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# [`decimal`](#module-decimal "decimal: Implementation of the General Decimal Arithmetic Specification.") --- 十进制定点和浮点运算
**源码：** [Lib/decimal.py](https://github.com/python/cpython/tree/3.7/Lib/decimal.py) \[https://github.com/python/cpython/tree/3.7/Lib/decimal.py\]
- - - - - -
[`decimal`](#module-decimal "decimal: Implementation of the General Decimal Arithmetic Specification.") 模块为快速正确舍入的十进制浮点运算提供支持。 它提供了 [`float`](functions.xhtml#float "float") 数据类型以外的几个优点：
- Decimal “基于一个浮点模型，它是为人们设计的，并且必然具有最重要的指导原则 —— 计算机必须提供与人们在学校学习的算法相同的算法。” —— 摘自十进制算术规范。
- 十进制数字可以准确表示。 相比之下，数字如 `1.1` 和 `2.2` 在二进制浮点中没有精确的表示。 最终用户通常不希望``1.1 + 2.2``显示为 `3.3000000000000003` ，就像二进制浮点一样。
- 精确性延续到算术中。 在十进制浮点数中，`0.1 + 0.1 + 0.1 - 0.3` 恰好等于零。 在二进制浮点数中，结果为 `5.5511151231257827e-017` 。 虽然接近于零，但差异妨碍了可靠的相等性检验，并且差异可能会累积。 因此，在具有严格相等不变量的会计应用程序中， decimal 是首选。
- 十进制模块包含一个重要位置的概念，因此 `1.30 + 1.20` 是 `2.50` 。 保留尾随零以表示重要性。 这是货币申请的惯常陈述。 对于乘法，“教科书”方法使用被乘数中的所有数字。 例如， `1.3 * 1.2` 给出 `1.56` 而 `1.30 * 1.20` 给出 `1.5600` 。
- 与基于硬件的二进制浮点不同，十进制模块具有用户可更改的精度（默认为28个位置），可以与给定问题所需的一样大：
```
>>> from decimal import *
>>> getcontext().prec = 6
>>> Decimal(1) / Decimal(7)
Decimal('0.142857')
>>> getcontext().prec = 28
>>> Decimal(1) / Decimal(7)
Decimal('0.1428571428571428571428571429')
```
- 二进制和十进制浮点都是根据已发布的标准实现的。 虽然内置浮点类型只公开其功能的一小部分，但十进制模块公开了标准的所有必需部分。 在需要时，程序员可以完全控制舍入和信号处理。 这包括通过使用异常来阻止任何不精确操作来强制执行精确算术的选项。
- 十进制模块旨在支持“无偏见，精确的非连续十进制算术（有时称为定点算术）和舍入浮点算术”。 —— 摘自十进制算术规范。
模块设计以三个概念为中心：十进制数，算术上下文和信号。
十进制数是不可变的。 它有一个符号，系数数字和一个指数。 为了保持重要性，系数数字不会截断尾随零。十进制数也包括特殊值，例如 `Infinity` ，`-Infinity` ，和 `NaN` 。 该标准还区分 `-0` 和 `+0` 。
算术的上下文是指定精度、舍入规则、指数限制、指示操作结果的标志以及确定符号是否被视为异常的陷阱启用器的环境。 舍入选项包括 [`ROUND_CEILING`](#decimal.ROUND_CEILING "decimal.ROUND_CEILING") 、 [`ROUND_DOWN`](#decimal.ROUND_DOWN "decimal.ROUND_DOWN") 、 [`ROUND_FLOOR`](#decimal.ROUND_FLOOR "decimal.ROUND_FLOOR") 、 [`ROUND_HALF_DOWN`](#decimal.ROUND_HALF_DOWN "decimal.ROUND_HALF_DOWN"), [`ROUND_HALF_EVEN`](#decimal.ROUND_HALF_EVEN "decimal.ROUND_HALF_EVEN") 、 [`ROUND_HALF_UP`](#decimal.ROUND_HALF_UP "decimal.ROUND_HALF_UP") 、 [`ROUND_UP`](#decimal.ROUND_UP "decimal.ROUND_UP") 以及 [`ROUND_05UP`](#decimal.ROUND_05UP "decimal.ROUND_05UP").
信号是在计算过程中出现的异常条件组。 根据应用程序的需要，信号可能会被忽略，被视为信息，或被视为异常。 十进制模块中的信号有：[`Clamped`](#decimal.Clamped "decimal.Clamped") 、 [`InvalidOperation`](#decimal.InvalidOperation "decimal.InvalidOperation") 、 [`DivisionByZero`](#decimal.DivisionByZero "decimal.DivisionByZero") 、 [`Inexact`](#decimal.Inexact "decimal.Inexact") 、 [`Rounded`](#decimal.Rounded "decimal.Rounded") 、 [`Subnormal`](#decimal.Subnormal "decimal.Subnormal") 、 [`Overflow`](#decimal.Overflow "decimal.Overflow") 、 [`Underflow`](#decimal.Underflow "decimal.Underflow") 以及 [`FloatOperation`](#decimal.FloatOperation "decimal.FloatOperation") 。
对于每个信号，都有一个标志和一个陷阱启动器。 遇到信号时，其标志设置为 1 ，然后，如果陷阱启用器设置为 1 ，则引发异常。 标志是粘性的，因此用户需要在监控计算之前重置它们。
参见
- IBM的通用十进制算术规范， [The General Decimal Arithmetic Specification](http://speleotrove.com/decimal/decarith.html) \[http://speleotrove.com/decimal/decarith.html\].
## 快速入门教程
通常使用小数的开始是导入模块，使用 [`getcontext()`](#decimal.getcontext "decimal.getcontext") 查看当前上下文，并在必要时为精度、舍入或启用的陷阱设置新值:
```
>>> from decimal import *
>>> getcontext()
Context(prec=28, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999,
capitals=1, clamp=0, flags=[], traps=[Overflow, DivisionByZero,
InvalidOperation])
>>> getcontext().prec = 7 # Set a new precision
```
可以从整数、字符串、浮点数或元组构造十进制实例。 从整数或浮点构造将执行该整数或浮点值的精确转换。 十进制数包括特殊值，例如 `NaN` 代表“非数字”，正的和负的 `Infinity`，和 `-0`
```
>>> getcontext().prec = 28
>>> Decimal(10)
Decimal('10')
>>> Decimal('3.14')
Decimal('3.14')
>>> Decimal(3.14)
Decimal('3.140000000000000124344978758017532527446746826171875')
>>> Decimal((0, (3, 1, 4), -2))
Decimal('3.14')
>>> Decimal(str(2.0 ** 0.5))
Decimal('1.4142135623730951')
>>> Decimal(2) ** Decimal('0.5')
Decimal('1.414213562373095048801688724')
>>> Decimal('NaN')
Decimal('NaN')
>>> Decimal('-Infinity')
Decimal('-Infinity')
```
如果 [`FloatOperation`](#decimal.FloatOperation "decimal.FloatOperation") 信号被捕获，构造函数中的小数和浮点数的意外混合或排序比较会引发异常
```
>>> c = getcontext()
>>> c.traps[FloatOperation] = True
>>> Decimal(3.14)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
decimal.FloatOperation: [<class 'decimal.FloatOperation'>]
>>> Decimal('3.5') < 3.7
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
decimal.FloatOperation: [<class 'decimal.FloatOperation'>]
>>> Decimal('3.5') == 3.5
True
```
3\.3 新版功能.
新 Decimal 的重要性仅由输入的位数决定。 上下文精度和舍入仅在算术运算期间发挥作用。
```
>>> getcontext().prec = 6
>>> Decimal('3.0')
Decimal('3.0')
>>> Decimal('3.1415926535')
Decimal('3.1415926535')
>>> Decimal('3.1415926535') + Decimal('2.7182818285')
Decimal('5.85987')
>>> getcontext().rounding = ROUND_UP
>>> Decimal('3.1415926535') + Decimal('2.7182818285')
Decimal('5.85988')
```
如果超出了C版本的内部限制，则构造一个十进制将引发 [`InvalidOperation`](#decimal.InvalidOperation "decimal.InvalidOperation")
```
>>> Decimal("1e9999999999999999999")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
decimal.InvalidOperation: [<class 'decimal.InvalidOperation'>]
```
在 3.3 版更改.
小数与 Python 的其余部分很好地交互。 这是一个小的十进制浮点飞行杂技团：
```
>>> data = list(map(Decimal, '1.34 1.87 3.45 2.35 1.00 0.03 9.25'.split()))
>>> max(data)
Decimal('9.25')
>>> min(data)
Decimal('0.03')
>>> sorted(data)
[Decimal('0.03'), Decimal('1.00'), Decimal('1.34'), Decimal('1.87'),
Decimal('2.35'), Decimal('3.45'), Decimal('9.25')]
>>> sum(data)
Decimal('19.29')
>>> a,b,c = data[:3]
>>> str(a)
'1.34'
>>> float(a)
1.34
>>> round(a, 1)
Decimal('1.3')
>>> int(a)
1
>>> a * 5
Decimal('6.70')
>>> a * b
Decimal('2.5058')
>>> c % a
Decimal('0.77')
```
Decimal 也可以使用一些数学函数：
```
>>> getcontext().prec = 28
>>> Decimal(2).sqrt()
Decimal('1.414213562373095048801688724')
>>> Decimal(1).exp()
Decimal('2.718281828459045235360287471')
>>> Decimal('10').ln()
Decimal('2.302585092994045684017991455')
>>> Decimal('10').log10()
Decimal('1')
```
`quantize()` 方法将数字四舍五入为固定指数。 此方法对于将结果舍入到固定的位置的货币应用程序非常有用：
```
>>> Decimal('7.325').quantize(Decimal('.01'), rounding=ROUND_DOWN)
Decimal('7.32')
>>> Decimal('7.325').quantize(Decimal('1.'), rounding=ROUND_UP)
Decimal('8')
```
如上所示，[`getcontext()`](#decimal.getcontext "decimal.getcontext") 函数访问当前上下文并允许更改设置。 这种方法满足大多数应用程序的需求。
对于更高级的工作，使用 Context() 构造函数创建备用上下文可能很有用。 要使用备用活动，请使用 [`setcontext()`](#decimal.setcontext "decimal.setcontext") 函数。
根据标准，[`decimal`](#module-decimal "decimal: Implementation of the General Decimal Arithmetic Specification.") 模块提供了两个现成的标准上下文 [`BasicContext`](#decimal.BasicContext "decimal.BasicContext") 和 [`ExtendedContext`](#decimal.ExtendedContext "decimal.ExtendedContext") 。 前者对调试特别有用，因为许多陷阱都已启用：
```
>>> myothercontext = Context(prec=60, rounding=ROUND_HALF_DOWN)
>>> setcontext(myothercontext)
>>> Decimal(1) / Decimal(7)
Decimal('0.142857142857142857142857142857142857142857142857142857142857')
>>> ExtendedContext
Context(prec=9, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999,
capitals=1, clamp=0, flags=[], traps=[])
>>> setcontext(ExtendedContext)
>>> Decimal(1) / Decimal(7)
Decimal('0.142857143')
>>> Decimal(42) / Decimal(0)
Decimal('Infinity')
>>> setcontext(BasicContext)
>>> Decimal(42) / Decimal(0)
Traceback (most recent call last):
File "<pyshell#143>", line 1, in -toplevel-
Decimal(42) / Decimal(0)
DivisionByZero: x / 0
```
上下文还具有用于监视计算期间遇到的异常情况的信号标志。 标志保持设置直到明确清除，因此最好通过使用 `clear_flags()` 方法清除每组受监控计算之前的标志。:
```
>>> setcontext(ExtendedContext)
>>> getcontext().clear_flags()
>>> Decimal(355) / Decimal(113)
Decimal('3.14159292')
>>> getcontext()
Context(prec=9, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999,
capitals=1, clamp=0, flags=[Inexact, Rounded], traps=[])
```
*flags* 条目显示对 `Pi` 的有理逼近被舍入（超出上下文精度的数字被抛弃）并且结果是不精确的（一些丢弃的数字不为零）。
使用上下文的 `traps` 字段中的字典设置单个陷阱：
```
>>> setcontext(ExtendedContext)
>>> Decimal(1) / Decimal(0)
Decimal('Infinity')
>>> getcontext().traps[DivisionByZero] = 1
>>> Decimal(1) / Decimal(0)
Traceback (most recent call last):
File "<pyshell#112>", line 1, in -toplevel-
Decimal(1) / Decimal(0)
DivisionByZero: x / 0
```
大多数程序仅在程序开始时调整当前上下文一次。 并且，在许多应用程序中，数据在循环内单个强制转换为 [`Decimal`](#decimal.Decimal "decimal.Decimal") 。 通过创建上下文集和小数，程序的大部分操作数据与其他 Python 数字类型没有区别。
## Decimal 对象
*class* `decimal.``Decimal`(*value="0"*, *context=None*)根据 *value* 构造一个新的 [`Decimal`](#decimal.Decimal "decimal.Decimal") 对象。
*value* 可以是整数，字符串，元组，[`float`](functions.xhtml#float "float") ，或另一个 [`Decimal`](#decimal.Decimal "decimal.Decimal") 对象。 如果没有给出 *value*，则返回 `Decimal('0')`。 如果 *value* 是一个字符串，它应该在前导和尾随空格字符以及下划线被删除之后符合十进制数字字符串语法:
```
sign ::= '+' | '-'
digit ::= '0' | '1' | '2' | '3' | '4' | '5' | '6' | '7' | '8' | '9'
indicator ::= 'e' | 'E'
digits ::= digit [digit]...
decimal-part ::= digits '.' [digits] | ['.'] digits
exponent-part ::= indicator [sign] digits
infinity ::= 'Infinity' | 'Inf'
nan ::= 'NaN' [digits] | 'sNaN' [digits]
numeric-value ::= decimal-part [exponent-part] | infinity
numeric-string ::= [sign] numeric-value | [sign] nan
```
当上面出现 `digit` 时也允许其他十进制数码。 其中包括来自各种其他语言系统的十进制数码（例如阿拉伯-印地语和天城文的数码）以及全宽数码 `'\uff10'` 到 `'\uff19'`。
如果 *value* 是一个 [`tuple`](stdtypes.xhtml#tuple "tuple") ，它应该有三个组件，一个符号（ `0` 表示正数或 `1` 表示负数），一个数字的 [`tuple`](stdtypes.xhtml#tuple "tuple") 和整数指数。 例如， `Decimal((0, (1, 4, 1, 4), -3))` 返回 `Decimal('1.414')`。
如果 *value* 是 [`float`](functions.xhtml#float "float") ，则二进制浮点值无损地转换为其精确的十进制等效值。 此转换通常需要53位或更多位数的精度。 例如， `Decimal(float('1.1'))` 转换为``Decimal('1.100000000000000088817841970012523233890533447265625')``。
*context* 精度不会影响存储的位数。 这完全由 *value* 中的位数决定。 例如，`Decimal('3.00000')` 记录所有五个零，即使上下文精度只有三。
*context* 参数的目的是确定 *value* 是格式错误的字符串时该怎么做。 如果上下文陷阱 [`InvalidOperation`](#decimal.InvalidOperation "decimal.InvalidOperation")，则引发异常；否则，构造函数返回一个新的 Decimal，其值为 `NaN`。
构造完成后， [`Decimal`](#decimal.Decimal "decimal.Decimal") 对象是不可变的。
在 3.2 版更改: 现在允许构造函数的参数为 [`float`](functions.xhtml#float "float") 实例。
在 3.3 版更改: [`float`](functions.xhtml#float "float") 参数在设置 [`FloatOperation`](#decimal.FloatOperation "decimal.FloatOperation") 陷阱时引发异常。 默认情况下，陷阱已关闭。
在 3.6 版更改: 允许下划线进行分组，就像代码中的整数和浮点文字一样。
十进制浮点对象与其他内置数值类型共享许多属性，例如 [`float`](functions.xhtml#float "float") 和 [`int`](functions.xhtml#int "int") 。 所有常用的数学运算和特殊方法都适用。 同样，十进制对象可以复制、pickle、打印、用作字典键、用作集合元素、比较、排序和强制转换为另一种类型（例如 [`float`](functions.xhtml#float "float") 或 [`int`](functions.xhtml#int "int") ）。
算术对十进制对象和算术对整数和浮点数有一些小的差别。 当余数运算符 `%` 应用于Decimal对象时，结果的符号是 *被除数* 的符号，而不是除数的符号:
```
>>> (-7) % 4
1
>>> Decimal(-7) % Decimal(4)
Decimal('-3')
```
整数除法运算符 `//` 的行为类似，返回真商的整数部分（截断为零）而不是它的向下取整，以便保留通常的标识 `x == (x // y) * y + x % y`:
```
>>> -7 // 4
-2
>>> Decimal(-7) // Decimal(4)
Decimal('-1')
```
`%` 和 `//` 运算符实现了 `remainder` 和 `divide-integer` 操作（分别），如规范中所述。
十进制对象通常不能与浮点数或 [`fractions.Fraction`](fractions.xhtml#fractions.Fraction "fractions.Fraction") 实例在算术运算中结合使用：例如,尝试将 [`Decimal`](#decimal.Decimal "decimal.Decimal") 加到 [`float`](functions.xhtml#float "float") ，将引发 [`TypeError`](exceptions.xhtml#TypeError "TypeError")。 但是，可以使用 Python 的比较运算符来比较 [`Decimal`](#decimal.Decimal "decimal.Decimal") 实例 `x` 和另一个数字 `y` 。 这样可以避免在对不同类型的数字进行相等比较时混淆结果。
在 3.2 版更改: 现在完全支持 [`Decimal`](#decimal.Decimal "decimal.Decimal") 实例和其他数字类型之间的混合类型比较。
除了标准的数字属性，十进制浮点对象还有许多专门的方法：
`adjusted`()在移出系数最右边的数字之后返回调整后的指数，直到只剩下前导数字：`Decimal('321e+5').adjusted()` 返回 7 。 用于确定最高有效位相对于小数点的位置。
`as_integer_ratio`()返回一对 `(n, d)` 整数，表示给定的 [`Decimal`](#decimal.Decimal "decimal.Decimal") 实例作为分数、最简形式项并带有正分母:
```
>>> Decimal('-3.14').as_integer_ratio()
(-157, 50)
```
转换是精确的。 在 Infinity 上引发 OverflowError ，在 NaN 上引起 ValueError 。
3\.6 新版功能.
`as_tuple`()返回一个 [named tuple](../glossary.xhtml#term-named-tuple) 表示的数字： `DecimalTuple(sign, digits, exponent)`。
`canonical`()返回参数的规范编码。 目前，一个 [`Decimal`](#decimal.Decimal "decimal.Decimal") 实例的编码始终是规范的，因此该操作返回其参数不变。
`compare`(*other*, *context=None*)比较两个 Decimal 实例的值。 [`compare()`](#decimal.Decimal.compare "decimal.Decimal.compare") 返回一个 Decimal 实例，如果任一操作数是 NaN ，那么结果是 NaN
```
a or b is a NaN ==> Decimal('NaN')
a < b ==> Decimal('-1')
a == b ==> Decimal('0')
a > b ==> Decimal('1')
```
`compare_signal`(*other*, *context=None*)除了所有 NaN 信号之外，此操作与 [`compare()`](#decimal.Decimal.compare "decimal.Decimal.compare") 方法相同。 也就是说，如果两个操作数都不是信令NaN，那么任何静默的 NaN 操作数都被视为信令NaN。
`compare_total`(*other*, *context=None*)使用它们的抽象表示而不是它们的数值来比较两个操作数。 类似于 [`compare()`](#decimal.Decimal.compare "decimal.Decimal.compare") 方法，但结果给出了一个总排序 [`Decimal`](#decimal.Decimal "decimal.Decimal") 实例。 两个 [`Decimal`](#decimal.Decimal "decimal.Decimal") 实例具有相同的数值但不同的表示形式在此排序中比较不相等：
```
>>> Decimal('12.0').compare_total(Decimal('12'))
Decimal('-1')
```
静默和发出信号的 NaN 也包括在总排序中。 这个函数的结果是 `Decimal('0')` 如果两个操作数具有相同的表示，或是 `Decimal('-1')` 如果第一个操作数的总顺序低于第二个操作数，或是 `Decimal('1')` 如果第一个操作数在总顺序中高于第二个操作数。 有关总排序的详细信息，请参阅规范。
此操作不受上下文影响且静默：不更改任何标志且不执行舍入。 作为例外，如果无法准确转换第二个操作数，则C版本可能会引发InvalidOperation。
`compare_total_mag`(*other*, *context=None*)比较两个操作数使用它们的抽象表示而不是它们的值，如 [`compare_total()`](#decimal.Decimal.compare_total "decimal.Decimal.compare_total")，但忽略每个操作数的符号。 `x.compare_total_mag(y)` 相当于 `x.copy_abs().compare_total(y.copy_abs())`。
此操作不受上下文影响且静默：不更改任何标志且不执行舍入。 作为例外，如果无法准确转换第二个操作数，则C版本可能会引发InvalidOperation。
`conjugate`()只返回self，这种方法只符合 Decimal 规范。
`copy_abs`()返回参数的绝对值。 此操作不受上下文影响并且是静默的：没有更改标志且不执行舍入。
`copy_negate`()回到参数的否定。 此操作不受上下文影响并且是静默的：没有标志更改且不执行舍入。
`copy_sign`(*other*, *context=None*)返回第一个操作数的副本，其符号设置为与第二个操作数的符号相同。 例如：
```
>>> Decimal('2.3').copy_sign(Decimal('-1.5'))
Decimal('-2.3')
```
此操作不受上下文影响且静默：不更改任何标志且不执行舍入。 作为例外，如果无法准确转换第二个操作数，则C版本可能会引发InvalidOperation。
`exp`(*context=None*)返回给定数字的（自然）指数函数``e\*\*x``的值。结果使用 [`ROUND_HALF_EVEN`](#decimal.ROUND_HALF_EVEN "decimal.ROUND_HALF_EVEN") 舍入模式正确舍入。
```
>>> Decimal(1).exp()
Decimal('2.718281828459045235360287471')
>>> Decimal(321).exp()
Decimal('2.561702493119680037517373933E+139')
```
`from_float`(*f*)将浮点数转换为十进制数的类方法。
注意， Decimal.from\_float(0.1) 与 Decimal('0.1') 不同。 由于 0.1 在二进制浮点中不能精确表示，因此该值存储为最接近的可表示值，即 0x1.999999999999ap-4 。 十进制的等效值是`0.1000000000000000055511151231257827021181583404541015625`。
注解
从 Python 3.2 开始，[`Decimal`](#decimal.Decimal "decimal.Decimal") 实例也可以直接从 [`float`](functions.xhtml#float "float") 构造。
```
>>> Decimal.from_float(0.1)
Decimal('0.1000000000000000055511151231257827021181583404541015625')
>>> Decimal.from_float(float('nan'))
Decimal('NaN')
>>> Decimal.from_float(float('inf'))
Decimal('Infinity')
>>> Decimal.from_float(float('-inf'))
Decimal('-Infinity')
```
3\.1 新版功能.
`fma`(*other*, *third*, *context=None*)混合乘法加法。 返回 self\*other+third ，中间乘积 self\*other 没有四舍五入。
```
>>> Decimal(2).fma(3, 5)
Decimal('11')
```
`is_canonical`()如果参数是规范的，则为返回 [`True`](constants.xhtml#True "True")，否则为 [`False`](constants.xhtml#False "False") 。 目前，[`Decimal`](#decimal.Decimal "decimal.Decimal") 实例总是规范的，所以这个操作总是返回 [`True`](constants.xhtml#True "True") 。
`is_finite`()如果参数是一个有限的数，则返回为 [`True`](constants.xhtml#True "True") ；如果参数为无穷大或 NaN ，则返回为 [`False`](constants.xhtml#False "False")。
`is_infinite`()如果参数为正负无穷大，则返回为 [`True`](constants.xhtml#True "True") ，否则为 [`False`](constants.xhtml#False "False") 。
`is_nan`()如果参数为 NaN （无论是否静默），则返回为 [`True`](constants.xhtml#True "True") ，否则为 [`False`](constants.xhtml#False "False") 。
`is_normal`(*context=None*)如果参数是一个有限正规数，返回 [`True`](constants.xhtml#True "True")，如果参数是0、次正规数、无穷大或是NaN，返回 [`False`](constants.xhtml#False "False")。
`is_qnan`()如果参数为静默 NaN，返回 [`True`](constants.xhtml#True "True")，否则返回 [`False`](constants.xhtml#False "False")。
`is_signed`()如果参数带有负号，则返回为 [`True`](constants.xhtml#True "True")，否则返回 [`False`](constants.xhtml#False "False")。注意，0 和 NaN 都可带有符号。
`is_snan`()如果参数为显式 NaN，则返回 [`True`](constants.xhtml#True "True")，否则返回 [`False`](constants.xhtml#False "False")。
`is_subnormal`(*context=None*)如果参数为次正规数，则返回 [`True`](constants.xhtml#True "True")，否则返回 [`False`](constants.xhtml#False "False")。
`is_zero`()如果参数是0（正负皆可），则返回 [`True`](constants.xhtml#True "True")，否则返回 [`False`](constants.xhtml#False "False")。
`ln`(*context=None*)Return the natural (base e) logarithm of the operand. The result is correctly rounded using the [`ROUND_HALF_EVEN`](#decimal.ROUND_HALF_EVEN "decimal.ROUND_HALF_EVEN") rounding mode.
`log10`(*context=None*)Return the base ten logarithm of the operand. The result is correctly rounded using the [`ROUND_HALF_EVEN`](#decimal.ROUND_HALF_EVEN "decimal.ROUND_HALF_EVEN") rounding mode.
`logb`(*context=None*)For a nonzero number, return the adjusted exponent of its operand as a [`Decimal`](#decimal.Decimal "decimal.Decimal") instance. If the operand is a zero then `Decimal('-Infinity')` is returned and the [`DivisionByZero`](#decimal.DivisionByZero "decimal.DivisionByZero") flag is raised. If the operand is an infinity then `Decimal('Infinity')` is returned.
`logical_and`(*other*, *context=None*)[`logical_and()`](#decimal.Decimal.logical_and "decimal.Decimal.logical_and") is a logical operation which takes two *logical operands* (see [Logical operands](#logical-operands-label)). The result is the digit-wise `and` of the two operands.
`logical_invert`(*context=None*)[`logical_invert()`](#decimal.Decimal.logical_invert "decimal.Decimal.logical_invert") is a logical operation. The result is the digit-wise inversion of the operand.
`logical_or`(*other*, *context=None*)[`logical_or()`](#decimal.Decimal.logical_or "decimal.Decimal.logical_or") is a logical operation which takes two *logical operands* (see [Logical operands](#logical-operands-label)). The result is the digit-wise `or` of the two operands.
`logical_xor`(*other*, *context=None*)[`logical_xor()`](#decimal.Decimal.logical_xor "decimal.Decimal.logical_xor") is a logical operation which takes two *logical operands* (see [Logical operands](#logical-operands-label)). The result is the digit-wise exclusive or of the two operands.
`max`(*other*, *context=None*)Like `max(self, other)` except that the context rounding rule is applied before returning and that `NaN` values are either signaled or ignored (depending on the context and whether they are signaling or quiet).
`max_mag`(*other*, *context=None*)Similar to the [`max()`](#decimal.Decimal.max "decimal.Decimal.max") method, but the comparison is done using the absolute values of the operands.
`min`(*other*, *context=None*)Like `min(self, other)` except that the context rounding rule is applied before returning and that `NaN` values are either signaled or ignored (depending on the context and whether they are signaling or quiet).
`min_mag`(*other*, *context=None*)Similar to the [`min()`](#decimal.Decimal.min "decimal.Decimal.min") method, but the comparison is done using the absolute values of the operands.
`next_minus`(*context=None*)Return the largest number representable in the given context (or in the current thread's context if no context is given) that is smaller than the given operand.
`next_plus`(*context=None*)Return the smallest number representable in the given context (or in the current thread's context if no context is given) that is larger than the given operand.
`next_toward`(*other*, *context=None*)If the two operands are unequal, return the number closest to the first operand in the direction of the second operand. If both operands are numerically equal, return a copy of the first operand with the sign set to be the same as the sign of the second operand.
`normalize`(*context=None*)Normalize the number by stripping the rightmost trailing zeros and converting any result equal to `Decimal('0')` to `Decimal('0e0')`. Used for producing canonical values for attributes of an equivalence class. For example, `Decimal('32.100')` and `Decimal('0.321000e+2')` both normalize to the equivalent value `Decimal('32.1')`.
`number_class`(*context=None*)Return a string describing the *class* of the operand. The returned value is one of the following ten strings.
- `"-Infinity"`, indicating that the operand is negative infinity.
- `"-Normal"`, indicating that the operand is a negative normal number.
- `"-Subnormal"`, indicating that the operand is negative and subnormal.
- `"-Zero"`, indicating that the operand is a negative zero.
- `"+Zero"`, indicating that the operand is a positive zero.
- `"+Subnormal"`, indicating that the operand is positive and subnormal.
- `"+Normal"`, indicating that the operand is a positive normal number.
- `"+Infinity"`, indicating that the operand is positive infinity.
- `"NaN"`, indicating that the operand is a quiet NaN (Not a Number).
- `"sNaN"`, indicating that the operand is a signaling NaN.
`quantize`(*exp*, *rounding=None*, *context=None*)Return a value equal to the first operand after rounding and having the exponent of the second operand.
```
>>> Decimal('1.41421356').quantize(Decimal('1.000'))
Decimal('1.414')
```
Unlike other operations, if the length of the coefficient after the quantize operation would be greater than precision, then an [`InvalidOperation`](#decimal.InvalidOperation "decimal.InvalidOperation") is signaled. This guarantees that, unless there is an error condition, the quantized exponent is always equal to that of the right-hand operand.
Also unlike other operations, quantize never signals Underflow, even if the result is subnormal and inexact.
If the exponent of the second operand is larger than that of the first then rounding may be necessary. In this case, the rounding mode is determined by the `rounding` argument if given, else by the given `context` argument; if neither argument is given the rounding mode of the current thread's context is used.
An error is returned whenever the resulting exponent is greater than `Emax` or less than `Etiny`.
`radix`()Return `Decimal(10)`, the radix (base) in which the [`Decimal`](#decimal.Decimal "decimal.Decimal")class does all its arithmetic. Included for compatibility with the specification.
`remainder_near`(*other*, *context=None*)Return the remainder from dividing *self* by *other*. This differs from `self % other` in that the sign of the remainder is chosen so as to minimize its absolute value. More precisely, the return value is `self - n * other` where `n` is the integer nearest to the exact value of `self / other`, and if two integers are equally near then the even one is chosen.
If the result is zero then its sign will be the sign of *self*.
```
>>> Decimal(18).remainder_near(Decimal(10))
Decimal('-2')
>>> Decimal(25).remainder_near(Decimal(10))
Decimal('5')
>>> Decimal(35).remainder_near(Decimal(10))
Decimal('-5')
```
`rotate`(*other*, *context=None*)Return the result of rotating the digits of the first operand by an amount specified by the second operand. The second operand must be an integer in the range -precision through precision. The absolute value of the second operand gives the number of places to rotate. If the second operand is positive then rotation is to the left; otherwise rotation is to the right. The coefficient of the first operand is padded on the left with zeros to length precision if necessary. The sign and exponent of the first operand are unchanged.
`same_quantum`(*other*, *context=None*)Test whether self and other have the same exponent or whether both are `NaN`.
此操作不受上下文影响且静默：不更改任何标志且不执行舍入。 作为例外，如果无法准确转换第二个操作数，则C版本可能会引发InvalidOperation。
`scaleb`(*other*, *context=None*)Return the first operand with exponent adjusted by the second. Equivalently, return the first operand multiplied by `10**other`. The second operand must be an integer.
`shift`(*other*, *context=None*)Return the result of shifting the digits of the first operand by an amount specified by the second operand. The second operand must be an integer in the range -precision through precision. The absolute value of the second operand gives the number of places to shift. If the second operand is positive then the shift is to the left; otherwise the shift is to the right. Digits shifted into the coefficient are zeros. The sign and exponent of the first operand are unchanged.
`sqrt`(*context=None*)Return the square root of the argument to full precision.
`to_eng_string`(*context=None*)Convert to a string, using engineering notation if an exponent is needed.
Engineering notation has an exponent which is a multiple of 3. This can leave up to 3 digits to the left of the decimal place and may require the addition of either one or two trailing zeros.
For example, this converts `Decimal('123E+1')` to `Decimal('1.23E+3')`.
`to_integral`(*rounding=None*, *context=None*)Identical to the [`to_integral_value()`](#decimal.Decimal.to_integral_value "decimal.Decimal.to_integral_value") method. The `to_integral`name has been kept for compatibility with older versions.
`to_integral_exact`(*rounding=None*, *context=None*)Round to the nearest integer, signaling [`Inexact`](#decimal.Inexact "decimal.Inexact") or [`Rounded`](#decimal.Rounded "decimal.Rounded") as appropriate if rounding occurs. The rounding mode is determined by the `rounding` parameter if given, else by the given `context`. If neither parameter is given then the rounding mode of the current context is used.
`to_integral_value`(*rounding=None*, *context=None*)Round to the nearest integer without signaling [`Inexact`](#decimal.Inexact "decimal.Inexact") or [`Rounded`](#decimal.Rounded "decimal.Rounded"). If given, applies *rounding*; otherwise, uses the rounding method in either the supplied *context* or the current context.
### Logical operands
The `logical_and()`, `logical_invert()`, `logical_or()`, and `logical_xor()` methods expect their arguments to be *logical operands*. A *logical operand* is a [`Decimal`](#decimal.Decimal "decimal.Decimal") instance whose exponent and sign are both zero, and whose digits are all either `0` or `1`.
## Context objects
Contexts are environments for arithmetic operations. They govern precision, set rules for rounding, determine which signals are treated as exceptions, and limit the range for exponents.
Each thread has its own current context which is accessed or changed using the [`getcontext()`](#decimal.getcontext "decimal.getcontext") and [`setcontext()`](#decimal.setcontext "decimal.setcontext") functions:
`decimal.``getcontext`()Return the current context for the active thread.
`decimal.``setcontext`(*c*)Set the current context for the active thread to *c*.
You can also use the [`with`](../reference/compound_stmts.xhtml#with) statement and the [`localcontext()`](#decimal.localcontext "decimal.localcontext")function to temporarily change the active context.
`decimal.``localcontext`(*ctx=None*)Return a context manager that will set the current context for the active thread to a copy of *ctx* on entry to the with-statement and restore the previous context when exiting the with-statement. If no context is specified, a copy of the current context is used.
For example, the following code sets the current decimal precision to 42 places, performs a calculation, and then automatically restores the previous context:
```
from decimal import localcontext
with localcontext() as ctx:
ctx.prec = 42 # Perform a high precision calculation
s = calculate_something()
s = +s # Round the final result back to the default precision
```
New contexts can also be created using the [`Context`](#decimal.Context "decimal.Context") constructor described below. In addition, the module provides three pre-made contexts:
*class* `decimal.``BasicContext`This is a standard context defined by the General Decimal Arithmetic Specification. Precision is set to nine. Rounding is set to [`ROUND_HALF_UP`](#decimal.ROUND_HALF_UP "decimal.ROUND_HALF_UP"). All flags are cleared. All traps are enabled (treated as exceptions) except [`Inexact`](#decimal.Inexact "decimal.Inexact"), [`Rounded`](#decimal.Rounded "decimal.Rounded"), and [`Subnormal`](#decimal.Subnormal "decimal.Subnormal").
Because many of the traps are enabled, this context is useful for debugging.
*class* `decimal.``ExtendedContext`This is a standard context defined by the General Decimal Arithmetic Specification. Precision is set to nine. Rounding is set to [`ROUND_HALF_EVEN`](#decimal.ROUND_HALF_EVEN "decimal.ROUND_HALF_EVEN"). All flags are cleared. No traps are enabled (so that exceptions are not raised during computations).
Because the traps are disabled, this context is useful for applications that prefer to have result value of `NaN` or `Infinity` instead of raising exceptions. This allows an application to complete a run in the presence of conditions that would otherwise halt the program.
*class* `decimal.``DefaultContext`This context is used by the [`Context`](#decimal.Context "decimal.Context") constructor as a prototype for new contexts. Changing a field (such a precision) has the effect of changing the default for new contexts created by the [`Context`](#decimal.Context "decimal.Context") constructor.
This context is most useful in multi-threaded environments. Changing one of the fields before threads are started has the effect of setting system-wide defaults. Changing the fields after threads have started is not recommended as it would require thread synchronization to prevent race conditions.
In single threaded environments, it is preferable to not use this context at all. Instead, simply create contexts explicitly as described below.
The default values are `prec`=`28`, `rounding`=[`ROUND_HALF_EVEN`](#decimal.ROUND_HALF_EVEN "decimal.ROUND_HALF_EVEN"), and enabled traps for [`Overflow`](#decimal.Overflow "decimal.Overflow"), [`InvalidOperation`](#decimal.InvalidOperation "decimal.InvalidOperation"), and [`DivisionByZero`](#decimal.DivisionByZero "decimal.DivisionByZero").
In addition to the three supplied contexts, new contexts can be created with the [`Context`](#decimal.Context "decimal.Context") constructor.
*class* `decimal.``Context`(*prec=None*, *rounding=None*, *Emin=None*, *Emax=None*, *capitals=None*, *clamp=None*, *flags=None*, *traps=None*)Creates a new context. If a field is not specified or is [`None`](constants.xhtml#None "None"), the default values are copied from the [`DefaultContext`](#decimal.DefaultContext "decimal.DefaultContext"). If the *flags*field is not specified or is [`None`](constants.xhtml#None "None"), all flags are cleared.
*prec* is an integer in the range \[`1`, [`MAX_PREC`](#decimal.MAX_PREC "decimal.MAX_PREC")\] that sets the precision for arithmetic operations in the context.
The *rounding* option is one of the constants listed in the section [Rounding Modes](#rounding-modes).
The *traps* and *flags* fields list any signals to be set. Generally, new contexts should only set traps and leave the flags clear.
The *Emin* and *Emax* fields are integers specifying the outer limits allowable for exponents. *Emin* must be in the range \[[`MIN_EMIN`](#decimal.MIN_EMIN "decimal.MIN_EMIN"), `0`\], *Emax* in the range \[`0`, [`MAX_EMAX`](#decimal.MAX_EMAX "decimal.MAX_EMAX")\].
The *capitals* field is either `0` or `1` (the default). If set to `1`, exponents are printed with a capital `E`; otherwise, a lowercase `e` is used: `Decimal('6.02e+23')`.
The *clamp* field is either `0` (the default) or `1`. If set to `1`, the exponent `e` of a [`Decimal`](#decimal.Decimal "decimal.Decimal")instance representable in this context is strictly limited to the range `Emin - prec + 1 <= e <= Emax - prec + 1`. If *clamp* is `0` then a weaker condition holds: the adjusted exponent of the [`Decimal`](#decimal.Decimal "decimal.Decimal") instance is at most `Emax`. When *clamp* is `1`, a large normal number will, where possible, have its exponent reduced and a corresponding number of zeros added to its coefficient, in order to fit the exponent constraints; this preserves the value of the number but loses information about significant trailing zeros. For example:
```
>>> Context(prec=6, Emax=999, clamp=1).create_decimal('1.23e999')
Decimal('1.23000E+999')
```
A *clamp* value of `1` allows compatibility with the fixed-width decimal interchange formats specified in IEEE 754.
The [`Context`](#decimal.Context "decimal.Context") class defines several general purpose methods as well as a large number of methods for doing arithmetic directly in a given context. In addition, for each of the [`Decimal`](#decimal.Decimal "decimal.Decimal") methods described above (with the exception of the `adjusted()` and `as_tuple()` methods) there is a corresponding [`Context`](#decimal.Context "decimal.Context") method. For example, for a [`Context`](#decimal.Context "decimal.Context")instance `C` and [`Decimal`](#decimal.Decimal "decimal.Decimal") instance `x`, `C.exp(x)` is equivalent to `x.exp(context=C)`. Each [`Context`](#decimal.Context "decimal.Context") method accepts a Python integer (an instance of [`int`](functions.xhtml#int "int")) anywhere that a Decimal instance is accepted.
`clear_flags`()Resets all of the flags to `0`.
`clear_traps`()Resets all of the traps to `0`.
3\.3 新版功能.
`copy`()Return a duplicate of the context.
`copy_decimal`(*num*)Return a copy of the Decimal instance num.
`create_decimal`(*num*)Creates a new Decimal instance from *num* but using *self* as context. Unlike the [`Decimal`](#decimal.Decimal "decimal.Decimal") constructor, the context precision, rounding method, flags, and traps are applied to the conversion.
This is useful because constants are often given to a greater precision than is needed by the application. Another benefit is that rounding immediately eliminates unintended effects from digits beyond the current precision. In the following example, using unrounded inputs means that adding zero to a sum can change the result:
```
>>> getcontext().prec = 3
>>> Decimal('3.4445') + Decimal('1.0023')
Decimal('4.45')
>>> Decimal('3.4445') + Decimal(0) + Decimal('1.0023')
Decimal('4.44')
```
This method implements the to-number operation of the IBM specification. If the argument is a string, no leading or trailing whitespace or underscores are permitted.
`create_decimal_from_float`(*f*)Creates a new Decimal instance from a float *f* but rounding using *self*as the context. Unlike the [`Decimal.from_float()`](#decimal.Decimal.from_float "decimal.Decimal.from_float") class method, the context precision, rounding method, flags, and traps are applied to the conversion.
```
>>> context = Context(prec=5, rounding=ROUND_DOWN)
>>> context.create_decimal_from_float(math.pi)
Decimal('3.1415')
>>> context = Context(prec=5, traps=[Inexact])
>>> context.create_decimal_from_float(math.pi)
Traceback (most recent call last):
...
decimal.Inexact: None
```
3\.1 新版功能.
`Etiny`()Returns a value equal to `Emin - prec + 1` which is the minimum exponent value for subnormal results. When underflow occurs, the exponent is set to [`Etiny`](#decimal.Context.Etiny "decimal.Context.Etiny").
`Etop`()Returns a value equal to `Emax - prec + 1`.
The usual approach to working with decimals is to create [`Decimal`](#decimal.Decimal "decimal.Decimal")instances and then apply arithmetic operations which take place within the current context for the active thread. An alternative approach is to use context methods for calculating within a specific context. The methods are similar to those for the [`Decimal`](#decimal.Decimal "decimal.Decimal") class and are only briefly recounted here.
`abs`(*x*)Returns the absolute value of *x*.
`add`(*x*, *y*)Return the sum of *x* and *y*.
`canonical`(*x*)Returns the same Decimal object *x*.
`compare`(*x*, *y*)Compares *x* and *y* numerically.
`compare_signal`(*x*, *y*)Compares the values of the two operands numerically.
`compare_total`(*x*, *y*)Compares two operands using their abstract representation.
`compare_total_mag`(*x*, *y*)Compares two operands using their abstract representation, ignoring sign.
`copy_abs`(*x*)Returns a copy of *x* with the sign set to 0.
`copy_negate`(*x*)Returns a copy of *x* with the sign inverted.
`copy_sign`(*x*, *y*)Copies the sign from *y* to *x*.
`divide`(*x*, *y*)Return *x* divided by *y*.
`divide_int`(*x*, *y*)Return *x* divided by *y*, truncated to an integer.
`divmod`(*x*, *y*)Divides two numbers and returns the integer part of the result.
`exp`(*x*)Returns e \*\* x.
`fma`(*x*, *y*, *z*)Returns *x* multiplied by *y*, plus *z*.
`is_canonical`(*x*)Returns `True` if *x* is canonical; otherwise returns `False`.
`is_finite`(*x*)Returns `True` if *x* is finite; otherwise returns `False`.
`is_infinite`(*x*)Returns `True` if *x* is infinite; otherwise returns `False`.
`is_nan`(*x*)Returns `True` if *x* is a qNaN or sNaN; otherwise returns `False`.
`is_normal`(*x*)Returns `True` if *x* is a normal number; otherwise returns `False`.
`is_qnan`(*x*)Returns `True` if *x* is a quiet NaN; otherwise returns `False`.
`is_signed`(*x*)Returns `True` if *x* is negative; otherwise returns `False`.
`is_snan`(*x*)Returns `True` if *x* is a signaling NaN; otherwise returns `False`.
`is_subnormal`(*x*)Returns `True` if *x* is subnormal; otherwise returns `False`.
`is_zero`(*x*)Returns `True` if *x* is a zero; otherwise returns `False`.
`ln`(*x*)Returns the natural (base e) logarithm of *x*.
`log10`(*x*)Returns the base 10 logarithm of *x*.
`logb`(*x*)Returns the exponent of the magnitude of the operand's MSD.
`logical_and`(*x*, *y*)Applies the logical operation *and* between each operand's digits.
`logical_invert`(*x*)Invert all the digits in *x*.
`logical_or`(*x*, *y*)Applies the logical operation *or* between each operand's digits.
`logical_xor`(*x*, *y*)Applies the logical operation *xor* between each operand's digits.
`max`(*x*, *y*)Compares two values numerically and returns the maximum.
`max_mag`(*x*, *y*)Compares the values numerically with their sign ignored.
`min`(*x*, *y*)Compares two values numerically and returns the minimum.
`min_mag`(*x*, *y*)Compares the values numerically with their sign ignored.
`minus`(*x*)Minus corresponds to the unary prefix minus operator in Python.
`multiply`(*x*, *y*)Return the product of *x* and *y*.
`next_minus`(*x*)Returns the largest representable number smaller than *x*.
`next_plus`(*x*)Returns the smallest representable number larger than *x*.
`next_toward`(*x*, *y*)Returns the number closest to *x*, in direction towards *y*.
`normalize`(*x*)Reduces *x* to its simplest form.
`number_class`(*x*)Returns an indication of the class of *x*.
`plus`(*x*)Plus corresponds to the unary prefix plus operator in Python. This operation applies the context precision and rounding, so it is *not* an identity operation.
`power`(*x*, *y*, *modulo=None*)Return `x` to the power of `y`, reduced modulo `modulo` if given.
With two arguments, compute `x**y`. If `x` is negative then `y`must be integral. The result will be inexact unless `y` is integral and the result is finite and can be expressed exactly in 'precision' digits. The rounding mode of the context is used. Results are always correctly-rounded in the Python version.
在 3.3 版更改: The C module computes [`power()`](#decimal.Context.power "decimal.Context.power") in terms of the correctly-rounded [`exp()`](#decimal.Context.exp "decimal.Context.exp") and [`ln()`](#decimal.Context.ln "decimal.Context.ln") functions. The result is well-defined but only "almost always correctly-rounded".
With three arguments, compute `(x**y) % modulo`. For the three argument form, the following restrictions on the arguments hold:
> - all three arguments must be integral
> - `y` must be nonnegative
> - at least one of `x` or `y` must be nonzero
> - `modulo` must be nonzero and have at most 'precision' digits
The value resulting from `Context.power(x, y, modulo)` is equal to the value that would be obtained by computing
```
(x**y)
% modulo
```
with unbounded precision, but is computed more efficiently. The exponent of the result is zero, regardless of the exponents of `x`, `y` and `modulo`. The result is always exact.
`quantize`(*x*, *y*)Returns a value equal to *x* (rounded), having the exponent of *y*.
`radix`()Just returns 10, as this is Decimal, :)
`remainder`(*x*, *y*)Returns the remainder from integer division.
The sign of the result, if non-zero, is the same as that of the original dividend.
`remainder_near`(*x*, *y*)Returns `x - y * n`, where *n* is the integer nearest the exact value of `x / y` (if the result is 0 then its sign will be the sign of *x*).
`rotate`(*x*, *y*)Returns a rotated copy of *x*, *y* times.
`same_quantum`(*x*, *y*)Returns `True` if the two operands have the same exponent.
`scaleb`(*x*, *y*)Returns the first operand after adding the second value its exp.
`shift`(*x*, *y*)Returns a shifted copy of *x*, *y* times.
`sqrt`(*x*)Square root of a non-negative number to context precision.
`subtract`(*x*, *y*)Return the difference between *x* and *y*.
`to_eng_string`(*x*)Convert to a string, using engineering notation if an exponent is needed.
Engineering notation has an exponent which is a multiple of 3. This can leave up to 3 digits to the left of the decimal place and may require the addition of either one or two trailing zeros.
`to_integral_exact`(*x*)Rounds to an integer.
`to_sci_string`(*x*)Converts a number to a string using scientific notation.
## 常数
The constants in this section are only relevant for the C module. They are also included in the pure Python version for compatibility.
32-bit
64-bit
`decimal.``MAX_PREC``425000000`
`999999999999999999`
`decimal.``MAX_EMAX``425000000`
`999999999999999999`
`decimal.``MIN_EMIN``-425000000`
`-999999999999999999`
`decimal.``MIN_ETINY``-849999999`
`-1999999999999999997`
`decimal.``HAVE_THREADS`The default value is `True`. If Python is compiled without threads, the C version automatically disables the expensive thread local context machinery. In this case, the value is `False`.
## Rounding modes
`decimal.``ROUND_CEILING`Round towards `Infinity`.
`decimal.``ROUND_DOWN`Round towards zero.
`decimal.``ROUND_FLOOR`Round towards `-Infinity`.
`decimal.``ROUND_HALF_DOWN`Round to nearest with ties going towards zero.
`decimal.``ROUND_HALF_EVEN`Round to nearest with ties going to nearest even integer.
`decimal.``ROUND_HALF_UP`Round to nearest with ties going away from zero.
`decimal.``ROUND_UP`Round away from zero.
`decimal.``ROUND_05UP`Round away from zero if last digit after rounding towards zero would have been 0 or 5; otherwise round towards zero.
## Signals
Signals represent conditions that arise during computation. Each corresponds to one context flag and one context trap enabler.
The context flag is set whenever the condition is encountered. After the computation, flags may be checked for informational purposes (for instance, to determine whether a computation was exact). After checking the flags, be sure to clear all flags before starting the next computation.
If the context's trap enabler is set for the signal, then the condition causes a Python exception to be raised. For example, if the [`DivisionByZero`](#decimal.DivisionByZero "decimal.DivisionByZero") trap is set, then a [`DivisionByZero`](#decimal.DivisionByZero "decimal.DivisionByZero") exception is raised upon encountering the condition.
*class* `decimal.``Clamped`Altered an exponent to fit representation constraints.
Typically, clamping occurs when an exponent falls outside the context's `Emin` and `Emax` limits. If possible, the exponent is reduced to fit by adding zeros to the coefficient.
*class* `decimal.``DecimalException`Base class for other signals and a subclass of [`ArithmeticError`](exceptions.xhtml#ArithmeticError "ArithmeticError").
*class* `decimal.``DivisionByZero`Signals the division of a non-infinite number by zero.
Can occur with division, modulo division, or when raising a number to a negative power. If this signal is not trapped, returns `Infinity` or `-Infinity` with the sign determined by the inputs to the calculation.
*class* `decimal.``Inexact`Indicates that rounding occurred and the result is not exact.
Signals when non-zero digits were discarded during rounding. The rounded result is returned. The signal flag or trap is used to detect when results are inexact.
*class* `decimal.``InvalidOperation`An invalid operation was performed.
Indicates that an operation was requested that does not make sense. If not trapped, returns `NaN`. Possible causes include:
```
Infinity - Infinity
0 * Infinity
Infinity / Infinity
x % 0
Infinity % x
sqrt(-x) and x > 0
0 ** 0
x ** (non-integer)
x ** Infinity
```
*class* `decimal.``Overflow`Numerical overflow.
Indicates the exponent is larger than `Emax` after rounding has occurred. If not trapped, the result depends on the rounding mode, either pulling inward to the largest representable finite number or rounding outward to `Infinity`. In either case, [`Inexact`](#decimal.Inexact "decimal.Inexact") and [`Rounded`](#decimal.Rounded "decimal.Rounded")are also signaled.
*class* `decimal.``Rounded`Rounding occurred though possibly no information was lost.
Signaled whenever rounding discards digits; even if those digits are zero (such as rounding `5.00` to `5.0`). If not trapped, returns the result unchanged. This signal is used to detect loss of significant digits.
*class* `decimal.``Subnormal`Exponent was lower than `Emin` prior to rounding.
Occurs when an operation result is subnormal (the exponent is too small). If not trapped, returns the result unchanged.
*class* `decimal.``Underflow`Numerical underflow with result rounded to zero.
Occurs when a subnormal result is pushed to zero by rounding. [`Inexact`](#decimal.Inexact "decimal.Inexact")and [`Subnormal`](#decimal.Subnormal "decimal.Subnormal") are also signaled.
*class* `decimal.``FloatOperation`Enable stricter semantics for mixing floats and Decimals.
If the signal is not trapped (default), mixing floats and Decimals is permitted in the [`Decimal`](#decimal.Decimal "decimal.Decimal") constructor, [`create_decimal()`](#decimal.Context.create_decimal "decimal.Context.create_decimal") and all comparison operators. Both conversion and comparisons are exact. Any occurrence of a mixed operation is silently recorded by setting [`FloatOperation`](#decimal.FloatOperation "decimal.FloatOperation") in the context flags. Explicit conversions with [`from_float()`](#decimal.Decimal.from_float "decimal.Decimal.from_float")or [`create_decimal_from_float()`](#decimal.Context.create_decimal_from_float "decimal.Context.create_decimal_from_float") do not set the flag.
Otherwise (the signal is trapped), only equality comparisons and explicit conversions are silent. All other mixed operations raise [`FloatOperation`](#decimal.FloatOperation "decimal.FloatOperation").
The following table summarizes the hierarchy of signals:
```
exceptions.ArithmeticError(exceptions.Exception)
DecimalException
Clamped
DivisionByZero(DecimalException, exceptions.ZeroDivisionError)
Inexact
Overflow(Inexact, Rounded)
Underflow(Inexact, Rounded, Subnormal)
InvalidOperation
Rounded
Subnormal
FloatOperation(DecimalException, exceptions.TypeError)
```
## Floating Point Notes
### Mitigating round-off error with increased precision
The use of decimal floating point eliminates decimal representation error (making it possible to represent `0.1` exactly); however, some operations can still incur round-off error when non-zero digits exceed the fixed precision.
The effects of round-off error can be amplified by the addition or subtraction of nearly offsetting quantities resulting in loss of significance. Knuth provides two instructive examples where rounded floating point arithmetic with insufficient precision causes the breakdown of the associative and distributive properties of addition:
```
# Examples from Seminumerical Algorithms, Section 4.2.2.
>>> from decimal import Decimal, getcontext
>>> getcontext().prec = 8
>>> u, v, w = Decimal(11111113), Decimal(-11111111), Decimal('7.51111111')
>>> (u + v) + w
Decimal('9.5111111')
>>> u + (v + w)
Decimal('10')
>>> u, v, w = Decimal(20000), Decimal(-6), Decimal('6.0000003')
>>> (u*v) + (u*w)
Decimal('0.01')
>>> u * (v+w)
Decimal('0.0060000')
```
The [`decimal`](#module-decimal "decimal: Implementation of the General Decimal Arithmetic Specification.") module makes it possible to restore the identities by expanding the precision sufficiently to avoid loss of significance:
```
>>> getcontext().prec = 20
>>> u, v, w = Decimal(11111113), Decimal(-11111111), Decimal('7.51111111')
>>> (u + v) + w
Decimal('9.51111111')
>>> u + (v + w)
Decimal('9.51111111')
>>>
>>> u, v, w = Decimal(20000), Decimal(-6), Decimal('6.0000003')
>>> (u*v) + (u*w)
Decimal('0.0060000')
>>> u * (v+w)
Decimal('0.0060000')
```
### Special values
The number system for the [`decimal`](#module-decimal "decimal: Implementation of the General Decimal Arithmetic Specification.") module provides special values including `NaN`, `sNaN`, `-Infinity`, `Infinity`, and two zeros, `+0` and `-0`.
Infinities can be constructed directly with: `Decimal('Infinity')`. Also, they can arise from dividing by zero when the [`DivisionByZero`](#decimal.DivisionByZero "decimal.DivisionByZero") signal is not trapped. Likewise, when the [`Overflow`](#decimal.Overflow "decimal.Overflow") signal is not trapped, infinity can result from rounding beyond the limits of the largest representable number.
The infinities are signed (affine) and can be used in arithmetic operations where they get treated as very large, indeterminate numbers. For instance, adding a constant to infinity gives another infinite result.
Some operations are indeterminate and return `NaN`, or if the [`InvalidOperation`](#decimal.InvalidOperation "decimal.InvalidOperation") signal is trapped, raise an exception. For example, `0/0` returns `NaN` which means "not a number". This variety of `NaN` is quiet and, once created, will flow through other computations always resulting in another `NaN`. This behavior can be useful for a series of computations that occasionally have missing inputs --- it allows the calculation to proceed while flagging specific results as invalid.
A variant is `sNaN` which signals rather than remaining quiet after every operation. This is a useful return value when an invalid result needs to interrupt a calculation for special handling.
The behavior of Python's comparison operators can be a little surprising where a `NaN` is involved. A test for equality where one of the operands is a quiet or signaling `NaN` always returns [`False`](constants.xhtml#False "False") (even when doing `Decimal('NaN')==Decimal('NaN')`), while a test for inequality always returns [`True`](constants.xhtml#True "True"). An attempt to compare two Decimals using any of the `<`, `<=`, `>` or `>=` operators will raise the [`InvalidOperation`](#decimal.InvalidOperation "decimal.InvalidOperation") signal if either operand is a `NaN`, and return [`False`](constants.xhtml#False "False") if this signal is not trapped. Note that the General Decimal Arithmetic specification does not specify the behavior of direct comparisons; these rules for comparisons involving a `NaN` were taken from the IEEE 854 standard (see Table 3 in section 5.7). To ensure strict standards-compliance, use the `compare()`and `compare-signal()` methods instead.
The signed zeros can result from calculations that underflow. They keep the sign that would have resulted if the calculation had been carried out to greater precision. Since their magnitude is zero, both positive and negative zeros are treated as equal and their sign is informational.
In addition to the two signed zeros which are distinct yet equal, there are various representations of zero with differing precisions yet equivalent in value. This takes a bit of getting used to. For an eye accustomed to normalized floating point representations, it is not immediately obvious that the following calculation returns a value equal to zero:
```
>>> 1 / Decimal('Infinity')
Decimal('0E-1000026')
```
## Working with threads
The [`getcontext()`](#decimal.getcontext "decimal.getcontext") function accesses a different [`Context`](#decimal.Context "decimal.Context") object for each thread. Having separate thread contexts means that threads may make changes (such as `getcontext().prec=10`) without interfering with other threads.
Likewise, the [`setcontext()`](#decimal.setcontext "decimal.setcontext") function automatically assigns its target to the current thread.
If [`setcontext()`](#decimal.setcontext "decimal.setcontext") has not been called before [`getcontext()`](#decimal.getcontext "decimal.getcontext"), then [`getcontext()`](#decimal.getcontext "decimal.getcontext") will automatically create a new context for use in the current thread.
The new context is copied from a prototype context called *DefaultContext*. To control the defaults so that each thread will use the same values throughout the application, directly modify the *DefaultContext* object. This should be done *before* any threads are started so that there won't be a race condition between threads calling [`getcontext()`](#decimal.getcontext "decimal.getcontext"). For example:
```
# Set applicationwide defaults for all threads about to be launched
DefaultContext.prec = 12
DefaultContext.rounding = ROUND_DOWN
DefaultContext.traps = ExtendedContext.traps.copy()
DefaultContext.traps[InvalidOperation] = 1
setcontext(DefaultContext)
# Afterwards, the threads can be started
t1.start()
t2.start()
t3.start()
. . .
```
## Recipes
Here are a few recipes that serve as utility functions and that demonstrate ways to work with the [`Decimal`](#decimal.Decimal "decimal.Decimal") class:
```
def moneyfmt(value, places=2, curr='', sep=',', dp='.',
pos='', neg='-', trailneg=''):
"""Convert Decimal to a money formatted string.
places: required number of places after the decimal point
curr: optional currency symbol before the sign (may be blank)
sep: optional grouping separator (comma, period, space, or blank)
dp: decimal point indicator (comma or period)
only specify as blank when places is zero
pos: optional sign for positive numbers: '+', space or blank
neg: optional sign for negative numbers: '-', '(', space or blank
trailneg:optional trailing minus indicator: '-', ')', space or blank
>>> d = Decimal('-1234567.8901')
>>> moneyfmt(d, curr='$')
'-$1,234,567.89'
>>> moneyfmt(d, places=0, sep='.', dp='', neg='', trailneg='-')
'1.234.568-'
>>> moneyfmt(d, curr='$', neg='(', trailneg=')')
'($1,234,567.89)'
>>> moneyfmt(Decimal(123456789), sep=' ')
'123 456 789.00'
>>> moneyfmt(Decimal('-0.02'), neg='<', trailneg='>')
'<0.02>'
"""
q = Decimal(10) ** -places # 2 places --> '0.01'
sign, digits, exp = value.quantize(q).as_tuple()
result = []
digits = list(map(str, digits))
build, next = result.append, digits.pop
if sign:
build(trailneg)
for i in range(places):
build(next() if digits else '0')
if places:
build(dp)
if not digits:
build('0')
i = 0
while digits:
build(next())
i += 1
if i == 3 and digits:
i = 0
build(sep)
build(curr)
build(neg if sign else pos)
return ''.join(reversed(result))
def pi():
"""Compute Pi to the current precision.
>>> print(pi())
3.141592653589793238462643383
"""
getcontext().prec += 2 # extra digits for intermediate steps
three = Decimal(3) # substitute "three=3.0" for regular floats
lasts, t, s, n, na, d, da = 0, three, 3, 1, 0, 0, 24
while s != lasts:
lasts = s
n, na = n+na, na+8
d, da = d+da, da+32
t = (t * n) / d
s += t
getcontext().prec -= 2
return +s # unary plus applies the new precision
def exp(x):
"""Return e raised to the power of x. Result type matches input type.
>>> print(exp(Decimal(1)))
2.718281828459045235360287471
>>> print(exp(Decimal(2)))
7.389056098930650227230427461
>>> print(exp(2.0))
7.38905609893
>>> print(exp(2+0j))
(7.38905609893+0j)
"""
getcontext().prec += 2
i, lasts, s, fact, num = 0, 0, 1, 1, 1
while s != lasts:
lasts = s
i += 1
fact *= i
num *= x
s += num / fact
getcontext().prec -= 2
return +s
def cos(x):
"""Return the cosine of x as measured in radians.
The Taylor series approximation works best for a small value of x.
For larger values, first compute x = x % (2 * pi).
>>> print(cos(Decimal('0.5')))
0.8775825618903727161162815826
>>> print(cos(0.5))
0.87758256189
>>> print(cos(0.5+0j))
(0.87758256189+0j)
"""
getcontext().prec += 2
i, lasts, s, fact, num, sign = 0, 0, 1, 1, 1, 1
while s != lasts:
lasts = s
i += 2
fact *= i * (i-1)
num *= x * x
sign *= -1
s += num / fact * sign
getcontext().prec -= 2
return +s
def sin(x):
"""Return the sine of x as measured in radians.
The Taylor series approximation works best for a small value of x.
For larger values, first compute x = x % (2 * pi).
>>> print(sin(Decimal('0.5')))
0.4794255386042030002732879352
>>> print(sin(0.5))
0.479425538604
>>> print(sin(0.5+0j))
(0.479425538604+0j)
"""
getcontext().prec += 2
i, lasts, s, fact, num, sign = 1, 0, x, 1, x, 1
while s != lasts:
lasts = s
i += 2
fact *= i * (i-1)
num *= x * x
sign *= -1
s += num / fact * sign
getcontext().prec -= 2
return +s
```
## Decimal FAQ
Q. It is cumbersome to type `decimal.Decimal('1234.5')`. Is there a way to minimize typing when using the interactive interpreter?
A. Some users abbreviate the constructor to just a single letter:
```
>>> D = decimal.Decimal
>>> D('1.23') + D('3.45')
Decimal('4.68')
```
Q. In a fixed-point application with two decimal places, some inputs have many places and need to be rounded. Others are not supposed to have excess digits and need to be validated. What methods should be used?
A. The `quantize()` method rounds to a fixed number of decimal places. If the [`Inexact`](#decimal.Inexact "decimal.Inexact") trap is set, it is also useful for validation:
```
>>> TWOPLACES = Decimal(10) ** -2 # same as Decimal('0.01')
```
```
>>> # Round to two places
>>> Decimal('3.214').quantize(TWOPLACES)
Decimal('3.21')
```
```
>>> # Validate that a number does not exceed two places
>>> Decimal('3.21').quantize(TWOPLACES, context=Context(traps=[Inexact]))
Decimal('3.21')
```
```
>>> Decimal('3.214').quantize(TWOPLACES, context=Context(traps=[Inexact]))
Traceback (most recent call last):
...
Inexact: None
```
Q. Once I have valid two place inputs, how do I maintain that invariant throughout an application?
A. Some operations like addition, subtraction, and multiplication by an integer will automatically preserve fixed point. Others operations, like division and non-integer multiplication, will change the number of decimal places and need to be followed-up with a `quantize()` step:
```
>>> a = Decimal('102.72') # Initial fixed-point values
>>> b = Decimal('3.17')
>>> a + b # Addition preserves fixed-point
Decimal('105.89')
>>> a - b
Decimal('99.55')
>>> a * 42 # So does integer multiplication
Decimal('4314.24')
>>> (a * b).quantize(TWOPLACES) # Must quantize non-integer multiplication
Decimal('325.62')
>>> (b / a).quantize(TWOPLACES) # And quantize division
Decimal('0.03')
```
In developing fixed-point applications, it is convenient to define functions to handle the `quantize()` step:
```
>>> def mul(x, y, fp=TWOPLACES):
... return (x * y).quantize(fp)
>>> def div(x, y, fp=TWOPLACES):
... return (x / y).quantize(fp)
```
```
>>> mul(a, b) # Automatically preserve fixed-point
Decimal('325.62')
>>> div(b, a)
Decimal('0.03')
```
Q. There are many ways to express the same value. The numbers `200`, `200.000`, `2E2`, and `02E+4` all have the same value at various precisions. Is there a way to transform them to a single recognizable canonical value?
A. The `normalize()` method maps all equivalent values to a single representative:
```
>>> values = map(Decimal, '200 200.000 2E2 .02E+4'.split())
>>> [v.normalize() for v in values]
[Decimal('2E+2'), Decimal('2E+2'), Decimal('2E+2'), Decimal('2E+2')]
```
Q. Some decimal values always print with exponential notation. Is there a way to get a non-exponential representation?
A. For some values, exponential notation is the only way to express the number of significant places in the coefficient. For example, expressing `5.0E+3` as `5000` keeps the value constant but cannot show the original's two-place significance.
If an application does not care about tracking significance, it is easy to remove the exponent and trailing zeroes, losing significance, but keeping the value unchanged:
```
>>> def remove_exponent(d):
... return d.quantize(Decimal(1)) if d == d.to_integral() else d.normalize()
```
```
>>> remove_exponent(Decimal('5E+3'))
Decimal('5000')
```
Q. Is there a way to convert a regular float to a [`Decimal`](#decimal.Decimal "decimal.Decimal")?
A. Yes, any binary floating point number can be exactly expressed as a Decimal though an exact conversion may take more precision than intuition would suggest:
```
>>> Decimal(math.pi)
Decimal('3.141592653589793115997963468544185161590576171875')
```
Q. Within a complex calculation, how can I make sure that I haven't gotten a spurious result because of insufficient precision or rounding anomalies.
A. The decimal module makes it easy to test results. A best practice is to re-run calculations using greater precision and with various rounding modes. Widely differing results indicate insufficient precision, rounding mode issues, ill-conditioned inputs, or a numerically unstable algorithm.
Q. I noticed that context precision is applied to the results of operations but not to the inputs. Is there anything to watch out for when mixing values of different precisions?
A. Yes. The principle is that all values are considered to be exact and so is the arithmetic on those values. Only the results are rounded. The advantage for inputs is that "what you type is what you get". A disadvantage is that the results can look odd if you forget that the inputs haven't been rounded:
```
>>> getcontext().prec = 3
>>> Decimal('3.104') + Decimal('2.104')
Decimal('5.21')
>>> Decimal('3.104') + Decimal('0.000') + Decimal('2.104')
Decimal('5.20')
```
The solution is either to increase precision or to force rounding of inputs using the unary plus operation:
```
>>> getcontext().prec = 3
>>> +Decimal('1.23456789') # unary plus triggers rounding
Decimal('1.23')
```
Alternatively, inputs can be rounded upon creation using the [`Context.create_decimal()`](#decimal.Context.create_decimal "decimal.Context.create_decimal") method:
```
>>> Context(prec=5, rounding=ROUND_DOWN).create_decimal('1.2345678')
Decimal('1.2345')
```
Q. Is the CPython implementation fast for large numbers?
A. Yes. In the CPython and PyPy3 implementations, the C/CFFI versions of the decimal module integrate the high speed [libmpdec](https://www.bytereef.org/mpdecimal/doc/libmpdec/index.html) \[https://www.bytereef.org/mpdecimal/doc/libmpdec/index.html\] library for arbitrary precision correctly-rounded decimal floating point arithmetic. `libmpdec` uses [Karatsuba multiplication](https://en.wikipedia.org/wiki/Karatsuba_algorithm) \[https://en.wikipedia.org/wiki/Karatsuba\_algorithm\]for medium-sized numbers and the [Number Theoretic Transform](https://en.wikipedia.org/wiki/Discrete_Fourier_transform_(general)#Number-theoretic_transform) \[https://en.wikipedia.org/wiki/Discrete\_Fourier\_transform\_(general)#Number-theoretic\_transform\]for very large numbers. However, to realize this performance gain, the context needs to be set for unrounded calculations.
```
>>> c = getcontext()
>>> c.prec = MAX_PREC
>>> c.Emax = MAX_EMAX
>>> c.Emin = MIN_EMIN
```
3\.3 新版功能.
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- 性能优化
- Build and C API Changes
- 弃用
- Porting to Python 3.3
- What's New In Python 3.2
- PEP 384: Defining a Stable ABI
- PEP 389: Argparse Command Line Parsing Module
- PEP 391: Dictionary Based Configuration for Logging
- PEP 3148: The concurrent.futures module
- PEP 3147: PYC Repository Directories
- PEP 3149: ABI Version Tagged .so Files
- PEP 3333: Python Web Server Gateway Interface v1.0.1
- 其他语言特性修改
- New, Improved, and Deprecated Modules
- 多线程
- 性能优化
- Unicode
- Codecs
- 文档
- IDLE
- Code Repository
- Build and C API Changes
- Porting to Python 3.2
- What's New In Python 3.1
- PEP 372: Ordered Dictionaries
- PEP 378: Format Specifier for Thousands Separator
- 其他语言特性修改
- New, Improved, and Deprecated Modules
- 性能优化
- IDLE
- Build and C API Changes
- Porting to Python 3.1
- What's New In Python 3.0
- Common Stumbling Blocks
- Overview Of Syntax Changes
- Changes Already Present In Python 2.6
- Library Changes
- PEP 3101: A New Approach To String Formatting
- Changes To Exceptions
- Miscellaneous Other Changes
- Build and C API Changes
- 性能
- Porting To Python 3.0
- What's New in Python 2.7
- The Future for Python 2.x
- Changes to the Handling of Deprecation Warnings
- Python 3.1 Features
- PEP 372: Adding an Ordered Dictionary to collections
- PEP 378: Format Specifier for Thousands Separator
- PEP 389: The argparse Module for Parsing Command Lines
- PEP 391: Dictionary-Based Configuration For Logging
- PEP 3106: Dictionary Views
- PEP 3137: The memoryview Object
- 其他语言特性修改
- New and Improved Modules
- Build and C API Changes
- Other Changes and Fixes
- Porting to Python 2.7
- New Features Added to Python 2.7 Maintenance Releases
- Acknowledgements
- Python 2.6 有什么新变化
- Python 3.0
- Changes to the Development Process
- PEP 343: The 'with' statement
- PEP 366: Explicit Relative Imports From a Main Module
- PEP 370: Per-user site-packages Directory
- PEP 371: The multiprocessing Package
- PEP 3101: Advanced String Formatting
- PEP 3105: print As a Function
- PEP 3110: Exception-Handling Changes
- PEP 3112: Byte Literals
- PEP 3116: New I/O Library
- PEP 3118: Revised Buffer Protocol
- PEP 3119: Abstract Base Classes
- PEP 3127: Integer Literal Support and Syntax
- PEP 3129: Class Decorators
- PEP 3141: A Type Hierarchy for Numbers
- 其他语言特性修改
- New and Improved Modules
- Deprecations and Removals
- Build and C API Changes
- Porting to Python 2.6
- Acknowledgements
- What's New in Python 2.5
- PEP 308: Conditional Expressions
- PEP 309: Partial Function Application
- PEP 314: Metadata for Python Software Packages v1.1
- PEP 328: Absolute and Relative Imports
- PEP 338: Executing Modules as Scripts
- PEP 341: Unified try/except/finally
- PEP 342: New Generator Features
- PEP 343: The 'with' statement
- PEP 352: Exceptions as New-Style Classes
- PEP 353: Using ssize_t as the index type
- PEP 357: The 'index' method
- 其他语言特性修改
- New, Improved, and Removed Modules
- Build and C API Changes
- Porting to Python 2.5
- Acknowledgements
- What's New in Python 2.4
- PEP 218: Built-In Set Objects
- PEP 237: Unifying Long Integers and Integers
- PEP 289: Generator Expressions
- PEP 292: Simpler String Substitutions
- PEP 318: Decorators for Functions and Methods
- PEP 322: Reverse Iteration
- PEP 324: New subprocess Module
- PEP 327: Decimal Data Type
- PEP 328: Multi-line Imports
- PEP 331: Locale-Independent Float/String Conversions
- 其他语言特性修改
- New, Improved, and Deprecated Modules
- Build and C API Changes
- Porting to Python 2.4
- Acknowledgements
- What's New in Python 2.3
- PEP 218: A Standard Set Datatype
- PEP 255: Simple Generators
- PEP 263: Source Code Encodings
- PEP 273: Importing Modules from ZIP Archives
- PEP 277: Unicode file name support for Windows NT
- PEP 278: Universal Newline Support
- PEP 279: enumerate()
- PEP 282: The logging Package
- PEP 285: A Boolean Type
- PEP 293: Codec Error Handling Callbacks
- PEP 301: Package Index and Metadata for Distutils
- PEP 302: New Import Hooks
- PEP 305: Comma-separated Files
- PEP 307: Pickle Enhancements
- Extended Slices
- 其他语言特性修改
- New, Improved, and Deprecated Modules
- Pymalloc: A Specialized Object Allocator
- Build and C API Changes
- Other Changes and Fixes
- Porting to Python 2.3
- Acknowledgements
- What's New in Python 2.2
- 概述
- PEPs 252 and 253: Type and Class Changes
- PEP 234: Iterators
- PEP 255: Simple Generators
- PEP 237: Unifying Long Integers and Integers
- PEP 238: Changing the Division Operator
- Unicode Changes
- PEP 227: Nested Scopes
- New and Improved Modules
- Interpreter Changes and Fixes
- Other Changes and Fixes
- Acknowledgements
- What's New in Python 2.1
- 概述
- PEP 227: Nested Scopes
- PEP 236: future Directives
- PEP 207: Rich Comparisons
- PEP 230: Warning Framework
- PEP 229: New Build System
- PEP 205: Weak References
- PEP 232: Function Attributes
- PEP 235: Importing Modules on Case-Insensitive Platforms
- PEP 217: Interactive Display Hook
- PEP 208: New Coercion Model
- PEP 241: Metadata in Python Packages
- New and Improved Modules
- Other Changes and Fixes
- Acknowledgements
- What's New in Python 2.0
- 概述
- What About Python 1.6?
- New Development Process
- Unicode
- 列表推导式
- Augmented Assignment
- 字符串的方法
- Garbage Collection of Cycles
- Other Core Changes
- Porting to 2.0
- Extending/Embedding Changes
- Distutils: Making Modules Easy to Install
- XML Modules
- Module changes
- New modules
- IDLE Improvements
- Deleted and Deprecated Modules
- Acknowledgements
- 更新日志
- Python 下一版
- Python 3.7.3 最终版
- Python 3.7.3 发布候选版 1
- Python 3.7.2 最终版
- Python 3.7.2 发布候选版 1
- Python 3.7.1 最终版
- Python 3.7.1 RC 2版本
- Python 3.7.1 发布候选版 1
- Python 3.7.0 正式版
- Python 3.7.0 release candidate 1
- Python 3.7.0 beta 5
- Python 3.7.0 beta 4
- Python 3.7.0 beta 3
- Python 3.7.0 beta 2
- Python 3.7.0 beta 1
- Python 3.7.0 alpha 4
- Python 3.7.0 alpha 3
- Python 3.7.0 alpha 2
- Python 3.7.0 alpha 1
- Python 3.6.6 final
- Python 3.6.6 RC 1
- Python 3.6.5 final
- Python 3.6.5 release candidate 1
- Python 3.6.4 final
- Python 3.6.4 release candidate 1
- Python 3.6.3 final
- Python 3.6.3 release candidate 1
- Python 3.6.2 final
- Python 3.6.2 release candidate 2
- Python 3.6.2 release candidate 1
- Python 3.6.1 final
- Python 3.6.1 release candidate 1
- Python 3.6.0 final
- Python 3.6.0 release candidate 2
- Python 3.6.0 release candidate 1
- Python 3.6.0 beta 4
- Python 3.6.0 beta 3
- Python 3.6.0 beta 2
- Python 3.6.0 beta 1
- Python 3.6.0 alpha 4
- Python 3.6.0 alpha 3
- Python 3.6.0 alpha 2
- Python 3.6.0 alpha 1
- Python 3.5.5 final
- Python 3.5.5 release candidate 1
- Python 3.5.4 final
- Python 3.5.4 release candidate 1
- Python 3.5.3 final
- Python 3.5.3 release candidate 1
- Python 3.5.2 final
- Python 3.5.2 release candidate 1
- Python 3.5.1 final
- Python 3.5.1 release candidate 1
- Python 3.5.0 final
- Python 3.5.0 release candidate 4
- Python 3.5.0 release candidate 3
- Python 3.5.0 release candidate 2
- Python 3.5.0 release candidate 1
- Python 3.5.0 beta 4
- Python 3.5.0 beta 3
- Python 3.5.0 beta 2
- Python 3.5.0 beta 1
- Python 3.5.0 alpha 4
- Python 3.5.0 alpha 3
- Python 3.5.0 alpha 2
- Python 3.5.0 alpha 1
- Python 教程
- 课前甜点
- 使用 Python 解释器
- 调用解释器
- 解释器的运行环境
- Python 的非正式介绍
- Python 作为计算器使用
- 走向编程的第一步
- 其他流程控制工具
- if 语句
- for 语句
- range() 函数
- break 和 continue 语句，以及循环中的 else 子句
- pass 语句
- 定义函数
- 函数定义的更多形式
- 小插曲：编码风格
- 数据结构
- 列表的更多特性
- del 语句
- 元组和序列
- 集合
- 字典
- 循环的技巧
- 深入条件控制
- 序列和其它类型的比较
- 模块
- 有关模块的更多信息
- 标准模块
- dir() 函数
- 包
- 输入输出
- 更漂亮的输出格式
- 读写文件
- 错误和异常
- 语法错误
- 异常
- 处理异常
- 抛出异常
- 用户自定义异常
- 定义清理操作
- 预定义的清理操作
- 类
- 名称和对象
- Python 作用域和命名空间
- 初探类
- 补充说明
- 继承
- 私有变量
- 杂项说明
- 迭代器
- 生成器
- 生成器表达式
- 标准库简介
- 操作系统接口
- 文件通配符
- 命令行参数
- 错误输出重定向和程序终止
- 字符串模式匹配
- 数学
- 互联网访问
- 日期和时间
- 数据压缩
- 性能测量
- 质量控制
- 自带电池
- 标准库简介 —— 第二部分
- 格式化输出
- 模板
- 使用二进制数据记录格式
- 多线程
- 日志
- 弱引用
- 用于操作列表的工具
- 十进制浮点运算
- 虚拟环境和包
- 概述
- 创建虚拟环境
- 使用pip管理包
- 接下来？
- 交互式编辑和编辑历史
- Tab 补全和编辑历史
- 默认交互式解释器的替代品
- 浮点算术：争议和限制
- 表示性错误
- 附录
- 交互模式
- 安装和使用 Python
- 命令行与环境
- 命令行
- 环境变量
- 在Unix平台中使用Python
- 获取最新版本的Python
- 构建Python
- 与Python相关的路径和文件
- 杂项
- 编辑器和集成开发环境
- 在Windows上使用 Python
- 完整安装程序
- Microsoft Store包
- nuget.org 安装包
- 可嵌入的包
- 替代捆绑包
- 配置Python
- 适用于Windows的Python启动器
- 查找模块
- 附加模块
- 在Windows上编译Python
- 其他平台
- 在苹果系统上使用 Python
- 获取和安装 MacPython
- IDE
- 安装额外的 Python 包
- Mac 上的图形界面编程
- 在 Mac 上分发 Python 应用程序
- 其他资源
- Python 语言参考
- 概述
- 其他实现
- 标注
- 词法分析
- 行结构
- 其他形符
- 标识符和关键字
- 字面值
- 运算符
- 分隔符
- 数据模型
- 对象、值与类型
- 标准类型层级结构
- 特殊方法名称
- 协程
- 执行模型
- 程序的结构
- 命名与绑定
- 异常
- 导入系统
- importlib
- 包
- 搜索
- 加载
- 基于路径的查找器
- 替换标准导入系统
- Package Relative Imports
- 有关 main 的特殊事项
- 开放问题项
- 参考文献
- 表达式
- 算术转换
- 原子
- 原型
- await 表达式
- 幂运算符
- 一元算术和位运算
- 二元算术运算符
- 移位运算
- 二元位运算
- 比较运算
- 布尔运算
- 条件表达式
- lambda 表达式
- 表达式列表
- 求值顺序
- 运算符优先级
- 简单语句
- 表达式语句
- 赋值语句
- assert 语句
- pass 语句
- del 语句
- return 语句
- yield 语句
- raise 语句
- break 语句
- continue 语句
- import 语句
- global 语句
- nonlocal 语句
- 复合语句
- if 语句
- while 语句
- for 语句
- try 语句
- with 语句
- 函数定义
- 类定义
- 协程
- 最高层级组件
- 完整的 Python 程序
- 文件输入
- 交互式输入
- 表达式输入
- 完整的语法规范
- Python 标准库
- 概述
- 可用性注释
- 内置函数
- 内置常量
- 由 site 模块添加的常量
- 内置类型
- 逻辑值检测
- 布尔运算 — and, or, not
- 比较
- 数字类型 — int, float, complex
- 迭代器类型
- 序列类型 — list, tuple, range
- 文本序列类型 — str
- 二进制序列类型 — bytes, bytearray, memoryview
- 集合类型 — set, frozenset
- 映射类型 — dict
- 上下文管理器类型
- 其他内置类型
- 特殊属性
- 内置异常
- 基类
- 具体异常
- 警告
- 异常层次结构
- 文本处理服务
- string — 常见的字符串操作
- re — 正则表达式操作
- 模块 difflib 是一个计算差异的助手
- textwrap — Text wrapping and filling
- unicodedata — Unicode 数据库
- stringprep — Internet String Preparation
- readline — GNU readline interface
- rlcompleter — GNU readline的完成函数
- 二进制数据服务
- struct — Interpret bytes as packed binary data
- codecs — Codec registry and base classes
- 数据类型
- datetime — 基础日期/时间数据类型
- calendar — General calendar-related functions
- collections — 容器数据类型
- collections.abc — 容器的抽象基类
- heapq — 堆队列算法
- bisect — Array bisection algorithm
- array — Efficient arrays of numeric values
- weakref — 弱引用
- types — Dynamic type creation and names for built-in types
- copy — 浅层 (shallow) 和深层 (deep) 复制操作
- pprint — 数据美化输出
- reprlib — Alternate repr() implementation
- enum — Support for enumerations
- 数字和数学模块
- numbers — 数字的抽象基类
- math — 数学函数
- cmath — Mathematical functions for complex numbers
- decimal — 十进制定点和浮点运算
- fractions — 分数
- random — 生成伪随机数
- statistics — Mathematical statistics functions
- 函数式编程模块
- itertools — 为高效循环而创建迭代器的函数
- functools — 高阶函数和可调用对象上的操作
- operator — 标准运算符替代函数
- 文件和目录访问
- pathlib — 面向对象的文件系统路径
- os.path — 常见路径操作
- fileinput — Iterate over lines from multiple input streams
- stat — Interpreting stat() results
- filecmp — File and Directory Comparisons
- tempfile — Generate temporary files and directories
- glob — Unix style pathname pattern expansion
- fnmatch — Unix filename pattern matching
- linecache — Random access to text lines
- shutil — High-level file operations
- macpath — Mac OS 9 路径操作函数
- 数据持久化
- pickle —— Python 对象序列化
- copyreg — Register pickle support functions
- shelve — Python object persistence
- marshal — Internal Python object serialization
- dbm — Interfaces to Unix “databases”
- sqlite3 — SQLite 数据库 DB-API 2.0 接口模块
- 数据压缩和存档
- zlib — 与 gzip 兼容的压缩
- gzip — 对 gzip 格式的支持
- bz2 — 对 bzip2 压缩算法的支持
- lzma — 用 LZMA 算法压缩
- zipfile — 在 ZIP 归档中工作
- tarfile — Read and write tar archive files
- 文件格式
- csv — CSV 文件读写
- configparser — Configuration file parser
- netrc — netrc file processing
- xdrlib — Encode and decode XDR data
- plistlib — Generate and parse Mac OS X .plist files
- 加密服务
- hashlib — 安全哈希与消息摘要
- hmac — 基于密钥的消息验证
- secrets — Generate secure random numbers for managing secrets
- 通用操作系统服务
- os — 操作系统接口模块
- io — 处理流的核心工具
- time — 时间的访问和转换
- argparse — 命令行选项、参数和子命令解析器
- getopt — C-style parser for command line options
- 模块 logging — Python 的日志记录工具
- logging.config — 日志记录配置
- logging.handlers — Logging handlers
- getpass — 便携式密码输入工具
- curses — 终端字符单元显示的处理
- curses.textpad — Text input widget for curses programs
- curses.ascii — Utilities for ASCII characters
- curses.panel — A panel stack extension for curses
- platform — Access to underlying platform's identifying data
- errno — Standard errno system symbols
- ctypes — Python 的外部函数库
- 并发执行
- threading — 基于线程的并行
- multiprocessing — 基于进程的并行
- concurrent 包
- concurrent.futures — 启动并行任务
- subprocess — 子进程管理
- sched — 事件调度器
- queue — 一个同步的队列类
- _thread — 底层多线程 API
- _dummy_thread — _thread 的替代模块
- dummy_threading — 可直接替代 threading 模块。
- contextvars — Context Variables
- Context Variables
- Manual Context Management
- asyncio support
- 网络和进程间通信
- asyncio — 异步 I/O
- socket — 底层网络接口
- ssl — TLS/SSL wrapper for socket objects
- select — Waiting for I/O completion
- selectors — 高级 I/O 复用库
- asyncore — 异步socket处理器
- asynchat — 异步 socket 指令/响应 处理器
- signal — Set handlers for asynchronous events
- mmap — Memory-mapped file support
- 互联网数据处理
- email — 电子邮件与 MIME 处理包
- json — JSON 编码和解码器
- mailcap — Mailcap file handling
- mailbox — Manipulate mailboxes in various formats
- mimetypes — Map filenames to MIME types
- base64 — Base16, Base32, Base64, Base85 数据编码
- binhex — 对binhex4文件进行编码和解码
- binascii — 二进制和 ASCII 码互转
- quopri — Encode and decode MIME quoted-printable data
- uu — Encode and decode uuencode files
- 结构化标记处理工具
- html — 超文本标记语言支持
- html.parser — 简单的 HTML 和 XHTML 解析器
- html.entities — HTML 一般实体的定义
- XML处理模块
- xml.etree.ElementTree — The ElementTree XML API
- xml.dom — The Document Object Model API
- xml.dom.minidom — Minimal DOM implementation
- xml.dom.pulldom — Support for building partial DOM trees
- xml.sax — Support for SAX2 parsers
- xml.sax.handler — Base classes for SAX handlers
- xml.sax.saxutils — SAX Utilities
- xml.sax.xmlreader — Interface for XML parsers
- xml.parsers.expat — Fast XML parsing using Expat
- 互联网协议和支持
- webbrowser — 方便的Web浏览器控制器
- cgi — Common Gateway Interface support
- cgitb — Traceback manager for CGI scripts
- wsgiref — WSGI Utilities and Reference Implementation
- urllib — URL 处理模块
- urllib.request — 用于打开 URL 的可扩展库
- urllib.response — Response classes used by urllib
- urllib.parse — Parse URLs into components
- urllib.error — Exception classes raised by urllib.request
- urllib.robotparser — Parser for robots.txt
- http — HTTP 模块
- http.client — HTTP协议客户端
- ftplib — FTP protocol client
- poplib — POP3 protocol client
- imaplib — IMAP4 protocol client
- nntplib — NNTP protocol client
- smtplib —SMTP协议客户端
- smtpd — SMTP Server
- telnetlib — Telnet client
- uuid — UUID objects according to RFC 4122
- socketserver — A framework for network servers
- http.server — HTTP 服务器
- http.cookies — HTTP state management
- http.cookiejar — Cookie handling for HTTP clients
- xmlrpc — XMLRPC 服务端与客户端模块
- xmlrpc.client — XML-RPC client access
- xmlrpc.server — Basic XML-RPC servers
- ipaddress — IPv4/IPv6 manipulation library
- 多媒体服务
- audioop — Manipulate raw audio data
- aifc — Read and write AIFF and AIFC files
- sunau — 读写 Sun AU 文件
- wave — 读写WAV格式文件
- chunk — Read IFF chunked data
- colorsys — Conversions between color systems
- imghdr — 推测图像类型
- sndhdr — 推测声音文件的类型
- ossaudiodev — Access to OSS-compatible audio devices
- 国际化
- gettext — 多语种国际化服务
- locale — 国际化服务
- 程序框架
- turtle — 海龟绘图
- cmd — 支持面向行的命令解释器
- shlex — Simple lexical analysis
- Tk图形用户界面(GUI)
- tkinter — Tcl/Tk的Python接口
- tkinter.ttk — Tk themed widgets
- tkinter.tix — Extension widgets for Tk
- tkinter.scrolledtext — 滚动文字控件
- IDLE
- 其他图形用户界面（GUI）包
- 开发工具
- typing — 类型标注支持
- pydoc — Documentation generator and online help system
- doctest — Test interactive Python examples
- unittest — 单元测试框架
- unittest.mock — mock object library
- unittest.mock 上手指南
- 2to3 - 自动将 Python 2 代码转为 Python 3 代码
- test — Regression tests package for Python
- test.support — Utilities for the Python test suite
- test.support.script_helper — Utilities for the Python execution tests
- 调试和分析
- bdb — Debugger framework
- faulthandler — Dump the Python traceback
- pdb — The Python Debugger
- The Python Profilers
- timeit — 测量小代码片段的执行时间
- trace — Trace or track Python statement execution
- tracemalloc — Trace memory allocations
- 软件打包和分发
- distutils — 构建和安装 Python 模块
- ensurepip — Bootstrapping the pip installer
- venv — 创建虚拟环境
- zipapp — Manage executable Python zip archives
- Python运行时服务
- sys — 系统相关的参数和函数
- sysconfig — Provide access to Python's configuration information
- builtins — 内建对象
- main — 顶层脚本环境
- warnings — Warning control
- dataclasses — 数据类
- contextlib — Utilities for with-statement contexts
- abc — 抽象基类
- atexit — 退出处理器
- traceback — Print or retrieve a stack traceback
- future — Future 语句定义
- gc — 垃圾回收器接口
- inspect — 检查对象
- site — Site-specific configuration hook
- 自定义 Python 解释器
- code — Interpreter base classes
- codeop — Compile Python code
- 导入模块
- zipimport — Import modules from Zip archives
- pkgutil — Package extension utility
- modulefinder — 查找脚本使用的模块
- runpy — Locating and executing Python modules
- importlib — The implementation of import
- Python 语言服务
- parser — Access Python parse trees
- ast — 抽象语法树
- symtable — Access to the compiler's symbol tables
- symbol — 与 Python 解析树一起使用的常量
- token — 与Python解析树一起使用的常量
- keyword — 检验Python关键字
- tokenize — Tokenizer for Python source
- tabnanny — 模糊缩进检测
- pyclbr — Python class browser support
- py_compile — Compile Python source files
- compileall — Byte-compile Python libraries
- dis — Python 字节码反汇编器
- pickletools — Tools for pickle developers
- 杂项服务
- formatter — Generic output formatting
- Windows系统相关模块
- msilib — Read and write Microsoft Installer files
- msvcrt — Useful routines from the MS VC++ runtime
- winreg — Windows 注册表访问
- winsound — Sound-playing interface for Windows
- Unix 专有服务
- posix — The most common POSIX system calls
- pwd — 用户密码数据库
- spwd — The shadow password database
- grp — The group database
- crypt — Function to check Unix passwords
- termios — POSIX style tty control
- tty — 终端控制功能
- pty — Pseudo-terminal utilities
- fcntl — The fcntl and ioctl system calls
- pipes — Interface to shell pipelines
- resource — Resource usage information
- nis — Interface to Sun's NIS (Yellow Pages)
- Unix syslog 库例程
- 被取代的模块
- optparse — Parser for command line options
- imp — Access the import internals
- 未创建文档的模块
- 平台特定模块
- 扩展和嵌入 Python 解释器
- 推荐的第三方工具
- 不使用第三方工具创建扩展
- 使用 C 或 C++ 扩展 Python
- 自定义扩展类型：教程
- 定义扩展类型：已分类主题
- 构建C/C++扩展
- 在Windows平台编译C和C++扩展
- 在更大的应用程序中嵌入 CPython 运行时
- Embedding Python in Another Application
- Python/C API 参考手册
- 概述
- 代码标准
- 包含文件
- 有用的宏
- 对象、类型和引用计数
- 异常
- 嵌入Python
- 调试构建
- 稳定的应用程序二进制接口
- The Very High Level Layer
- Reference Counting
- 异常处理
- Printing and clearing
- 抛出异常
- Issuing warnings
- Querying the error indicator
- Signal Handling
- Exception Classes
- Exception Objects
- Unicode Exception Objects
- Recursion Control
- 标准异常
- 标准警告类别
- 工具
- 操作系统实用程序
- 系统功能
- 过程控制
- 导入模块
- Data marshalling support
- 语句解释及变量编译
- 字符串转换与格式化
- 反射
- 编解码器注册与支持功能
- 抽象对象层
- Object Protocol
- 数字协议
- Sequence Protocol
- Mapping Protocol
- 迭代器协议
- 缓冲协议
- Old Buffer Protocol
- 具体的对象层
- 基本对象
- 数值对象
- 序列对象
- 容器对象
- 函数对象
- 其他对象
- Initialization, Finalization, and Threads
- 在Python初始化之前
- 全局配置变量
- Initializing and finalizing the interpreter
- Process-wide parameters
- Thread State and the Global Interpreter Lock
- Sub-interpreter support
- Asynchronous Notifications
- Profiling and Tracing
- Advanced Debugger Support
- Thread Local Storage Support
- 内存管理
- 概述
- 原始内存接口
- Memory Interface
- 对象分配器
- 默认内存分配器
- Customize Memory Allocators
- The pymalloc allocator
- tracemalloc C API
- 示例
- 对象实现支持
- 在堆中分配对象
- Common Object Structures
- Type 对象
- Number Object Structures
- Mapping Object Structures
- Sequence Object Structures
- Buffer Object Structures
- Async Object Structures
- 使对象类型支持循环垃圾回收
- API 和 ABI 版本管理
- 分发 Python 模块
- 关键术语
- 开源许可与协作
- 安装工具
- 阅读指南
- 我该如何...？
- ...为我的项目选择一个名字？
- ...创建和分发二进制扩展？
- 安装 Python 模块
- 关键术语
- 基本使用
- 我应如何 ...？
- ... 在 Python 3.4 之前的 Python 版本中安装 pip ？
- ... 只为当前用户安装软件包？
- ... 安装科学计算类 Python 软件包？
- ... 使用并行安装的多个 Python 版本？
- 常见的安装问题
- 在 Linux 的系统 Python 版本上安装
- 未安装 pip
- 安装二进制编译扩展
- Python 常用指引
- 将 Python 2 代码迁移到 Python 3
- 简要说明
- 详情
- 将扩展模块移植到 Python 3
- 条件编译
- 对象API的更改
- 模块初始化和状态
- CObject 替换为 Capsule
- 其他选项
- Curses Programming with Python
- What is curses?
- Starting and ending a curses application
- Windows and Pads
- Displaying Text
- User Input
- For More Information
- 实现描述器
- 摘要
- 定义和简介
- 描述器协议
- 发起调用描述符
- 描述符示例
- Properties
- 函数和方法
- Static Methods and Class Methods
- 函数式编程指引
- 概述
- 迭代器
- 生成器表达式和列表推导式
- 生成器
- 内置函数
- itertools 模块
- The functools module
- Small functions and the lambda expression
- Revision History and Acknowledgements
- 引用文献
- 日志 HOWTO
- 日志基础教程
- 进阶日志教程
- 日志级别
- 有用的处理程序
- 记录日志中引发的异常
- 使用任意对象作为消息
- 优化
- 日志操作手册
- 在多个模块中使用日志
- 在多线程中使用日志
- 使用多个日志处理器和多种格式化
- 在多个地方记录日志
- 日志服务器配置示例
- 处理日志处理器的阻塞
- Sending and receiving logging events across a network
- Adding contextual information to your logging output
- Logging to a single file from multiple processes
- Using file rotation
- Use of alternative formatting styles
- Customizing LogRecord
- Subclassing QueueHandler - a ZeroMQ example
- Subclassing QueueListener - a ZeroMQ example
- An example dictionary-based configuration
- Using a rotator and namer to customize log rotation processing
- A more elaborate multiprocessing example
- Inserting a BOM into messages sent to a SysLogHandler
- Implementing structured logging
- Customizing handlers with dictConfig()
- Using particular formatting styles throughout your application
- Configuring filters with dictConfig()
- Customized exception formatting
- Speaking logging messages
- Buffering logging messages and outputting them conditionally
- Formatting times using UTC (GMT) via configuration
- Using a context manager for selective logging
- 正则表达式HOWTO
- 概述
- 简单模式
- 使用正则表达式
- 更多模式能力
- 修改字符串
- 常见问题
- 反馈
- 套接字编程指南
- 套接字
- 创建套接字
- 使用一个套接字
- 断开连接
- 非阻塞的套接字
- 排序指南
- 基本排序
- 关键函数
- Operator 模块函数
- 升序和降序
- 排序稳定性和排序复杂度
- 使用装饰-排序-去装饰的旧方法
- 使用 cmp 参数的旧方法
- 其它
- Unicode 指南
- Unicode 概述
- Python's Unicode Support
- Reading and Writing Unicode Data
- Acknowledgements
- 如何使用urllib包获取网络资源
- 概述
- Fetching URLs
- 处理异常
- info and geturl
- Openers and Handlers
- Basic Authentication
- Proxies
- Sockets and Layers
- 脚注
- Argparse 教程
- 概念
- 基础
- 位置参数介绍
- Introducing Optional arguments
- Combining Positional and Optional arguments
- Getting a little more advanced
- Conclusion
- ipaddress模块介绍
- 创建 Address/Network/Interface 对象
- 审查 Address/Network/Interface 对象
- Network 作为 Address 列表
- 比较
- 将IP地址与其他模块一起使用
- 实例创建失败时获取更多详细信息
- Argument Clinic How-To
- The Goals Of Argument Clinic
- Basic Concepts And Usage
- Converting Your First Function
- Advanced Topics
- 使用 DTrace 和 SystemTap 检测CPython
- Enabling the static markers
- Static DTrace probes
- Static SystemTap markers
- Available static markers
- SystemTap Tapsets
- 示例
- Python 常见问题
- Python常见问题
- 一般信息
- 现实世界中的 Python
- 编程常见问题
- 一般问题
- 核心语言
- 数字和字符串
- 性能
- 序列（元组/列表）
- 对象
- 模块
- 设计和历史常见问题
- 为什么Python使用缩进来分组语句？
- 为什么简单的算术运算得到奇怪的结果？
- 为什么浮点计算不准确？
- 为什么Python字符串是不可变的？
- 为什么必须在方法定义和调用中显式使用“self”？
- 为什么不能在表达式中赋值？
- 为什么Python对某些功能（例如list.index()）使用方法来实现，而其他功能（例如len(List)）使用函数实现？
- 为什么 join()是一个字符串方法而不是列表或元组方法？
- 异常有多快？
- 为什么Python中没有switch或case语句？
- 难道不能在解释器中模拟线程，而非得依赖特定于操作系统的线程实现吗？
- 为什么lambda表达式不能包含语句？
- 可以将Python编译为机器代码，C或其他语言吗？
- Python如何管理内存？
- 为什么CPython不使用更传统的垃圾回收方案？
- CPython退出时为什么不释放所有内存？
- 为什么有单独的元组和列表数据类型？
- 列表是如何在CPython中实现的？
- 字典是如何在CPython中实现的？
- 为什么字典key必须是不可变的？
- 为什么 list.sort() 没有返回排序列表？
- 如何在Python中指定和实施接口规范？
- 为什么没有goto？
- 为什么原始字符串（r-strings）不能以反斜杠结尾？
- 为什么Python没有属性赋值的“with”语句？
- 为什么 if/while/def/class语句需要冒号？
- 为什么Python在列表和元组的末尾允许使用逗号？
- 代码库和插件 FAQ
- 通用的代码库问题
- 通用任务
- 线程相关
- 输入输出
- 网络 / Internet 编程
- 数据库
- 数学和数字
- 扩展/嵌入常见问题
- 可以使用C语言中创建自己的函数吗？
- 可以使用C++语言中创建自己的函数吗？
- C很难写，有没有其他选择？
- 如何从C执行任意Python语句？
- 如何从C中评估任意Python表达式？
- 如何从Python对象中提取C的值？
- 如何使用Py_BuildValue()创建任意长度的元组？
- 如何从C调用对象的方法？
- 如何捕获PyErr_Print()（或打印到stdout / stderr的任何内容）的输出？
- 如何从C访问用Python编写的模块？
- 如何从Python接口到C ++对象？
- 我使用Setup文件添加了一个模块，为什么make失败了？
- 如何调试扩展？
- 我想在Linux系统上编译一个Python模块，但是缺少一些文件。为什么?
- 如何区分“输入不完整”和“输入无效”？
- 如何找到未定义的g++符号__builtin_new或__pure_virtual？
- 能否创建一个对象类，其中部分方法在C中实现，而其他方法在Python中实现（例如通过继承）？
- Python在Windows上的常见问题
- 我怎样在Windows下运行一个Python程序？
- 我怎么让 Python 脚本可执行？
- 为什么有时候 Python 程序会启动缓慢？
- 我怎样使用Python脚本制作可执行文件？
- *.pyd 文件和DLL文件相同吗？
- 我怎样将Python嵌入一个Windows程序？
- 如何让编辑器不要在我的 Python 源代码中插入 tab ？
- 如何在不阻塞的情况下检查按键？
- 图形用户界面（GUI）常见问题
- 图形界面常见问题
- Python 是否有平台无关的图形界面工具包？
- 有哪些Python的GUI工具是某个平台专用的？
- 有关Tkinter的问题
- “为什么我的电脑上安装了 Python ？”
- 什么是Python？
- 为什么我的电脑上安装了 Python ？
- 我能删除 Python 吗？
- 术语对照表
- 文档说明
- Python 文档贡献者
- 解决 Bug
- 文档错误
- 使用 Python 的错误追踪系统
- 开始为 Python 贡献您的知识
- 版权
- 历史和许可证
- 软件历史
- 访问Python或以其他方式使用Python的条款和条件
- Python 3.7.3 的 PSF 许可协议
- Python 2.0 的 BeOpen.com 许可协议
- Python 1.6.1 的 CNRI 许可协议
- Python 0.9.0 至 1.2 的 CWI 许可协议
- 集成软件的许可和认可
- Mersenne Twister
- 套接字
- Asynchronous socket services
- Cookie management
- Execution tracing
- UUencode and UUdecode functions
- XML Remote Procedure Calls
- test_epoll
- Select kqueue
- SipHash24
- strtod and dtoa
- OpenSSL
- expat
- libffi
- zlib
- cfuhash
- libmpdec