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If
assert 1 == 1
is fine, then why does:
assert np.nan == np.nan
cause an assertion error?
What's even more confusing, this is OK:
assert np.nan != np.nan
What's the best way to test for nan?
922
We can replace NaN values with 0 to get rid of NaN values. This is done by using fillna() function. This function will check the NaN values in the dataframe columns and fill the given value.
Nan means “Not a number”, this is because inside your cube function, you're not calling the square function, but getting it's contents.
Introduction to NumPy NaN. In Python, NumPy NAN stands for not a number and is defined as a substitute for declaring value which are numerical values that are missing values in an array as NumPy is used to deal with arrays in Python and this can be initialized using numpy.
nan is np. nan is True and one is two is also True . If you check the id of one and two using id(one) and id(two) , the same id will be displayed.
NaN has the property that it doesn't equal itself, you should use np.isnan to test NaN values, here np.isnan(np.nan) will yield True:
In[5]:
np.nan == np.nan
Out[5]: False
In[6]:
np.nan != np.nan
Out[6]: True
In[7]:
np.isnan(np.nan)
Out[7]: True
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