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.
Lectures from a Python workshop I taught in 2013 at the University of Pittsburgh. These are introductory slides to teach important aspects of the Python language.
Update: python limits the number of recursion calls, so computing the factorial as shown in the slides may not be generally usable.
Introducción al Análisis y diseño de algoritmosluzenith_g
The document discusses algorithms and their analysis. It defines an algorithm as a well-defined computational procedure that takes inputs and produces outputs. It discusses analyzing algorithms to determine their time and space complexity, and how this involves determining how the resources required grow with the size of the problem. It provides examples of analyzing simple algorithms and determining whether they have linear, quadratic, or other complexity.
The document discusses algorithms and their analysis. It defines an algorithm as a well-defined computational procedure that takes inputs and produces outputs. It discusses analyzing algorithms based on their time complexity, space complexity, and correctness. It provides examples of analyzing simple algorithms and calculating their complexity based on the number of elementary operations.
An Introduction to Part of C++ STL for OI. Introduced the common use of STL algorithms and containers, especially those are helpful to OI.
Also with some examples.
This document provides an overview of pointers in C programming. It discusses seven rules for pointers, including that pointers are integer variables that store memory addresses, how to dereference and reference pointers, NULL pointers, and arithmetic operations on pointers. It also covers dynamic memory allocation using malloc, calloc, realloc, and free and different approaches to 2D arrays. Finally, it discusses function pointers and their uses, including as callback functions.
This presentation about Python Interview Questions will help you crack your next Python interview with ease. The video includes interview questions on Numbers, lists, tuples, arrays, functions, regular expressions, strings, and files. We also look into concepts such as multithreading, deep copy, and shallow copy, pickling and unpickling. This video also covers Python libraries such as matplotlib, pandas, numpy,scikit and the programming paradigms followed by Python. It also covers Python library interview questions, libraries such as matplotlib, pandas, numpy and scikit. This video is ideal for both beginners as well as experienced professionals who are appearing for Python programming job interviews. Learn what are the most important Python interview questions and answers and know what will set you apart in the interview process.
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
This document discusses various techniques for optimizing Python code, including:
1. Using the right algorithms and data structures to minimize time complexity, such as choosing lists, sets or dictionaries based on needed functionality.
2. Leveraging Python-specific optimizations like string concatenation, lookups, loops and imports.
3. Profiling code with tools like timeit, cProfile and visualizers to identify bottlenecks before optimizing.
4. Optimizing only after validating a performance need and starting with general strategies before rewriting hotspots in Python or other languages. Premature optimization can complicate code.
Python for Scientific Computing -- Ricardo Cruzrpmcruz
This document discusses Python for scientific computing. It provides notes on NumPy, the fundamental package for scientific computing in Python. NumPy allows vectorized mathematical operations on multidimensional arrays in a simple and efficient manner. The notes cover common NumPy operations and syntax as compared to MATLAB and R. Pandas is also introduced as a package for data manipulation and analysis based on the concept of data frames from R. Examples are given of generating fake data to demonstrate modeling capabilities in Python.
Pythran: Static compiler for high performance by Mehdi Amini PyData SV 2014PyData
Pythran is a an ahead of time compiler that turns modules written in a large subset of Python into C++ meta-programs that can be compiled into efficient native modules. It targets mainly compute intensive part of the code, hence it comes as no surprise that it focuses on scientific applications that makes extensive use of Numpy. Under the hood, Pythran inter-procedurally analyses the program and performs high level optimizations and parallel code generation. Parallelism can be found implicitly in Python intrinsics or Numpy operations, or explicitly specified by the programmer using OpenMP directives directly in the Python source code. Either way, the input code remains fully compatible with the Python interpreter. While the idea is similar to Parakeet or Numba, the approach differs significantly: the code generation is not performed at runtime but offline. Pythran generates C++11 heavily templated code that makes use of the NT2 meta-programming library and relies on any standard-compliant compiler to generate the binary code. We propose to walk through some examples and benchmarks, exposing the current state of what Pythran provides as well as the limit of the approach.
The document discusses time and space complexity analysis for algorithms, including using Big O notation to describe an algorithm's efficiency. It provides examples of time complexity for different codes, such as O(n) for a simple loop and O(n^2) for a double loop. The document also covers space complexity and how to estimate the complexity of a problem based on input size constraints.
Imagine writing a pure Python library which can achieve the performance of Fortran or C/C++.
To this end we have developed Pyccel, which translates Python code to either Fortran or C, and makes the generated code callable from Python. The generated Fortran or C code is not only fast, but also human-readable; hence it can easily be profiled and optimized for the target machine.
Pyccel has a focus on high-performance computing applications, where the efficient usage of the available hardware resources is fundamental.
To this end it provides type annotations, function decorators, and OpenMP pragmas.
Pyccel is easy to use, is almost completely written in Python, and compares favourably against other Python accelerators.
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.
NumPy is a Python library that provides multidimensional array and matrix objects to perform scientific computing. It contains efficient functions for operations on arrays like arithmetic, aggregation, copying, indexing, slicing, and reshaping. NumPy arrays have advantages over native Python sequences like fixed size and efficient mathematical operations. Common NumPy operations include elementwise arithmetic, aggregation functions, copying and transposing arrays, changing array shapes, and indexing/slicing arrays.
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.
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.
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The Ring programming language version 1.7 book - Part 83 of 196Mahmoud Samir Fayed
The document describes several low-level functions in Ring that provide access to the virtual machine and runtime environment. These include functions to call the garbage collector, get and set pointers, allocate memory, compare pointers, and get lists of functions, classes, packages, memory scopes, call stacks, and loaded files.
SciPy and NumPy are Python packages that provide scientific computing capabilities. NumPy provides multidimensional array objects and fast linear algebra functions. SciPy builds on NumPy and adds modules for optimization, integration, signal and image processing, and more. Together, NumPy and SciPy give Python powerful data analysis and visualization capabilities. The community contributes to both projects to expand their functionality. Memory mapped arrays in NumPy allow working with large datasets that exceed system memory.
From JVM to .NET languages, from minor coding idioms to system-level architectures, functional programming is enjoying a long overdue surge in interest. Functional programming is certainly not a new idea and, although not apparently as mainstream as object-oriented and procedural programming, many of its concepts are also more familiar than many programmers believe. This talk examines functional and declarative programming styles from the point of view of coding patterns, little languages and programming techniques already familiar to many programmers.
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How To Develop A Cryptocurrency Exchange - Slideshare.pptxlaravinson24
A fast, practical guide to building your own cryptocurrency exchange — from choosing the right type (CEX, DEX, Hybrid) to legal compliance, tech stack, features, liquidity strategies, and scaling. Perfect for startups, developers, and entrepreneurs entering the crypto space.
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This presentation about Python Interview Questions will help you crack your next Python interview with ease. The video includes interview questions on Numbers, lists, tuples, arrays, functions, regular expressions, strings, and files. We also look into concepts such as multithreading, deep copy, and shallow copy, pickling and unpickling. This video also covers Python libraries such as matplotlib, pandas, numpy,scikit and the programming paradigms followed by Python. It also covers Python library interview questions, libraries such as matplotlib, pandas, numpy and scikit. This video is ideal for both beginners as well as experienced professionals who are appearing for Python programming job interviews. Learn what are the most important Python interview questions and answers and know what will set you apart in the interview process.
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The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
This document discusses various techniques for optimizing Python code, including:
1. Using the right algorithms and data structures to minimize time complexity, such as choosing lists, sets or dictionaries based on needed functionality.
2. Leveraging Python-specific optimizations like string concatenation, lookups, loops and imports.
3. Profiling code with tools like timeit, cProfile and visualizers to identify bottlenecks before optimizing.
4. Optimizing only after validating a performance need and starting with general strategies before rewriting hotspots in Python or other languages. Premature optimization can complicate code.
Python for Scientific Computing -- Ricardo Cruzrpmcruz
This document discusses Python for scientific computing. It provides notes on NumPy, the fundamental package for scientific computing in Python. NumPy allows vectorized mathematical operations on multidimensional arrays in a simple and efficient manner. The notes cover common NumPy operations and syntax as compared to MATLAB and R. Pandas is also introduced as a package for data manipulation and analysis based on the concept of data frames from R. Examples are given of generating fake data to demonstrate modeling capabilities in Python.
Pythran: Static compiler for high performance by Mehdi Amini PyData SV 2014PyData
Pythran is a an ahead of time compiler that turns modules written in a large subset of Python into C++ meta-programs that can be compiled into efficient native modules. It targets mainly compute intensive part of the code, hence it comes as no surprise that it focuses on scientific applications that makes extensive use of Numpy. Under the hood, Pythran inter-procedurally analyses the program and performs high level optimizations and parallel code generation. Parallelism can be found implicitly in Python intrinsics or Numpy operations, or explicitly specified by the programmer using OpenMP directives directly in the Python source code. Either way, the input code remains fully compatible with the Python interpreter. While the idea is similar to Parakeet or Numba, the approach differs significantly: the code generation is not performed at runtime but offline. Pythran generates C++11 heavily templated code that makes use of the NT2 meta-programming library and relies on any standard-compliant compiler to generate the binary code. We propose to walk through some examples and benchmarks, exposing the current state of what Pythran provides as well as the limit of the approach.
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Imagine writing a pure Python library which can achieve the performance of Fortran or C/C++.
To this end we have developed Pyccel, which translates Python code to either Fortran or C, and makes the generated code callable from Python. The generated Fortran or C code is not only fast, but also human-readable; hence it can easily be profiled and optimized for the target machine.
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To this end it provides type annotations, function decorators, and OpenMP pragmas.
Pyccel is easy to use, is almost completely written in Python, and compares favourably against other Python accelerators.
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.
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The document describes several low-level functions in Ring that provide access to the virtual machine and runtime environment. These include functions to call the garbage collector, get and set pointers, allocate memory, compare pointers, and get lists of functions, classes, packages, memory scopes, call stacks, and loaded files.
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2. BUILT-IN
FUNCTION
The built-in function
are pre defined
functions, there are
68 Library function in
python, these
function perform a
Specific tasks and
can be used in
program.
4. abs() function
• The python abs() function used to return the
absolute value of a number.
• The argument can be integer and floating
point number.
• It takes only one argument, a number whose
absolute value is to be returned.
5. E x a m p l e :
a = - 2 0
p r i n t ( ‘ A b s o l u t e v a l u e o f - 2 0 i s : ’ , a b s ( a ) )
b = - 2 0 . 8 3
p r i n t ( ‘ A b s o l u t e v a l u e o f - 2 0 . 8 3 i s : ’ , a b s ( b ) )
O u t p u t :
Absolute value of -20 is: 20
Absolute value of -20.83 is: 20.83
6. bin() function
The python bin() function is used to return the
binary representation of a specific integer. A
result always starts with the prefix 0b.
7. E x a m p l e :
x = 1 0
y = b i n ( x )
p r i n t ( y )
O u t p u t :
0 b 1 0 1 0
8. bool() function
The python bool() converts a value to
Boolean (true or false) using the standard truth
Testing procedure.
9. E x a m p l e :
t e s t 1 = [ ]
p r i n t ( t e s t 1 , ’ i s ’ , b o o l ( t e s t 1 ) )
t e s t 2 = [ 1 ]
p r i n t ( t e s t 2 , ’ i s ’ , b o o l ( t e s t 2 ) )
O u t p u t :
[ ] i s f a l s e
[ 1 ] i s t r u e
10. sum() function
As the name says, python sum() function is
used to get the sum of numbers of an iterable.
11. E x a m p l e :
s = s u m ( [ 1 , 2 , 3 ] )
p r i n t ( s )
s 1 = s u m ( [ s ] , 1 0 )
p r i n t ( s 1 )
O u t p u t :
6
1 6
12. help() function
• Python help()function is used to get help
Related to the object passed during the call.
• It takes an optional parameter and returns
help information.
• It shows the python help console.It internally
calls python’s help function.
13. E x a m p l e :
i n f o = h e l p ( )
p r i n t ( i n f o )
O u t p u t :
W e l c o m e t o P y t h o n 3 . 1 !
T h i s i s t h e o n l i n e h e l p u t i l i t y .
14. len() function
The python len() function is used to return the
length (the number of items) of an object
15. E x a m p l e :
s t r A = ‘ P y t h o n ’
p r i n t ( l e n ( s t r A ) )
O u t p u t :
6
16. min() function
• Python min() function is used to get the
smallest element from the collection.
• This function takes two arguments, first is a
collection of elements and second is key, and
returns the smallest element from the collect.
17. E x a m p l e :
s m a l l = m i n ( 1 8 , 0 7 , 4 5 , 1 7 )
s m a l l 1 = m i n ( 1 8 . 2 5 , 1 8 . 2 4 , 1 8 . 2 3 )
O u t p u t :
0 7
1 8 . 2 3
18. pow() function
• The python pow() function is used to compute
the power of a number. It returns x to the
power of y.
• If the third arguments(z) is given, it returns x
to the power of y modules z.
19. E x a m p l e :
p r i n t ( p o w ( 2 , 3 ) )
p r i n t ( p o w ( - 2 , 3 ) )
p r i n t ( p o w ( 2 , - 3 ) )
p r i n t ( p o w ( - 2 , - 3 ) )
O u t p u t :
8
- 8
0 . 1 2 5
- 0 . 1 2 5
20. print() function
The python print() function prints the given
object to the screen or other standard output
devices.
21. E x a m p l e :
x , y, z = " a p p l e " , " b a n a n a " , " C h e r r y "
p r i n t ( x , y, z , s e p = ‘ , ’ , e n d = ‘ . ’ )
a = b = c = " a p p l e "
p r i n t ( a , b , c , s e p = ‘ , ’ , e n d = ‘ . ’ )
O u t p u t :
a p p l e , b a n a n a , C h e r r y .
a p p l e , a p p l e , a p p l e .
22. range() function
• The python range() function return as
immutable sequence of numbers starting from
0 by default.
• Increments by 1(by default) and ends at a
specified number.
23. E x a m p l e :
p r i n t ( l i s t ( r a n g e ( 0 ) ) )
p r i n t ( l i s t ( r a n g e ( 4 ) ) )
p r i n t ( l i s t ( r a n g e ( 1 , 7 ) ) )
O u t p u t :
[ ]
[ 0 , 1 , 2 , 3 ]
[ 1 , 2 , 3 , 4 , 5 , 6 ]
24. round() function
The python round() function rounds off the
digits of a number and returns the floating
point numbers.
25. E x a m p l e :
p r i n t ( r o u n d ( 1 0 . 4 ) )
p r i n t ( r o u n d ( 1 7 . 7 ) )
O u t p u t :
1 0
1 8
26. type() function
The python type() returns the type of the
specified object if a single argument is
passed to the type() built in function.
27. E x a m p l e :
l i s t 1 = [ 4 , 5 ]
p r i n t ( t y p e ( l i s t 1 ) )
d i c t 1 = { 4 : ’ f o u r ’ , 5 : ’ f i v e ’ }
p r i n t ( t y p e ( d i c t 1 ) )
O u t p u t :
< c l a s s ‘ l i s t ’ >
< c l a s s ‘ d i c t ’ >