math4tots - 10 months ago 100

Python Question

I need to perform some calculations a large list of numbers.

Do array.array or numpy.array offer significant performance boost over typical arrays?

I don't have to do complicated manipulations on the arrays, I just need to be able to access and modify values,

e.g.

`import numpy`

x = numpy.array([0] * 1000000)

for i in range(1,len(x)):

x[i] = x[i-1] + i

So I will not really be needing concatenation, slicing, etc.

Also, it looks like array throws an error if I try to assign values that don't fit in C long:

`import numpy`

a = numpy.array([0])

a[0] += 1232234234234324353453453

print(a)

On console I get:

`a[0] += 1232234234234324353453453`

OverflowError: Python int too large to convert to C long

Is there a variation of array that lets me put in unbounded Python integers?

Or would doing it that way take away the point of having arrays in the first place?

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Answer Source

Your first example could be speed up. Python loop and access to individual items in a numpy array are slow. Use vectorized operations instead:

```
import numpy as np
x = np.arange(1000000).cumsum()
```

You can put unbounded Python integers to numpy array:

```
a = np.array([0], dtype=object)
a[0] += 1232234234234324353453453
```

Arithmetic operations compared to fixed-sized C integers would be slower in this case.

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