Durchschnittliche Bewertung6 Bewertungen
- Gesamt 4.7/5
- Benutzerfreundlichkeit 4.5/5
- Kundenservice 4/5
- Funktionen 5/5
- Preis-Leistungs-Verhältnis 5/5
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Anaconda is platform that allows data scientists to deploy machine learning projects quickly, delivering insights to decision makers
Die hilfreichsten Reviews für Anaconda
Bewertet am 25.1.2020
Data Science platform with extensive functionality and ease of version management
Kommentare: I love Anaconda overall thanks to its extensive set of features, platforms it contains and ability to manage installed libraries and install new ones. Version management is very critical for a developer. For example, I recently needed a function that was brought to Pandas just recently, which was 'Int64' datatype that enabled NaN values for Int datatype in DataFrame. I realized I needed to update Pandas. Rather than going through painful process of library update and management, I have gone through this process with ease.
I loved that:
- Almost all libraries come default with Anaconda, such as Pandas, NumPy, Matplotlib, Seaborn, Sci-kit Learn.
- Even the libraries that does not come default in Anaconda can be easily installed via Anaconda user interface because the namespace contains such information. For example XGBoost, CatBoost, LightGBM, Imbalanced-learn, tsfresh libraries can be installed easily with no need to pip or other command-line interface command.
- Maintenance and update of installed libraries are managed easily and automatically in Anaconda. You can see your installed library's current version and if there's any update, it automatically checks and notify you so that you can update them all with one click only.
- Anaconda comes with platforms such as Jupyter Notebook, Spyder, Orange, VSCode and more. So you can develop your Python/R script in any of those according to your preference.
There are two drawbacks I have seen so far:
- As more and more libraries are installed, Anaconda opening becomes slower.
- Update of libraries at once is relatively slow but I guess that's understandable, comparing it to all the labor otherwise that would be carried out by the developer.
Bewertet am 6.9.2019
Toolkit for Data Science 101
Kommentare: I use Anaconda for 2 years with great satisfaction. It lacks some packages but that's still fine, it has so much credit already.
Vorteile: It comes with fundamental Python libraries for Data Science and Machine Learning such as Pandas for data manipulation, NumPy for linear algebra, Sci-kit Learn for machine learning algorithms, SciPy for statistics tools. This is simply the starter kid. It eases the burden of newbies, removing the effort to install all these manually. Not to mention it comes with the most important data science tool Jupyter Notebook.
Nachteile: It does not contain some packages such as XGBoost, CatBoost, LightGBM, Imbalanced-Learn, MLXtend, etc. These are maybe not Data Science 101 but 201 packages that are needed for more advanced needs.
Bewertet am 4.2.2020
The best Python IDE
Kommentare: Overall I would recommend all programmers that code in the python language to use Anaconda.
Vorteile: The ease of use and the layout of the IDE. Other IDE are not programmer friendly but anaconda is. I especially love the documentation help that the IDE provides. It makes programming a lot easier.
Nachteile: The least I like about this software is nothing at all. I believe this is the best IDE out there for python. It also supports other programming languages too. It would be great if it could compile C++ too.
Bewertet am 8.9.2019
Vorteile: I think this is the best way of setting up Python/ML environments on Windows
Nachteile: A little clunky and heavy. I prefer just using the prompt rather than GUI
Bewertet am 29.12.2019
Matlab users, you have got a new home
Kommentare: In short: easy and productive. Endless possibilities and a programming shell that makes it easy to "learn by doing".
Vorteile: The shell reminds me of Matlab, which made my learning curve easier and faster. I can keep on eye at the values taken by (most) variables in my program and build up the code step by step, testing instructions and "seeing" results as they are stored in memory. Access to Pyhton's statistical and data-science functions is made easy.
Nachteile: It is hard to find drawbacks, but I may mention the fact that the huge number of functions available can make you feel lost at times. Also, context help is good, but can always be improved.