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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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Anaconda is a versatile tool that has found widespread use across various departments and teams within organizations. It is highly …
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Popular Features

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  • Data Transformations (25)
  • Visualization (24)
  • Extend Existing Data Sources (23)
  • Interactive Data Analysis (23)
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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

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Platform Connectivity

Ability to connect to a wide variety of data sources

Avg 8.4

Data Exploration

Ability to explore data and develop insights

Avg 8.4

Data Preparation

Ability to prepare data for analysis

Avg 8.2

Platform Data Modeling

Building predictive data models

Avg 8.5

Model Deployment

Tools for deploying models into production

Avg 8.6
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Product Details

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 Integrations

Anaconda Technical Details

Deployment TypesOn-premise, Software as a Service (SaaS), Cloud, or Web-Based
Operating SystemsWindows, Linux, Mac
Mobile ApplicationNo
Supported CountriesGlobal

Frequently Asked Questions

Dataiku, Domino Enterprise MLOps Platform, and Posit are common alternatives for Anaconda.

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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Reviews and Ratings


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!

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.

  1. 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.

  2. 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.

  3. 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


(1-25 of 37)
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Ammar Aboalrub | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Since my beginnings in the programming path I have been using it, and it is very difficult to do without it. It provided me with some software that I needed such as Spyder Editor and its scientific script because you will need it in most of your projects such as NumPy, Dusk, Pandas, Matplotlib, and others.
  • 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.
We said Anaconda for Python does data science activities, Anaconda for Python does it perfectly, and it's open-source too, Anaconda includes many very suitable for beginners standard data science packages and science libraries inside. It is easy to install on any operating system you want, and it is considered the best data science version control tool at present.
Score 9 out of 10
Vetted Review
Verified User
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
It is an excellent IDE for writing Python scripts. It supports too many libraries and APIs, and provides very good help.
Zayed Rais | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
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.
Anaconda is well suited for data science projects. If you are working with multiple projects, it [is] easy to build different environments for the requirements of the project. Easy interaction with [the] notebook for data collection, pre-processing, transforming, training, and visualizing. Sometimes, we are unable to update the libraries due to some security patches.
Score 7 out of 10
Vetted Review
Verified User
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.
  • 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.
A must-try for smaller data analytics teams who seek project reproducibility, multiple language support and extensive community support. For bigger teams, consider the enterprise version, which makes it easy for app, API deployment, authentication, custom repository, and sharing of work spaces.
Jay Thakkar | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
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.
  • 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.
Started with learning Python through Jupytor Notebook.
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.
June 28, 2021

Anaconda Review

Dilip Jain | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
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.
To design an end-to-end solution or machine learning model, Anaconda is the one that can easily manage all the libraries and we can set the environment according to the project requirement. 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. But in the case of designing the analytics dashboards and all then we give less priority to Anaconda but we can use analytics tools like Tableau or PowerBI.
Score 9 out of 10
Vetted Review
Verified User
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.
As a Data Analyst, it is my job to analyze large datasets using complex mathematical models. Anaconda provides a one-stop destination with tools like PyCharm, Jupyter, Spyder, and RStudio. One case where it is well suited is for someone who has just started his/her career in this field. The ability to install Anaconda requires zero to little skills and its UI is a lot easier for a beginner to try. On the other hand, for a professional, its ability to handle large data sets could be improved. From my experience, it has happened a lot that the system would crash with big files.
Gabriel Krahn | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
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.
Anaconda is a good choice when you have to build different environments to perform different tasks (for example, one environment with Python 3.7 + TensorFlow and the other with Julia + TensorFlow.jl or even Flux). The fact that it supports an easy switch between different environments (if you ignore the part about your installation getting bigger and bigger) is a big win situation.
Score 10 out of 10
Vetted Review
Verified User
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.
  • 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.
This will suit to any kind of work now days. We have built many data science applications using Anaconda Navigator. This is very easy to use and can be used for any work. We have used it for Image processing projects and worked very much accurately as we were able to install all the latest packages.
Score 10 out of 10
Vetted Review
Verified User
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.
Being a Data Science and Analyst professional Anaconda is the go to place for all the softwares.
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.
Score 7 out of 10
Vetted Review
Verified User
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
[Anaconda] is appropriate if you have a employee force of more than ten people it helps in automating the work of setting up the systems so that people can work. It is very helpful and reduces a lot of time which is wasted on doing something which is not productive.
Fernanda Ministerio | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
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

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.

Score 7 out of 10
Vetted Review
Verified User
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.
Anaconda is definitely good when it comes to large-scale projects in python requiring different versions of python as a dependency on project packages and use cases. It consumes heavy memory and is not suitable for smaller projects and is likely overkill for the same. If the user is new to anaconda, it takes time to get comfortable with it.
Score 9 out of 10
Vetted Review
Verified User
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
Anaconda is best suited for small to medium-sized projects. It can help you to quickly set up a data analytics environment to work on with Python and R programming languages.
Score 9 out of 10
Vetted Review
Verified User
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
Anaconda is great for setting a standard development environment for beginners. it is a very complete base deployment and does not require anything else to start running some basic datascience packages. while it allows you to install the packages via 'conda' the packages are not always the latest compared to pip.
April 16, 2021

Review for Anaconda

Tigran Petrosyan | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
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
Anaconda have a lot of scientific libraries of Python which I use in my everyday work (Pandas, Numpy, Seaborn, matplotlib, etc). Jupiter Notebook is a best option for me if I have small tasks or small projects which I must do using Python. However if I have large projects I prefer to use PyCharm.
Score 9 out of 10
Vetted Review
Verified User
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.
Anaconda (specifically Jupyter Notebook) is well suited for sharing code with other teams that are less programming-focused. It is very easy for me to open a pre-written code notebook, enter some values in the initial inputs box, press Run, and see my results. It is simple enough to open the notebook, and run the code, so we can use it in our production area and train the assembly technicians on how to operate it. If the code is properly written, it is easy to know when something went wrong.
Juande Santander-Vela | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
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.
Anaconda, and Miniconda, are easy to deploy, scientifically-ready Python distributions, especially well-suited for the fields of science, astronomy, and engineering. We are using Miniconda for finer-grained customization of environments in containers for deployment.

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.
Ryan McGarry | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
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.
Anaconda is excellent for quickly setting up new users on a software development team, and providing them with necessary dependencies.
Xiaotong Song | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
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.
  • Free
  • Integration
  • clear ENV
If you are a data scientist or data engineer who is not only doing ad hoc analysis by oneself. You must have used Anaconda to maintain a clean python environment for others to recreate so that the team of people from other teams can work on the same project.
Score 10 out of 10
Vetted Review
Verified User
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.
  • Virtual environment
  • Version control
  • One spot for data science tools
Anaconda is the best data science version control tool in the market. With Anaconda, you can easily create, remove, and switch environments to run different scripts. What is more, you can also use it to export the current environment automatically into yaml file that can be used to recreate the same environment.
Score 7 out of 10
Vetted Review
Verified User
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.
Anaconda is very suitable for a research team/lab/department which has many data scientists who want to apply some Python-based analytic programming and want to cooperate in sharing the results easily. It is not very well suited for final production environment deployment.
February 18, 2020

Anaconda for Python

Score 10 out of 10
Vetted Review
Verified User
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.
Best suited for getting started with Python, handling different libraries, version control, etc. I have not found any deal-breaking shortcomings yet.
Score 10 out of 10
Vetted Review
Verified User
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.
Anaconda is an all-in-one package. It is free and easy to install in any operating system. I'm using Anaconda in physics and engineering-related results. It is very suitable in data analysis too. You can work with big data in a very simple way. It has Spyder, Jupyter and many more installed in one platform. Jupyter Notebook is very nice feature in Anaconda. I believe it is appropriate for every problem and in every field.
Score 10 out of 10
Vetted Review
Verified User
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.
For anyone who's starting data analytics, Anaconda is great because you don't have to deal with installing and knowing all these Python and R packages yourself. Moreover, you learn them as you start using them via Anaconda. If you're going to read, manipulate, preprocess, and write data, Anaconda is great. If you need data visualization, Anaconda has Jupyter Notebook, as well as Matplotlib, and Seaborn. If you need forecasting and prediction, whether it is classification or regression or even unsupervised learning, Anaconda provides the Sci-kit Learn library. Furthermore, you can install Catboost, XGBoost, LGBM via Anaconda, which uses the Sci-kit Learn interface.
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