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# Generating Arrays (arange, linspace, zeros, ones)

NumPy offers built-in functions that let you create arrays quickly without typing values manually.

These functions are especially useful for generating test data or initializing arrays for computations.

<br/>

## `np.arange(start, stop, step)`

Generates evenly spaced values from `start` to `stop` (excluding `stop`).

```python title="np.arange(start, stop, step)"
np.arange(0, 10, 2)  # [0 2 4 6 8]
```

<br/>

## `np.linspace(start, stop, num)`

Generates a specific number of evenly spaced values *including the stop value*.

```python title="np.linspace(start, stop, num)"
np.linspace(0, 1, 5)  # [0.   0.25 0.5  0.75 1.0]
```

<br/>

## `np.zeros(shape)` and `np.ones(shape)`

Create arrays filled with zeros or ones.
Pass a shape like `(3,)` or `(2, 3)`.

```python title="np.zeros(shape)"
np.zeros((2, 2))  
# [[0. 0.]
#  [0. 0.]]
```

```python title="np.ones(shape)"
np.ones((2, 3))  
# [[1. 1. 1.]
#  [1. 1. 1.]]
```

<br/>

## Summary

* `arange`: values spaced by step (like `range()`)
* `linspace`: values spaced by number of points
* `zeros` / `ones`: fill arrays with fixed values

The `np.linspace` function generates a specified number of evenly spaced values between a start and stop point, including the stop value itself. This is useful when you need a precise division of values for plotting graphs or simulations.

### Using NumPy's array creation functions

Generating Arrays (arange, linspace, zeros, ones)

np.arange(start, stop, step)

np.linspace(start, stop, num)

np.zeros(shape) and np.ones(shape)

Summary

Using NumPy's array creation functions

`np.arange(start, stop, step)`

`np.linspace(start, stop, num)`

`np.zeros(shape)` and `np.ones(shape)`