Puzzle 18. Off with Their NaNs

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Puzzle 18

Off with Their NaNs

	import numpy as np
	import pandas as pd

	s = pd.Series([1, np.nan, 3])
	print(s[~(s == np.nan)])

Guess the Output
	Try to guess what the output is before moving to the next page.

This code will print:

	0 1.0
	1 NaN
	2 3.0
	dtype: float64

We covered some of the floating-point oddities in the puzzle Puzzle 12, Multiplying . NaN (or np.nan) is another oddity. The name NaN stands for not a number. It serves two purposes: illegal computation and missing values.

Here’s an example of a bad computation:

	In [1]: np.float64(0)/np.float64(0)
	RuntimeWarning: invalid value encountered in
	double_scalars np.float64(0)/np.float64(0)
	Out[1]: nan

You see a warning but not an exception, and the return value is nan.

nan does not equal any number, including itself.

	In [2]: np.nan == np.nan
	Out[2]: False

To check that a value is nan, you need to use a special function such as pandas.isnull:

	In [3]: pd.isnull(np.nan)
	Out[3]: True

You can use pandas.isnull to fix this teaser.

not_nan_fixed.py

	import numpy as np
	import pandas as pd

	s = pd.Series([1, np.nan, 3])
	print(s[~pd.isnull(s)])

pandas.isnull works with all Pandas “missing” values: None, pandas.NaT (not a time), and the new pandas.NA.

Floating points have several other special “numbers” such as inf (infinity), -inf, -0, +0, and others. You can learn more about them in the following links.

Table of Contents for Puzzle 18. Off with Their NaNs

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Table of Contents for
Puzzle 18. Off with Their NaNs