How do you perform a split operation using NumPy to separate data into x and y?

To perform a split operation using NumPy to separate data into `x` (features) and `y` (target variable), you typically need to slice the NumPy array along the appropriate axis. Here's how you can do it:

Assuming you have your data in a NumPy array called `data`, and you want to separate the last column as the target variable `y`, and the rest of the columns as the features `x`, you can follow these steps:

1. Import NumPy:

First, make sure you have NumPy imported.

import numpy as np

2. Load or Define Your Data:

Ensure your data is in a NumPy array. For example:

data = np.array([[1, 2, 3, 4],
               [5, 6, 7, 8],
               [9, 10, 11, 12]])

3. Split the Data:

Use array slicing to separate `x` and `y`. Here’s how to split the data, assuming `y` is the last column:

x = data[:, :-1] # All rows, all columns except the last one
y = data[:, -1] # All rows, only the last column

Here's a complete example:

import numpy as np

# Sample data
data = np.array([[1, 2, 3, 4],
               [5, 6, 7, 8],
               [9, 10, 11, 12]])

# Splitting data into x and y
x = data[:, :-1]
y = data[:, -1]

# Printing the results
print("x (Features):")
print(x)
print("y (Target Variable):")
print(y)

Explanation:

- `data[:, :-1]` selects all rows (`:`) and all columns except the last one (`:-1`), which are assigned to `x`.

- `data[:, -1]` selects all rows (`:`) and only the last column (`-1`), which is assigned to `y`.

4. Verify the Split:

Print `x` and `y` to confirm the split.

print("x (Features):", x)
print("y (Target Variable):", y)

This method is effective for separating features and target variables in a structured NumPy array, which is common in data science and machine learning tasks.

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