In 1990, the Internet became increasingly popular. While it was limited to a few websites running on slow servers, developers wanted to be able to develop programs for it. While languages like C++ allowed developers to write native applications in performant ways and languages like Java had the ecosystem to maintain a write once run anywhere paradigm, there were no languages that allowed easy code scripting to be run within another application. With this came Perl, Ruby and most importantly Python.
Python was created by Guido van Rossum as a scripting language for developers. Python was intended to be an interpreted language. This meant the interpreter, which in the case of languages like Python or Ruby replaced the compiler, would read code directly and produce the output. These interpreters were often written in a lower level language like C, and while they were not as performant as writing C code, interpreters allowed developers to write simpler code than they would have in a lower level language. These interpreters quickly became useful on the Internet as it allowed these languages to become useful in websites.
Python was intended to be a general-purpose programming language. It was based on C++ and Java, copying their object-oriented paradigm. However, using an object-oriented paradigm in Python is not required. Python is ultimately a procedural scripting language and usually runs a single file on the command line interpreter. Unlike C++ and Java, however, Python does not have explicit datatypes nor strong typing. This means a variable in Python can go from being a floating point to a integer without having specified the data type. This is because interpreted languages are often smarter than lower level languages because the interpreter can assign a floating point data type to any number with a decimal and an integer to any other. However, this can take time, making Python slower than these languages.
Because Python is so easy to script and write it has become very popular among data scientists. Because Python comes with many string interpretation libraries built in and has great math libraries that have been created based on C bindings, its popularity has increased. Using tools like Numpy and Scipy developers can do machine learning using Python and conduct data analytics. Because Numpy and Scipy are based on MATLAB bindings, Python is certain to have the performance of matrix multiplications that are needed in data analytics. Python also comes built-in with many plotting tools that make it easy for developers to visualize data.
Because Python has the capability to be object-oriented, this can make it a lot easier for data scientists as well. Object-oriented programming is extremely useful in trying to structure data, and while using classes in Python, developers can more easily keep track of data and run models on structured data. Because many data developers collect is often unstructured in the form of text files, Python has the tools to not only collect the data but organize it and analyze it.
Python can be run in many ways, but ultimately has two popular ways. Many developers usually download Python and run it directly from the command line to test out scripts when writing libraries. However, data scientists prefer to use software tools called notebooks. Notebooks allow developers to break up chunks of code and control output and organization of code. With libraries like Matplotlib, creating reports for data analytics projects can be much easier. Depending on what Python is used for, there are many ways to write it.
Programming Languages and Resources for Software Developers
The most common programming languages for software engineers are C, C++, Python, and Java. Also, for building native mobile Apps, iOS Swift and Java Android are used for building iPhone and Android Apps respectively.
Python coding is well suited for those interested in pursuing a career in software engineering; however, other options are system admin, web design and development and mobile App design and development. It is advisable to consult with an IT career counselor to understand what career options best fits your skills. For instance, if you want to be a software engineer, learning HTML and CSS might not fit the bill. Here is an excellent article for learning more on coding and technology career roadmap. Once you know what career path you wish to pursue, you can make a plan on what, when, and how to learn. There are lots of online resources for learning coding and technology in general. For teenagers and high school students, High School Technology Services offers variety of hands-on training. For adults and professionals, Coding Bootcamps and DC Web Makers Companies offer basic to advance project-based programming and technology classes.