However, the problem is, how do you pick the best environment for development when there are so many options? Is the one Python editor better than others? Such concerns often become an issue that beginner developers face. They not only make your work much easier as well as logical they also enhance the coding experience and efficiency. There is no better way to build in Python than by using an IDE (Integrated Development Environment). It is very suitable for scientific computing. ![]() Pyzo: It is a cross-platform Python IDE, written based on Python 3.WinPython: The free Python distribution includes common scientific computing packages and Spyder ide development environment, but only supports Windows.At present, it only supports Python 2 version. It is mainly used for engineering projects such as numerical calculation, data analysis, and data visualization. Python(x,y): It is software developed based on Python, QT (graphical user interface), and Spyder (interactive development environment).Anaconda is widely used at present, so it is recommended to install it. In addition to supporting windows, it also supports Linux and MAC systems. Anaconda: This is an open-source Python distribution, which contains more than 180 science packages such as NumPy and SciPy and their dependencies.All the above software packages can be installed by installing an integrated platform. This kind of platform includes common numerical computing and machine learning libraries, such as NumPy, Matplotlib, SciPy, IPython, etc., and it can automatically solve the dependency between packages. First, we need to know what is SciPy stack? In fact, it is an integrated platform for scientific computing software packages.The following describes how to install SciPy packages using the SciPy stack. We need to solve the dependency problem of SciPy, so it is not recommended to use pip to install the SciPy package. Note: when installing SciPy directly using PIP under windows, an error will be reported.Therefore, it is a good choice to install them at the same time. SciPy can be regarded as an extension of the NumPy library, which adds many engineering calculation functions on the basis of NumPy. In real projects, NumPy is usually used together with the SciPy package.Type "help", "copyright", "credits" or "license" for more information. ![]()
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