The advantage is that you can see your history of calculation & use constants as in a program. You may argue that excel formula is a good tool, but it may be difficult for a real time access, when you want to try something as it wont keep the log. You can go back and check your mistakes!!

Lets start with day-today mathematics.

Add

>>> 85+63

148

>>>

Substract

>>> 85-63

22

>>>

Multiply

>>> 10*8

80

>>>

Division

>>> 10/8

1.25

>>>

The `%` (modulo) operator gives reminder of a division

>>> 10%8

2

>>>

Square-root & Power

>>> (-1)**2

1

>>> (2)**-10

0.0009765625

>>>

>>> (2)**.5

1.4142135623730951

>>>

Exponential

>>> (10)**-10

1e-10

>>> 1e-10

### Programmers Mathematics(Binary operations)

**Binary to Decimal**

>>> int(“1101”,2)

13

or

>>> 0b1101

13

**Decimal to binary**

>>> bin(15)

‘0b1111’

>>>

**Hex to decimal **

>>> int(“1101”,16)

4353

>>> int(“ffff”,16)

65535

>>>

or

>>> ord(‘x0D’)

13

**Decimal to hex**

>>> hex(255)

‘0xff’

**Negation**

>>> -0b1111

-15

**Invertion**

>>> ~0b1111

-16

>>>

**Shift Left & right**

>>> 55<<2

220

>>> 55>>2

13

>>>

**Summary**

Operator | Description |
---|---|

lambda |
Lambda expression |

if – else |
Conditional expression |

or |
Boolean OR |

and |
Boolean AND |

not x |
Boolean NOT |

in, not in, is, is not, <, <=, >, >=, <>, !=, == |
Comparisons, including membership tests and identity tests, |

| |
Bitwise OR |

^ |
Bitwise XOR |

& |
Bitwise AND |

<<, >> |
Shifts |

+, - |
Addition and subtraction |

*, /, //, % |
Multiplication, division, remainder [8] |

+x, -x, ~x |
Positive, negative, bitwise NOT |

** |
Exponentiation [9] |

x[index], x[index:index], x(arguments...), x.attribute |
Subscription, slicing, call, attribute reference |

(expressions...), [expressions...], {key:datum...}, `expressions...` |
Binding or tuple display, list display, dictionary display, string conversion |

### Scientific Mathematics

Python has 2 math modules- math for integer mathematics and cmath for complex math. This part is copied from http://docs.python.org/library/math.html

Enter “import math” before using math module

#### Constants

`math.``pi`- The mathematical constant π = 3.141592…, to available precision.

`math.``e`- The mathematical constant e = 2.718281…, to available precision.

#### Number-theoretic and representation functions

`math.``ceil`(*x*)- Return the ceiling of
*x*as a float, the smallest integer value greater than or equal to*x*.

`math.``copysign`(*x*,*y*)- Return
*x*with the sign of*y*. On a platform that supports signed zeros,`copysign(1.0, -0.0)`returns*-1.0*.

`math.``fabs`(*x*)- Return the absolute value of
*x*.

`math.``factorial`(*x*)- Return
*x*factorial. Raises`ValueError`if*x*is not integral or is negative.

`math.``floor`(*x*)- Return the floor of
*x*as a float, the largest integer value less than or equal to*x*.

`math.``fmod`(*x*,*y*)- Return
`fmod(x, y)`, as defined by the platform C library. Note that the Python expression`x % y`may not return the same result. The intent of the C standard is that`fmod(x, y)`be exactly (mathematically; to infinite precision) equal to`x - n*y`for some integer*n*such that the result has the same sign as*x*and magnitude less than`abs(y)`. Python’s`x % y`returns a result with the sign of*y*instead, and may not be exactly computable for float arguments. For example,`fmod(-1e-100, 1e100)`is`-1e-100`, but the result of Python’s`-1e-100 % 1e100`is`1e100-1e-100`, which cannot be represented exactly as a float, and rounds to the surprising`1e100`. For this reason, function`fmod()`is generally preferred when working with floats, while Python’s`x % y`is preferred when working with integers.

`math.``frexp`(*x*)- Return the mantissa and exponent of
*x*as the pair`(m, e)`.*m*is a float and*e*is an integer such that`x == m * 2**e`exactly. If*x*is zero, returns`(0.0, 0)`, otherwise`0.5 <= abs(m) < 1`. This is used to “pick apart” the internal representation of a float in a portable way.

`math.``fsum`(*iterable*)- Return an accurate floating point sum of values in the iterable. Avoids loss of precision by tracking multiple intermediate partial sums:
>>> sum([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1]) 0.9999999999999999 >>> fsum([.1, .1, .1, .1, .1, .1, .1, .1, .1, .1]) 1.0

The algorithm’s accuracy depends on IEEE-754 arithmetic guarantees and the typical case where the rounding mode is half-even. On some non-Windows builds, the underlying C library uses extended precision addition and may occasionally double-round an intermediate sum causing it to be off in its least significant bit.

For further discussion and two alternative approaches, see the ASPN cookbook recipes for accurate floating point summation.

`math.``isinf`(*x*)- Check if the float
*x*is positive or negative infinity.

`math.``isnan`(*x*)- Check if the float
*x*is a NaN (not a number). For more information on NaNs, see the IEEE 754 standards.

`math.``ldexp`(*x*,*i*)- Return
`x * (2**i)`. This is essentially the inverse of function`frexp()`.

`math.``modf`(*x*)- Return the fractional and integer parts of
*x*. Both results carry the sign of*x*and are floats.

`math.``trunc`(*x*)- Return the
`Real`value*x*truncated to an`Integral`(usually a long integer). Uses the`__trunc__`method.

Note that `frexp()` and `modf()` have a different call/return pattern than their C equivalents: they take a single argument and return a pair of values, rather than returning their second return value through an ‘output parameter’ (there is no such thing in Python).

For the `ceil()`, `floor()`, and `modf()` functions, note that *all* floating-point numbers of sufficiently large magnitude are exact integers. Python floats typically carry no more than 53 bits of precision (the same as the platform C double type), in which case any float *x* with `abs(x) >= 2**52` necessarily has no fractional bits.

#### Power and logarithmic functions

`math.``exp`(*x*)- Return
`e**x`.

`math.``expm1`(*x*)- Return
`e**x - 1`. For small floats*x*, the subtraction in`exp(x) - 1`can result in a significant loss of precision; the`expm1()`function provides a way to compute this quantity to full precision:>>> from math import exp, expm1 >>> exp(1e-5) - 1 # gives result accurate to 11 places 1.0000050000069649e-05 >>> expm1(1e-5) # result accurate to full precision 1.0000050000166668e-05

`math.``log`(*x*[,*base*])- With one argument, return the natural logarithm of
*x*(to base*e*).With two arguments, return the logarithm of*x*to the given*base*, calculated as`log(x)/log(base)`.

`math.``log1p`(*x*)- Return the natural logarithm of
*1+x*(base*e*). The result is calculated in a way which is accurate for*x*near zero.

`math.``log10`(*x*)- Return the base-10 logarithm of
*x*. This is usually more accurate than`log(x, 10)`.

`math.``pow`(*x*,*y*)- Return
`x`raised to the power`y`. Exceptional cases follow Annex ‘F’ of the C99 standard as far as possible. In particular,`pow(1.0, x)`and`pow(x, 0.0)`always return`1.0`, even when`x`is a zero or a NaN. If both`x`and`y`are finite,`x`is negative, and`y`is not an integer then`pow(x, y)`is undefined, and raises`ValueError`.

`math.``sqrt`(*x*)- Return the square root of
*x*.

#### Trigonometric functions

`math.``acos`(*x*)¶- Return the arc cosine of
*x*, in radians.

`math.``asin`(*x*)- Return the arc sine of
*x*, in radians.

`math.``atan`(*x*)- Return the arc tangent of
*x*, in radians.

`math.``atan2`(*y*,*x*)- Return
`atan(y / x)`, in radians. The result is between`-pi`and`pi`. The vector in the plane from the origin to point`(x, y)`makes this angle with the positive X axis. The point of`atan2()`is that the signs of both inputs are known to it, so it can compute the correct quadrant for the angle. For example,`atan(1)`and`atan2(1, 1)`are both`pi/4`, but`atan2(-1, -1)`is`-3*pi/4`.

`math.``cos`(*x*)- Return the cosine of
*x*radians.

`math.``hypot`(*x*,*y*)- Return the Euclidean norm,
`sqrt(x*x + y*y)`. This is the length of the vector from the origin to point`(x, y)`.

`math.``sin`(*x*)- Return the sine of
*x*radians.

`math.``tan`(*x*)- Return the tangent of
*x*radians.

#### Angular conversion

`math.``degrees`(*x*)- Converts angle
*x*from radians to degrees.

`math.``radians`(*x*)- Converts angle
*x*from degrees to radians.

#### Hyperbolic functions

`math.``acosh`(*x*)- Return the inverse hyperbolic cosine of
*x*.

`math.``asinh`(*x*)- Return the inverse hyperbolic sine of
*x*.

`math.``atanh`(*x*)- Return the inverse hyperbolic tangent of
*x*.New in version 2.6.

`math.``cosh`(*x*)- Return the hyperbolic cosine of
*x*.

`math.``sinh`(*x*)- Return the hyperbolic sine of
*x*.

`math.``tanh`(*x*)- Return the hyperbolic tangent of
*x*.

##### Special functions

`math.``erf`(*x*)- Return the error function at
*x*.

`math.``erfc`(*x*)- Return the complementary error function at
*x*.

`math.``gamma`(*x*)- Return the Gamma function at
*x*.

`math.``lgamma`(*x*)- Return the natural logarithm of the absolute value of the Gamma function at
*x*.

### 2 Comments

### Leave a comment

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Usually i don’t provide feedback to blogs, but yours has some rare content on python hence wanted to thank you.

if you want accuracy and are willing to sacrifice the total range of

numbers that Python’s IEEE754 double-precision floats give you, then

use the decimal.Decimal class instead — better precision, smaller

range.

however, if you wish to stick with floats, use the string format

operator and tell it you want 17 places after the decimal point:

>>> x=7./13

>>> x

0.53846153846153844

>>> str(x)

’0.538461538462′

>>> ‘%.17f’ % x

’0.53846153846153844′