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Informatics PracticesClass 11Introduction Chapter to NumPy

Introduction Chapter to NumPy | Class 11 Informatics Practices Notes

By ConceptScroll Team · Published on 17 July 2026 · 3 min read

Introduction Chapter to NumPy – this guide gives you a concise, exam-ready overview of Introduction Chapter to NumPy from Class 11 Informatics Practices, written by ConceptScroll editors and reviewed against the latest NCERT textbook.

6.3 NumPy ARRAY

NumPy arrays, officially called ndarray, are specialized data structures designed to store numerical data efficiently. Unlike Python lists, which can hold elements of different types and are stored non-contiguously, NumPy arrays require all elements to be of the same data type and store them contiguously in memory. This design allows for faster computation and supports element-wise operations such as addition, multiplication, and division. NumPy arrays are versatile, supporting multi-dimensional data like vectors and matrices, and come with a rich set of built-in functions for creation, manipulation, and transformation. The chapter highlights the differences between lists and arrays, emphasizing arrays' efficiency and suitability for scientific computing. Creating arrays from lists is straightforward using numpy.array(), and arrays can be one-dimensional or multi-dimensional. Attributes like ndim, shape, size, dtype, and itemsize provide metadata about arrays, helping understand their structure and memory usage. Additional array creation functions like zeros(), ones(), and arange() facilitate initializing arrays with specific values or sequences.

📊 Diagram: No specific diagrams, but tables comparing list vs array and examples of array attributes.

🔗 Connection: Leads to detailed understanding of array indexing and slicing, essential for manipulating array data.

Frequently asked questions

1. What is NumPy? How to install it?

NumPy is a Python library used for numerical computing. It provides support for arrays, matrices, and many mathematical functions to operate on these data structures efficiently. To install NumPy, you can use the pip package manager by running the command: pip install numpy in your command prompt or terminal.

2. What is an array and how is it different from a list? What is the name of the built-in array class in NumPy?

An array is a collection of elements of the same data type stored at contiguous memory locations. Unlike a Python list, which can hold elements of different data types and is more flexible but less efficient, a NumPy array (ndarray) is optimized for numerical operations and uses less memory. The built-in array class in NumPy is called 'ndarray' (N-dimensional array).

3. What do you understand by rank of an ndarray?

The rank of an ndarray refers to the number of dimensions or axes it has. For example, a 1-D array has rank 1, a 2-D array has rank 2, and so on. It indicates how many indices are needed to specify an element in the array.

4. Create the following NumPy arrays: a) A 1-D array called zeros having 10 elements and all the elements are set to zero. b) A 1-D array called vowels having the elements 'a', 'e', 'i', 'o' and 'u'. c) A 2-D array called ones having 2 rows and 5 columns and all the elements are set to 1 and dtype as int. d) Use nested Python lists to create a 2-D array called myarray1 having 3 rows and 3 columns and store the following data: 2.7, -2, -19 0, 3.4, 99.9 10.6, 0, 13 e) A 2-D array called myarray2 using arange() having 3 rows and 5 columns with start value = 4, step size 4 and dtype as float.

a) zeros = np.zeros(10)

b) vowels = np.array(['a', 'e', 'i', 'o', 'u'])

c) ones = np.ones((2,5), dtype=int)

d) myarray1 = np.array([[2.7, -2, -19], [0, 3.4, 99.9], [10.6, 0, 13]])

e) myarray2 = np.arange(4, 4 + 354, 4, dtype=float).reshape(3,5)

Explanation:

  • np.zeros(10) creates a 1-D array of 10 zeros.
  • np.array with list of vowels creates a 1-D array of characters.
  • np.ones with shape (2,5) and dtype int creates a 2-D array of ones.
  • Nested lists are passed to np.array to create mya

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