numpy.pv() in Python Last Updated : 29 Nov, 2018 Comments Improve Suggest changes Like Article Like Report numpy.fv(rate, nper, pmt, fv, when = 'end') : This financial function helps user to compute future values. Parameters : rate : [array_like] Rate of interest as decimal (not per cent) per period nper : [array_like] total compounding periods pmt : [array_like] fixed payment fv : [array_like, optional] future value. Default = 0.0 when : at the beginning (when = {‘begin’, 1}) or the end (when = {‘end’, 0}) of each period. Default is {‘end’, 0} Return : present value as per given parameters. Equation being solved : fv + pv*(1 + rate)**nper + pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) = 0 or when rate == 0 fv + pv + pmt * nper = 0 Code 1 : Working Python ## Python program explaining pv() function import numpy as np ''' Question : What is the present value (e.g., the initial investment) of an investment that needs to total $15692.93 after 10 years of saving $100 every month? Assume the interest rate is 5% (annually) compounded monthly. ''' # rate np pmt fv Solution = np.pv(0.05/12, 10*12, -100, 15692.93) print("present value (fv) : ", Solution) Output : present value (fv) : -100.000671316 Reference : https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.pv.html Comment More infoAdvertise with us Next Article numpy.pv() in Python mohit gupta_omg :) Follow Improve Article Tags : Python Python-numpy Python numpy-Financial Functions Practice Tags : python Similar Reads numpy.pmt() in Python numpy.pmt(rate, nper, pv, fv, when = ‘end’): This financial function helps user to compute payment value as per the principal and interest. Parameters : rate : [scalar or (M, )array] Rate of interest as decimal (not per cent) per period nper : [scalar or (M, )array] total compounding periods fv : [s 1 min read numpy.ptp() in Python numpy.ptp()function plays an important role in statistics by finding out Range of given numbers. Range = max value - min value.  Syntax : ndarray.ptp(arr, axis=None, out=None) Parameters : arr :input array. axis :axis along which we want the range value. Otherwise, it will consider arr to be flatte 2 min read numpy.ppmt() in Python numpy.ppmt(rate, nper, pv, fv, when = ‘end’) : This financial function helps user to compute payment value as per the principal value only. Parameters : rate : [scalar or (M, )array] Rate of interest as decimal (not per cent) per period nper : [scalar or (M, )array] total compounding periods fv : [s 2 min read numpy.polyval() in Python numpy.polyval(p, x) method evaluates a polynomial at specific values. If 'N' is the length of polynomial 'p', then this function returns the value Parameters : p : [array_like or poly1D] polynomial coefficients are given in decreasing order of powers. If the second parameter (root) is set to True th 2 min read numpy.polydiv() in Python The numpy.polydiv() method evaluates the division of two polynomials and returns the quotient and remainder of the polynomial division. Syntax : numpy.polydiv(p1, p2) Parameters : p1 : [array_like or poly1D]Coefficients of dividend polynomial. p2 : [array_like or poly1D]Coefficients of divisor polyn 1 min read numpy.fv() in Python numpy.fv(rate, nper, pmt, pv, when = 'end') : This financial function helps user to compute future values. Parameters : rate : [scalar or (M, )array] Rate of interest as decimal (not per cent) per period nper : [scalar or (M, )array] total compounding periods pmt : [scalar or (M, )array] fixed payme 1 min read numpy.polymul() in Python The numpy.polymul() method evaluates the product of two polynomials and returns the polynomial resulting from the multiplication of two input polynomials 'p1' and 'p2'. Syntax : numpy.polymul(p1, p2) Parameters : p1 : [array_like or poly1D]Input polynomial 1. p2 : [array_like or poly1D]Input polynom 1 min read numpy.polyder() in Python The numpy.polyder() method evaluates the derivative of a polynomial with specified order. Syntax :numpy.polyder(p, m) Parameters : p : [array_like or poly1D]the polynomial coefficients are given in decreasing order of powers. If the second parameter (root) is set to True then array values are the ro 2 min read numpy.std() in Python numpy.std() is a function provided by the NumPy library that calculates the standard deviation of an array or a set of values. Standard deviation is a measure of the amount of variation or dispersion of a set of values.\text{Standard Deviation} = \sqrt{\text{mean} \left( (x - x.\text{mean}())^2 \rig 3 min read numpy.var() in Python numpy.var(arr, axis = None) : Compute the variance of the given data (array elements) along the specified axis(if any). Example : x = 1 1 1 1 1 Standard Deviation = 0 . Variance = 0 y = 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4 Step 1 : Mean of distribution 4 = 7 Step 2 : Summat 3 min read Like