How access multiple column values in a numpy structured array in python?

Question

I am new to numpy and still learning it. I created a structured array as below:

name = ['Alice', 'Beth', 'Cathy', 'Dorothy']
studentid = [1,2,3,4]
score = [85.4, 90.4, 87.66, 78.9]
student_data = np.zeros(4, dtype={'names':('name', 'studentId', 'score'), 
                         'formats':('U10', 'i4', 'f8')})
student_data['name'] = name
student_data['studentId'] = studentid
student_data['score']=score

Inorder to get the names of people who have scores greater than 85, I wrote this:

student_data[student_data['score'] > 85]['name']

But if I try to retrieve another column along with 'name', I am getting an error:

IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

For example, I tried the below ways:

student_data[student_data['score'] < 90]['name','studentid']
student_data[student_data['score'] < 90]['name']['studentid']

Both of them result in the error:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-189-7ad7c151f0e3> in <module>
----> 1 student_data[student_data['score'] < 90]['name']['studentid']

IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices

Could anyone let me know what is the mistake I am doing here & how can I retrieve multiple columns on conditional basis ?

The relevant docs: numpy.org/doc/1.18/user/…. "the index is a list of field names." — hpaulj
– hpaulj, Commented Jun 6, 2020 at 6:51
Does this answer your question? How to access multiple fields from a structured numpy.array? — Joe
– Joe, Commented Jun 6, 2020 at 9:09

Quang Hoang · Accepted Answer · 2020-06-06 06:28:34Z

1

You can access with array of columns:

student_data[student_data['score'] < 90][['name','studentid']]

Output:

array([('Alice', 1), ('Cathy', 3), ('Dorothy', 4)],
      dtype={'names':['name','studentId'], 'formats':['<U10','<i4'], 'offsets':[0,40], 'itemsize':52})

answered Jun 6, 2020 at 6:28

Quang Hoang

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sushanth · Accepted Answer · 2020-06-06 06:36:05Z

1

You can use np.where

print(student_data[np.where(student_data['score'] < 90)][['name', 'studentId']])

[('Alice', 1) ('Cathy', 3) ('Dorothy', 4)]

edited Jun 6, 2020 at 6:36

answered Jun 6, 2020 at 6:29

sushanth

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