YouTipNumpy Ndarray Object
# NumPy Ndarray Object
One of the most important features of NumPy is its N-dimensional array object, `ndarray`, which is a collection of elements of the same type, indexed starting from 0.
The `ndarray` object is a multidimensional array used to store elements of the same type.
Each element in an `ndarray` occupies the same amount of memory space.
The internal structure of an `ndarray` consists of the following:
* A pointer to the data (a block of data in memory or a memory-mapped file).
* The data type or `dtype`, describing the layout of fixed-size values in the array.
* A tuple representing the shape of the array, indicating the size of each dimension.
* A tuple of strides, where the integers indicate the number of bytes to "step" in order to move to the next element in the current dimension.
Internal structure of an ndarray:
!(#)
Strides can be negative, which would cause the array to move backwards in memory, as seen in slices like `obj[::-1]` or `obj[:,::-1]`.
Creating an `ndarray` is as simple as calling NumPy's `array` function:
```python
numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)
**Parameter Description:**
| Name | Description |
| --- | --- |
| object | Array or nested sequence |
| dtype | The desired data type for the array elements, optional |
| copy | Whether to copy the object, optional |
| order | The memory layout of the array: 'C' for row-major (C-style), 'F' for column-major (Fortran or MATLAB-style), 'A' for any (default) |
| subok | If True, sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default) |
| ndmin | Specifies the minimum number of dimensions the resulting array should have |
### Examples
The following examples will help us understand better.
## Example 1
```python
import numpy as np
a = np.array([1,2,3])
print(a)
The output is as follows:
## Example 2
```python
# More than one dimension
import numpy as np
a = np.array([[1, 2], [3, 4]])
print(a)
The output is as follows:
[
]
## Example 3
```python
# Minimum dimensions
import numpy as np
a = np.array([1, 2, 3, 4, 5], ndmin = 2)
print(a)
The output is as follows:
[]
## Example 4
```python
# dtype parameter
import numpy as np
a = np.array([1, 2, 3], dtype = complex)
print(a)
The output is as follows:
[1.+0.j 2.+0.j 3.+0.j]
The `ndarray` object is composed of a contiguous one-dimensional part of computer memory, combined with an indexing scheme that maps each element to a location in the memory block. The memory block stores elements in row-major order (C-style) or column-major order (FORTRAN or MATLAB-style, i.e., the aforementioned F-style).