pytutorial/numpy/new_matrix/README.md

# New matrices 
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## The goal

Making a new matrix...

Questions to [David Rotermund](mailto:davrot@uni-bremen.de)

Using **import numpy as np** is the standard. 

## Simple example -- new [np.zeros()](https://numpy.org/doc/stable/reference/generated/numpy.zeros.html)

Define the size of your new matrix with a tuple, e.g.​

```python
M = numpy.zeros((DIM_0, DIM_1, DIM_2, …))​
```

### 1d

```python
import numpy as np

M = np.zeros((2))
print(M)
```

Output:

```python
[0. 0.]
```

### 2d

```python
import numpy as np

M = np.zeros((2, 3))
print(M)
```

Output:

```python
[[0. 0. 0.]
 [0. 0. 0.]]
```

### 3d

```python
import numpy as np

M = np.zeros((2, 3, 4))
print(M)
```

Output:

```python
[[[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]

 [[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]]
```

## Simple example -- recycle [np.zeros_like()](https://numpy.org/doc/stable/reference/generated/numpy.zeros_like.html)

If you have a matrix with the same size ​you want then you can use zeros_like. This will also copy other properties like the data type.

as a prototype use​

N = numpy.zeros_like(M) ​

```python
import numpy as np

M = np.zeros((2, 3, 4))

N = np.zeros_like(M)
print(N)
```

Output:

```python
[[[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]

 [[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]]
```

## Remember unpacking

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This is an optional topic!

```python
import numpy as np

d = (3, 4)
M = np.zeros((2, *d))

print(M)
```

## [From shape or value](https://numpy.org/doc/stable/reference/routines.array-creation.html#from-shape-or-value)

|||
|---|---|
|[empty](https://numpy.org/doc/stable/reference/generated/numpy.empty.html#numpy.empty)(shape[, dtype, order, like])|Return a new array of given shape and type, without initializing entries.|
|[empty_like](https://numpy.org/doc/stable/reference/generated/numpy.empty_like.html#numpy.empty_like)(prototype[, dtype, order, subok, ...])|Return a new array with the same shape and type as a given array.|
|[eye](https://numpy.org/doc/stable/reference/generated/numpy.eye.html#numpy.eye)(N[, M, k, dtype, order, like])|Return a 2-D array with ones on the diagonal and zeros elsewhere.|
|[identity](https://numpy.org/doc/stable/reference/generated/numpy.identity.html#numpy.identity)(n[, dtype, like])|Return the identity array.|
|[ones](https://numpy.org/doc/stable/reference/generated/numpy.ones.html#numpy.ones)(shape[, dtype, order, like])|Return a new array of given shape and type, filled with ones.|
|[ones_like](https://numpy.org/doc/stable/reference/generated/numpy.ones_like.html#numpy.ones_like)(a[, dtype, order, subok, shape])|Return an array of ones with the same shape and type as a given array.|
|[zeros](https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros)(shape[, dtype, order, like])|Return a new array of given shape and type, filled with zeros.|
|[zeros_like](https://numpy.org/doc/stable/reference/generated/numpy.zeros_like.html#numpy.zeros_like)(a[, dtype, order, subok, shape])|Return an array of zeros with the same shape and type as a given array.|
|[full](https://numpy.org/doc/stable/reference/generated/numpy.full.html#numpy.full)(shape, fill_value[, dtype, order, like])|Return a new array of given shape and type, filled with fill_value.|
|[full_like](https://numpy.org/doc/stable/reference/generated/numpy.full_like.html#numpy.full_like)(a, fill_value[, dtype, order, ...])|Return a full array with the same shape and type as a given array.|