Question 1

What is NumPy? How to install it?

Accepted Answer

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. — NumPy stands for Numerical Python and is widely used for scientific computing. Installation via pip is straightforward and ensures you get the latest stable version.

Question 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?

Accepted Answer

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). — Arrays provide efficient storage and operations for homogeneous data, whereas lists are more general-purpose but less efficient for numerical computations. NumPy's ndarray class is designed for fast array operations.

Question 3

What do you understand by rank of an ndarray?

Accepted Answer

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. — Rank is a fundamental property of arrays that helps in understanding their structure and how to access elements.

Question 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.

Accepted Answer

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 + 3*5*4, 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 myarray1.
- np.arange creates a sequence starting at 4, stepping by 4, total 15 elements, reshaped to 3x5. — Each part uses appropriate NumPy functions to create arrays as specified. Reshape is used to get the desired dimensions.

Question 5

Using the arrays created in Question 4 above, write NumPy commands for the following:

a) Find the dimensions, shape, size, data type of the items and itemsize of arrays zeros, vowels, ones, myarray1 and myarray2.
b) Reshape the array ones to have all the 10 elements in a single row.
c) Display the 2nd and 3rd element of the array vowels.
d) Display all elements in the 2nd and 3rd row of the array myarray1.
e) Display the elements in the 1st and 2nd column of the array myarray1.
f) Display the elements in the 1st column of the 2nd and 3rd row of the array myarray1.
g) Reverse the array of vowels.

Accepted Answer

a) zeros.ndim, zeros.shape, zeros.size, zeros.dtype, zeros.itemsize
   vowels.ndim, vowels.shape, vowels.size, vowels.dtype, vowels.itemsize
   ones.ndim, ones.shape, ones.size, ones.dtype, ones.itemsize
   myarray1.ndim, myarray1.shape, myarray1.size, myarray1.dtype, myarray1.itemsize
   myarray2.ndim, myarray2.shape, myarray2.size, myarray2.dtype, myarray2.itemsize

b) ones.reshape(1, 10)

c) vowels[1:3]

d) myarray1[1:3, :]

e) myarray1[:, 0:2]

f) myarray1[1:3, 0]

g) vowels[::-1]

Explanation:
- ndim gives number of dimensions.
- shape gives the shape tuple.
- size gives total number of elements.
- dtype gives data type of elements.
- itemsize gives size in bytes of each element.
- Slicing is used to access specified elements or rows/columns.
- Reversing array uses slicing with step -1. — Each command uses NumPy attributes and slicing to retrieve or manipulate the arrays as required.

Question 6

Using the arrays created in Question 4 above, write NumPy commands for the following:

a) Divide all elements of array ones by 3.
b) Add the arrays myarray1 and myarray2.
c) Subtract myarray1 from myarray2 and store the result in a new array.
d) Multiply myarray1 and myarray2 elementwise.
e) Do the matrix multiplication of myarray1 and myarray2 and store the result in a new array myarray3.
f) Divide myarray1 by myarray2.
g) Find the cube of all elements of myarray1 and divide the resulting array by 2.
h) Find the square root of all elements of myarray2 and divide the resulting array by 2. The result should be rounded to two places of decimals.

Accepted Answer

a) ones / 3

b) myarray1 + myarray2

c) myarray2 - myarray1

d) myarray1 * myarray2

e) myarray3 = np.matmul(myarray1, myarray2)

f) myarray1 / myarray2

g) (myarray1 ** 3) / 2

h) np.round(np.sqrt(myarray2) / 2, 2)

Explanation:
- Arithmetic operations on arrays are elementwise by default.
- Matrix multiplication uses np.matmul or the @ operator.
- np.sqrt computes square root elementwise.
- np.round rounds the elements to specified decimal places. — These commands demonstrate elementwise arithmetic, matrix multiplication, and use of mathematical functions in NumPy.

Question 7

Using the arrays created in Question 4 above, write NumPy commands for the following:

a) Find the transpose of ones and myarray2.
b) Sort the array vowels in reverse.
c) Sort the array myarray1 such that it brings the lowest value of the column in the first row and so on.

Accepted Answer

a) ones.T
   myarray2.T

b) np.sort(vowels)[::-1]

c) np.sort(myarray1, axis=0)

Explanation:
- .T attribute returns the transpose of an array.
- np.sort sorts array elements; reversing with [::-1] gives descending order.
- Sorting myarray1 along axis=0 sorts each column independently, bringing lowest values to first row. — Transpose changes rows to columns and vice versa. Sorting can be done ascending or descending by reversing the sorted array.

Question 8

Using the arrays created in Question 4 above, write NumPy commands for the following:

a) Use NumPy.split() to split the array myarray2 into 5 arrays columnwise. Store your resulting arrays in myarray2A, myarray2B, myarray2C, myarray2D and myarray2E. Print the arrays myarray2A, myarray2B, myarray2C, myarray2D and myarray2E.
b) Split the array zeros at array index 2, 5, 7, 8 and store the resulting arrays in zerosA, zerosB, zerosC and zerosD and print them.
c) Concatenate the arrays myarray2A, myarray2B and myarray2C into an array having 3 rows and 3 columns.

Accepted Answer

a) myarray2A, myarray2B, myarray2C, myarray2D, myarray2E = np.split(myarray2, 5, axis=1)
   print(myarray2A, myarray2B, myarray2C, myarray2D, myarray2E)

b) zerosA, zerosB, zerosC, zerosD = np.split(zeros, [2, 5, 7, 8])
   print(zerosA, zerosB, zerosC, zerosD)

c) np.concatenate((myarray2A, myarray2B, myarray2C), axis=1)

Explanation:
- np.split splits arrays at specified indices along given axis.
- axis=1 splits columnwise.
- np.concatenate joins arrays along specified axis. — Splitting and concatenation are useful for dividing and combining arrays as needed.

Question 9

Create a 2-D array called myarray4 using arange() having 14 rows and 3 columns with start value = -1, step size 0.25 having. Split this array row wise into 3 equal parts and print the result.

Accepted Answer

myarray4 = np.arange(-1, -1 + 14*3*0.25, 0.25).reshape(14, 3)

parts = np.array_split(myarray4, 3, axis=0)

for part in parts:
    print(part)

Explanation:
- np.arange creates values starting at -1, stepping by 0.25, total 42 elements (14*3).
- reshape to 14 rows and 3 columns.
- np.array_split splits array into 3 parts row-wise (axis=0).
- Printing each part shows the split arrays. — Using arange and reshape creates the required array. array_split divides it into equal parts along rows.

Question 10

Using the myarray4 created in the above questions, write commands for the following:

a) Find the sum of all elements.
b) Find the sum of all elements row wise.
c) Find the sum of all elements column wise.
d) Find the max of all elements.
e) Find the min of all elements in each row.
f) Find the mean of all elements in each row.
g) Find the standard deviation column wise.

Accepted Answer

a) np.sum(myarray4)

b) np.sum(myarray4, axis=1)

c) np.sum(myarray4, axis=0)

d) np.max(myarray4)

e) np.min(myarray4, axis=1)

f) np.mean(myarray4, axis=1)

g) np.std(myarray4, axis=0)

Explanation:
- np.sum with no axis sums all elements.
- axis=1 sums row-wise.
- axis=0 sums column-wise.
- np.max finds maximum element.
- np.min with axis=1 finds minimum in each row.
- np.mean with axis=1 finds mean in each row.
- np.std with axis=0 finds standard deviation column-wise. — NumPy provides functions to compute aggregate statistics along specified axes.

Question 11

Q1. Import the Iris dataset from the file 'C:/NCERT/Iris.txt' using NumPy's genfromtxt function, skipping the header row and using comma as delimiter. Store the data as float type.

Accepted Answer

Use the numpy genfromtxt function as follows:
import numpy as np
iris = np.genfromtxt('C:/NCERT/Iris.txt', skip_header=1, delimiter=', ', dtype=float) — The genfromtxt function reads data from a text file. skip_header=1 skips the first row (header). delimiter=', ' specifies comma and space as delimiter. dtype=float ensures data is read as floating point numbers.

Question 12

Q2. From the Iris dataset array, drop the 4th column (index 4) and keep only columns 0, 1, 2, 3, and 5 for the first 30 rows.

Accepted Answer

Use array slicing and indexing as:
iris = iris[0:30, [0, 1, 2, 3, 5]] — The slicing iris[0:30, [0,1,2,3,5]] selects rows from 0 to 29 and columns at indices 0,1,2,3,5, effectively dropping the 4th column (index 4).

Question 13

Q3. Find the shape, number of dimensions, and size of the Iris dataset array after slicing.

Accepted Answer

Use the following commands:
iris.shape  # returns (30, 5)
iris.ndim   # returns 2
iris.size   # returns 150 — The shape attribute gives the dimensions of the array (rows, columns). ndim gives the number of dimensions. size gives the total number of elements (rows × columns).

Question 14

Q4. Split the Iris dataset array into three arrays, each corresponding to a different species, with 10 rows each.

Accepted Answer

Use numpy split function:
iris1, iris2, iris3 = np.split(iris, [10, 20], axis=0) — The np.split function splits the array at row indices 10 and 20 along axis 0 (rows), resulting in three arrays each with 10 rows.

Question 15

Q5. Print the three arrays obtained by splitting the Iris dataset into species.

Accepted Answer

Simply print the arrays:
print(iris1)
print(iris2)
print(iris3)

The arrays contain the data for each species respectively. — Printing the arrays confirms the data for each species is correctly separated into iris1, iris2, and iris3.

Introduction Chapter to NumPy

Introduction Chapter to NumPy — Study Notes

6.1 INTRODUCTION

6.2 ARRAY

6.3 NumPy ARRAY

Practice Questions — Introduction Chapter to NumPy

All 8 Chapters in Informatics Practices