NaN-Aware Functions (nanmean, nansum)

NaN-aware functions like np.nanmean and np.nansum compute the usual statistics while quietly skipping missing values, so a single NaN no longer poisons your result.

Learn NaN-Aware Functions (nanmean, nansum) in our free NumPy course — a beginner-friendly interactive lesson with worked examples, a practice exercise and a…

Part of the free Numpy course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

You'll see how an ordinary reduction collapses to NaN, swap in the nan* family, detect missing values with np.isnan , and run NaN-aware reductions along an axis.

A NaN ("not a number") infects any arithmetic it touches, so a plain mean() or sum() over data with even one NaN returns nan . The nan* family — np.nanmean , np.nansum , and friends — ignores the NaNs and computes the statistic over the valid values only.

The whole reduction toolbox has a NaN-aware twin: nanmax , nanmin , nanstd , nanvar , nanmedian , and nanpercentile .

You cannot test for NaN with == because NaN is not equal to anything — not even itself. Use np.isnan(arr) , which returns a boolean mask. Sum the mask to count missing values, or invert it with ~ to keep only the valid ones.

Just like their normal counterparts, the nan* functions accept an axis argument, so you can summarize each row or column while ignoring the NaNs scattered through a 2D array. nanpercentile reads cut points the same way.

One caution: if an entire row or column is NaN, nanmean has nothing to average and returns nan for that slice (with a warning) — a useful signal that the data is fully missing there.

Replace each ___ so the program shows the poisoned mean, then the NaN-aware mean.

Expected output: nan then 22.0 . (Answers: mean , nanmean .)

x == np.nan is always False because NaN is not equal to itself.

✅ Fix: use np.isnan(x) to detect missing values.

A single NaN poisons any normal reduction over the array.

✅ Fix: switch to the NaN-aware twin — np.nanmean , np.nansum , np.nanstd , and so on.

A sensor stream has gaps recorded as NaN. Count the missing readings, then compute the NaN-aware mean, max, min, and standard deviation.

Lesson complete — NaN-aware functions unlocked!

You can now spot a poisoned reduction, reach for np.nanmean and the rest of the nan* family, detect missing values with np.isnan , and run NaN-aware reductions along any axis.

🚀 Up next: Checkpoint — Scientific Computing — combine masking, FFT, histograms, stats, and linalg.

Practice quiz

Why does a normal arr.mean() return nan when arr has one NaN?

  • A single NaN poisons the whole reduction
  • mean is broken
  • It rounds to nan
  • NaN counts as zero

Answer: A single NaN poisons the whole reduction. Arithmetic touching NaN yields NaN, so one NaN poisons the result.

For data = [1.0, 2.0, np.nan, 4.0, 5.0], what does np.nanmean(data) return?

  • nan
  • 3.0
  • 12.0
  • 2.4

Answer: 3.0. nanmean skips the NaN and averages 1, 2, 4, 5 to get 3.0.

What does np.nansum([1.0, 2.0, np.nan, 4.0, 5.0]) return?

  • nan
  • 10.0
  • 12.0
  • 15.0

Answer: 12.0. nansum ignores the NaN and adds 1+2+4+5 = 12.0.

Which function correctly detects NaN values?

  • x == np.nan
  • x is np.nan
  • np.isnan(x)
  • x != x only

Answer: np.isnan(x). np.isnan(x) is the reliable element-wise NaN check; == always fails.

Why can't you test for NaN with the == operator?

  • == is too slow
  • NaN is not equal to anything, not even itself
  • == only works on ints
  • It raises an error

Answer: NaN is not equal to anything, not even itself. By IEEE rules NaN never equals anything, so x == np.nan is always False.

How do you count NaN values in array a?

  • len(a)
  • a.count()
  • np.isnan(a).sum()
  • a.sum()

Answer: np.isnan(a).sum(). np.isnan(a) gives a boolean mask; .sum() counts the True entries.

Which of these is NOT part of the nan-aware family?

  • np.nanstd
  • np.nanmax
  • np.nanmedian
  • np.nanconcat

Answer: np.nanconcat. There is no np.nanconcat; nanstd, nanmax, and nanmedian all exist.

What does np.nanmean([[1.0,2.0,np.nan],[4.0,np.nan,6.0]], axis=0) return?

Down each column skipping NaN: [2.5, 2.0, 6.0].

What is np.nanmean essentially equivalent to?

  • a.sum()
  • a.mean() * 2
  • Filling NaN with 0 first

It drops the NaNs, then averages the remaining valid values.

If an entire row is NaN, what does nanmean return for that slice?

  • nan (with a warning)
  • 0.0
  • the row length
  • infinity

Answer: nan (with a warning). With nothing to average, nanmean returns nan and warns for that slice.