Is there a way to store NaN in a Numpy array of integers? I get: a=np.array([1],dtype=long) a[0]=np.nan Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: cannot convert float NaN to integer Answers: Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you … Read more
I am trying to use imshow in matplotlib to plot data as a heatmap, but some of the values are NaNs. I’d like the NaNs to be rendered as a special color not found in the colormap.
Is there a quick way of replacing all NaN values in a numpy array with (say) the linearly interpolated values?
I am curious why a simple concatenation of two data frames in pandas:
For my unittest, I want to check if two arrays are identical. Reduced example:
sorted([2, float('nan'), 1]) returns [2, nan, 1]
I have a pandas dataframe (df), and I want to do something like:
Given a pandas dataframe containing possible NaN values scattered here and there:
How can I calculate matrix mean values along a matrix, but to remove nan values from calculation? (For R people, think na.rm = TRUE).
Is there any method to replace values with None in Pandas in Python?