The Input Statement in Core Python .pptxKavitha713564
The document discusses different ways to accept input from the user in Python programs. It explains the input(), raw_input(), and int() functions for accepting input as a string, integer, or other data type. It also covers using command line arguments with the sys module to access arguments passed when running a Python program from the terminal. The getopt and argparse modules provide additional functionality for parsing command line arguments in a program.
Lecture 5 – Computing with Numbers (Math Lib).pptxjovannyflex
This document provides an overview of numeric data types and operations in Python. It discusses integers and floats, and how they represent numeric values differently. Functions for determining data types like type() and converting between types are presented. Core numeric operations like addition, subtraction, multiplication, division, and modulo are covered. The use of accumulators and loops to calculate factorial and solve other numeric problems is demonstrated. Finally, the math library module is introduced for accessing advanced math functions like square root, trigonometric functions, rounding, and random number generation.
Lecture 5 – Computing with Numbers (Math Lib).pptxjovannyflex
This document provides an overview of numeric data types and operations in Python. It discusses integers and floats, and how they represent numeric values differently. Functions for determining data types like type() and converting between types are presented. Core numeric operations like addition, subtraction, multiplication, division, and modulus are covered. The use of accumulators and loops to calculate factorial and solve other numeric problems is demonstrated. Finally, the math library module is introduced for accessing advanced math functions like square root, trigonometric functions, rounding, and random number generation.
This individual assignment submission contains code snippets and explanations for various Python programming concepts and techniques. It includes programs to prompt a user for input, calculate pay and grades, define functions, use data types like lists and dictionaries, and work with classes and objects. The submission also contains questions about Python concepts like loops, variables, modules, file handling, and database usage. Overall, the document demonstrates an understanding of core Python programming and problem-solving skills.
The document discusses the Number class in Java. It provides methods for converting between primitive numeric types like int and their corresponding wrapper classes like Integer. The Number class is an abstract class that all wrapper classes like Integer extend. It contains methods for common operations on numbers like comparison, conversion to primitive types, parsing strings, and mathematical operations. The document lists and describes over 20 methods in the Number class.
This document discusses Python functions. It covers built-in functions, user-defined functions, fruitful and void functions, parameters and arguments, recursion, and strings. Specifically, it provides examples and explanations of various math functions like abs(), ceil(), exp(), and more. It also covers defining new functions, importing functions from modules, boolean functions, and using for loops to traverse strings.
Python supports common data types like numbers, strings, booleans, lists, tuples, sets and dictionaries. Numbers can be integers, floating point numbers or complex numbers. Strings are sequences of characters that can be indexed and manipulated. Booleans represent true and false values. Lists are mutable sequences that can hold heterogeneous data types. Tuples are immutable sequences similar to lists. Sets hold unique elements without defined ordering. Dictionaries store mappings of unique keys to values.
The document discusses arrays and array data structures. It defines an array as a set of index-value pairs where each index maps to a single value. It then describes the common array abstract data type (ADT) with methods like create, retrieve, and store for manipulating arrays. The document also discusses sparse matrix data structures and provides an ADT for sparse matrices with methods like create, transpose, add, and multiply.
This document provides an overview of higher-order functions in Python. It discusses functions as parameters, examples of higher-order functions like map, filter and reduce, and how they work. It also covers anonymous functions, examples and problems demonstrating the use of map, filter and reduce. Additional topics covered include regular expressions, metacharacters, and solving problems using regex patterns.
Here we learn Python strings methods in the hope that it will meet your educational needs.
There are a number of Python built-in methods that can be used to manipulate strings.
There is one thing to keep in mind when using string methods: they all return new values. In other words, they do not change the original string in any way.
It is imperative to note that Python strings methods return rewritten values. The original string remains unchanged.
Python Strings Tutorial explains strings in more detail.
The following is an in-depth explanation of Python Strings Methods, with examples for each method.
Capitalize() method generates a string with the first character in upper case, and the remaining characters in lower case.
The document provides information about error handling in Python programming. It discusses different types of exceptions that may occur during program execution and how to handle them using try, except, else and finally blocks. It gives examples of programs that handle errors from inputting non-integer values or dividing by zero. The document also covers other Python programming concepts like lists, random numbers, and comparing Python to C/C++.
Functions are reusable blocks of code that perform a specific task. They help reduce complexity, improve reusability and maintainability of code. There are built-in functions predefined in modules and user-defined functions. Built-in functions include type conversion, math operations etc. User-defined functions are created using the def keyword and defined with a name, parameters and indented block of code. Functions are called by their name with actual parameters. This transfers program control to the function block, executes code, then returns control to calling block.
The document discusses Python data types. It describes the numeric data types integer, float, and complex which are used to represent numbers. Integer is a whole number without decimals, float has decimals, and complex numbers have real and imaginary parts. None is described as a null value. Strings are arrays of characters and can be indexed. Tuples and lists are ordered collections that can hold heterogeneous data types. Sets are unordered collections of unique items. Dictionaries are unordered collections of key-value pairs that allow accessing values via keys.
This document contains a presentation on self-learning modules in Python. It discusses:
1. Assigning modules to different students for learning.
2. Modules, packages, and libraries as different ways to reuse code in Python. A module is a file with the .py extension, a package is a folder containing modules, and a library is a collection of packages.
3. The Python standard library contains built-in functions and modules that are part of the Python installation. Common modules discussed include math, random, and urllib.
Python lambda functions with filter, map & reduce functionARVIND PANDE
Lambda functions allow the creation of small anonymous functions and can be passed as arguments to other functions. The map() function applies a lambda function to each element of a list and returns a new list. The filter() function filters a list based on the return value of a lambda function. The reduce() function iteratively applies a lambda function to consecutive pairs in a list and returns a single value. User-defined functions in Python can perform tasks like converting between temperature scales, finding max/min/average of lists, generating Fibonacci series, reversing strings, summing digits in numbers, and calculating powers using recursion.
This document provides an overview of the core built-in data types in Python, including numbers, strings, lists, tuples, and dictionaries. It defines each data type, provides examples of their usage, and demonstrates basic programs utilizing each type. The core data types covered are integers, floating point numbers, complex numbers, strings, lists, tuples, and dictionaries.
The document discusses arrays and sparse matrices as data structures. It defines array and sparse matrix abstract data types, including methods for creating, accessing, and manipulating the structures. Examples are given of representing polynomials using arrays or as a sparse matrix to illustrate different implementations of these data structures.
This document discusses parsing and parsers. It introduces parsing as breaking a string into tokens through lexing and extracting meaning through parsing. It then defines a Parser type in Swift and provides some basic building blocks like Parser.result(), Parser.zero(), and Parser.item(). It shows how parsers can be combined using functions like bind() and satisfy() to parse characters, words, and make choices. Finally, it mentions some references on monadic parser combinators.
This document provides an overview of Java 8 lambda expressions. It begins with an introduction and background on anonymous classes in previous Java versions. The main topics covered include lambda syntax, lambda expressions vs closures, capturing variables in lambda expressions, type inference, and functional interfaces. It also discusses stream operations like filter, map, flatMap, and peek. Finally, it covers parallelism and how streams can leverage multiple cores to improve performance.
The role of the lexical analyzer
Specification of tokens
Finite state machines
From a regular expressions to an NFA
Convert NFA to DFA
Transforming grammars and regular expressions
Transforming automata to grammars
Language for specifying lexical analyzers
This document discusses Python functions. It covers built-in functions, user-defined functions, fruitful and void functions, parameters and arguments, recursion, and strings. Specifically, it provides examples and explanations of various math functions like abs(), ceil(), exp(), and more. It also covers defining new functions, importing functions from modules, boolean functions, and using for loops to traverse strings.
Python supports common data types like numbers, strings, booleans, lists, tuples, sets and dictionaries. Numbers can be integers, floating point numbers or complex numbers. Strings are sequences of characters that can be indexed and manipulated. Booleans represent true and false values. Lists are mutable sequences that can hold heterogeneous data types. Tuples are immutable sequences similar to lists. Sets hold unique elements without defined ordering. Dictionaries store mappings of unique keys to values.
The document discusses arrays and array data structures. It defines an array as a set of index-value pairs where each index maps to a single value. It then describes the common array abstract data type (ADT) with methods like create, retrieve, and store for manipulating arrays. The document also discusses sparse matrix data structures and provides an ADT for sparse matrices with methods like create, transpose, add, and multiply.
This document provides an overview of higher-order functions in Python. It discusses functions as parameters, examples of higher-order functions like map, filter and reduce, and how they work. It also covers anonymous functions, examples and problems demonstrating the use of map, filter and reduce. Additional topics covered include regular expressions, metacharacters, and solving problems using regex patterns.
Here we learn Python strings methods in the hope that it will meet your educational needs.
There are a number of Python built-in methods that can be used to manipulate strings.
There is one thing to keep in mind when using string methods: they all return new values. In other words, they do not change the original string in any way.
It is imperative to note that Python strings methods return rewritten values. The original string remains unchanged.
Python Strings Tutorial explains strings in more detail.
The following is an in-depth explanation of Python Strings Methods, with examples for each method.
Capitalize() method generates a string with the first character in upper case, and the remaining characters in lower case.
The document provides information about error handling in Python programming. It discusses different types of exceptions that may occur during program execution and how to handle them using try, except, else and finally blocks. It gives examples of programs that handle errors from inputting non-integer values or dividing by zero. The document also covers other Python programming concepts like lists, random numbers, and comparing Python to C/C++.
Functions are reusable blocks of code that perform a specific task. They help reduce complexity, improve reusability and maintainability of code. There are built-in functions predefined in modules and user-defined functions. Built-in functions include type conversion, math operations etc. User-defined functions are created using the def keyword and defined with a name, parameters and indented block of code. Functions are called by their name with actual parameters. This transfers program control to the function block, executes code, then returns control to calling block.
The document discusses Python data types. It describes the numeric data types integer, float, and complex which are used to represent numbers. Integer is a whole number without decimals, float has decimals, and complex numbers have real and imaginary parts. None is described as a null value. Strings are arrays of characters and can be indexed. Tuples and lists are ordered collections that can hold heterogeneous data types. Sets are unordered collections of unique items. Dictionaries are unordered collections of key-value pairs that allow accessing values via keys.
This document contains a presentation on self-learning modules in Python. It discusses:
1. Assigning modules to different students for learning.
2. Modules, packages, and libraries as different ways to reuse code in Python. A module is a file with the .py extension, a package is a folder containing modules, and a library is a collection of packages.
3. The Python standard library contains built-in functions and modules that are part of the Python installation. Common modules discussed include math, random, and urllib.
Python lambda functions with filter, map & reduce functionARVIND PANDE
Lambda functions allow the creation of small anonymous functions and can be passed as arguments to other functions. The map() function applies a lambda function to each element of a list and returns a new list. The filter() function filters a list based on the return value of a lambda function. The reduce() function iteratively applies a lambda function to consecutive pairs in a list and returns a single value. User-defined functions in Python can perform tasks like converting between temperature scales, finding max/min/average of lists, generating Fibonacci series, reversing strings, summing digits in numbers, and calculating powers using recursion.
This document provides an overview of the core built-in data types in Python, including numbers, strings, lists, tuples, and dictionaries. It defines each data type, provides examples of their usage, and demonstrates basic programs utilizing each type. The core data types covered are integers, floating point numbers, complex numbers, strings, lists, tuples, and dictionaries.
The document discusses arrays and sparse matrices as data structures. It defines array and sparse matrix abstract data types, including methods for creating, accessing, and manipulating the structures. Examples are given of representing polynomials using arrays or as a sparse matrix to illustrate different implementations of these data structures.
This document discusses parsing and parsers. It introduces parsing as breaking a string into tokens through lexing and extracting meaning through parsing. It then defines a Parser type in Swift and provides some basic building blocks like Parser.result(), Parser.zero(), and Parser.item(). It shows how parsers can be combined using functions like bind() and satisfy() to parse characters, words, and make choices. Finally, it mentions some references on monadic parser combinators.
This document provides an overview of Java 8 lambda expressions. It begins with an introduction and background on anonymous classes in previous Java versions. The main topics covered include lambda syntax, lambda expressions vs closures, capturing variables in lambda expressions, type inference, and functional interfaces. It also discusses stream operations like filter, map, flatMap, and peek. Finally, it covers parallelism and how streams can leverage multiple cores to improve performance.
The role of the lexical analyzer
Specification of tokens
Finite state machines
From a regular expressions to an NFA
Convert NFA to DFA
Transforming grammars and regular expressions
Transforming automata to grammars
Language for specifying lexical analyzers
its all about Artificial Intelligence(Ai) and Machine Learning and not on advanced level you can study before the exam or can check for some information on Ai for project
How to use nRF24L01 module with ArduinoCircuitDigest
Learn how to wirelessly transmit sensor data using nRF24L01 and Arduino Uno. A simple project demonstrating real-time communication with DHT11 and OLED display.
"Boiler Feed Pump (BFP): Working, Applications, Advantages, and Limitations E...Infopitaara
A Boiler Feed Pump (BFP) is a critical component in thermal power plants. It supplies high-pressure water (feedwater) to the boiler, ensuring continuous steam generation.
⚙️ How a Boiler Feed Pump Works
Water Collection:
Feedwater is collected from the deaerator or feedwater tank.
Pressurization:
The pump increases water pressure using multiple impellers/stages in centrifugal types.
Discharge to Boiler:
Pressurized water is then supplied to the boiler drum or economizer section, depending on design.
🌀 Types of Boiler Feed Pumps
Centrifugal Pumps (most common):
Multistage for higher pressure.
Used in large thermal power stations.
Positive Displacement Pumps (less common):
For smaller or specific applications.
Precise flow control but less efficient for large volumes.
🛠️ Key Operations and Controls
Recirculation Line: Protects the pump from overheating at low flow.
Throttle Valve: Regulates flow based on boiler demand.
Control System: Often automated via DCS/PLC for variable load conditions.
Sealing & Cooling Systems: Prevent leakage and maintain pump health.
⚠️ Common BFP Issues
Cavitation due to low NPSH (Net Positive Suction Head).
Seal or bearing failure.
Overheating from improper flow or recirculation.
This paper proposes a shoulder inverse kinematics (IK) technique. Shoulder complex is comprised of the sternum, clavicle, ribs, scapula, humerus, and four joints.
ELectronics Boards & Product Testing_Shiju.pdfShiju Jacob
This presentation provides a high level insight about DFT analysis and test coverage calculation, finalizing test strategy, and types of tests at different levels of the product.
The Fluke 925 is a vane anemometer, a handheld device designed to measure wind speed, air flow (volume), and temperature. It features a separate sensor and display unit, allowing greater flexibility and ease of use in tight or hard-to-reach spaces. The Fluke 925 is particularly suitable for HVAC (heating, ventilation, and air conditioning) maintenance in both residential and commercial buildings, offering a durable and cost-effective solution for routine airflow diagnostics.
Analysis of reinforced concrete deep beam is based on simplified approximate method due to the complexity of the exact analysis. The complexity is due to a number of parameters affecting its response. To evaluate some of this parameters, finite element study of the structural behavior of the reinforced self-compacting concrete deep beam was carried out using Abaqus finite element modeling tool. The model was validated against experimental data from the literature. The parametric effects of varied concrete compressive strength, vertical web reinforcement ratio and horizontal web reinforcement ratio on the beam were tested on eight (8) different specimens under four points loads. The results of the validation work showed good agreement with the experimental studies. The parametric study revealed that the concrete compressive strength most significantly influenced the specimens’ response with the average of 41.1% and 49 % increment in the diagonal cracking and ultimate load respectively due to doubling of concrete compressive strength. Although the increase in horizontal web reinforcement ratio from 0.31 % to 0.63 % lead to average of 6.24 % increment on the diagonal cracking load, it does not influence the ultimate strength and the load-deflection response of the beams. Similar variation in vertical web reinforcement ratio leads to an average of 2.4 % and 15 % increment in cracking and ultimate load respectively with no appreciable effect on the load-deflection response.
Value Stream Mapping Worskshops for Intelligent Continuous SecurityMarc Hornbeek
This presentation provides detailed guidance and tools for conducting Current State and Future State Value Stream Mapping workshops for Intelligent Continuous Security.
3. Python abs()
The abs() method returns the absolute
value of the given number. If the number
is a complex number, abs() returns its
magnitude.
The syntax of abs() method is:
abs(num)
4. Python abs()
integer = -20print('Absolute value of -20 is:',
abs(integer))
#random floating number
floating = -30.33
print('Absolute value of -30.33 is:',
abs(floating))
When you run the program, the output will be:
Absolute value of -20 is: 20
Absolute value of -30.33 is: 30.33
5. Python all()
The all() method returns True when all
elements in the given iterable are true. If not, it
returns False.
The syntax of all() method is:
all(iterable)
all() Parameters
The all() method takes a single parameter:
iterable - any iterable (list, tuple, dictionary,
etc.) which contains the elements
6. Python all()
Return Value from all()
The all() method returns:
True - If all elements in an iterable are true
False - If any element in an iterable is false
7. Python all()
l = [1, 3, 4, 5]
print(all(l))
# all values false
l = [0, False]
print(all(l))
# one false value
l = [1, 3, 4, 0]
print(all(l))# one true value
l = [0, False, 5]
print(all(l))
# empty iterable
l = []
print(all(l))
8. Python all()
When you run the program, the output will be:
True
False
False
False
True
9. Python any()
The any() method returns True if any element of an
iterable is True. If not, any() returns False.
The syntax of any() is:
any(iterable)
any() Parameters
The any() method takes an iterable (list, string,
dictionary etc.) in Python.
10. Python any()
Return Value from any()
any() returns:
True: if at least one element of an iterable is true
False: if all elements are false or if an iterable is
empty
11. Python any()
l = [1, 3, 4, 0]
print(any(l))
l = [0, False]
print(any(l))
l = [0, False, 5]
print(any(l))
l = []print(any(l))
13. Python complex()
The complex() method returns a complex number
when real and imaginary parts are provided, or it
converts a string to a complex number.
The syntax of complex() is:
complex([real[, imag]])
14. Python complex()
complex() Parameters
In general, the complex() method takes two
parameters:
real - real part. If real is omitted, it defaults to 0.
imag - imaginary part. If imag is omitted, it default to
0.
If the first parameter passed to this method is a
string, it will be interpreted as a complex number. In
this case, second parameter shouldn't be passed.
15. Python complex()
Return Value from complex()
As suggested by the name, the complex() method
returns a complex number.
If the string passed to this method is not a valid
complex number, ValueError exception is raised.
16. Python complex()
z = complex(2, -3)
print(z)z = complex(1)
print(z)z = complex()
print(z)
z = complex('5-9j')
print(z)
17. Python float()
The float() method returns a floating point number
from a number or a string.
The syntax for float() is:
float([x])
float() Parameters
The float() method takes a single parameter:
x (Optional) - number or string that needs to be
converted to floating point number
If it's a string, the string should contain decimal
points
19. Python float()
Return value from float()
The float() method returns:
Equivalent floating point number if an argument is
passed
0.0 if no arguments passed
OverflowError exception if the argument is outside
the range of Python float
20. Python float()
# for integers
print(float(10))
# for floats
print(float(11.22))
# for string floats
print(float("-13.33"))
# for string floats with whitespaces
print(float(" -24.45n"))
# string float error
print(float("abc"))
21. Python float()
When you run the program, the output will be:
10.0
11.22
-13.33
-24.45
ValueError: could not convert string to float: 'abc'
22. Python help()
The help() method calls the built-in Python help
system.
The syntax of help() is:
help(object)
help() Parameters
The help() method takes maximum of one
parameter.
object (optional) - you want to generate the help of
the given object
23. Python help()
How help() works in Python?
The help() method is used for interactive use. It's
recommenced to try it in your interpreter when you
need help to write Python program and use
Python modules.
24. Python help()
object is passed to help() (not a string)
Try these on Python shell.
>>> help(list)
>>> help(dict)
>>> help(print)
>>> help([1, 2, 3])
25. Python help()
object is passed to help() (not a string)
Try these on Python shell.
>>> help(list)
>>> help(dict)
>>> help(print)
>>> help([1, 2, 3])
26. Python help()
To quit the help utility and return to the interpreter,
you need to type quit and press enter.
help > quit
27. Python hex()
The hex() function converts an integer number to the
corresponding hexadecimal string.
The syntax of hex() is:
hex(x)
hex() Parameters
The hex() function takes a single argument.
x - integer number (int object or it has to
define __index__() method that returns an integer)
28. Python hex()
Return Value from hex()
The hex() function converts an integer to the
corresponding hexadecimal number in string form
and returns it.
The returned hexadecimal string starts with prefix
"0x" indicating it's in hexadecimal form.
29. Python hex()
number = 435
print(number, 'in hex =', hex(number))
number = 0print(number, 'in hex =', hex(number))
number = -34print(number, 'in hex =', hex(number))
returnType = type(hex(number))
print('Return type from hex() is', returnType)
30. Python hex()
When you run the program, the output will be:
435 in hex = 0x1b3
0 in hex = 0x0
-34 in hex = -0x22
Return type from hex() is <class 'str'>
31. Python int()
The int() method returns an integer object from any
number or string.
The syntax of int() method is:
int(x=0, base=10)
32. Python int()
int() Parameters
The int() method takes two arguments:
x - Number or string to be converted to integer
object.
Default argument is zero.
base - Base of the number in x.
Can be 0 (code literal) or 2-36.
33. Python int()
Return value from int()
The int() method returns:
an integer object from the given number or string,
treats default base as 10
(No parameters) returns 0
(If base given) treats the string in the given base (0,
2, 8, 10, 16)
35. Python int()
When you run the program, the output will be:
int(123) is: 123
int(123.23) is: 123
int('123') is: 123
36. Python list() Function
When you run the program, the output will be:
int(123) is: 123
int(123.23) is: 123
int('123') is: 123
The syntax of list() constructor is:
list([iterable])
37. Python list() Function
list() Parameters
Python list() constructor takes a single argument:
iterable (Optional) - an object that could be a
sequence (string, tuples) or collection
(set, dictionary) or iterator object
38. Python list() Function
Return value from list()
The list() constructor returns a mutable sequence list
of elements.
If no parameters are passed, it creates an empty list
If iterable is passed as parameter, it creates a list of
elements in the iterable
40. Python list() Function
# vowel list
vowelList = ['a', 'e', 'i', 'o', 'u']
print(list(vowelList))
41. Python list() Function
When you run the program, the output will be:
[]
['a', 'e', 'i', 'o', 'u']
['a', 'e', 'i', 'o', 'u']
['a', 'e', 'i', 'o', 'u']
42. Python max()
The max() method returns the largest element in an
iterable or largest of two or more parameters.
Differnt syntaxes of max() are:
max(iterable, *iterables[,key, default])
max(arg1, arg2, *args[, key])
43. Python max()
max() Parameters
max() has two forms of arguments it can work with.
max(iterable, *iterables[, key, default])
iterable - sequence (tuple, string), collection
(set, dictionary) or an iterator object whose largest
element is to be found
*iterables (Optional) - any number of iterables whose
largest is to be found
key (Optional) - key function where the iterables are
passed and comparison is performed based on its return
value
default (Optional) - default value if the given iterable is
empty
44. Python max()
max(arg1, arg2, *args[, key])
arg1 - mandatory first object for comparison (could be
number, string or other object)
arg2 - mandatory second object for comparison (could be
number, string or other object)
*args(Optional) - other objects for comparison
key - key function where each argument is passed, and
comparison is performed based on its return value
45. Python max()
max(arg1, arg2, *args[, key])
arg1 - mandatory first object for comparison (could be
number, string or other object)
arg2 - mandatory second object for comparison (could be
number, string or other object)
*args(Optional) - other objects for comparison
key - key function where each argument is passed, and
comparison is performed based on its return value
46. Python max()
# using max(arg1, arg2, *args)
print('Maximum is:', max(1, 3, 2, 5, 4))
# using max(iterable)
num = [1, 3, 2, 8, 5, 10, 6]
print('Maximum is:', max(num))
47. Python max()
When you run the program, the output will be:
Maximum is: 5
Maximum is: 10