pip install forex_python
Enter the amount: 10 From Currency: EUR To Currency: INR EUR To INR 10 888.9
What is the result of the following python code?
x = {1: "a", 2: "b"}
y = x.keys()
print(y)
The code you provided creates a dictionary x with keys 1 and 2, and then assigns the keys of the dictionary to the variable y. Finally, it prints the values of y. However, in Python 3, y will be a view object (dict_keys) representing the keys of the dictionary. To see the keys as a list, you can convert it to a list:
x = {1: "a", 2: "b"}
y = list(x.keys())
print(y)
Output: [1, 2]
Dictionary Creation:
x = {1: "a", 2: "b"}
Here, a dictionary x is created with keys 1 and 2, each associated with a corresponding value ("a" and "b").
Getting Keys:
y = x.keys()
In this line, the keys() method is used on the dictionary x to obtain a view object that represents the keys of the dictionary. The dict_keys view is a dynamic view of the dictionary's keys.
Printing:
print(y)
This line prints the result of y, which is the dict_keys view. However, in Python 3, this view is not automatically converted to a list when printed.
If you want to see the keys as a list, you can convert the dict_keys view to a list, like this:
y = list(x.keys())
print(y)
Output:
[1, 2]
The final output, after converting the dict_keys view to a list, is a list containing the keys of the dictionary x. In this case, it's [1, 2].
a = []
b = [a.append(i) for i in range(5)]
print(a)
print(b)
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In this Python code:
i = j = [3]
i += j
print(i, j)
Both i and j are assigned the same list [3]. The += operator is used to extend the list i by appending the elements of list j to it.
After the execution of the code, the output will be:
[3, 3] [3, 3]
Here's the breakdown:
i = j = [3]: Both i and j are assigned the list [3].
i += j: The += operator modifies the list i by appending the elements of list j to it. So, i becomes [3, 3].
print(i, j): Prints the values of i and j.
As a result, both i and j are [3, 3].
What is the output of the following python code?
x = 5
y = x > 3
print(y)
a) 5
b) 3
c) True
d) False
a = "240"
print(a.zfill(4))
The zfill() method in Python is used to pad a string with zeros (0) on the left side until the string reaches the specified width. In your case, you've specified a width of 4.
Here's how it works:
a = "240"
result = a.zfill(4)
print(result)
Output:
0240
In this example, the original string "240" has a length of 3. The zfill(4) method pads zeros on the left side to make the length of the string 4. Therefore, the result is "0240".
my_list=[11,22,33,44,55]
del my_list[:]
print(my_list)
The above code deletes all elements from the list my_list using the del statement with the slice [:]. This is a common way to clear a list in Python.
Here's a breakdown of the code:
del my_list[:]: This deletes all elements in the list. The [:] is a slice that includes all elements of the list.
After executing this code, the list my_list will be empty. If you print my_list after the deletion, you will get:
[]
So, the output of the provided code will be an empty list ([]).
a = (10, '20', 30)
print(min(a))
The min() function compares the elements in the tuple lexicographically (in the case of strings) or numerically (in the case of numbers) and returns the smallest element. If the elements are of different types, it may raise a TypeError.
In your case, the tuple a contains elements of different types: an integer (10), a string ('20'), and another integer (30). The min() function will compare them based on their natural order. In this case, it will compare the integer 10, the string '20', and the integer 30.
The comparison is done lexicographically for strings, so '20' is considered smaller than both 10 and 30. Therefore, the output of the min(a) expression will be '20'.
If you run the provided code, the output will be:
20
Learn how to play faster and looser and more casual with code, skimming and copying code from the Internet.
Discuss code flow and the order that your computer reads the code you write. This introduces us to a whole other level of thinking in code.
Logic can be harnessed to do useful stuff. We'll make it concrete by performing tasks like building an anagram finder.
Apply Python by robocall and spam text yourself through the Twilio API.
Understanding the flow of running code is a major part of learning to think in code and of coding itself. In this course we will study the flow of code through several demonstrations and walkthroughs. We'll experience turning logic into useful work by running Python that automatically reads all of Shakespeare, and by setting Python up to give you a call on the phone. In technical terms, this course will demonstrate Python loops, list comprehensions, and conditional statements, while at a higher level we'll discuss code style and good practices for code.
Understand what creative thinking techniques are
Comprehend their importance in tackling global challenges as well as in everyday problem-solving scenarios
Select and apply the appropriate technique based on the opportunity to seize or the problem to tackle
In today’s ever-growing and changing world, being able to think creatively and innovatively are essential skills. It can sometimes be challenging to step back and reflect in an environment which is fast paced or when you are required to assimilate large amounts of information. Making sense of or communicating new ideas in an innovative and engaging way, approaching problems from fresh angles, and producing novel solutions are all traits which are highly sought after by employers.
This course will equip you with a ‘tool-box’, introducing you to a selection of behaviours and techniques that will augment your innate creativity. Some of the tools are suited to use on your own and others work well for a group, enabling you to leverage the power of several minds. You can pick and choose which of these tools or techniques suit your needs and interests, focusing on some or all of the selected approaches and in the order that fits best for you.
The practical approach of this course enables you to acquire an essential skill-set for generating ideas, with plenty of:
- Fun e-tivities and exercises;
- Practical lectures and tips;
- Video representations of the techniques in action.
By the end of this course you should be able to:
- Pick a type of brainstorming you think will be useful to apply to a challenge
- Use alphabet brainstorming in tackling a challenge
- Use grid brainstorming in tackling a challenge
- Use a morphological chart to synthesise a solution to a challenge
- Use the TRIZ contradiction matrix to identify recommended inventive principles
- Apply SCAMPER to a range of challenges
The greatest innovators aren’t necessarily the people who have the most original idea. Often, they are people- or teams- that have harnessed their creativity to develop a new perspective or more effective way of communicating an idea. You can train your imagination to seize opportunities, break away from routine and habit, and tap into your natural creativity.
Join this course and a community of practitioners in CREATIVITY!
c = True or False
print(not not c)
In the first line, True or False evaluates to True because it uses the logical OR operator (or). This means that the variable c is assigned the value True.
In the second line, not not c is equivalent to not (not c). The not operator negates the value, so not c is not True, which is False. Then, the outer not negates this result again, making it not False, which evaluates to True.
So, the final output of the code will be:
True
This is because c is assigned the value True, and the double not operation results in True.
a. Class attributes and object attributes are same.
Answer
False
b. A class data member is useful when all objects of the same class must
share a common item of information.
Answer
True
c. If a class has a data member and three objects are created from this class,
then each object would have its own data member.
Answer
True
d. A class can have class data as well as class methods.
Answer
True
e. Usually data in a class is kept private and the data is accessed /
manipulated through object methods of the class.
Answer
True
f. Member functions of an object have to be called explicitly, whereas, the
_init_( ) method gets called automatically.
Answer
True
g. A constructor gets called whenever an object gets instantiated.
Answer
True
h. The _init_( ) method never returns a value.
Answer
True
i. When an object goes out of scope, its _del_( ) method gets called
automatically.
Answer
True
j. The self variable always contains the address of the object using which
the method/data is being accessed.
Answer
True
k. The self variable can be used even outside the class.
Answer
False
l. The _init_( ) method gets called only once during the lifetime of an
object.
Answer
True
m. By default, instance data and methods in a class are public.
Answer
True
n. In a class 2 constructors can coexist-a 0-argument constructor and a 2-
argument constructor.
Answer
True
1 Print the length of the string "Hello, world!".
2 Print the sum of the numbers 1 and 2.
3 Print the Fibonacci sequence up to the 10th term.
4 Check whether a number is prime or not.
5 Find the factorial of a number.
6 Print a multiplication table for a given number.
7 Check whether a string is a palindrome or not.
8 Convert a temperature from Celsius to Fahrenheit.
9 Convert a temperature from Fahrenheit to Celsius.
10 Check whether a year is a leap year or not.
11 Write a function to check whether a string is a pangram or not.
12 Write a function to find the longest common substring of two strings.
13 Write a function to reverse a string.
14 Write a function to check whether a number is a perfect square or not.
15 Write a function to find the greatest common divisor (GCD) of two numbers.
16 Write a function to find the least common multiple (LCM) of two numbers.
17 Write a function to check whether a number is an Armstrong number or not.
18 Write a function to find the kth permutation of a given set of numbers.
19 Write a function to find the next lexicographical permutation of a given string.
20 Write a function to check whether a graph is bipartite or not.
21 Write a function to find the shortest path between two nodes in a graph.
22 Write a function to find the minimum spanning tree of a graph.
23 Write a function to find the diameter of a tree.
24 Write a function to check whether a tree is a binary search tree or not.
25 Write a function to insert a node into a binary search tree.
26 Write a function to delete a node from a binary search tree.
27 Write a function to search for a node in a binary search tree.
28 Write a function to find the inorder successor of a node in a binary search tree.
29 Write a function to find the preorder successor of a node in a binary search tree.
30 Write a function to find the postorder successor of a node in a binary search tree.
31 Write a function to find the level order traversal of a binary tree.
32 Write a function to find the height of a binary tree.
33 Write a function to check whether a binary tree is balanced or not.
34 Write a function to convert an infix expression to a postfix expression.
35 Write a function to convert a postfix expression to an infix expression.
36 Write a function to evaluate a postfix expression.
37 Write a function to find the maximum depth of a nested list.
38 Write a function to flatten a nested list.
39 Write a function to check whether a linked list is a palindrome or not.
40 Write a function to reverse a linked list.
41 Write a function to insert a node into a linked list.
42 Write a function to delete a node from a linked list.
43 Write a function to search for a node in a linked list.
44 Write a function to find the middle node in a linked list.
45 Write a function to find the nth node from the end of a linked list.
46 Write a function to merge two sorted linked lists.
47 Write a function to find the intersection point of two linked lists.
48 Write a function to find the union of two linked lists.
49 Write a function to check whether a linked list is circular or not.
50 Write a function to find the length of a linked list.
51 Write a function to find the median of a list of numbers.
52 Write a function to find the mode of a list of numbers.
53 Write a function to find the range of a list of numbers.
54 Write a function to find the variance of a list of numbers.
55 Write a function to find the standard deviation of a list of numbers.
56 Write a function to find the covariance of two lists of numbers.
57 Write a function to find the correlation coefficient of two lists of numbers.
58 Write a function to fit a linear regression model to a dataset.
59 Write a function to fit a logistic regression model to a dataset.
60 Write a function to perform k-means clustering on a dataset.
61 Write a function to implement the Naive Bayes classification algorithm.
62 Write a function to implement the Support Vector Machine (SVM) classification algorithm.
63 Write a function to implement the Random Forest classification algorithm.
64 Write a function to implement the K-Nearest Neighbors (KNN) classification algorithm.
65 Write a function to implement the Decision Tree classification algorithm.
66 Write a function to implement the Naive Bayes classification algorithm for spam filtering.
67 Write a function to implement the Support Vector Machine (SVM) classification algorithm for sentiment analysis.
68 Write a function to implement the Random Forest classification algorithm for image recognition.
69 Write a function to implement the K-Nearest Neighbors (KNN) classification algorithm for handwriting recognition.
70 Write a function to implement the Decision Tree classification algorithm for medical diagnosis.
71 Define a class to represent a bank account.
72 Define a class to represent a student.
73 Define a class to represent a book.
74 Define a class to represent an employee.
75 Define a class to represent a car.
76 Implement inheritance in a class hierarchy for animals.
77 Implement polymorphism in a class hierarchy for shapes.
78 Implement encapsulation in a class to represent a bank account.
79 Implement abstraction in a class to represent a
80 Implement a stack data structure using Python.
81 Implement a queue data structure using Python.
82 Implement a linked list data structure using
83 Implement a double-linked list data structure using Python.
84 Implement a circular linked list data structure using Python.
85 Implement a binary search tree data structure using Python.
86 Implement an AVL tree data structure using Python.
87 Implement a red-black tree data structure using Python.
88 Implement a hash table data structure using Python.
89 Implement a heap data structure using Python.
90 Implement a graph data structure using Python.
91 Implement a directed graph data structure using Python.
92 Implement a weighted graph data structure using Python.
93 Implement a breadth-first search algorithm for traversing a graph.
94 Implement a depth-first search algorithm for traversing a graph.
95 Implement Dijkstra's algorithm for finding the shortest path in a weighted graph.
96 Implement Kruskal's algorithm for finding the minimum spanning tree of a weighted graph.
97 Implement Prim's algorithm for finding the minimum spanning tree of a weighted graph.
98 Implement a sorting algorithm, such as bubble sort, insertion sort, merge sort, or quick sort.
99 Implement a searching algorithm, such as binary search or linear search.
100 Write a Python program to solve a real-world problem, such as building a simple web scraper, a text summarizer, or a financial data analyzer.
print(False == False) in [False]
print (False == (False in [False]))
print (False == False in [False])
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list1 = ["1.0", "a", "0.1", "1", "-1"]
list2 = sorted(list1, key=lambda x: float(x) if x.isdigit() else float('inf'))
print(list2)
list1 = ["1.0", "a", "0.1", "1", "-1"]
This line creates a list named list1 and assigns it the values ["1.0", "a", "0.1", "1", "-1"].
list2 = sorted(list1, key=lambda x: float(x) if x.isdigit() else float('inf'))
This line creates a list named list2 and assigns it the sorted values of list1. The key argument specifies that the sorting should be done by converting the elements to floats if they are digits, and otherwise using the value float('inf'). This means that the strings "-1", "a", and "0.1" will be sorted as if they were the numbers -1, inf, and 0.1, respectively.
print(list2)
This line prints the value of list2 to the console.
Here is a table that summarizes the steps involved in sorting the list:
Step Action
1 Create a list named list1 and assign it the values ["1.0", "a", "0.1", "1", "-1"].
2 Create a list named list2 and assign it the sorted values of list1.
3 Sort list2 using the key argument, which specifies that the sorting should be done by converting the elements to floats if they are digits, and otherwise using the value float('inf').
4 Print the value of list2 to the console.
How many objects are created in the following code snippet?
a = 10
b = a
c = b
Answer : one
a is assigned the value 10, creating an integer object with the value 10.
b is assigned the value of a, so it refers to the same integer object as a. No new object is created in this step; it just points to the existing object.
c is assigned the value of b, so it also refers to the same integer object as a and b. Again, no new object is created in this step.
a = 20
b = 40
print(globals( ))
print(locals( ))
Code :
# Download YouTube videos using Python
pip install yt-dlp
import yt_dlp
# Enter the url for the download
url = input("Enter video url: ")
ydl_opts = {}
with yt_dlp. YoutubeDL (yd1_opts) as ydl:
ydl.download ([url])
print("Video downloaded successfully!")
#clcoding.com
Enter video url: https://youtu.be/b1kbLwvqugk
pip install pubchempy
import pubchempy as pcp
# Define the chemical formula of the hydrocarbon
chemical_formula = input("Enter chemical Formula : ")
try:
# Search PubChem for the compound by its chemical formula
compound = pcp.get_compounds(chemical_formula, 'formula')[0]
# Display information about the compound
print(f"Name: {compound.iupac_name}")
print(f"Common Name: {compound.synonyms[0]}")
print(f"Molecular Weight: {compound.molecular_weight}")
print(f"Formula: {compound.molecular_formula}")
# You can access more properties as needed
except IndexError:
print(f"No information found for {chemical_formula}.Please check formula.")
#clcoding.com
.png)
import sys, getopt
sys.argv =['C:\\a.py', '-h', 'word1', 'word2']
options, arguments = getopt.getopt(sys.argv[1:],'s:t:h')
print(options)
In Python, you can store a hexadecimal value like E0A485 in a bytes data type using the bytes.fromhex() method. Here's an example:
hex_value = "E0A485"
bytes_data = bytes.fromhex(hex_value)
print(bytes_data)
This code will output a bytes object representing the hexadecimal value:
b'\xe0\xa4\x85'
Each pair of hexadecimal digits is converted to its corresponding byte value in the bytes object. In this example, E0 becomes \xe0, A4 becomes \xa4, and 85 becomes \x85.
The second edition of Python 101 (50,000+ eBook readers alone) uses Python 3 to teach you how to program successfully. This book can be read by people with no programming experience, although understanding basic computer terminology is recommended.
While Python 101 teaches you all the basics that you need to know, it also dives into many intermediate level topics too. This book is split up into four sections:
The Python Language
Intermediate Topics
Creating Sample Applications
Distributing Your Code
This book has been completely rewritten from the ground up. There are many new chapters and coverage of new features in Python 3.
Another new feature is that Python 101 has Review Questions for every chapter. The book also includes an answer key in the back.
Unlike most beginner Python books, Python 101 explains how to create simple applications. It also goes on to teach you how to distribute your code as a Windows executable.
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c = "clcoding"
print(c[:-2])
print(c[-2:])
here are the step-by-step solutions:
Step 1:
Define the variable c and assign the string "clcoding" to it.
c = "clcoding"
Step 2:
Print the substring of c that starts at the beginning of the string and goes up to the second to last character. This can be done using the slicing syntax c[:-2].
print(c[:-2])
Output:
clcodi
Step 3:
Print the substring of c that starts at the second to last character and goes up to the end of the string. This can be done using the slicing syntax c[-2:].
print(c[-2:])
Output:
ng
a. Dictionary elements can be accessed using position-based index.
Answer
False
b. Dictionaries are immutable.
Answer
False
c. Insertion order is preserved by a dictionary.
Answer
False
d. The very first key - value pair in a dictionary d can be accessed using the
expression d[0].
Answer
False
e. courses.clear( ) will delete the dictionary object called courses.
Answer
False
f. It is possible to nest dictionaries.
Answer
True
g. It is possible to hold multiple values against a key in a dictionary.
Answer
True
Let's go through each statement one by one:
a. Dictionary elements can be accessed using position-based index.
Answer: False
Explanation: Dictionary elements are accessed using keys, not position-based indices. You use the key to retrieve the associated value.
b. Dictionaries are immutable.
Answer: False
Explanation: Dictionaries are mutable, meaning you can modify their content by adding, removing, or updating key-value pairs.
c. Insertion order is preserved by a dictionary.
Answer: False
Explanation: Prior to Python 3.7, dictionaries did not guarantee order preservation. However, starting from Python 3.7, the insertion order is guaranteed to be preserved.
d. The very first key-value pair in a dictionary d can be accessed using the expression d[0].
Answer: False
Explanation: Dictionary elements are not accessed by numerical indices but by keys. There is no guarantee that the keys are numerical, so attempting to access d[0] would not necessarily give you the first key-value pair.
e. courses.clear() will delete the dictionary object called courses.
Answer: False
Explanation: courses.clear() will remove all items from the dictionary called courses, but the dictionary object itself still exists.
f. It is possible to nest dictionaries.
Answer: True
Explanation: Yes, it is possible to have dictionaries as values within another dictionary, creating nested or hierarchical structures.
g. It is possible to hold multiple values against a key in a dictionary.
Answer: True
Explanation: Yes, a key in a dictionary can have a list, tuple, set, or another dictionary as its associated value, allowing you to store multiple values against a single key.
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Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more.
Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis.
After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.
Work with vectors and matrices using NumPy
Plot and visualize data with Matplotlib
Perform data analysis tasks with Pandas and SciPy
Review statistical modeling and machine learning with statsmodels and scikit-learn
Optimize Python code using Numba and Cython
Who This Book Is For
Developers who want to understand how to use Python and its related ecosystem for numerical computing.
Unlock the Power of Deep Learning Embark on an extraordinary journey into the realm of cutting-edge technology with The Little Book of Deep Learning . Discover the secrets behind one of the most revolutionary advancements of our time, and witness how it is transforming industries across the globe. Explore the intricate world of neural networks and artificial intelligence as you delve into the minds of experts and pioneers. Gain unparalleled insights into the principles, algorithms, and applications of deep learning, unraveling complex concepts with ease. From image recognition to natural language processing, uncover the limitless possibilities that await you within these pages. Witness how deep learning is reshaping medicine, finance, entertainment, and more, igniting a new era of innovation. Written by a leading authority in the field, this captivating book distills the essence of deep learning, providing a comprehensive yet accessible guide for both beginners and seasoned professionals. Its engaging narrative and practical examples will empower you to harness the true potential of this transformative technology. Don't miss your chance to join the ranks of those who have unlocked the power of deep learning. Whether you're a student, researcher, or industry enthusiast, The Little Book of Deep Learning is your gateway to a world of unlimited possibilities.
Getting started with Python Language
- Deep Learning - https://lnkd.in/gxpnZ6Sa
- Dive into Deep Learning - d2l.ai
- Machine Learning Engineering - https://lnkd.in/eVCAYh4
- Python Data Science Handbook - https://lnkd.in/ehfZ-Tx
- Probabilistic Machine Learning - https://lnkd.in/gcSBFgk
- Machine Learning Yearning - https://lnkd.in/d3bC2d2R
%20Any%20Machine%20Learning%20Problem%20(PDF%20Book).jpg)
This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.
Table of contents:
- Setting up your working environment
- Supervised vs unsupervised learning
- Cross-validation
- Evaluation metrics
- Arranging machine learning projects
- Approaching categorical variables
- Feature engineering
- Feature selection
- Hyperparameter optimization
- Approaching image classification & segmentation
- Approaching text classification/regression
- Approaching ensembling and stacking
- Approaching reproducible code & model serving
There are no sub-headings. Important terms are written in bold.
I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, please create an issue on github repo: https://github.com/abhishekkrthakur/approachingalmost
tpl1 = ('A',) - Tuple
tpl1 = ('A') - String
t = tpl[::-1] - Sorts tuple
('A', 'B', 'C', 'D') - tuple of strings
[(1, 2), (2, 3), (4, 5)] - list of tuples
tpl = tuple(range(2, 5)) - (2, 3, 4)
([1, 2], [3, 4], [5, 6]) - tuple of lists
t = tuple('Ajooba') - tuple of length 6
tpl1 = ('A',) - This creates a tuple named tpl1 containing a single element 'A'. Note the comma after 'A', which is essential for creating a tuple with a single element.
tpl1 = ('A') - This actually creates a string, not a tuple. To create a tuple with a single element, you need to include a comma: tpl1 = ('A',).
t = tpl[::-1] - This reverses the order of elements in the tuple tpl. The [::-1] slicing notation is used to reverse the sequence.
('A', 'B', 'C', 'D') - This is a tuple of strings with four elements: 'A', 'B', 'C', and 'D'.
[(1, 2), (2, 3), (4, 5)] - This is a list of tuples, where each tuple contains two integers.
tpl = tuple(range(2, 5)) - This creates a tuple named tpl with elements generated using range(2, 5), resulting in the tuple (2, 3, 4).
([1, 2], [3, 4], [5, 6]) - This is a tuple of lists, where each list contains two integers.
t = tuple('Ajooba') - This creates a tuple named t from the characters of the string 'Ajooba'. The resulting tuple has six elements, one for each character.
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.
After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.
The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis.
To get the most out of this book, open a Python interpreter and type along with the many code samples.
Buy : Python Programming for Data Analysis
PDF Download :
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Function Definition:
def fun(a, *args, s='!'):
The function fun is defined to take at least one argument a, followed by any number of additional positional arguments (*args), and an optional keyword argument s with a default value of '!'.
Print the First Argument and Suffix:
print(a, s)
This line prints the value of the first argument a followed by the value of the keyword argument s.
Loop through Additional Arguments:
for i in args:
print(i, s)
This loop iterates through any additional positional arguments provided (if any) and prints each one followed by the value of the keyword argument s.
Function Call:
fun(10)
The function is called with the argument 10. Since no additional positional arguments are provided, only the first print statement is executed.
When you run this code, it will output:
10 !
s = [a + b for a in ['They ', 'We '] for b in ['are gone!', 'have come!']]
print(s)
This code uses list comprehension to create a list s by concatenating elements from two nested lists. Here's the breakdown:
s = [a + b for a in ['They ', 'We '] for b in ['are gone!', 'have come!']]
Two nested for loops are used in the list comprehension.
The outer loop iterates over elements a in the list ['They ', 'We '].
The inner loop iterates over elements b in the list ['are gone!', 'have come!'].
The expression a + b concatenates the current elements from both loops.
The result is a new list s containing all possible concatenations of elements from the outer and inner loops.
If you print the list s, you will get:
['They are gone!', 'They have come!', 'We are gone!', 'We have come!']
This is because it combines each element from the first list with each element from the second list, resulting in all possible combinations.
a = set()
for n in range(21, 30):
if n % 2 == 0:
a.add(n)
print(a)
Let's break down the code step by step:
Initialize an empty set:
a = set()
A new empty set named a is created.
Loop through a range of numbers (21 to 29):
for n in range(21, 30):
The for loop iterates over the numbers from 21 to 29 (inclusive).
Check if the number is even:
if n % 2 == 0:
The if statement checks if the current number (n) is even by using the modulo operator (%) to check if it's divisible by 2.
Add even numbers to the set:
a.add(n)
If the condition in the if statement is true (meaning n is even), the current even number is added to the set a using the add method.
Print the set after the loop:
print(a)
The print(a) statement is outside the for loop, so it will be executed after the loop has finished. It prints the final contents of the set a.
In summary, when you run this code, it will output the set of even numbers between 21 and 29. In this specific case, the set a will contain the even numbers 22, 24, 26, and 28.
a. Tuple comprehension offers a fast and compact way to generate a tuple.
Answer
True
b. List comprehension and dictionary comprehension can be nested.
Answer
True
c. A list being used in a list comprehension cannot be modified when it is
being iterated.
Answer
True
d. Sets being immutable cannot be used in comprehension.
Answer
False
e. Comprehensions can be used to create a list, set or a dictionary.
Answer
True
s.PNG)
Describe what is AI, its applications, use cases, and how it is transforming our lives
Explain terms like Machine Learning, Deep Learning and Neural Networks
Describe several issues and ethical concerns surrounding AI
Articulate advice from experts about learning and starting a career in AI
In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project.
This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.
a = 10
if a in (30, 40, 50):
print('Hello')
else:
print('Hi')
There are 6 modules in this course
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.
Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as "data science in action".
The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.
This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments.
The course covers the three main types of process mining.
1. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log.
2. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa.
3. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases.
Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development.
The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field.
After taking this course you should:
- have a good understanding of Business Process Intelligence techniques (in particular process mining),
- understand the role of Big Data in today’s society,
- be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification,
- be able to apply basic process discovery techniques to learn a process model from an event log (both manually and using tools),
- be able to apply basic conformance checking techniques to compare event logs and process models (both manually and using tools),
- be able to extend a process model with information extracted from the event log (e.g., show bottlenecks),
- have a good understanding of the data needed to start a process mining project,
- be able to characterize the questions that can be answered based on such event data,
- explain how process mining can also be used for operational support (prediction and recommendation), and
- be able to conduct process mining projects in a structured manner.
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