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I supposed that
data[data.agefm.isnull()] and
data[data.agefm == numpy.nan] are equivalent. But no, the first truly returns rows where agefm is NaN, but the second returns an empty DataFrame. I thank that omitted values are always equal to np.nan, but it seems wrong.
agefm column has float64 type:
(Pdb) data.agefm.describe() count 2079.000000 mean 20.686388 std 5.002383 min 10.000000 25% 17.000000 50% 20.000000 75% 23.000000 max 46.000000 Name: agefm, dtype: float64 Could you explain me please, what does data[data.agefm == np.nan] mean exactly?
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Javascript null represents the intentional absence of any object value. The undefined property indicates that the variable has not been assigned a value or not declared at all. The NaN property represents a “Not-a-Number” value. The NaN property indicates that a value is not a legitimate number.
To check for NaN values in a Numpy array you can use the np. isnan() method. This outputs a boolean mask of the size that of the original array. The output array has true for the indices which are NaNs in the original array and false for the rest.
nan is a single object that always has the same id, no matter which variable you assign it to. np. nan is np. nan is True and one is two is also True .
np.nan allows for vectorized operations; its a float value, while None , by definition, forces object type, which basically disables all efficiency in numpy.
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 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 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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