In this Python NumPy blog, I will explain what the **np.broadcast.reset() function in Python** is in detail with the help of an illustrative example.

**The numpy.broadcast.reset() function is a lesser-known part of the broadcasting toolkit. It resets the iterator of the numpy.broadcast object to its initial condition. This function is typically used in advanced scenarios where the same broadcasting object is needed for multiple operations, and there’s a requirement to reset its state between these operations.**

## np.broadcast.reset() function in Python syntax

The basic syntax of **numpy.broadcast.reset() function in Python** is straightforward:

`broadcast_obj.reset()`

Here, broadcast_obj is an instance of numpy.broadcast() in Python.

**Note:** There is no requirement for any parameters in the **np.broadcast.reset()** function in Python and it does not return any values. Instead, it resets the internal iterator of the numpy.broadcast object.

## NumPy reset index using np.broadcast.reset() function

The **np.broadcast.reset()** function in Python is used to reset the iterators of the output produced by the broadcasting process. Let’s see how it works:

```
import numpy as np
Plants = np.array([[1], [2], [3], [4]])
water_required = np.array([5, 6, 7, 8])
Total_water_required = np.broadcast(Plants, water_required)
print("Current broadcast index:", Total_water_required.index)
print("Next two values in broadcast:", next(Total_water_required), next(Total_water_required))
print("New broadcast index:", Total_water_required.index)
Total_water_required.reset()
print("Reset broadcast index:", Total_water_required.index)
```

**Output:** Here,

- We invoke the numpy
**broadcast() function**, supplying it with the arrays “Plants” and “water_required” as arguments. - The outcome is stored in a variable named “Total_water_required”.
- We print the present index of the result after broadcasting the arrays in Python.
- Then, we get the next iterator values from the broadcasted result in Python.
- Now, we print the new index value from the recently broadcasted outcome.
- To reset the value, we use the
**np.broadcast.reset()**function in Python. - Then, We print the new index value of the broadcasted result.

```
Current broadcast index: 0
Next two values in broadcast: (1, 5) (1, 6)
New broadcast index: 2
Reset broadcast index: 0
```

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

## Conclusion

To summarize, the **np.broadcast.reset() function in Python** is used to reset the internal iterator of a numpy.broadcast object. This function is particularly useful in advanced scenarios where the same broadcasting object needs to be iterated over multiple times, allowing for efficient reuse in complex array operations.

Understanding this function, along with the broader concept of broadcasting, can be beneficial for those working on complex numerical computations and custom array operations in Python.

You may also like to read:

- np.savetxt() function in Python
- NumPy Reset Index of an Array in Python
- np.count() function in Python

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etcâ€¦ for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.