Headstart¶

Overview¶

The document demonstrate about the landscape of basic headstart for Python, started with constant, data types and structures.

Components¶

Constants¶

Following table that introduce the built-in constants supported

A small number of constants live in the built-in namespace. They are:

Constant	Description
`False`	The false value of the bool type
`True`	The true value of the bool type
`None`	An object frequently used to represent the absence of a value
`NotImplemented`	Special value indicating that an operation is not implemented
`Ellipsis`	an object frequently used to indicate that something is omitted. Aka of `...`
`__debug__`	This constant is true if Python was not started with an -O option.
`Ellipsis`	an object frequently used to indicate that something is omitted. Aka of `...`

Note:

. Assignments to False are illegal and raise a SyntaxError. . Assignments to True are illegal and raise a SyntaxError. . Assignments to None are illegal and raise a SyntaxError.

Assignment to Ellipsis is possible, but assignment to ... raises a SyntaxError. Ellipsis is the sole instance of the types.EllipsisType type.

Objects¶

Objects are Python’s abstraction for data. All data in a Python program is represented by objects or by relations between objects.

Data Types¶

Python has a rich set of built-in data types, which are illustrated in the diagram below.

Group	Type	Built-in (T\|F)	Description
Numeric	`int`	True
	`float`	True
	`complex`	True
Boolean	`bool`	True
Text Sequence	`str`	True
Binary Sequence	`bytes`	True
	`bytearray`	True
	`memoryview`	True
Datetime	`datetime`	False, `datetime`	A combination of a date and a time
	`date`	False, `datetime`	A native date
	`time`	False, `datetime`	An idealized time, independent of any particular day
	`timezone`	False, `datetime`	A class that implements the tzinfo abstract base class as a fixed offset from the UTC
	`timedelta`	False, `datetime`	A duration expressing the difference between two datetime or date, to microsecond resolution.
None	`None`	True
Sequence	`list`	True
	`tuple`	True
	`range`	True
Set	`set`	True
	`frozen`	True
Mapping	`dict`	True
Containers	`namedtuple`	False, `colectition`	named tuple factory function for creating tuple subclasses with named fields
	`deque`	False, `colectition`	list-like container with fast appends and pops on either end
	`ChainMap`	False, `colectition`	dict-like class for creating a single view of multiple mappings
	`Counter`	False, `colectition`	dict subclass for counting hashable objects
	`OrderedDict`	False, `colectition`	dict subclass that remembers the order entries were added
	`defaultdict`	False, `colectition`	dict subclass that calls a factory function to supply missing values
	`UserDict`	False, `colectition`	wrapper around dictionary objects for easier dict subclassing
	`UserList`	False, `colectition`	wrapper around list objects for easier list subclassing
	`UserString`	False, `colectition`	wrapper around string objects for easier string subclassing

Python provides a set of primitive data-types that do not rely on any other modules. |These include the numeric types, sequences, mappings, sets, and frozensets, as well as the None type and boolean types.

2.2 Core Native Data Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3 Additional Container Data Types in the Standard Library . . . . . . . . . . . . . . . . . . . 24

from datetime import datetime, date
>>> 1699842778.2149107
1699842778.2149107
>>> a = 1699842778.2149107
>>> import datetime
>>> datetime.datetime.utcfromtimestamp(a)
datetime.datetime(2023, 11, 13, 2, 32, 58, 214911)

Text Data `str`¶

The string is representation, you can see this in very form.

Methods	Description	Example
`len()`	Returns the length of the string	`len("hello")`
`upper()`	Returns a string in uppercase	`"hello".upper()`
`lower()`	Returns a string in lowercase	`"hello".lower()`
`strip()`	Removes leading and trailing whitespace	`" hello ".strip()`
`lstrip()`	Removes leading whitespace	`" hello ".lstrip()`
`rstrip()`	Removes trailing whitespace	`" hello ".rstrip()`
`split()`	Splits a string into a list of words	`"hello world".split()`
`join()`	Joins a list of words into a string	`" ".join(["hello", "world"])`
`replace()`	Replaces a substring with another substring	`"hello world".replace("hello", "goodbye")`
`find()`	Returns the index of the first occurrence of a substring	`"hello world".find("world")`
`index()`	Returns the index of the first occurrence of a substring	`"hello world".index("world")`
`count()`	Returns the number of occurrences of a substring	`"hello world".count("o")`
`startswith()`	Returns True if the string starts with the given substring	`"hello world".startswith("hello")`
`endswith()`	Returns True if the string ends with the given substring	`"hello world".endswith("world")`
`isalpha()`	Returns True if all characters in the string are alphabets	`"hello".isalpha()`
`isalnum()`	Returns True if all characters in the string are alphanumeric	`"hello123".isalnum()`
`isdecimal()`	Returns True if all characters in the string are decimal characters	`"123.45".isdecimal()`
`isspace()`	Returns True if all characters in the string are whitespace characters	`" ".isspace()`

Iterator

Arguments

Results

Example

accumulate()

p [,func]

p0, p0+p1, p0+p1+p2, …

accumulate([1,2,3,4,5]) → 1 3 6 10 15

The description of data

Working with number¶

Limitation of float or

The list and dict¶

Dictationry (`dict`)¶

The dictionary is very

Transformation between data-types¶

Make new form¶

Package `itertools` that support for efficient looping¶

The itertools package provides a variety of functions to efficiently loop over iterable objects.

See more: Python3 - Itertools

Reference¶

Python - Data Model

https://www.geeksforgeeks.org/namedtuple-in-python/?ref=lbp