data source.
- I'd like to see some themes for night coders like myself. Some good UI would be appreciated.
- Open source, leading to zero sketchy things running in the background.
- Easy to install packages.
- Multiple environments are easy to configure and also encouraged.
- Anaconda gets bigger and bigger with each package or dependency that you own, making it a huge pain to move environments around.
Used department and Teams wise as per the requirement from the stakeholders. Not every team is using this navigator.
- Complete package to build or work on data science projects.
- All the latest modules/packages are installed very easy just with anaconda prompt.
- We can use Jupyter notebook from it very easily and together we can work on Spyder as well.
- It works very fast, if the system has 16GB ram then its data processing speed is also very high.
- More graphics need in Spyder book. If you work for couple of years then you will be bored with the graphics.
- Extra tools are required for making it secure. We uses extra tools for adding Username /Password to Jupyter.
- R Studio Hangs a lot when open from Anaconda Navigator.
- It provides easy access to software like Jupyter, Spyder, R and QT Console etc.
- Easy installation of Anaconda even without much technical knowledge.
- Easy to navigate through files in Jupyter and also to install new libraries.
- R Studio in Anaconda is easy to use for complex machine learning algorithms.
- It can have a cloud interface to store the work.
- Compatible for large size files.
- I used R Studio for building Machine Learning models, Many times when I tried to run the entire code together the software would crash. It would lead to loss of data and changes I made.
Easy to access Jupyter, RStudio and gives direct access to your files in your PC. It is compatible to install as many libraries required for the work you do. I have worked with large live data for a project on RStudio and it let me easily connect to it, though the system crashed sometimes when I tried to execute the entire code but it always created a recovered file of the changes I made. So that was one of the features I really liked.
- It has both graphical and command line interface available
- The community is also very good and supporting
- It can improve the time of loading all the contents
- It can also improve its memory and ram requirements
- Some softwares should also be integrated like PyCharm
Currently we are working on predicting the client requirements in our company. So we are using there preference and choice or decision made by them and according to that we will provide the recommendation.
- Provide support for multiple liberary and have pre loaded functionality.
- It has the support for the python and many other languages.
- It's automatically install the main function.
- It has multiplatform support
- Anaconda consume almost every resources of the computer
- It is very heavy software.
- Suitable for the small projects more.
- Multiplatform (multiple operating systems)
- It aggregates several important systems in the same visualization, facilitating the work of new professionals in data analysis and science
- Anaconda makes programming easier on Jupyter Notebook
- Needs to be optimized to consume less RAM on machines
- It is a great tool for the development of small projects but not for large projects
- Anaconda could have more documentation translated into other languages, facilitating the entry of users from non-English-speaking countries
When choosing Python or R for software development, you choose a large language ecosystem with a wide variety of packages covering all programming needs. But in addition to libraries for everything from GUI development to machine learning, you can also choose from a variety of tool runtimes and their libraries; some runtimes may be more suited to the use case you have at hand than others.
Anaconda has versions optimized for special use cases. Anaconda was designed for Python developers who need a distribution supported by a commercial provider and with support plans for companies. The main use cases for Anaconda Python are mathematics, statistics, engineering, data analysis, machine learning, and related applications.
Anaconda groups together many of the most common libraries for commercial and scientific work in Python--SciPy, NumPy, Numba, and so on--and makes it much more personalized through a package management system.
Anaconda stands out from the other distributions for the way it integrates all these pieces. When installed, Anaconda offers a desktop application--Anaconda Navigator--that makes all aspects of the Anaconda environment available through a convenient user interface. Finding components, customizing them, and working with them is much easier with Anaconda than with CPython.
Another benefit is the way Anaconda handles components from outside the Python ecosystem, if they are prioritized for a specific package. Conda conda packages, created specifically for Anaconda, deal with the installation of Python packages and external third-party software requirements.
Since Anaconda includes so many useful libraries and can install even more with just a few keys, the size of an Anaconda installation can be much larger than that of other competitors. This can be an issue in situations where you have resource constraints.
- Python environment management.
- Package management.
- Out of the box installed with commonly used packages.
- Support for R as well.
- Has a learning curve before getting comfortable.
- Pretty heavy installation due to included packages.
- Only great for larger projects.
- Requires a lot of memory to run kernels.
- Support for multiple opensource libraries
- Easy to deploy and develop
- Containerization of code is fast and easy
- Irregular security updates
- No support for integration with version control tools
- makes installation of python very easy
- great environment manager
- very easy to install python packages
- pricing could be improved to allow better entry for team usage
- some of the packages in pip not available via 'conda'
- the package manager is kind of slow
- First of all it is very easy to install and it is user friendly. You just download a Anaconda from its official site and you can start using it for coding (I usually code using Jupiter Notebook) Compared to PyCharm it is easier navigate in Anaconda(Jupiter Notebook)
- For me it is a best environment to use if I have small projects. Jupiter Notebook is running tasks much faster compared to PyCharm and other IDE's.
- In my work I usually need different scientific packages that are not commonly used. As Anaconda have thousands of libraries it helps me making my job easier
- As I use Anaconda mainly for Jupyter Notebook I will provide cons of Jupiter Notebook, First of all it consumes a lot of RAM.
- Jupyter Notebook is a good tool for small projects. However it can not handle large projects very well as it is not structured(whereas in PyCharm you can create a project and have all files related to that project in 1 place)
- It takes some time to load Anaconda. Sometimes it even makes computer to freeze
- User interface is easy enough for a layman to navigate.
- User interface has all the tools required to write code.
- Jupyter Notebook is easy to get lost in when there is lots of code. A way to minimize the sections to watch the progress would be a lot better.
- Management of custom environments
- Support for standardizing deployments
- Deployment in containers using Miniconda
- Update of Conda packages is becoming slower. The 4.7 update was welcome, but seems to be regressing again.
We are not using the customer supported version of Anaconda, and instead, we are relying on the community edition, based on the Open Source of all of our software. Hence, I am not evaluating Anaconda's support. Also, we are not making use as a company of the multi-language support in Anaconda, but I have tried the SciJava, R, and Julia support in Anaconda.
- It provides a smooth, intuitive GUI to automate setting up a development environment.
- Helps install new compilers without user input
- Assists with finding and installing necessary dependencies.
- Anaconda could greatly benefit by integrating with Git and other versioning software.
- The software's default installation is relatively bloated, slower on older machines, and could be improved by allowing for a lean default installation environment.
- Anaconda has an issue with supporting the current version of Computer-Vision, a commonly used machine learning package.
- Anaconda itself already carries the most popular Python packages so for most developers it is sufficient enough to deal with the normal work requirements.
- The Jupyter Notebook is a very encouraging feature which allows the researcher to apply the data analysis in an intuitive way. It provides step by step understanding the data, processing the data, visualizing the data and trying out the different methodology and algorithm
- Both the old version of Python and the new version of Python are supported, giving a very good backward compatibility of some old Python codes developed beforehand.
- Although some other users mentioned the installation is "simple", we did encounter some challenge in a highly controlled environment (due to security reasons).
- Jupyter Notebook is extremely slow when the client/server side of the network's speed/bandwidth is not balanced.
- Bootstrapping Anaconda takes too long, sometimes I even started doubting it would respond any more.
- If there are extra python packages you need but are not by default installed by Anaconda, then some efforts will be required to figure out how to put them in the right place.
- Handle different environments with different versions of python and its libraries. This is a handy feature because some tools like PSSE run only with Python 2.7.
- Anaconda preinstalls the most useful libraries and packages.
- It's a little slow at startup. If it were a little faster, that would add significantly to the experience.
- Data analysis.
- Machine learning.
- It is very easy to install and run in any operating system.
- I'm not sure Anaconda needs improvement.
- Contains every fundamental package about data analytics and machine learning.
- It is very easy to install further packages.
- It's great that it contains a lot of stuff but it is very slow to boot and is a heavy product.