Extract specific values from array in Python

Question

I want to calculate means and averages in a data set, but for many reasons that I can't go into here, my array contains my values and some "filler" values (which are currently set to -1000).

How can I calculate the mean (for example) on only the non -1000 values?

res=[-1000 for x in range(0,10)]
res[1]=2
res[5]=3
res[7]=4
#something like this?
np.mean(res>-1000)

#the result should be the mean value of 2,3 and 4 (3)

MVCE

res=[1.0, 1.0, 1.0, 1.0, 1.0, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000, -1000]
#for instance
print(np.mean(res[res > -1000]))

jpp · Accepted Answer · 2018-05-02 14:56:37Z

3

Since you tagged numpy, you should use it for indexing / slicing. Here's an example:

res = np.array([-1000 for x in range(0,10)])
res[1]=2
res[5]=3
res[7]=4

output = np.mean(res[res > -1000])  # 3.0

Read the numpy docs for more details on indexing logic.

edited May 2, 2018 at 14:56

answered May 2, 2018 at 14:43

jpp

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5 Comments

Jessica Chambers Over a year ago

as is I get this error "TypeError: unorderable types: list() > int()" but I'll read up on the doc

jpp Over a year ago

@JessicaChambers, I'm not sure. Did you copy / paste the above code exactly? You might also want to supply a minimal reproducible example of your problem.

Jessica Chambers Over a year ago

when I copy it exactly it works, as soon as I put it in my code it throws that error... does the list being full of floats (not ints) make a difference? Otherwise there should be no difference... I will add a MCVE to the original post, just to be sure

jpp Over a year ago

@JessicaChambers, No, int vs float makes no difference.

jpp Over a year ago

@JessicaChambers, In your example, you need to convert your object to an array first: res = np.array(res)

Venkata S S K M Chaitanya · Accepted Answer · 2018-05-02 15:00:13Z

2

Why use another library when you have your friend filter method

import statistics
number_list = [2, -1000, 3, 4, -1000, -1000]
not_1000 = list(filter(lambda x: x != -1000, number_list))
not_1000_mean = statistics.mean(not_1000)

edited May 2, 2018 at 15:00

answered May 2, 2018 at 14:53

Venkata S S K M Chaitanya

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