Nicholas Nicholas - 1 month ago 4x
Python Question

How do I deal with Pandas Series data type that has NaN?

What happens when using max() and min() on pandas.core.series.Series type that has NaN in it? Is this a bug? See below,

%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

mydata = pd.DataFrame(np.random.standard_normal((100,1)), columns=['No NaN'])
mydata['Has NaN'] = mydata['No NaN'] / mydata['No NaN'].shift(1)

# Both return NaN!
print(min(mydata['Has NaN']), max(mydata['Has NaN']))
# Still why False? Isn't float('nan') a singleton like None?
print(min(mydata['Has NaN']) == max(mydata['Has NaN']))
# But this time works well!
print(min([1, 2, 3, float('nan')]))


# When Series data type that has NaN bumps into min() and max(), what should
# I do? E.g.,
n, bins, patches = plt.hist(mydata['Has NaN'], 10)
except ValueError as e:
print(e, '\nSeems "range" argument in hist() has problem!')


First, you shouldn't use the Python built-in max or min when dealing with pandas or numpy, especially when you are working with nan.

Since 'nan' is the first item of mydata['Has NaN'], it is never replaced in either max or min because (as stated in the docs):

The not-a-number values float('NaN') and Decimal('NaN') are special. They are identical to themselves (x is x is true) but are not equal to themselves (x == x is false). Additionally, comparing any number to a not-a-number value will return False. For example, both 3 < float('NaN') and float('NaN') < 3 will return False.

Instead, use the pandas max and min methods:

In [4]: mydata['Has NaN'].min()
Out[4]: -176.9844930355774

In [5]: mydata['Has NaN'].max()
Out[5]: 12.684033138603787

With regards to the histogram, it seems this is a known issue with plt.hist, see here and here.

It should be fairly straightforward to deal with for now, though:

n, bins, patches = plt.hist(mydata['Has NaN'][~mydata['Has NaN'].isnull()], 10)

enter image description here