New in version 2.4.

The `decimal` module provides support for decimal floating point
arithmetic. It offers several advantages over the `float` datatype:

Decimal “is based on a floating-point model which was designed with people in mind, and necessarily has a paramount guiding principle – computers must provide an arithmetic that works in the same way as the arithmetic that people learn at school.” – excerpt from the decimal arithmetic specification.

Decimal numbers can be represented exactly. In contrast, numbers like

`1.1`do not have an exact representation in binary floating point. End users typically would not expect`1.1`to display as`1.1000000000000001`as it does with binary floating point.The exactness carries over into arithmetic. In decimal floating point,

`0.1 + 0.1 + 0.1 - 0.3`is exactly equal to zero. In binary floating point, the result is`5.5511151231257827e-017`. While near to zero, the differences prevent reliable equality testing and differences can accumulate. For this reason, decimal is preferred in accounting applications which have strict equality invariants.The decimal module incorporates a notion of significant places so that

`1.30 + 1.20`is`2.50`. The trailing zero is kept to indicate significance. This is the customary presentation for monetary applications. For multiplication, the “schoolbook” approach uses all the figures in the multiplicands. For instance,`1.3 * 1.2`gives`1.56`while`1.30 * 1.20`gives`1.5600`.Unlike hardware based binary floating point, the decimal module has a user alterable precision (defaulting to 28 places) which can be as large as needed for a given problem:

>>> getcontext().prec = 6 >>> Decimal(1) / Decimal(7) Decimal('0.142857') >>> getcontext().prec = 28 >>> Decimal(1) / Decimal(7) Decimal('0.1428571428571428571428571429')

Both binary and decimal floating point are implemented in terms of published standards. While the built-in float type exposes only a modest portion of its capabilities, the decimal module exposes all required parts of the standard. When needed, the programmer has full control over rounding and signal handling. This includes an option to enforce exact arithmetic by using exceptions to block any inexact operations.

The decimal module was designed to support “without prejudice, both exact unrounded decimal arithmetic (sometimes called fixed-point arithmetic) and rounded floating-point arithmetic.” – excerpt from the decimal arithmetic specification.

The module design is centered around three concepts: the decimal number, the context for arithmetic, and signals.

A decimal number is immutable. It has a sign, coefficient digits, and
an exponent. To preserve significance, the coefficient digits do not
truncate trailing zeros. Decimals also include special values such as
`Infinity`, `-Infinity`, and `NaN`. The standard also
differentiates `-0` from `+0`.

The context for arithmetic is an environment specifying precision,
rounding rules, limits on exponents, flags indicating the results of
operations, and trap enablers which determine whether signals are
treated as exceptions. Rounding options include `ROUND_CEILING`,
`ROUND_DOWN`, `ROUND_FLOOR`, `ROUND_HALF_DOWN`,
`ROUND_HALF_EVEN`, `ROUND_HALF_UP`, `ROUND_UP`, and
`ROUND_05UP`.

Signals are groups of exceptional conditions arising during the course
of computation. Depending on the needs of the application, signals
may be ignored, considered as informational, or treated as exceptions.
The signals in the decimal module are: `Clamped`,
`InvalidOperation`, `DivisionByZero`, `Inexact`, `Rounded`,
`Subnormal`, `Overflow`, and `Underflow`.

For each signal there is a flag and a trap enabler. When a signal is encountered, its flag is set to one, then, if the trap enabler is set to one, an exception is raised. Flags are sticky, so the user needs to reset them before monitoring a calculation.

See also:

- IBM’s General Decimal Arithmetic Specification, The General Decimal Arithmetic Specification.
- IEEE standard 854-1987, Unofficial IEEE 854 Text.

The usual start to using decimals is importing the module, viewing the
current context with `getcontext()` and, if necessary, setting new
values for precision, rounding, or enabled traps:

>>> from decimal import * >>> getcontext() Context(prec=28, rounding=ROUND_HALF_EVEN, Emin=-999999999, Emax=999999999, capitals=1, flags=[], traps=[Overflow, DivisionByZero, InvalidOperation])>>> getcontext().prec = 7 # Set a new precision

Decimal instances can be constructed from integers, strings, or
tuples. To create a Decimal from a `float`, first convert it to a
string. This serves as an explicit reminder of the details of the
conversion (including representation error). Decimal numbers include
special values such as `NaN` which stands for “Not a number”,
positive and negative `Infinity`, and `-0`.

```
>>> getcontext().prec = 28
>>> Decimal(10)
Decimal('10')
>>> Decimal('3.14')
Decimal('3.14')
>>> Decimal((0, (3, 1, 4), -2))
Decimal('3.14')
>>> Decimal(str(2.0 ** 0.5))
Decimal('1.41421356237')
>>> Decimal(2) ** Decimal('0.5')
Decimal('1.414213562373095048801688724')
>>> Decimal('NaN')
Decimal('NaN')
>>> Decimal('-Infinity')
Decimal('-Infinity')
```

The significance of a new Decimal is determined solely by the number of digits input. Context precision and rounding only come into play during arithmetic operations.

```
>>> 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')
```

Decimals interact well with much of the rest of Python. Here is a small decimal floating point flying circus:

```
>>> data = 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)
1.3400000000000001
>>> round(a, 1) # round() first converts to binary floating point
1.3
>>> int(a)
1
>>> a * 5
Decimal('6.70')
>>> a * b
Decimal('2.5058')
>>> c % a
Decimal('0.77')
```

And some mathematical functions are also available to Decimal:

```
>>> 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')
```

The `quantize()` method rounds a number to a fixed exponent. This
method is useful for monetary applications that often round results to
a fixed number of places:

```
>>> Decimal('7.325').quantize(Decimal('.01'), rounding=ROUND_DOWN)
Decimal('7.32')
>>> Decimal('7.325').quantize(Decimal('1.'), rounding=ROUND_UP)
Decimal('8')
```

As shown above, the `getcontext()` function accesses the current
context and allows the settings to be changed. This approach meets
the needs of most applications.

For more advanced work, it may be useful to create alternate contexts
using the Context() constructor. To make an alternate active, use the
`setcontext()` function.

In accordance with the standard, the `Decimal` module provides two
ready to use standard contexts, `BasicContext` and
`ExtendedContext`. The former is especially useful for debugging
because many of the traps are enabled:

>>> 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=-999999999, Emax=999999999, capitals=1, 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

Contexts also have signal flags for monitoring exceptional conditions
encountered during computations. The flags remain set until
explicitly cleared, so it is best to clear the flags before each set
of monitored computations by using the `clear_flags()` method.

```
>>> setcontext(ExtendedContext)
>>> getcontext().clear_flags()
>>> Decimal(355) / Decimal(113)
Decimal('3.14159292')
>>> getcontext()
Context(prec=9, rounding=ROUND_HALF_EVEN, Emin=-999999999, Emax=999999999,
capitals=1, flags=[Rounded, Inexact], traps=[])
```

The *flags* entry shows that the rational approximation to `Pi` was
rounded (digits beyond the context precision were thrown away) and
that the result is inexact (some of the discarded digits were non-
zero).

Individual traps are set using the dictionary in the `traps` field
of a context:

```
>>> 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
```

Most programs adjust the current context only once, at the beginning
of the program. And, in many applications, data is converted to
`Decimal` with a single cast inside a loop. With context set and
decimals created, the bulk of the program manipulates the data no
differently than with other Python numeric types.

class class decimal.Decimal([value[, context]])

Construct a new

Decimalobject based fromvalue.

valuecan be an integer, string, tuple, or anotherDecimalobject. If novalueis given, returnsDecimal('0'). Ifvalueis a string, it should conform to the decimal numeric string syntax after leading and trailing whitespace characters are removed: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] nanIf

valueis atuple, it should have three components, a sign (0for positive or1for negative), atupleof digits, and an integer exponent. For example,Decimal((0, (1, 4, 1, 4), -3))returnsDecimal('1.414').The

contextprecision does not affect how many digits are stored. That is determined exclusively by the number of digits invalue. For example,Decimal('3.00000')records all five zeros even if the context precision is only three.The purpose of the

contextargument is determining what to do ifvalueis a malformed string. If the context trapsInvalidOperation, an exception is raised; otherwise, the constructor returns a new Decimal with the value ofNaN.Once constructed,

Decimalobjects are immutable.Changed in version 2.6: leading and trailing whitespace characters are permitted when creating a Decimal instance from a string.

Decimal floating point objects share many properties with the other built-in numeric types such as

floatandint. All of the usual math operations and special methods apply. Likewise, decimal objects can be copied, pickled, printed, used as dictionary keys, used as set elements, compared, sorted, and coerced to another type (such asfloatorlong).In addition to the standard numeric properties, decimal floating point objects also have a number of specialized methods:

adjusted()

Return the adjusted exponent after shifting out the coefficient’s rightmost digits until only the lead digit remains:Decimal('321e+5').adjusted()returns seven. Used for determining the position of the most significant digit with respect to the decimal point.as_tuple()

Return a

named tuplerepresentation of the number:DecimalTuple(sign, digits, exponent).Changed in version 2.6: Use a named tuple.

canonical()

Return the canonical encoding of the argument. Currently, the encoding of a

Decimalinstance is always canonical, so this operation returns its argument unchanged.New in version 2.6.

compare(other[, context])

Compare the values of two Decimal instances. This operation behaves in the same way as the usual comparison method

__cmp__(), except thatcompare()returns a Decimal instance rather than an integer, and if either operand is a NaN then the result is a 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])

This operation is identical to the

compare()method, except that all NaNs signal. That is, if neither operand is a signaling NaN then any quiet NaN operand is treated as though it were a signaling NaN.New in version 2.6.

compare_total(other)

Compare two operands using their abstract representation rather than their numerical value. Similar to the

compare()method, but the result gives a total ordering onDecimalinstances. TwoDecimalinstances with the same numeric value but different representations compare unequal in this ordering:>>> Decimal('12.0').compare_total(Decimal('12')) Decimal('-1')Quiet and signaling NaNs are also included in the total ordering. The result of this function is

Decimal('0')if both operands have the same representation,Decimal('-1')if the first operand is lower in the total order than the second, andDecimal('1')if the first operand is higher in the total order than the second operand. See the specification for details of the total order.New in version 2.6.

compare_total_mag(other)

Compare two operands using their abstract representation rather than their value as in

compare_total(), but ignoring the sign of each operand.x.compare_total_mag(y)is equivalent tox.copy_abs().compare_total(y.copy_abs()).New in version 2.6.

conjugate()

Just returns self, this method is only to comply with the Decimal Specification.

New in version 2.6.

copy_abs()

Return the absolute value of the argument. This operation is unaffected by the context and is quiet: no flags are changed and no rounding is performed.

New in version 2.6.

copy_negate()

Return the negation of the argument. This operation is unaffected by the context and is quiet: no flags are changed and no rounding is performed.

New in version 2.6.

copy_sign(other)

Return a copy of the first operand with the sign set to be the same as the sign of the second operand. For example:

>>> Decimal('2.3').copy_sign(Decimal('-1.5')) Decimal('-2.3')This operation is unaffected by the context and is quiet: no flags are changed and no rounding is performed.

New in version 2.6.

exp([context])

Return the value of the (natural) exponential function

e**xat the given number. The result is correctly rounded using theROUND_HALF_EVENrounding mode.>>> Decimal(1).exp() Decimal('2.718281828459045235360287471') >>> Decimal(321).exp() Decimal('2.561702493119680037517373933E+139')New in version 2.6.

fma(other, third[, context])

Fused multiply-add. Return self*other+third with no rounding of the intermediate product self*other.

>>> Decimal(2).fma(3, 5) Decimal('11')New in version 2.6.

is_canonical()

Return

Trueif the argument is canonical andFalseotherwise. Currently, aDecimalinstance is always canonical, so this operation always returnsTrue.New in version 2.6.

is_finite()

Return

Trueif the argument is a finite number, andFalseif the argument is an infinity or a NaN.New in version 2.6.

is_infinite()

Return

Trueif the argument is either positive or negative infinity andFalseotherwise.New in version 2.6.

is_nan()

Return

Trueif the argument is a (quiet or signaling) NaN andFalseotherwise.New in version 2.6.

is_normal()

Return

Trueif the argument is anormalfinite number. ReturnFalseif the argument is zero, subnormal, infinite or a NaN.New in version 2.6.

is_qnan()

Return

Trueif the argument is a quiet NaN, andFalseotherwise.New in version 2.6.

is_signed()

Return

Trueif the argument has a negative sign andFalseotherwise. Note that zeros and NaNs can both carry signs.New in version 2.6.

is_snan()

Return

Trueif the argument is a signaling NaN andFalseotherwise.New in version 2.6.

is_subnormal()

Return

Trueif the argument is subnormal, andFalseotherwise.New in version 2.6.

is_zero()

Return

Trueif the argument is a (positive or negative) zero andFalseotherwise.New in version 2.6.

ln([context])

Return the natural (base e) logarithm of the operand. The result is correctly rounded using the

ROUND_HALF_EVENrounding mode.New in version 2.6.

log10([context])

Return the base ten logarithm of the operand. The result is correctly rounded using the

ROUND_HALF_EVENrounding mode.New in version 2.6.

logb([context])

For a nonzero number, return the adjusted exponent of its operand as a

Decimalinstance. If the operand is a zero thenDecimal('-Infinity')is returned and theDivisionByZeroflag is raised. If the operand is an infinity thenDecimal('Infinity')is returned.New in version 2.6.

logical_and(other[, context])

logical_and()is a logical operation which takes twological operands(seeLogical operands). The result is the digit-wiseandof the two operands.New in version 2.6.

logical_invert(other[, context])

logical_invert()is a logical operation. The argument must be alogical operand(seeLogical operands). The result is the digit-wise inversion of the operand.New in version 2.6.

logical_or(other[, context])

logical_or()is a logical operation which takes twological operands(seeLogical operands). The result is the digit- wiseorof the two operands.New in version 2.6.

logical_xor(other[, context])

logical_xor()is a logical operation which takes twological operands(seeLogical operands). The result is the digit-wise exclusive or of the two operands.New in version 2.6.

max(other[, context])

Likemax(self, other)except that the context rounding rule is applied before returning and thatNaNvalues are either signaled or ignored (depending on the context and whether they are signaling or quiet).max_mag(other[, context])

Similar to the

max()method, but the comparison is done using the absolute values of the operands.New in version 2.6.

min(other[, context])

Likemin(self, other)except that the context rounding rule is applied before returning and thatNaNvalues are either signaled or ignored (depending on the context and whether they are signaling or quiet).min_mag(other[, context])

Similar to the

min()method, but the comparison is done using the absolute values of the operands.New in version 2.6.

next_minus([context])

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.

New in version 2.6.

next_plus([context])

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.

New in version 2.6.

next_toward(other[, context])

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.

New in version 2.6.

normalize([context])

Normalize the number by stripping the rightmost trailing zeros and converting any result equal toDecimal('0')toDecimal('0e0'). Used for producing canonical values for members of an equivalence class. For example,Decimal('32.100')andDecimal('0.321000e+2')both normalize to the equivalent valueDecimal('32.1').number_class([context])

Return a string describing the

classof 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.New in version 2.6.

quantize(exp[, rounding[, context[, watchexp]]])

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

InvalidOperationis 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

roundingargument if given, else by the givencontextargument; if neither argument is given the rounding mode of the current thread’s context is used.If

watchexpis set (default), then an error is returned whenever the resulting exponent is greater thanEmaxor less thanEtiny.radix()

Return

Decimal(10), the radix (base) in which theDecimalclass does all its arithmetic. Included for compatibility with the specification.New in version 2.6.

remainder_near(other[, context])

Compute the modulo as either a positive or negative value depending on which is closest to zero. For instance,

Decimal(10).remainder_near(6)returnsDecimal('-2')which is closer to zero thanDecimal('4').If both are equally close, the one chosen will have the same sign as

self.rotate(other[, context])

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.

New in version 2.6.

same_quantum(other[, context])

Test whether self and other have the same exponent or whether both areNaN.scaleb(other[, context])

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.New in version 2.6.

shift(other[, context])

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.

New in version 2.6.

sqrt([context])

Return the square root of the argument to full precision.to_eng_string([context])

Convert to an engineering-type string.

Engineering notation has an exponent which is a multiple of 3, so there are up to 3 digits left of the decimal place. For example, converts

Decimal('123E+1')toDecimal('1.23E+3')to_integral([rounding[, context]])

Identical to theto_integral_value()method. Theto_integralname has been kept for compatibility with older versions.to_integral_exact([rounding[, context]])

Round to the nearest integer, signaling

InexactorRoundedas appropriate if rounding occurs. The rounding mode is determined by theroundingparameter if given, else by the givencontext. If neither parameter is given then the rounding mode of the current context is used.New in version 2.6.

to_integral_value([rounding[, context]])

Round to the nearest integer without signaling

InexactorRounded. If given, appliesrounding; otherwise, uses the rounding method in either the suppliedcontextor the current context.Changed in version 2.6: renamed from

to_integraltoto_integral_value. The old name remains valid for compatibility.

The `logical_and()`, `logical_invert()`, `logical_or()`, and
`logical_xor()` methods expect their arguments to be *logical
operands*. A *logical operand* is a `Decimal` instance whose
exponent and sign are both zero, and whose digits are all either `0`
or `1`.

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()` and `setcontext()` functions:

decimal.getcontext()

Return the current context for the active thread.

decimal.setcontext(c)

Set the current context for the active thread toc.

Beginning with Python 2.5, you can also use the `with` statement and
the `localcontext()` function to temporarily change the active
context.

decimal.localcontext([c])

Return a context manager that will set the current context for the active thread to a copy of

con 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.New in version 2.5.

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` constructor
described below. In addition, the module provides three pre-made
contexts:

class 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. All flags are cleared. All traps are enabled (treated as exceptions) exceptInexact,Rounded, andSubnormal.Because many of the traps are enabled, this context is useful for debugging.

class 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. 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

NaNorInfinityinstead of raising exceptions. This allows an application to complete a run in the presence of conditions that would otherwise halt the program.

class class decimal.DefaultContext

This context is used by the

Contextconstructor as a prototype for new contexts. Changing a field (such a precision) has the effect of changing the default for new contexts creating by theContextconstructor.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 precision=28, rounding=ROUND_HALF_EVEN, and enabled traps for Overflow, InvalidOperation, and DivisionByZero.

In addition to the three supplied contexts, new contexts can be
created with the `Context` constructor.

class class decimal.Context(prec=None, rounding=None, traps=None, flags=None, Emin=None, Emax=None, capitals=1)

Creates a new context. If a field is not specified or is

None, the default values are copied from theDefaultContext. If theflagsfield is not specified or isNone, all flags are cleared.The

precfield is a positive integer that sets the precision for arithmetic operations in the context.The

roundingoption is one of:

ROUND_CEILING(towardsInfinity),ROUND_DOWN(towards zero),ROUND_FLOOR(towards-Infinity),ROUND_HALF_DOWN(to nearest with ties going towards zero),ROUND_HALF_EVEN(to nearest with ties going to nearest even integer),ROUND_HALF_UP(to nearest with ties going away from zero), orROUND_UP(away from zero).ROUND_05UP(away from zero if last digit after rounding towards zero would have been 0 or 5; otherwise towards zero)The

trapsandflagsfields list any signals to be set. Generally, new contexts should only set traps and leave the flags clear.The

EminandEmaxfields are integers specifying the outer limits allowable for exponents.The

capitalsfield is either0or1(the default). If set to1, exponents are printed with a capitalE; otherwise, a lowercaseeis used:Decimal('6.02e+23').Changed in version 2.6: The

ROUND_05UProunding mode was added.The

Contextclass 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 theDecimalmethods described above (with the exception of theadjusted()andas_tuple()methods) there is a correspondingContextmethod. For example,C.exp(x)is equivalent tox.exp(context=C).clear_flags()

Resets all of the flags to0.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

numbut usingselfas context. Unlike theDecimalconstructor, 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 is permitted.

Etiny()

Returns a value equal toEmin - prec + 1which is the minimum exponent value for subnormal results. When underflow occurs, the exponent is set toEtiny.Etop()

Returns a value equal toEmax - prec + 1.The usual approach to working with decimals is to create

Decimalinstances 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 theDecimalclass and are only briefly recounted here.abs(x)

Returns the absolute value ofx.add(x, y)

Return the sum ofxandy.canonical(x)

Returns the same Decimal objectx.compare(x, y)

Comparesxandynumerically.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 ofxwith the sign set to 0.copy_negate(x)

Returns a copy ofxwith the sign inverted.copy_sign(x, y)

Copies the sign fromytox.divide(x, y)

Returnxdivided byy.divide_int(x, y)

Returnxdivided byy, truncated to an integer.divmod(x, y)

Divides two numbers and returns the integer part of the result.exp(x)

Returnse *x*.fma(x, y, z)

Returnsxmultiplied byy, plusz.is_canonical(x)

Returns True ifxis canonical; otherwise returns False.is_finite(x)

Returns True ifxis finite; otherwise returns False.is_infinite(x)

Returns True ifxis infinite; otherwise returns False.is_nan(x)

Returns True ifxis a qNaN or sNaN; otherwise returns False.is_normal(x)

Returns True ifxis a normal number; otherwise returns False.is_qnan(x)

Returns True ifxis a quiet NaN; otherwise returns False.is_signed(x)

Returns True ifxis negative; otherwise returns False.is_snan(x)

Returns True ifxis a signaling NaN; otherwise returns False.is_subnormal(x)

Returns True ifxis subnormal; otherwise returns False.is_zero(x)

Returns True ifxis a zero; otherwise returns False.ln(x)

Returns the natural (base e) logarithm ofx.log10(x)

Returns the base 10 logarithm ofx.logb(x)

Returns the exponent of the magnitude of the operand’s MSD.logical_and(x, y)

Applies the logical operationandbetween each operand’s digits.logical_invert(x)

Invert all the digits inx.logical_or(x, y)

Applies the logical operationorbetween each operand’s digits.logical_xor(x, y)

Applies the logical operationxorbetween 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 ofxandy.next_minus(x)

Returns the largest representable number smaller thanx.next_plus(x)

Returns the smallest representable number larger thanx.next_toward(x, y)

Returns the number closest tox, in direction towardsy.normalize(x)

Reducesxto its simplest form.number_class(x)

Returns an indication of the class ofx.plus(x)

Plus corresponds to the unary prefix plus operator in Python. This operation applies the context precision and rounding, so it isnotan identity operation.power(x, y[, modulo])

Return

xto the power ofy, reduced modulomoduloif given.With two arguments, compute

x**y. Ifxis negative thenymust be integral. The result will be inexact unlessyis integral and the result is finite and can be expressed exactly in ‘precision’ digits. The result should always be correctly rounded, using the rounding mode of the current thread’s context.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
ymust be nonnegative- at least one of
xorymust be nonzeromodulomust be nonzero and have at most ‘precision’ digitsThe result of

Context.power(x, y, modulo)is identical to the result that would be obtained by computing(x**y) % modulowith unbounded precision, but is computed more efficiently. It is always exact.Changed in version 2.6:

ymay now be nonintegral inx**y. Stricter requirements for the three-argument version.quantize(x, y)

Returns a value equal tox(rounded), having the exponent ofy.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)

Returnsx - y * n, wherenis the integer nearest the exact value ofx / y(if the result is 0 then its sign will be the sign ofx).rotate(x, y)

Returns a rotated copy ofx,ytimes.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 ofx,ytimes.sqrt(x)

Square root of a non-negative number to context precision.subtract(x, y)

Return the difference betweenxandy.to_eng_string(x)

Converts a number to a string, using scientific notation.to_integral_exact(x)

Rounds to an integer.to_sci_string(x)

Converts a number to a string using scientific notation.

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` trap is set, then a `DivisionByZero` exception is
raised upon encountering the condition.

class class decimal.Clamped

Altered an exponent to fit representation constraints.

Typically, clamping occurs when an exponent falls outside the context’s

EminandEmaxlimits. If possible, the exponent is reduced to fit by adding zeros to the coefficient.

class class decimal.DecimalException

Base class for other signals and a subclass ofArithmeticError.

class 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

Infinityor-Infinitywith the sign determined by the inputs to the calculation.

class 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 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 x._rescale( non-integer ) sqrt(-x) and x > 0 0 ** 0 x ** (non-integer) x ** Infinity

class class decimal.Overflow

Numerical overflow.

Indicates the exponent is larger than

Emaxafter 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 toInfinity. In either case,InexactandRoundedare also signaled.

class 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

5.00to5.0). If not trapped, returns the result unchanged. This signal is used to detect loss of significant digits.

class class decimal.Subnormal

Exponent was lower than

Eminprior to rounding.Occurs when an operation result is subnormal (the exponent is too small). If not trapped, returns the result unchanged.

class class decimal.Underflow

Numerical underflow with result rounded to zero.

Occurs when a subnormal result is pushed to zero by rounding.

InexactandSubnormalare also signaled.

The following table summarizes the hierarchy of signals:

- exceptions.ArithmeticError(exceptions.StandardError)

- DecimalException
Clamped DivisionByZero(DecimalException, exceptions.ZeroDivisionError) Inexact

Overflow(Inexact, Rounded) Underflow(Inexact, Rounded, Subnormal)InvalidOperation Rounded Subnormal

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 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')
```

The number system for the `decimal` 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`
signal is not trapped. Likewise, when the `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` 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` (even when doing `Decimal('NaN')==Decimal('NaN')`), while
a test for inequality always returns `True`. An attempt to compare
two Decimals using any of the `<`, `<=`, `>` or `>=` operators
will raise the `InvalidOperation` signal if either operand is a
`NaN`, and return `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-1000000026')
```

The `getcontext()` function accesses a different `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()` function automatically assigns its
target to the current thread.

If `setcontext()` has not been called before `getcontext()`, then
`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()`. 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()

. . .

Here are a few recipes that serve as utility functions and that
demonstrate ways to work with the `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 zeropos: 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 = map(str, digits) build, next = result.append, digits.pop if sign:

build(trailneg)

- for i in range(places):
- build(next() if digits else ‘0’)
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 += tgetcontext().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 / factgetcontext().prec -= 2 return +s

- def cos(x):
“”“Return the cosine of x as measured in radians.

>>> 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 * signgetcontext().prec -= 2 return +s

- def sin(x):
“”“Return the sine of x as measured in radians.

>>> 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 * signgetcontext().prec -= 2 return +s

Q. It is cumbersome to type `decimal.Decimal('1234.5')`. Is there a
way to minimize typing when using the interactive interpreter?

- 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` 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')

- Is there a way to convert a regular float to a
`Decimal`?

A. Yes, all binary floating point numbers can be exactly expressed as
a Decimal. An exact conversion may take more precision than intuition
would suggest, so we trap `Inexact` to signal a need for more
precision:

- def float_to_decimal(f):
“Convert a floating point number to a Decimal with no loss of information” n, d = f.as_integer_ratio() numerator, denominator = Decimal(n), Decimal(d) ctx = Context(prec=60) result = ctx.divide(numerator, denominator) while ctx.flags[Inexact]:

ctx.flags[Inexact] = False ctx.prec *= 2 result = ctx.divide(numerator, denominator)return result

>>> float_to_decimal(math.pi) Decimal('3.141592653589793115997963468544185161590576171875')

Q. Why isn’t the `float_to_decimal()` routine included in the
module?

A. There is some question about whether it is advisable to mix binary and decimal floating point. Also, its use requires some care to avoid the representation issues associated with binary floating point:

```
>>> float_to_decimal(1.1)
Decimal('1.100000000000000088817841970012523233890533447265625')
```

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()` method:

```
>>> Context(prec=5, rounding=ROUND_DOWN).create_decimal('1.2345678')
Decimal('1.2345')
```