Thursday, August 13, 2026

NumPy

NumPy for Data Science

A Practical Python Tutorial with Examples and Output

Introduction to NumPy

NumPy (Numerical Python) is one of the most important Python libraries for numerical and scientific computing. It provides a powerful multidimensional array object called ndarray and many functions for mathematical, statistical, logical and array-based operations.

NumPy is widely used in Data Science, Machine Learning, Artificial Intelligence, Scientific Computing, Image Processing and numerical analysis.

Why Use NumPy?

Fast numerical calculations
Multidimensional arrays
Efficient mathematical operations
Array indexing and slicing
Statistical functions such as mean, median and standard deviation
Random number generation
Matrix and linear algebra operations
Useful foundation for Pandas, SciPy, scikit-learn and many scientific Python tools

1. Installing NumPy

NumPy can normally be installed using pip.

pip install numpy

After installation, import NumPy using the alias np.

import numpy as np
print(np.__version__)

Output

2.5.0

Note: The exact version displayed depends on the NumPy version installed on your computer.

2. Creating a NumPy Array

The basic NumPy data structure is the ndarray, a multidimensional array whose elements have a common data type.

import numpy as np

arr = np.array([10, 20, 30, 40, 50])

print(arr)

Output

[10 20 30 40 50]

3. Creating a Two-Dimensional Array

A two-dimensional NumPy array can be created using nested lists.

import numpy as np

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

print(arr)

Output

[[1 2 3]
 [4 5 6]]

4. Checking Dimensions

The ndim attribute tells us the number of dimensions of an array.

import numpy as np

a = np.array([1, 2, 3])

b = np.array([
    [1, 2],
    [3, 4]
])

print("Dimensions of a:", a.ndim)
print("Dimensions of b:", b.ndim)

Output

Dimensions of a: 1
Dimensions of b: 2

5. Shape of an Array

The shape attribute gives the size of the array along each dimension.

import numpy as np

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

print(arr.shape)

Output

(2, 3)

Here, 2 represents rows and 3 represents columns.

6. Size of an Array

The size attribute returns the total number of elements.

import numpy as np

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

print(arr.size)

Output

7. Data Type of an Array

The dtype attribute tells us the data type of array elements.

import numpy as np

arr = np.array([10, 20, 30])

print(arr.dtype)

Output

int64

The exact integer dtype can vary depending on the platform.

8. Array Indexing

Array elements can be accessed using their index. Python uses zero-based indexing.

import numpy as np

arr = np.array([10, 20, 30, 40, 50])

print(arr[0])
print(arr[2])
print(arr[-1])

Output

10
30
50

9. Array Slicing

Slicing allows us to select a portion of an array.

import numpy as np

arr = np.array([10, 20, 30, 40, 50])

print(arr[1:4])
print(arr[:3])
print(arr[2:])

Output

[20 30 40]
[10 20 30]
[30 40 50]

10. Creating Arrays with arange()

np.arange() creates values within a specified range.

import numpy as np

arr = np.arange(1, 11)

print(arr)

Output

[ 1  2  3  4  5  6  7  8  9 10]

11. Creating an Array of Zeros

import numpy as np

arr = np.zeros(5)

print(arr)

Output

[0. 0. 0. 0. 0.]

12. Creating an Array of Ones

import numpy as np

arr = np.ones(5)

print(arr)

Output

[1. 1. 1. 1. 1.]

13. Mathematical Operations on Arrays

NumPy supports element-wise arithmetic operations on arrays.

import numpy as np

a = np.array([10, 20, 30])
b = np.array([1, 2, 3])

print("Addition:", a + b)
print("Subtraction:", a - b)
print("Multiplication:", a * b)
print("Division:", a / b)

Output

Addition: [11 22 33]
Subtraction: [ 9 18 27]
Multiplication: [10 40 90]
Division: [10. 10. 10.]

14. Sum of Array Elements

import numpy as np

arr = np.array([10, 20, 30, 40, 50])

print("Sum =", np.sum(arr))

Output

Sum = 150

15. Mean, Median and Standard Deviation

import numpy as np

marks = np.array([60, 70, 80, 90, 100])

print("Mean =", np.mean(marks))
print("Median =", np.median(marks))
print("Standard Deviation =", np.std(marks))

Output

Mean = 80.0
Median = 80.0
Standard Deviation = 14.142135623730951

16. Minimum and Maximum

import numpy as np

arr = np.array([25, 10, 45, 30, 15])

print("Minimum =", np.min(arr))
print("Maximum =", np.max(arr))

Output

Minimum = 10
Maximum = 45

17. Reshaping an Array

The reshape() operation changes the structure of an array without changing the number of elements.

import numpy as np

arr = np.arange(1, 7)

new_arr = arr.reshape(2, 3)

print(new_arr)

Output

[[1 2 3]
 [4 5 6]]

18. Sorting an Array

import numpy as np

arr = np.array([50, 10, 40, 20, 30])

print(np.sort(arr))

Output

[10 20 30 40 50]

19. Finding Unique Values

import numpy as np

arr = np.array([10, 20, 20, 30, 30, 30, 40])

print(np.unique(arr))

Output

[10 20 30 40]

20. Filtering Array Elements

Boolean conditions can be used to select elements satisfying a particular condition.

import numpy as np

marks = np.array([35, 45, 60, 75, 90])

result = marks[marks >= 50]

print(result)

Output

[60 75 90]

21. Random Numbers

NumPy provides facilities for random simulation and random-number generation.

import numpy as np

np.random.seed(10)

arr = np.random.randint(1, 101, 5)

print(arr)

Output

[10 16 65 29 90]

22. Matrix Multiplication

NumPy supports matrix multiplication and linear-algebra operations.

import numpy as np

A = np.array([
    [1, 2],
    [3, 4]
])

B = np.array([
    [5, 6],
    [7, 8]
])

C = A @ B

print(C)

Output

[[19 22]
 [43 50]]

23. Transpose of a Matrix

import numpy as np

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

print(A.T)

Output

[[1 4]
 [2 5]
 [3 6]]

24. Concatenating Arrays

import numpy as np

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

result = np.concatenate((a, b))

print(result)

Output

[1 2 3 4 5 6]

25. Practical Example — Student Marks

NumPy can be used to perform quick numerical analysis of student marks.

import numpy as np

marks = np.array([65, 72, 81, 55, 90])

print("Marks:", marks)
print("Average:", np.mean(marks))
print("Highest:", np.max(marks))
print("Lowest:", np.min(marks))
print("Passed:", marks[marks >= 40])

Output

Marks: [65 72 81 55 90]
Average: 72.6
Highest: 90
Lowest: 55
Passed: [65 72 81 55 90]

Important NumPy Attributes

Attribute	Purpose
`ndim`	Number of dimensions
`shape`	Dimensions of the array
`size`	Total number of elements
`dtype`	Data type of elements
`T`	Transpose of an array

Important NumPy Functions

Function	Use
`np.array()`	Create an array
`np.arange()`	Generate a range of values
`np.zeros()`	Create an array of zeros
`np.ones()`	Create an array of ones
`np.sum()`	Calculate sum
`np.mean()`	Calculate mean
`np.median()`	Calculate median
`np.std()`	Calculate standard deviation
`np.min()`	Find minimum
`np.max()`	Find maximum
`np.sort()`	Sort array values
`np.unique()`	Find unique values
`np.concatenate()`	Join arrays

NumPy and Data Science

NumPy forms an important numerical foundation for the Python data-science ecosystem. Its multidimensional arrays and numerical routines are useful for data preparation, mathematical calculations, statistics, simulations and machine-learning workflows.

Student Tip: Before learning Pandas and Machine Learning, understand NumPy arrays, indexing, slicing, reshaping, mathematical operations and basic statistics.

Summary

In this tutorial we learned the fundamental concepts of NumPy:

Installing and importing NumPy
Creating one-dimensional and multidimensional arrays
Indexing and slicing
Array dimensions, shape, size and data type
Creating arrays using zeros, ones and arange
Arithmetic operations
Statistical operations
Sorting and filtering
Reshaping arrays
Random number generation
Matrix operations
Transpose and concatenation

Created by Bijan Krishna Paul

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Thursday, August 13, 2026

NumPy

NumPy for Data Science

Introduction to NumPy

Why Use NumPy?

1. Installing NumPy

Output

2. Creating a NumPy Array

Output

3. Creating a Two-Dimensional Array

Output

4. Checking Dimensions

Output

5. Shape of an Array

Output

6. Size of an Array

Output

7. Data Type of an Array

Output

8. Array Indexing

Output

9. Array Slicing

Output

10. Creating Arrays with arange()

Output

11. Creating an Array of Zeros

Output

12. Creating an Array of Ones

Output

13. Mathematical Operations on Arrays

Output

14. Sum of Array Elements

Output

15. Mean, Median and Standard Deviation

Output

16. Minimum and Maximum

Output

17. Reshaping an Array

Output

18. Sorting an Array

Output

19. Finding Unique Values

Output

20. Filtering Array Elements

Output

21. Random Numbers

Output

22. Matrix Multiplication

Output

23. Transpose of a Matrix

Output

24. Concatenating Arrays

Output

25. Practical Example — Student Marks

Output

Important NumPy Attributes

Important NumPy Functions

NumPy and Data Science

Summary

Created by Bijan Krishna Paul

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