CODEWITHSUNDEEP
pythonarraysmathnumpytrigonometry
Srivatsan
Srivatsan

Reputation: 9363

Calculating cosine values for an array in Python

I have this array named a of 1242 numbers. I need to get the cosine value for all the numbers in Python.

When I use : cos_ra = math.cos(a) I get an error stating:

TypeError: only length-1 arrays can be converted to Python scalars

How can I solve this problem??

Thanks in advance

Upvotes: 4

Views: 22506

Answers (5)

Choung young jae
Choung young jae

Reputation: 33

Easy Way Motivated by the answer of zhangxaochen.

np.cos(np.arange(start, end, step))

Hope this helps!

Upvotes: 2

Ashwini Chaudhary
Ashwini Chaudhary

Reputation: 251096

Problem is you're using numpy.math.cos here, which expects you to pass a scalar. Use numpy.cos if you want to apply cos to an iterable.

In [30]: import numpy as np

In [31]: np.cos(np.array([1, 2, 3]))                                                             
Out[31]: array([ 0.54030231, -0.41614684, -0.9899925 ])

Error:

In [32]: np.math.cos(np.array([1, 2, 3]))                                                        
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-32-8ce0f3c0df04> in <module>()
----> 1 np.math.cos(np.array([1, 2, 3]))

TypeError: only length-1 arrays can be converted to Python scalars

Upvotes: 6

Danstahr
Danstahr

Reputation: 4319

The problem is that math.cos expect to get a number as argument while you are trying to pass a list. You need to call math.cos on each of the list elements.

Try using map : 

map(math.cos, a)

Upvotes: 3

jayelm
jayelm

Reputation: 7678

math.cos() can only be called on individual values, not lists.

Another alternative, using list comprehension:

cos_ra = [math.cos(i) for i in a]

Upvotes: 1

zhangxaochen
zhangxaochen

Reputation: 34047

use numpy:

In [178]: from numpy import *

In [179]: a=range(1242)

In [180]: b=np.cos(a)

In [181]: b
Out[181]: 
array([ 1.        ,  0.54030231, -0.41614684, ...,  0.35068442,
       -0.59855667, -0.99748752])

besides, numpy array operations are very fast:

In [182]: %timeit b=np.cos(a)  #numpy is the fastest
10000 loops, best of 3: 165 us per loop

In [183]: %timeit cos_ra = [math.cos(i) for i in a]
1000 loops, best of 3: 225 us per loop

In [184]: %timeit map(math.cos, a)
10000 loops, best of 3: 173 us per loop

Upvotes: 3

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