Numpy,Python Library, Pandas, AI, Machine Learning

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Index ❑ NumPy Introduction ❑ The need of NumPy ❑ Importance and features of NumPy ❑ Advantages of NumPy ❑ NumPy Environment Setup ❑ Using Numpy ❑ Ndarray in NumPy ❑ Creating a ndarray ❑ Accessing Elements (Indexing and Slicing) 1 ❑ Basic Array Operation ❑ NumPy Array Axis ❑ Understanding Array Dimensions ❑ Higher-Dimensional Arrays ❑ Important Attributes of an ndarray Objects ❑ Reshaping the array objects ❑ Array Concatenation and Stacking ❑ Splitting Arrays ❑ Flattening Arrays with flatten() ❑ NumPy Datatypes

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Index 2 ❑ Trigonometric and Statistical Functions ❑ Introduction to Broadcasting ❑ NumPy Array Iteration ❑ NumPy Bitwise Operators ❑ NumPy Sorting and Searching ❑ Assignment ❑ Numpy Array from Existing Data ❑ Array Creation using numpy.arange() ❑ Array Creation using numpy.linspace() ❑ Creating Identity and Diagonal Matrices ❑ Finding square root and standard deviation ❑ Linear Algebra Operations(dot products, matrix multiplication)

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png NumPy Introduction 3 ❑ NumPy stands for numeric python which is a python package for the computation and processing of the multidimensional and single dimensional array elements. ❑ Travis Oliphant created NumPy package in 2005 by injecting the features of the ancestor module Numeric into another module Numarray. ❑ It is an extension module of Python which is mostly written in C. It provides various functions which are capable of performing the numeric computations with a high speed.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png The need of NumPy ❑ With the revolution of data science, data analysis libraries like NumPy, SciPy, Pandas, etc. have seen a lot of growth. With a much easier syntax than other programming languages, python is the first choice language for the data scientist. ❑ NumPy provides a convenient and efficient way to handle the vast amount of data. NumPy is also very convenient with Matrix multiplication and data reshaping. NumPy is fast which makes it reasonable to work with a large set of data. ❑ NumPy provides a convenient and efficient way to handle the vast amount of data. NumPy is also very convenient with Matrix multiplication and data reshaping. NumPy is fast which makes it reasonable to work with a large set of data. 4

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Importance and features of NumPy 5 ❑ Efficient Data Handling: - Fast and efficient processing of large datasets. - Provides support for array-oriented computing. ❑ Matrix Operations: - Built-in functions for matrix multiplication, linear algebra, and data reshaping. - Supports Fourier Transform and other scientific computations. ❑ Multidimensional Arrays: - Efficient implementation and manipulation of multidimensional arrays. - Convenient tools for data reshaping and processing. ❑ Integration with Other Libraries: - Works seamlessly with SciPy and Matplotlib. - Widely used as a replacement for MATLAB due to Python’s simplicity and versatility.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Advantages of NumPy ❑ NumPy performs array-oriented computing. ❑ It efficiently implements the multidimensional arrays. ❑ It performs scientific computations. ❑ It is capable of performing Fourier Transform and reshaping the data stored in multidimensional arrays. ❑ NumPy provides the in-built functions for linear algebra and random number generation. 6

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png NumPy Environment Setup 7 NumPy doesn't come bundled with Python. We have to install it using the python pip installer. Execute the following command. ❑ Open Command Prompt or Terminal: To install NumPy, you need to use the command line. open the Command Prompt by typing `cmd` in the search bar. ❑ Install NumPy Using pip: Type the following command and press Enter: pip install numpy This command will download and install the latest version of NumPy.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png ❑ Import the numpy package in a python program as: import numpy or import numpy as np 9

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png ❑ Ndarray is the n-dimensional array object defined in the numpy which stores the collection of the similar type of elements. In other words, we can define a ndarray as the collection of the data type (dtype) objects. ❑ The ndarray object can be accessed by using the 0 based indexing. Each element of the Array object contains the same size in the memory. Key Features: ❑ 0-Based Indexing: Access elements using indices starting from 0. ❑ Memory Efficiency: Each element occupies the same amount of memory space. Why Use `ndarray`? ❑ Supports efficient computation with large datasets. ❑ Enables fast operations on arrays like slicing, reshaping, and broadcasting. 10

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Creating a ndarray 10 ❑ The ndarray object can be created by using the array array() function of the numpy module. ❑ Step 1: Import NumPy Module import numpy ❑ Step 2: Use the `array` Routine Create an `ndarray` using `numpy.array()`. Example: #Create a Simple Array: import numpy a = numpy.array([1, 2, 3]) print(a) Output: [1 2 3]

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue… 11 ❑ To create a two-dimensional array object, use the following syntax. # ndarray of two dimensions import numpy as np a = np.array([[1,2,3], [4,5,6], [7,8,9]]) ❑ To create a multi-dimensional array object, use the following syntax. # ndarray of more than one dimensions import numpy as np a = np.array([[values…..], [values…..]])

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Accessing Elements (Indexing and Slicing) 12 ❑ Indexing allows access to specific elements in an array, while slicing provides a way to extract subsets of the array. NumPy supports both simple and advanced indexing techniques. ❑ Understanding how to access and manipulate elements in arrays is fundamental for data analysis and manipulation. Examples: Indexing: import numpy as np a = np.array([1, 2, 3, 4]) print(a[0]) Output: 1 b = np.array([[1, 2, 3], [4, 5, 6]]) print(b[1, 2]) Output: 6

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue… 13 ❑ Slicing in the NumPy array is the way to extract a range of elements from an array. Slicing in the array is performed in the same way as it is performed in the python list. ❑ Slicing extracts a subset of an array using a range of indices. It returns a new array containing elements from the specified start index up to, but not including, the stop index. Example 1: import numpy as np a = np.array([[1,2],[3,4],[5,6]]) print(a[0,1]) print(a[2,0]) Output: 2 5 The above program prints the 2nd element from the 0th index and 0th element from the 2nd index of the array.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Basic Array Operation 14 ❑ Arithmetic Operations: Element-wise addition, subtraction, multiplication, and division: import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) print(a + b) Output: [5 7 9] print(a * b) Output: [ 4 10 18] ❑ Array Methods: `sum()`, `mean()`, `max()`, `min()`: import numpy as np a = np.array([1, 2, 3]) print(a.sum()) # Output: 6 print(a.mean()) # Output: 2.0

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue…. 15 ❑ The NumPy provides the max(), min(), and sum() functions which are used to find the maximum, minimum, and sum of the array elements respectively. ❑ max(): max() function is used to display largest value from a given array list. Example 1: import numpy as np x = np.array([1,2,3,10,15,4]) print("The maximum element:", x.max()) Output: The maximum element: 15

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue…. 16 ❑ min(): The min() function is used to display smallest value from a given array list. Example 1: import numpy as np x = np.array([1,2,3,10,15,4]) print("The minimum element:", x.min()) Output: The minimum element: 1

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue…. 17 ❑ sum(): The sum() function is used to display total of the values. Example 1: import numpy as np a = np.array([1,2,3,10,15,4]) print("The some of elements:",a.sum()) Output: The some of elements: 35

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png NumPy Array Axis 18 A NumPy multi-dimensional array is represented by the axis. ❑ axis=0 represents the columns ❑ axis =1 represents the rows.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Example 19 import numpy as np a = np.array([[1,2,30],[10,15,4]]) print("The array:",a) print("The maximum elements of columns:",a.max(axis = 0)) print("The minimum element of rows",a.min(axis = 1)) print("The sum of all rows",a.sum(axis = 1)) Output: The array: [[1 2 30] [10 15 4]] The maximum elements of columns: [10 15 30] The minimum element of rows [1 4] The sum of all rows [33 29]

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Understanding Array Dimensions 20 ❑ Array Dimensions: Refers to the number of axes or coordinates needed to index an array. ❑ 1-D Array: A simple list of elements. ❑ 2-D Array: A matrix with rows and columns. ❑ Higher Dimensions: Arrays with more than two axes, useful for more complex data structures. Example for 1-D Array: import numpy as np a = np.array([1, 2, 3]) print(a.ndim) # Output: 1 print(a.shape) # Output: (3,)

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Example 21 Example for 2-D Array: import numpy as np b = np.array([[1, 2], [3, 4]]) print(b.ndim) print(b.shape) Output: 2 (2, 2)

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Higher-Dimensional Arrays 22 - 3-D Array: Contains multiple 2-D arrays (e.g., a stack of matrices). - N-D Arrays: Can have more than three dimensions, used for complex data like multidimensional data grids. Code Example for 3-D Array: import numpy as np c = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) print(c.ndim) Output: 3 print(c.shape) Output: (2, 2, 2)

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Important Attributes of an ndarray Objects 23 ❑ ndarray.ndim Returns the number of dimensions (axes) of the array. Example: a = numpy.array([[1, 2, 3], [4, 5, 6]]) print(a.ndim) Output: 2 ❑ ndarray.shape Returns a tuple representing the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the number of axes, ndim.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue… 24 Example: import numpy as np a = numpy.array([[1, 2, 3], [4, 5, 6]]) print(a.shape) Output: (2, 3)

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png 25 ❑ ndarray.size Returns the total number of elements in the array. Example: a = numpy.array([[1, 2, 3], [4, 5, 6]]) print(a.size) Output: 6 ❑ ndarray.dtype Returns the data type of the elements in the array. Example: a = numpy.array([1, 2, 3]) print(a.dtype) Output: int64 (or int32, depending on the system) Continue…

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png 26 ❑ ndarray.itemsize Returns the **size (in bytes) of each element in the array. Example: a = numpy.array([1, 2, 3]) print(a.itemsize) Output: 8 (if dtype is int64) ❑ ndarray.nbytes Returns the **total number of bytes** consumed by the elements of the array. Example: a = numpy.array([1, 2, 3]) print(a.nbytes) Output: 24 (3 elements * 8 bytes each) Continue…

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png 26 ❑ ndarray.T Returns the transpose of the array. Example: a = numpy.array([[1, 2, 3], [4, 5, 6]]) print(a.T) Output: [[1, 4], [2, 5], [3, 6]] ❑ ndarray.flat Returns a 1-D iterator over the array, allowing iteration over all elements in a flattened format. Example: a = np.array([[1, 2], [3, 4]]) print(list(a.flat)) Output: [1, 2, 3, 4] Continue…

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png 27 ❑ reshape() is used to reshape array objects. By the shape of the array, we mean the number of rows and columns of a multi-dimensional array. However, the numpy module provides us the way to reshape the array by changing the number of rows and columns of the multi-dimensional array. Example: import numpy as np a = np.array([[1,2],[3,4],[5,6]]) print("printing the original array..") print(a) a=a.reshape(2,3) print("printing the reshaped array..") print(a) Reshaping the array objects

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png 27 Output: printing the original array.. [[1 2] [3 4] [5 6]] printing the reshaped array.. [[1 2 3] [4 5 6]] Continue….

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Array Concatenation and Stacking 30 ❑ Concatenation : The concatenate() function allows you to join arrays along an existing axis. For example, you can join two 1D arrays or two 2D arrays along rows or columns. ❑ Stacking : vstack() and hstack() are used for vertical and horizontal stacking of arrays, respectively. vstack() stacks arrays vertically (row-wise), while hstack() stacks them horizontally (column-wise). Example: arr1 = np.array([1, 2]) arr2 = np.array([3, 4]) concatenated_arr = np.concatenate((arr1, arr2)) stacked_arr = np.vstack((arr1, arr2)) print(concatenated_arr) print(stacked_arr) Output: [1 2 3 4] [[1 2] [3 4]]

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Splitting Arrays 31 ❑ split() function The `split()` function is used to split an array into multiple sub-arrays. You can specify how many parts the array should be split into, and it returns a list of sub-arrays. Example: arr = np.array([1, 2, 3, 4, 5, 6]) split_arr = np.split(arr, 3) print(split_arr) Output: [array([1, 2]), array([3, 4]), array([5, 6])] Explanation: Here, the array `[1, 2, 3, 4, 5, 6]` is split into 3 equal sub-arrays: `[1, 2]`, `[3, 4]`, and `[5, 6]`.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Flattening Arrays with flatten() 32 ❑ Flattening Arrays with flatten() : The `flatten()` method converts a multi-dimensional array into a 1D array. This is particularly useful when you need to work with the array data in a linear form. Example: arr = np.array([[1, 2, 3], [4, 5, 6]]) flat_arr = arr.flatten() print(flat_arr) Output: [1 2 3 4 5 6] Explanation: The 2D array `[[1, 2, 3], [4, 5, 6]]` is flattened into a 1D array `[1, 2, 3, 4, 5, 6]`. This helps to easily manipulate or iterate over the array data.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png NumPy Datatypes 33 SN Data type Description 1 bool_ It represents the boolean value indicating true or false. It is stored as a byte. 2 int_ It is the default type of integer. It is identical to long type in C that contains 64 bit or 32-bit integer. 3 intc It is similar to the C integer (c int) as it represents 32 or 64-bit int. 4 intp It represents the integers which are used for indexing. 5 int8 It is the 8-bit integer identical to a byte. The range of the value is -128 to 127. 6 int16 It is the 2-byte (16-bit) integer. The range is -32768 to 32767. 7 int32 It is the 4-byte (32-bit) integer. The range is -2147483648 to 2147483647. 8 int64 It is the 8-byte (64-bit) integer. The range is -9223372036854775808 to 9223372036854775807. 9 uint8 It is the 1-byte (8-bit) unsigned integer. 10 uint16 It is the 2-byte (16-bit) unsigned integer. 11 uint32 It is the 4-byte (32-bit) unsigned integer.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue…. 34 SN Data type Description 12 uint64 It is the 8 bytes (64-bit) unsigned integer. 13 float_ It is identical to float64. 14 float16 It is the half-precision float. 5 bits are reserved for the exponent. 10 bits are reserved for mantissa, and 1 bit is reserved for the sign. 15 float32 It is a single precision float. 8 bits are reserved for the exponent, 23 bits are reserved for mantissa, and 1 bit is reserved for the sign. 16 float64 It is the double precision float. 11 bits are reserved for the exponent, 52 bits are reserved for mantissa, 1 bit is used for the sign. 17 complex_ It is identical to complex128.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue…. 35 SN Data type Description 12 uint64 It is the 8 bytes (64-bit) unsigned integer. 13 float_ It is identical to float64. 14 float16 It is the half-precision float. 5 bits are reserved for the exponent. 10 bits are reserved for mantissa, and 1 bit is reserved for the sign. 15 float32 It is a single precision float. 8 bits are reserved for the exponent, 23 bits are reserved for mantissa, and 1 bit is reserved for the sign. 16 float64 It is the double precision float. 11 bits are reserved for the exponent, 52 bits are reserved for mantissa, 1 bit is used for the sign. 17 complex_ It is identical to complex128.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Numpy Array from Existing Data 36 ❑ NumPy provides us the way to create an array by using the existing data. ❑ This routine is used to create an array by using the existing data in the form of lists, or tuples. This routine is useful in the scenario where we need to convert a python sequence into the numpy array object. numpy.asarray(sequence, dtype = None, order = None) ❑ It accepts the following parameters. sequence: It is the python sequence which is to be converted into the python array. dtype: It is the data type of each item of the array. order: It can be set to C or F. The default is C.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue… 37 ❑ Creating numpy array using the list import numpy as np l=[1,2,3,4,5,6,7] a = np.asarray(l) ❑ Creating a numpy array using Tuple import numpy as np l=(1,2,3,4,5,6,7) a = np.asarray(l) ❑ Creating a numpy array using more than one list import numpy as np l=[[1,2,3,4,5,6,7],[8,9,12,13,14,15,16]] a = np.asarray(l)

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Array Creation using numpy.arange() 38 ❑ Using numpy.arange() numpy.arange() creates arrays with evenly spaced values within a specified range. It is similar to Python's range() but returns an array instead of a list. Example: import numpy as np a = np.arange(5) b = np.arange(1, 10, 2) print(a) print(b) Output: [0 1 2 3 4] [1 3 5 7 9]

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Array Creation using numpy.linspace() 39 ❑ Using numpy.linspace() : numpy.linspace() generates arrays with a specified number of evenly spaced values between a start and end point. This is useful for creating ranges with a specific number of elements. Example: import numpy as np a = np.linspace(0, 1, 5) b = np.linspace(1, 10, 4) print(a) print(b) Output: [0. 0.25 0.5 0.75 1. ] [ 1. 4. 7. 10.]

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Creating Identity and Diagonal Matrices 40 ❑ Identity matrices have ones on the diagonal and zeros elsewhere. Diagonal matrices have non-zero values only on the diagonal. Both are important for linear algebra operations. Example: import numpy as np a = np.eye(3) # Creates a 3x3 identity matrix b = np.diag([1, 2, 3]) # Creates a diagonal matrix with [1, 2, 3] on the diagonal print(a) print(b) Output: [[1. 0. 0.] [0. 1. 0.] [0. 0. 1.]] [[1 0 0] [0 2 0] [0 0 3]]

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Finding square root and standard deviation 41 ❑ The sqrt() and std() functions associated with the numpy array are used to find the square root and standard deviation of the array elements respectively. ❑ Standard deviation means how much each element of the array varies from the mean value of the numpy array. Example: import numpy as np a = np.array([[1,2,30],[10,15,4]]) print(“Square root: ”np.sqrt(a)) print(“Standard Deviation ”np.std(a)) Output: Square Root: [[1. 1.41421356 5.47722558] [3.16227766 3.87298335 2. ]] Standard Deviation: 10.044346115546242

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Linear Algebra Operations(dot products, matrix multiplication) 42 ❑ NumPy provides efficient functions for linear algebra operations such as dot products, matrix multiplication, and solving linear systems. The `dot()` function is used for matrix multiplication, while `@` is the shorthand operator for dot products. Example: arr1 = np.array([[1, 2], [3, 4]]) arr2 = np.array([[5, 6], [7, 8]]) dot_product = np.dot(arr1, arr2) matrix_mult = arr1 @ arr2 # Same as np.dot(arr1, arr2) print(dot_product) print(matrix_mult) Output: [[19 22] [43 50]] [[19 22] [43 50]]

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Trigonometric and Statistical Functions 43 ❑ NumPy includes a wide range of trigonometric functions (such as `sin()`, `cos()`, `tan()`) and statistical functions (such as `min()`, `max()`, `percentile()`) for handling scientific computations and data analysis. Example: angles = np.array([0, np.pi / 2, np.pi]) sine_values = np.sin(angles) max_val = np.max(angles) print(sine_values) # Sine of the angles print(max_val) # Maximum value in the array Output: [0.0000000e+00 1.0000000e+00 1.2246468e-16] 3.141592653589793 Explanation: The `sin()` function calculates the sine of each angle in radians, and `max()` returns the maximum value from the array of angles.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue… 44 Numpy statistical functions: ❑ numpy.amin() and numpy.amax() :These functions are used to find the minimum and maximum of the array elements along the specified axis respectively. ❑ Syntax: amin(array_name, axis) amax(array_name, axis) ❑ The axis will be 1: for row wise operation 0: for column wise operation

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Example 45 import numpy as np array = np.array([[4, 9, 2], [7, 1, 8], [6, 3, 5]]) # Finding the minimum and maximum along axis 1 (row-wise) min_row = np.amin(array, axis=1) max_row = np.amax(array, axis=1) # Finding the minimum and maximum along axis 0 (column-wise) min_col = np.amin(array, axis=0) max_col = np.amax(array, axis=0) print("Original Array:n", array) print("nRow-wise minimum:", min_row) print("Row-wise maximum:", max_row) print("nColumn-wise minimum:", min_col) print("Column-wise maximum:", max_col)

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Output 46 Original Array: [[4 9 2] [7 1 8] [6 3 5]] Row-wise minimum: [2 1 3] Row-wise maximum: [9 8 6] Column-wise minimum: [4 1 2] Column-wise maximum: [7 9 8] Note: axis=1 performs the operation row-wise (across the columns for each row). axis=0 performs the operation column-wise (across the rows for each column).

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Introduction to Broadcasting 47 ❑ Broadcasting is a technique in NumPy that allows you to perform operations on arrays of different shapes as if they have the same shape. Smaller arrays are automatically “broadcasted” to match the shape of the larger array. Example: If you add a scalar (like a single number) to an array, NumPy will "stretch" the scalar across the array: arr = np.array([1, 2, 3]) result = arr + 5 # Broadcasting the scalar 5 print(result) Output: [6 7 8] Explanation: The number `5` is broadcasted across all elements of the array `[1, 2, 3]`, and the addition happens element-wise.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Examples of Broadcasting 48 ❑ Broadcasting works when NumPy can match dimensions or "expand" the smaller array to the larger one. This works for operations like addition, subtraction, multiplication, and more. Example : import numpy as np a = np.array([[1,2,3,4],[2,4,5,6],[10,20,39,3]]) b = np.array([2,4,6,8]) print("nprinting array a..") print(a) print("nprinting array b..") print(b) print("nAdding arrays a and b ..") c = a + b; print(c)

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png NumPy Array Iteration 49 ❑ NumPy provides an iterator object, i.e., nditer which can be used to iterate over the given array using python standard Iterator interface. Example: import numpy as np a = np.array([[1,2,3,4],[2,4,5,6],[10,20,39,3]]) print("Printing array:") print(a); print("Iterating over the array:") for x in np.nditer(a): print(x,end=' ')

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png NumPy Bitwise Operators 50 SN Operator Description 1 bitwise_and It is used to calculate the bitwise and operation between the corresponding array elements. 2 bitwise_or It is used to calculate the bitwise or operation between the corresponding array elements. 3 invert It is used to calculate the bitwise not the operation of the array elements. 4 left_shift It is used to shift the bits of the binary representation of the elements to the left. 5 right_shift It is used to shift the bits of the binary representation of the elements to the right.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Example 51 import numpy as np a = np.array([5, 7, 10, 12]) # Binary: 101, 111, 1010, 1100 b = np.array([3, 6, 12, 15]) # Binary: 011, 110, 1100, 1111 # Bitwise AND and_result = np.bitwise_and(a, b) print("Bitwise AND:", and_result) # Bitwise OR or_result = np.bitwise_or(a, b) print("Bitwise OR:", or_result) # Bitwise NOT on array 'b' not_result_b = np.invert(b) print("Bitwise NOT on b:", not_result_b) print("binary representation of a:",bin(a)) print("binary representation of b:",bin(b))

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Output 52 Output: binary representation of a: 101, 111, 1010, 1100 binary representation of b: 011, 110, 1100, 1111 Bitwise AND: [ 1 6 8 12] Bitwise OR: [ 7 7 14 15] Bitwise NOT on b: [-4 -7 -13 -16]

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png NumPy Sorting and Searching 53 ❑ Numpy provides a variety of functions for sorting and searching. There are various sorting algorithms like quicksort, merge sort and heapsort which is implemented using the numpy.sort() function. Syntax: numpy.sort(a, axis, kind, order) ❑ It accepts the following parameters. Input It represents the input array which is to be sorted. axis It represents the axis along which the array is to be sorted. If the axis is not mentioned, then the sorting is done along the last available axis.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue… 54 ❑ Kind It represents the type of sorting algorithm which is to be used while sorting. The default is quick sort. ❑ Order It represents the filed according to which the array is to be sorted in the case if the array contains the fields.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Example 55 import numpy as np a = np.array([[10,2,3],[4,5,6],[7,8,9]]) print("Sorting along the columns:") print(np.sort(a)) print("Sorting along the rows:") print(np.sort(a, 0)) Output: Sorting along the columns: [[ 2 3 10] [ 4 5 6] [ 7 8 9]] Sorting along the rows: [[ 4 2 3] [ 7 5 6] [10 8 9]]

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue… 56 ❑ NumPy provides several functions for searching for specific elements or indices in an array. ❑ numpy.nonzero() :This function is used to find the location of the non-zero elements from the array. Example import numpy as np b = np.array([12, 90, 380, 12, 211]) print("printing original array",b) print("printing location of the non-zero elements") print(b.nonzero()) Output: printing original array [ 12 90 380 12 211] printing location of the non-zero elements (array([0, 1, 2, 3, 4]),)

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Continue… 57 ❑ numpy.where(): This function is used to return the indices of all the elements which satisfies a particular condition. Example arr = np.array([5, 1, 9, 3, 7]) # Find the indices where the element is greater than 5 indices = np.where(arr > 5) print("Indices where elements are greater than 5:", indices) # Use the indices to get the elements elements = arr[indices] print("Elements greater than 5:", elements)

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Output 58 Indices where elements are greater than 5: (array([2, 4]),) Elements greater than 5: [9 7]

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png Assignment 59 ❑ Write a Python script to create a NumPy ndarray from a list of lists. Then, reshape the ndarray into a different shape and print both the original and reshaped arrays. ❑ Create a 3x3 NumPy array with random integers. Perform slicing operations to extract specific rows and columns. ❑ Generate a 4x4 array of random numbers. Write functions to find the maximum, minimum, sum, square root, and standard deviation of the array elements. ❑ Create two 1-dimensional NumPy arrays and concatenate them into a single array. ❑ Write a Python program to create a NumPy array with random integers. Sort the array in ascending order and then search for a specific element within the array.

Module: M2-R5: Web Designing & Publishing [Unit 1: Introduction to Web Design] Course: NIELIT ‘O’ Level (IT) home-2741413_960_720.png 60 Thank You

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