NumPy is one of the best Python data science libraries . It comes with a built-in data structure "Array", which standard Python lacks. Although Standard Python provides Python List and a dedicated Python array module as an alternative, but both of them are not efficient. Here in this Python tutorial we will go through the NumPy Arrays in Python and discuss how to initialize and use a NumPy array with the help of some examples.

##
**
What is Python NumPy Array?
**

The NumPy array is a Python container data structure, similar to the Python List. But unlike a Python list, the Python NumPy Array can store only elements of similar data types. The Python Numpy Arrays are also known as
```
ndarray,
```

and to define a NumPy array we can use
```
array()
```

Function.
**
Note:
**
*
before using numpy in your Python program make sure that numpy is installed for your Python environment. Else use
pip install numpy
the command to install numpy
*

###
**
Numpy Array Syntax
**

```
import numpy as np
arr = np.arrray(object,dtype=None, *, copy=True, order='K', subok=False, ndmin=0, like=None)
```

###
**
NumPy Array() function Parameters
**

**
object (mandatory)
**
should be an array-like structure such as Python List, and it should contain data elements of a similar data type. Apart from the object parameter, all the other parameters of
```
NumPy array()
```

function are optional to know more about all the parameters check the
official documentation of NumPy array
.

###
**
Create a NumPy Array
**

The straightforward way to create a numpy array by using the Python list.

`my_list = [1,2,4,5,7,10]`

Once you defined the list then using the numpy.array() function you can convert it into the NumPy array object.

`numpy_array = numpy.array(my_list )`

**
Example
**

```
import numpy as np
my_list = [1,2,4,5,7,10]
numpy_array = np.array(my_list)
print(numpy_array)
print(type(numpy_array))
```

**
Output
**

```
[ 1 2 4 5 7 10]
<class 'numpy.ndarray'>
```

###
**
Mathematical Operations on NumPy array
**

Mathematical or Aithematic operations like additions, multiplication, division, and subtraction are where the NumPy Array excel as compared to Standard Python List and Arrays. Using the +, -, / and * operators we can perform mathematical operations on the elements of the array.

**
Example
**

```
>>> import numpy as np
>>> my_list = [1,2,4,5,7,10]
>>> numpy_array = np.array(my_list)
>>> #addition
>>> numpy_array + 10
array([11, 12, 14, 15, 17, 20])
>>> #substraction
>>> numpy_array -10
array([-9, -8, -6, -5, -3, 0])
>>> #multiplication
>>> numpy_array * 100
array([ 100, 200, 400, 500, 700, 1000])
>>> #division
>>> numpy_array / 100
array([0.01, 0.02, 0.04, 0.05, 0.07, 0.1 ])
>>> #floor division
>>> numpy_array // 2
array([0, 1, 2, 2, 3, 5], dtype=int32)
>>> #modulo
>>> numpy_array % 2
array([1, 0, 0, 1, 1, 0], dtype=int32)
```

###
**
Shape, datatype, and size of NumPy Array
**

Once you have defined a numpy array, you can check its shape, size, and datatype. The
```
```

is the attribute of the Python Numpy Array and it returns a tuple of integer numbers values representing the number of rows and columns of the array. The
**
shape
**
**
size
**
Property of array returns the total number of elements present in the array. The

**Property of NumPy array returns the data type of elements.**

```
dtype
```

####
**
1-Dimensional NumPy Array
**

```
import numpy as np
my_list = [1,2,3,4,5]
one_d = np.array(my_list)
print("The shape of one_d array: ", one_d.shape)
print("The total number of elements in one_d array: ", one_d.size)
print("The data type of one_d array: ", one_d.dtype)
```

**
Output
**

```
The shape of one_d array: (5,)
The total number of elements in one_d array: 5
The data type of one_d array: int32
```

####
**
2-Dimensional NumPy Array
**

```
import numpy as np
my_list = [[1,2,3,4,5],
[5,7,8,9,10]
]
two_d = np.array(my_list)
print("The shape of two_d array: ", two_d.shape)
print("The total number of elements in two_d array: ", two_d.size)
print("The data type of two_d array: ", two_d.dtype)
```

**
Output
**

```
The shape of two_d array: (2, 5)
The total number of elements in two_d array: 10
The data type of two_d array: int32
```

####
**
3-Dimensional NumPy Array
**

```
import numpy as np
my_list = [
[
[1,2,3,4,5],
[5,7,8,9,10]
],
[
[11,12,13,14,15],
[16,17,18,19,20]
]
]
three_d = np.array(my_list)
print("The shape of three_d array: ", three_d.shape)
print("The total number of elements in three_d array: ", three_d.size)
print("The data type of three_d array: ", three_d.dtype)
```

**
Output
**

```
The shape of three_d array: (2, 2, 5)
The total number of elements in three_d array: 20
The data type of three_d array: int32
```

##
**
Conclusion
**

In this Python tutorial, you learned what is a Python NumPy array and how to initialize it in Python. In this tutorial, we have only discussed examples up to 3-D array, but numpy is capable of defining n-d Arrays. And if we directly compare it with Python List and Python standard Array module the NumPy arrays are way faster and efficient.

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