Anaconda Reviews

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Reviews (1-22 of 22)

Juande Santander-Vela | TrustRadius Reviewer
March 17, 2020

Good way to standardize Python-based developments & support for desktop users.

Score 9 out of 10
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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.
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Ryan McGarry | TrustRadius Reviewer
March 04, 2020

Anaconda provides a vast array of compilers and tools for every developer - and is easy enough for non-developers to setup.

Score 9 out of 10
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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.
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Xiaotong Song | TrustRadius Reviewer
March 03, 2020

Anaconda turned a group into a team

Score 9 out of 10
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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.
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Anonymous | TrustRadius Reviewer
May 01, 2020

Easily share code with other departments

Score 9 out of 10
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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.
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Anonymous | TrustRadius Reviewer
February 18, 2020

Anaconda - The tool to master for Python based data analytic tasks

Score 7 out of 10
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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.
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Anonymous | TrustRadius Reviewer
February 18, 2020

Anaconda for Python

Score 10 out of 10
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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.
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Anonymous | TrustRadius Reviewer
January 22, 2020

All-in-one data science package

Score 10 out of 10
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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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Anonymous | TrustRadius Reviewer
February 28, 2020

Anaconda: A data scientist's best friend

Score 10 out of 10
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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.
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Anonymous | TrustRadius Reviewer
February 14, 2020

Anaconda is the best distribution which includes all in one

Score 10 out of 10
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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.
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Anonymous | TrustRadius Reviewer
November 19, 2019

Anaconda - a platform made for Py.

Score 8 out of 10
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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.
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Matthew Deakyne | TrustRadius Reviewer
March 13, 2019

Anaconda: The Data Science Starter Kit.

Score 7 out of 10
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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.
Anaconda is excellent if all you do is data science. If you are already a python developer, then it may be more frustrating having multiple ways to manage your distribution and your packages. It has very clear use cases and makes starting off in data science much easier than figuring out all you'd need to install in Vanilla Python.
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Daniel Blazquez | TrustRadius Reviewer
November 05, 2018

Data science experiments in no time

Score 10 out of 10
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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
Anaconda shines if you need to set up a data analysis or data science lab in no time. Newcomers to Python or Jupiter can be up and running in minutes and playing with the most popular packages. I think the Anaconda Cloud package could benefit from some UX improvements to clarify the migration process. Integration with external data sources could be improved as well.
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Maike Holthuijzen | TrustRadius Reviewer
July 12, 2018

A simple and powerful open-source Python distribution

Score 9 out of 10
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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.
Anaconda is great for academic and private organizations that cannot afford more expensive Python/R package managers. Also, it is more appropriate for intermediate to advanced Python users--Anaconda can be somewhat frustrating for beginners, as it takes some practice to get comfortable with the workflow. I find it particularly useful for working in teams, because if everyone uses the same package manager, it is easier to troubleshoot issues and makes for reproducible research. For wealthier organizations, a premium package management system (with tech support) would be ideal. Anaconda is also great for people working independently on code development.
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SURA SREENIVASULU | TrustRadius Reviewer
September 06, 2018

Anaconda as an analytics platform for beginners

Score 10 out of 10
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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.
Anaconda is a very good choice for all the beginners who are new to analytics.
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Anonymous | TrustRadius Reviewer
November 28, 2018

Anaconda - Flexible Enterprise Data Science Solution

Score 8 out of 10
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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).
Useful for collaborating across multiple teams on data projects. Also great for distributed workflows which require more processing power than a local machine.

Less useful for quick exploratory analysis. Need to host datasets outside of local.
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Anonymous | TrustRadius Reviewer
August 30, 2018

Anaconda - the easiest and quickest way to get going on ML

Score 9 out of 10
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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
If your organization is reliant on Machine Learning to solve real world business problems, Anaconda is very well suited for that need. It can be a bit of a pain to install all the necessary dependencies for Python to do Machine Learning. Anaconda takes care of all the installation of appropriate libraries. If you're organization is reliant on GitHub or other code repositories, it's a bit cumbersome to have that in Anaconda, so it might not be the solution for you.
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Mauricio Quiroga-Pascal Ortega | TrustRadius Reviewer
March 09, 2018

ANACONDA REVIEW

Score 10 out of 10
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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
Anaconda is the best solution when you need to make more basic algorithm training. However, when the client necessity if completely new or there're poor libraries, anaconda is too basic.

When designing algorithms, I find ai-one to be very useful. Other tools that more suitable than Anaconda for more complex tasks are protege, biffblue and Nervana Neon
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Alejandro Daniel Copati | TrustRadius Reviewer
March 16, 2018

Anaconda, the Python best way

Score 8 out of 10
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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.
Anaconda is highly recommended for all types of programmers who use Python.

For beginners and advanced users, it is perfect because it helps maintain the order of programs and projects, it is extremely easy to get and download packages, and the way in which the environment handles libraries is friendly.

For professional users, all the above applies, and also allows large developments and projects do not lose their functionality or modularization, because the program is responsible for managing all this and not the user.
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Luciana Montivero | TrustRadius Reviewer
March 16, 2018

Python programming made easy

Score 8 out of 10
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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.
When using different Python libraries and frameworks, this is software you are looking for. Besides the bugs, it's easy to use, and not as hard as it could be to set up. Also, it's great for analytics. But when doing complex projects perhaps you should think about using something else.
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Ayush Choukse | TrustRadius Reviewer
November 02, 2016

The best platform for Python

Score 9 out of 10
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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.
It eats everything and everything has a wrapper or API for it. Python is lingua franca by now, more than Java.
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Alexander Lubyansky | TrustRadius Reviewer
August 08, 2016

The best platform for Python analytics.

Score 10 out of 10
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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.
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.
Read Alexander Lubyansky's full review
Anonymous | TrustRadius Reviewer
May 16, 2017

Anaconda for Python library management

Score 8 out of 10
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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
Anaconda is great for managing Python libraries and frameworks. It does get a little convoluted when you already have some packages installed, though, and managing the different PATHs and versions of things is fairly annoying. It is much better to have anaconda perform a fresh install of Python and to have it manage all your Python needs, much less confusing.
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Feature Scorecard Summary

Connect to Multiple Data Sources (9)
9.0
Extend Existing Data Sources (9)
9.0
Automatic Data Format Detection (8)
8.2
MDM Integration (5)
8.2
Visualization (11)
8.4
Interactive Data Analysis (11)
8.0
Interactive Data Cleaning and Enrichment (10)
8.5
Data Transformations (11)
9.0
Data Encryption (9)
7.4
Built-in Processors (8)
8.2
Multiple Model Development Languages and Tools (9)
9.3
Automated Machine Learning (9)
7.8
Single platform for multiple model development (10)
8.8
Self-Service Model Delivery (7)
8.2
Flexible Model Publishing Options (7)
8.5
Security, Governance, and Cost Controls (6)
8.3

About Anaconda

Anaconda is an open source Python distribution / data discovery & analytics platform.

Anaconda Technical Details

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