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Anaconda is a versatile tool that has found widespread use across various departments and teams within organizations. It is highly …
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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.…
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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
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per month
Starter Tier
$9
Cloud
per month
Business Tier
$50
Cloud
per month per user
Entry-level set up fee?
- No setup fee
For the latest information on pricing, visitwww.anaconda.com/pricing
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Product Demos
Introducing Anaconda Distribution for Python in Excel
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Introducing: Anaconda Assistant
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Anaconda for Open-Source Security with Python and R
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AI Development in the Enterprise with Anaconda's Data Science Platform
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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 |
|---|---|
| Operating Systems | Windows, Linux, Mac |
| Mobile Application | No |
| Supported Countries | Global |
Frequently Asked Questions
Reviewers rate Connect to Multiple Data Sources highest, with a score of 9.8.
The most common users of Anaconda are from Enterprises (1,001+ employees).
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(143)
Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Business Problems Solved
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- 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
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March 13, 2019
Anaconda: The Data Science Starter Kit.
Anaconda is being used for data analysis purposes. We use it to mainly manage python distributions and to preload scientific packages that make working with data very easy. It's used by pockets on campus, mostly those who have research needs. It relieves us from having to purchase expensive software like SAS or SPSS and uses both Python and R.
- 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.
November 28, 2018
Anaconda - Flexible Enterprise Data Science Solution
Anaconda is use by all the different analytics teams at my company. It solves for a unified, easy to install, toolkit with all the base scientific packages an analyst might need.
- 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).
November 06, 2018
Data science experiments in no time
As part of an overall strategic assessment, we decided to test the possibilities of several data lakes by conducting a collection of data science experiments. Anaconda was instrumental to get the data science experiments up and running in record time, including testing different data analysis packages, generation of notebooks, and the sharing of the results to a larger team.
- Integration of the most popular and useful Python packages
- Managing multiple execution environments
- Management of package dependencies
- Easier migration to cloud sharing
September 06, 2018
Anaconda as an analytics platform for beginners
Score 10 out of 10
Vetted Review
Verified User
I use Anaconda, Jupyter notebooks to automate few routine excel sheets for getting desired modifications on them, also I use Python Pandas, other visual libraries for sales data analysis for dashboards, identify opportunities, for business enhancement etc.
- 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.
August 30, 2018
Anaconda - the easiest and quickest way to get going on ML
We use Anaconda for all of our Machine Learning projects in Data Analytics and Reporting department. Primarily we use Jupyter Notebook, Spyder and RStudio functionality to create various machine learning algorithms to solve real world business problems, such as how to keep users in our game longer and how to better monetize their experience.
- 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
Anaconda is used by most members of my department who use Python. Since Anaconda is a cross-platform program, it makes collaboration among Mac/PC/Linux users relatively painless. Anaconda's package management system helps us maintain the most up-to-date Python libraries, which is important for working on code development within our department. Anaconda helps us avoid problems with installing python libraries that sometimes arise when installing libraries using pip. This, in turn, allows us to spend more of our time developing code and building software rather than troubleshoot issues with installing libraries. Anaconda offers several IDEs for python (and R), which makes writing code and debugging easier.
- Installing packages is very easy with Anaconda. Anaconda comes with 'anaconda navigator', a terminal-like utility from which you can easily install R packages and python libraries.
- Launching R and python IDEs as well as Jupyter notebooks from anaconda navigator is simple, and Anaconda makes it very easy to keep these packages up-to-date.
- I really like the fact that if you don't want to install the full version of Anaconda, you can opt to install a lightweight version (called Miniconda) that includes less python libraries and only core conda. I've installed it when I didn't want to take up as much disk space as Anaconda requires, but it works just the same.
- Although I have generally had positive experiences with Anaconda, I have had trouble installing specific python libraries. I tried to remedy the solution by updating other packages, but in the end, things got really messed up, and I ended up having to uninstall and reinstall a total of about 4 times over the past 2 years.
- If you have the free version of Anaconda, there is not much support. Googling questions and error messages are helpful, but there were times when I wished I would have been able to ask technical support to help me troubleshoot issues.
- There were a few times when I tried to install tensorflow and tensorboard via Anaconda on a PC, but I could not get them to install properly. Anaconda allows you to create 'environments' , which allow you to install specific versions of python and associated libraries. You can keep your environments separate so they do not conflict with one another. Anyway, I ended up having to create several 'conda envrionments' just so I could use tensforflow/tensorboard and a few other utilities to avoid errors. This was somewhat annoying, because every time I wanted to run a specific model, I'd have to open up the specific conda environment with the appropriate python libraries.
March 16, 2018
Anaconda, the Python best way
Anaconda is a very useful python environment where you can work on both code projects and jupyter notebooks, all in one place. It is easy to manage python packages and install and uninstall add-ons, without the need to go through the terminal of the device you are using.
I use Anaconda for the development of Neural Networks and for Image Processing, due to the simplicity when installing the packages I need for the development of my works.
- The most useful thing is the Jupyter notebook that Anaconda has inside the platform. You can use your browser to manage them and launch everything from your file system.
- Anaconda exists for Python 2 and Python 3. So, you can use it despite which Python you use.
- It's very easy to install, and it's multiplatform (Windows, OS X, Linux).
- Friendly manage of Python packages.
- Some Python packages are not included to Anaconda, so you have to install them using different ways, like using pip, for example.
- Sometimes you get stuck because Anaconda still have some little bugs.
- Anaconda is a little slow when it's initializing.
March 16, 2018
Python programming made easy
I used Anaconda in my company to program in Python. It is used all across the Information Technology department when using this programming language and Jupyter and Spider. It is specially used to work with libraries as it's made easy with this software!
- It's really easy to use and implement, something that is not always usual with this kind of software
- One of the best things Anaconda does is managing Python libraries and packages
- You can easily install your preferred Python version, something handy considering the differences between the diverse versions of Python
- Sometimes it takes too much time to initialize
- Some of the packages are not already charged so you need to upload them by hand.
March 09, 2018
ANACONDA REVIEW
I use Anaconda myself, for Python, Spider and R. It’s used by the whole organization. In my area, we use Anaconda for importing libraries to train predictive algorithms that help our clients to estimate value sources.
- Anaconda has iPython- Notebook that facilitates code writing in Python
- It's very easy to install tour preferred the Python version
- The risk of messing up the libraries is completely eliminated
- It's hard to get security updates when you leave the system packages
- There're some PyPI packages that Anaconda doesn't have. This obligates the user to package it by herself or using pip
- Anaconda isn't as fast as PyPI publications
May 16, 2017
Anaconda for Python library management
Anaconda Is largely used by myself, not by my organization, for management of my Python and Jupyter packages. Generally it does a pretty good job of importing and updating libraries that I use for utilization within Python programs.
- Manage Python packages
- Install Python and Jupyter notebook frameworks
- Utilization of Python and Jupyter notebook shells
- It is difficult to manage everything when you already have libraries or frameworks installed
- Fairly slow initialization
- Account requirements for some programs
November 02, 2016
The best platform for Python
If you are doing Python analytics, it's possible but nearly pointless to roll your own distribution. There are only two main analytics distributions, and Anaconda is the better one. So use Anaconda. As a distribution, if you are doing other Python stuff, then Anaconda holds a lesser utility.
- Anaconda (i.e. Python with lots of packages and the fabulous iPython/Jupyter Notebook) does analytics well. In analytics, or "data science" or whatever buzzword, you have to pick your poison: Python, R, or SAS. Python is the only one that's good at doing other things as well.
- Like the visualization...The quality of the built-in types of scientific visualization in Python vs. R and their aesthetics is up for grabs. However, Python can do a whole lot of different kinds of visualization above and beyond R. Similarly, JavaScript probably can do more/better visualization than Python, but it's not meant for analytics. Anaconda has enough visualization packages to get you started.
- It's still a little buggy. Especially the launcher.
- It's not always easy to set up. It's not exactly difficult: a Google search away for most things, but silly stuff like path names, installing custom fonts and colors. That kind of thing.
August 08, 2016
The best platform for Python analytics.
Score 10 out of 10
Vetted Review
Verified User
I'm still a bit new here, but we have many different places where we do analytics. Some people like SAS, some like R, some like Python, some like SQL, some like Excel, etc. Anaconda is the "no duh" default distribution for doing analytics or anything scientific in Python. In particular, once you do your pull from the server, it's really necessary to have a powerful tool for data analysis. SQL "can" do a lot of things, but it is just horrible for analytics. Like using vice grips to brush your teeth.
- Anaconda (i.e. Python with lots of packages and the fabulous iPython/Jupyter Notebook) does analytics well. In analytics, or "data science" or whatever buzzword, you have to pick your poison: Python, R, or SAS. Python is the only one that's good at doing other things as well.
- Like visualization...The quality of the built in types of scientific visualization in Python vs. R and their aesthetics is up for grabs. However, Python can do a whole lot of different kinds of visualization above and beyond R. Similarly, JavaScript probably can do more/better visualization than Python, but it's not meant for analytics. Anaconda has enough visualization packages to get you started.
- It's still a little buggy. Especially the launcher.
- It's not always easy to set up. It's not exactly difficult: a Google search away for most things, but silly stuff like path names, installing custom fonts and colors. That kind of thing.