Hello Python Enthusiasts!

In the world of Python programming, few things are more frustrating than grappling with bug-infested code. Fewer still are obscure errors that leave you scratching your head. One such error is the “**axis 1 is out of bounds for array of dimension 1**” error message that occasionally pops up when handling data with the Python NumPy library. Today, I’ll be walking you through this often-misunderstood error.

Contents

## What is “axis 1 is out of bounds for array of dimension 1”?

This error generally occurs when dealing with NumPy arrays. Arrays in NumPy are multi-dimensional and you can access specific segments through its axis. However, when you try to access an axis that doesn’t exist, Python throws up an “**axis 1 is out of bounds for array of dimension 1**” error. Essentially, this error means you’re trying to access a plane or dimension in the array that doesn’t exist.

## What Causes the “axis 1 is out of bounds for array of dimension 1” error?

The underlying cause of this error is referencing an axis, specifically axis 1, in a one-dimensional NumPy array. Simply put, you’re trying to find a second dimension (dimension 1 as per zero-index order) where there is none since the array is one-dimensional.

## Code Examples

Let’s take a look at it in action!

```
import numpy as np
# a 1D array
y_pred = np.array([0.1, 0.3, 0.6, 0.2])
# trying to find the index of the maximum value along axis 1
y_class = np.argmax(y_pred, axis = 1)
```

Executing this code will return an IndexError: “**axis 1 is out of bounds for array of dimension 1**“!

## How to resolve “axis 1 is out of bounds for array of dimension 1” error?

To resolve this error, correctly address the dimension of the array. In the case of a one-dimensional array, you can change axis from 1 to 0. This change is due to the fact that a one-dimensional array only has one axis (0), and axis 1 does not exist.

## Solution Code Examples

Here are two potential solutions, specific to our earlier example:

```
# Solution 1:
import numpy as np
# a 1D array
y_pred = np.array([0.1, 0.3, 0.6, 0.2])
# index of the maximum value along axis 1
y_class = np.argmax(y_pred, axis=0)
print(y_class)
```

The output of this program is “2”, which means the value “0.6” in the `y_pred`

array is the maximum value and its index is “2”.

```
# Solution 2:
import numpy as np
# a 2D array
y_pred = np.array([[0.1, 0.3],
[0.6, 0.2]])
# index of the maximum value along axis 1
y_class = np.argmax(y_pred, axis=1)
print(y_class)
```

The output of this program is “[1, 0]”, which signifies the maximum value’s indices for each sub-array along axis 1.

## FAQs

**1. Can I use an axis value greater than 1?**

Yes, you can use axis values more than 1 when dealing with multi-dimensional arrays (>2D). Each number corresponds to a different axis and the limit is set by the dimension of the array.

**2. Does this error occur only with “argmax” function?**

No, this error is not exclusive to `np.argmax`

. It can occur with any function that requires specifying an axis in an array, such as `np.sum`

, `np.average`

, `np.argmax`

, etc.

**3. Is a one-dimensional array the same as a single column or row of a two-dimensional array?**

No, a row/column of a 2D array is not the same as a 1D array. They can be accessed differently, and specifying axis=1 on a strictly 1D array would still result in “axis 1 is out of bounds” error.

## Conclusion

Understanding the dimensions of our data and how to properly interact with these dimensions is crucial to effectively use NumPy and manipulate data arrays. Next time you encounter the “**axis 1 is out of bounds for array of dimension 1**” error, remember, it’s just Python’s way of telling you to check the array dimensions before calling for a non-existent axis!

Hopefully, this tutorial aids in your Python learning journey and helps you navigate through the tricky world of multi-dimensional arrays. Until next time, code on, Pythonistas!

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