numpy.pv() in Python

numpy.fv() in Python

Last Updated : 29 Nov, 2018

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 payment pv : [scalar or (M, )array] present value when : at the beginning (when = {‘begin’, 1}) or the end (when = {‘end’, 0}) of each period. Default is {‘end’, 0}

Return :

value at the end of nper periods

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 fv() function

import numpy as np
'''
Question : 

Future value after 10 years of saving $100 now, 
with an additional monthly savings of $100. 
Assume the interest rate is 5% (annually) 
compounded monthly ?
'''

#                 rate       np      pmt    pv
Solution = np.fv(0.05/12, 10*12, -100, -100)

print("Solution : ", Solution)

Output :

Solution :  15692.9288943

References : https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.fv.html#numpy.fv .

numpy.pv() in Python

mohit gupta_omg :)

Improve

Article Tags :

Practice Tags :

python

Similar Reads

numpy.fabs() in Python

numpy.fabs() function is used to compute the absolute values element-wise. This function returns the absolute values (positive magnitude) of the data in arr. It always return absolute values in floats. Syntax : numpy.fabs(arr, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, u

numpy.pv() in Python

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]

NumPy Array in Python

NumPy (Numerical Python) is a powerful library for numerical computations in Python. It is commonly referred to multidimensional container that holds the same data type. It is the core data structure of the NumPy library and is optimized for numerical and scientific computation in Python. Table of C

numpy.random.f() in Python

With the help of numpy.random.f() method, we can get the random samples of F distribution and return the random samples of numpy array by using this method. Syntax : numpy.random.f(dfnum, dfden, size=None) Return : Return the random samples as numpy array. Example #1 : In this example we can see tha

Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python.Besides its obvious scientific uses, Numpy can also be used as an efficient m

numpy.load() in Python

numpy.load() function return the input array from a disk file with npy extension(.npy). Syntax : numpy.load(file, mmap_mode=None, allow_pickle=True, fix_imports=True, encoding='ASCII') Parameters: file : : file-like object, string, or pathlib.Path.The file to read. File-like objects must support the

numpy.ipmt() in Python

numpy.ipmt(rate, nper, pv, fv, when = â€˜endâ€™) : This financial function helps user to compute payment value as per the interest only. i.e. returns the interest part. Parameters : rate : [scalar or (M, )array] Rate of interest as decimal (not per cent) per period nper : [scalar or (M, )array] total co

numpy.rint() in Python

The numpy.rint() is a mathematical function that rounds elements of the array to the nearest integer. Syntax : numpy.rint(x[, out]) = ufunc â€˜rintâ€™) Parameters : array : [array_like] Input array. Return : An array with all array elements being rounded off, having same type and shape as input. Code #1

numpy.zeros() in Python

numpy.zeros() function creates a new array of specified shapes and types, filled with zeros. It is beneficial when you need a placeholder array to initialize variables or store intermediate results. We can create 1D array using numpy.zeros().Let's understand with the help of an example:Pythonimport

numpy.finfo() function â€“ Python

numpy.finfo() function shows machine limits for floating point types. Syntax : numpy.finfo(dtype) Parameters : dtype : [float, dtype, or instance] Kind of floating point data-type about which to get information. Return : Machine parameters for floating point types. Code #1 : Python3 # Python program