mostUsedModules.txt

1) abc - This tiny module delivers the environment you need for creating abstract base classes.

2) argparse - This module contains tools that make it easier to create user-friendly interfaces from the level of the command line.

3) asyncio - This is a huge module that delivers the framework and environment for asynchronous programming. 
    It was added to Python 3.4 as a temporary module and worked as an alternative to the popular framework called Twisted. 
    In short, asyncio is handy if you want to create asynchronous, concurrent, and single-threaded code. 
    The module launches a loop where the asynchronous code is executed as a task. 
    The module executes another task when one task is inactive (for example, waiting for server response).

4) base64 - This well-known module delivers a tool for coding and decoding binary code into a format that can be displayed - and the other way round.

5) collections - This module offers specialized container data types that work as an alternative to basic contained types for general purposes such as dict, list, set and tuple.

6) copy - Everyone uses this tiny module that contains tools for deep copying of container-type data. 
    Its most famous function is deepcopy - if not for this function, copying lists and dictionaries would become torture for developers.

7) csv - Delivers functionalities for exporting and importing tabular data in CSV format. 
    The module allows developers to say “load data” or “save data” from a CSV file.

8) datetime - A simple and one of the most popular modules in Python. 
    It delivers tools that make it easier to work with dates and times. The most popular classes are datetime, timezone, and timedelta, but date,time, and tzinfo can also be helpful.

9) decimal - The module delivers a data type called Decimal. 
    Its main advantage is the correct rounding of decimal numbers which is extremely important in billing systems - even a slight distortion with several hundred thousand operations can change the final result significantly.

10) functools - The functools module comes in handy for higher-order functions, 
    i.e., the functions that act on or return other functions. 
    You can treat any callable object as a function for this module.

11) hashlib - This handy module implements a standard interface to numerous secure hash and message digest algorithms like the FIPS secure hash algorithms SHA1, SHA224, SHA256, SHA384, and SHA512 (defined in FIPS 180-2), as well as RSA’s MD5 algorithm (defined in Internet RFC 1321). 
    Note the terminology: “secure hash” and “message digest” are interchangeable.

12) http - This package collects several modules for working with the HyperText Transfer Protocol, 
    such as http.client (low-level HTTP protocol client), 
    http.server (includes basic HTTP server classes based on socketserver), 
    http.cookies (with utilities for implementing state management with cookies) and 
    http.cookiejar (that provides persistence of cookies).

13) importlib - The package provides the implementation of the import statement (and the import() function) in Python source code. 
    The components to implement import are exposed, making it easier for developers to create custom objects to participate in the import process.

14) itertools - This useful module implements iterator building blocks inspired by constructs from APL, Haskell, and SML (all in a form that matches Python programs). 
    It standardizes a core collection of quick, memory-efficient tools you can use on their own or combine to construct specialized tools efficiently in pure Python.

15) inspect - The module offers several useful functions that help developers get information about live objects such as modules, classes, methods, functions, tracebacks, frame objects, and code objects in Python code. 
    It provides four main functions: type checking, getting source code, inspecting classes and functions, and examining the interpreter stack. 
    You can use it to retrieve the source code of a method, examine the contents of a class, or just get all the relevant information to display a detailed traceback.

16) json - JSON (JavaScript Object Notation) is a lightweight data-interchange format that was inspired by JavaScript object literal syntax. 
    The module json exposes an API that looks similar to the standard library marshal and pickle modules.

17) logging - This module defines functions and classes that provide a flexible event-logging system for applications and libraries. 
    It’s a good idea to use it because it ensures that all Python modules can log in - your application log may include your messages integrated with messages from third-party modules.

18) math - This module gives developers access to the mathematical functions implemented directly in the C language. 
    You can’t use them with complex numbers, which is good if you’re not looking to learn a lot of mathematics required to understand complex problems.

19) multiprocessing - This handy package supports spawning processes with the help of an API similar to the threading module. 
    It provides local and remote concurrency using sub processes instead of threads to side-step the Global Interpreter Lock. 
    Developers use it to take full advantage of multiple processors on a given machine. 
    It runs on both Unix and Windows.

20) os - This module offers a portable method of using operating system-dependent functionality.

21) pdb - This module defines an interactive source code debugger. 
    It supports single stepping at the source line level, setting (conditional) breakpoints, an inspection of stack frames, source code listing, and more.

22) random - This useful module implements pseudo-random number generators for various distributions. 
    For example, you get a uniform selection from a range of integers. 
    For sequences, there is a uniform selection of a random element (as well as a function to generate a random permutation of a list in place and for random sampling without replacement).

23) re - This module provides regular expression matching operations similar to those you get in Perl. 
    You can search Unicode and 8-bit strings, but they can’t be mixed.

24) shutil - The module provides several high-level operations on files and collections of files, mainly functions that support file copying and removal.

25) sys - The module offers access to variables used or maintained by the interpreter and functions that interact strongly with the interpreter.

26) threading - This useful module builds higher-level threading interfaces on top of the lower-level _thread module.

27) types - The module defines utility functions that support the dynamic creation of new types. 
    It defines names for object types used by the standard Python interpreter but not exposed as builtins. 
    It also offers additional type-related utility classes and functions that aren’t builtins.

28) unittest - Originally inspired by JUnit, 
    the module works similarly to major unit testing frameworks in other programming languages. 
    It supports a broad range of functions: test automation, sharing of setup, shutdown code for tests, test aggregation into collections, and more.

29) urllib - This handy package collects several modules for working with URLs: urllib.request (opening and reading URLs), urllib.error (includes exceptions raised by urllib.request), urllib.parse (for parsing URLs), and urllib.robotparser (for parsing robots.txt files).

30) uuid - The module provides immutable UUID objects, as well as the following functions for generating version 1, 3, 4, and 5 UUIDs: uuid1(), uuid3(), uuid4(), uuid5().