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Questions and Answers
What type of error occurs when executable statements encounter unexpected conditions in Python?
Which error in Python arises from misunderstanding program requirements or misconceptions regarding code functioning?
What type of defensive programming practice does Python recommend to mitigate errors?
Which error in Python results from invalid statements, incorrect indentation, or unmatched parentheses?
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In Python, what error occurs when attempting operations like dividing by zero or breaching array bounds?
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What is a common practice to quickly verify and troubleshoot Python code expressions and statements?
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Which data type in Python is used for whole numbers?
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What is a key characteristic of a Tuple data type in Python?
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Which data type is used to store a sequence of characters in Python?
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What type of collection in Python allows each element to appear only once?
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Which data type is suitable for holding key-value pairs in Python?
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When dealing with intricate logic or large datasets in Python, what is likely to happen frequently?
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Study Notes
Python: Exploring Data Types and Errors
Python has emerged as a robust tool for both scientists and researchers alike, thanks to its simplicity, versatile data structures, and abundant libraries for various domains. Here, we delve deeper into two crucial aspects of working with Python—data types and errors—to enhance your knowledge while keeping things accessible and practical.
Data Types
In Python, data resides in objects known as variables. These variables hold values belonging to specific types, each having unique characteristics and capabilities. Below, we summarize Python's primary built-in data types:
-
Integer: Whole numbers, e.g.,
1,-5, etc. -
Float: Numbers containing decimal points, e.g.,
3.14,1.5. -
String: Sequences of characters encased in single quotes (
'') or double quotes (""), e.g.,'Hello World!',"My string". -
List: Ordered collections of items contained within square brackets (
[]):[1, 'string', True]. -
Tuple: Similar to lists but immutable once created, i.e., elements cannot be altered after initialization:
(1, 'str', False). -
Dictionary: Collections of key-value pairs held inside curly braces ({}), e.g.,
{'name': 'John', 'age': 30}. -
Set: Unordered collection of unique items, e.g.,
{1, 3, 5, 4}(each element appears exactly once).
Handling Errors
Errors occur frequently during coding, especially when dealing with intricate logic or large datasets. These mistakes may halt execution abruptly, causing frustration and delay. Python equips us with mechanisms to detect and manage errors effectively.
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Syntax Error. Syntax errors emerge when syntax rules aren't followed properly, leading to invalid statements, incorrect indentation, or unmatched parentheses.
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Runtime Error. Runtime errors manifest when executable statements encounter unexpected conditions, resulting in abrupt termination. Examples include attempting to divide by zero (ZeroDivisionError), referencing a variable before declaring it (UnboundLocalError), or breaching array bounds (IndexError).
-
Logical Error. Logical errors arise due to misunderstanding program requirements or misconceptions regarding code functioning. Consequently, the correct outputs might differ from intended outcomes despite the absence of obvious syntax or runtime flaws.
To mitigate errors, Python encourages defensive programming practices, such as checking data validity, verifying input integrity, comprehensively documenting code, and thoroughly testing scripts across various scenarios. Additionally, Python's interactive shell (REPL) allows quick verification and troubleshooting of expressions and statements, providing immediate feedback.
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Description
Enhance your Python programming knowledge by exploring data types (integer, float, string, list, tuple, dictionary, set) and error handling techniques (syntax errors, runtime errors, logical errors). Learn how to effectively manage errors, increase code integrity, and prevent common coding mistakes.
