If you’re new to this beautiful language, welcome aboard! But before we dive deep into the world of data science with Python, some essential syntax rules that will make your life easier and your code more readable.

Before anything else: line breaks matter in Python. Unlike SQL or other programming languages, you can’t just write a long line without any spaces or indentations. If you do, you might get an error message that looks like this: “SyntaxError: invalid syntax”. So make sure to use proper formatting and avoid writing your code on one single line!

Another important Python rule is case sensitivity. This means that uppercase letters matter in variable names or keywords. For example, if you write “AND” instead of “and”, it won’t work. To be safe, always stick to lowercase letters for variables and use capital letters only when necessary (like in class names).

Speaking of variables, let me give you a quick tip: don’t use one-letter variable names! It might seem like a good idea at first, but trust me, it will make your code harder to read and understand. Instead, choose descriptive names that explain what the variable is for (like “age” or “total_sales”).

Now some best practices for formatting your Python code. First of all, use comments! They are a great way to add explanations to your code and make it more readable. To write a comment in Python, simply put the # character at the beginning of the line you want to explain.

Another useful tip is to follow PEP 8 style guidelines for variable names and indentation. This will help you avoid common mistakes and keep your code consistent with other Python projects out there. For example, use lowercase letters for variables (like “total_sales”) and capitalize the first letter of each word in class or function names (like “MyClass” or “myFunction”).

Finally, some common exceptions that might trip you up as a beginner. For example, remember to use True and False instead of TRUE or true for Boolean values! And always make sure to indent your code correctly when using loops or conditional statements (like if/else).