Best Practices for Structuring Python Programs

Once known as a skill-set specifically for computer scientists and engineers, programming is now a top data science skill across fields and industries. Many of the most popular programming languages have become essential learning for anyone who wants to start a career as a Data Scientist or developer. This is because working with big data requires the manipulation and analysis of complex database management systems and collections that need to be cleaned, organized, and analyzed using more efficient and streamlined methods. Learning to program is one of the methods that make it easier to manage and understand big data. 

When choosing a programming language to learn, consider that the data science industry continues to generate opportunities for professionals who have training in Python. Python is a versatile open-source language that can be used for anything from analyzing a dataset to automating machine learning models. In addition to learning how to program with Python, it is also important to develop knowledge of best practices when it comes to structuring Python programs and writing code. This is why Python data scientists and developers must understand how to execute code using control-flow statements and structures, as well as identify common mistakes when programming with the Python language.

What is Control Flow?

Control flow is commonly understood as the order in which a program or script is structured. In any programming language, control flow not only dictates the structure of your code, but also describes the statements, instructions, and protocols of the code. These statements include the calls or actions that are written within the program such as instructions for the way that a dataset should be read or how a particular set of commands should be completed. Within the Python programming language, control-flow statements are structured on conditionals, loops, and functions. 

Specifically, conditionals are “if-then” statements used within programming to instruct a machine how to respond when decisions need to be made given a specific circumstance. In addition to an order of operations for the machine, conditionals include instructions for a series of commands or steps. Loops are also a type of instructional statement for machines, but instead of creating an “if-then” structure, a loop is a scripted form of the “repeat” command. Loops are programmed to repeat a series of steps over and over again until a specific condition or end goal is met. 

Finally, functions encompass any section of code which can be used multiple times by a Data Scientist or developer. Functions are often written for particular tasks or actions that are required for a specific reason and routine. Functions are a common component of data science libraries and packages that come pre-programmed with code that can be used just by calling on a specific function. In addition to conditionals, loops, and functions, there are several structures unique to programming with Python.

The Three Structures of Python Programs

For data scientists and developers using the Python programming language to write code, control-flow statements are generally structured in three different forms: sequential, selection, and repetition. Sequential statements are the most common structure for Python code because this structure indicates that the statements within a program will be executed as a sequence, or one after the other. Sequential statements include line-by-line code, which means that in order for the entire script to execute properly, each line of code must be free of errors. 

Selection statements are another type of conditional statement. They begin with an “if” statement which is only executed based on the truth of any following statements. There are several different types of selection statements that correspond to the types of conditions that need to be met to satisfy the conditions of the statement. Selection statements are also known as “branching statements” or “decision control statements, ” because they are similar in form and function to decision trees, diagrams in which paths are dictated by choices. 

As the name indicates, repetition statements are statements or a script meant to be repeated. Operating as a type of loop, repetition statements include both “for loops” and “while loops.” “For loops” operate through the repetition of particular data types such as lists, dictionaries, sets, or tuples. In contrast, “while loops” repeat a sequence until a designated end-point or condition is reached. Loops are used when a particular series of steps or data need to be repeatedly checked or accumulated.

Mistakes to Look Out for When Structuring Python Programs

There are several common mistakes that data scientists and developers should look out for when writing Python code. One of the most common mistakes when structuring a Python program are errors in the dataset itself. When preparing to code a data science project, it is important to familiarize yourself with all of the file names and variables in the dataset and any libraries and packages that are being used. In addition, any missing values or spelling errors in the dataset should be rectified during the data cleaning and organizing process. 

Another common mistake that can hinder the programming process are issues with calling a particular function or variable, as well as properly structuring your code. This could be as simple as not being fully aware of all of the functions available within a data science library or not knowing the names or designations of variables within a dataset. Before running more complex analyses, it is important to get descriptions of the functions and variables available within the packages and libraries that you are using. For most Python libraries, this is as simple as using the “help()” function to describe any objects or methods available to you. Then you can move on to writing your programs and scripts with less chance of error.

Want to Learn More About Programming with Python? 

Programming with Python is one of many essential skills that data scientists and developers can learn to get ahead in the industry. Noble Desktop’s Data Science classes offer hands-on instruction in using the Python programming language to develop data science projects, from cleaning and analyzing data to developing machine learning models and creating visualizations. 

Beginner data scientists can benefit from the Python for Data Science Bootcamp which introduces the fundamentals of structuring programs with Python and ends with training in predictive analytics. Another option is the immersive Python Programming Bootcamp which gives prospective data scientists and developers experience structuring Python code and working with real-world datasets. 

In addition to these offerings, Noble Desktop has several Python Classes and programs that can help increase your skills and knowledge of this popular programming language!

Introduction to Data Analytics Technologies

Whether it’s training or the industry itself, data analysis is one of the key components of data science. Encompassing a variety of tools and techniques, data analytics is how we decipher data, turning numbers and patterns into information and stories. Many data scientists utilize data analysis tools to garner important insights from their research and data collection, digging deeper into the data and parsing it using software that utilizes machine learning and algorithms. In addition, more traditional data analytics tools rely on statistics and mathematical functions to uncover patterns and important information. Data science students and professionals can greatly benefit from learning more about the latest data analytics technologies on the market!