Overview
What is Anaconda?
Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and research.…
The best and easiest data analysis tool
Anaconda: Best IDE for Python
Best IDE for Data Science Projects
Anaconda - a platform made for Py.
Awesome tool for Data Scientists
Advance mathematics operation is easily done …
Anaconda Review
One stop data science destination - Anaconda
Powerful environment to work on what you want with what you want (not ironic!)
Complete Data Science software suit.
Anaconda for Data Science!
Hemant's review of Anaconda
Data Science and Anaconda
Big guns for big scale projects: Anaconda
Anaconda - your one stop platform for data science
Anaconda for beginner data scientists!
Awards
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Popular Features
- Data Transformations (25)9.696%
- Visualization (24)9.696%
- Extend Existing Data Sources (23)8.989%
- Interactive Data Analysis (23)8.989%
Pricing
Free Tier
$0
Starter Tier
$9
Business Tier
$50
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Product Demos
Introducing Anaconda Distribution for Python in Excel
Introducing: Anaconda Assistant
Anaconda for Open-Source Security with Python and R
AI Development in the Enterprise with Anaconda's Data Science Platform
Features
Platform Connectivity
Ability to connect to a wide variety of data sources
- 9.8Connect to Multiple Data Sources(22) Ratings
Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion
- 8.9Extend Existing Data Sources(23) Ratings
Use R or Python to create custom connectors for any APIs or databases
- 9.6Automatic Data Format Detection(21) Ratings
Automatic detection of data formats and schemas
- 9.6MDM Integration(14) Ratings
Integration with MDM and metadata dictionaries
Data Exploration
Ability to explore data and develop insights
- 9.6Visualization(24) Ratings
The product’s support and tooling for analysis and visualization of data.
- 8.9Interactive Data Analysis(23) Ratings
Ability to analyze data interactively using Python or R Notebooks
Data Preparation
Ability to prepare data for analysis
- 8.8Interactive Data Cleaning and Enrichment(23) Ratings
Access to visual processors for data wrangling
- 9.6Data Transformations(25) Ratings
Use visual tools for standard transformations
- 9.7Data Encryption(19) Ratings
Data encryption to ensure data privacy
- 9.6Built-in Processors(20) Ratings
Library of processors for data quality checks
Platform Data Modeling
Building predictive data models
- 9.6Multiple Model Development Languages and Tools(22) Ratings
Access to multiple popular languages, tools, and packages such as R, Python, SAS, Jupyter, RStudio, etc.
- 8.9Automated Machine Learning(21) Ratings
Tools to help automate algorithm development
- 8.9Single platform for multiple model development(23) Ratings
Single place to build, validate, deliver, and monitor many different models
- 9.7Self-Service Model Delivery(18) Ratings
Multiple model delivery modes to comply with existing workflows
Model Deployment
Tools for deploying models into production
- 9.6Flexible Model Publishing Options(20) Ratings
Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.
- 9.5Security, Governance, and Cost Controls(19) Ratings
Built-in controls to mitigate compliance and audit risk with user activity tracking
Product Details
- About
- Integrations
- Competitors
- Tech Details
- FAQs
What is Anaconda?
Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and research. Anaconda also provides enterprise-grade security to open-source software through the Premium Repository.
Anaconda Features
Platform Connectivity Features
- Supported: Extend Existing Data Sources
Data Exploration Features
- Supported: Visualization
- Supported: Interactive Data Analysis
Data Preparation Features
- Supported: Data Transformations
- Supported: Data Encryption
Platform Data Modeling Features
- Supported: Multiple Model Development Languages and Tools
- Supported: Automated Machine Learning
- Supported: Single platform for multiple model development
- Supported: Self-Service Model Delivery
Model Deployment Features
- Supported: Flexible Model Publishing Options
- Supported: Security, Governance, and Cost Controls
Anaconda Competitors
Anaconda Technical Details
Deployment Types | On-premise, Software as a Service (SaaS), Cloud, or Web-Based |
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Operating Systems | Windows, Linux, Mac |
Mobile Application | No |
Supported Countries | Global |
Frequently Asked Questions
Comparisons
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Reviews and Ratings
(141)Community Insights
- Business Problems Solved
- Pros
- Cons
- Recommendations
Anaconda is a versatile tool that has found widespread use across various departments and teams within organizations. It is highly regarded by users for its ability to import libraries, train predictive algorithms, and estimate value sources. This makes it an invaluable tool for data scientists and machine learning engineers who rely on it for real-world problem-solving and project development. Anaconda's package management system is particularly appreciated as it helps maintain up-to-date Python libraries, saving time and avoiding installation issues. Additionally, its cross-platform program facilitates seamless collaboration among Mac/PC/Linux users.
One of the key use cases of Anaconda is in the field of business intelligence and data science. Professionals in this domain utilize Anaconda for analysis, forecasting, and answering critical questions. Organizations also leverage Anaconda to identify the impact of COVID-19 on different products by analyzing customer survey data. The software's robust capabilities make it an ideal choice for managing large-scale projects with multiple dependencies, ensuring reproducibility of analysis, and providing a standardized working environment. Furthermore, Anaconda serves as a comprehensive data analysis environment, particularly when coupled with the user-friendly Jupyter Notebook.
In addition to its applications in data science and business intelligence, Anaconda finds utility in other areas such as banking departments for coding complex tasks like risk prediction and evaluation. It also supports software development objectives by enabling quick setup of development environments for employees. The product is widely used in analytics-based projects, including building small applications with Spyder and reporting and visualization with R and Orange. Moreover, researchers in fields like engineering and geoscience often turn to Anaconda as a research platform for prototyping custom algorithms and sharing progress with teammates.
Overall, Anaconda proves itself as an indispensable tool that streamlines coding workflows, ensures version control, enhances collaboration among teams, simplifies package management, enables efficient scripting in Python, offers a wide range of libraries and packages for various domains, automates routine tasks like excel sheet modifications, and provides a robust environment for data analysis and visualization.
Anaconda as a one-stop destination: Many users have found Anaconda to be a convenient and comprehensive solution for data science and programming tools. It has been praised by multiple reviewers for providing important tools such as Jupyter, Spyder, and R in one platform.
User-friendly interface: The simplicity and ease of use of Anaconda's user interface have been appreciated by many reviewers. They have found it intuitive and easy to navigate through files in Jupyter, as well as install new libraries.
Flexibility in working with Python environments: Users have highlighted the flexibility of Anaconda in working with multiple Python environments based on their requirements. This feature has been found useful for different use cases by several reviewers.
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Slow performance and high resource consumption: Several users have expressed frustration with the slow performance of Anaconda, particularly when it comes to bootstrapping the software and loading its contents. Additionally, some reviewers have mentioned that Anaconda can consume a significant amount of RAM, making it unsuitable for large projects or older machines.
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Difficulty in installing packages and libraries: Many users have encountered challenges when installing specific Python libraries using Anaconda's package manager, conda. Some reviewers had to uninstall and reinstall Anaconda multiple times to resolve issues with library installation. Others found it confusing to work with Anaconda alongside other Python packages and versions on their machine.
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Lack of support and technical troubleshooting difficulties: A number of users have mentioned the lack of support for the free version of Anaconda, making it difficult to troubleshoot issues without technical assistance. Reviewers felt frustrated when encountering software crashes while running code in Anaconda, leading to data loss. They also expressed dissatisfaction with the irregular security updates and the lack of integration with version control tools.
Users commonly recommend Anaconda as an excellent IDE tool for Python developers. They appreciate its user-friendly interface and the positive coding experience it provides. Users also find it easy to manage libraries in different programming languages. Additionally, they value the availability of helpful training materials and tutorials, particularly for beginners in data science and machine learning. As a result, users suggest starting with Anaconda for beginners and using it for projects involving Python programming. Furthermore, they recommend considering PyCharm as a more sophisticated IDE alternative.
Attribute Ratings
Reviews
(1-25 of 28)The best and easiest data analysis tool
- Ease of downloading anaconda
- Open source, anyone can download it
- it used in data science and big data analysis.
- Extensive community support on social media and the internet.
- I wish to add several times in cases when downloading Anaconda such as Spyder.
Best IDE for Data Science Projects
- 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.
Anaconda - a platform made for Py.
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.
Awesome tool for Data Scientists
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.
Anaconda Review
- 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.
One stop data science destination - Anaconda
- 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.
Complete Data Science software suit.
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.
Anaconda for Data Science!
- 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.
Data Science and Anaconda
- 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.
Big guns for big scale projects: Anaconda
- 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
- Responsive
- Containerization of code is fast and easy
- Irregular security updates
- No support for integration with version control tools
Anaconda for beginner data scientists!
- 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
Easily share code with other departments
- 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.
Anaconda turned a group into a team
Anaconda: A data scientist's best friend
Anaconda - The tool to master for Python based data analytic tasks
- 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.
Anaconda for Python
- 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.
Anaconda is the best distribution which includes all in one
- Data analysis.
- Machine learning.
- It is very easy to install and run in any operating system.
- I'm not sure Anaconda needs improvement.
All-in-one data science package
- 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.
Anaconda: The Data Science Starter Kit.
- Clear install story. There are a lot of ways to install python. There's only one way to install anaconda. This makes teaching and standardizing much easier.
- Batteries included. It's easy to install things in python, but anaconda ships with most of what you need out of the box. This helps with standardization and reproducibility.
- Good integrations with Jupyter and other visual tools. Jupyter is really convenient when learning various python packages. Anaconda makes these tools easy to launch and to use.
- Doesn't play well with other Python. I use python for more than data science, and whenever I have multiple versions of python on my machine —some using conda, some using Pipenv, some using poetry— it can get really confusing. If Anaconda is all you use, then it works really well.
- Not all packages are available in Anaconda. Conda install doesn't always work for all PyPI packages. This adds to the frustration above - as you have to install some packages outside of conda, and then figure out how to use them internally.
- Visualizations don't always work like you'd hope. This is getting better, but creating interactive graphics doesn't always work well in this context.
Anaconda - Flexible Enterprise Data Science Solution
- Package Management. Some packages are difficult to install on different platforms. This is simplified with Anaconda.
- Dedicated servers. More control over security.
- Collaboration. Analysts can interact with and checkout notebooks and datasets.
- Requires dedicated administration.
- Expensive.
- Removes some control from end-users (analysts).
Less useful for quick exploratory analysis. Need to host datasets outside of local.
Data science experiments in no time
- Integration of the most popular and useful Python packages
- Managing multiple execution environments
- Management of package dependencies
- Easier migration to cloud sharing
Anaconda as an analytics platform for beginners
- Anaconda is one platform with all the necessary toolset for data analysis. It is very easy to set up on any OS.
- Adding new libraries and configuring them is quite simple and easy. Traditionally this task using command prompt is not simple. Updating the existing libraries is also easy.
- I experimented many platforms and tools before deciding on Anaconda as this platform helps with lots of business insights for enhancing my work function.
- Sharing the dashboards to team with very good visuals is easy as Jupyter Notebooks offers various forms of sharing.
- For someone who is new, the learning curve is very simple. Excellent community help.
- I am not sure as I have not explored more on Anaconda platform if we can create workflows of different tasks on data using pre-defined templates. For example, KNIME offers this kind of approach.
- Once some analysis is completed and if the result has to be presented, currently I see the only option is sharing the Jupyter notebook.
Anaconda - the easiest and quickest way to get going on ML
- Everything is in one place, so it's very convinient
- It's easy to switch between multiple functionalities
- Performance and Speed - Python and R run smoothly and efficiently.
- User Interface could be a little bit more clearer.
- Error messaging can definitely be improved