I think there is problem with different index values, so where concat cannot align get NaN: aaa = pd.DataFrame([0,1,0,1,0,0], columns=[‘prediction’], index=[4,5,8,7,10,12]) print(aaa) prediction 4 0 5 1 8 0 7 1 10 0 12 0 bbb = pd.DataFrame([0,0,1,0,1,1], columns=[‘groundTruth’]) print(bbb) groundTruth 0 0 1 0 2 1 3 0 4 1 5 1 print (pd.concat([aaa, … Read more
Two ways, which are more or less equivalent: if (slope != slope) { // handle nan here } Or #include <math.h> … if (isnan(slope)) { // handle nan here } (man isnan will give you more information, or you can read all about it in the C standard) Alternatively, you could detect that the denominator … Read more
a = a[~np.isnan(a)]
The 1 is converted to a complex number first, 1 + 0j, which then leads to an inf * 0 multiplication, resulting in a nan. (inf + 0j) * 1 (inf + 0j) * (1 + 0j) inf * 1 + inf * 0j + 0j * 1 + 0j * 0j # ^ this … Read more
json.dumps has an allow_nan parameter, which defaults to True. NaN, Infinity and -Infinity are not part of JSON, but they are standard in Javascript, so they’re commonly used extensions. If the recipient can’t handle them, set allow_nan=False. But then you’ll get ValueError when you try to serialise NaN.
Simply pass the desired value as first argument, like 0, math.inf or, here, np.nan. The constructor then initializes and fills the value array to the size specified by arguments index and columns: >>> import numpy as np >>> import pandas as pd >>> df = pd.DataFrame(np.nan, index=[0, 1, 2, 3], columns=[‘A’, ‘B’]) >>> df A … Read more
For versions of numpy prior to 1.19, this is probably the best approach in situations that don’t specifically involve unit tests: >>> ((a == b) | (numpy.isnan(a) & numpy.isnan(b))).all() True However, modern versions provide the array_equal function with a new keyword argument, equal_nan, which fits the bill exactly. This was first pointed out by flyingdutchman; … Read more
Hrm, it appears I can use a masked array to do this: masked_array = np.ma.array (a, mask=np.isnan(a)) cmap = matplotlib.cm.jet cmap.set_bad(‘white’,1.) ax.imshow(masked_array, interpolation=’nearest’, cmap=cmap) This should suffice, though I’m still open to suggestions. :]
Actually in later versions of pandas this will give a TypeError: df.replace(‘-‘, None) TypeError: If “to_replace” and “value” are both None then regex must be a mapping You can do it by passing either a list or a dictionary: In [11]: df.replace(‘-‘, df.replace([‘-‘], [None]) # or .replace(‘-‘, {0: None}) Out[11]: 0 0 None 1 3 … Read more
The count() method returns the number of non-NaN values in each column: >>> df1.count() a 3 b 2 d 1 dtype: int64 Similarly, count(axis=1) returns the number of non-NaN values in each row.