### Navigation

- index
- modules |
- next |
- previous |
- NumPy v1.3 Manual (DRAFT) »
- NumPy Reference »
- Routines »
- Financial functions »

# numpy.fv

`numpy.``fv`(rate, nper, pmt, pv, when='end')

  Compute the future value.

  Parameters: | rate : scalar or array_like of shape(M, ) Rate of interest as decimal (not per cent) per period nper : scalar or array_like of shape(M, ) Number of compounding periods pmt : scalar or array_like of shape(M, ) Payment pv : scalar or array_like of shape(M, ) Present value when : {{‘begin’, 1}, {‘end’, 0}}, {string, int}, optional When payments are due (‘begin’ (1) or ‘end’ (0)). Defaults to {‘end’, 0}.
  Returns: | out : ndarray Future values. If all input is scalar, returns a scalar float. If any input is array_like, returns future values for each input element. If multiple inputs are array_like, they all must have the same shape.

  Notes

  The future value is computed by solving the equation:

      fv +
      pv*(1+rate)**nper +
      pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) == 0

  or, when `rate == 0`:

      fv + pv + pmt * nper == 0

  Examples

  What is the 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?

      >>> np.fv(0.05/12, 10*12, -100, -100)
      15692.928894335748

  By convention, the negative sign represents cash flow out (i.e. money not available today). Thus, saving $100 a month at 5% annual interest leads to $15,692.93 available to spend in 10 years.

  If any input is array_like, returns an array of equal shape. Let’s compare different interest rates from the example above.

      >>> a = np.array((0.05, 0.06, 0.07))/12
      >>> np.fv(a, 10*12, -100, -100)
      array([ 15692.92889434,  16569.87435405,  17509.44688102])

### Navigation

- index
- modules |
- next |
- previous |
- NumPy v1.3 Manual (DRAFT) »
- NumPy Reference »
- Routines »
- Financial functions »