Anaconda: Best IDE for Python
I use Spyder for developing my Python codes a lot. And Anaconda provides an excellent IDE to accomplish my tasks.
Anaconda
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Anaconda
Starting at $14.95 per month
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Popular Features
View all 16 featuresData Transformations (25)
8.8
88%
Extend Existing Data Sources (23)
8.7
87%
Interactive Data Analysis (23)
8.6
86%
Visualization (24)
8.6
86%
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View all pricingCommercial Edition
$14.95
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Team Edition
10,000
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Enterprise Edition
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- No setup fee
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- Free Trial
- Free/Freemium Version
- Premium Consulting / Integration Services
Features Scorecard
Platform Connectivity
8.4
84%
Data Exploration
8.6
86%
Data Preparation
8.5
85%
Platform Data Modeling
8.6
86%
Model Deployment
8.2
82%
Product Details
What is Anaconda?
Anaconda is an open source Python distribution / data discovery & analytics platform.
Anaconda Video
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Anaconda Technical Details
| Deployment Types | SaaS |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
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November 05, 2021
Anaconda: Best IDE for Python
I use Spyder for developing my Python codes a lot. And Anaconda provides an excellent IDE to accomplish my tasks.
- Profiling
- Several IDEs
- User-friendly
- Using better graphics for spyder
October 07, 2021
Best IDE for Data Science Projects
Anaconda is the best tool for the data scientist to [develop] the machine learning project [under] a single umbrella. It is used [throughout] the whole organization. We are using the Anaconda for Python [and] R to do the data science activities end-end process, i.e. importing the statistical/ML/Visualization libraries to train and visualize the data/reports.
- 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.
September 11, 2021
Anaconda - a platform made for Py.
Anaconda open-source distribution is a flexible platform enabling users to work with several popular data analytics languages such as R and Python.
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.
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.
September 09, 2021
Awesome tool for Data Scientists
Anaconda is mainly used for Python programming like data science and computer vision script.
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.
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.
June 28, 2021
Anaconda Review
I am a machine learning engineer and certified data scientist who is solving some real-world problems and used to teach students. I generally used to work on the project that is beneficial for me as well as the society to make life easier. I used to create machine learning models and host them on the cloud. I used Anaconda as my primary software to work on my projects. Best for setting your Python environment. Anaconda is the best data science version control tool in the present time. This is the best solution that is packed with lots of ideas and good features. With anaconda, you can easily create, remove, and switch environments to run.
- 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.
June 25, 2021
One stop data science destination - Anaconda
My previous organization used Anaconda, Jupyter Notebook specifically to run sales forecasting codes in Python. At the time, it was specifically used by the E-Commerce and Buying team to make buying decisions. The ease of using Anaconda Navigator was a very big plus point for my organization as they could save a ton of time and money that was needed behind the training.
- 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.
We used Anaconda to develop solutions to analyze blocks of information from customer tickets in order to gather information about our monthly resource x workforce relation.
- 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.
June 15, 2021
Complete Data Science software suit.
The teams which are working on analytics-based projects are using Anaconda/Anaconda Navigator for various other tools. Like building small applications using python on Spyder is used in it. Also for reporting and visualization R and Orange has used it.
Used department and Teams wise as per the requirement from the stakeholders. Not every team is using this navigator.
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.
June 12, 2021
Anaconda for Data Science!
Anaconda is being used by the Business Intelligence and Data Science department at my organization. It is used widely for analysis, forecasting and answering questions. For example, the commercial department wanted to know which of their three products was most affected by COVID-19. So using Anaconda Jupyter Notebook and data from surveys conducted with customers we could come to a conclusion. It was easy to represent the findings in visual forms.
- 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.
May 21, 2021
Hemant's review of Anaconda
Anaconda is currently used as the complete python environment setup tool. It has been easy for us to automate the process of setting up the coding environment for all of our employees. We have created separate environments for separate purposes. Anaconda has been very useful. Not only [do] we have every requirement at one place but we can also manage it more efficiently and debug problems more easily
- Anaconda has support for many different things like Spyder idle, Jupyter Notebook, vs code, r studio
- 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
May 21, 2021
My Anaconda Review
Anaconda is not just a tool it is a complete package to build and deployment of the project related go machine learning , neural networks, artificial intelligence. It is loaded with the pre built library. Anaconda provide the facilities for the data visualization. Starting with the normal python script you can even create the whole data science project along with the deployment. In my second year of college I use the Anaconda [to] build my machine learning project which is based on predicting the food item in the given plate. For that i done all the computing using this software only.
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.
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.
May 16, 2021
Data Science and Anaconda
The company has several departments and distributed units that are adopting the use of data science to improve institutional performance. Anaconda has been used as a tool to support professionals who improve data and their results for the management of the organization. We still have a lot to evolve in data management, integration, standardization, and data improvement; but the continued use of Anaconda will allow us to identify our bottlenecks and make better decisions.
- 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
May 10, 2021
Big guns for big scale projects: Anaconda
Anaconda is a great package manager for large-scale projects with multiple dependencies and support for multiple versions of python. It offers us out-of-the-box capabilities for major common data science use cases and projects. Really robust in terms of switching execution environment and offers granular control over the Conda virtual environments. It is used across our organization as it has really great community support and they offer solutions in case we are stuck as well.
- 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.
Our team is using Anaconda as a python distributions tool for running python code. It contains and supports the maximum number of python libraries and packages. It has helped us to set up a complete data analysis environment with the help of Jupyter Notebook.
- Support for multiple opensource libraries
- Easy to deploy and develop
- Responsive
- Containerization of code is fast and easy
- Irregular security updates
- No support for integration with version control tools
April 30, 2021
Anaconda for beginner data scientists!
Anaconda is a standard installation for our python coders. Our python coders stretch across multiple departments so it should be considered being used by the whole organization. Anaconda makes it easy for us to standardize getting a base working environment ready for everyone. From there it makes is easy for users to install required packages.
- 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
April 16, 2021
Review for Anaconda
Anaconda is mainly used only by some of the departments in our bank. Specifically in our department it is mainly used as a environment for coding in python although some of my colleagues use it as a environment for coding in R. As risk managers we usually do some complex tasks using python (to predict exchange rate fluctuations and evaluate market and credit risks).
- 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
May 02, 2020
Easily share code with other departments
Anaconda Navigator is used across a few departments as a way to share code that is used to analyze data from our products. The data is stored in the cloud. Some engineers write the code to analyze and print results for manufacturing tests. The manufacturing team can then easily run the code to receive the results of the tests.
- 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.
The usage of Anaconda is not yet standard through the organization, but many people at my organization use it as the best way to create a standardized Python environment. In particular, the Miniconda distribution is preferred for deployment of Python-based containers, as it allows for a better, finer-grained installation in containers. For desktop users, the full Anaconda distribution is used, as it comes with several packages that are used throughout the organization: Astropy, NumPy, Matplotlib, Pandas, and others.
- 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.
March 05, 2020
Anaconda provides a vast array of compilers and tools for every developer - and is easy enough for non-developers to setup.
We use Anaconda to support software development objectives for our staff. It helps us reduce "time waste" by quickly onboarding employees and setting up the majority of their development environments, so they have all of their necessary tools.
- 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.
March 03, 2020
Anaconda turned a group into a team
Anaconda is being used in the entire organization of my company. It ensures that data science teams across the whole organization manage our python environment and make sure the repeatability of the packages that we built internally as well as the notebooks and projects created by different teams.
February 28, 2020
Anaconda: A data scientist's best friend
Anaconda is widely used in my organization to set up the python environment and perform version control. By setting up the environment yaml file, you can ensure the other users can run the analysis based on the same environment. Also, Anaconda provides other tools such as RStudio/spyder via the navigator.
February 18, 2020
Anaconda - The tool to master for Python based data analytic tasks
The data science and operation research team in our company majorly uses Python as the programming language. So Anaconda was chosen to provide one research platform, allowing the data scientists to work on one unified environment, across different OS, using the same language while being able to share the work progress as well as results and promote the team efforts.
- 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.
February 18, 2020
Anaconda for Python
Anaconda is used mainly for handling different Python versions and packages and being used for data analysis tasks on collaborative projects in the ECE department.
- 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.
February 14, 2020
Anaconda is the best distribution which includes all in one
We use the Anaconda package for physics and engineering research. We get large data in accelerator physics experiments. We use Anaconda for many purpose, but especially for its Python libraries. We have mainly used this platform for data analysis and making a nice plot. Many faculty, staff and students are using it in their research.
- Data analysis.
- Machine learning.
- It is very easy to install and run in any operating system.
- I'm not sure Anaconda needs improvement.
January 22, 2020
All-in-one data science package
We're using it in our department for data-related business needs, data retrieval, data manipulation, data preprocessing, visualization, forecasting, and prediction. So whether the business problem is a simple data analytics problem or complex modeling, Anaconda is used in our department. We use Anaconda for its Python libraries that come as a package, which is great. Not to mention that it eases the pain of updating all packages that would otherwise be carried out one by one for each. We use Panda's library within Anaconda to read, manipulate, preprocess and write the data. We use Numpy for mathematical functions. We use Matplotlib and Seaborn for data analysis and visualization. Finally and most importantly, we use Sci-kit Learn to create predictive models because it contains almost all the algorithms we need. Sadly, it does not contain XGBoost, CatBoost or LBGM however it is easy to install those with Anaconda because that's what Anaconda is for - helping managing all these packages, whether it is installation or simply updating.
- 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.