Conditional statements in Python help your program make decisions based on conditions. They let you run certain code only when a rule is true, and run a different block of code when the rule is false. In data analysis, conditional logic is used for filtering, data cleaning, categorisation, validation checks, and creating new fields based on business rules.
The main keywords used for conditional statements are if, elif, and else. The if block runs when the condition is true. The elif block is used when you have more than one condition to check, and it runs only if the previous conditions were false. The else block runs when none of the conditions match. This structure is useful when you need clear decision flow, such as assigning categories based on score ranges or choosing an action based on a status value.
Conditions are built using comparison operators such as ==, !=, >, <, >=, and <=. You can also combine conditions using logical operators like and, or, and not. For example, you can check if a value is within a range, or if multiple requirements are satisfied at the same time. Parentheses can be used to make complex conditions easier to read and to control the order of evaluation.
Indentation is very important in Python. The code inside an if, elif, or else block must be indented consistently, otherwise Python will throw an error. Another useful idea is nesting, where you place one conditional statement inside another. This helps when rules depend on earlier decisions, but it should be used carefully so the logic stays readable.
In real datasets, conditional statements help you handle missing values, flag outliers, apply discounts, mark high-priority cases, and validate data rules before running deeper analysis. Learning conditional statements properly makes your code more accurate and your analysis more reliable.
