Numbers:
Python's built-in core data types are in some cases also called object types. There are four built-in data types for numbers:
Integer
Normal integers
e.g. 4321
Octal literals (base 8)
A number prefixed by a 0 (zero) will be interpreted as an octal number
example:
>>> a = 010
>>> print a
8 Alternatively, an octal number can be defined with "0o" as a prefix: >>> a = o10
>>> print a
8
Hexadecimal literals (base 16)
Hexadecimal literals have to be prefixed either by "0x" or "0X".
example:
>>> hex_number = 0xA0F
>>> print hex_number
2575
Long integers
these numbers are of unlimited size
e.g.42000000000000000000L
Floating-point numbers
for example: 42.11, 3.1415e-10
Complex numbers
Complex numbers are written as <real part> + <imaginary part>j
examples:
>>> x = 3 + 4j
>>> y = 2 - 3j
>>> z = x + y
>>> print z
(5+1j)
String：
有三种方式定义字符串：
1.'xxxxx'
2."xxxxxx"
3.'''xxxxxxxx'''
字符串的一些操作：
1."Hello"+"World"
2."xx"*3 -> "xxxxxx"
3."Python"[0] will return in "P"
4."Python"[2:4]
4.len("Python")
字符串不可被改变
If both a and b are strings, "a is b" checks if they have the same identity, i.e. share the same memory location. If "a is b" is True, then it trivially follows that "a == b" has to be True as well. But "a == b" True doesn't imply that "a is b" is True as well!
>>> a = "Linux"
>>> b = "Linux"
>>> a is b
True
The Pitfalls of Repetitions
In our previous examples we applied the repetition operator on strings and flat lists. We can apply it to nested lists as well:
>>> x = ["a","b","c"]
>>> y = [x] * 4
>>> y
[['a', 'b', 'c'], ['a', 'b', 'c'], ['a', 'b', 'c'], ['a', 'b', 'c']]
>>> y[0][0] = "p"
>>> y
[['p', 'b', 'c'], ['p', 'b', 'c'], ['p', 'b', 'c'], ['p', 'b', 'c']]
>>>
complex(real[,imag]])
创建一个值为real + imag * j的复数或者转化一个字符串或数为复数。如果第一个参数为字符串，则不需要指定第二个参数。
参数real: int, long, float或字符串；
参数imag: int, long, float。
>>> complex(1, 2)
(1 + 2j)
#数字
>>> complex(1)
(1 + 0j)
#当做字符串处理
>>> complex("1")
(1 + 0j)
#注意：这个地方在“+”号两边不能有空格，也就是不能写成"1 + 2j"，应该是"1+2j"，否则会报错
>>> complex("1+2j")
(1 + 2j)
