Created .ones() entry for Python Numpy (#6898)

* Created .ones() entry for Python Numpy

* Update ones.md

* Update ones.md

* added a codebyte example, formatted the parameters, fixed format issues

* Update ones.md

* fixed format

* Update ones.md

* Update ones.md

* Format

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Author: arisdelacruz

content/numpy/concepts/built-in-functions/terms/ones/ones.md

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+---
+Title: '.ones()'
+Description: 'Creates a new array of the given shape and type, filled with ones.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Arrays'
+  - 'Functions'
+  - 'Linear Algebra'
+  - 'NumPy'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/data-science'
+---
+
+In NumPy, the **`.ones()`** function creates a new array of the given shape and type, filled with ones.
+
+This function is particularly useful when there is a need to initialize an array with a placeholder value of `1`, which might be multiplied by another value or used in various numerical computations later.
+
+## Syntax
+
+```pseudo
+numpy.ones(shape, dtype=None, order='C', *, like=None)
+```
+
+**Parameters:**
+
+- `shape` (`int` or tuple of `int`): Defines the dimensions of the new array.
+  - If an int, a 1-D array of that length is created.
+  - If a tuple of ints, an array with those dimensions is created (e.g., (2, 3) for a 2x3 array).
+- `dtype` (optional): The desired data type for the array elements. Defaults to `float64`. Examples: `int32`, `bool_`, `float`.
+- `order`: ({'C', 'F'}, optional): Controls whether the array is stored in row-major or column-major memory layout.
+  - `'C'` (default): Row-major (C-style)
+  - `'F'`: Column-major (Fortran-style)
+- `like`(array_like, optional): Reference object for creating arrays not strictly of NumPy type. If `None`, a NumPy array is returned.
+
+**Return value:**
+
+- `out` (ndarray): An array of ones of the given shape, `dtype`, and order.
+
+## Example
+
+This example demonstrates the usage of the `.ones()` function:
+
+```py
+import numpy as np
+
+# Create a 1-D array with 5 ones (default dtype is float64)
+arr1 = np.ones(5)
+print("Array 1:\n", arr1)
+print("Data type of Array 1:", arr1.dtype)
+print("Shape of Array 1:", arr1.shape)
+
+# Create a 2-D array (3 rows, 4 columns) of ones with integer type
+arr2 = np.ones((3, 4), dtype=int)
+print("\nArray 2:\n", arr2)
+print("Data type of Array 2:", arr2.dtype)
+print("Shape of Array 2:", arr2.shape)
+
+# Create a 3-D array of ones
+arr3 = np.ones((2, 3, 2))
+print("\nArray 3:\n", arr3)
+print("Data type of Array 3:", arr3.dtype)
+print("Shape of Array 3:", arr3.shape)
+```
+
+Here is the output:
+
+```shell
+Array 1:
+ [1. 1. 1. 1. 1.]
+Data type of Array 1: float64
+Shape of Array 1: (5,)
+
+Array 2:
+ [[1 1 1 1]
+  [1 1 1 1]
+  [1 1 1 1]]
+Data type of Array 2: int64
+Shape of Array 2: (3, 4)
+
+Array 3:
+ [[[1. 1.]
+   [1. 1.]
+   [1. 1.]]
+
+  [[1. 1.]
+   [1. 1.]
+   [1. 1.]]]
+Data type of Array 3: float64
+Shape of Array 3: (2, 3, 2)
+```
+
+## Codebyte Example: Add Bias Term to a Feature Matrix in Machine Learning
+
+In many machine learning algorithms (like linear regression), there is a need to add a bias column of 1s to the input feature matrix. Here's how `numpy.ones()` can be used:
+
+```codebyte/python
+import numpy as np
+
+# Feature matrix (e.g., 4 samples with 3 features each)
+X = np.array([
+  [5.1, 3.5, 1.4],
+  [4.9, 3.0, 1.4],
+  [6.2, 3.4, 5.4],
+  [5.9, 3.0, 5.1]
+])
+
+# Create a column of ones for the bias term
+bias = np.ones((X.shape[0], 1))
+
+# Concatenate bias and features
+X_with_bias = np.hstack((bias, X))
+
+print("Feature Matrix with Bias Term:\n", X_with_bias)
+```