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# [`decimal`](#module-decimal "decimal: Implementation of the General Decimal Arithmetic Specification.") --- 十進制定點和浮點運算
**源碼：** [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.") 模塊為快速正確舍入的十進制浮點運算提供支持。 它提供了 [`float`](functions.xhtml#float "float") 數據類型以外的幾個優點：
- Decimal “基于一個浮點模型，它是為人們設計的，并且必然具有最重要的指導原則 —— 計算機必須提供與人們在學校學習的算法相同的算法。” —— 摘自十進制算術規范。
- 十進制數字可以準確表示。 相比之下，數字如 `1.1` 和 `2.2` 在二進制浮點中沒有精確的表示。 最終用戶通常不希望``1.1 + 2.2``顯示為 `3.3000000000000003` ，就像二進制浮點一樣。
- 精確性延續到算術中。 在十進制浮點數中，`0.1 + 0.1 + 0.1 - 0.3` 恰好等于零。 在二進制浮點數中，結果為 `5.5511151231257827e-017` 。 雖然接近于零，但差異妨礙了可靠的相等性檢驗，并且差異可能會累積。 因此，在具有嚴格相等不變量的會計應用程序中， decimal 是首選。
- 十進制模塊包含一個重要位置的概念，因此 `1.30 + 1.20` 是 `2.50` 。 保留尾隨零以表示重要性。 這是貨幣申請的慣常陳述。 對于乘法，“教科書”方法使用被乘數中的所有數字。 例如， `1.3 * 1.2` 給出 `1.56` 而 `1.30 * 1.20` 給出 `1.5600` 。
- 與基于硬件的二進制浮點不同，十進制模塊具有用戶可更改的精度（默認為28個位置），可以與給定問題所需的一樣大：
```
>>> from decimal import *
>>> getcontext().prec = 6
>>> Decimal(1) / Decimal(7)
Decimal('0.142857')
>>> getcontext().prec = 28
>>> Decimal(1) / Decimal(7)
Decimal('0.1428571428571428571428571429')
```
- 二進制和十進制浮點都是根據已發布的標準實現的。 雖然內置浮點類型只公開其功能的一小部分，但十進制模塊公開了標準的所有必需部分。 在需要時，程序員可以完全控制舍入和信號處理。 這包括通過使用異常來阻止任何不精確操作來強制執行精確算術的選項。
- 十進制模塊旨在支持“無偏見，精確的非連續十進制算術（有時稱為定點算術）和舍入浮點算術”。 —— 摘自十進制算術規范。
模塊設計以三個概念為中心：十進制數，算術上下文和信號。
十進制數是不可變的。 它有一個符號，系數數字和一個指數。 為了保持重要性，系數數字不會截斷尾隨零。十進制數也包括特殊值，例如 `Infinity` ，`-Infinity` ，和 `NaN` 。 該標準還區分 `-0` 和 `+0` 。
算術的上下文是指定精度、舍入規則、指數限制、指示操作結果的標志以及確定符號是否被視為異常的陷阱啟用器的環境。 舍入選項包括 [`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").
信號是在計算過程中出現的異常條件組。 根據應用程序的需要，信號可能會被忽略，被視為信息，或被視為異常。 十進制模塊中的信號有：[`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") 。
對于每個信號，都有一個標志和一個陷阱啟動器。 遇到信號時，其標志設置為 1 ，然后，如果陷阱啟用器設置為 1 ，則引發異常。 標志是粘性的，因此用戶需要在監控計算之前重置它們。
參見
- IBM的通用十進制算術規范， [The General Decimal Arithmetic Specification](http://speleotrove.com/decimal/decarith.html) \[http://speleotrove.com/decimal/decarith.html\].
## 快速入門教程
通常使用小數的開始是導入模塊，使用 [`getcontext()`](#decimal.getcontext "decimal.getcontext") 查看當前上下文，并在必要時為精度、舍入或啟用的陷阱設置新值:
```
>>> 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
```
可以從整數、字符串、浮點數或元組構造十進制實例。 從整數或浮點構造將執行該整數或浮點值的精確轉換。 十進制數包括特殊值，例如 `NaN` 代表“非數字”，正的和負的 `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") 信號被捕獲，構造函數中的小數和浮點數的意外混合或排序比較會引發異常
```
>>> 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 的重要性僅由輸入的位數決定。 上下文精度和舍入僅在算術運算期間發揮作用。
```
>>> 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版本的內部限制，則構造一個十進制將引發 [`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.
小數與 Python 的其余部分很好地交互。 這是一個小的十進制浮點飛行雜技團：
```
>>> 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 也可以使用一些數學函數：
```
>>> 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()` 方法將數字四舍五入為固定指數。 此方法對于將結果舍入到固定的位置的貨幣應用程序非常有用：
```
>>> 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") 函數訪問當前上下文并允許更改設置。 這種方法滿足大多數應用程序的需求。
對于更高級的工作，使用 Context() 構造函數創建備用上下文可能很有用。 要使用備用活動，請使用 [`setcontext()`](#decimal.setcontext "decimal.setcontext") 函數。
根據標準，[`decimal`](#module-decimal "decimal: Implementation of the General Decimal Arithmetic Specification.") 模塊提供了兩個現成的標準上下文 [`BasicContext`](#decimal.BasicContext "decimal.BasicContext") 和 [`ExtendedContext`](#decimal.ExtendedContext "decimal.ExtendedContext") 。 前者對調試特別有用，因為許多陷阱都已啟用：
```
>>> 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
```
上下文還具有用于監視計算期間遇到的異常情況的信號標志。 標志保持設置直到明確清除，因此最好通過使用 `clear_flags()` 方法清除每組受監控計算之前的標志。:
```
>>> 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* 條目顯示對 `Pi` 的有理逼近被舍入（超出上下文精度的數字被拋棄）并且結果是不精確的（一些丟棄的數字不為零）。
使用上下文的 `traps` 字段中的字典設置單個陷阱：
```
>>> 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
```
大多數程序僅在程序開始時調整當前上下文一次。 并且，在許多應用程序中，數據在循環內單個強制轉換為 [`Decimal`](#decimal.Decimal "decimal.Decimal") 。 通過創建上下文集和小數，程序的大部分操作數據與其他 Python 數字類型沒有區別。
## Decimal 對象
*class* `decimal.``Decimal`(*value="0"*, *context=None*)根據 *value* 構造一個新的 [`Decimal`](#decimal.Decimal "decimal.Decimal") 對象。
*value* 可以是整數，字符串，元組，[`float`](functions.xhtml#float "float") ，或另一個 [`Decimal`](#decimal.Decimal "decimal.Decimal") 對象。 如果沒有給出 *value*，則返回 `Decimal('0')`。 如果 *value* 是一個字符串，它應該在前導和尾隨空格字符以及下劃線被刪除之后符合十進制數字字符串語法:
```
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
```
當上面出現 `digit` 時也允許其他十進制數碼。 其中包括來自各種其他語言系統的十進制數碼（例如阿拉伯-印地語和天城文的數碼）以及全寬數碼 `'\uff10'` 到 `'\uff19'`。
如果 *value* 是一個 [`tuple`](stdtypes.xhtml#tuple "tuple") ，它應該有三個組件，一個符號（ `0` 表示正數或 `1` 表示負數），一個數字的 [`tuple`](stdtypes.xhtml#tuple "tuple") 和整數指數。 例如， `Decimal((0, (1, 4, 1, 4), -3))` 返回 `Decimal('1.414')`。
如果 *value* 是 [`float`](functions.xhtml#float "float") ，則二進制浮點值無損地轉換為其精確的十進制等效值。 此轉換通常需要53位或更多位數的精度。 例如， `Decimal(float('1.1'))` 轉換為``Decimal('1.100000000000000088817841970012523233890533447265625')``。
*context* 精度不會影響存儲的位數。 這完全由 *value* 中的位數決定。 例如，`Decimal('3.00000')` 記錄所有五個零，即使上下文精度只有三。
*context* 參數的目的是確定 *value* 是格式錯誤的字符串時該怎么做。 如果上下文陷阱 [`InvalidOperation`](#decimal.InvalidOperation "decimal.InvalidOperation")，則引發異常；否則，構造函數返回一個新的 Decimal，其值為 `NaN`。
構造完成后， [`Decimal`](#decimal.Decimal "decimal.Decimal") 對象是不可變的。
在 3.2 版更改: 現在允許構造函數的參數為 [`float`](functions.xhtml#float "float") 實例。
在 3.3 版更改: [`float`](functions.xhtml#float "float") 參數在設置 [`FloatOperation`](#decimal.FloatOperation "decimal.FloatOperation") 陷阱時引發異常。 默認情況下，陷阱已關閉。
在 3.6 版更改: 允許下劃線進行分組，就像代碼中的整數和浮點文字一樣。
十進制浮點對象與其他內置數值類型共享許多屬性，例如 [`float`](functions.xhtml#float "float") 和 [`int`](functions.xhtml#int "int") 。 所有常用的數學運算和特殊方法都適用。 同樣，十進制對象可以復制、pickle、打印、用作字典鍵、用作集合元素、比較、排序和強制轉換為另一種類型（例如 [`float`](functions.xhtml#float "float") 或 [`int`](functions.xhtml#int "int") ）。
算術對十進制對象和算術對整數和浮點數有一些小的差別。 當余數運算符 `%` 應用于Decimal對象時，結果的符號是 *被除數* 的符號，而不是除數的符號:
```
>>> (-7) % 4
1
>>> Decimal(-7) % Decimal(4)
Decimal('-3')
```
整數除法運算符 `//` 的行為類似，返回真商的整數部分（截斷為零）而不是它的向下取整，以便保留通常的標識 `x == (x // y) * y + x % y`:
```
>>> -7 // 4
-2
>>> Decimal(-7) // Decimal(4)
Decimal('-1')
```
`%` 和 `//` 運算符實現了 `remainder` 和 `divide-integer` 操作（分別），如規范中所述。
十進制對象通常不能與浮點數或 [`fractions.Fraction`](fractions.xhtml#fractions.Fraction "fractions.Fraction") 實例在算術運算中結合使用：例如,嘗試將 [`Decimal`](#decimal.Decimal "decimal.Decimal") 加到 [`float`](functions.xhtml#float "float") ，將引發 [`TypeError`](exceptions.xhtml#TypeError "TypeError")。 但是，可以使用 Python 的比較運算符來比較 [`Decimal`](#decimal.Decimal "decimal.Decimal") 實例 `x` 和另一個數字 `y` 。 這樣可以避免在對不同類型的數字進行相等比較時混淆結果。
在 3.2 版更改: 現在完全支持 [`Decimal`](#decimal.Decimal "decimal.Decimal") 實例和其他數字類型之間的混合類型比較。
除了標準的數字屬性，十進制浮點對象還有許多專門的方法：
`adjusted`()在移出系數最右邊的數字之后返回調整后的指數，直到只剩下前導數字：`Decimal('321e+5').adjusted()` 返回 7 。 用于確定最高有效位相對于小數點的位置。
`as_integer_ratio`()返回一對 `(n, d)` 整數，表示給定的 [`Decimal`](#decimal.Decimal "decimal.Decimal") 實例作為分數、最簡形式項并帶有正分母:
```
>>> Decimal('-3.14').as_integer_ratio()
(-157, 50)
```
轉換是精確的。 在 Infinity 上引發 OverflowError ，在 NaN 上引起 ValueError 。
3\.6 新版功能.
`as_tuple`()返回一個 [named tuple](../glossary.xhtml#term-named-tuple) 表示的數字： `DecimalTuple(sign, digits, exponent)`。
`canonical`()返回參數的規范編碼。 目前，一個 [`Decimal`](#decimal.Decimal "decimal.Decimal") 實例的編碼始終是規范的，因此該操作返回其參數不變。
`compare`(*other*, *context=None*)比較兩個 Decimal 實例的值。 [`compare()`](#decimal.Decimal.compare "decimal.Decimal.compare") 返回一個 Decimal 實例，如果任一操作數是 NaN ，那么結果是 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 信號之外，此操作與 [`compare()`](#decimal.Decimal.compare "decimal.Decimal.compare") 方法相同。 也就是說，如果兩個操作數都不是信令NaN，那么任何靜默的 NaN 操作數都被視為信令NaN。
`compare_total`(*other*, *context=None*)使用它們的抽象表示而不是它們的數值來比較兩個操作數。 類似于 [`compare()`](#decimal.Decimal.compare "decimal.Decimal.compare") 方法，但結果給出了一個總排序 [`Decimal`](#decimal.Decimal "decimal.Decimal") 實例。 兩個 [`Decimal`](#decimal.Decimal "decimal.Decimal") 實例具有相同的數值但不同的表示形式在此排序中比較不相等：
```
>>> Decimal('12.0').compare_total(Decimal('12'))
Decimal('-1')
```
靜默和發出信號的 NaN 也包括在總排序中。 這個函數的結果是 `Decimal('0')` 如果兩個操作數具有相同的表示，或是 `Decimal('-1')` 如果第一個操作數的總順序低于第二個操作數，或是 `Decimal('1')` 如果第一個操作數在總順序中高于第二個操作數。 有關總排序的詳細信息，請參閱規范。
此操作不受上下文影響且靜默：不更改任何標志且不執行舍入。 作為例外，如果無法準確轉換第二個操作數，則C版本可能會引發InvalidOperation。
`compare_total_mag`(*other*, *context=None*)比較兩個操作數使用它們的抽象表示而不是它們的值，如 [`compare_total()`](#decimal.Decimal.compare_total "decimal.Decimal.compare_total")，但忽略每個操作數的符號。 `x.compare_total_mag(y)` 相當于 `x.copy_abs().compare_total(y.copy_abs())`。
此操作不受上下文影響且靜默：不更改任何標志且不執行舍入。 作為例外，如果無法準確轉換第二個操作數，則C版本可能會引發InvalidOperation。
`conjugate`()只返回self，這種方法只符合 Decimal 規范。
`copy_abs`()返回參數的絕對值。 此操作不受上下文影響并且是靜默的：沒有更改標志且不執行舍入。
`copy_negate`()回到參數的否定。 此操作不受上下文影響并且是靜默的：沒有標志更改且不執行舍入。
`copy_sign`(*other*, *context=None*)返回第一個操作數的副本，其符號設置為與第二個操作數的符號相同。 例如：
```
>>> Decimal('2.3').copy_sign(Decimal('-1.5'))
Decimal('-2.3')
```
此操作不受上下文影響且靜默：不更改任何標志且不執行舍入。 作為例外，如果無法準確轉換第二個操作數，則C版本可能會引發InvalidOperation。
`exp`(*context=None*)返回給定數字的（自然）指數函數``e\*\*x``的值。結果使用 [`ROUND_HALF_EVEN`](#decimal.ROUND_HALF_EVEN "decimal.ROUND_HALF_EVEN") 舍入模式正確舍入。
```
>>> Decimal(1).exp()
Decimal('2.718281828459045235360287471')
>>> Decimal(321).exp()
Decimal('2.561702493119680037517373933E+139')
```
`from_float`(*f*)將浮點數轉換為十進制數的類方法。
注意， Decimal.from\_float(0.1) 與 Decimal('0.1') 不同。 由于 0.1 在二進制浮點中不能精確表示，因此該值存儲為最接近的可表示值，即 0x1.999999999999ap-4 。 十進制的等效值是`0.1000000000000000055511151231257827021181583404541015625`。
注解
從 Python 3.2 開始，[`Decimal`](#decimal.Decimal "decimal.Decimal") 實例也可以直接從 [`float`](functions.xhtml#float "float") 構造。
```
>>> 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 ，中間乘積 self\*other 沒有四舍五入。
```
>>> Decimal(2).fma(3, 5)
Decimal('11')
```
`is_canonical`()如果參數是規范的，則為返回 [`True`](constants.xhtml#True "True")，否則為 [`False`](constants.xhtml#False "False") 。 目前，[`Decimal`](#decimal.Decimal "decimal.Decimal") 實例總是規范的，所以這個操作總是返回 [`True`](constants.xhtml#True "True") 。
`is_finite`()如果參數是一個有限的數，則返回為 [`True`](constants.xhtml#True "True") ；如果參數為無窮大或 NaN ，則返回為 [`False`](constants.xhtml#False "False")。
`is_infinite`()如果參數為正負無窮大，則返回為 [`True`](constants.xhtml#True "True") ，否則為 [`False`](constants.xhtml#False "False") 。
`is_nan`()如果參數為 NaN （無論是否靜默），則返回為 [`True`](constants.xhtml#True "True") ，否則為 [`False`](constants.xhtml#False "False") 。
`is_normal`(*context=None*)如果參數是一個有限正規數，返回 [`True`](constants.xhtml#True "True")，如果參數是0、次正規數、無窮大或是NaN，返回 [`False`](constants.xhtml#False "False")。
`is_qnan`()如果參數為靜默 NaN，返回 [`True`](constants.xhtml#True "True")，否則返回 [`False`](constants.xhtml#False "False")。
`is_signed`()如果參數帶有負號，則返回為 [`True`](constants.xhtml#True "True")，否則返回 [`False`](constants.xhtml#False "False")。注意，0 和 NaN 都可帶有符號。
`is_snan`()如果參數為顯式 NaN，則返回 [`True`](constants.xhtml#True "True")，否則返回 [`False`](constants.xhtml#False "False")。
`is_subnormal`(*context=None*)如果參數為次正規數，則返回 [`True`](constants.xhtml#True "True")，否則返回 [`False`](constants.xhtml#False "False")。
`is_zero`()如果參數是0（正負皆可），則返回 [`True`](constants.xhtml#True "True")，否則返回 [`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響且靜默：不更改任何標志且不執行舍入。 作為例外，如果無法準確轉換第二個操作數，則C版本可能會引發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.
## 常數
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
```
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- 已棄用的 Python 模塊、函數和方法
- 已棄用的 C API 函數和類型
- 平臺支持的移除
- API 與特性的移除
- 移除的模塊
- Windows 專屬的改變
- 移植到 Python 3.7
- Python 3.7.1 中的重要變化
- Python 3.7.2 中的重要變化
- Python 3.6 有什么新變化A
- 摘要 - 發布重點
- 新的特性
- 其他語言特性修改
- 新增模塊
- 改進的模塊
- 性能優化
- Build and C API Changes
- 其他改進
- 棄用
- 移除
- 移植到Python 3.6
- Python 3.6.2 中的重要變化
- Python 3.6.4 中的重要變化
- Python 3.6.5 中的重要變化
- Python 3.6.7 中的重要變化
- Python 3.5 有什么新變化
- 摘要 - 發布重點
- 新的特性
- 其他語言特性修改
- 新增模塊
- 改進的模塊
- Other module-level changes
- 性能優化
- Build and C API Changes
- 棄用
- 移除
- Porting to Python 3.5
- Notable changes in Python 3.5.4
- What's New In Python 3.4
- 摘要 - 發布重點
- 新的特性
- 新增模塊
- 改進的模塊
- CPython Implementation Changes
- 棄用
- 移除
- Porting to Python 3.4
- Changed in 3.4.3
- What's New In Python 3.3
- 摘要 - 發布重點
- PEP 405: Virtual Environments
- PEP 420: Implicit Namespace Packages
- PEP 3118: New memoryview implementation and buffer protocol documentation
- PEP 393: Flexible String Representation
- PEP 397: Python Launcher for Windows
- PEP 3151: Reworking the OS and IO exception hierarchy
- PEP 380: Syntax for Delegating to a Subgenerator
- PEP 409: Suppressing exception context
- PEP 414: Explicit Unicode literals
- PEP 3155: Qualified name for classes and functions
- PEP 412: Key-Sharing Dictionary
- PEP 362: Function Signature Object
- PEP 421: Adding sys.implementation
- Using importlib as the Implementation of Import
- 其他語言特性修改
- A Finer-Grained Import Lock
- Builtin functions and types
- 新增模塊
- 改進的模塊
- 性能優化
- Build and C API Changes
- 棄用
- 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
- 其他語言特性修改
- New, Improved, and Deprecated Modules
- 多線程
- 性能優化
- Unicode
- Codecs
- 文檔
- 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
- 其他語言特性修改
- New, Improved, and Deprecated Modules
- 性能優化
- 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
- 其他語言特性修改
- 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 有什么新變化
- 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
- 其他語言特性修改
- 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
- 其他語言特性修改
- 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
- 其他語言特性修改
- 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
- 其他語言特性修改
- 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
- 列表推導式
- 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 最終版
- Python 3.7.3 發布候選版 1
- Python 3.7.2 最終版
- Python 3.7.2 發布候選版 1
- Python 3.7.1 最終版
- Python 3.7.1 RC 2版本
- Python 3.7.1 發布候選版 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
- 課前甜點
- 使用 Python 解釋器
- 調用解釋器
- 解釋器的運行環境
- Python 的非正式介紹
- Python 作為計算器使用
- 走向編程的第一步
- 其他流程控制工具
- if 語句
- for 語句
- range() 函數
- break 和 continue 語句，以及循環中的 else 子句
- pass 語句
- 定義函數
- 函數定義的更多形式
- 小插曲：編碼風格
- 數據結構
- 列表的更多特性
- del 語句
- 元組和序列
- 集合
- 字典
- 循環的技巧
- 深入條件控制
- 序列和其它類型的比較
- 模塊
- 有關模塊的更多信息
- 標準模塊
- dir() 函數
- 包
- 輸入輸出
- 更漂亮的輸出格式
- 讀寫文件
- 錯誤和異常
- 語法錯誤
- 異常
- 處理異常
- 拋出異常
- 用戶自定義異常
- 定義清理操作
- 預定義的清理操作
- 類
- 名稱和對象
- Python 作用域和命名空間
- 初探類
- 補充說明
- 繼承
- 私有變量
- 雜項說明
- 迭代器
- 生成器
- 生成器表達式
- 標準庫簡介
- 操作系統接口
- 文件通配符
- 命令行參數
- 錯誤輸出重定向和程序終止
- 字符串模式匹配
- 數學
- 互聯網訪問
- 日期和時間
- 數據壓縮
- 性能測量
- 質量控制
- 自帶電池
- 標準庫簡介 —— 第二部分
- 格式化輸出
- 模板
- 使用二進制數據記錄格式
- 多線程
- 日志
- 弱引用
- 用于操作列表的工具
- 十進制浮點運算
- 虛擬環境和包
- 概述
- 創建虛擬環境
- 使用pip管理包
- 接下來？
- 交互式編輯和編輯歷史
- Tab 補全和編輯歷史
- 默認交互式解釋器的替代品
- 浮點算術：爭議和限制
- 表示性錯誤
- 附錄
- 交互模式
- 安裝和使用 Python
- 命令行與環境
- 命令行
- 環境變量
- 在Unix平臺中使用Python
- 獲取最新版本的Python
- 構建Python
- 與Python相關的路徑和文件
- 雜項
- 編輯器和集成開發環境
- 在Windows上使用 Python
- 完整安裝程序
- Microsoft Store包
- nuget.org 安裝包
- 可嵌入的包
- 替代捆綁包
- 配置Python
- 適用于Windows的Python啟動器
- 查找模塊
- 附加模塊
- 在Windows上編譯Python
- 其他平臺
- 在蘋果系統上使用 Python
- 獲取和安裝 MacPython
- IDE
- 安裝額外的 Python 包
- Mac 上的圖形界面編程
- 在 Mac 上分發 Python 應用程序
- 其他資源
- Python 語言參考
- 概述
- 其他實現
- 標注
- 詞法分析
- 行結構
- 其他形符
- 標識符和關鍵字
- 字面值
- 運算符
- 分隔符
- 數據模型
- 對象、值與類型
- 標準類型層級結構
- 特殊方法名稱
- 協程
- 執行模型
- 程序的結構
- 命名與綁定
- 異常
- 導入系統
- importlib
- 包
- 搜索
- 加載
- 基于路徑的查找器
- 替換標準導入系統
- Package Relative Imports
- 有關 main 的特殊事項
- 開放問題項
- 參考文獻
- 表達式
- 算術轉換
- 原子
- 原型
- await 表達式
- 冪運算符
- 一元算術和位運算
- 二元算術運算符
- 移位運算
- 二元位運算
- 比較運算
- 布爾運算
- 條件表達式
- lambda 表達式
- 表達式列表
- 求值順序
- 運算符優先級
- 簡單語句
- 表達式語句
- 賦值語句
- assert 語句
- pass 語句
- del 語句
- return 語句
- yield 語句
- raise 語句
- break 語句
- continue 語句
- import 語句
- global 語句
- nonlocal 語句
- 復合語句
- if 語句
- while 語句
- for 語句
- try 語句
- with 語句
- 函數定義
- 類定義
- 協程
- 最高層級組件
- 完整的 Python 程序
- 文件輸入
- 交互式輸入
- 表達式輸入
- 完整的語法規范
- Python 標準庫
- 概述
- 可用性注釋
- 內置函數
- 內置常量
- 由 site 模塊添加的常量
- 內置類型
- 邏輯值檢測
- 布爾運算 — and, or, not
- 比較
- 數字類型 — int, float, complex
- 迭代器類型
- 序列類型 — list, tuple, range
- 文本序列類型 — str
- 二進制序列類型 — bytes, bytearray, memoryview
- 集合類型 — set, frozenset
- 映射類型 — dict
- 上下文管理器類型
- 其他內置類型
- 特殊屬性
- 內置異常
- 基類
- 具體異常
- 警告
- 異常層次結構
- 文本處理服務
- string — 常見的字符串操作
- re — 正則表達式操作
- 模塊 difflib 是一個計算差異的助手
- textwrap — Text wrapping and filling
- unicodedata — Unicode 數據庫
- stringprep — Internet String Preparation
- readline — GNU readline interface
- rlcompleter — GNU readline的完成函數
- 二進制數據服務
- struct — Interpret bytes as packed binary data
- codecs — Codec registry and base classes
- 數據類型
- datetime — 基礎日期/時間數據類型
- calendar — General calendar-related functions
- collections — 容器數據類型
- collections.abc — 容器的抽象基類
- heapq — 堆隊列算法
- 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 — 淺層 (shallow) 和深層 (deep) 復制操作
- pprint — 數據美化輸出
- reprlib — Alternate repr() implementation
- enum — Support for enumerations
- 數字和數學模塊
- numbers — 數字的抽象基類
- math — 數學函數
- cmath — Mathematical functions for complex numbers
- decimal — 十進制定點和浮點運算
- fractions — 分數
- random — 生成偽隨機數
- statistics — Mathematical statistics functions
- 函數式編程模塊
- itertools — 為高效循環而創建迭代器的函數
- functools — 高階函數和可調用對象上的操作
- operator — 標準運算符替代函數
- 文件和目錄訪問
- pathlib — 面向對象的文件系統路徑
- os.path — 常見路徑操作
- 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 路徑操作函數
- 數據持久化
- pickle —— Python 對象序列化
- copyreg — Register pickle support functions
- shelve — Python object persistence
- marshal — Internal Python object serialization
- dbm — Interfaces to Unix “databases”
- sqlite3 — SQLite 數據庫 DB-API 2.0 接口模塊
- 數據壓縮和存檔
- zlib — 與 gzip 兼容的壓縮
- gzip — 對 gzip 格式的支持
- bz2 — 對 bzip2 壓縮算法的支持
- lzma — 用 LZMA 算法壓縮
- zipfile — 在 ZIP 歸檔中工作
- tarfile — Read and write tar archive files
- 文件格式
- csv — CSV 文件讀寫
- configparser — Configuration file parser
- netrc — netrc file processing
- xdrlib — Encode and decode XDR data
- plistlib — Generate and parse Mac OS X .plist files
- 加密服務
- hashlib — 安全哈希與消息摘要
- hmac — 基于密鑰的消息驗證
- secrets — Generate secure random numbers for managing secrets
- 通用操作系統服務
- os — 操作系統接口模塊
- io — 處理流的核心工具
- time — 時間的訪問和轉換
- argparse — 命令行選項、參數和子命令解析器
- getopt — C-style parser for command line options
- 模塊 logging — Python 的日志記錄工具
- logging.config — 日志記錄配置
- logging.handlers — Logging handlers
- getpass — 便攜式密碼輸入工具
- curses — 終端字符單元顯示的處理
- 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 的外部函數庫
- 并發執行
- threading — 基于線程的并行
- multiprocessing — 基于進程的并行
- concurrent 包
- concurrent.futures — 啟動并行任務
- subprocess — 子進程管理
- sched — 事件調度器
- queue — 一個同步的隊列類
- _thread — 底層多線程 API
- _dummy_thread — _thread 的替代模塊
- dummy_threading — 可直接替代 threading 模塊。
- contextvars — Context Variables
- Context Variables
- Manual Context Management
- asyncio support
- 網絡和進程間通信
- asyncio — 異步 I/O
- socket — 底層網絡接口
- ssl — TLS/SSL wrapper for socket objects
- select — Waiting for I/O completion
- selectors — 高級 I/O 復用庫
- asyncore — 異步socket處理器
- asynchat — 異步 socket 指令/響應 處理器
- signal — Set handlers for asynchronous events
- mmap — Memory-mapped file support
- 互聯網數據處理
- email — 電子郵件與 MIME 處理包
- json — JSON 編碼和解碼器
- mailcap — Mailcap file handling
- mailbox — Manipulate mailboxes in various formats
- mimetypes — Map filenames to MIME types
- base64 — Base16, Base32, Base64, Base85 數據編碼
- binhex — 對binhex4文件進行編碼和解碼
- binascii — 二進制和 ASCII 碼互轉
- quopri — Encode and decode MIME quoted-printable data
- uu — Encode and decode uuencode files
- 結構化標記處理工具
- html — 超文本標記語言支持
- html.parser — 簡單的 HTML 和 XHTML 解析器
- html.entities — HTML 一般實體的定義
- XML處理模塊
- 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
- 互聯網協議和支持
- webbrowser — 方便的Web瀏覽器控制器
- cgi — Common Gateway Interface support
- cgitb — Traceback manager for CGI scripts
- wsgiref — WSGI Utilities and Reference Implementation
- urllib — URL 處理模塊
- urllib.request — 用于打開 URL 的可擴展庫
- 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 模塊
- http.client — HTTP協議客戶端
- ftplib — FTP protocol client
- poplib — POP3 protocol client
- imaplib — IMAP4 protocol client
- nntplib — NNTP protocol client
- smtplib —SMTP協議客戶端
- smtpd — SMTP Server
- telnetlib — Telnet client
- uuid — UUID objects according to RFC 4122
- socketserver — A framework for network servers
- http.server — HTTP 服務器
- http.cookies — HTTP state management
- http.cookiejar — Cookie handling for HTTP clients
- xmlrpc — XMLRPC 服務端與客戶端模塊
- xmlrpc.client — XML-RPC client access
- xmlrpc.server — Basic XML-RPC servers
- ipaddress — IPv4/IPv6 manipulation library
- 多媒體服務
- audioop — Manipulate raw audio data
- aifc — Read and write AIFF and AIFC files
- sunau — 讀寫 Sun AU 文件
- wave — 讀寫WAV格式文件
- chunk — Read IFF chunked data
- colorsys — Conversions between color systems
- imghdr — 推測圖像類型
- sndhdr — 推測聲音文件的類型
- ossaudiodev — Access to OSS-compatible audio devices
- 國際化
- gettext — 多語種國際化服務
- locale — 國際化服務
- 程序框架
- turtle — 海龜繪圖
- cmd — 支持面向行的命令解釋器
- shlex — Simple lexical analysis
- Tk圖形用戶界面(GUI)
- tkinter — Tcl/Tk的Python接口
- tkinter.ttk — Tk themed widgets
- tkinter.tix — Extension widgets for Tk
- tkinter.scrolledtext — 滾動文字控件
- IDLE
- 其他圖形用戶界面（GUI）包
- 開發工具
- typing — 類型標注支持
- pydoc — Documentation generator and online help system
- doctest — Test interactive Python examples
- unittest — 單元測試框架
- unittest.mock — mock object library
- unittest.mock 上手指南
- 2to3 - 自動將 Python 2 代碼轉為 Python 3 代碼
- test — Regression tests package for Python
- test.support — Utilities for the Python test suite
- test.support.script_helper — Utilities for the Python execution tests
- 調試和分析
- bdb — Debugger framework
- faulthandler — Dump the Python traceback
- pdb — The Python Debugger
- The Python Profilers
- timeit — 測量小代碼片段的執行時間
- trace — Trace or track Python statement execution
- tracemalloc — Trace memory allocations
- 軟件打包和分發
- distutils — 構建和安裝 Python 模塊
- ensurepip — Bootstrapping the pip installer
- venv — 創建虛擬環境
- zipapp — Manage executable Python zip archives
- Python運行時服務
- sys — 系統相關的參數和函數
- sysconfig — Provide access to Python's configuration information
- builtins — 內建對象
- main — 頂層腳本環境
- warnings — Warning control
- dataclasses — 數據類
- contextlib — Utilities for with-statement contexts
- abc — 抽象基類
- atexit — 退出處理器
- traceback — Print or retrieve a stack traceback
- future — Future 語句定義
- gc — 垃圾回收器接口
- inspect — 檢查對象
- site — Site-specific configuration hook
- 自定義 Python 解釋器
- code — Interpreter base classes
- codeop — Compile Python code
- 導入模塊
- zipimport — Import modules from Zip archives
- pkgutil — Package extension utility
- modulefinder — 查找腳本使用的模塊
- runpy — Locating and executing Python modules
- importlib — The implementation of import
- Python 語言服務
- parser — Access Python parse trees
- ast — 抽象語法樹
- symtable — Access to the compiler's symbol tables
- symbol — 與 Python 解析樹一起使用的常量
- token — 與Python解析樹一起使用的常量
- keyword — 檢驗Python關鍵字
- tokenize — Tokenizer for Python source
- tabnanny — 模糊縮進檢測
- pyclbr — Python class browser support
- py_compile — Compile Python source files
- compileall — Byte-compile Python libraries
- dis — Python 字節碼反匯編器
- pickletools — Tools for pickle developers
- 雜項服務
- formatter — Generic output formatting
- Windows系統相關模塊
- msilib — Read and write Microsoft Installer files
- msvcrt — Useful routines from the MS VC++ runtime
- winreg — Windows 注冊表訪問
- winsound — Sound-playing interface for Windows
- Unix 專有服務
- posix — The most common POSIX system calls
- pwd — 用戶密碼數據庫
- spwd — The shadow password database
- grp — The group database
- crypt — Function to check Unix passwords
- termios — POSIX style tty control
- tty — 終端控制功能
- 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 庫例程
- 被取代的模塊
- optparse — Parser for command line options
- imp — Access the import internals
- 未創建文檔的模塊
- 平臺特定模塊
- 擴展和嵌入 Python 解釋器
- 推薦的第三方工具
- 不使用第三方工具創建擴展
- 使用 C 或 C++ 擴展 Python
- 自定義擴展類型：教程
- 定義擴展類型：已分類主題
- 構建C/C++擴展
- 在Windows平臺編譯C和C++擴展
- 在更大的應用程序中嵌入 CPython 運行時
- Embedding Python in Another Application
- Python/C API 參考手冊
- 概述
- 代碼標準
- 包含文件
- 有用的宏
- 對象、類型和引用計數
- 異常
- 嵌入Python
- 調試構建
- 穩定的應用程序二進制接口
- The Very High Level Layer
- Reference Counting
- 異常處理
- Printing and clearing
- 拋出異常
- Issuing warnings
- Querying the error indicator
- Signal Handling
- Exception Classes
- Exception Objects
- Unicode Exception Objects
- Recursion Control
- 標準異常
- 標準警告類別
- 工具
- 操作系統實用程序
- 系統功能
- 過程控制
- 導入模塊
- Data marshalling support
- 語句解釋及變量編譯
- 字符串轉換與格式化
- 反射
- 編解碼器注冊與支持功能
- 抽象對象層
- Object Protocol
- 數字協議
- Sequence Protocol
- Mapping Protocol
- 迭代器協議
- 緩沖協議
- Old Buffer Protocol
- 具體的對象層
- 基本對象
- 數值對象
- 序列對象
- 容器對象
- 函數對象
- 其他對象
- Initialization, Finalization, and Threads
- 在Python初始化之前
- 全局配置變量
- 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
- 內存管理
- 概述
- 原始內存接口
- Memory Interface
- 對象分配器
- 默認內存分配器
- Customize Memory Allocators
- The pymalloc allocator
- tracemalloc C API
- 示例
- 對象實現支持
- 在堆中分配對象
- Common Object Structures
- Type 對象
- Number Object Structures
- Mapping Object Structures
- Sequence Object Structures
- Buffer Object Structures
- Async Object Structures
- 使對象類型支持循環垃圾回收
- API 和 ABI 版本管理
- 分發 Python 模塊
- 關鍵術語
- 開源許可與協作
- 安裝工具
- 閱讀指南
- 我該如何...？
- ...為我的項目選擇一個名字？
- ...創建和分發二進制擴展？
- 安裝 Python 模塊
- 關鍵術語
- 基本使用
- 我應如何 ...？
- ... 在 Python 3.4 之前的 Python 版本中安裝 pip ？
- ... 只為當前用戶安裝軟件包？
- ... 安裝科學計算類 Python 軟件包？
- ... 使用并行安裝的多個 Python 版本？
- 常見的安裝問題
- 在 Linux 的系統 Python 版本上安裝
- 未安裝 pip
- 安裝二進制編譯擴展
- Python 常用指引
- 將 Python 2 代碼遷移到 Python 3
- 簡要說明
- 詳情
- 將擴展模塊移植到 Python 3
- 條件編譯
- 對象API的更改
- 模塊初始化和狀態
- CObject 替換為 Capsule
- 其他選項
- Curses Programming with Python
- What is curses?
- Starting and ending a curses application
- Windows and Pads
- Displaying Text
- User Input
- For More Information
- 實現描述器
- 摘要
- 定義和簡介
- 描述器協議
- 發起調用描述符
- 描述符示例
- Properties
- 函數和方法
- Static Methods and Class Methods
- 函數式編程指引
- 概述
- 迭代器
- 生成器表達式和列表推導式
- 生成器
- 內置函數
- itertools 模塊
- The functools module
- Small functions and the lambda expression
- Revision History and Acknowledgements
- 引用文獻
- 日志 HOWTO
- 日志基礎教程
- 進階日志教程
- 日志級別
- 有用的處理程序
- 記錄日志中引發的異常
- 使用任意對象作為消息
- 優化
- 日志操作手冊
- 在多個模塊中使用日志
- 在多線程中使用日志
- 使用多個日志處理器和多種格式化
- 在多個地方記錄日志
- 日志服務器配置示例
- 處理日志處理器的阻塞
- 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
- 正則表達式HOWTO
- 概述
- 簡單模式
- 使用正則表達式
- 更多模式能力
- 修改字符串
- 常見問題
- 反饋
- 套接字編程指南
- 套接字
- 創建套接字
- 使用一個套接字
- 斷開連接
- 非阻塞的套接字
- 排序指南
- 基本排序
- 關鍵函數
- Operator 模塊函數
- 升序和降序
- 排序穩定性和排序復雜度
- 使用裝飾-排序-去裝飾的舊方法
- 使用 cmp 參數的舊方法
- 其它
- Unicode 指南
- Unicode 概述
- Python's Unicode Support
- Reading and Writing Unicode Data
- Acknowledgements
- 如何使用urllib包獲取網絡資源
- 概述
- Fetching URLs
- 處理異常
- info and geturl
- Openers and Handlers
- Basic Authentication
- Proxies
- Sockets and Layers
- 腳注
- Argparse 教程
- 概念
- 基礎
- 位置參數介紹
- Introducing Optional arguments
- Combining Positional and Optional arguments
- Getting a little more advanced
- Conclusion
- ipaddress模塊介紹
- 創建 Address/Network/Interface 對象
- 審查 Address/Network/Interface 對象
- Network 作為 Address 列表
- 比較
- 將IP地址與其他模塊一起使用
- 實例創建失敗時獲取更多詳細信息
- Argument Clinic How-To
- The Goals Of Argument Clinic
- Basic Concepts And Usage
- Converting Your First Function
- Advanced Topics
- 使用 DTrace 和 SystemTap 檢測CPython
- Enabling the static markers
- Static DTrace probes
- Static SystemTap markers
- Available static markers
- SystemTap Tapsets
- 示例
- Python 常見問題
- Python常見問題
- 一般信息
- 現實世界中的 Python
- 編程常見問題
- 一般問題
- 核心語言
- 數字和字符串
- 性能
- 序列（元組/列表）
- 對象
- 模塊
- 設計和歷史常見問題
- 為什么Python使用縮進來分組語句？
- 為什么簡單的算術運算得到奇怪的結果？
- 為什么浮點計算不準確？
- 為什么Python字符串是不可變的？
- 為什么必須在方法定義和調用中顯式使用“self”？
- 為什么不能在表達式中賦值？
- 為什么Python對某些功能（例如list.index()）使用方法來實現，而其他功能（例如len(List)）使用函數實現？
- 為什么 join()是一個字符串方法而不是列表或元組方法？
- 異常有多快？
- 為什么Python中沒有switch或case語句？
- 難道不能在解釋器中模擬線程，而非得依賴特定于操作系統的線程實現嗎？
- 為什么lambda表達式不能包含語句？
- 可以將Python編譯為機器代碼，C或其他語言嗎？
- Python如何管理內存？
- 為什么CPython不使用更傳統的垃圾回收方案？
- CPython退出時為什么不釋放所有內存？
- 為什么有單獨的元組和列表數據類型？
- 列表是如何在CPython中實現的？
- 字典是如何在CPython中實現的？
- 為什么字典key必須是不可變的？
- 為什么 list.sort() 沒有返回排序列表？
- 如何在Python中指定和實施接口規范？
- 為什么沒有goto？
- 為什么原始字符串（r-strings）不能以反斜杠結尾？
- 為什么Python沒有屬性賦值的“with”語句？
- 為什么 if/while/def/class語句需要冒號？
- 為什么Python在列表和元組的末尾允許使用逗號？
- 代碼庫和插件 FAQ
- 通用的代碼庫問題
- 通用任務
- 線程相關
- 輸入輸出
- 網絡 / Internet 編程
- 數據庫
- 數學和數字
- 擴展/嵌入常見問題
- 可以使用C語言中創建自己的函數嗎？
- 可以使用C++語言中創建自己的函數嗎？
- C很難寫，有沒有其他選擇？
- 如何從C執行任意Python語句？
- 如何從C中評估任意Python表達式？
- 如何從Python對象中提取C的值？
- 如何使用Py_BuildValue()創建任意長度的元組？
- 如何從C調用對象的方法？
- 如何捕獲PyErr_Print()（或打印到stdout / stderr的任何內容）的輸出？
- 如何從C訪問用Python編寫的模塊？
- 如何從Python接口到C ++對象？
- 我使用Setup文件添加了一個模塊，為什么make失敗了？
- 如何調試擴展？
- 我想在Linux系統上編譯一個Python模塊，但是缺少一些文件。為什么?
- 如何區分“輸入不完整”和“輸入無效”？
- 如何找到未定義的g++符號__builtin_new或__pure_virtual？
- 能否創建一個對象類，其中部分方法在C中實現，而其他方法在Python中實現（例如通過繼承）？
- Python在Windows上的常見問題
- 我怎樣在Windows下運行一個Python程序？
- 我怎么讓 Python 腳本可執行？
- 為什么有時候 Python 程序會啟動緩慢？
- 我怎樣使用Python腳本制作可執行文件？
- *.pyd 文件和DLL文件相同嗎？
- 我怎樣將Python嵌入一個Windows程序？
- 如何讓編輯器不要在我的 Python 源代碼中插入 tab ？
- 如何在不阻塞的情況下檢查按鍵？
- 圖形用戶界面（GUI）常見問題
- 圖形界面常見問題
- Python 是否有平臺無關的圖形界面工具包？
- 有哪些Python的GUI工具是某個平臺專用的？
- 有關Tkinter的問題
- “為什么我的電腦上安裝了 Python ？”
- 什么是Python？
- 為什么我的電腦上安裝了 Python ？
- 我能刪除 Python 嗎？
- 術語對照表
- 文檔說明
- Python 文檔貢獻者
- 解決 Bug
- 文檔錯誤
- 使用 Python 的錯誤追蹤系統
- 開始為 Python 貢獻您的知識
- 版權
- 歷史和許可證
- 軟件歷史
- 訪問Python或以其他方式使用Python的條款和條件
- Python 3.7.3 的 PSF 許可協議
- Python 2.0 的 BeOpen.com 許可協議
- Python 1.6.1 的 CNRI 許可協議
- Python 0.9.0 至 1.2 的 CWI 許可協議
- 集成軟件的許可和認可
- 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