The difference between comparison to np.nan and isnull()

np.nan is not comparable to np.nan… directly.

np.nan == np.nan

False

While

np.isnan(np.nan)

True

Could also do

pd.isnull(np.nan)

True

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examples
Filters nothing because nothing is equal to np.nan

s = pd.Series([1., np.nan, 2.])
s[s != np.nan]

0    1.0
1    NaN
2    2.0
dtype: float64

Filters out the null

s = pd.Series([1., np.nan, 2.])
s[s.notnull()]

0    1.0
2    2.0
dtype: float64

Use odd comparison behavior to get what we want anyway. If np.nan != np.nan is True then

s = pd.Series([1., np.nan, 2.])
s[s == s]

0    1.0
2    2.0
dtype: float64

Just dropna

s = pd.Series([1., np.nan, 2.])
s.dropna()

0    1.0
2    2.0
dtype: float64