Py Notes 2

get random item in list python

To get a random item from a list in Python, you can use the random.choice() method from the random module. Here's how you can do it:

import random

# Your list
my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Select a random item
random_item = random.choice(my_list)

print(random_item)

This code snippet will select a random item from my_list each time it is executed.

how to run subprocess with multiple threads in python

Running subprocesses with multiple threads in Python involves using the subprocess module to start external processes and the threading or concurrent.futures.ThreadPoolExecutor module to manage multiple threads. Each thread can be used to handle a subprocess, allowing you to run and manage multiple subprocesses concurrently. Here's an example using concurrent.futures.ThreadPoolExecutor for a cleaner and more efficient approach:

Using `concurrent.futures.ThreadPoolExecutor`

This method is preferred for running tasks concurrently and is available in Python 3.2 and later. It abstracts away much of the boilerplate code associated with managing threads directly.

import subprocess
import concurrent.futures

# Define a function to run a subprocess
def run_subprocess(command):
    """Runs the subprocess specified by command."""
    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
    return result.stdout

# List of commands to run (each command is a list as expected by subprocess.run)
commands = [
    ['ls', '-l'],
    ['echo', 'Hello World'],
    ['uname', '-a']
]

# Use ThreadPoolExecutor to run subprocesses in parallel
with concurrent.futures.ThreadPoolExecutor() as executor:
    # Map the function and arguments to the executor
    results = executor.map(run_subprocess, commands)

    # Iterate through the results and print each
    for result in results:
        print(result)

This script defines a function run_subprocess that executes a given subprocess command. It then creates a list of commands to run. Using ThreadPoolExecutor, it maps each command to the run_subprocess function, running each command in a separate thread. The executor.map method automatically handles creating threads and assigning tasks to them. Finally, it prints the stdout output of each command.

Remember, running subprocesses in parallel can be powerful, but it also requires careful management of resources, especially if the subprocesses are resource-intensive or if you're running many of them in parallel. Always test your implementation under conditions similar to your production environment to ensure performance and stability.

get the last line of the text file python

To get the last line of a text file in Python, one efficient way is to seek towards the end of the file and then read backwards until you find a newline character. This method is especially useful for large files, as it avoids loading the entire file into memory. Here's how you can do it:

def get_last_line(file_path):
    with open(file_path, 'rb') as file:
        file.seek(-2, os.SEEK_END)  # Go to the second last byte before EOF
        while file.read(1) != b'\n':  # Keep moving backwards until you find a newline
            file.seek(-2, os.SEEK_CUR)
        last_line = file.readline().decode()  # Read and decode the last line
    return last_line

# Example usage
file_path = 'path/to/your/file.txt'
last_line = get_last_line(file_path)
print('Last line of the file:', last_line)

This function opens a file in binary mode ('rb') to allow for backward seeking, then moves the cursor back from the end of the file until it hits the beginning of the last line. It reads this line, decodes it to a string (assuming a UTF-8 encoding or similar), and returns it.

This method is particularly efficient for large files, as it doesn't require reading the whole file into memory. However, it assumes that the file ends with a newline character; if the file's last line doesn't end with a newline, consider adjusting the logic to handle such cases appropriately.

create alias method in python

Creating an alias for a method in Python can be done simply by assigning the method to a new variable name. This can be useful for shortening method names, improving code readability, or creating more intuitive names for methods obtained from libraries or base classes.

Here's a basic example to illustrate how to create an alias for a method within a class:

class MyClass:
    def original_method(self):
        print("This is the original method.")

    # Create an alias for original_method
    alias_method = original_method

# Creating an instance of MyClass
obj = MyClass()

# Both calls will execute the same method
obj.original_method()
obj.alias_method()

In this example, alias_method is an alias for original_method. Both obj.original_method() and obj.alias_method() will produce the same output, showing that both names refer to the same method.

Aliasing Functions and Methods from Modules

You can also create aliases for functions and methods from imported modules. For example, if you wanted to create a shorter alias for a function from the math module, you could do the following:

import math

# Create an alias for the math.sqrt function
sqrt = math.sqrt

# Use the alias
print(sqrt(4))

This makes sqrt an alias for math.sqrt, allowing you to call sqrt(4) instead of math.sqrt(4).

Important Considerations

Scope: Aliases will only be available in the scope where they are defined. If you define an alias inside a function, it won't be accessible outside of that function.
Memory: Creating an alias does not duplicate the method or function. Both the original and the alias refer to the same memory location, so changes affecting the method/function (though rare in practice for functions) will reflect through both the original and the alias.
Usage: While aliasing can make some names more readable or manageable, overuse or creating aliases with non-descriptive names can make code harder to understand and maintain. It's important to use this technique judiciously.

remove less than 2 letter string in list + python

To remove strings from a list that are less than 2 letters long in Python, you can use a list comprehension. This is a concise and efficient way to create a new list that only includes items that meet a certain condition. Here's how you can do it:

# Original list with strings of various lengths
my_list = ['a', 'be', 'see', 'd', 'efg', 'h']

# Use a list comprehension to filter out strings shorter than 2 letters
filtered_list = [item for item in my_list if len(item) >= 2]

print(filtered_list)

In this example, filtered_list will contain only the strings from my_list that are 2 letters long or longer. The output will be:

['be', 'see', 'efg']

List comprehensions are a powerful feature in Python that allow for clear and concise code when manipulating collections.

get random character in python

To get a random character in Python, you can use the random.choice() function from the random module. First, decide on the set of characters you want to select from. For example, you might want a random lowercase letter, in which case you can use the string ascii_lowercase from the string module, which contains all lowercase letters.

Here's an example that shows how to get a random lowercase letter:

import random
import string

# Select a random character from lowercase letters
random_character = random.choice(string.ascii_lowercase)

print(random_character)

If you want a random character from a different set, like uppercase letters, digits, or a custom set of characters, you can modify the source string accordingly. Here are a few examples:

For uppercase letters: string.ascii_uppercase
For digits: string.digits
For a custom set of characters: Define your own string, e.g., 'abc123'

Here's an example with a custom set of characters:

import random

# Define a custom set of characters
characters = 'abc123XYZ!@#'

# Select a random character from the custom set
random_character = random.choice(characters)

print(random_character)

Using random.choice(), you can easily select a random character from any sequence of characters you define.

ternary operator python

The ternary operator in Python provides a way to condense a simple if-else block into a single line of code. It allows for a concise expression of condition-based assignments or decisions. The syntax for the ternary operator in Python is:

value_if_true if condition else value_if_false

Here's a basic example to demonstrate its use:

# Condition
a = 10
b = 5

# Ternary operator to find the maximum value
max_value = a if a > b else b

print(max_value)

In this example, max_value will be assigned the value of a if a is greater than b, otherwise, it will be assigned the value of b. This effectively replaces the following more verbose if-else statement:

if a > b:
    max_value = a
else:
    max_value = b

The ternary operator is particularly useful for simple conditional assignments and can make the code more readable and concise.

download file in requests python

To download a file from the web using Python and save it with the same filename as it has on the server, you can use the requests library for downloading and the os module to extract the filename from the URL. Here's how you can do it:

Install the requests library if you haven't already, by running pip install requests.
Use the requests library to make a GET request to the file's URL.
Extract the filename from the URL using os.path.basename.
Save the file using the extracted filename.

Here's a step-by-step code example:

import requests
import os

# URL of the file you want to download
url = 'https://example.com/path/to/file.jpg'

# Extract the filename
filename = os.path.basename(url)

# Perform the GET request
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    # Open a file with the same filename as in the URL and write the content
    with open(filename, 'wb') as file:
        file.write(response.content)
    print(f'File "{filename}" downloaded successfully!')
else:
    print(f'Failed to download the file. Status code: {response.status_code}')

Make sure to replace 'https://example.com/path/to/file.jpg' with the actual URL of the file you wish to download. This script will download the file and save it in the current working directory with the same name as it appears in the URL.

Note:

The os.path.basename function is used to extract the filename part of the URL. This works well for simple URLs but might not always provide the desired results for URLs with complex query parameters or in cases where the URL does not contain the filename directly. In such scenarios, you may need to parse the URL more carefully or use the Content-Disposition header if provided by the server's response.
Always be cautious when downloading files from untrusted sources, as they can potentially harm your system.

download file with same filename + python

Install the requests library if you haven't already, by running pip install requests.
Use the requests library to make a GET request to the file's URL.
Extract the filename from the URL using os.path.basename.
Save the file using the extracted filename.

Here's a step-by-step code example:

import requests
import os

# URL of the file you want to download
url = 'https://example.com/path/to/file.jpg'

# Extract the filename
filename = os.path.basename(url)

# Perform the GET request
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    # Open a file with the same filename as in the URL and write the content
    with open(filename, 'wb') as file:
        file.write(response.content)
    print(f'File "{filename}" downloaded successfully!')
else:
    print(f'Failed to download the file. Status code: {response.status_code}')

Note:

The os.path.basename function is used to extract the filename part of the URL. This works well for simple URLs but might not always provide the desired results for URLs with complex query parameters or in cases where the URL does not contain the filename directly. In such scenarios, you may need to parse the URL more carefully or use the Content-Disposition header if provided by the server's response.
Always be cautious when downloading files from untrusted sources, as they can potentially harm your system.

get last item in a list in python

To get the last item in a list in Python, you can use negative indexing. Here’s how you do it:

my_list = [1, 2, 3, 4, 5]
last_item = my_list[-1]

print(last_item)

In this example, last_item will be 5, which is the last element of my_list. Negative indices start from the end of the list, with -1 being the last item, -2 being the second to last item, and so on.

format with two decimals python

To format a number with two decimal places in Python, you can use the format() function or an f-string (if you're using Python 3.6 or newer). Here are examples of both methods:

Using `format()` Function

number = 3.14159
formatted_number = format(number, '.2f')
print(formatted_number)

Using an f-string

number = 3.14159
formatted_number = f"{number:.2f}"
print(formatted_number)

Both of these examples will output 3.14, formatting the original number to two decimal places.

overwrite into file python

To overwrite the contents of a file in Python, you can open the file in write mode ('w') using the open() function. When you open a file in write mode, it truncates the file to zero length if it already exists or creates it if it doesn't. Here's how you can do it:

# Define the file name
file_name = 'example.txt'

# Text to write into the file
text_to_write = 'This text will overwrite any existing content in the file.'

# Open the file in write mode ('w') and write the text
with open(file_name, 'w') as file:
    file.write(text_to_write)

print(f'Content written to {file_name}')

This code will create a file named example.txt (or overwrite it if it already exists) in the current working directory, and then write the string This text will overwrite any existing content in the file. into the file. The with statement ensures that the file is properly closed after writing, even if an error occurs during the process.

empty the file in python

To empty or truncate a file to zero length in Python, you can open the file in write mode ('w') without writing anything to it. Opening a file in write mode automatically truncates the file to zero length. Here’s how you can do it:

# Define the file name
file_name = 'example.txt'

# Open the file in write mode to truncate it to zero length
with open(file_name, 'w'):
    pass  # No need to write anything, just opening in 'w' mode truncates the file

print(f'{file_name} has been emptied.')

This code snippet effectively empties the file example.txt by simply opening it in write mode and then immediately closing it (the pass statement is just a placeholder that does nothing). The with statement ensures that the file is properly closed after being opened.

read a file line py line python

To read a file line by line in Python, you can use a with statement to open the file and then iterate over it in a loop. Each iteration of the loop reads a single line from the file until the end of the file is reached. Here's how you can do it:

# Define the file name
file_name = 'example.txt'

# Open the file in read mode ('r')
with open(file_name, 'r') as file:
    # Iterate over each line in the file
    for line in file:
        # Print the current line. Use line.strip() to remove the newline character at the end
        print(line.strip())

This code will print each line from example.txt to the console, removing any trailing newline characters from each line with strip() to avoid double-spacing caused by the print statement’s own newline.

two bars together + python matplotlib

Creating a chart with two bars side by side for each category in Python using Matplotlib can be achieved by carefully positioning the bars on the x-axis. This involves adjusting the bar positions so that they don’t overlap but appear next to each other for each category or group. Here's how you can do it:

Prepare your data: Decide on the categories (groups) and the values for each of the two sets of bars you want to display.
Calculate positions for each set of bars: You'll need to offset the positions of the two sets of bars so that they appear side by side rather than on top of each other.
Plot the bars: Use Matplotlib to plot the bars at their respective positions.
Adjust the x-axis labels: Make sure the labels of your categories are centered between the two bars.
Add a legend: To make clear what each set of bars represents.

Here's an example that puts this into action:

import matplotlib.pyplot as plt
import numpy as np

# Categories
categories = ['Category 1', 'Category 2', 'Category 3']

# Data for each set of bars
values_set1 = [10, 20, 15]
values_set2 = [12, 18, 14]

# Positions of the categories
x = np.arange(len(categories))

# Width of a bar
width = 0.4

# Plotting
fig, ax = plt.subplots()
bars1 = ax.bar(x - width/2, values_set1, width, label='Set 1')
bars2 = ax.bar(x + width/2, values_set2, width, label='Set 2')

# Adding some text for labels, title, and custom x-axis tick labels
ax.set_ylabel('Scores')
ax.set_title('Scores by category and set')
ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.legend()

# Display the plot
plt.show()

In this example, width determines the width of each bar, and by subtracting or adding half of this width from the x positions (x - width/2 and x + width/2), we position the two sets of bars side by side for each category. Adjusting width or the calculations for x positions can change how close together or far apart the bars appear.

subtract from small number and get a complete number instead of minus in python

If you're looking to subtract one number from another and want to ensure the result is always non-negative, you can use the abs() function in Python, which returns the absolute value of the subtraction, thereby ensuring you never end up with a negative result. Here's a simple example:

number1 = 5
number2 = 10

# Subtract number1 from number2 and get the absolute value
result = abs(number1 - number2)

print(result)

In this case, result will be 5, which is the absolute value of (5 - 10). This way, regardless of which number is larger, you will always get a non-negative result from the subtraction.

However, if by "complete number" you mean you specifically want to ensure that you subtract the smaller number from the larger one to avoid a negative result, you can decide the order of subtraction based on a conditional check. Here's how you might do it:

number1 = 5
number2 = 10

# Ensure subtraction is from the larger number
result = number2 - number1 if number2 > number1 else number1 - number2

print(result)

This method also results in result being 5, but it does so by ensuring the subtraction is always performed with the larger number first. This approach gives you a positive result without necessarily using abs(), which might be more intuitive depending on your specific needs.

how to convert minus number to complete number in python

If you have a negative number and you want to convert it to its absolute value (i.e., remove the negative sign), you can use the abs() function in Python. Here's how you can do it:

# Example with a negative number
negative_number = -5

# Convert negative number to its absolute value
absolute_value = abs(negative_number)

print(absolute_value)  # Output: 5

The abs() function returns the absolute value of a number, which is its distance from zero without regard to its sign. So abs(-5) evaluates to 5, removing the negative sign from the number.

If you want to ensure that a subtraction operation always results in a positive number, you can use the abs() function as shown in my previous response. This is useful for scenarios where you're subtracting smaller numbers from larger ones and want to ensure the result is always non-negative.

how to convert minus number to complete number in python

If by "complete number" you mean you want to convert negative numbers to their positive equivalent (i.e., remove the negative sign), you can simply use the abs() function in Python. Here's how you can do it:

# Example with a negative number
negative_number = -5

# Convert negative number to its positive equivalent
positive_number = abs(negative_number)

print(positive_number)  # Output: 5

The abs() function returns the absolute value of a number, which is its distance from zero without regard to its sign. So abs(-5) evaluates to 5, effectively removing the negative sign from the number.

This method works for both negative and positive numbers. If the number is already positive, abs() leaves it unchanged.

string to dictionary + python

To convert a string representation of a dictionary into a Python dictionary, you can use the ast.literal_eval() function from the ast module, or you can use the json.loads() function from the json module. Both methods are safe and efficient for converting string representations of dictionaries into actual dictionaries. Here's how you can do it using both methods:

Using ast.literal_eval():

import ast

# String representation of a dictionary
string_dict = '{"key1": "value1", "key2": "value2", "key3": "value3"}'

# Convert string to dictionary using ast.literal_eval()
dictionary = ast.literal_eval(string_dict)

print(dictionary)

Using json.loads():

import json

# String representation of a dictionary
string_dict = '{"key1": "value1", "key2": "value2", "key3": "value3"}'

# Convert string to dictionary using json.loads()
dictionary = json.loads(string_dict)

print(dictionary)

Both of these methods will produce the following output:

{'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}

Choose the method that suits your needs best. If you're working with JSON-like strings, using json.loads() might be more appropriate. Otherwise, ast.literal_eval() provides a more generic solution for evaluating Python literals.

get random item in python

To get a random item from a sequence in Python, you can use the random.choice() function from the random module. This function selects a random element from a non-empty sequence. Here's how you can use it:

import random

# Example list
my_list = [1, 2, 3, 4, 5]

# Get a random item from the list
random_item = random.choice(my_list)

print(random_item)

In this example, random_item will contain a random element from my_list. Each element has an equal chance of being selected. This function works with other sequence types as well, such as strings, tuples, or custom-defined sequences.

how to call dynamic method based on user arguments?

To call a dynamic method based on user arguments, you can use Python's built-in getattr() function to dynamically retrieve the method of an object based on its name. Here's a basic example demonstrating how to achieve this:

class MyClass:
    def method1(self):
        print("Method 1 called")
    
    def method2(self):
        print("Method 2 called")
    
    def method3(self):
        print("Method 3 called")

# Create an instance of MyClass
obj = MyClass()

# User input to select the method
user_input = input("Enter method name (method1, method2, method3): ")

# Call the method dynamically based on user input
method_to_call = getattr(obj, user_input, None)
if method_to_call is not None and callable(method_to_call):
    method_to_call()
else:
    print("Invalid method name.")

In this example, the user inputs the name of the method they want to call (method1, method2, or method3). The getattr() function retrieves the method of the MyClass object based on the user input. If the method exists and is callable, it is called dynamically. If the method name provided by the user is invalid or does not exist, an appropriate message is displayed.

Make sure to handle cases where the method name provided by the user doesn't exist in the class to avoid runtime errors. Also, ensure that user input is sanitized to prevent any security vulnerabilities.

identify the last element in enumerate + python

To identify the last element in an enumeration in Python, you can compare the index of the current element with the length of the iterable being enumerated. If the current index equals the length of the iterable minus one, then it is the last element. Here's how you can do it:

my_list = ['apple', 'banana', 'cherry', 'date']

# Enumerate the list
for i, item in enumerate(my_list):
    if i == len(my_list) - 1:
        print(f'The last item is: {item}')
    else:
        print(item)

This code snippet iterates through my_list and prints each item. If the index i is equal to len(my_list) - 1, it prints a message indicating that the current item is the last one.

Another way to identify the last element is to use a flag variable to mark it, like this:

my_list = ['apple', 'banana', 'cherry', 'date']
last_index = len(my_list) - 1

# Enumerate the list
for i, item in enumerate(my_list):
    if i == last_index:
        print(f'The last item is: {item}')
    else:
        print(item)

This approach calculates the index of the last element only once, which can be more efficient if you're iterating over a large iterable. Both methods achieve the same result; choose the one that suits your preference or requirements.

identify whether the current item as a last item in enumerate + python

You can identify whether the current item is the last item in an enumeration by comparing the index of the current item with the index of the last item in the enumeration. Here's how you can do it:

my_list = ['apple', 'banana', 'cherry', 'date']

# Enumerate the list
for i, item in enumerate(my_list):
    if i == len(my_list) - 1:
        print(f'{item} is the last item.')
    else:
        print(f'{item} is not the last item.')

In this code snippet, i represents the index of the current item, and len(my_list) - 1 represents the index of the last item in the list. If i equals len(my_list) - 1, then the current item is the last item in the enumeration.

Another way to achieve the same result is to use a flag variable to mark the last iteration. Here's how you can do it:

my_list = ['apple', 'banana', 'cherry', 'date']
last_index = len(my_list) - 1

# Enumerate the list
for i, item in enumerate(my_list):
    if i == last_index:
        print(f'{item} is the last item.')
    else:
        print(f'{item} is not the last item.')

Both methods yield the same result. Choose the one that suits your preference or requirements.

dictionary keys as list python

If you want to extract the keys of a dictionary and store them as a list in Python, you can simply use the keys() method of the dictionary. Here's how you can do it:

# Your dictionary
my_dict = {'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}

# Extract keys as a list
keys_list = list(my_dict.keys())

print(keys_list)

In this code snippet, my_dict.keys() returns a view object that contains the keys of the dictionary my_dict. By passing this view object to the list() constructor, you can convert it into a list. The resulting keys_list will contain all the keys of the dictionary as elements.

Alternatively, you can directly use a list comprehension to achieve the same result:

keys_list = [key for key in my_dict.keys()]

Both methods will produce the same output, a list containing the keys of the dictionary. Choose the one that suits your preference or fits better with your existing code.

throw error python

To throw an error in Python, you can use the raise statement followed by the type of error you want to raise. Here's a basic example:

# Raise a ValueError
raise ValueError("This is a ValueError example")

In this example, ValueError is raised with the message "This is a ValueError example".

You can also use a custom exception by creating a new class that inherits from Exception:

# Define a custom exception class
class CustomError(Exception):
    pass

# Raise a custom error
raise CustomError("This is a custom error example")

In this example, a custom exception named CustomError is defined and raised with the message "This is a custom error example".

You can handle these errors using a try-except block:

try:
    raise ValueError("This is a ValueError example")
except ValueError as e:
    print("Caught ValueError:", e)

This block will catch the ValueError exception and print its message. You can similarly handle custom exceptions.

result = business.get_search_result(query)

I need to find the process time in this line

To measure the time taken by a specific line of code or function in Python, you can use the time module. This module provides a simple way to capture the start and end times around a piece of code, allowing you to calculate the duration it takes to execute that code.

Here’s how you can use the time module to measure the processing time of the business.get_search_result(query) function call:

Step-by-Step Code

import time

# Start timing
start_time = time.time()

# Execute the function
result = business.get_search_result(query)

# End timing
end_time = time.time()

# Calculate the duration
duration = end_time - start_time

print(f"The function took {duration} seconds to complete.")

Explanation

Import the Time Module: The time module is part of Python's standard library, so you don't need to install anything extra.
Start Timing: time.time() returns the current time in seconds since the Epoch (January 1, 1970, 00:00:00 UTC). You capture this time just before your function executes.
Function Execution: Run the function for which you want to measure the execution time.
End Timing: Immediately after the function completes, capture the time again.
Calculate Duration: The difference between end_time and start_time gives you the duration in seconds that the function took to execute.
Output the Duration: Print or otherwise use the duration information. This can be helpful for logging, debugging, or performance tuning.

Using this method, you can accurately gauge the runtime of any function or code block in Python. This is particularly useful when you need to optimize performance or diagnose slow execution issues in your applications.

8.523752927780151

I want only 3 decimals

To format a floating-point number to three decimal places in Python, you can use various string formatting methods. Here are a few common approaches:

1. Using the `format` function:

number = 8.523752927780151
formatted_number = format(number, ".3f")
print(formatted_number)

2. Using formatted string literals (f-strings):

number = 8.523752927780151
formatted_number = f"{number:.3f}"
print(formatted_number)

3. Using the `round` function:

number = 8.523752927780151
rounded_number = round(number, 3)
print(rounded_number)

Explanation:

Format Function: The format function is versatile and can be used for various types of data formatting. .3f specifies that you want the number formatted as a float with three decimal places.
F-Strings: This method is concise and embeds Python expressions inside string constants. It’s available from Python 3.6 onwards.
Round Function: The round function is straightforward and rounds the number to the specified number of decimal places. Note that rounding can behave differently for numbers exactly halfway between two intervals due to floating-point arithmetic rules.

Choose the method that best fits the style of your existing code or your personal preference. The formatted string literals or f-strings are generally the most readable and are widely used in modern Python code.

start_time = time.time()
result = business.get_search_result(query)
duration = time.time() - start_time

I don't want to do like this every time. Instead, I want to have a decorator function which will return process time of specific function like below

@decorator
def get_search_result

Creating a decorator to measure the execution time of a function is a great way to refactor repetitive timing code and make your Python scripts cleaner and more Pythonic. Below, I’ll provide a simple example of how you can create such a decorator and apply it to functions you wish to time.

Step 1: Define the Decorator

First, let's define a decorator that measures the execution time of any function it wraps:

import time

def time_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        duration = time.time() - start_time
        print(f"{func.__name__} took {duration:.3f} seconds to complete.")
        return result
    return wrapper

Explanation:

Decorator Function: time_decorator is the decorator that takes a function func as an argument.
Wrapper Function: wrapper replaces the decorated function. It takes any positional and keyword arguments (*args and **kwargs) to pass them to the original function.
Timing: Inside the wrapper, we record the start time, execute the function, and calculate the duration by subtracting the start time from the end time.
Return the Result: The original function's result is returned after printing the duration.

Step 2: Apply the Decorator

Now, you can use this decorator to measure the execution time of any function by prefixing the function definition with @time_decorator:

@time_decorator
def get_search_result(query):
    # Simulate some computations or database queries
    time.sleep(2)  # Simulating delay
    return f"Result for {query}"

# Use the function
result = get_search_result("example query")

Output:

This will print something like:

get_search_result took 2.003 seconds to complete.

Advantages of Using Decorators:

Non-intrusive: You can add timing functionality to any function without modifying its internal code.
Reusability: The same decorator can be applied to any number of functions across your projects.
Cleaner Code: It keeps the timing logic separate from the business logic, making the code cleaner and easier to read.

This approach is particularly useful in testing and development phases when you are concerned with performance and need to monitor how long various parts of your application take to execute.

I want to return process_time whoever calls the method "get_search_result"

If you want the decorator to not only print the execution time but also return the execution time to whoever calls the method, you can adjust the decorator to return both the result of the function and its execution time. This often involves returning a tuple or a custom data structure that includes both the original function's result and the timing data.

Modified Decorator to Return Execution Time

Here’s how you can modify the decorator to return the execution time along with the function’s result:

import time

def time_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        duration = time.time() - start_time
        print(f"{func.__name__} took {duration:.3f} seconds to complete.")
        return result, duration  # Return a tuple containing the result and the execution time
    return wrapper

# Example usage
@time_decorator
def get_search_result(query):
    time.sleep(2)  # Simulate processing delay
    return f"Result for {query}"

# Call the function
result, process_time = get_search_result("example query")
print(f"Process time: {process_time:.3f} seconds")

Explanation

Return Tuple: The wrapper function now returns a tuple where the first element is the result of the function and the second element is the duration (execution time).
Printing and Returning: The function's execution time is both printed (for immediate feedback during debugging or logging) and returned so that callers can programmatically use it.

Handling Decorator with Multiple Return Values

Using this decorator changes the signature of the returned value, which could affect how you handle the function’s output throughout your code. Callers of the function must now expect two return values (a tuple) instead of one. This change needs to be managed in the codebase to avoid errors.

Alternative: Custom Class or Namedtuple

If you prefer a cleaner or more explicit way of handling multiple return values, you can use a named tuple or a simple class to make the returns more descriptive:

from collections import namedtuple

ResultWithTime = namedtuple('ResultWithTime', ['result', 'execution_time'])

def time_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        duration = time.time() - start_time
        return ResultWithTime(result, duration)
    return wrapper

# Usage remains similar, but now more structured
result_with_time = get_search_result("example query")
print(f"Result: {result_with_time.result}, Process time: {result_with_time.execution_time:.3f} seconds")

This approach enhances readability and maintenance by explicitly defining what is being returned, which can be especially useful in large codebases or API-like scenarios.

PreviousPy Notes NextPy Notes 3

Last updated 1 year ago

Using concurrent.futures.ThreadPoolExecutor

Aliasing Functions and Methods from Modules

Important Considerations

Using format() Function

Using an f-string

Step-by-Step Code

Explanation

1. Using the format function:

2. Using formatted string literals (f-strings):

3. Using the round function:

Explanation:

Step 1: Define the Decorator

Explanation:

Step 2: Apply the Decorator

Output:

Advantages of Using Decorators:

Modified Decorator to Return Execution Time

Explanation

Handling Decorator with Multiple Return Values

Alternative: Custom Class or Namedtuple

Using `concurrent.futures.ThreadPoolExecutor`

Using `format()` Function

1. Using the `format` function:

3. Using the `round` function: