The interpreted high-level programming language is developed for general-purpose programming. With a running course of almost 3 decades, Python has garnered enormous popularity among the programming community. This is because each of them has their own strengths and weaknesses. The text editor supports plugins written in Node. Although Atom is available for a number of programming language, it shows an exceptional love for Python with its interesting data science features. However, you need to install the Data Atom plugin first to access the feature. Furthermore, you can visualize results in Atom without the need of opening any other window. Yet another Atom plugin that will benefit Python data scientists is the Markdown Preview Plus. This provides support for editing as well as visualizing Markdown files, allowing you to preview, render LaTeX equations, etc. It allows you jupyter notebook ide create as well as manipulate notebook documents called notebooks. For Python data scientists, Jupyter Notebook is a must-have as it offers one of the most intuitive and interactive data science environments. Moreover, it is a perfect tool for those just starting out with data science. You can easily see and edit the code with Jupyter Notebook, allowing you to create impressive presentations. By using visualization libraries like Matplotlib and Seaborn, you can display the graphs in the same document as the jupyter notebook ide is in. PyCharm to Python is what Eclipse is to Java. The full-featured Integrated Development Environment is available both in free and paid versions, dubbed Community and Professional editions, respectively. This means you can work easily with array viewers and interactive plots while working on data science projects. This makes it opportune for web development too. Once you finish the installation, PyCharm can be readily used for editing, running, writing, and debugging the Python code. To start with a new Python project, you need to simply open a fresh file and start writing down the code. In addition to offering direct debugging and running features, PyCharm also offers support for source control and full-sized projects. If you have some experience withthen you will know that Rodeo shares many of its traits with it. It comes bundled with the Anaconda package manager, which is the standard distribution of. Build especially for data science projects, Spyder flaunts a smooth learning curve allowing you to jupyter notebook ide it in a flash. The online help option allows you to look for specific information about libraries while side-by-side developing a project. Hence, it is opportune to go for when switching from R to Python. It allows displaying data using a table-based layout. Well, this depends entirely on the kind of requirements you need to fulfill. After working for a decade in Infosys and Sapient, he started his first startup, Leno, to solve a hyperlocal book-sharing problem. He is interested in product marketing, and analytics. His latest venture recommends the best and online programming courses for every programming language. All the tutorials are submitted and voted by the programming community.