import turtle
def draw_ring(color, x, y):
turtle.penup()
turtle.color(color)
turtle.goto(x, y)
turtle.pendown()
turtle.circle(50)
turtle.speed(5)
turtle.width(5)
draw_ring("blue", -120, 0)
draw_ring("black", 0, 0)
draw_ring("red", 120, 0)
draw_ring("yellow", -60, -50)
draw_ring("green", 60, -50)
turtle.hideturtle()
turtle.done()
#clcoding.com
In Python, dictionaries are compared based on their keys and corresponding values. When you use the != operator to compare two dictionaries, it checks if there is any difference between them.
Here’s the explanation for the given code:
dict1 = {"a": 1, "b": 2}
dict2 = {"a": 1, "b": 3}
print(dict1 != dict2)
dict1 is created with keys "a" and "b" having values 1 and 2, respectively.
dict2 is created with keys "a" and "b" having values 1 and 3, respectively.
The comparison dict1 != dict2 checks if dict1 is not equal to dict2.
Python compares each key-value pair in dict1 with the corresponding key-value pair in dict2.
Both dictionaries have the same keys: "a" and "b".
For key "a", both dictionaries have the value 1. So, these are equal.
For key "b", dict1 has the value 2 and dict2 has the value 3. These are not equal.
Since there is at least one key-value pair that differs ("b": 2 in dict1 vs. "b": 3 in dict2), the dictionaries are considered not equal.
Result:
The expression dict1 != dict2 evaluates to True.
Therefore, the output of print(dict1 != dict2) will be True, indicating that dict1 is not equal to dict2.
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This Python code defines a function and then calls it with specific arguments. Let's break it down step by step:
Function Definition:
def func(x, y):
return x + y
def func(x, y):: This line defines a function named func that takes two parameters, x and y.
return x + y: This line specifies that the function will return the sum of x and y.
Function Call:
print(func(y=2, x=3))
func(y=2, x=3): This calls the func function with x set to 3 and y set to 2. The order of the arguments doesn't matter here because they are passed as keyword arguments.
The function func adds x and y, so 3 + 2 results in 5.
print(5): The print function then outputs the result, which is 5.
Putting it all together, the code defines a function that adds two numbers, then calls the function with x as 3 and y as 2, and prints the result, which is 5.
The code provided consists of two parts: defining a dictionary and using the fromkeys method to create a new dictionary. Let's break it down:
Defining a Dictionary:
d = {'a': 1, 'b': 2, 'c': 3}
Here, d is a dictionary with three key-value pairs:
'a' maps to 1
'b' maps to 2
'c' maps to 3
Using dict.fromkeys:
print(dict.fromkeys(d, 0))
The dict.fromkeys method is used to create a new dictionary from the keys of an existing iterable (in this case, the dictionary d). The method signature is:
dict.fromkeys(iterable, value)
iterable: An iterable containing keys.
value: The value to assign to each key in the new dictionary.
In this code, d is used as the iterable. When d is used as an iterable, it provides its keys ('a', 'b', and 'c'). The second argument is 0, which means all keys in the new dictionary will have the value 0.
Therefore, the new dictionary created by dict.fromkeys(d, 0) will have the same keys as d but with all values set to 0:
{'a': 0, 'b': 0, 'c': 0}
Output:
The print statement will output:
{'a': 0, 'b': 0, 'c': 0}
In summary, the code defines a dictionary d and then creates a new dictionary with the same keys as d but with all values set to 0, and prints this new dictionary.
7. Error Prevention
Reason: PEP 8 includes guidelines that help prevent common errors, such as mixing tabs and spaces for indentation.
Without PEP 8:
def calculate_sum(a, b):
return a + b
print("Sum calculated")
Cell In[16], line 3
print("Sum calculated")
^
IndentationError: unexpected indent
With PEP 8:
def calculate_sum(a, b):
return a + b
print("Sum calculated")
Sum calculated
6. Community Standard
Reason: PEP 8 is the de facto standard for Python code. Following it ensures your code aligns with what other Python developers expect, making it easier for others to read and contribute to your projects.
Without PEP 8:
class Person:
def __init__(self,name,age):
self.name=name
self.age=age
def getDetails(self):
return self.name + " is " + str(self.age)
With PEP 8:
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
def get_details(self):
return f"{self.name} is {self.age}"
5. Professionalism
Reason: Following PEP 8 shows that you care about writing high-quality code. It demonstrates professionalism and attention to detail, which are valuable traits in any developer.
Without PEP 8:
def square(x):return x*x
With PEP 8:
def square(x):
return x * x
4. Maintainability
Reason: Code that adheres to a standard style is easier to maintain and update. PEP 8’s guidelines help you write code that is more maintainable in the long run.
Without PEP 8:
def processData(data):
result = data["name"].upper() + " is " + str(data["age"]) + " years old"
return result
With PEP 8:
def process_data(data):
result = f"{data['name'].upper()} is {data['age']} years old"
return result
#clcoding.com
3. Collaboration
Reason: When everyone on a team follows the same style guide, it’s easier for team members to read and understand each other’s code, making collaboration smoother.
Without PEP 8:
def fetchData():
# fetch data from API
data = {"name":"John","age":30}
return data
With PEP 8:
def fetch_data():
# Fetch data from API
data = {"name": "John", "age": 30}
return data
2. Readability
Reason: Readable code is easier to understand and debug. PEP 8 encourages practices that make your code more readable.
Without PEP 8:
def add(a,b):return a+b
With PEP 8:
def add(a, b):
return a + b
1. Consistency
Reason: Consistent code is easier to read and understand. PEP 8 provides a standard style guide that promotes consistency across different projects and among different developers.
Without PEP 8:
def my_function():print("Hello"); print("World")
my_function()
Hello
World
With PEP 8:
def my_function():
print("Hello")
print("World")
my_function()
Hello
World
In Python, memoryview is a built-in class that allows you to access the memory of an object without copying it. This can be useful for performance reasons, especially when dealing with large data sets or when you want to manipulate data in place.
Here's a breakdown of the code snippet you provided:
x = memoryview(b'clcoding')
print(type(x))
Creating a memoryview object:
b'clcoding' is a bytes object. The b prefix indicates that this is a bytes literal, which is a sequence of bytes.
memoryview(b'clcoding') creates a memoryview object that exposes the memory of the bytes object b'clcoding' without copying it.
Printing the type of the memoryview object:
print(type(x)) prints the type of the object x.
When you run this code, you will get the following output:
<class 'memoryview'>
This output indicates that x is an instance of the memoryview class.
Why use memoryview?
Efficiency: It allows you to access and manipulate data without copying it, which can save memory and improve performance.
Slicing and indexing: You can use memoryview to slice and index data structures such as bytes, bytearray, and other objects that support the buffer protocol.
Interoperability: It can be useful when working with binary data and interfacing with C/C++ extensions or other low-level APIs.
Example Usage
Here's a simple example to demonstrate the usage of memoryview:
data = b'clcoding'
mv = memoryview(data)
# Accessing elements
print(mv[0]) # Output: 99 (ASCII value of 'c')
# Slicing
print(mv[1:4]) # Output: <memory at 0x...> (a slice of the memoryview)
# Converting back to bytes
print(mv.tobytes()) # Output: b'clcoding'
In this example:
mv[0] accesses the first byte, which is 99, the ASCII value of 'c'.
mv[1:4] creates a new memoryview object that is a slice of the original memoryview.
mv.tobytes() converts the memoryview back to a bytes object.
Using memoryview is particularly beneficial when you need to work with large data structures efficiently.
Example 5: Summing Non-Negative Numbers
Using continue to skip negative numbers and sum the non-negative ones.
numbers = [10, -5, 20, -10, 30]
total = 0
for num in numbers:
if num < 0:
continue # Skip negative numbers
total += num
print(f"Total sum of non-negative numbers is: {total}")
#clcoding.com
Total sum of non-negative numbers is: 60
Example 4: Finding the First Negative Number
Using break to find and print the first negative number in a list.
numbers = [10, 20, -5, 30, -10]
for num in numbers:
if num < 0:
print(f"First negative number is: {num}")
break
#clcoding.com
First negative number is: -5
Example 3: Skipping a Specific Number and Stopping at Another
Combining continue and break to skip the number 3 and stop at 7.
for i in range(1, 11):
if i == 3:
continue # Skip the number 3
if i == 7:
break # Stop the loop when i is 7
print(i)
#clcoding.com
1
2
4
5
6
Example 2: Stopping at a Specific Number
Using break to stop the loop when encountering the number 5.
for i in range(1, 11):
if i == 5:
break
print(i)
#clcoding.com
1
2
3
4
Example 1: Skipping Even Numbers
Using continue to skip even numbers in a loop.
for i in range(1, 11):
if i % 2 == 0:
continue
print(i)
#clcoding.com
1
3
5
7
9
Class Definition
Class Vehicle:
class Vehicle:
def __init__(self, color):
self.color = color
Class Declaration: class Vehicle: defines a class named Vehicle.
Constructor: def __init__(self, color): defines the constructor method (__init__). It initializes a new instance of Vehicle.
Instance Variable: self.color = color assigns the value of the parameter color to the instance variable self.color.
Class Car (Inheritance):
class Car(Vehicle):
def __init__(self, color, model):
super().__init__(color)
self.model = model
Class Declaration with Inheritance: class Car(Vehicle): defines a class named Car that inherits from the Vehicle class.
Constructor: def __init__(self, color, model): defines the constructor method (__init__). It initializes a new instance of Car.
Calling Superclass Constructor: super().__init__(color) calls the constructor of the superclass Vehicle to initialize the color attribute.
Instance Variable: self.model = model assigns the value of the parameter model to the instance variable self.model.
Object Instantiation
Creating an Instance:
my_car = Car("Red", "Toyota")
This line creates an instance of the Car class with color set to "Red" and model set to "Toyota". The constructor of the Vehicle class is also called to initialize the color attribute.
Accessing Attributes
Printing the Model:
print(my_car.model)
This line prints the model attribute of the my_car object, which is "Toyota".
Complete Code
Here is the complete code for clarity:
class Vehicle:
def __init__(self, color):
self.color = color
class Car(Vehicle):
def __init__(self, color, model):
super().__init__(color)
self.model = model
my_car = Car("Red", "Toyota")
print(my_car.model)
Output
The output of the code is: Toyota
Explanation Summary
Vehicle Class: Defines a vehicle with a color.
Car Class: Inherits from Vehicle and adds a model attribute.
Object Creation: An instance of Car is created with color "Red" and model "Toyota".
Attribute Access: The model attribute of my_car is printed, displaying "Toyota".
5. Interactive Line Graph using Plotly
import plotly.express as px
# Sample data
data = {
'x': [1, 2, 3, 4, 5],
'y': [2, 3, 5, 7, 11]
}
# Creating an interactive line plot
fig = px.line(data, x='x', y='y',
title='Interactive Line Graph using Plotly', markers=True)
fig.show()
4. Line Graph using Seaborn
import seaborn as sns
import matplotlib.pyplot as plt
# Sample data
data = {
'x': [1, 2, 3, 4, 5],
'y': [2, 3, 5, 7, 11]
}
# Creating a Seaborn line plot
sns.lineplot(x='x', y='y', data=data, marker='o')
plt.title('Line Graph using Seaborn')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.grid(True)
plt.show()
3. Line Graph with Error Bars using Matplotlib
import matplotlib.pyplot as plt
import numpy as np
# Sample data
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 3, 5, 7, 11])
yerr = np.array([0.5, 0.4, 0.3, 0.2, 0.1])
# Plotting the line graph with error bars
plt.errorbar(x, y, yerr=yerr, fmt='-o', capsize=5)
plt.title('Line Graph with Error Bars')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.grid(True)
plt.show()
2. Multiple Line Graphs using Matplotlib
import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4, 5]
y1 = [2, 3, 5, 7, 11]
y2 = [1, 4, 6, 8, 10]
# Plotting multiple line graphs
plt.plot(x, y1, label='Line 1', marker='o')
plt.plot(x, y2, label='Line 2', marker='s')
plt.title('Multiple Line Graphs')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
plt.grid(True)
plt.show()
1. Basic Line Graph using Matplotlib
import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
# Plotting the line graph
plt.plot(x, y, marker='o')
plt.title('Basic Line Graph')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.grid(True)
plt.show()
Let's break down the code and explain what each part does:
my_list = [1, 2, 3, 4, 5]
my_list[1:3] = []
print(my_list)
Step-by-Step Explanation
my_list = [1, 2, 3, 4, 5]
This line initializes a list named my_list with the elements [1, 2, 3, 4, 5].
my_list[1:3] = []
my_list[1:3] is a slice of the list from index 1 to index 3, but not including index 3. In this case, my_list[1:3] refers to the sublist [2, 3].
The assignment my_list[1:3] = [] replaces the slice [2, 3] with an empty list [], effectively removing the elements 2 and 3 from the list.
print(my_list)
This line prints the modified list.
After the slice assignment, my_list is modified to remove the elements at indices 1 and 2 (the elements 2 and 3). The resulting list is:
[1, 4, 5]
Visual Breakdown
Let's visualize the process:
Initial list: [1, 2, 3, 4, 5]
Slice my_list[1:3] refers to [2, 3]
Assigning [] to the slice removes [2, 3]
Resulting list: [1, 4, 5]
Full Code with Output
Here is the complete code along with its output:
my_list = [1, 2, 3, 4, 5]
my_list[1:3] = []
print(my_list) # Output: [1, 4, 5]
The output is [1, 4, 5], as explained.
def trace(func):
"""A decorator that traces function calls."""
def wrapper(*args, **kwargs):
print(f"TRACE: calling {func.__name__}() with {args}, {kwargs}")
result = func(*args, **kwargs)
print(f"TRACE: {func.__name__}() returned {result}")
return result
return wrapper
@trace
def compute_area(length: float, width: float) -> float:
"""Compute the area of a rectangle."""
return length * width
area = compute_area(3.0, 4.5)
# clcoding.com
TRACE: calling compute_area() with (3.0, 4.5), {}
TRACE: compute_area() returned 13.5
def debug(func):
"""A decorator that prints the function
signature and return value."""
def wrapper(*args, **kwargs):
print(f"Calling {func.__name__} with {args} and {kwargs}")
result = func(*args, **kwargs)
print(f"{func.__name__} returned {result}")
return result
return wrapper
@debug
def add(a, b):
"""Add two numbers."""
return a + b
add(3, 4)
# clcoding.com
Calling add with (3, 4) and {}
add returned 7
7
square = lambda x: x * x
print(square(5))
# clcoding.com
25
def square(x):
"""Return the square of x."""
return x * x
def apply_function(func, value):
"""Apply a function to a value
and return the result."""
return func(value)
result = apply_function(square, 4)
print(result)
# clcoding.com
16
def divide(x, y):
"""
Divide x by y and return the result.
:param x: numerator
:param y: denominator
:return: division result
"""
return x / y
result = divide(10, 2)
print(result)
# clcoding.com
5.0
def multiply(x: int, y: int) -> int:
"""Multiply two integers
and return the result."""
return x * y
result = multiply(3, 4)
print(result)
# clcoding.com
12
def print_info(**kwargs):
"""Print key-value pairs
from keyword arguments."""
for key, value in kwargs.items():
print(f"{key}: {value}")
print_info(name="Clcoding", age=30, city="Earth")
# clcoding.com
name: Clcoding
age: 30
city: Earth
def sum_all(*args):
"""Sum all given arguments."""
return sum(args)
total = sum_all(1, 2, 3, 4, 5)
print(total)
# clcoding.com
15
def greet(name, greeting="Hello"):
"""Greet someone with a provided
greeting or a default greeting."""
return f"{greeting}, {name}!"
message = greet("Clcoding")
print(message)
message = greet("Python", "Hi")
print(message)
# clcoding.com
Hello, Clcoding!
Hi, Python!
1. Basic Function Definition
def add(a, b):
"""Add two numbers and
return the result."""
return a + b
result = add(3, 5)
print(result)
# clcoding.com
8
# Creating a simple list
my_list = [1, 2, 3, 4, 5]
print(my_list)
#clcoding.com
[1, 2, 3, 4, 5]
# Accessing elements by index
my_list = [1, 2, 3, 4, 5]
first_element = my_list[0]
last_element = my_list[-1]
print( "First:", first_element, "Last:", last_element)
#clcoding.com
First: 1 Last: 5
# Slicing a list
my_list = [1, 2, 3, 4, 5]
sub_list = my_list[1:4]
print(sub_list)
#clcoding.com
[2, 3, 4]
# Appending and extending a list
my_list = [1, 2, 3, 4, 5]
my_list.append(6)
my_list.extend([7, 8])
print(my_list)
#clcoding.com
[1, 2, 3, 4, 5, 6, 7, 8]
# Removing elements
my_list = [1, 2, 3, 4, 5]
my_list.remove(2)
popped_element = my_list.pop()
print(my_list)
print(popped_element)
#clcoding.com
[1, 3, 4]
5
# Using list comprehensions
my_list = [1, 2, 3, 4, 5]
squared_list = [x**2 for x in my_list]
print(squared_list)
#clcoding.com
[1, 4, 9, 16, 25]
# Creating and accessing nested lists
nested_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
element = nested_list[1][2]
print(nested_list, element)
#clcoding.com
[[1, 2, 3], [4, 5, 6], [7, 8, 9]] 6
# Using built-in functions and methods
my_list = [1, 2, 3, 4, 5]
length = len(my_list)
sorted_list = sorted(my_list)
my_list.sort(reverse=True)
print(length, sorted_list, my_list)
#clcoding.com
5 [1, 2, 3, 4, 5] [5, 4, 3, 2, 1]
# Iterating over a list
for element in my_list:
print(element)
5
4
3
2
1
# Using advanced operations like map, filter, and reduce
my_list = [1, 2, 3, 4, 5]
from functools import reduce
# Map: applying a function to each element
doubled_list = list(map(lambda x: x * 2, my_list))
print(doubled_list)
# Filter: filtering elements based on a condition
even_list = list(filter(lambda x: x % 2 == 0, my_list))
print(even_list)
# Reduce: reducing the list to a single value
sum_of_elements = reduce(lambda x, y: x + y, my_list)
print(sum_of_elements)
#clcoding.com
[2, 4, 6, 8, 10]
[2, 4]
15
6. Fraction Module for Rational Numbers
The fractions module provides support for rational number arithmetic.
from fractions import Fraction
a = Fraction(1, 3)
b = Fraction(2, 3)
c = a + b
print(c)
#clcoding.com
1
5. Decimal Module for Precise Calculations
For financial and other applications requiring precise decimal representation, the decimal module is very useful.
from decimal import Decimal, getcontext
getcontext().prec = 6
a = Decimal('0.1')
b = Decimal('0.2')
c = a + b
print(c)
#clcoding.com
0.3
s.PNG)
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