Intro to Python
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form and are open source.
Often, programmers fall in love with Python because of the increased productivity it provides. Since there is no compilation step, the edit-test-debug cycle is incredibly fast. Debugging Python programs is easy: a bug or bad input will never cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception. When the program doesn't catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on. The debugger is written in Python itself, testifying to Python's introspective power. On the other hand, often the quickest way to debug a program is to add a few print statements to the source: the fast edit-test-debug cycle makes this simple approach very effective.
What are Data Types?
It specifies the type of values a data variable can take, and so drives the type of operations or calculations that can be applied to it. The most common type of data variables:
Integers(int): As the name suggests, it can contain integers only and no fractions or decimals or non-numerics. Example: 1, 2, 345, 678
Float: It is to store fractions, decimals or scientific notations; character e or E followed by a positive or negative integer represents scientific notation. Example: 27.566, 2.8, 7e2
Boolean(bool): This data type can take only two values -true or false. It can be represented by 1 or 0 as well.
String(str): It is used to store characters or texts rather than numbers. It can also take numbers but they won’t act as numbers i.e.you can’t perform addition on a number stored in a string.
What are Data Structures?
If you want to analyze data and process it, you have to store it somewhere and data structures are the solution. So a data structure is a particular format for organizing, processing, retrieving and storing data.
List: A list is a collection of other objects floats, integers, complex numbers, strings or even other lists.A list is created by placing all the items(elements)inside a square bracket [ ], separated by commas.
Dictionary: In python, a dictionary consists of key-value pairs.The value can be accessed by a unique key in the dictionary.
Tuples: Tuples are similar to lists but they are immutable i.e. the elements comprising a tuple cannot be changed.It is not possible to add or remove elements from a tuple. A tuple is created by placing all the elements inside round brackets ().
Array: An array is a data structure that stores a sequential collection of the same type of elements so an array maintains homogeneity. An array can be one dimensional or multi-dimensional.They are always rectangular so that all rows have the same number of elements.
Matrix: A matrix is like a subset of array but it always has two dimensions and follows the rules of linear algebra.
Vectors: By definition Vectors are the geometrical objects that have both magnitude and direction. It is defined by a line having an arrow the length of the line is its magnitude and the arrow specifies the direction. In python vectors are one dimensional dynamic arrays so unlike arrays it provides the ability to quickly change storage capacity.
Resources
Python 3.0 ( Important: Python 3.10 is the latest version with recent updates)