Py Notes 3

string ljust in python

In Python, the string method .ljust(width, fillchar) returns a left-justified string within a given minimum width. If the width is less than or equal to the length of the string, the original string is returned. If the width is greater, the method pads the string on the right with the specified fillchar (default is a space) until the string reaches the given width. Here's the basic syntax:

string.ljust(width[, fillchar])

width: The minimum width of the resulting string. If the width is less than the original string, the original string is returned.
fillchar (optional): The character used for padding. If not specified, a space is used. It must be a single character, not multiple characters.

Here's an example:

text = "hello"
# Left justify with spaces to make it 10 characters long
print(text.ljust(10))
# Left justify with asterisks
print(text.ljust(10, '*'))

Output:

hello     
hello*****

This demonstrates how ljust can be used to ensure a string reaches a certain length by padding it on the right with either spaces (default behavior) or a specified character.

python f strings tutorial how to use f strings for string formatting

Python's f-strings, introduced in Python 3.6, offer a more readable, concise, and convenient way to format strings compared to older methods like % formatting or the str.format() method. An f-string is a literal string prefixed with 'f' or 'F', which contains expressions inside curly braces {}. These expressions are replaced with their values.

Basic Usage

To create an f-string, prefix the string with f or F and include any variables or expressions you want to format inside {}. Here's a simple example:

name = "John"
age = 30
greeting = f"Hello, my name is {name} and I am {age} years old."
print(greeting)

This will output:

Hello, my name is John and I am 30 years old.

Expressions Inside F-Strings

One of the advantages of f-strings is that you can put any valid Python expression inside the curly braces:

x = 10
y = 5
print(f"{x} times {y} is {x * y}")

Output:

10 times 5 is 50

Formatting Specifiers

You can also use formatting specifiers with f-strings to control the formatting of numbers, dates, and more. Formatting specifiers follow a colon : after the expression:

import decimal
price = decimal.Decimal("1234.5678")
print(f"The price is {price:.2f}")

Output:

The price is 1234.57

This rounds the decimal to two places.

Date Formatting

F-strings work well with datetime objects, allowing for easy formatting:

from datetime import datetime
now = datetime.now()
print(f"The current time is {now:%Y-%m-%d %H:%M}")

Output (varies depending on the current time):

The current time is 2024-04-06 12:34

Multiline F-Strings

F-strings can span multiple lines, which is useful for longer strings:

name = "John"
occupation = "developer"
message = (
    f"Name: {name}\n"
    f"Occupation: {occupation}"
)
print(message)

Escaping Braces

If you need to include literal { or } characters in an f-string, double them:

print(f"{{This will appear inside curly braces}}")

Output:

{This will appear inside curly braces}

Summary

Python's f-strings provide a modern and efficient way to format strings. They are both concise and powerful, allowing for inline expressions, formatting, and multiline strings. Remember to prefix your string with f and include your variables or expressions within {} to take advantage of this feature.

How to install pyenv?

Installing pyenv can significantly simplify Python version management on your system, allowing you to switch between multiple versions of Python easily. Here's a general guide to installing pyenv on various operating systems. Note that specific steps might vary based on the exact OS version you are using and any updates to pyenv installation procedures after my last update in April 2023.

Prerequisites

Git: Ensure Git is installed on your system as pyenv will be cloned from its GitHub repository.
Build dependencies: Installing Python versions requires various build dependencies depending on your operating system.

On macOS

Install Homebrew: If you don't have Homebrew (a package manager for macOS), install it by running the following in your terminal:
```
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
```
Install pyenv: Use Homebrew to install pyenv:
```
brew install pyenv
```
Configuration: Add pyenv to your shell to enable shims and autocompletion. Execute the following command and follow the on-screen instructions:
```
echo -e 'if command -v pyenv 1>/dev/null 2>&1; then\n  eval "$(pyenv init --path)"\nfi' >> ~/.zprofile
```
Restart your terminal, or run source ~/.zprofile to apply the changes.

On Linux

Install dependencies: Depending on your Linux distribution, you will need to install prerequisites. For Ubuntu/Debian, you can install common build dependencies with:

sudo apt-get update; sudo apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev \
libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev \
xz-utils tk-dev libffi-dev liblzma-dev python-openssl git

Clone pyenv: Clone the pyenv repository to ~/.pyenv:

git clone https://github.com/pyenv/pyenv.git ~/.pyenv

Configure your profile: Add pyenv to your shell so that it initializes correctly. Add the following lines to your shell's startup file (.bashrc, .zshrc, etc.):
```
export PYENV_ROOT="$HOME/.pyenv"
export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init --path)"
```
Restart your terminal or run source ~/.bashrc (or the equivalent for your shell) to apply the changes.

On Windows

For Windows, it's recommended to use pyenv-win instead of pyenv, as pyenv is primarily designed for Unix-like environments.

Install pyenv-win: You can install pyenv-win via chocolatey, a package manager for Windows:
```
choco install pyenv-win
```
Configuration: Add pyenv to your path manually or ensure it's added by the installation process. You might need to restart your terminal or your computer to apply the changes.

Verifying Installation

After installation, you can verify that pyenv is correctly installed by running:

pyenv --version

This command should output the version of pyenv you have installed. With pyenv installed, you can now install different versions of Python, set global or local Python versions for your projects, and much more. To learn more about using pyenv, you can visit the official GitHub repository of pyenv.

loop key value dictionary python

Looping through key-value pairs in a Python dictionary is a common task, allowing you to access both the keys and their corresponding values within the loop. Here's how you can do it using various methods:

Method 1: Using `.items()`

The .items() method returns a view object that displays a list of dictionary's key-value tuple pairs. It's the most straightforward way to loop through both keys and values:

my_dict = {'apple': 1, 'banana': 2, 'cherry': 3}

for key, value in my_dict.items():
    print(f"Key: {key}, Value: {value}")

Method 2: Looping Through Keys and Accessing Values

Alternatively, you can loop through the keys and then use them to access the values:

my_dict = {'apple': 1, 'banana': 2, 'cherry': 3}

for key in my_dict:
    value = my_dict[key]
    print(f"Key: {key}, Value: {value}")

This method is slightly less efficient than using .items(), especially with large dictionaries, because it accesses the dictionary to get each value by its key inside the loop.

Method 3: Using `.keys()` and `.values()` (When Only Keys or Values are Needed)

If you only need the keys or the values, you can loop through them using .keys() or .values() respectively:

# Looping through keys
for key in my_dict.keys():
    print(f"Key: {key}")

# Looping through values
for value in my_dict.values():
    print(f"Value: {value}")

While .keys() is not necessary for looping through just the keys (you can do that directly with for key in my_dict:), it's shown here for completeness.

Method 4: Python Dictionary Comprehension (Advanced)

Dictionary comprehension is a compact way to process all items in a dictionary. While it's not "looping" in the traditional sense of iterating with a for loop, it lets you create a new dictionary by iterating over an existing one:

my_dict = {'apple': 1, 'banana': 2, 'cherry': 3}

# Create a new dictionary with keys and values swapped
new_dict = {value: key for key, value in my_dict.items()}
print(new_dict)

This method is useful for scenarios where you need to construct a new dictionary from an existing one based on some condition or operation applied to the keys and values.

Each of these methods serves different purposes and can be chosen based on what you need to achieve in your loop.

format exception in python

Formatting exceptions in Python is crucial for debugging and logging purposes, allowing you to present error information clearly and effectively. When an exception occurs, you can catch it using a try-except block and then format the exception information for output or logging. Here's how you can do this in various ways:

Basic Exception Catching

First, let's see a simple try-except block:

try:
    # Code block where you suspect an exception may occur
    1 / 0
except Exception as e:
    print(f"An error occurred: {e}")

This will print a basic message with the exception information.

Using `traceback` for Detailed Information

For more detailed information, including the traceback, you can use the traceback module, which allows you to format and print stack traces of exceptions:

import traceback

try:
    1 / 0
except Exception:
    print("An exception occurred:")
    traceback.print_exc()

The traceback.print_exc() function prints the detailed traceback to stderr by default.

Capturing Exception Information to a String

If you need to capture the exception information in a string (for logging to a file or processing further), you can use traceback.format_exc():

try:
    1 / 0
except Exception:
    error_message = traceback.format_exc()
    print(f"An exception occurred:\n{error_message}")

Custom Formatting

For custom formatting, especially when you want to log specific parts of an exception or create a custom error message, you can directly access the exception's attributes (args, __str__(), etc.) or use any combination of the methods shown above to format the exception as needed.

try:
    1 / 0
except Exception as e:
    custom_message = f"Error type: {type(e).__name__}, Arguments: {e.args}"
    print(custom_message)

Formatting Exceptions Without Catching

Sometimes, you might want to format and log exceptions without explicitly catching them (for example, in an error handler or logger). You can use sys.exc_info() to get the last exception information and then format it using traceback:

import sys
import traceback

def log_last_exception():
    exc_type, exc_value, exc_traceback = sys.exc_info()
    traceback_details = traceback.format_exception(exc_type, exc_value, exc_traceback)
    error_message = ''.join(traceback_details)
    print(f"An exception occurred:\n{error_message}")

# Simulate an error
try:
    1 / 0
except:
    log_last_exception()

This method captures the last exception globally and is particularly useful in contexts where the exception might not be directly accessible (e.g., in a logging utility).

Using these techniques, you can effectively manage and format exceptions in Python, making your error handling and logging much more informative and helpful for debugging purposes.

find the similarity metric between two strings in python

In Python, you can compute the similarity metric between two strings using various methods depending on the type of similarity you are interested in. Common approaches include the Levenshtein Distance (also known as Edit Distance), Jaccard similarity, and Cosine similarity, among others. I'll show you how to use a couple of these methods:

1. Levenshtein Distance (Edit Distance)

The Levenshtein Distance measures how many single-character edits (insertions, deletions, or substitutions) are required to change one word into the other. This metric is particularly useful for spelling correction and fuzzy matching.

You can use the python-Levenshtein library to calculate it:

# You might need to install the library using pip
# pip install python-Levenshtein

import Levenshtein as lev

str1 = "kitten"
str2 = "sitting"

distance = lev.distance(str1, str2)
similarity = 1 - distance / max(len(str1), len(str2))

print(f"Levenshtein distance: {distance}")
print(f"Similarity: {similarity:.2f}")

2. Jaccard Similarity

The Jaccard similarity measures the similarity between two sets. To apply this to strings, you can treat each string as a set of characters or words, depending on the granularity you need.

def jaccard_similarity(str1, str2):
    set1 = set(str1)
    set2 = set(str2)
    intersection = set1.intersection(set2)
    union = set1.union(set2)
    similarity = len(intersection) / len(union)
    return similarity

str1 = "apple"
str2 = "apple pie"

similarity = jaccard_similarity(str1, str2)
print(f"Jaccard similarity: {similarity:.2f}")

3. Cosine Similarity

Cosine similarity is often used in text mining and information retrieval to measure how similar two documents are irrespective of their size. This method is especially useful when comparing documents or strings where the occurrence of certain words (or characters) is more important than their order.

To compute the cosine similarity between two strings, you first need to vectorize the strings, typically using the bag-of-words model or TF-IDF (Term Frequency-Inverse Document Frequency). However, this approach is more complex and often involves using libraries such as scikit-learn.

Here’s a simplified example using character frequency (for demonstration purposes):

from collections import Counter
from math import sqrt

# Function to calculate cosine similarity
def cosine_similarity(str1, str2):
    # Count character occurrences
    c1 = Counter(str1)
    c2 = Counter(str2)
    
    # Convert counters to vectors
    all_items = set(c1.keys()).union(c2.keys())
    vec1 = [c1.get(k, 0) for k in all_items]
    vec2 = [c2.get(k, 0) for k in all_items]
    
    # Calculate dot product and magnitudes
    dot_product = sum(a*b for a, b in zip(vec1, vec2))
    magnitude1 = sqrt(sum(a*a for a in vec1))
    magnitude2 = sqrt(sum(a*a for a in vec2))
    
    # Compute cosine similarity
    return dot_product / (magnitude1 * magnitude2)

str1 = "I like apples"
str2 = "I like oranges"

similarity = cosine_similarity(str1, str2)
print(f"Cosine similarity: {similarity:.2f}")

These methods can be used to calculate similarity between two strings in Python, each having its own use cases and suitability depending on the application.

show e-04 in full format in python

If you have a floating-point number represented in scientific notation, such as e-04 (which stands for (10^{-4})), and you want to display it in its full decimal format in Python, you can use string formatting options available in Python. The format function or an f-string (formatted string literals introduced in Python 3.6) can be used to achieve this.

Here's how you can do it:

Using the `format` Function

The format function can be used to convert a number into a string and display it in various formats, including the full decimal format.

number = 5e-4  # This is 0.0005

# Using the format function with the 'f' format specifier
# The '.20f' specifies that you want to display the number with 20 decimal places
formatted_number = format(number, '.20f')
print(formatted_number)

Using an F-String

With Python 3.6 and later, you can use f-strings for a more readable way to format strings. Like with format, you can specify the number of decimal places you want.

number = 5e-4  # This is 0.0005

# Using an f-string with the 'f' format specifier
formatted_number = f"{number:.20f}"
print(formatted_number)

Both of these methods will display the number 0.0005 in its full decimal format. The .20f specifies that you want to format the number as a floating point with 20 digits after the decimal point. You can adjust the number of decimal places by changing the number in the format specifier (for example, .10f for 10 decimal places).

Remember that floating-point arithmetic can introduce some rounding errors, so the number displayed may not be exactly as you expect when you get to the very last decimal places. This is a limitation of how floating-point numbers are represented in computer memory and not specific to Python.

apply Decimal in ndarray in python

Using Python's decimal.Decimal objects within a NumPy ndarray can be challenging because NumPy is optimized for fixed-size, native data types like integers and floats, and it does not natively support arbitrary-precision arithmetic or the decimal data type found in Python's standard library. However, there are workarounds to use Decimal objects with arrays, albeit with some trade-offs in performance and convenience.

Here's how you can create an array of Decimal objects, though it won't be a NumPy ndarray but rather a standard Python list or a numpy array with dtype=object. This approach allows you to use Decimal for precise decimal arithmetic while sacrificing some of NumPy's speed and functionality optimized for numerical data types.

Using Python Lists with Decimal

from decimal import Decimal

# Create a list of Decimal objects
decimals = [Decimal("0.1"), Decimal("0.2"), Decimal("0.3")]

# Operations with Decimal retain their precision
result = sum(decimals)
print(result)  # Decimal('0.6')

Creating a NumPy Array of Decimal Objects

You can create a NumPy array that holds Decimal objects by specifying dtype=object when creating the array. However, keep in mind that operations on such arrays won't be as fast or as memory-efficient as operations on arrays of native numerical types.

import numpy as np
from decimal import Decimal

# Create a NumPy array of Decimal objects
decimals_np = np.array([Decimal("0.1"), Decimal("0.2"), Decimal("0.3")], dtype=object)

# Example operation: summing the array elements
result_np = np.sum(decimals_np)
print(result_np)  # Outputs: 0.6 as a Decimal

Considerations

Performance: Arrays of dtype=object do not benefit from NumPy's optimizations for numerical computations. Operations on such arrays will generally be slower and use more memory than operations on arrays of native data types.
Functionality: Not all NumPy functions and methods are designed to work with arrays of objects or will work as expected. You might encounter limitations or need to fallback on Python's built-in functions or list comprehensions for certain operations.
Precision vs. Performance: If your application requires the precision of Decimal, this approach may be necessary. However, if you can tolerate the precision of Python's float or NumPy's floating-point types, you would benefit from better performance and broader functionality.

When working with financial or scientific calculations that demand exact decimal representation, using Decimal in an array (despite the limitations) could be essential. Always consider the trade-offs between precision and performance for your specific use case.

how to disable python warnings

Disabling warnings in Python can be useful in situations where you're aware of the warnings and have deemed them non-critical to your application's functionality. However, it's generally best practice to address warnings directly, as they can often indicate potential issues that could affect your program's correctness or performance.

If you still need to disable warnings, Python provides several ways to do this, primarily through the warnings module.

Using the `warnings` Module

You can control the display of warnings using the warnings module. To ignore all warnings, you can use the warnings.filterwarnings() function:

import warnings

# Ignore all warnings
warnings.filterwarnings("ignore")

This will suppress all warnings from being displayed. If you want to re-enable warnings later in your program, you can use:

# Reset the warnings filter to default
warnings.filterwarnings("default")

Suppressing Specific Types of Warnings

You can also target specific kinds of warnings for suppression. For example, to ignore only DeprecationWarning:

import warnings

# Ignore DeprecationWarning
warnings.filterwarnings("ignore", category=DeprecationWarning)

Or to ignore warnings from a specific module:

import warnings

# Ignore all warnings from a specific module
warnings.filterwarnings("ignore", module="some_module_name")

Using the `-W` Command-Line Option

When running Python scripts, you can use the -W command-line option to control warnings. For example, to ignore all warnings, you can run your script like this:

python -W ignore your_script.py

Environment Variable

You can also set the PYTHONWARNINGS environment variable to ignore warnings. This method is useful when you want to suppress warnings globally in your environment, not just in a specific script.

For example, in Unix-like systems, you can export this variable:

export PYTHONWARNINGS="ignore"

And on Windows:

set PYTHONWARNINGS=ignore

Note

Suppressing warnings can hide important issues. Use this capability judiciously, and consider addressing the root causes of warnings where feasible. For development, it's often beneficial to enable even more warnings than usual, rather than suppressing them, to help identify and correct potential problems early.

how to apply filterwarnings in the project + python

To apply filterwarnings effectively across your Python project, you'll want to configure the warnings filter at a point in your application where it's guaranteed to execute before any warnings could be triggered. This setup usually depends on the structure of your project and the point of entry of your application.

Here are steps and tips on how to apply filterwarnings in your Python project:

1. Import the `warnings` Module at the Project Entry Point

Your project's entry point might be a main script or the main module that gets executed when your project starts. At the very top of this entry point file, import the warnings module and use warnings.filterwarnings() to configure your warnings filter.

For example, if main.py is your entry point, you might have:

# main.py
import warnings

# Ignore all warnings
warnings.filterwarnings("ignore")

# The rest of your imports and code follow
import other_module

def main():
    # Your main code here
    pass

if __name__ == "__main__":
    main()

2. Configuring Filters for Specific Warnings

You might not want to suppress all warnings since some could be indicative of significant issues. Instead, target specific types of warnings or warnings triggered under certain conditions.

For example, to ignore only DeprecationWarnings:

warnings.filterwarnings("ignore", category=DeprecationWarning)

Or to ignore warnings coming from a specific module:

warnings.filterwarnings("ignore", module="some_module_name")

3. Use a Configuration File for Complex Filters

For more complex projects or when you want to centralize your warnings configuration, you can use a Python script dedicated to configuring warnings. This script can then be imported at the start of your project's entry point.

For example, you could have a config_warnings.py file:

# config_warnings.py
import warnings

def configure():
    warnings.filterwarnings("ignore", category=DeprecationWarning)
    # Add more filter configurations as needed

And in your main.py or equivalent:

# main.py
import config_warnings
config_warnings.configure()

# The rest of your imports and code

4. Dynamically Adjusting Filters

You might find it useful to adjust warnings filters dynamically based on the runtime context (e.g., development vs. production environment). You can wrap your filter configurations in functions that you call under certain conditions.

5. Consider the Impact

While suppressing warnings can clean up your application's output, it's essential to carefully consider which warnings to ignore. Some warnings, especially DeprecationWarnings, can inform you about future-breaking changes or bad practices that should be addressed.

Conclusion

By strategically placing filterwarnings calls and configuring them according to the needs of your project, you can manage Python warnings effectively. Just ensure that this configuration is done early in the application's lifecycle to ensure that all relevant warnings are caught and filtered according to your settings.

default import file in all python

If you're looking to have a specific module or file automatically imported in all your Python scripts without explicitly using the import statement each time, there are a few approaches you can consider. However, it's important to note that automatically importing modules globally across all Python scripts is not a standard practice and can lead to maintainability issues or unexpected behavior in larger projects. Python doesn't have a built-in, straightforward way to ensure a module is imported in every script universally.

That said, here are some methods to achieve a similar effect:

1. Modifying the Python Startup File

Python executes the code in a startup file (if it exists) whenever the Python interpreter starts. You can use this to automatically import modules.

For interactive sessions, you can set up a .pythonrc.py file and point the PYTHONSTARTUP environment variable to it. This doesn't affect scripts run directly as files, but it works for interactive sessions.
```
export PYTHONSTARTUP=~/.pythonrc.py
```
Then, in ~/.pythonrc.py, add your imports:
```
# ~/.pythonrc.py
import mymodule
```
For IPython, you can use IPython's startup configuration to automatically import modules in every IPython session.

2. Custom Python Environment

You can create a custom Python environment that includes a wrapper around the Python interpreter. This wrapper can set up the environment, including importing modules before executing any script. This approach requires modifying the way you run Python scripts, which might not be desirable.

3. Utilizing a Project-wide Setup Module

For a specific project, you can have a convention where each script starts by importing a common setup module. This module can include all the necessary global imports. While this method still requires an import statement at the beginning of each file, it centralizes your imports into a single location.

# common_setup.py
import sys
import os
# Any other global imports

# config.py
from common_setup import *
# Your script continues from here

4. Editing Site-packages

Python executes site-specific configuration scripts during startup, including sitecustomize.py and usercustomize.py, if they are found in the site-packages directories. You can add imports to these files to make certain modules available globally. However, this is generally discouraged because it affects all Python projects on the system and can lead to hard-to-debug issues.

5. Virtual Environments

If your project uses a virtual environment, you can include the automatic import in the environment's startup files, similarly to the site-packages method. This confines the effect to the virtual environment and doesn't impact the global Python installation.

Conclusion

While there are ways to automate imports across your Python projects, most methods have significant drawbacks or scope limitations. The most Pythonic approach is to explicitly import what you need in each script or module. This keeps your code clear and maintainable. For project-wide utilities, consider a project-specific common setup module that you import explicitly in your project's scripts.

logging.basicConfig + print only on file + python

To configure Python's logging to output messages only to a file, bypassing the console entirely, you can use the logging.basicConfig() function with appropriate arguments. When you set up logging this way, it will direct the logging output to a file specified in the configuration, and by not setting a stream or configuring the console handler, it will not print log messages to the console.

Here's how you can do it:

import logging

# Configure logging to write to a file, ignoring console output
logging.basicConfig(filename='my_app.log', level=logging.DEBUG, filemode='w', format='%(asctime)s - %(levelname)s - %(message)s')

# Example usage
logging.debug('This is a debug message')
logging.info('This is an info message')
logging.warning('This is a warning message')
logging.error('This is an error message')
logging.critical('This is a critical message')

In this configuration:

filename: Specifies the name of the log file to write the log messages to.
level: Sets the threshold for the logger. Only messages with this level or higher will be tracked. logging.DEBUG means all levels of log messages will be tracked.
filemode: If set to 'w', the log file will be overwritten every time the application starts. If you prefer to append to the file instead, you can use 'a'.
format: Defines the format of the log messages. '%(asctime)s - %(levelname)s - %(message)s' will log the timestamp, the level of the message, and the message itself.

Remember, once basicConfig() is called, trying to call it again won't change the logging configuration in the current Python session. If you need to change the logging configuration (for example, during testing or if your application conditionally logs to different places), you would need to work directly with loggers and handlers rather than relying on basicConfig().

This setup ensures that all log messages are written to my_app.log and not printed to the console, allowing you to keep persistent records of your application's operation, which can be invaluable for troubleshooting and monitoring over time.

how to solve modulenotfounderror no module named in python

A ModuleNotFoundError in Python usually occurs when Python can't find the module you're trying to import. This can happen for several reasons. Here are some common causes and how to solve them:

1. Module Is Not Installed

If the module is third-party (not part of the standard library), you may not have it installed in your environment.

Solution: Install the module using pip. For example, if you're trying to import requests but get a ModuleNotFoundError, you can install it via:

pip install requests

Ensure you're using the correct pip version if you're working with multiple Python versions (pip3 for Python 3).

2. Incorrect Virtual Environment

If you're using a virtual environment, the module might not be installed in it, even if it's installed globally.

Solution: Make sure your virtual environment is activated before running your script or installing packages. To activate a virtual environment:

On macOS/Linux:
```
source /path/to/venv/bin/activate
```
On Windows:
```
\path\to\venv\Scripts\activate
```

Then, install the required module within the activated environment.

3. Typographical Error in Module Name

A simple typo in the module name can cause this error.

Solution: Double-check the spelling of the module name in the import statement. Refer to the module's documentation if necessary.

4. Incorrect Python Path or System Path

Python might not be looking in the correct directory for your module, especially if it's a custom module or installed in a non-standard location.

Solution:

For custom modules, ensure that the module's directory is in your Python path. You can add a directory to your Python path temporarily by modifying sys.path:
```
import sys
sys.path.append('/path/to/module/directory')
```
For modules installed in non-standard locations, ensure the location is included in your PYTHONPATH environment variable.

5. Python Version Mismatch

The module might not be compatible with your version of Python, or you might be using the wrong version of pip to install it.

Solution: Check the module's documentation for version compatibility. Use pip corresponding to your Python version (pip2 for Python 2.x and pip3 for Python 3.x) or use Python's -m option to specify the version:

python3 -m pip install some_module

6. Package Is Not Available for Your Platform

Some Python packages might not be available or might have different names on different platforms.

Solution: Check the package documentation for platform-specific installation instructions.

7. Conflicting or Corrupted Installations

Conflicts between different installations of Python or corrupted environments/packages can cause this error.

Solution: Try uninstalling and reinstalling the problematic module. If that doesn't work, consider creating a new virtual environment. In extreme cases, you may need to reinstall Python itself.

Final Thoughts

Resolving a ModuleNotFoundError typically involves ensuring that you've correctly installed the module and that Python can find it. Start with the most common solutions like installing with pip and checking your environment, then move on to more complex solutions as needed.

check string available in list in python

To check if a string is present in a list in Python, you can use the in operator. This operator checks for membership and returns True if the specified string is found in the list, and False otherwise. It's a straightforward and efficient way to perform this check.

Here's a basic example:

# Define a list of strings
my_list = ['apple', 'banana', 'cherry']

# String to check
my_string = 'banana'

# Check if the string is in the list
if my_string in my_list:
    print(f"'{my_string}' is in the list.")
else:
    print(f"'{my_string}' is not in the list.")

This will output:

'banana' is in the list.

Case-Insensitive Checking

If you need to perform a case-insensitive check (where 'Banana', 'BANANA', etc., should also be considered as present if 'banana' is in the list), you could convert both the list items and the string to check to lower case (or upper case) before performing the check:

# Define a list of strings
my_list = ['Apple', 'banana', 'Cherry']

# String to check, in a different case
my_string = 'BANANA'

# Convert list items to lower case and check if the lower case string is in the lower-cased list
if my_string.lower() in [item.lower() for item in my_list]:
    print(f"'{my_string}' (case-insensitive) is in the list.")
else:
    print(f"'{my_string}' (case-insensitive) is not in the list.")

This will output:

'BANANA' (case-insensitive) is in the list.

This method ensures that the case of the characters does not affect the outcome of the membership test.

get file extension python

To get the file extension in Python, you can use methods from the os.path module, specifically os.path.splitext(), which splits the filepath into a tuple containing the path without the file extension and the file extension itself. Here's how you can do it:

import os

# Example file path
file_path = 'example.txt'

# Split the extension from the path and normalize it to lowercase
root, ext = os.path.splitext(file_path)
print(f"The extension of the file is: '{ext}'")  # Output: '.txt'

The ext variable will contain the file extension including the dot (.txt in this case).

If you're working with paths that may come from URLs or you need a method that works independently of the operating system's path syntax, you can achieve similar functionality using the pathlib module (available in Python 3.4 and later). Here’s how you can do it with pathlib.Path:

from pathlib import Path

# Example file path
file_path = Path('example.txt')

# Get the suffix (file extension)
ext = file_path.suffix
print(f"The extension of the file is: '{ext}'")  # Output: '.txt'

The suffix attribute of a Path object contains the file extension as a string, including the dot. If you're dealing with filenames that might have multiple extensions (like archive.tar.gz), suffix will give you the last extension (.gz in this case). If you need all parts of the extension, Path.suffixes can be used, which returns a list of all extensions.

Both os.path and pathlib are part of the Python Standard Library, so you can use them without installing external packages.

call rest api in python

To call a REST API in Python, you can use the requests library, which simplifies the process of making HTTP requests. If you don't already have the requests library installed, you can install it using pip:

pip install requests

Here's a basic example of how to make a GET request to a REST API and handle the response:

import requests

# The URL of the API endpoint
url = 'https://api.example.com/data'

# Making a GET request
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    # Parse JSON response
    data = response.json()
    print(data)
else:
    print(f"Failed to get data: {response.status_code}")

Making a POST Request

If you need to make a POST request, for instance, to create a new resource or submit data to a server, you can do so by providing a data payload:

import requests

# The URL of the API endpoint
url = 'https://api.example.com/data'

# Data to be sent (as a dictionary)
payload = {'key': 'value', 'another_key': 'another_value'}

# Making a POST request
response = requests.post(url, json=payload)

# Check if the request was successful
if response.status_code == 201:  # 201 Created
    # Parse JSON response
    data = response.json()
    print(data)
else:
    print(f"Failed to post data: {response.status_code}")

Handling Exceptions

It's also a good idea to handle exceptions that might occur during the request. For example, a network problem could raise a ConnectionError, or a timeout might raise a Timeout exception:

import requests

url = 'https://api.example.com/data'

try:
    response = requests.get(url, timeout=10)
    response.raise_for_status()  # Raises an HTTPError if the status is 4xx, 5xx
    data = response.json()
    print(data)
except requests.exceptions.HTTPError as http_err:
    print(f'HTTP error occurred: {http_err}')  # Python 3.6+
except requests.exceptions.ConnectionError as conn_err:
    print(f'Connection error occurred: {conn_err}')
except requests.exceptions.Timeout as timeout_err:
    print(f'Timeout error occurred: {timeout_err}')
except requests.exceptions.RequestException as req_err:
    print(f'Error occurred: {req_err}')

This example demonstrates basic usage patterns for the requests library, including handling common exceptions. The requests library is versatile and supports many more HTTP methods and options for headers, cookies, and session handling, making it well-suited for interacting with REST APIs.

print json with tab + python

To print JSON data with indentation (using tabs for example) in Python, you can use the json module, which provides a dumps() method to serialize Python objects to a JSON-formatted string. By default, json.dumps() uses spaces for indentation, but you can replace spaces with tabs after serialization for display purposes.

Here's how to do it:

import json

# Sample Python dictionary
data = {
    "name": "John",
    "age": 30,
    "city": "New York"
}

# Convert to JSON string with indentation
json_string = json.dumps(data, indent=4)

# Replace 4 spaces with a tab
json_string_with_tabs = json_string.replace('    ', '\t')

print(json_string_with_tabs)

However, note that while this method replaces spaces with tabs in the output for visual purposes, the resulting JSON string with literal tab characters () may not be considered valid JSON in all contexts. JSON standards specify spaces, not tabs, for pretty-printing. This approach is primarily for visualization or specific contexts where tabbed indentation is required for the display.

If you're working with JSON data for configuration files, data interchange, or APIs, it's generally best to stick with spaces for indentation to ensure compatibility.

add headers in request post

To add headers to a POST request using the requests library in Python, you can use the headers parameter of the requests.post() method. Headers are often required for specifying content type, authentication tokens, and other necessary information for the server to correctly process the request.

Here's a basic example of how to include headers in a POST request:

import requests

# The URL of the API endpoint
url = 'https://api.example.com/data'

# Headers to be sent with the POST request
headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer YOUR_ACCESS_TOKEN',
}

# Data to be sent with the request
data = {
    'key': 'value',
}

# Making the POST request with headers
response = requests.post(url, json=data, headers=headers)

# Check if the request was successful
if response.ok:
    # Process the response if needed
    print(response.json())
else:
    print(f"Failed to send POST request: {response.status_code}")

In this example:

headers is a dictionary where each key-value pair represents a header name and its value.
'Content-Type': 'application/json' tells the server that the body of the request contains JSON data.
'Authorization': 'Bearer YOUR_ACCESS_TOKEN' is an example of how you might pass an authorization token. Replace 'YOUR_ACCESS_TOKEN' with your actual token.
json=data automatically serializes the Python dictionary to a JSON-formatted string. If you need to send data as form-encoded instead of JSON, use data= instead of json=, and adjust the Content-Type header accordingly to 'application/x-www-form-urlencoded'.
response.ok is a quick way to check if the HTTP status code is less than 400, indicating success.

Remember to replace 'YOUR_ACCESS_TOKEN' with your actual access token and adjust the URL and data payload as necessary for your specific use case.

decrease 2 in each number in a list + python

To decrease each number in a list by 2 in Python, you can use a list comprehension. List comprehensions provide a concise way to create lists by iterating over an iterable and applying an expression to each item. Here's how you can do it:

# Original list of numbers
numbers = [5, 10, 15, 20, 25]

# Decrease each number by 2
updated_numbers = [number - 2 for number in numbers]

print(updated_numbers)

This code iterates over each element in the numbers list, subtracts 2 from each element, and then stores the result in the updated_numbers list. The output will be:

[3, 8, 13, 18, 23]

This demonstrates how you can efficiently perform operations on list items and generate new lists using list comprehensions in Python.

how-do-i-check-whether-a-file-exists-without-exceptions in python

In Python, you can check if a file exists without using exceptions by utilizing the os.path.exists() function from the os module. This function returns True if the file or directory specified by the path exists, and False otherwise. It's a straightforward way to perform this check without handling exceptions explicitly.

Here's how to use os.path.exists():

import os

# Specify the file path
file_path = 'example.txt'

# Check if the file exists
if os.path.exists(file_path):
    print(f"The file {file_path} exists.")
else:
    print(f"The file {file_path} does not exist.")

This method is useful for simple checks. However, if you're going to work with the file immediately after checking and you're concerned about potential race conditions (e.g., the file being deleted between the check and the subsequent file operation), it might be safer to just try the operation and handle exceptions as necessary.

For Python 3.4 and newer, you can also use the pathlib module, which provides an object-oriented approach:

from pathlib import Path

# Specify the file path
file_path = Path('example.txt')

# Check if the file exists
if file_path.exists():
    print(f"The file {file_path} exists.")
else:
    print(f"The file {file_path} does not exist.")

The pathlib approach is more modern and integrates well with other file path-related operations.

replace key name in dictionary in python

To replace a key name in a Python dictionary, you can't directly rename a key. Instead, you'll need to add a new key with the desired name, copy the value from the old key, and then delete the old key. Here's how you can do it:

# Sample dictionary
my_dict = {'oldKey': 'value', 'anotherKey': 'anotherValue'}

# Replace 'oldKey' with 'newKey'
my_dict['newKey'] = my_dict.pop('oldKey')

print(my_dict)

In this example:

The value associated with 'oldKey' is copied to a new key named 'newKey' using the assignment operation.
The .pop('oldKey') method removes 'oldKey' from the dictionary and returns its value. This value is then assigned to 'newKey', effectively renaming the key without losing the value.
'oldKey' is removed from the dictionary as part of the .pop() operation.

This approach ensures that the dictionary is updated in-place without creating a new dictionary, preserving any references to the original dictionary object.

do lowercase all keys in a dictionary in python

To convert all keys in a Python dictionary to lowercase, you can use a dictionary comprehension. This approach creates a new dictionary where each key is the lowercase version of the keys from the original dictionary. Here's how you can do it:

# Original dictionary
my_dict = {'Name': 'John', 'Age': 30, 'CITY': 'New York'}

# Convert all keys to lowercase
lowercase_dict = {k.lower(): v for k, v in my_dict.items()}

print(lowercase_dict)

This will output:

{'name': 'John', 'age': 30, 'city': 'New York'}

Dictionary comprehensions provide a concise and readable way to transform the keys and values of a dictionary. In this case, k.lower() is used to convert each key to lowercase, while v is used directly since we're not altering the values.

sublist with random items in python

To create a sublist containing random items from an existing list in Python, you can use the random.sample() function from the random module. This function allows you to specify the number of items you want in the sublist. It returns a new list with the specified number of items chosen randomly from the input list, without repeating any items.

Here’s how to do it:

import random

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

# Number of items you want in the sublist
n = 5

# Generate the sublist with n random items
sublist = random.sample(my_list, n)

print(sublist)

In this example, n is the number of random items you want to include in your sublist. The random.sample() function ensures that the items are randomly selected without duplication.

Please note that the value of n should not exceed the length of the original list. If it does, random.sample() will raise a ValueError.

check number inside string python

To check if a string contains any number in Python, you can use several approaches. Here are two common methods:

Method 1: Using any() with a List Comprehension

You can use the any() function along with a list comprehension to check if any character in the string is a digit. The str.isdigit() method is used to determine if a character is a digit.

my_string = "Hello123"
contains_number = any(char.isdigit() for char in my_string)

print(contains_number)  # Output: True

Method 2: Using Regular Expressions

Another way to check for the presence of numbers within a string is by using the re module, which provides regular expression matching operations. You can search for digits using the pattern '\d', which matches any decimal digit.

import re

my_string = "Hello123"
contains_number = bool(re.search(r'\d', my_string))

print(contains_number)  # Output: True

Both methods will return True if the string contains at least one numeral and False otherwise. The first method is more straightforward and Pythonic for simple checks, while the second method is more powerful and flexible, allowing for more complex pattern matching if needed.

list1 - list2 in python

If you want to subtract one list from another in Python, meaning you want to remove all elements of list2 from list1, you can use a list comprehension or the filter() function. This operation is not a subtraction in the mathematical sense but rather a way to filter out elements.

Using List Comprehension

A list comprehension provides a concise way to create a new list by iterating over list1 and including only those elements not present in list2.

list1 = [1, 2, 3, 4, 5]
list2 = [2, 4]

result = [item for item in list1 if item not in list2]

print(result)  # Output: [1, 3, 5]

Using the `filter()` Function

Alternatively, you can use the filter() function, which offers a more functional programming approach. You filter list1 by a function that returns True for elements not in list2.

list1 = [1, 2, 3, 4, 5]
list2 = [2, 4]

result = list(filter(lambda item: item not in list2, list1))

print(result)  # Output: [1, 3, 5]

Considerations

These methods effectively create a new list containing the elements of list1 that are not found in list2.
The result does not modify the original lists.
If you need to perform this operation with lists containing complex objects (like dictionaries or custom objects), make sure you have a proper comparison mechanism in place (like implementing __eq__ method for custom objects).

These approaches are suitable for lists with primitive data types or objects that can be directly compared. For more complex scenarios, you might need to tailor the comparison mechanism to your specific needs.

PreviousPy Notes 2 NextPy Notes 4

Last updated 1 year ago

Basic Usage

Expressions Inside F-Strings

Formatting Specifiers

Date Formatting

Multiline F-Strings

Escaping Braces

Summary

Prerequisites

On macOS

On Linux

On Windows

Verifying Installation

Method 1: Using .items()

Method 2: Looping Through Keys and Accessing Values

Method 3: Using .keys() and .values() (When Only Keys or Values are Needed)

Method 4: Python Dictionary Comprehension (Advanced)

Basic Exception Catching

Using traceback for Detailed Information

Capturing Exception Information to a String

Custom Formatting

Formatting Exceptions Without Catching

1. Levenshtein Distance (Edit Distance)

2. Jaccard Similarity

3. Cosine Similarity

Using the format Function

Using an F-String

Using Python Lists with Decimal

Creating a NumPy Array of Decimal Objects

Considerations

Using the warnings Module

Suppressing Specific Types of Warnings

Using the -W Command-Line Option

Environment Variable

Note

1. Import the warnings Module at the Project Entry Point

2. Configuring Filters for Specific Warnings

3. Use a Configuration File for Complex Filters

4. Dynamically Adjusting Filters

5. Consider the Impact

Conclusion

1. Modifying the Python Startup File

2. Custom Python Environment

3. Utilizing a Project-wide Setup Module

4. Editing Site-packages

5. Virtual Environments

Conclusion

1. Module Is Not Installed

2. Incorrect Virtual Environment

3. Typographical Error in Module Name

4. Incorrect Python Path or System Path

5. Python Version Mismatch

6. Package Is Not Available for Your Platform

7. Conflicting or Corrupted Installations

Final Thoughts

Case-Insensitive Checking

Making a POST Request

Handling Exceptions

Method 1: Using any() with a List Comprehension

Method 2: Using Regular Expressions

Using List Comprehension

Using the filter() Function

Considerations

Method 1: Using `.items()`

Method 3: Using `.keys()` and `.values()` (When Only Keys or Values are Needed)

Using `traceback` for Detailed Information

Using the `format` Function

Using the `warnings` Module

Using the `-W` Command-Line Option

1. Import the `warnings` Module at the Project Entry Point

Using the `filter()` Function