2.1 Basic Programming Concepts and Data Types

Programming fundamentals are the building blocks of scientific computing. They include variables, data types, and operators, which allow us to store, categorize, and manipulate information efficiently. Understanding these basics is crucial for writing effective code and solving complex problems.

Data handling and manipulation are essential skills in scientific computing. These techniques enable us to input data, process it, and output results. Mastering type conversions and data manipulation functions allows us to transform and analyze information effectively, leading to meaningful insights.

Programming Fundamentals

Fundamentals of programming

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• Variables store data in memory with unique names for easy access and modification
• Declaration assigns memory space while initialization sets initial value (x = 5)
• Naming conventions improve readability (camelCase, snake_case)
• Data types categorize and organize information for efficient processing
• Numeric types handle mathematical operations (integers, floats)
• Boolean represents true/false values for logical operations
• Characters and strings store text data ('a', "Hello")
• Operators perform calculations and comparisons on variables
• Arithmetic operators execute mathematical operations (+ adds, - subtracts)
• Comparison operators evaluate relationships between values (== checks equality)
• Logical operators combine or modify boolean expressions (and, or, not)
• Assignment operators store values in variables (= assigns, += adds and assigns)

Primitive vs complex data types

• Primitive data types store single values directly in memory
• Integer whole numbers without decimals (-1, 0, 42)
• Float represents decimal numbers with varying precision (3.14, -0.5)
• Boolean holds true or false values for logical operations
• Character stores single Unicode characters ('A', '7', '$')
• Complex data types organize multiple values or objects
• Arrays store fixed-size collections of same-type elements
• Lists allow dynamic resizing and mixed data types
• Tuples create immutable sequences of elements
• Dictionaries associate keys with values for fast lookups
• Scientific computing leverages both types for various tasks
• Primitive types handle simple calculations and control flow
• Arrays efficiently process large datasets (numerical simulations)
• Lists manage dynamic data structures (particle systems)
• Dictionaries organize structured data (molecular properties)

Data Handling and Manipulation

Input and output operations

• Input operations acquire data for program processing
• Reading from console prompts user for interactive input
• File input extracts data from external files (CSV, JSON)
• Command-line arguments pass initial values when launching program
• Output operations present results and information
• Writing to console displays text-based results to user
• File output saves processed data for later use or sharing
• Formatting enhances readability of output (tables, graphs)
• Error handling and input validation ensure data integrity
• Try-except blocks catch and manage runtime errors
• Input sanitization prevents malicious or incorrect data entry

Type conversions and data manipulation

• Type conversions transform data between different formats
• Implicit conversions automatically performed by the language (int to float)
• Explicit type casting manually converts data types (str(42))
• Numeric type conversions preserve or truncate precision
• String-number conversions parse text or format numbers
• Data manipulation processes and transforms information
• String functions modify text data (concatenation, slicing)
• Mathematical functions perform complex calculations (abs, round)
• Array operations efficiently handle large datasets (indexing, reshaping)
• Operator precedence determines calculation order (PEMDAS)
• Functions encapsulate reusable code for data manipulation
• Parameters allow flexible input to functions
• Return values provide processed results
• Lambda functions create quick, one-time operations