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os module provides OS-independent interface for operating system operations. Functions: os.path for path manipulation, os.environ for environment variables, os.walk for directory traversal. Consider platform differences, security implications.
pathlib provides object-oriented interface for file system paths. Path class methods: glob(), mkdir(), touch(), resolve(). More readable than os.path, handles platform differences. Consider migration from os.path.
time provides time-related functions: time() for timestamps, sleep() for delays, strftime() for formatting. Handle different time representations, platform differences. Consider timezone implications.
operator provides function equivalents of operators: itemgetter(), attrgetter() for data access, add(), mul() for arithmetic. Useful in functional programming, sorting. Consider performance implications.
platform provides system information: system(), machine(), python_version(). Access hardware, OS details, Python environment. Consider cross-platform compatibility, information security.
concurrent.futures provides high-level interface for asynchronous execution: ThreadPoolExecutor for I/O-bound tasks, ProcessPoolExecutor for CPU-bound tasks. Supports map(), submit(), as_completed(). Handle thread/process management, exceptions.
urllib handles URL operations: request handling, parsing, encoding. Modules: request for HTTP, parse for URL parsing, error for exceptions. Consider error handling, security, timeout configuration.
enum provides enumeration support: Enum class, auto() for automatic values. Supports unique/non-unique values, custom behavior. Consider type safety, value comparison, serialization needs.
statistics provides statistical functions: mean(), median(), mode(), stdev(). Supports different types of averages, variance calculations. Consider numerical stability, data types, population vs sample.
Collections module provides specialized container types: defaultdict (automatic default values), Counter (counting hashable objects), deque (double-ended queue), namedtuple (tuple with named fields), OrderedDict (ordered dictionary pre-3.7). Each optimized for specific use cases and performance requirements.
itertools provides functions for efficient iteration: combinations(), permutations(), product() for combinatorics; cycle(), repeat() for infinite iterators; chain(), islice() for iterator manipulation. Memory efficient for large datasets.
json module handles JSON encoding/decoding. Methods: dumps()/loads() for strings, dump()/load() for files. Supports custom serialization with default/object_hook. Handle encoding issues, pretty printing, security considerations.
random provides pseudo-random number generation: randint(), choice(), shuffle(). Supports different distributions, seeding. Consider cryptographic needs (use secrets instead), reproducibility requirements.
multiprocessing provides process-based parallelism: Process class, Pool for worker pools. Handle process creation, communication, synchronization. Consider GIL bypass, resource sharing, cleanup.
datetime module handles dates and times. Use datetime.datetime with tzinfo for timezone awareness, pytz for reliable timezone handling. Methods: astimezone(), replace(tzinfo=), timezone conversions. Consider DST transitions, UTC conversions.
re module provides regular expression operations: compile(), match(), search(), findall(). Supports patterns, groups, flags. Consider using raw strings (r'pattern'), pre-compilation for performance. Handle different regex patterns and flags.
logging provides flexible event logging: different levels (DEBUG to CRITICAL), handlers, formatters. Configure using basicConfig() or dictConfig(). Consider log rotation, formatting, handling in different environments.
functools provides tools for functional programming: partial() for partial application, lru_cache() for caching, reduce() for reduction operations. Includes wraps() for preserving function metadata. Consider performance implications.
argparse handles command-line argument parsing: argument types, help messages, subcommands. Supports required/optional arguments, default values, custom actions. Consider user interface design, error handling.
csv handles CSV file operations: reading, writing, different dialects. Supports DictReader/DictWriter for named columns. Handle different formats, encoding issues, custom dialects. Consider large file handling.
pickle handles Python object serialization: dump()/load() for files, dumps()/loads() for bytes. Consider security implications, version compatibility. Handle custom object serialization, protocol versions.
sys provides system-specific parameters/functions: sys.path for module search, sys.argv for command arguments, sys.stdin/stdout/stderr for I/O. Handle interpreter interaction, system limitations.
subprocess manages external processes: run(), Popen for process control. Handle command execution, I/O redirection, process communication. Consider security, platform differences, error handling.
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