Loops in Python are used to repeat a set of instructions multiple times. They are helpful when you want to process many items one by one, such as rows in a dataset, values in a list, or files in a folder. In data analysis, loops are used for tasks like cleaning values, applying rules, calculating totals, and running repeated checks. Even though libraries like Pandas reduce the need for loops in many cases, loops are still important to understand because they explain how iteration works and they are useful for custom logic.
Python mainly uses two types of loops: for loops and while loops.
A for loop is used when you want to go through each item in a sequence, such as a list, tuple, set, dictionary, or a range of numbers. For example, you can loop through a list of sales values and compute something for each value. You can also loop through dictionary keys and values when you need to build mappings or summaries.
A while loop is used when you want to repeat something until a condition becomes false. This is useful when you do not know in advance how many times the loop should run. For example, you might keep asking for valid input until the user provides it, or keep retrying an operation until a success condition is met.
Two important keywords used inside loops are break and continue. Break stops the loop completely, while continue skips the current step and moves to the next iteration. Loops can also be nested, meaning one loop inside another, but nesting should be used carefully because it can make code slower and harder to read.
In real analysis tasks, loops help you understand how data is processed step-by-step. Once you are comfortable with loops, you can write logic that handles complex cases and improves the flexibility of your Python scripts.
