Well, it may seem a little strange that something called “not a number” is considered a number, but NaN is still a numeric type, despite that fact 🙂 NaN just means the specific value cannot be represented within the limitations of the numeric type (although that could be said for all numbers that have to … Read more
According to the IEEE standard, NaN values have the odd property that comparisons involving them are always false. That is, for a float f, f != f will be true only if f is NaN. Note that, as some comments below have pointed out, not all compilers respect this when optimizing code. For any compiler … Read more
size includes NaN values, count does not: In [46]: df = pd.DataFrame({‘a’:[0,0,1,2,2,2], ‘b’:[1,2,3,4,np.NaN,4], ‘c’:np.random.randn(6)}) df Out[46]: a b c 0 0 1 1.067627 1 0 2 0.554691 2 1 3 0.458084 3 2 4 0.426635 4 2 NaN -2.238091 5 2 4 1.256943 In [48]: print(df.groupby([‘a’])[‘b’].count()) print(df.groupby([‘a’])[‘b’].size()) a 0 2 1 1 2 2 Name: … Read more
RukTech’s answer, df.set_value(‘C’, ‘x’, 10), is far and away faster than the options I’ve suggested below. However, it has been slated for deprecation. Going forward, the recommended method is .iat/.at. Why df.xs(‘C’)[‘x’]=10 does not work: df.xs(‘C’) by default, returns a new dataframe with a copy of the data, so df.xs(‘C’)[‘x’]=10 modifies this new dataframe only. … Read more
I believe DataFrame.fillna() will do this for you. Link to Docs for a dataframe and for a Series. Example: In [7]: df Out[7]: 0 1 0 NaN NaN 1 -0.494375 0.570994 2 NaN NaN 3 1.876360 -0.229738 4 NaN NaN In [8]: df.fillna(0) Out[8]: 0 1 0 0.000000 0.000000 1 -0.494375 0.570994 2 0.000000 0.000000 … Read more
You could use the fillna method on the DataFrame and specify the method as ffill (forward fill): >>> df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]]) >>> df.fillna(method=’ffill’) 0 1 2 0 1 2 3 1 4 2 3 2 4 2 9 This method… propagate[s] last valid observation forward to next … Read more
How do you check that a number is NaN in JavaScript?
Don’t drop, just take the rows where EPS is not NA: df = df[df[‘EPS’].notna()]
I was a member of the IEEE-754 committee, I’ll try to help clarify things a bit. First off, floating-point numbers are not real numbers, and floating-point arithmetic does not satisfy the axioms of real arithmetic. Trichotomy is not the only property of real arithmetic that does not hold for floats, nor even the most important. … Read more