Permutations Calculator P(n,r)

Calculate P(n,r) = n!/(n-r)! — ordered arrangements of r items from n. Use when order matters: rankings, race finishes, passwords without repetition.

🔢 Combinatorics📐 P(n,r) = n! / (n-r)!🔢 Math
Total items n
Items arranged r
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Formula & Reference

VariableSymbolFormulaUnits
Permutations Calculator P(n,r)P(n,r) = n! / (n-r)!positive integer

Why Order Changes the Count

A permutation counts ordered selections. Choosing A then B is different from choosing B then A, so the number of arrangements of r distinct items selected from n distinct items is P(n,r)=n!/(n-r)!. The factorial ratio cancels the unused tail and leaves n(n-1)...(n-r+1), exactly r decreasing factors.

Permutations connect directly to combinations. A combination first selects an unordered group, and the r chosen objects can then be arranged in r! orders. Therefore P(n,r)=C(n,r)r!. This identity is a quick way to decide which count a problem needs: if changing the order creates a new outcome, use a permutation.

SituationFormulaOrder?
Choose and arrange r from nP(n,r)=n!/(n-r)!Matters
Choose r from nC(n,r)=n!/[r!(n-r)!]Does not matter
r positions with repetition allowednrMatters

Special cases are useful checks: P(n,0)=1 because there is one empty arrangement, P(n,1)=n, and P(n,n)=n!. The calculator is intended for nonnegative integer n and r with r≤n.

Decision workflow: first ask whether the r selected positions are distinguishable. A gold-silver-bronze podium, president-vice president pair, or ordered password slot makes order matter. A committee or unordered subset usually does not. Then ask whether the same object can be reused. Standard P(n,r) assumes no repetition; if each of r positions independently allows any of n choices, the count is nr.

The factorial notation is compact, but multiplying only the r needed factors is often easier. P(12,3)=12×11×10=1,320; there is no need to compute 12! and 9! separately. This also makes the counting logic visible: 12 choices for the first position, then 11, then 10.

Permutation counts grow very quickly. P(20,10)=20×19×...×11, already a very large integer. In probability problems, the permutation count may appear in a denominator when all ordered outcomes are equally likely. Always define the sample space before turning a count into a probability.

Integer inputs matter. “5.7 objects” or “2.3 ranked places” has no ordinary combinatorial meaning, so n and r should be whole numbers. If a software tool rounds decimal entries automatically, the rounded problem is mathematically different from the one typed.

Step-by-Step Examples

Example 1
Medal podium

P(8,3)

  • 8×7×6=336 ways
✓ 336
Example 2
PIN code

P(10,4) no repeats

  • 10×9×8×7=5,040
✓ 5,040
Example 3
Race top 3

P(5,3)

  • 5×4×3=60 finish orders
✓ 60
Example 4
Two officers from six people

President and vice president: P(6,2)

  • 6 choices for president × 5 remaining choices for vice president = 30.
✓ 30
Example 5
Choose no positions

P(10,0)

  • The empty arrangement is one valid outcome, so the product of zero factors is 1.
✓ 1

Real-World Applications

Common Mistakes to Avoid

⚠️
C vs P

Use P when order matters.

⚠️
P(n,n)=n!

All arrangements of n items.

⚠️
With repetition

n^r when repeats are allowed.

⚠️
Using decimal n or r

Permutation counts require whole numbers of objects and positions. Do not silently round 5.7 objects or 2.3 positions into a different counting problem.

Frequently Asked Questions

P vs C?
P: order matters. C: order doesn't. P=C×r!
P(n,n)?
n! — all arrangements.
With repetition?
n^r, not n!/(n-r)!
Circular permutations?
(n-1)! for circular arrangements.
Probability connection?
P(specific order)=1/P(n,r).
Why is P(n,0)=1?
There is exactly one way to choose and arrange zero objects: the empty arrangement. This convention also makes factorial identities work consistently.
How do permutations relate to combinations?
Each unordered group of r items can be arranged in r! orders, so P(n,r)=C(n,r)×r!.

Related Math Calculators

Formula Explorer connections

Interpretation: This relationship counts discrete structures, analyzes logic and sets, or describes algorithmic growth. Assumption: Define the sample space, recurrence, logical variables and counting constraints precisely. Avoid double counting and distinguish worst, average and best cases.

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