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

NumPy gives you built-in functions to quickly create arrays — without manually typing the values.

These are useful for building test data or initializing arrays.

 

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

Creates evenly spaced values from start to stop (excluding stop).

```python
np.arange(0, 10, 2) # [0 2 4 6 8]
```

 

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

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

```python
np.linspace(0, 1, 5) # [0. 0.25 0.5 0.75 1.0]
```

 

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

Create arrays filled with all 0s or all 1s.
Pass in a shape like `(3,)` or `(2, 3)`.

```python
np.zeros((2, 2)) # [[0. 0.]
 # [0. 0.]]
```

 

## Summary

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

 

## What’s Next

You’ll now try generating different types of arrays using these tools in Jupyter.

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

What’s Next

Using NumPy's array creation functions

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

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

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