Learn about all array data types in Python

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Array types are the basic array structures in various programming languages. In this article, we will take a look at Python’s implementation of various “array” types.

• list
• tuple
• array.array
• str
• bytes
• bytearray

It is not accurate to call all of the above types arrays. Arrays are thought of as a broad concept. Lists, sequences, and arrays are all understood as array-like data types.

Note that all the code in this article is inPython3.7Run the ^_^

0x00 Mutable dynamic list List

List is probably the most commonly used array type in Python. It is mutable, dynamically scalable, and can store all objects in Python without specifying the type of element to store.

Very simple to use

>>> arr = ["one"."two"."three"]
>>> arr[0]
'one'
# Dynamic capacity expansion
>>> arr.append(4)
>>> arr
['one'.'two'.'three'And 4)Delete an element
>>> del arr[2]
>>> arr
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0x01 Immutable Tuple

Tuple operates like a list. It is immutable and cannot be expanded. It can store all objects in Python without specifying the type of element to store.

>>> t = 'one'.'two',3
>>> t
('one'.'two', 3)
>>> t.append(4)
AttributeError: 'tuple' object has no attribute 'append'
>>> del t[0]
TypeError: 'tuple' object doesn't support item deletion
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A tuple can use the + operator, which creates a new tuple object to store data in.

>>> t+(1,)
('one'.'two', 3, 1)
>>> tcopy = t+(1,)
>>> tcopy
('one'.'two', 3, 1)
>>> id(tcopy)
4604415336
>>> id(t)
4605245696
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You can see that the addresses of the two objects are different after tuple executes the + operator

0x02 array.array

If you want to use data structures like “arrays” in other languages in Python, you need the array module. It is mutable and stores values of the same type, not objects.

Because array is used with specified element data types, it has more efficient spatial performance than both lists and tuples.

Specify element data type as' float 'when used
>>> arr = array.array('f', (1.0, 1.5, 2.0, 2.5)) >>> ARR Array ('f', [1.0, 1.5, 2.0, 2.5])
# modify an element>>> ARR [1]=12.45 >>> ARR Array ('f', [1.0, 12.449999809265137, 2.0, 2.5])
Delete an element
>>> del arr[2]
>>> arr
array('f'And [1.0, 12.449999809265137, 2.5])Add an element>>> Arr.appEnd (4.89) >>> ARr array('f', [1.0, 12.449999809265137, 2.5, 4.889999866485596])
An error will be reported if you store string data in an array of floating point numbers
>>> arr[0]='hello'
TypeError: must be real number, not str
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The following table lists the data types of the elements in the array

0x03 String sequence STR

Python3 uses STR objects to represent a sequence of text characters (see how similar this is to the String in Java). It features immutable Unicode character sequences.

Each of its elements in STR is a string object.

>>> s ='123abc'
>>> s
'123abc'
>>> s[0]
'1'
>>> s[2]
'3'
# strings are immutable sequences in which elements cannot be deleted
>>> del s[1]
TypeError: 'str' object doesnDeletion # delete from list >>> sn = list(s) >>> sn ['1', '2', '3', 'a', 'b', 'c']
>>> sn.append(9)
>>> sn
['1', '2', '3', 'a', 'b', 'c>>> type(s[2]) str'>
>>> type(s)
<class 'str'>
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The STR object can also perform the + operation, which also generates a new object for storage.

>>> s2 = s+'33'
>>> s2
'123abc33'
>>> id(s2)
4605193648
>>> id(s)
4552640416
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0x04 bytes

Bytes objects are used to store sequences of bytes and are characterized by immutable storage that can store values from 0 to 256.

>>> b = bytes([0,2,4,8])
>>> b[2]
4
>>> b
b'\x00\x02\x04\x08'
>>> b[0]=33
TypeError: 'bytes' object does not support item assignment
>>> del b[0]
TypeError: 'bytes' object doesn't support item deletion
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0x05 bytearray

The bytearray object, similar to bytes, is used to store sequences of bytes. It features a mutable, dynamically scalable byte array.

> > > ba = bytearray containing (,3,5,7,9) (1) > > > ba bytearray containing (b'\x01\x03\x05\x07\t')
>>> ba[1]
3
Delete an element
>>> del ba[1]
>>> ba
bytearray(b'\x01\x05\x07\t')
>>> ba[0]=2
>>> ba[0]
2
Add an element
>>> ba.append(6)
Only bytes can be added
>>> ba.append(s)
TypeError: 'str' object cannot be interpreted as an integer
>>> ba
bytearray(b'\x02\x05\x07\t\x06')
# bytes range from 0 to 256
>>> ba[2]=288
ValueError: byte must be in range(0, 256)
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Bytearray can be converted to bytes objects, but not very efficiently.

Converting # bytearray to bytes will generate a new object
>>> bn = bytes(ba)
>>> id(bn)
4604114344
>>> id(ba)
4552473544
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0x06 Each type converts to each other

tuple->list

>>> tuple(l)
('a'.'b'.'c')
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list->tuple

>>> t
('a'.'b'.'c')
>>> list(t)
['a'.'b'.'c']
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str->list

>>> l = list('abc')
>>> l
['a'.'b'.'c']
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list->str

>>> l
['a'.'b'.'c'] > > >' '.join(l)
'abc'
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str->bytes

>>> s = '123'
>>> bytes(s)
TypeError: string argument without an encoding
>>> bytes(s,encoding='utf-8')
b'123'
Or use the encode() method of STR
>>> s.encode()
b'123'
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bytes->str

>>> b = b'124'
>>> b
b'124'
>>> type(b)
<class 'bytes'>
>>> str(b,encoding='utf-8')
'124'
# or decode() using bytes
>>> b.decode()
'124'
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0 x07 summary

These data types are native to Python, and you should choose the appropriate data type based on your actual requirements. For example, when you want to store multiple element types, use a list or tuple. Array, on the other hand, has relatively good spatial performance, but it can only store a single type.

I believe there are many business scenarios where a list or tuple will do the job, but other data structures need to be understood as well, whether we’re doing some basic components, thinking about the performance of the data structure, or reading someone else’s code.

0x08 Learning Materials

• Docs.python.org/3.1/library…
• Docs.python.org/zh-cn/3/lib…
• Docs.python.org/3/library/s…