I recently had to construct a couple of numpy arrays from a handful of files. I quickly did something like this:

```
files = list_of_files()
arr = np.array([], dtype=np.float32)
for f in files:
iarr = get_arr_from_file(f)
arr = np.concatenate([arr, iarr])
```

This was taking a lot longer than I thought it should. There’s a very
simple reason: NumPy arrays have to be contiguous in memory so I was
copying my `arr`

variable `len(files)`

times to construct a final
`arr`

(and every iteration of the loop `arr`

was getting larger). This
was of course unnecessary.

A better (and more pythonic) way to do it:

```
files = list_of_files()
arrs = [get_arr_from_file(f) for f in files]
arr = np.concatenate(arrs)
```

So when it comes to repetitive NumPy concatenations… avoid it.

A quick test in IPython:

```
import numpy as np
def bad():
arr = np.array([], dtype=np.float32)
for i in range(100):
iarr = np.random.randn(100000)
arr = np.concatenate([arr, iarr])
return arr
def good():
arrs = [np.random.randn(100000) for i in range(100)]
return np.concatenate(arrs)
%timeit bad()
%timeit good()
```

The output:

```
1.73 s ± 2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
247 ms ± 2.52 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```

Quite the difference!