Array sheet
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The document outlines a series of programming exercises involving one and two dimensional arrays in C++, including reversing and performing operations on a 1D array, searching arrays, scalar multiplication, finding intersections, unions, and complements of arrays, manipulating 2D arrays by replacing values and positions, performing matrix addition, multiplication, and column swapping, checking for magic squares, and an optional bonus exercise to create an X O game. Students are to write C++ programs to complete the various array and matrix manipulation tasks outlined in the seven sections.
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![October 6 UNI
Department of Computer Science
Structured Programming
Practice Lab (Arrays)
One dimensional array
1- Write a C++ program that accepts an array of 20 elements then apply the following
operations :
A. Reverse the order of the array and display it.
B. Display the sum and the average of the array.
C. Allow user to input a number and search for this number in the array.
2- Write a program to read 20 values from the user, ask the user about a number N, and
display how many numbers of N in this array.
3- Write a program that accepts an array of ten elements and an integer value then find
scalar multiplication.
4- Write a program to find the intersection between array of 10 elements and array o3
elements. For example: if A[10] = { 1,2,3,4,5,6,7,8,9,10 }and B [3] = {2, 5, 11}, result is
2, 5.
5- Write a program to find the union between array of 10 elements and array o3 elements.
For example: if A[10] = { 1,2,3,4,5,6,7,8,9,10 }and B [3] = {2, 5, 11}, result is
1,2,3,4,5,6,7,8,9,10, 11.
6- Write a program to find the complement of an array of 3 elements from an array of 10
elements. For example: if A[10] = { 1,2,3,4,5,6,7,8,9,10 }and B [3] = {2, 5, 11}, result is
1,3,4,6,7,8,9,10.](https://image.slidesharecdn.com/arraysheet-161228174445/85/Array-sheet-1-320.jpg)
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C programming session 04
1. Arrays allow storing a collection of related data items and can be one-dimensional or multidimensional.
2. Two-dimensional arrays are arrays of one-dimensional arrays with two indices to access elements.
3. Preprocessor directives like #include and #define are instructions to the compiler and are not part of the C language itself. They expand the scope of the programming environment.
Lewis jssap3 e_labman02
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1) Declaring and populating an array of words from user input and displaying the array.
2) Declaring an integer array, assigning values based on index * 3, and printing selected elements.
3) Removing duplicate strings from an array of user-input strings.
4) Counting vowels in an array of characters.
5) Reading integers into an array, finding the maximum and minimum values, swapping their positions, and printing the result.
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Data Manipulation with Numpy and Pandas in PythonStarting with N
Data Manipulation with Numpy and Pandas in Python
Starting with Numpy
#load the library and check its version, just to make sure we aren't using an older version
import numpy as np
np.__version__
'1.12.1'
#create a list comprising numbers from 0 to 9
L = list(range(10))
#converting integers to string - this style of handling lists is known as list comprehension.
#List comprehension offers a versatile way to handle list manipulations tasks easily. We'll learn about them in future tutorials. Here's an example.
[str(c) for c in L]
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
[type(item) for item in L]
[int, int, int, int, int, int, int, int, int, int]
Creating Arrays
Numpy arrays are homogeneous in nature, i.e., they comprise one data type (integer, float, double, etc.) unlike lists.
#creating arrays
np.zeros(10, dtype='int')
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
#creating a 3 row x 5 column matrix
np.ones((3,5), dtype=float)
array([[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.]])
#creating a matrix with a predefined value
np.full((3,5),1.23)
array([[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23]])
#create an array with a set sequence
np.arange(0, 20, 2)
array([0, 2, 4, 6, 8,10,12,14,16,18])
#create an array of even space between the given range of values
np.linspace(0, 1, 5)
array([ 0., 0.25, 0.5 , 0.75, 1.])
#create a 3x3 array with mean 0 and standard deviation 1 in a given dimension
np.random.normal(0, 1, (3,3))
array([[ 0.72432142, -0.90024075, 0.27363808],
[ 0.88426129, 1.45096856, -1.03547109],
[-0.42930994, -1.02284441, -1.59753603]])
#create an identity matrix
np.eye(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
#set a random seed
np.random.seed(0)
x1 = np.random.randint(10, size=6) #one dimension
x2 = np.random.randint(10, size=(3,4)) #two dimension
x3 = np.random.randint(10, size=(3,4,5)) #three dimension
print("x3 ndim:", x3.ndim)
print("x3 shape:", x3.shape)
print("x3 size: ", x3.size)
('x3 ndim:', 3)
('x3 shape:', (3, 4, 5))
('x3 size: ', 60)
Array Indexing
The important thing to remember is that indexing in python starts at zero.
x1 = np.array([4, 3, 4, 4, 8, 4])
x1
array([4, 3, 4, 4, 8, 4])
#assess value to index zero
x1[0]
4
#assess fifth value
x1[4]
8
#get the last value
x1[-1]
4
#get the second last value
x1[-2]
8
#in a multidimensional array, we need to specify row and column index
x2
array([[3, 7, 5, 5],
[0, 1, 5, 9],
[3, 0, 5, 0]])
#1st row and 2nd column value
x2[2,3]
0
#3rd row and last value from the 3rd column
x2[2,-1]
0
#replace value at 0,0 index
x2[0,0] = 12
x2
array([[12, 7, 5, 5],
[ 0, 1, 5, 9],
[ 3, 0, 5, 0]])
Array Slicing
Now, we'll learn to access multiple or a range of elements from an array.
x = np.arange(10)
x
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
#from start to 4th position
x[: ...
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