It is not a surprise anymore that Python is one of the most popular, successful, and widely used programming languages among developers and data scientists. There are numerous reasons behind that, but primarily:
First, the Python programming language has a very simple syntax equivalent to mathematics that can be easily learned and grasped with enough practice. Second, it offers amazing libraries, tools, etc. for data science and computing.
Python is adored because of its versatility, ease of access, and usage. The language creates wonders in the worlds of Data Science, Machine Learning, Computing, Artificial Intelligence, IoT and much more.
There are various reasons beyond Python being a go-to programming language. In this article, we will talk about the most widely used tools for Python development across the world. So if you are a beginner or looking forward to learning about the best tool for Python development, then this is the right place for you.
Jupyter notebook is a popular open source tool. The name Jupyter is a representation of the programming languages Julia, Python, and R, which it supports. This tool can be used for interactive development, sharing documentation containing live code, code execution, and creating visualizations. Jupyter Python works on a console-based approach for documentation authoring, and also provides you with the ability to add notes along with the written code.
This tool is widely used by data engineers, data scientists, and machine learning engineers to solve complex problems related to data. Because Jupyter solves and simplifies the creation and execution of code, despite running many lines of code, you will only have to execute one cell of code.
If you are new to Jupyter and want to give it a trial run, then you might want to visit here. And you don’t even need to install it to try it, as it runs on a web browser, and you can host your code on a local machine or local server as well.
Let’s understand a bit more about Jupyter Notebook:
- Text and HTML: You can insert plain text or annotated text in markdown syntax to generate HTML, at any point in the document. CSS styles can also be used in templates to generate the notebook.
- Code and Output: Imagine a place where you can write your code and see the output right there; Jupyter was built for that. Jupyter Notebook is made for Python coding, but you can add support for any other language such as Julia, R and execute the code. The output will appear immediately after the code, which helps a lot, as you can run a block of code anytime and in any order you wish to.
- Visualizations: You can also generate graphs and maps from code, by using the modules, Plotly, Bokeh, or Matplotlib. Just like your code output, these graphs or charts will appear immediately after the code. However, if the outcome is required, we can configure the code to appear in other files.
- Data: You can provide data in.ipynb format, or it can also be imported programmatically into Notebook, for example, to download it from a public repository or to make a connection and access it via a database.
- Multimedia: Jupyter is built on web technology, so it can support and display all types of multimedia supported on a web page.
Benefits of the Jupyter Notebook
- Data Visualization: Jupyter Notebook is used to render the data as graphics, which is pretty amazing.
- Live interaction with code: Jupyter code can be edited, and the outcome can be changed however a coder wants by manipulating the code.
- Documentation: Documentation is very easy in Jupyter, you can explain the code, line by line along with your feedback. Even after an interactive explanation, your code will be functional.
- Code Sharing: With Jupyter, you can view your code, execute it and display the results directly on your browser.
PyCharm IDE is one of the best development tools for Python. This IDE consists of a smart code editor, intuitive auto completion, navigation, a GUI based test runner, a powerful debugger, and much more. It also supports testing assistance, intuitive automated code refactoring, and code inspection.
To extend its scientific computations, NumPy and Matplotlib can be easily integrated.
Benefits of PyCharm
- Intelligent Python Assistance: By providing smart code completion, code inspections, rich navigation abilities, quick fixes, and automated code refactoring, this IDE is the best to work with.
- Scientific Tools: PyCharm can be integrated with IPython Notebook, and it has an interactive console that supports Anaconda and many scientific packages, including NumPy and Matplotlib.
- Web Development Frameworks: PyCharm offers support for the most popularly used frameworks such as Django, Pyramid, Google App Engine, Flask, etc.
- Built-in Development Tools: PyCharm also provides built-in developer tools by integrating debuggers and test runners. One of them is Python Profiler, which is a built-in terminal tool.
- Remote Development: You can run, test, debug, or deploy applications on remote hosts, virtual machines, or environments with remote interpreters. which is an integrated SSH terminal.
Francois Chollet, a Google engineer, made Keras, an open-source, high-level neural network library, in 2015. It is a deep learning API that is written in Python. This tool is used to support neural network computations and create a deep learning network. This tool is modular, user-friendly, and easily extensible.
As an open-source tool, it encourages contributors to make ongoing improvements by making neural network creation easier. Furthermore, troubleshooting is made easy by actionable and detailed error messages.
Advantages of Keras:
- User-Friendly and Fast Deployment of Code: Keras is a user-friendly neural network API, which is the best for implementing NLP and deep learning algorithms. NLP can be built in a few lines of code with accessible parameters and functions.
- Quality Documentation: Keras has one of the best-documented systems. The documentation makes you familiar with each function in a very structured and sequential way. The codes, along with the examples given, are straightforward to understand with a moderate knowledge of Python.
- GPU support: Keras lets you teach models on single or multiple GPUs. It supports data parallelism and processes a large amount of data.
- Pretrained Models: Keras also provides some pre-trained machine learning models with their weights. These models can be used to make predictions.
- Multiple Backend Support: Keras allows multiple backends; for example, TensorFlow and CNTK are the most common backends used. Different backends can be chosen according to the project requirements.
Python development is well-known around the world and has numerous applications in machine learning, data science, and engineering. Hence, we hope that the light shed on these three top tools can help you grow in your journey. Other than these, there are some other tools used, such as Scikit-Learn, SciPy, Theano, Selenium, RobotFramework, etc.