*Last updated: 2023-03-16.*
# tf_quant_finance.rates.analytics.cashflows.present_value
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Computes present value of a stream of cashflows given discount factors.
```python
tf_quant_finance.rates.analytics.cashflows.present_value(
cashflows, discount_factors, dtype=None, name=None
)
```
```python
# 2 and 3 year bonds with 1000 face value and 4%, 6% semi-annual coupons.
# Note that the first four entries in the cashflows are the cashflows of
# the first bond (group=0) and the next six are the cashflows of the second
# bond (group=1).
cashflows = [[20, 20, 20, 1020, 0, 0],
[30, 30, 30, 30, 30, 1030]]
# Corresponding discount factors for the cashflows
discount_factors = [[0.96, 0.93, 0.9, 0.87, 1.0, 1.0],
[0.97, 0.95, 0.93, 0.9, 0.88, 0.86]]
present_values = present_value(
cashflows, discount_factors, dtype=np.float64)
# Expected: [943.2, 1024.7]
```
#### Args:
* `cashflows`: A real `Tensor` of shape `batch_shape + [n]`. The set of
cashflows of underlyings. `n` is the number of cashflows per bond
and `batch_shape` is the number of bonds. Bonds with different number
of cashflows should be padded to a common number `n`.
* `discount_factors`: A `Tensor` of the same `dtype` as `cashflows` and of
compatible shape. The set of discount factors corresponding to the
cashflows.
* `dtype`: `tf.Dtype`. If supplied the dtype for the input and output `Tensor`s.
Default value: None which maps to the default dtype inferred from
`cashflows`.
* `name`: Python str. The name to give to the ops created by this function.
Default value: None which maps to 'present_value'.
#### Returns:
Real `Tensor` of shape `batch_shape`. The present values of the cashflows.