# Numpy: Beyond normal np.save
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## The goal
Let's [save and load data under numpy](https://numpy.org/doc/stable/reference/routines.io.html). This can be more complicated than expected.
Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
## Normal [np.save](https://numpy.org/doc/stable/reference/generated/numpy.save.html) and [np.load](https://numpy.org/doc/stable/reference/generated/numpy.load.html)
A normal np.save and np.load cycle may look like this:
```python
import numpy as np
rng = np.random.default_rng()
a_original: np.ndarray = rng.random((100, 10))
np.save("a.npy", a_original)
a_load: np.ndarray = np.load("a.npy")
print(np.abs(a_original - a_load).sum()) # -> 0.0
```
## Saving non-standard numpy data
You may have tried this and wondered why it doesn't work
```python
import numpy as np
rng = np.random.default_rng()
a_original: list = []
a_original.append(rng.random((100, 10)))
a_original.append(rng.random((100, 10)))
a_original.append("now something completely different")
np.save("b.npy", a_original)
a_load: np.ndarray = np.load("b.npy")
```
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/data_1/davrot/MRI_Fruits/test/test.py in line 12
26 a_original.append("now something completely different")
28 np.save("b.npy", a_original)
---> 29 a_load: np.ndarray = np.load("b.npy")
[...]
```
This is how you have to do it:
```python
import numpy as np
rng = np.random.default_rng()
a_original: list = []
a_original.append(rng.random((100, 10)))
a_original.append(rng.random((100, 10)))
a_original.append("now something completely different")
np.save("b.npy", np.array(a_original, dtype=object))
a_load: np.ndarray = np.load("b.npy", allow_pickle=True)
print(np.abs(a_original[0] - a_load[0]).sum()) # -> 0.0
print(np.abs(a_original[1] - a_load[1]).sum()) # -> 0.0
print(a_load[2]) # -> now something completely different
```
## [np.savez](https://numpy.org/doc/stable/reference/generated/numpy.savez.html)
We can save more than one variable into one file. We need to use np.savez for this. Now the file extension is npz instead of npy. This is required!
```python
import numpy as np
rng = np.random.default_rng()
a_original = rng.random((100, 10))
b_original = rng.random((100, 10))
c_original = rng.random((100, 10))
np.savez("c.npz", a_original=a_original, b_original=b_original, c_original=c_original)
np_file = np.load("c.npz")
np_file_keys: list = list(np_file.keys())
print(np_file_keys) # -> ['a_original', 'b_original', 'c_original']
```
Please don't use savez like this because this can cause human errors down the road:
```python
import numpy as np
rng = np.random.default_rng()
a_original = rng.random((100, 10))
b_original = rng.random((100, 10))
c_original = rng.random((100, 10))
# np.savez("c.npz", a_original=a_original, b_original=b_original, c_original=c_original)
np.savez("d.npz", a_original, b_original, c_original)
np_file = np.load("d.npz")
np_file_keys: list = list(np_file.keys())
print(np_file_keys) # -> ['arr_0', 'arr_1', 'arr_2']
```
You don't need to keep the variable name but keep it human readable:
```python
import numpy as np
rng = np.random.default_rng()
a_original = rng.random((100, 10))
b_original = rng.random((100, 10))
c_original = rng.random((100, 10))
d_original = rng.random((100, 10))
np.savez("e.npz", what=a_original, a=b_original, nice=c_original, day=d_original)
np_file = np.load("e.npz")
np_file_keys: list = list(np_file.keys())
print(np_file_keys) # -> ['what', 'a', 'nice', 'day']
```
Now we can work with the file and the stored variables:
```python
import numpy as np
rng = np.random.default_rng()
a_original = rng.random((100, 10))
b_original = rng.random((100, 10))
c_original = rng.random((100, 10))
np.savez("c.npz", a_original=a_original, b_original=b_original, c_original=c_original)
np_file = np.load("c.npz")
print(np.abs(a_original - np_file["a_original"]).sum()) # -> 0.0
print(np.abs(b_original - np_file["b_original"]).sum()) # -> 0.0
print(np.abs(c_original - np_file["c_original"]).sum()) # -> 0.0
```
## [np.savez_compressed](https://numpy.org/doc/stable/reference/generated/numpy.savez_compressed.html)
We can compress the data too:
```python
import numpy as np
rng = np.random.default_rng()
a_original = rng.random((100, 10))
b_original = rng.random((100, 10))
c_original = rng.random((100, 10))
np.savez_compressed(
"f.npz", a_original=a_original, b_original=b_original, c_original=c_original
)
np_file = np.load("f.npz")
print(np.abs(a_original - np_file["a_original"]).sum()) # -> 0.0
print(np.abs(b_original - np_file["b_original"]).sum()) # -> 0.0
print(np.abs(c_original - np_file["c_original"]).sum()) # -> 0.0
```