NumPy is a fundamental Python library for numerical computing. Key elements in a <a target="_blank" href="https://www.almabetter.com/bytes/cheat-sheet/numpy">NumPy cheat sheet</a> include:
<ul>
<li>Importing: Use <code>import numpy as np</code> to access NumPy functions.
</li>
<li>Arrays: Create arrays with <code>np.array([1, 2, 3])</code>.
</li>
<li>Array Operations: Perform element-wise operations like <code>+</code>, <code>-</code>, <code>*</code>, <code>/</code>, and functions such as <code>np.sum()</code>, <code>np.mean()</code>, <code>np.std()</code>.
</li>
<li>Reshape: Change the shape of arrays using <code>np.reshape()</code>.
</li>
<li>Indexing: Access elements with <code>array[index]</code> or slicing <code>array[start:end]</code>.
</li>
<li>Linspace and Arange: Generate ranges of values using <code>np.linspace(start, stop, num)</code> and <code>np.arange(start, stop, step)</code>.
</li>
<li>Random: Generate random numbers with <code>np.random.random(size)</code>.
</li>
</ul>
These features make NumPy essential for efficient data manipulation.

NumPy is a fundamental Python library for numerical computing. Key elements in a [NumPy cheat sheet](https://www.almabetter.com/bytes/cheat-sheet/numpy) include:

* **Importing**: Use `import numpy as np` to access NumPy functions.
    
* **Arrays**: Create arrays with `np.array([1, 2, 3])`.
    
* **Array Operations**: Perform element-wise operations like `+`, `-`, `*`, `/`, and functions such as `np.sum()`, `np.mean()`, `np.std()`.
    
* **Reshape**: Change the shape of arrays using `np.reshape()`.
    
* **Indexing**: Access elements with `array[index]` or slicing `array[start:end]`.
    
* **Linspace and Arange**: Generate ranges of values using `np.linspace(start, stop, num)` and `np.arange(start, stop, step)`.
    
* **Random**: Generate random numbers with `np.random.random(size)`.
    

These features make NumPy essential for efficient data manipulation.

Introduction to Numpy