In this Python NumPy tutorial, you will learn about **python numpy array**, how to **create an array using Python NumPy** and, also we will check**:**

- Numpy array creation
- Numpy.empty method
- Numpy.zeros method
- Numpy.ones methods
- Numpy.reshape method
- Python Numpy array example
- Python numpy array size
- Create Numpy ndarray object
- What is Array Dimension
- 0-D arrays in Numpy
- 1-D arrays in Numpy
- 2-D arrays in Numpy
- 3-D arrays in Numpy
- What’s the difference between the Numpy array and the python list?

## How to create Numpy array

**NumPy **offers many functions for creating arrays in Python with initial placeholder content. Also, it minimizes the necessity of growing arrays, which is an expensive operation. For example NumPy.empty, NumPy.zeros methods we can use to create arrays in Python using NumPy, etc.

## Numpy.empty method

Let’s see **Numpy.empty method** to create an array in Python.

The **np.empty()** method is used to **create an uninitialized array** of the specified arrays of specified shapes and data types. It contains junk values.

```
import numpy as np
my_arr = np.empty((2,2), dtype = int)
print(my_arr)
```

You can refer to the below screenshot to see the output for **Numpy.empty methods**.

This is how to **create an uninitialized array in Python using NumPy**.

Read: Python program to print element in an array

## Numpy.zeros method

Let us see **Numpy.zeros methods** in Python NumPy to create an array.

The **numpy.zeros()** is used to **create the NumPy array with the specified shape** where each NumPy array item is initialized to 0.

```
import numpy as np
my_arr = np.zeros((3,3), dtype = int)
print(my_arr)
```

You can refer to the below screenshot to see the output for **Numpy.zeros methods**.

This is how we can use the **Python Numpy.zeros method** to create an array.

Read: Python NumPy Random + Examples

## Numpy.ones method

Now, we will see **Numpy.ones method** to create a NumPy arrary in Python.

The **np.ones() **is used to create the NumPy array with the specified shape where each NumPy array item is initialized to 1.

```
import numpy as np
my_arr = np.ones((3,3), dtype = int)
print(my_arr)
```

You can refer to the below screenshot to see the output for **Numpy.ones methods**

This is how to **create a NumPy array with the specified shape in Python**.

Read: Python concatenate arrays

## NumPy.reshape method

Let us see, how to use **NumPy.reshape method** in Python.

The **numPy.reshape()** method is used to shape an array without changing data of array. The shape array with 2 rows and 3 columns.

```
import numpy as np
my_arr = np.arange(6).reshape(2, 3)
print("\nArray reshaped with 2 rows and 3 columns : \n", my_arr)
```

You can refer to the below screenshot to see the output for **NumPy.reshape methods**

This is how we can work with, **NumPy.reshape method** in Python.

These are various **python numpy array functions**.

## Python NumPy array example

Let’s see **Python NumPy array example**.

- Firstly we need to
**import NumPy as np**. - Then we have declared the variable as
**my_arr = np.array([101, 102, 103, 104, 105])** - At last to get the output
**print(my_arr).**

**Example:**

```
import numpy as np
my_arr = np.array([101, 102, 103, 104, 105])
print(my_arr)
```

You can refer to the below screenshot to see the output for **Python NumPy array example**.

Here, we saw a simple example of **Python NumPy array**.

Read: How to write Python array to CSV

## Python numpy array size

Here, we will see** Python numpy array size**.

To get the size (number of all elements) in numpy array we will use **size** attributes to get the array size in the output.

**Example:**

```
import numpy as np
my_arr = np.array([0,1,2], dtype=np.float64)
print("Size of the array: ", my_arr.size)
```

You can refer to the below screenshot to see the output for **python numpy array size**

This is how to get the **Python NumPy array size**.

## Create NumPy ndarray object

Now, we will see how to **create NumPy ndarray object** in Python.

**Numpy** is used to work with array, the array object in numpy is called ndarray.

```
import numpy as np
my_arr = np.array([5,6,7,8,9])
print(my_arr)
print(type(my_arr))
```

You can refer to the below screenshot to see the output for** Create NumPy ndarray object**

This is how to work with **NumPy ndarray in Python**.

Read: Python shape of an array

## Python NumPy array dimensions

A dimension is a direction in which you can vary the specification of an array’s elements. Dimension in an array is one level of array depth. Let us see a few examples of **python numpy array dimensions**.

## 0-D arrays in Numpy

Lets us see how to create a **0-D arrays in Numpy**.

- The
**0-D arrays**in Numpy are scalar and they cannot be accessed via indexing. - Firstly we will
**import numpy as np**. - The
**0-D arrays**are the elements in an array. Also, each value in an array is a 0-D array.

```
import numpy as np
my_arr = np.array(50)
print(my_arr)
```

You can refer to the below screenshot to see the output for **0-D arrays in Numpy**.

This is how to work with **0-D arrays in Numpy** **python**.

## 1-D arrays in Numpy

Now, we will see **1-D arrays in Numpy** python.

**1-D arrays**in numpy are one dimension that can be thought of a list where you can access the elements with the help of indexing.- Firstly we will
**import numpy as np**. - The array which has 0-D arrays as its elements is called 1-D arrays.

```
import numpy as np
my_arr = np.array([10, 11, 12, 13, 14])
print(my_arr)
```

You can refer to the below screenshot to see the output for** 1-D arrays in Numpy**.

This is how to work with** 1-D arrays in Numpy in python**

## 2-D arrays in Numpy

Now, we will see how to create a** 2-D arrays in Numpy** in Python.

**2-D arrays**in numpy are two dimensions array that can be distinguished based on the number of square brackets used.- Firstly we will
**import numpy as np**. - The array which has 1-D arrays as its elements is called 2-D arrays.

```
import numpy as np
my_arr = np.array([[11, 12, 13], [14, 15, 16]])
print(my_arr)
```

You can refer to the below screenshot to see the output for **2-D arrays in Numpy**.

This is how to work with** 2-D arrays in Numpy** **python**.

Read: Python Array with Examples

## 3-D arrays in Numpy

Here, you will see **3-D arrays in Numpy** python

- The
**3-D arrays**in numpy are the three-dimension array that can have three square brackets with the numpy array. - Firstly we will
**import numpy as np**. - The array which has 2-D arrays as its elements is called 3-D arrays.

```
import numpy as np
my_arr = np.array([[[11, 12, 13], [14, 15, 16]], [[11, 12, 13], [14, 15, 16]]])
print(my_arr)
```

You can refer to the below screenshot to see the output for **3-D arrays in Numpy**

This is how to work with** 3-D arrays in Numpy** **python**.

## Difference between NumPy array and Python list

NumPy Array | Python List |

Numpy arrays are faster and more compact. | Python lists are not much faster and compact. |

Can store only one data type in an array at any time. | Able to store different data types in the same list. |

NumPy gives an enormous range of fast and efficient ways of creating arrays and manipulating numerical data inside them. | While python lists can contain different data types within a single list. |

You may like the following Python tutorials:

- How to Convert Python string to byte array with Examples
- Create an empty array in Python
- Convert string to float in Python
- Check if NumPy Array is Empty in Python
- Python NumPy zeros + Examples

In this **python numpy array tutorial**, we learned about** Python NumPy Array **and also we have seen how to use it like:

- Numpy array creation
- Numpy.empty method
- Numpy.zeros method
- Numpy.ones method
- Numpy.reshape method
- Python Numpy array example
- Python numpy array size
- Create Numpy ndarray object
- What is Array Dimension in python
- Python 0-D arrays in Numpy
- Python 1-D arrays in Numpy
- Python 2-D arrays in Numpy
- Python 3-D arrays in Numpy
- Difference between Numpy array and the python list

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.