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 the array but it’s slow to process. NumPy array is a powerful N-dimensional array object and is used 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:

# importing numpy module
import numpy as np

# creating list
list = [1, 2, 3, 4]

# creating numpy array
sample_array = np.array(list)

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:

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:

# 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:

# 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:

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:

# 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: 

# 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:

#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:

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:

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:

import numpy as np

np.linspace(3.5, 10, 3)

Output:

array([ 3.5 ,  6.75, 10.  ])

Example 2:

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:

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:

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:

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]])

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