Interesting facts about Dictionaries

 Dictionary Methods

Dictionaries come with several handy methods such as setdefault, update, pop, popitem, and clear.

my_dict = {'name': 'Alice', 'age': 25}

# setdefault

my_dict.setdefault('city', 'Unknown')

print(my_dict)  

# update

my_dict.update({'age': 26, 'city': 'New York'})

print(my_dict)  

# pop

age = my_dict.pop('age')

print(age)  # Output: 26

print(my_dict)  

# popitem

item = my_dict.popitem()

print(item) 

print(my_dict)  

# clear

my_dict.clear()

print(my_dict)  

#clcoding.com

{'name': 'Alice', 'age': 25, 'city': 'Unknown'}

{'name': 'Alice', 'age': 26, 'city': 'New York'}

26

{'name': 'Alice', 'city': 'New York'}

('city', 'New York')

{'name': 'Alice'}

{}

Ordered Dictionaries

As of Python 3.7, dictionaries maintain insertion order by default. The OrderedDict from the collections module was used for this purpose in earlier versions.

my_dict = {'first': 1, 'second': 2, 'third': 3}

print(my_dict)  

#clcoding.com

{'first': 1, 'second': 2, 'third': 3}

Merging Dictionaries

Starting with Python 3.9, you can use the | operator to merge dictionaries.

dict1 = {'a': 1, 'b': 2}

dict2 = {'b': 3, 'c': 4}

merged_dict = dict1 | dict2

print(merged_dict)  

#clcoding.com

{'a': 1, 'b': 3, 'c': 4}

Dictionary Views

The keys, values, and items methods return dictionary view objects, which are dynamic and reflect changes in the dictionary.

my_dict = {'name': 'Alice', 'age': 25}

keys_view = my_dict.keys()

print(keys_view)  

my_dict['city'] = 'New York'

print(keys_view) 

#clcoding.com

dict_keys(['name', 'age'])

dict_keys(['name', 'age', 'city'])

Default Values with get and defaultdict

Using the get method, you can provide a default value if the key is not found.

my_dict = {'name': 'Alice'}

print(my_dict.get('age', 'Not Found'))  

Not Found

With defaultdict from the collections module, you can provide default values for missing keys.

from collections import defaultdict

dd = defaultdict(int)

dd['a'] += 1

print(dd)  

#clcoding.com

defaultdict(<class 'int'>, {'a': 1})

Dictionary Comprehensions

Just like list comprehensions, you can create dictionaries using dictionary comprehensions.

squares = {x: x*x for x in range(6)}

print(squares)  

#clcoding.com

{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

Iterating Through Dictionaries

You can iterate through keys, values, or key-value pairs in a dictionary.

my_dict = {'name': 'Alice', 'age': 25}

for key in my_dict:

    print(key)  

for value in my_dict.values():

    print(value)  

for key, value in my_dict.items():

    print(key, value)  

#clcoding.com

name

age

Alice

25

name Alice

age 25

Keys Must Be Immutable and Unique

The keys in a dictionary must be immutable types (like strings, numbers, or tuples) and must be unique.

my_dict = {(1, 2): 'tuple key', 'name': 'Alice', 3.14: 'pi'}

print(my_dict)  

#clcoding.com

{(1, 2): 'tuple key', 'name': 'Alice', 3.14: 'pi'}

Efficient Lookup Time

Dictionaries have average O(1) time complexity for lookups, insertions, and deletions due to their underlying hash table implementation.

my_dict = {'a': 1, 'b': 2, 'c': 3}

print(my_dict['b'])  

#clcoding.com

2

Dynamic and Mutable

Dictionaries are mutable, which means you can change their content without changing their identity.

my_dict = {'name': 'Alice', 'age': 25}

my_dict['age'] = 26

my_dict['city'] = 'New York'

print(my_dict)  

#clcoding.com

{'name': 'Alice', 'age': 26, 'city': 'New York'}

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Python Coding challenge - Day 206 | What is the output of the following Python Code?

 

Code:

dict_a = {"a": 1, "b": 2}

dict_b = {"a": 1, "b": 2}

print(dict_a is dict_b)

print(dict_a == dict_b)

Solution and Explanation: 

This code creates two dictionaries, dict_a and dict_b, with identical key-value pairs. Then it prints the results of two different comparisons:

print(dict_a is dict_b): This checks whether dict_a and dict_b refer to the same object in memory. In this case, they are two separate dictionary objects, so the output will be False.

print(dict_a == dict_b): This checks whether the contents of dict_a and dict_b are the same. Since they have the same key-value pairs, the output will be True.

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Python Coding challenge - Day 205 | What is the output of the following Python Code?

 

Code: 

num = [5, 6]

*midd, lst = num, num[-1]  

print(midd, lst)

Solution and Explanation: 

Let's break down the code step by step:

num = [5, 6]: This line creates a list called num containing two integers, 5 and 6.

*midd, lst = num, num[-1]: Here, we're using extended iterable unpacking. Let's dissect this line:

*midd: The * operator is used to gather any remaining items in the iterable (in this case, the list num) into a list. So, midd will contain all elements of num except the last one.

, lst: This part assigns the last item of the num list to the variable lst. In this case, it's assigning 6 to lst.

Therefore, after this line executes, midd will be [5] and lst will be 6.

print(midd, lst): This line prints the variables midd and lst. So, it will output [5] 6.

So, overall, the code snippet initializes a list num with two elements [5, 6], then it unpacks this list into two variables: midd, which contains all elements of num except the last one ([5]), and lst, which contains the last element of num (6). Finally, it prints the values of midd and lst.

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Python Coding challenge - Day 204 | What is the output of the following Python Code?

 

Code:

num = [7, 8, 9]

*mid, last = num[:-1]  

print(mid, last)

Solution and Explanation:

let's break down the code:

num = [7, 8, 9]

*mid, last = num[:-1]  

print(mid, last)

num = [7, 8, 9]: This line creates a list called num containing the elements 7, 8, and 9.

*mid, last = num[:-1]: This line is a bit more complex. It's using extended unpacking and slicing. Let's break it down:

num[:-1]: This slices the list num from the beginning to the second-to-last element. So, it creates a new list [7, 8].

*mid, last: Here, the *mid notation is used to capture multiple items from the sliced list except the last one, and last captures the last item. So, mid will contain [7, 8], and last will contain 9.

print(mid, last): This line prints the values of mid and last.

So, when you run this code, it will print:

[7, 8] 9

This demonstrates how you can use extended unpacking along with slicing to split a list into multiple variables in Python.

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Python Libraries for Financial Analysis and Portfolio Management

 

import statsmodels.api as sm

import numpy as np

# Generate some sample data

x = np.random.rand(100)

y = 2 * x + np.random.randn(100)

# Fit a linear regression model

model = sm.OLS(y, sm.add_constant(x)).fit()

print("Regression coefficients:", model.params)

print("R-squared:", model.rsquared)

#clcoding.com 

import pandas as pd

# Create a simple DataFrame

data = {'Name': ['Alice', 'Bob', 'Charlie'],

        'Age': [25, 30, 35],

        'Salary': [50000, 60000, 70000]}

df = pd.DataFrame(data)

# Perform data analysis

print("DataFrame head:")

print(df.head())

print("\nAverage salary:", df['Salary'].mean())

#clcoding.com 

import numpy as np

# Create a simple array

arr = np.array([1, 2, 3, 4, 5])

# Perform numerical operations

print("Sum:", np.sum(arr))

print("Mean:", np.mean(arr))

print("Standard deviation:", np.std(arr))

#clcoding.com 

from ibapi.client import EClient

from ibapi.wrapper import EWrapper

class MyWrapper(EWrapper):

    def __init__(self):

        super().__init__()

class MyClient(EClient):

    def __init__(self, wrapper):

        EClient.__init__(self, wrapper)

app = MyClient(MyWrapper())

app.connect("127.0.0.1", 7497, clientId=1)

app.run()

#clcoding.com 

import numpy as np

from scipy import optimize

# Define a simple objective function

def objective(x):

    return x**2 + 10*np.sin(x)

# Optimize the objective function

result = optimize.minimize(objective, x0=0)

print("Minimum value found at:", result.x)

print("Objective function value at minimum:", result.fun)

#clcoding.com 

from riskfolio.Portfolio import Portfolio

# Create a simple portfolio

data = {'Asset1': [0.05, 0.1, 0.15],

        'Asset2': [0.08, 0.12, 0.18],

        'Asset3': [0.06, 0.11, 0.14]}

portfolio = Portfolio(returns=data)

# Perform portfolio optimization

portfolio.optimize()

print("Optimal weights:", portfolio.w)

print("Expected return:", portfolio.mu)

print("Volatility:", portfolio.sigma)

#clcoding.com 

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Python Coding challenge - Day 203 | What is the output of the following Python Code?

 

Code:

num = [1, 2, 3]

*middle, last = num

print(middle, last)

Solution and Explanation:

Let's break down the code step by step:

num = [1, 2, 3]: This line initializes a list called num with three elements: 1, 2, and 3.

*middle, last = num: This line uses a feature called "extended iterable unpacking". Here, *middle is used to capture all elements except the last one from the list num, and last captures the last element of the list.

*middle: This syntax with the * operator before a variable name is used to capture multiple elements from an iterable (like a list) into a new list. In this case, it captures the first two elements [1, 2] into the variable middle.

last: This variable captures the last element of the list, which is 3, and assigns it to the variable last.

print(middle, last): This line prints out the values of the variables middle and last.

Putting it all together, if we execute this code, it will print:

[1, 2] 3

This is because middle contains the first two elements [1, 2] from the list num, and last contains the last element 3 from the list num.

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Happy Mother's Day!

 

Code:

import pyfiglet

from termcolor import colored

import random

def get_random_font_style():

    # List of available Figlet font styles

    font_styles = pyfiglet.FigletFont.getFonts() 

    # Choose a random font style from the list

    return random.choice(font_styles)

def get_random_color():

    # List of available colors

    colors = ['red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white']

    # Choose a random color from the list

    return random.choice(colors)

def wish_mothers_day():

    # Get a random font style and color

    random_font_style = get_random_font_style()

    random_color = get_random_color()

    # Create a Figlet font object with the random style

    font = pyfiglet.Figlet(font=random_font_style)

    # Print the decorative greeting in the chosen color

    print(colored(font.renderText("Happy Mother's Day!"), random_color))

wish_mothers_day()

#clcoding.com

Solution and Explanation:

This code generates a Mother's Day wish with a randomly selected font style and color using the pyfiglet and termcolor libraries in Python. Here's a breakdown of what each part of the code does:

import pyfiglet: Imports the pyfiglet library, which is used to create ASCII art text.

from termcolor import colored: Imports the colored function from the termcolor library, which is used to add color to text in the terminal.

import random: Imports the random module, which is used to generate random font styles and colors.

get_random_font_style(): Defines a function that returns a random font style chosen from the available Figlet font styles provided by pyfiglet.

get_random_color(): Defines a function that returns a random color chosen from a predefined list of colors.

wish_mothers_day(): Defines the main function responsible for printing the Mother's Day wish. Inside this function:

random_font_style is assigned a random font style using get_random_font_style().

random_color is assigned a random color using get_random_color().

A Figlet font object is created using the random font style.

The text "Happy Mother's Day!" is rendered using the chosen font style and colored with the chosen color using the colored function.

The rendered text is printed to the console.

wish_mothers_day(): Finally, the wish_mothers_day() function is called to generate and print the Mother's Day wish with a random font style and color.

This code allows you to produce colorful and decorative Mother's Day wishes each time it's run, adding a touch of randomness and visual appeal to the message.

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Type Hints in Python

 Type hints in Python are an excellent tool for improving code quality and maintainability, especially in larger projects. 

from typing import Optional

class TreeNode:

    def __init__(self, value: int) -> None:

        self.value = value

        self.left: Optional['TreeNode'] = None

        self.right: Optional['TreeNode'] = None

#clcoding.com

class LinkedListNode:

    def __init__(self, value: int, next_node: 'LinkedListNode' = None) -> None:

        self.value = value

        self.next = next_node

#clcoding.com

from typing import Optional

class TreeNode:

    def __init__(self, value: int) -> None:

        self.value = value

        self.left: Optional['TreeNode'] = None

        self.right: Optional['TreeNode'] = None

#clcoding.com

from typing import TypeVar

class Animal:

    def speak(self) -> str:

        raise NotImplementedError

class Dog(Animal):

    def speak(self) -> str:

        return "Woof!"

T = TypeVar('T', bound=Animal)

def make_sound(animal: T) -> str:

    return animal.speak()

#clcoding.com

from typing import Callable

def apply_func(func: Callable[[int, int], int], a: int, b: int) -> int:

    return func(a, b)

#clcoding.com

from typing import Iterable, TypeVar

T = TypeVar('T')

def process_items(items: Iterable[T]) -> None:

    for item in items:

        # Process each item

        pass

#clcoding.com

from typing import TypeVar, Iterable

T = TypeVar('T')

def first_element(items: Iterable[T]) -> T:

    return next(iter(items))

#clcoding.com

from typing import List, Tuple

Point = Tuple[int, int]

Polygon = List[Point]

def area(polygon: Polygon) -> float:

    # Calculate area of polygon

    pass

#clcoding.com

from typing import Union

def square_root(num: Union[int, float]) -> Union[int, float]:

    return num ** 0.5

#clcoding.com

from typing import Optional

def greet(name: Optional[str]) -> str:

    if name:

        return f"Hello, {name}!"

    else:

        return "Hello, anonymous!"

#clcoding.com

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Automating File Compression with gzip

 

Code:

import gzip

import shutil

def compress_file(input_file, output_file):

    with open(input_file, 'rb') as f_in:

        with gzip.open(output_file, 'wb') as f_out:

            shutil.copyfileobj(f_in, f_out)

# Example usage

input_filename = 'clcoding.txt'

output_filename = 'clcoding.txt.gz'

compress_file(input_filename, output_filename)

#clcoding.com 

Explanation:

Let's break down the code step by step:

import gzip: This line imports the gzip module, which provides functionalities for working with gzip-compressed files in Python. The gzip module allows you to create, read, and write gzip-compressed files.

import shutil: This line imports the shutil module, which provides a higher-level interface for file operations. In this script, shutil is used to copy the contents of one file to another efficiently.

def compress_file(input_file, output_file):: This line defines a function named compress_file that takes two parameters: input_file (the path to the file to be compressed) and output_file (the path where the compressed file will be saved).

with open(input_file, 'rb') as f_in:: This line opens the input file (input_file) in binary read mode ('rb') using the open() function. It uses a context manager (with statement) to automatically close the file when the block is exited. The file object is assigned to the variable f_in.

with gzip.open(output_file, 'wb') as f_out:: This line opens the output file (output_file) in gzip-compressed write mode ('wb') using gzip.open(). Similar to the previous with statement, it also uses a context manager to automatically close the file when the block is exited. The compressed file object is assigned to the variable f_out.

shutil.copyfileobj(f_in, f_out): This line copies the contents of the input file (f_in) to the gzip-compressed output file (f_out). It efficiently copies data between file objects without loading the entire file into memory.

input_filename = 'clcoding.txt': This line defines a variable input_filename and assigns it the name of the input file (clcoding.txt). This file will be compressed.

output_filename = 'clcoding.txt.gz': This line defines a variable output_filename and assigns it the name of the output file (clcoding.txt.gz). This file will store the compressed data.

compress_file(input_filename, output_filename): This line calls the compress_file function with the input and output filenames as arguments, which compresses the input file and saves the compressed data to the output file.

#clcoding.com: This line appears to be a comment, possibly indicating the source of the file (clcoding.com). Comments in Python are preceded by the # symbol and are ignored by the interpreter.

This script takes a file (clcoding.txt), compresses it using gzip, and saves the compressed data to a new file (clcoding.txt.gz).

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Python Coding challenge - Day 202 | What is the output of the following Python Code?

 

Code:

x = [1, 2, 3, 4, 5]

a, *_, b = x

print(a, b)

Solution and Explanation:

Let's break down the Python code step by step:

x = [1, 2, 3, 4, 5]

a, *_, b = x

print(a, b)

List Assignment: x = [1, 2, 3, 4, 5]

This line initializes a list named x with five elements: [1, 2, 3, 4, 5].

Extended Unpacking: a, *_, b = x

This line uses extended unpacking to assign values from the list x to variables a and b, while ignoring the rest of the elements.

The * (asterisk) operator is used to capture multiple values into a list. In this case, * is followed by an underscore (_), which is a common convention in Python to indicate that the variable is a placeholder and its value is not used.

The first element of x is assigned to a, and the last element is assigned to b, while all other elements are captured by the _ variable (which is ignored).

Print Statement: print(a, b)

This line prints the values of variables a and b separated by a space.

So, what does this code output?

a will be assigned the value 1, which is the first element of the list x.

b will be assigned the value 5, which is the last element of the list x.

The other elements of the list x are captured by the _ variable, but since they are not used in the print statement, they are ignored.

Therefore, the output of the code will be:

1 5

Read More

Donut Charts using Python

 

Code:

import matplotlib.pyplot as plt

# Data to plot

labels = ['A', 'B', 'C', 'D']

sizes = [15, 30, 45, 10]

# Plot

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)

# Draw a circle at the center of pie to make it look like a donut

centre_circle = plt.Circle((0,0),0.70,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

# Equal aspect ratio ensures that pie is drawn as a circle

plt.axis('equal')

plt.title('Basic Donut Chart')

plt.show()

#clcoding.com

Explanation:

In this code snippet, we're using the matplotlib.pyplot module, which is a powerful library in Python for creating static, animated, and interactive visualizations. We're importing it using the alias plt, which is a common convention for brevity.

Here's a breakdown of the code:

Importing matplotlib.pyplot: import matplotlib.pyplot as plt

This line imports the matplotlib.pyplot module and assigns it the alias plt, allowing us to reference it with the shorter name plt throughout the code.

labels = ['A', 'B', 'C', 'D']

sizes = [15, 30, 45, 10]

These lines define the data we want to visualize. labels contains the labels for each segment of the pie chart, and sizes contains the corresponding sizes or values for each segment.

Plotting the pie chart:

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)

Here, we use the plt.pie() function to create a pie chart. We pass sizes as the data to plot, labels to label each segment, autopct='%1.1f%%' to display the percentage for each segment, and startangle=140 to rotate the pie chart to start from the angle 140 degrees.

Drawing a circle to create a donut effect:

centre_circle = plt.Circle((0,0),0.70,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

These lines draw a white circle at the center of the pie chart, creating a donut-like appearance. The plt.Circle() function creates a circle with the specified parameters: center (0,0) and radius 0.70.

Setting equal aspect ratio:

plt.axis('equal')

This line ensures that the plot is displayed with equal aspect ratio, so the pie chart appears as a circle rather than an ellipse.

Adding a title and displaying the plot:

plt.title('Basic Donut Chart')

plt.show()

Here, we set the title of the plot to 'Basic Donut Chart' using plt.title(), and then plt.show() displays the plot on the screen.

This code generates a basic donut chart with four segments labeled A, B, C, and D, where the size of each segment is determined by the values in the sizes list.

Code:

import matplotlib.pyplot as plt

# Data to plot

labels = ['A', 'B', 'C', 'D']

sizes = [15, 30, 45, 10]

explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice

# Plot

plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', startangle=140)

# Draw a circle at the center of pie to make it look like a donut

centre_circle = plt.Circle((0,0),0.70,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

# Equal aspect ratio ensures that pie is drawn as a circle

plt.axis('equal')

plt.title('Donut Chart with Exploded Slices')

plt.show()

#clcoding.com

Explanation: 

This code snippet is similar to the previous one, but it adds exploding effect to one of the slices in the pie chart. Let's break down the code:

Importing matplotlib.pyplot:

import matplotlib.pyplot as plt

This line imports the matplotlib.pyplot module and assigns it the alias plt, allowing us to reference it with the shorter name plt throughout the code.

Data to plot:

labels = ['A', 'B', 'C', 'D']

sizes = [15, 30, 45, 10]

explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice

Here, labels contains the labels for each segment of the pie chart, sizes contains the corresponding sizes or values for each segment, and explode contains the magnitude of the explosion for each slice. In this case, we're exploding the second slice ('B') by 0.1.

Plotting the pie chart:

plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', startangle=140)

This line creates a pie chart using plt.pie(). The explode parameter is used to specify the amount by which to explode each slice. Here, we're exploding only the second slice ('B') by 0.1. Other parameters are similar to the previous example.

Drawing a circle to create a donut effect:

centre_circle = plt.Circle((0,0),0.70,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

This part is the same as before. It draws a white circle at the center of the pie chart, creating a donut-like appearance.

Setting equal aspect ratio:

plt.axis('equal')

This line ensures that the plot is displayed with an equal aspect ratio, so the pie chart appears as a circle.

Adding a title and displaying the plot:

plt.title('Donut Chart with Exploded Slices')

plt.show()

Here, we set the title of the plot to 'Donut Chart with Exploded Slices' and then display the plot.

This code generates a donut chart with four segments labeled A, B, C, and D, where the second slice ('B') is exploded outwards. The size of each segment is determined by the values in the sizes list.

Code:

import matplotlib.pyplot as plt

# Data to plot

labels = ['A', 'B', 'C', 'D']

sizes1 = [25, 30, 35, 10]

sizes2 = [20, 40, 20, 20]

# Plot

fig, ax = plt.subplots()

ax.pie(sizes1, radius=1.2, labels=labels, autopct='%1.1f%%', startangle=140)

ax.pie(sizes2, radius=1, startangle=140, colors=['red', 'green', 'blue', 'yellow'])

# Draw a circle at the center of pie to make it look like a donut

centre_circle = plt.Circle((0,0),0.8,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

# Equal aspect ratio ensures that pie is drawn as a circle

ax.set(aspect="equal")

plt.title('Donut Chart with Multiple Rings')

plt.show()

#clcoding.com

Explanation: 

This code snippet creates a donut chart with multiple rings, demonstrating the capability to display more than one dataset in the same chart. Let's dissect the code:

Importing matplotlib.pyplot:

import matplotlib.pyplot as plt

This line imports the matplotlib.pyplot module and assigns it the alias plt.

Data to plot:

labels = ['A', 'B', 'C', 'D']

sizes1 = [25, 30, 35, 10]

sizes2 = [20, 40, 20, 20]

Two sets of data are defined here: sizes1 and sizes2. Each set represents the values for different rings of the donut chart.

Plotting the donut chart:

fig, ax = plt.subplots()

ax.pie(sizes1, radius=1.2, labels=labels, autopct='%1.1f%%', startangle=140)

ax.pie(sizes2, radius=1, startangle=140, colors=['red', 'green', 'blue', 'yellow'])

This code creates a subplot (fig, ax = plt.subplots()) and then plots two pie charts on the same subplot using ax.pie().

The first ax.pie() call plots the outer ring (sizes1) with a larger radius (radius=1.2), while the second call plots the inner ring (sizes2) with a smaller radius (radius=1).

labels, autopct, and startangle parameters are used to configure the appearance of the pie charts.

Different colors are specified for the inner ring using the colors parameter.

Drawing a circle to create a donut effect:

centre_circle = plt.Circle((0,0),0.8,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

This part is similar to previous examples. It draws a white circle at the center of the pie chart to create the donut-like appearance.

Setting equal aspect ratio:

ax.set(aspect="equal")

This line sets the aspect ratio of the subplot to 'equal', ensuring that the pie charts are displayed as circles.

Adding a title and displaying the plot:

plt.title('Donut Chart with Multiple Rings')

plt.show()

Finally, the title of the plot is set to 'Donut Chart with Multiple Rings', and the plot is displayed.

This code generates a donut chart with two rings, each representing different datasets (sizes1 and sizes2). Each ring has its own set of labels and colors, and they are displayed concentrically to create the donut chart effect.

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Python Coding challenge - Day 201 | What is the output of the following Python Code?

 

Code:

class Powerizer(int):

    def __pow__(self, other):

        return super().__pow__(other ** 2)

p = Powerizer(2)

result = p ** 3

print(result)  

Solution and Explanation:

let's break down the code:

Class Definition:

class Powerizer(int):

    def __pow__(self, other):

        return super().__pow__(other ** 2)

Powerizer(int): This line defines a class named Powerizer that inherits from the int class. Instances of Powerizer will inherit all properties and methods of integers.

def __pow__(self, other): This method overrides the power behavior (__pow__) for instances of the Powerizer class. It's invoked when the ** operator is used with instances of Powerizer.

return super().__pow__(other ** 2): Inside the __pow__ method, it squares the other operand and then calls the __pow__ method of the superclass (which is int). It passes the squared other operand to the superclass method. Essentially, it calculates the power of the Powerizer instance with the squared value of other.

Object Instantiation:

p = Powerizer(2)

This line creates an instance of the Powerizer class with the value 2.

Power Operation:

result = p ** 3

This line performs a power operation using the ** operator. Since p is an instance of Powerizer, the overridden __pow__ method is invoked. The value 3 is passed as other. Inside the overridden __pow__ method, 3 is squared to 9. Then, the superclass method (int.__pow__) is called with the squared other value. Essentially, it calculates p raised to the power of 9.

print(result)

This line prints the value of result, which is the result of the power operation performed in the previous step.

So, the output of this code will be 512, which is the result of 2 raised to the power of 9 (2^9).

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Python Coding challenge - Day 200 | What is the output of the following Python Code?

 

Code:

class Decrementer(int):

    def __sub__(self, other):

        return super().__sub__(other - 1)

d = Decrementer(5)

result = d - 3

print(result)  

Solution and Explanation:

Class Definition:

class Decrementer(int):

    def __sub__(self, other):

        return super().__sub__(other - 1)

Decrementer(int): This line creates a class called Decrementer which inherits from the int class. Instances of Decrementer will inherit all the properties and methods of integers.

def __sub__(self, other): This method overrides the subtraction behavior (__sub__) for instances of the Decrementer class. It is called when the - operator is used with instances of Decrementer.

return super().__sub__(other - 1): Inside the __sub__ method, it subtracts 1 from the other operand and then calls the __sub__ method of the superclass (which is int). It passes the modified other operand to the superclass method. Essentially, it performs subtraction of the Decrementer instance with the modified value of other.

Object Instantiation:

d = Decrementer(5)

This line creates an instance of the Decrementer class with the value 5.

Subtraction Operation:

result = d - 3

This line performs a subtraction operation using the - operator. Since d is an instance of Decrementer, the overridden __sub__ method is invoked. The value 3 is passed as other. Inside the overridden __sub__ method, 1 is subtracted from other, making it 2. Then, the superclass method (int.__sub__) is called with the modified other value. Essentially, it subtracts 2 from d, resulting in the final value.

print(result)

This line prints the value of result, which is the result of the subtraction operation performed in the previous step.

So, the output of this code will be 3, which is the result of subtracting 3 - 1 from 5.

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Python Coding challenge - Day 199 | What is the output of the following Python Code?

 

Code:

class Incrementer(int):

    def __add__(self, other):

        return super().__add__(other + 1)

i = Incrementer(5)

result = i + 3

print(result)  

Solution and Explanation:

 let's break it down step by step:

Class Definition:

class Incrementer(int):

    def __add__(self, other):

        return super().__add__(other + 1)

Incrementer(int): This line defines a class named Incrementer that inherits from the int class. This means that instances of Incrementer will behave like integers but with additional functionality.

def __add__(self, other): This method overrides the addition behavior (__add__) of instances of the Incrementer class. Whenever the + operator is used with instances of Incrementer, this method is invoked.

return super().__add__(other + 1): Inside the __add__ method, it adds 1 to the other operand and then calls the __add__ method of the superclass (which is int in this case) using super(). It passes the modified other operand to the superclass method. Essentially, it performs addition of the Incrementer instance with the modified value of other.

Object Instantiation:

i = Incrementer(5)

This line creates an instance of the Incrementer class with the value 5. Since Incrementer inherits from int, it behaves like an integer but with the overridden __add__ method.

Addition Operation:

result = i + 3

This line performs an addition operation using the + operator. Since i is an instance of Incrementer, the overridden __add__ method is invoked. The value 3 is passed as other. Inside the overridden __add__ method, 1 is added to other, making it 4. Then, the superclass method (int.__add__) is called with the modified other value. In essence, it adds i to 4, resulting in the final value.

Print Result:

print(result)

This line prints the value of result, which is the result of the addition operation performed in the previous step.

So, the output of this code will be 9, which is the result of adding 5 (the value of i) to 3 + 1.

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Python Coding challenge - Day 198 | What is the output of the following Python Code?

 

Code:

class Quadrupler(int):

    def __mul__(self, other):

        return super().__mul__(other + 4)

q = Quadrupler(5)

result = q * 3

print(result)

Solution and Explanation:

let's delve into this code:

class Quadrupler(int):: This line establishes a new class named Quadrupler that inherits from the int class. As a result, Quadrupler inherits all the attributes and methods of the int class.

def __mul__(self, other):: This creates a special method __mul__() which overrides the multiplication behavior of instances of the Quadrupler class. This method is invoked when the * operator is utilized with instances of the Quadrupler class.

return super().__mul__(other + 4): Within the __mul__() method, it adds 4 to the other operand and then invokes the __mul__() method of the superclass (in this case, the int class) using super(). It transfers the adjusted other operand to the superclass method. Essentially, it performs multiplication of the Quadrupler instance with the modified value of other.

q = Quadrupler(5): This line instantiates an object of the Quadrupler class with the value 5. Since the Quadrupler class inherits from int, it can be initialized with an integer value.

result = q * 3: This line utilizes the * operator with the Quadrupler instance q and the integer 3. Since the Quadrupler class has overridden the __mul__() method, the overridden behavior is invoked. In this case, it adds 4 to the other operand (which is 3) and then performs multiplication.

print(result): Lastly, this line prints the value of result.

Now, let's follow the multiplication:

other is 3.

4 is added to other, making it 7.

The __mul__() method of the int class (the superclass) is invoked with 7 as the argument.

The superclass's __mul__() method multiplies the Quadrupler instance (q) by 7.

The outcome of this multiplication is assigned to result.

Finally, result is printed.

Hence, when you execute this code, it should output 35, which is the result of multiplying 5 by (3 + 4).

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Python Coding challenge - Day 197 | What is the output of the following Python Code?

 

Code:

class Tripler(int):

    def __mul__(self, other):

        return super().__mul__(other - 2)

t = Tripler(6)

result = t * 4

print(result)

Solution and Explanation:

let's break down the code step by step:

class Tripler(int):: This line defines a new class named Tripler that inherits from the int class. This means that Tripler inherits all the properties and methods of the int class.

def __mul__(self, other):: This defines a special method __mul__() which overrides the multiplication behavior of instances of the Tripler class. This method is called when the * operator is used with instances of the Tripler class.

return super().__mul__(other - 2): Inside the __mul__() method, it subtracts 2 from the other operand and then calls the __mul__() method of the superclass (in this case, the int class) using super(). It passes the modified other operand to the superclass method. Essentially, it performs multiplication of the Tripler instance with the modified value of other.

t = Tripler(6): This line creates an instance of the Tripler class with the value 6. The Tripler class inherits from int, so it can be initialized with an integer value.

result = t * 4: This line uses the * operator with the Tripler instance t and the integer 4. Since the Tripler class has overridden the __mul__() method, the overridden behavior is invoked. In this case, it subtracts 2 from the other operand (which is 4) and then performs multiplication.

print(result): Finally, this line prints the value of result.

Now, let's follow through the multiplication:

other is 4.

2 is subtracted from other, making it 2.

The __mul__() method of the int class (the superclass) is called with 2 as the argument.

The superclass's __mul__() method multiplies the Tripler instance (t) by 2.

The result of this multiplication is assigned to result.

Finally, result is printed.

So, when you run this code, it should output 12, which is the result of multiplying 6 by (4 - 2).

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Python Coding challenge - Day 196 | What is the output of the following Python Code?

Code:

class Doubler(int):

    def __mul__(self, other):

        return super().__mul__(other + 3)  

d = Doubler(3)

result = d * 5

print(result)

Solution and Explanation:

This code defines a class named Doubler which inherits from the int class. Here's a detailed breakdown of the code:

class Doubler(int):: This line defines a new class named Doubler that inherits from the int class. This means that Doubler inherits all the properties and methods of the int class.

def __mul__(self, other):: This defines a special method __mul__() which overrides the multiplication behavior of instances of the Doubler class. This method is called when the * operator is used with instances of the Doubler class.

return super().__mul__(other + 3): Inside the __mul__() method, it first adds 3 to the other operand and then calls the __mul__() method of the superclass (in this case, the int class) using super(). It passes the modified other operand to the superclass method. Essentially, it performs multiplication of the Doubler instance with the modified value of other.

d = Doubler(3): This line creates an instance of the Doubler class with the value 3. The Doubler class inherits from int, so it can be initialized with an integer value.

result = d * 5: This line uses the * operator with the Doubler instance d and the integer 5. Since the Doubler class has overridden the __mul__() method, the overridden behavior is invoked. In this case, it adds 3 to the other operand (which is 5) and then performs multiplication.

print(result): Finally, this line prints the value of result.

Now, let's follow through the multiplication:

other is 5.

3 is added to other, making it 8.

The __mul__() method of the int class (the superclass) is called with 8 as the argument.

The superclass's __mul__() method multiplies the Doubler instance (d) by 8.

The result of this multiplication is assigned to result.

Finally, result is printed.

So, when you run this code, it should output 24, which is the result of multiplying 3 by (5 + 3).

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Python Coding challenge - Day 195 | What is the output of the following Python Code?

 

Code: 

s = 'clcoding'

print(s[5:5])

Solution and Explanation:

Let's break down the code s = 'clcoding' and print(s[5:5]) step by step:

s = 'clcoding': This line of code assigns the string 'clcoding' to the variable s.

s[5:5]: This is called string slicing. Let's break it down:

s[5:5]: This specifies a substring of s starting from the 5th character (counting from 0) and ending at the 5th character. The start index is inclusive, while the end index is exclusive.In 'clcoding', the character at index 5 is 'i'.

Since the start and end indices are the same, this indicates an empty substring. In Python, when the start index is greater than or equal to the end index, an empty string is returned.

So, print(s[5:5]) would output an empty string, i.e., ''''

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Python Coding challenge - Day 194 | What is the output of the following Python Code?

 

Code: 

s = 'clcoding'

print(s[-6:-1:2])

Solution and Explanation:

Let's break down the code s = 'clcoding' and print(s[-6:-1:2]) step by step:

s = 'clcoding': This line of code assigns the string 'clcoding' to the variable s.

s[-6:-1:2]: This is called string slicing. Let's break it down:

s[-6:-1]: This specifies a substring of s starting from the 6th character from the end (counting from the right) and ending at the 1st character from the end. Negative indices in Python count from the end of the string.So, in 'clcoding', the characters at these positions are:

-6: 'c'

-5: 'l'

-4: 'c'

-3: 'o'

-2: 'd'

The substring extracted by s[-6:-1] is 'clcod'.

s[-6:-1:2]: This specifies that we want to take every second character from the substring 'clcod'.So, starting from the first character 'c', we take every second character:

'c': 0th position

'c': 2nd position

'd': 4th position

Thus, the final result printed would be 'ccd'.

So, print(s[-6:-1:2]) would output 'ccd'.

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Data Science: The Hard Parts: Techniques for Excelling at Data Science

 

This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline—machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.

Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.

With this book, you will:

Understand how data science creates value

Deliver compelling narratives to sell your data science project

Build a business case using unit economics principles

Create new features for a ML model using storytelling

Learn how to decompose KPIs

Perform growth decompositions to find root causes for changes in a metric

Daniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

PDF: Data Science: The Hard Parts: Techniques for Excelling at Data Science

Hard Copy: Data Science: The Hard Parts: Techniques for Excelling at Data Science

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Streamgraphs using Python

 

Code:

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(0, 10, 100)

y1 = np.sin(x)

y2 = np.cos(x)

plt.stackplot(x, y1, y2, baseline='wiggle')

plt.title('Streamgraph')

plt.show()

Explanation: 

This code snippet creates a streamgraph using Matplotlib, a popular plotting library in Python. Let's break down the code:

Importing Libraries:

import matplotlib.pyplot as plt

import numpy as np

matplotlib.pyplot as plt: This imports the pyplot module of Matplotlib and assigns it the alias plt, which is a common convention.

numpy as np: This imports the NumPy library and assigns it the alias np. NumPy is commonly used for numerical computing in Python.

Generating Data:

x = np.linspace(0, 10, 100)

y1 = np.sin(x)

y2 = np.cos(x)

np.linspace(0, 10, 100): This creates an array x of 100 evenly spaced numbers between 0 and 10.

np.sin(x): This calculates the sine of each value in x, resulting in an array y1.

np.cos(x): This calculates the cosine of each value in x, resulting in an array y2.

Creating the Streamgraph:

plt.stackplot(x, y1, y2, baseline='wiggle')

plt.stackplot(x, y1, y2, baseline='wiggle'): This function creates a stack plot (streamgraph) with the x-values from x and the y-values from y1 and y2. The baseline='wiggle' argument specifies that the baseline for the stacked areas should be wiggled, which can help to visually separate the layers in the streamgraph.

Setting Title:

plt.title('Streamgraph')

plt.title('Streamgraph'): This sets the title of the plot to "Streamgraph".

Displaying the Plot:

plt.show()

plt.show(): This command displays the plot on the screen. Without this command, the plot would not be shown.

Overall, the code generates a streamgraph showing the variations of sine and cosine functions over the range of 0 to 10. The streamgraph visually represents how these functions change over the given range, with the wiggled baseline helping to distinguish between the layers.

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Statistical Inference and Probability

 

An experienced author in the field of data analytics and statistics, John Macinnes has produced a straight-forward text that breaks down the complex topic of inferential statistics with accessible language and detailed examples. It covers a range of topics, including:

·       Probability and Sampling distributions

·       Inference and regression

·       Power, effect size and inverse probability

Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.

Hard Copy: Statistical Inference and Probability

PDF: Statistical Inference and Probability (The SAGE Quantitative Research Kit)

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Python Coding challenge - Day 193 | What is the output of the following Python Code?

 

Code:

class Doubler(int):

  def __mul__(self, other):

    return super().__mul__(other * 2)  

# Create an instance of Doubler

d = Doubler(3)

# Multiply by another number

result = d * 5

print(result)

Solution and Explanation:

Let's go through the code step by step:

We define a class Doubler that inherits from the built-in int class.

class Doubler(int):

We override the __mul__ method within the Doubler class. This method gets called when we use the * operator with instances of the Doubler class.

def __mul__(self, other):

Inside the __mul__ method, we double the value of other and then call the __mul__ method of the superclass (int) with this doubled value.

return super().__mul__(other * 2)

We create an instance of the Doubler class with the value 3.

d = Doubler(3)

We multiply this instance (d) by 5.

result = d * 5

When we perform this multiplication, the __mul__ method of the Doubler class is called. Inside this method:

other is the value 5.

We double the value of other (5) to get 10.

Then we call the __mul__ method of the superclass (int) with this doubled value, 10.

Thus, we're effectively performing 3 * 10, resulting in 30.

Finally, we print the result, which is 30.

So, the output of the code is 30.

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Python Coding challenge - Day 192 | What is the output of the following Python Code?

 

Code:

class MyClass:

    def __init__(self, x):

        self.x = x

    def __call__(self, y):

        return self.x * y

p1 = MyClass(2)

print(p1(3))

Solution and Explanation:

This code defines a class MyClass with an __init__ method and a __call__ method.

The __init__ method initializes an instance of the class with a parameter x, setting self.x to the value of x.

The __call__ method allows instances of the class to be called as if they were functions. It takes a parameter y and returns the product of self.x and y.

Then, an instance p1 of MyClass is created with x set to 2. When p1 is called with the argument 3 (p1(3)), it effectively calculates 2 * 3 and returns the result, which is 6.

So, when you run print(p1(3)), it prints 6.

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Python Coding challenge - Day 191 | What is the output of the following Python Code?

 

Code:

class MyClass:

    def __init__(self, x):

        self.x = x

p1 = MyClass(1)

p2 = MyClass(2)

p1.x = p2.x

del p2.x

print(p1.x)

Solution with Explanation:

let's break down the code step by step:

class MyClass:: This line defines a new class named MyClass. Classes are used to create new objects that bundle data (attributes) and functions (methods) together.

def __init__(self, x):: This is a special method called the constructor or initializer method. It's automatically called when a new instance of the class is created. In this case, it takes two parameters: self (which refers to the instance being created) and x (a value that initializes the x attribute of the instance).

self.x = x: Within the constructor, self.x refers to an attribute of the instance, and x is the value passed to the constructor. This line assigns the value of x passed to the constructor to the x attribute of the instance.

p1 = MyClass(1): This line creates a new instance of the MyClass class and assigns it to the variable p1. The value 1 is passed to the constructor, so p1.x will be set to 1.

p2 = MyClass(2): Similarly, this line creates another instance of the MyClass class and assigns it to the variable p2. The value 2 is passed to the constructor, so p2.x will be set to 2.

p1.x = p2.x: This line sets the value of the x attribute of p1 to be the same as the value of the x attribute of p2. After this line, both p1.x and p2.x will be 2, because p2.x is 2.

del p2.x: This line deletes the x attribute from the p2 instance. After this line, p2.x will raise an AttributeError because x no longer exists as an attribute of p2.

print(p1.x): Finally, this line prints the value of p1.x. Since p1.x was set to p2.x, which was 2 before it got deleted, the output will be 2.

So, the output of this code will be:

2

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