TrustRadius
Read Matthew Deakyne's full reviewRead Daniel Blazquez's full review Read this authenticated review Read this authenticated review Read Maike Holthuijzen's full review Read SURA SREENIVASULU's full review Read this authenticated review Read Mauricio Quiroga-Pascal Ortega's full review
Read Alejandro Daniel Copati's full reviewRead Luciana Montivero's full review Read Ayush Choukse's full review Read Alexander Lubyansky's full review Read this authenticated review
https://media.trustradius.com/product-logos/zB/ny/E8P9M3TGXBRK.pngAnacondaAnaconda: The Data Science Starter Kit.2019-03-13T01:38:34.007ZAnaconda 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.,7,Anaconda has helped us analyze academic data to inform business decisions.
Anaconda has helped us visualize complex data sets into understandable graphics.
Anaconda has helped us explore machine learning abilities and limitations.,Microsoft Power BI,Power BI For Office 365, OneNote,15,2,Data Analytics. Evaluating data sets.
Data Exploration. Visualizing relationships within data sets.
Reporting. Creating reproducible reports.,Introduction to programming environment. It works well as an introduction, as the install story is really clear.
Data literacy. Helping non-programmers and non-statisticians better understand the capabilities of machine learning and other computing techniques.
Data pipelines. Moving data from one system to another is handled by many different systems - anaconda and python make this fairly easy.,Replacing programs such as Excel.
Generating interactive websites.
Automating reporting across the University.,7,Installation. This is the highlight of anaconda. It's really easy to install.
Launching interactive consoles. It integrates with Jupyter really well.
Getting started. All the libraries you'd need are included.,Managing Python virtual environments.
Version control. You can use git, but better integration would be nice.
Developing interactive websites. It's not really intended for this.,No,8Matthew DeakyneData science experiments in no time2018-11-06T01:26:23.942ZAs 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,10,Extremely quick turnaround time to set up data science experiments
Reduction of troubleshooting time when deploying new packages and dependencies
Low risk environment due to the community edition,Asana, Zoho Books, EclipseDaniel BlazquezAnaconda - The tool to master for Python based data analytic tasks2019-01-17T21:08:38.800ZThe 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.,8,Anaconda helps a lot for standardizing the Python analytic environment, speeding up the research progress, saving a lot of extra setting up time.,MongoDBVerified UserAnaconda - Flexible Enterprise Data Science Solution2018-11-28T18:55:10.664ZAnaconda 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).,8,Centralized repository for all notebooks and analysis projects.
Solved for some security concerns from IT.
Saves money by avoiding ad-hoc computer resources for analysts.,Alteryx Analytics, SAS Advanced Analytics and DataRobot,Tableau Desktop, Google BigQuery, Amazon RedshiftVerified UserA simple and powerful open-source Python distribution2018-07-12T15:27:14.157ZAnaconda 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.,9,It has increased our productivity by allowing us to spend more time on code development and less time on troubleshooting library installation issues.
Because Anaconda helped us increase our efficiency while developing code / statistical models, we were able to complete our research objectives quickly. This allowed us to write manuscripts and publish our results quickly..
Anaconda makes it very easy to share code through Jupyter notebooks. This has been particularly valuable for helping other members of our department (not directly involved with software development) understand what the software developers are doing. This step also doubles as quality control, as a new set of eyes can spot small software bugs. Getting rid of software bugs early is extremely important and helps us save time and money.,RStudio,GitHub, Ubuntu Linux, Microsoft Office 365Maike HolthuijzenAnaconda as an analytics platform for beginners2018-09-06T17:29:39.576ZI 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.,10,I am using the Opensource,KNIME Analytics PlatformSURA SREENIVASULUAnaconda - the easiest and quickest way to get going on ML2018-08-30T17:19:56.334ZWe 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,9,We can get any new employee set-up on Python for Machine learning in minutes, without any assistance from IT. That's real $ savings.
We started to experiment with Machine Learning a lot more, which leads to creating new projects which can have a tremendous impact on the business.,PyCharm,Arm Treasure Data, Snowflake, RStudioVerified UserANACONDA REVIEW2018-03-09T16:06:12.077ZI 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,10,It has helped our organization to work collectively faster by using Anaconda's collaborative capabilities and adding other collaboration tools over.
By having an easy access and immediate use of libraries, developing times has decreased more than 20 %
There's an enormous data scientist shortage. Since Anaconda is very easy to use, we have to be able to convert several professionals into the data scientist. This is especially true for an economist, and this my case. I convert myself to Data Scientist thanks to my econometrics knowledge applied with Anaconda.,Alteryx Analytics, MicroStrategy Analytics and Power BI For Office 365,TensorFlow, Amazon Tensor Flow, Nvidia GridMauricio Quiroga-Pascal OrtegaAnaconda, the Python best way2018-03-16T19:51:22.840ZAnaconda 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.,8,We save a lot of programming time, since all the add-ons are in the same environment.
Being multiplatform, Anaconda is perfect for work teams with many people, since it supports all operating systems.
The only negative thing that can happen is that at first, the way of working in Anaconda can be a bit confusing. Once passed the learning period, its daily use is very comfortable.,PyCharm,PyCharm, MATLABAlejandro Daniel CopatiPython programming made easy2018-03-16T18:47:18.062ZI 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.,8,The project time was really improved, so we got a positive impact in the ROI as the times were shortened.,MySQL and Eclipse,Eclipse, Visual Studio IDE, MATLABLuciana MontiveroThe best platform for Python2016-11-02T21:10:02.976ZIf 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.,9,MySQL, MongoDBAyush ChoukseThe best platform for Python analytics.2016-08-08T16:46:06.873ZI'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.,10,Enthought CanopyAlexander LubyanskyAnaconda for Python library management2017-05-16T15:33:37.733ZAnaconda 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,8Verified UserUnspecified
Anaconda
51 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener noreferrer'>trScore algorithm: Learn more.</a>
Filter Ratings and Reviews
Filter 51 vetted Anaconda reviews and ratings
Reviews (1-13 of 13)
Companies can't remove reviews or game the system. Here's why.
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 05, 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
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.
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).
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.
September 06, 2018
Anaconda as an analytics platform for beginners
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
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
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.
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.
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.
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
Anaconda Scorecard Summary
Feature Scorecard Summary
About Anaconda
Anaconda is an open source Python distribution / data discovery & analytics platform.
Anaconda Technical Details
| Operating Systems: | Unspecified |
|---|---|
| Mobile Application: | No |