# NumPy Array – Python Tutorials

Welcome back friends, we are back with a new tutorial of NumPy, i.e. NumPy Array in the Python Tutorials series. What is a Numpy array? how it’s different from list arrays of python and also its implementation with examples.

Lets get started now…

## What is NumPy Array?

NumPy arrays are the main way we will use Numpy throughout the course. Numpy arrays essentially come in two flavors: vectors and matrices. Vectors are strictly 1-d arrays and matrices are 2-d (but you should note a matrix can still have only one row or one column).

Let’s begin our introduction by exploring how to create NumPy arrays.

## How to check NumPy Version?

If you want to know the version string is stored under __version__ attribute then follow below mentioned example:

``````import numpy as np

print(np.__version__)``````

### How to create NumPy arrays in Python?

The array object is likewise a list array it is denoted by ndarrays. we can pass lists, tuples in ndarrays.

We can create an ndarray by using the array() function.

``````import numpy as np

# list is passing in array function.
nparr = np.array([1, 2, 3, 4, 5,6])

print(nparr)

# for knowing the class or type of the object
print(type(nparr))``````
##### Output:
```[1 2 3 4 5 6]
<class 'numpy.ndarray'>```

## Dimensions in Arrays

In Python, there are multi-dimension arrays for instance 0-D array, 1-D array, etc.

#### 0 – Dimension Array:

You can consider this by its name 0 dimension array or scaler array.

Example:

``````import numpy as np

nparr = np.array(52)

print(nparr)``````

Output:

`52`

#### 1 – Dimension Array:

1-D array also called unidimensional array.

``````import numpy as np

nparr = np.array([4,7,5,9])

print(nparr)``````
``Output:[4,7,5,9]``

#### 2 – Dimension Array:

``````import numpy as np

nparr = np.array([[1, 2, 3], [4, 5, 6]])

print(nparr)``````
```Output:
[[1 2 3]
[4 5 6]]```

#### 3 – Dimension Array:

To create a 3-D array with two 2-D arrays, both containing two arrays values:

``````import numpy as np

nparr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [7, 8, 9]]])

print(nparr)``````
```Output:
[[[1 2 3]
[4 5 6]]

[[1 2 3]
[7 8 9]]]```

## Higher Dimensional Arrays

A higher dimensional array have number of dimensions.

``````import numpy as np

nparr = np.array([1, 2, 3, 4], ndmin=5)

print(nparr)
print('Total number of dimensions :', nparr.ndim)``````
```Output:
[[[[[1 2 3 4]]]]]
number of dimensions : 5```

## Python Built-in Methods

There are lots of built-in ways to generate Arrays, we are describing a few of them.

### arange

Return evenly spaced values within a given interval. For instance:

### zeros and ones

Generate arrays of zeros or ones.

### linspace

Return evenly spaced numbers over a specified interval.

```code: np.linspace(0,10,3)
Output: array([  0.,   5.,  10.])```
```Code: np.linspace(0,10,50)
Output: array([  0.        ,   0.20408163,   0.40816327,   0.6122449 ,          0.81632653,   1.02040816,   1.2244898 ,   1.42857143,          1.63265306,   1.83673469,   2.04081633,   2.24489796,          2.44897959,   2.65306122,   2.85714286,   3.06122449,          3.26530612,   3.46938776,   3.67346939,   3.87755102,          4.08163265,   4.28571429,   4.48979592,   4.69387755,          4.89795918,   5.10204082,   5.30612245,   5.51020408,          5.71428571,   5.91836735,   6.12244898,   6.32653061,          6.53061224,   6.73469388,   6.93877551,   7.14285714,          7.34693878,   7.55102041,   7.75510204,   7.95918367,          8.16326531,   8.36734694,   8.57142857,   8.7755102 ,          8.97959184,   9.18367347,   9.3877551 ,   9.59183673,          9.79591837,  10.        ])```

### eye

To create an identity matrix.

Code: np.eye(4)

```OutPut: array([[ 1.,  0.,  0.,  0.],
[ 0.,  1.,  0.,  0.],
[ 0.,  0.,  1.,  0.],
[ 0.,  0.,  0.,  1.]])```

### randn

Return a sample (or samples) from the “standard normal” distribution. Unlike rand which is uniform:

Code: np.random.randn(5,5)

Output:

This is all about Python NumPy array and some built-in methods. In the next tutorial, we will discuss the NumPy array indexing.

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