Anaconda vs. PyCharm

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Anaconda
Score 8.4 out of 10
N/A
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.
$0
per month
PyCharm
Score 9.1 out of 10
N/A
PyCharm is an extensive Integrated Development Environment (IDE) for Python developers. Its arsenal includes intelligent code completion, error detection, and rapid problem-solving features, all of which aim to bolster efficiency. The product supports programmers in composing orderly and maintainable code by offering PEP8 checks, testing assistance, intelligent refactorings, and inspections. Moreover, it caters to web development frameworks like Django and Flask by providing framework…
$9.90
per month per user
Pricing
AnacondaPyCharm
Editions & Modules
Free Tier
$0
per month
Starter Tier
$9
per month
Business Tier
$50
per month per user
Enterprise Tier
60.00+
per month per user
For Individuals
$99
per year per user
All Products Pack for Organizations
$249
per year per user
All Products Pack for Individuals
$289
per year per user
For Organizations
$779
per year per user
Offerings
Pricing Offerings
AnacondaPyCharm
Free Trial
NoYes
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
YesNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Community Pulse
AnacondaPyCharm
Considered Both Products
Anaconda
Chose Anaconda
Anaconda is way easier to set-up. On Anaconda we have users working on Machine Learning in minutes, where on PyCharm is takes a lot longer to set-up and often involves getting help from IT. PyCharm is easier to integrate with Code repositories (such as GitHub), so if that's …
Chose Anaconda
Anaconda is very strong in the environment and version control that make data science work much easier. The only thing that might be comparable to Anaconda would be using Kubernetes to control Docker. Another potential improvement would be replacing spyder with PyCharm and Atom …
Chose Anaconda
I know that PyCharm is a IDE and Anaconda is a distribution. However I use Anaconda largely due to Jupyter Notebook, which more or less does the same job as PyCharm. 1 year ago I decided to use Anaconda (Jupiyer Notebook) as it is easier to use it as a beginner(at least my …
Chose Anaconda
Some analyzed tools, such as PyCharm and Spyder, are simpler to use but still do not have all the libraries needed for those starting out in data science--or in institutions that need to grow in that direction. Anaconda is more robust but stable, more complete, and the …
Chose Anaconda
It is almost dishonest to compare Anaconda with PyCharm as they do different things in their basic forms unless you spend a lot of time configuring plugins on your PyCharm environment. Anaconda has a lot of things ready and you just need to install your libs and dependencies.
Chose Anaconda
There are several reasons why Anaconda is better to use for me including that it is much easier to use than Baycharm. Also, the user interface is not as complicated as that of Baycharm. Even Anaconda does not slow down my device, using PaySharm slowed down my device in an …
Chose Anaconda
In Anaconda, [it is easy] to find and install the required libraries. Here, we can work on multiple projects with different sets of the environment. [It is] easy to create the notebook for developing the ML model and deployment. Right now, it is the best data science version …
Chose Anaconda
On top of all the software that I have used, Anaconda is the best because in Anaconda we have built-in packages that provide no headache to install packages and we can design a separate environment for different projects. Anaconda has versions made for special use cases. …
Chose Anaconda
Free ware, better design ease of use
Chose Anaconda
Compare Anaconda to Unix coding system. You can use PIP to install and create requirement.txt to replace environment.yml to avoid using Anaconda. However, Anaconda is such an excellent tool to maintain your environment and check the version of your package and update the …
Chose Anaconda
I like SpyDER, which comes with Anaconda better for its intuitive layout and variable explorer options.
Chose Anaconda
Anaconda is the best Python environment because you have all the things you need all in one places, at the reach of your hand. You can download and manage libraries as you wish and is very easy to create new projects and API's for all your stuff.

It's Multiplatform so you don't …
PyCharm
Chose PyCharm
What differentiates PyCharm from other products is that it is built for a particular language (Python) and works great while doing it, without losing efficiency with the rest of languages. It's simple, easy to use, fast and efficient, what else could you need?
Chose PyCharm
PyCharm is the best tool to switch between different projects. One can connect to various technologies at a time. Package and plugin installation is easy. Dark and light mode helps in working according to the mood. One can extend it to IntelliJ, depending on the need for custom …
Chose PyCharm
PyCharm was selected due to it's first class treatment of Python. Visual Studio is more general "Do everything" IDE which contains a lot of features our team didn't need. PyCharm strikes the balance of power and complexity.
TrustRadius Insights
AnacondaPyCharm
Highlights

TrustRadius
Research Team Insight
Published

PyCharm and Anaconda are both tools used to aid Python developers.  Though they are independent tools, PyCharm and AnaConda can be used together for projects that can benefit from both tools.  PyCharm is an IDE built to make it easier to write Python code, by providing a text editor and debugging, among other features.  Anaconda is a Python distribution focused on data driven projects. Both tools popular with businesses of all sizes that use Python.

Features and Limitations

PyCharm and Anaconda both provide specialized features for Python development, but provide different base functionalities.

PyCharm is an IDE, meaning it is interfaced with directly by developers writing Python code.  PyCharm provides a text editor including coding assistance features such as code navigation through search, and color coding.  Additionally, PyCharm provides support for multiple platforms, as well as complementary front end coding languages such as HTML and JavaScript.  In essence, PyCharm is designed to make it as easy as possible to code in Python, though it does not include any packages by default.  PyCharm also includes built-in support for Anaconda.

Anaconda includes a basic text editor, but its primary role is that of a Python distribution.  Projects using Anaconda can access data science packages of their choice from a library of over 400 popular packages.  Data science projects can use Anaconda to easily load packages to save time and reduce written code.  Anaconda is an ideal tool for performing data science tasks whether a business is using PyCharm or not, but it isn’t ideal for non-data oriented projects.

Pricing

PyCharm professional is priced at $199.00 per year, though its price reduces each year beyond the first. 

Anaconda is free to use for individuals, but pricing for teams starts at $10,000.  Enterprises that need unique features such as custom repositories can reach out to the vendor for a quote.

Features
AnacondaPyCharm
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Anaconda
9.3
25 Ratings
10% above category average
PyCharm
-
Ratings
Connect to Multiple Data Sources9.822 Ratings00 Ratings
Extend Existing Data Sources8.024 Ratings00 Ratings
Automatic Data Format Detection9.721 Ratings00 Ratings
MDM Integration9.614 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Anaconda
8.5
25 Ratings
1% above category average
PyCharm
-
Ratings
Visualization9.025 Ratings00 Ratings
Interactive Data Analysis8.024 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Anaconda
9.0
26 Ratings
9% above category average
PyCharm
-
Ratings
Interactive Data Cleaning and Enrichment8.823 Ratings00 Ratings
Data Transformations8.026 Ratings00 Ratings
Data Encryption9.719 Ratings00 Ratings
Built-in Processors9.620 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Anaconda
9.2
24 Ratings
8% above category average
PyCharm
-
Ratings
Multiple Model Development Languages and Tools9.023 Ratings00 Ratings
Automated Machine Learning8.921 Ratings00 Ratings
Single platform for multiple model development10.024 Ratings00 Ratings
Self-Service Model Delivery9.019 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Anaconda
9.5
21 Ratings
10% above category average
PyCharm
-
Ratings
Flexible Model Publishing Options10.021 Ratings00 Ratings
Security, Governance, and Cost Controls9.020 Ratings00 Ratings
Best Alternatives
AnacondaPyCharm
Small Businesses
Jupyter Notebook
Jupyter Notebook
Score 8.9 out of 10
IntelliJ IDEA
IntelliJ IDEA
Score 9.4 out of 10
Medium-sized Companies
Posit
Posit
Score 9.9 out of 10
IntelliJ IDEA
IntelliJ IDEA
Score 9.4 out of 10
Enterprises
Posit
Posit
Score 9.9 out of 10
IntelliJ IDEA
IntelliJ IDEA
Score 9.4 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
AnacondaPyCharm
Likelihood to Recommend
10.0
(38 ratings)
8.7
(42 ratings)
Likelihood to Renew
7.0
(1 ratings)
10.0
(2 ratings)
Usability
9.0
(3 ratings)
9.0
(4 ratings)
Support Rating
8.9
(9 ratings)
8.3
(13 ratings)
User Testimonials
AnacondaPyCharm
Likelihood to Recommend
Anaconda
I have asked all my juniors to work with Anaconda and Pycharm only, as this is the best combination for now. Coming to use cases: 1. When you have multiple applications using multiple Python variants, it is a really good tool instead of Venv (I never like it). 2. If you have to work on multiple tools and you are someone who needs to work on data analytics, development, and machine learning, this is good. 3. If you have to work with both R and Python, then also this is a good tool, and it provides support for both.
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JetBrains
PyCharm is well suited to developing and deploying Python applications in the cloud using Kubernetes or serverless pipelines. The integration with GitLab is great; merges and rebates are easily done and help the developer move quickly. The search engine that allows you to search inside your code is also great. It is less appropriate for other languages.
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Pros
Anaconda
  • Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
  • Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
  • Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.
Read full review
JetBrains
  • Git integration is really essential as it allows anyone to visually see the local and remote changes, compare revisions without the need for complex commands.
  • Complex debugging tools are basked into the IDE. Controls like break on exception are sometimes very helpful to identify errors quickly.
  • Multiple runtimes - Python, Flask, Django, Docker are native the to IDE. This makes development and debugging and even more seamless.
  • Integrates with Jupyter and Markdown files as well. Side by side rendering and editing makes it simple to develop such files.
Read full review
Cons
Anaconda
  • 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.
Read full review
JetBrains
  • The biggest complaint I have about PyCharm is that it can use a lot of RAM which slows down the computer / IDE. I use the paid version, and have otherwise found nothing to complain about the interface, utility, and capabilities.
Read full review
Likelihood to Renew
Anaconda
It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
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JetBrains
It's perfect for our needs, cuts development time, is really helpful for newbies to understand projects structure
Read full review
Usability
Anaconda
I am giving this rating because I have been using this tool since 2017, and I was in college at that time. Initially, I hesitated to use it as I was not very aware of the workings of Python and how difficult it is to manage its dependency from project to project. Anaconda really helped me with that. The first machine-learning model that I deployed on the Live server was with Anaconda only. It was so managed that I only installed libraries from the requirement.txt file, and it started working. There was no need to manually install cuda or tensor flow as it was a very difficult job at that time. Graphical data modeling also provides tools for it, and they can be easily saved to the system and used anywhere.
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JetBrains
It's pretty easy to use, but if it's your first time using it, you need time to adapt. Nevertheless, it has a lot of options, and everything is pretty easy to find. The console has a lot of advantages and lets you accelerate your development from the first day.
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Support Rating
Anaconda
Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.
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JetBrains
I rate 10/10 because I have never needed a direct customer support from the JetBrains so far. Whenever and for whatever kind of problems I came across, I have been able to resolve it within the internet community, simply by Googling because turns out most of the time, it was me who lacked the proper information to use the IDE or simply make the proper configuration. I have never came across a bug in PyCharm either so it deserves 10/10 for overall support
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Alternatives Considered
Anaconda
I have experience using RStudio oustide of Anaconda. RStudio can be installed via anaconda, but I like to use RStudio separate from Anaconda when I am worin in R. I tend to use Anaconda for python and RStudio for working in R. Although installing libraries and packages can sometimes be tricky with both RStudio and Anaconda, I like installing R packages via RStudio. However, for anything python-related, Anaconda is my go to!
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JetBrains
When it comes to development and debugging PyCharm is better than Spyder as it provides good debugging support and top-quality code completion suggestions. Compared to Jupiter notebook it's easy to install required packages in PyCharm, also PyChram is a good option when we want to write production-grade code because it provides required suggestions.
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Return on Investment
Anaconda
  • 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.
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JetBrains
  • PyCharm has a very positive ROI for our BU. It has increased developer productivity exponentially.
  • Software quality has significantly improved. We are able to refactor/test/debug the code quicker/faster/better.
  • Our business unit is able to deliver faster. Customers are happier than ever.
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