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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.

6.10 LOADING ARRAYS FROM FILES

Loading data from external files into NumPy arrays is essential for handling real-world datasets. NumPy provides functions like loadtxt() and genfromtxt() to read data from text files, especially CSV (Comma Separated Values) files. loadtxt() reads data assuming uniform structure and no missing values, with parameters to skip header rows, specify delimiters, and define data types. The unpack parameter allows loading data row-wise or column-wise into separate arrays. genfromtxt() extends loadtxt() by handling missing or non-numeric data gracefully, converting such entries to NaN or specified fill values. These functions enable importing datasets for analysis and processing within Python programs. Proper understanding of parameters like skiprows, delimiter, dtype, and filling_values is crucial for accurate data loading.

📊 Diagram: Tables showing sample data in data.txt and dataMissing.txt illustrating file formats and missing data.

🧪 Activity: Activity 6.1: Write command to load data.txt including header row. Activity 6.2: Create a datafile and import data into multiple NumPy arrays column-wise using unpack parameter.

🔗 Connection: Prepares for saving NumPy arrays back to files for data persistence.

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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