# Basics of NumPy Arrays

NumPy stands for Numerical Python. It is a Python library used for working with an array. In Python, we use the list for purpose of the array but it’s slow to process. NumPy array is a powerful N-dimensional array object and its use in linear algebra, Fourier transform, and random number capabilities. It provides an array object much faster than traditional Python lists.

#### Types of Array:

1. One Dimensional Array
2. Multi-Dimensional Array

#### One Dimensional Array:

A one-dimensional array is a type of linear array. One Dimensional Array

Example:

## Python3

 `# importing numpy module ` `import` `numpy as np ` ` `  `# creating list  ` `list` `=` `[``1``, ``2``, ``3``, ``4``] ` ` `  `# creating numpy array ` `sample_array ``=` `np.array(list1) ` ` `  `print``(``"List in python : "``, ``list``) ` ` `  `print``(``"Numpy Array in python :"``, ` `      ``sample_array)`

Output:

```List in python :  [1, 2, 3, 4]
Numpy Array in python :  [1 2 3 4]
```

Check data type for list and array:

## Python3

 `print``(``type``(list_1)) ` ` `  `print``(``type``(sample_array))`

Output:

```<class 'list'>
<class 'numpy.ndarray'>
```

#### Multi-Dimensional Array:

Data in multidimensional arrays are stored in tabular form. Two Dimensional Array

Example:

## Python3

 `# importing numpy module ` `import` `numpy as np ` ` `  `# creating list  ` `list_1 ``=` `[``1``, ``2``, ``3``, ``4``] ` `list_2 ``=` `[``5``, ``6``, ``7``, ``8``] ` `list_3 ``=` `[``9``, ``10``, ``11``, ``12``] ` ` `  `# creating numpy array ` `sample_array ``=` `np.array([list_1,  ` `                         ``list_2, ` `                         ``list_3]) ` ` `  `print``(``"Numpy multi dimensional array in python\n"``, ` `      ``sample_array)`

Output:

```Numpy multi dimensional array in python
[[ 1  2  3  4]
[ 5  6  7  8]
[ 9 10 11 12]]
```

Note: use [ ] operators inside numpy.array() for multi-dimensional

#### Anatomy of an array :

1. Axis: The Axis of an array describes the order of the indexing into the array.

Axis 0 = one dimensional

Axis 1 = Two dimensional

Axis 2 = Three dimensional

2. Shape: The number of elements along with each axis. It is from a tuple.

Example:

## Python3

 `# importing numpy module ` `import` `numpy as np ` ` `  `# creating list  ` `list_1 ``=` `[``1``, ``2``, ``3``, ``4``] ` `list_2 ``=` `[``5``, ``6``, ``7``, ``8``] ` `list_3 ``=` `[``9``, ``10``, ``11``, ``12``] ` ` `  `# creating numpy array ` `sample_array ``=` `np.array([list_1, ` `                         ``list_2, ` `                         ``list_3]) ` ` `  `print``(``"Numpy array :"``) ` `print``(sample_array) ` ` `  `# print shape of the array ` `print``(``"Shape of the array :"``, ` `      ``sample_array.shape)`

Output:

```Numpy array :
[[ 1  2  3  4]
[ 5  6  7  8]
[ 9 10 11 12]]
Shape of the array :  (3, 4)
```

Example:

## Python3

 `import` `numpy as np ` ` `  `sample_array ``=` `np.array([[``0``, ``4``, ``2``], ` `                       ``[``3``, ``4``, ``5``], ` `                       ``[``23``, ``4``, ``5``], ` `                       ``[``2``, ``34``, ``5``], ` `                       ``[``5``, ``6``, ``7``]]) ` ` `  `print``(``"shape of the array :"``, ` `      ``sample_array.shape)`

Output:

```shape of the array :  (5, 3)
```

3. Rank: The rank of an array is simply the number of axes (or dimensions) it has.

The one-dimensional array has rank 1. Rank 1

The two-dimensional array has rank 2. Rank 2

4. Data type objects (dtype): Data type objects (dtype) is an instance of numpy.dtype class. It describes how the bytes in the fixed-size block of memory corresponding to an array item should be interpreted.

Example:

## Python3

 `# Import module ` `import` `numpy as np ` ` `  `# Creating the array  ` `sample_array_1 ``=` `np.array([[``0``, ``4``, ``2``]]) ` ` `  `sample_array_2 ``=` `np.array([``0.2``, ``0.4``, ``2.4``]) ` ` `  `# display data type ` `print``(``"Data type of the array 1 :"``, ` `      ``sample_array_1.dtype) ` ` `  `print``(``"Data type of array 2 :"``, ` `      ``sample_array_2.dtype)`

Output:

```Data type of the array 1 :  int32
Data type of array 2 :  float64
```

Some different way of creating Numpy Array :

1. numpy.array(): The Numpy array object in Numpy is called ndarray. We can create ndarray using numpy.array() function.

Syntax: numpy.array(parameter)

Example:

## Python3

 `# import module ` `import` `numpy as np ` ` `  `#creating a array ` ` `  `arr ``=` `np.array([``3``,``4``,``5``,``5``]) ` ` `  `print``(``"Array :"``,arr)`

Output:

```Array : [3 4 5 5]
```

2. numpy.fromiter(): The fromiter() function create a new one-dimensional array from an iterable object.

Syntax: numpy.fromiter(iterable, dtype, count=-1)

Example 1:

## Python3

 `#Import numpy module ` `import` `numpy as np ` ` `  `# iterable ` `iterable ``=` `(a``*``a ``for` `a ``in` `range``(``8``)) ` ` `  `arr ``=` `np.fromiter(iterable, ``float``) ` ` `  `print``(``"fromiter() array :"``,arr)`

Output:

fromiter() array :  [ 0.  1.  4.  9. 16. 25. 36. 49.]

Example 2:

## Python3

 `import` `numpy as np ` ` `  `var ``=` `"Geekforgeeks"` ` `  `arr ``=` `np.fromiter(var, dtype ``=` `'U2'``) ` ` `  `print``(``"fromiter() array :"``, ` `      ``arr)`

Output:

fromiter() array :  [‘G’ ‘e’ ‘e’ ‘k’ ‘f’ ‘o’ ‘r’ ‘g’ ‘e’ ‘e’ ‘k’ ‘s’]

3. numpy.arange(): This is an inbuilt NumPy function that returns evenly spaced values within a given interval.

Syntax: numpy.arange([start, ]stop, [step, ]dtype=None)

Example:

## Python3

 `import` `numpy as np ` ` `  `np.arange(``1``, ``20` `, ``2``,  ` `          ``dtype ``=` `np.float32)`

Output:

array([ 1.,  3.,  5.,  7.,  9., 11., 13., 15., 17., 19.], dtype=float32)

4. numpy.linspace(): This function returns evenly spaced numbers over a specified between two limits.

Syntax: numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)

Example 1:

## Python3

 `import` `numpy as np ` ` `  `np.linspace(``3.5``, ``10``, ``3``)`

Output:

```array([ 3.5 ,  6.75, 10.  ])
```

Example 2:

## Python3

 `import` `numpy as np ` ` `  `np.linspace(``3.5``, ``10``, ``3``,  ` `            ``dtype ``=` `np.int32)`

Output:

```array([ 3,  6, 10])
```

5. numpy.empty(): This function create a new array of given shape and type, without initializing value.

Syntax: numpy.empty(shape, dtype=float, order=’C’)

Example:

## Python3

 `import` `numpy as np ` ` `  `np.empty([``4``, ``3``], ` `         ``dtype ``=` `np.int32, ` `         ``order ``=` `'f'``)`

Output:

```array([[ 1,  5,  9],
[ 2,  6, 10],
[ 3,  7, 11],
[ 4,  8, 12]])
```

6. numpy.ones(): This function is used to get a new array of given shape and type, filled with ones(1).

Syntax: numpy.ones(shape, dtype=None, order=’C’)

Example:

## Python3

 `import` `numpy as np ` ` `  `np.ones([``4``, ``3``], ` `        ``dtype ``=` `np.int32, ` `        ``order ``=` `'f'``)`

Output:

```array([[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
```

7. numpy.zeros(): This function is used to get a new array of given shape and type, filled with zeros(0).

Syntax: numpy.ones(shape, dtype=None)

Example:

## Python3

 `import` `numpy as np ` `np.zeros([``4``, ``3``],  ` `         ``dtype ``=` `np.int32, ` `         ``order ``=` `'f'``)`

Output:

```array([[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]])
```

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.

My Personal Notes arrow_drop_up Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.

Article Tags :

Be the First to upvote.

Please write to us at contribute@geeksforgeeks.org to report any issue with the above content.