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:
- One Dimensional Array
- 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]])
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