Welcome to our blog series "Python Panorama Series"! In this series, we'll take you from the basics of Python to advanced topics, helping you master this powerful and flexible programming syntax. Whether you are new to programming or a developer with a certain foundation, this series will provide the knowledge and skills you need.
As a powerful and flexible programming language, Python has a rich data type system. This article provides a detailed introduction to every data type in Python, including numeric, sequence, map, set, Boolean, and None types. The characteristics of each data type, how to use it, and its application to practical problems will be discussed in depth. In addition, we will explore Python's dynamic typing feature and how to make full use of these data types in practical programming to simplify code and improve efficiency. At the end of the article, I will also share a very useful feature that you may not know yet.
1. Numerics
Python's numeric types include integers (Integers), floating-point numbers (Floating-point numbers), complex numbers (Complex numbers), Boolean values (Booleans) and binary types (Bytes).
# 整数
x = 10
print(type(x)) # <class 'int'>
# 浮点数
y = 20.5
print(type(y)) # <class 'float'>
# 复数
z = 2 + 3j
print(type(z)) # <class 'complex'>
# 布尔值
a = True
print(type(a)) # <class 'bool'>
# 二进制
b = b"Hello"
print(type(b)) # <class 'bytes'>Integer types in Python not only support regular decimal, but also binary (0b10), octal (0o10) and hexadecimal (0x10). They are designed to give Python a powerful ability to perform mathematical operations. It's worth noting that Python's integers have no size limit, which means you can handle very large integers without worrying about overflow.
2. Sequence type (Sequences)
Sequence types include Lists, Tuples, Strings, Byte Arrays, and Ranges.
# 列表
list_ = [1, 2, 3]
print(type(list_)) # <class 'list'>
# 元组
tuple_ = (1, 2, 3)
print(type(tuple_)) # <class 'tuple'>
# 字符串
str_ = "Hello, Python!"
print(type(str_)) # <class 'str'>
# 字节数组
bytes_array = bytearray(b"Hello")
print(type(bytes_array)) # <class 'bytearray'>
# 范围
range_ = range(5)
print(type(range_)) # <class 'range'>Lists are mutable, while tuples and strings are immutable. This feature determines their usage scenarios in Python programming. For example, we can use lists to store data that needs to be modified dynamically, tuples to store immutable sequences of data, and strings to handle text data.
3. Mappings
Python's mapping types include Dictionary.
# 字典
dict_ = {"name": "Python", "age": 30}
print(type(dict_)) # <class 'dict'>The performance advantage of the dictionary is that the lookup and insertion speed is very fast, independent of the size of the dictionary, because the internal implementation of the dictionary uses a hash table. This makes dictionaries ideal for storing large amounts of data, especially when we need to look up data quickly.
Dictionaries have been optimized to maintain insertion order since Python 3.7, which means that when we iterate through the dictionary, the elements will be in the same order as they were inserted. This makes dictionaries an alternative to ordered dictionaries (OrderedDict) in some cases.
4. Collection type (Sets)
Set and FrozenSet are two collection types in Python.
# 集合
set_ = {1, 2, 3}
print(type(set_)) # <class 'set'>
# 冻结集合
frozenset_ = frozenset([1, 2, 3])
print(type(frozenset_)) # <class 'frozenset'>Sets are very useful when dealing with some specific problems, such as removing duplicate elements, checking whether an element exists, finding intersection, union, difference, etc.
5. None type
Python has a special type called NoneType that has only one value: None. It is often used to represent missing or null values.
# None类型
none_ = None
print(type(none_)) # <class 'NoneType'>Using None can help us distinguish whether a variable has been assigned a value, or whether a function returned a valid result.
In a function, if there is no explicit return statement, Python will return None by default. This allows us to easily determine whether a function has an explicit return value.
**One More Thing...**
Python's data types are all classes. This means that we can handle these data like objects, call their methods, and even add properties to them.
# 给整数添加属性
x = 10
x.my_attribute = "Hello"
print(x.my_attribute) # "Hello"While this feature may not be commonly used, it opens up enormous possibilities for Python's dynamism. It is the embodiment of Python as an object-oriented language, and the embodiment of Python's "everything is an object" philosophy.
These are all the built-in data types of Python. Understanding and proficient application of these data types is the basis for improving Python programming skills. Hope this blog helps you understand Python's data types better. If you have any questions or ideas, please leave a message in the comment area.