The most important object defined in NumPy is an N-dimensional array type called ndarray. It describes the collection of items of the same type. Items in the collection can be accessed using a zero-based index.

Every item in an ndarray takes the same size of block in the memory. Each element in ndarray is an object of data-type object (called dtype).

Any item extracted from ndarray object (by slicing) is represented by a Python object of one of array scalar types. The following diagram shows a relationship between ndarray, data type object (dtype) and array scalar type −

An instance of ndarray class can be constructed by different array creation routines described later in the tutorial. The basic ndarray is created using an array function in NumPy as follows −

```
numpy.array
```

It creates an ndarray from any object exposing array interface, or from any method that returns an

```
array.numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)
```

Sr.No. | Parameter & Description | |||
---|---|---|---|---|

1 | object: Any object exposing the array interface method returns an array, or any (nested) sequence. |
|||

2 | dtype: Desired data type of array, optional |
|||

3 | copy: Optional. By default (true), the object is copied |
|||

4 | order: C (row major) or F (column major) or A (any) (default) |
|||

5 | subok: By default, returned array forced to be a base class array. If true, sub-classes passed through |
|||

6 | ndmin: Specifies minimum dimensions of resultant array |

In [5]:

```
import numpy as np
a = np.array([1,2,3])
print (a)
```

In [6]:

```
# more than one dimensions
import numpy as np
a = np.array([[1, 2], [3, 4]])
print (a)
```

In [7]:

```
# minimum dimensions
import numpy as np
a = np.array([1, 2, 3,4,5], ndmin = 2)
print (a)
```

The **ndarray object** consists of contiguous one-dimensional segment of computer memory, combined with an indexing scheme that maps each item to a location in the memory block.

Dolly Solanki

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