INF, NAN, and Null

These are special values that Analytica returns in particular conditions. You can also use them in expressions:

Inf means infinity -- e.g.,

1/0 → Inf

or a number larger than 1.796E308 (the largest number that your computer can represent explicitly) -- e.g.

1E307 * 100 → Inf

-Inf means negative infinity (or a number less than 1.796E308) -- e.g.

-1/0 → -Inf

NAN means "Not A Number" -- i.e. not a well-defined number nor infinity -- e.g.

0/0 → NAN

Sqrt(-1) → NAN

(If you enable Complex Numbers, Sqrt(-1) returns the valid imaginary number, 1j.)

Null means that there is no such value. For example, Slice and Subscript return Null if you try to get the nth slice over an Index with less than n values. For example:

Index Year := [2015, 2016, 2017]

Slice(Year, 4) → NULL

Variable X := Array(Year, [20, 23, 28])

X[Year = 2018] → NULL

More on INF and NAN

Calculations using INF and NAN follow ANSI (Association of National Standards Institutes) standards, which follow the laws of mathematics as far as possible:

1/Inf → 0

1/(-Inf) → 0

Inf + Inf → Inf

Inf - Inf → NAN

Expressions taking NAN as an operand or parameter give NAN as their result unless the expression has a well-defined logical or numerical value for any value of NAN:

True OR NAN → True

NaN AND False → False

IF True THEN 5 ELSE NAN → 5

More on NULL

When NULL appears in scalar operations, it generally produces a warning and evaluates to NULL, for example:

10 + NULL → NULL

NULL - 10 → NULL

1 AND NULL → NULL

Array-reducing functions ignore NULL. These examples demonstrate (assume A is indexed by I as indicated).

I:	1	2	3	4	5
A:	8	NULL	4	NULL	0

Sum(A, I) → 12

Average(A, I) → 4

JoinText(A, I, ', ') → "8, 4, 0"

Graphs will simply ignore (not show) any point whose value is NULL.

Array-reducing functions include Sum, Min, Max, ArgMin, ArgMax, Product, Average, JoinText, Irr, Npv. Array functions Sum, Min and Max also accept an optional parameter IgnoreNaN to ignore NaN values (which otherwise propagate, i.e. return NaN).

Regression also ignores any data points which have Y=Null, which is useful for missing data.