Valueerror: per-column arrays must each be 1-dimensional

Explanation of “ValueError: Per-Column Arrays Must Each Be 1-Dimensional”

The error message “ValueError: Per-Column Arrays Must Each Be 1-Dimensional” occurs when working with multidimensional arrays (e.g., NumPy arrays) and trying to perform an operation that expects 1-dimensional arrays for each column. This error typically arises when we try to apply an operation or method that cannot handle multidimensional arrays directly.

Example

Let’s consider an example where we have a 2-dimensional array with two columns:

“`python
import numpy as np

array = np.array([[1, 2], [3, 4], [5, 6]])
“`

If we try to apply an operation that expects 1-dimensional arrays for each column, such as calculating the mean using the `np.mean()` function, we will encounter the mentioned error:

“`python
column_mean = np.mean(array, axis=1)
“`

Output:

“`
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 1 dimension(s)
“`

The error occurs because the mean function expects a 1-dimensional array for each column, but we provided a 2-dimensional array.

Solution

To resolve this error, we need to ensure that we are providing 1-dimensional arrays for each column operation. One common way of achieving this is to access the desired column(s) as separate 1-dimensional arrays before applying the operation:

“`python
column_mean = np.mean(array[:, 0])
“`

Output:

“`
3.0
“`

In the above code, `array[:, 0]` accesses the first column of the 2-dimensional array as a 1-dimensional array. This allows us to calculate the mean without encountering the error.