Switch Match

 >>> def http(e):

...    match e:

...       case 400:

...          print('Error 400')

...       case 401:

...          print('Auth request')

...       case _:

...          print('Unknow error')

... 

>>> http(12)

Unknow error

>>> http(500)

Unknow error

>>> http(400)

Error 400

OpenAI API Realtime TTS and Speech to Text

 https://platform.openai.com/docs/guides/realtime-websocket?websocket-examples=python

getting key from a dict based on a value

 >>> users

{'Hans': 'active', 'Éléonore': 'inactive', '景太郎': 'active'}

>>> [a for a in users.keys() if users.get(a)=='active']

['Hans', '景太郎']

getting only str o int values from a dict

 >>> r

{'Name': 'Ambiorix', 'age': 34}

>>> {r[a]:a for a in r if type(r[a])==int}

{34: 'age'}

>>> {r[a]:a for a in r if type(r[a])==str}

{'Ambiorix': 'Name'}

dict practice

 >>> 

>>> 

>>> answer

'Ambiorix Rodriguez Placencio'

>>> chr(max({a:ord(a) for a in answer}.values()))

'z'

>>> chr(max({a:ord(a) for a in answer}.values()))

accessing to dict values

 Code that modifies a collection while iterating over that same collection can be tricky to get right. Instead, it is usually more straight-forward to loop over a copy of the collection or to create a new collection:

# Create a sample collection
users = {'Hans': 'active', 'Éléonore': 'inactive', '景太郎': 'active'}

# Strategy:  Iterate over a copy
for user, status in users.copy().items():
    if status == 'inactive':
        del users[user]

# Strategy:  Create a new collection
active_users = {}
for user, status in users.items():
    if status == 'active':
        active_users[user] = status

reading a file

import os

import sys

def search_key_value(file_path, search_key):

    # Open and read the file safely using a context manager

    with open(file_path, 'r') as file:

        lines = file.readlines()

    # Create a dictionary from the parsed list

    parsed_dict = {line.strip().split(',')[0]: line.strip().split(',')[1] for line in lines}

    # Return the value associated with the search key if it exists

    return parsed_dict.get(search_key)

# Example usage:

file_path = '/var/www/html/fwlist.conf'

sound_folder = '/var/lib/asterisk/sounds/en/custom/'

search_key = sys.argv[1]

file = search_key_value(file_path, search_key)

if file:

    print(f"The value for key '{search_key}' is: {file}")

    # Check if the corresponding file exists

    file_path = sound_folder + file

    print(1 if os.path.isfile(file_path) else 0)

else:

    print(f"Key '{search_key}' not found.")

    print(0)

#############

849,hello

829,goodbye

809,version_2

searching letters on key and values of a dict

 

>>> c

{'Ambiorix': 'Rodriguez', 'Katherine': 'Maria, 'Rosa: 'Perez'}

>>> [a for  a in  list(c.keys()) if 'k'.lower() in a.lower()]

['Katherine']

>>> [a for  a in  list(c.values()) if 'R'.lower() in a.lower()]

['Rodriguez']

python-realtime-twilio-transcription

 https://github.com/AssemblyAI-Community/python-realtime-twilio-transcription/blob/main/main.py

Regular try , exception finally and Custom Exception error.

try:

    n = 10

    if n < 20:

        raise ValueError("n must be at least 20")  # Raising a ValueError with a message

except ValueError as e:

    print("An error has occurred:", e)

finally:

    print("Code completed.")

--------

Custom  Exception error.

 class CustomError(Exception):

    pass

try:

    raise CustomError("This is a custom error message.")

except CustomError as e:

    print("An error has occurred:", e)

finally:

    print("Code completed.")

count lens of element on a list, and create a dict if value is greater than

>>> words

['cat', 'window', 'defenestrate']

>>> 

 >>> {a:len(a) for a in words if len(a)>5}

{'window': 6, 'defenestrate': 12}

Flow control and function

 https://docs.python.org/3/tutorial/controlflow.html#if-statements

google sheet reading pytthon

1. Create a Google Cloud Platform (GCP) project

Go to the Google Cloud Console by clicking the link below :

Create a Google Cloud project | Google Workspace | Google for Developers

A Google Cloud project is required to use Google Workspace APIs and build Google Workspace add-ons or apps. This…

developers.google.com

The link will direct you to this page.

Create a new project by clicking ‘Go to create a project button on the above page’ or directly by using the link below. If you already have a project, you can select that one instead.

Google Cloud Platform

Google Cloud Platform lets you build, deploy, and scale applications, websites, and services on the same infrastructure…

console.cloud.google.com

The link will direct you to this page. Enter a project name and create it.

2. Enable the Google Sheets API

In the Cloud Console, navigate to the “APIs & Services” > “Library” section.

Search for “Google Sheets API” and enable it for your project.

Click on the ‘Enable’ button.

3. Create credentials

After enabling the API, go to the “APIs & Services” > “Credentials” section.

Click on the ‘Create credentials’ button and generate an API key.

Copy the API key for future use.

4. Manage Google Sheet Access Permissions

By default, Google Sheets are private. To make them accessible, you’ll need to share the Google Sheet publicly. Navigate to your Google Sheet and click on the ‘Share’ button.

Ensure you choose ‘Anyone with the link’ and set the access to ‘View.

5. Access Sheet Data

Below is the API endpoint to access sheet data. Please update the sheet ID, sheet name, and API key accordingly. You can find the sheet ID in the URL of your Google Sheet page. Look for the string of characters between ‘/d/’ and ‘/edit’ in the URL, and that is your sheet ID.

https://sheets.googleapis.com/v4/spreadsheets/{SHEET_ID}/values/{SHEET_NAME}!A1:Z?alt=json&key={API_KEY}

Next, open the provided URL in your web browser, and you will receive a response similar to this.

{
  'range': 'demo_sheet!A1:Z1000',
  'majorDimension': 'ROWS',
  'values': [
    [
      'Email id',
      'Password',
      'count',
      'id'
    ],
    [
      'demo@gmail.com',
      'demopass',
      '60',
      '0'
    ]
  ]
}

6. Retrieve Sheet Data Using Python

Now, let’s write Python code to access this data.

import requests

def get_google_sheet_data(spreadsheet_id,sheet_name, api_key):
    # Construct the URL for the Google Sheets API
    url = f'https://sheets.googleapis.com/v4/spreadsheets/{spreadsheet_id}/values/{sheet_name}!A1:Z?alt=json&key={api_key}'

    try:
        # Make a GET request to retrieve data from the Google Sheets API
        response = requests.get(url)
        response.raise_for_status()  # Raise an exception for HTTP errors

        # Parse the JSON response
        data = response.json()
        return data

    except requests.exceptions.RequestException as e:
        # Handle any errors that occur during the request
        print(f"An error occurred: {e}")
        return None


# configurations
spreadsheet_id = ''
api_key = ''
sheet_name = "Sheet1"

sheet_data = get_google_sheet_data(spreadsheet_id,sheet_name, api_key)

if sheet_data:
    print(sheet_data)
else:
    print("Failed to fetch data from Google Sheets API.")

That’s all for now. Thank you for reading! Please feel free to share your suggestions in the comments below.

Python

Google Sheets

 
https://medium.com/@techworldthink/accessing-google-sheet-data-with-python-a-practical-guide-using-the-google-sheets-api-dc57759d387a

Calculadora class

class calculadora:

    def __init__(self, x, y):

        self.x = x

        self.y = y

    def suma(self):

        return self.x + self.y

    def mult(self):

        return self.x * self.y

    def div(self):

        return self.x / self.y

    def resta(self):

        return self.x - self.y

c1 = calculadora(24, 5)

print(c1.suma())

print(c1.mult())

print(c1.div())

print(c1.resta())

creating a dict from 2 list

Easy way

>>> d=dict(zip([1, 2, 3], ['sugar', 'spice', 'everything nice']))

>>> d

{1: 'sugar', 2: 'spice', 3: 'everything nice'}

>>> thedict={item[0]:item[1] for item in zip([1, 2, 3], ['sugar', 'spice', 'everything nice'])}

>>> thedict={item[0]:item[1] for item in zip([1, 2, 3], ['sugar', 'spice', 'everything nice'])}

>>> thedict

{1: 'sugar', 2: 'spice', 3: 'everything nice'}

------------------------------

>>> info={}

>>> [info.update({item[0]:item[1]}) for item in zip([1, 2, 3], ['sugar', 'spice', 'everything nice'])]

[None, None, None]

>>> info

{1: 'sugar', 2: 'spice', 3: 'everything nice'}

searching words on a list

 >>> words

['Ambiorix', 'RODRIGUEZ', 'Placencio', 42, 'AMIGo']

>>> [a for a in words if 'pl' in str(a).lower()]

['Placencio']

>>> [a for a in words if 'amb' in str(a).lower()]

['Ambiorix']

>>> [a for a in words if str(a).lower()=='amigo']

['AMIGo']

>>> l

[2.4, 3, 4, 'amigo', 'Rosa', 'Maria']

>>> [a for a in l if str('ro'.lower()) in str(a).lower()]

http handling reponse status

 import requests

url = 'https://example.com'

response = requests.get(url)

if response.status_code == 200:

    print("Success!")

elif response.status_code == 404:

    print("Not Found!")

elif response.status_code == 500:

    print("Server Error!")

# Add more status code checks as needed

else:

    print(f"Received unexpected status code: {response.status_code}")

-------------------

 import requests

from bs4 import BeautifulSoup

import csv

import json

url = 'https://example.com/data'  # Replace with actual URL

response = requests.get(url)

if response.status_code == 200:

    content_type = response.headers.get('Content-Type', '')

    if 'text/html' in content_type:

        soup = BeautifulSoup(response.text, 'html.parser')

        print("HTML content:")

        print(soup.prettify())

    elif 'application/json' in content_type:

        data = response.json()

        print("JSON data:")

        print(data)

    elif 'application/xml' in content_type or 'text/xml' in content_type:

        soup = BeautifulSoup(response.content, 'xml')

        print("XML content:")

        print(soup.prettify())

    elif 'text/plain' in content_type:

        text_content = response.text

        print("Plain text content:")

        print(text_content)

    elif 'text/csv' in content_type:

        csv_content = response.text

        print("CSV content:")

        reader = csv.reader(csv_content.splitlines())

        for row in reader:

            print(row)

    else:

        print("Received unsupported content type:", content_type)

else:

    print(f"Failed to retrieve data. Status code: {response.status_code}")

Disk usage

 import glob

import subprocess

from prettytable import PrettyTable

from math import log  # Import log from the math module

def convert_size(size_bytes):

    """Convert bytes to a human-readable format (KB, MB, GB)."""

    if size_bytes == 0:

        return "0 Bytes"

    size_names = ["Bytes", "KB", "MB", "GB"]

    i = int(log(size_bytes, 1024))

    p = 1024 ** i

    s = round(size_bytes / p, 2)

    return f"{s} {size_names[i]}"

# Function to fetch and sort disk usage, then print a pretty table

def test(path, extension):

    table = PrettyTable(field_names=["File", "Disk Usage"])

    

    # Collect file sizes and names

    usage_info = []

    for file in glob.glob(f'{path}*.{extension}'):

        if file:

            usage = subprocess.getoutput(f'du -b "{file}"')

            size, _ = usage.split()  # Get size, ignore the rest

            usage_info.append((file, int(size)))  # Store as a tuple of (file, size)

    

    # Sort by size (second element of the tuple)

    usage_info.sort(key=lambda x: x[1], reverse=True)

    # Add sorted data to the table with human-readable sizes

    for file, size in usage_info:

        human_readable_size = convert_size(size)

        table.add_row([file, human_readable_size])

    print(table)

# Get user input for directory path and file extension

path = input("Enter the directory path: ")

file_extension = input("Enter the file extension (without dot): ")

# Example usage

test(path, file_extension)

pip install prettytable

 import glob

import subprocess

from prettytable import PrettyTable

# One-liner with pretty printing

test = lambda a, b: (lambda table: [table.add_row([file, subprocess.getoutput(f'du -h "{file}"')]) for file in glob.glob(f'{a}*.{b}') if file] and print(table))(

    PrettyTable(field_names=["File", "Disk Usage"])

)

# Example usage

test('./', 'txt')

getting file size in a list

 

>>> c=glob.glob('*.wav')

>>> c

['tts_1.wav', 'ss-claro.wav', 'en-US-Studio-O.wav', 'no-disp.wav', 'ss-viva.wav']

>>> 

>>> [subprocess.getoutput(f'du -h {a}').split() for a in c]

[['168K', 'tts_1.wav'], ['768K', 'ss-claro.wav'], ['96K', 'en-US-Studio-O.wav'], ['352K', 'no-disp.wav'], ['508K', 'ss-viva.wav']]

>>> 

du -h *.wav

96K en-US-Studio-O.wav

352K no-disp.wav

------------

>>> fs=lambda a:glob.glob(f'*.{a}')

>>> fs('mp3')

['output.mp3', 'tts_1.mp3', '1.mp3']

----------

 fs=lambda path,files:glob.glob(f'{path}*.{files}')

>>> fs('/home/ambiorixg12/Downloads/','txt')

['/home/ambiorixg12/Downloads/index.php.txt', '/home/ambiorixg12/Downloads/MicroSIP_log (1).txt', '/home/ambiorixg12/Downloads/task.txt', '/home/ambiorixg12/Downloads/FC61FA41-E334-5D8C-B80E-C80638B68203.txt', '/home/ambiorixg12/Downloads/50225054-BCCC-57A2-8C29-5318E213BD05.txt']

>>> 

>>> test= lambda a,b: [subprocess.getoutput(f'du -h {a}').split() for a in fs(a,b)]

 test('/home/ambiorixg12/Downloads/','mp3')

------------------------------------------------------------

function  1

>>> fs=lambda a:glob.glob(f'*.{a}')

>>> fs('mp3')

['output.mp3', 'tts_1.mp3', '1.mp3']

function 2

>>> test= lambda a: [subprocess.getoutput(f'du -h {a}').split() for a in fs(a)]

>>> test('mp3')

[['12K', 'output.mp3'], ['32K', 'tts_1.mp3'], ['96K', '1.mp3']]

-------

serching files on the system from a list

1. import subprocess
2.  
3. c=subprocess.getoutput('ls')
4.  
5. r=[a for a in c.split() if 'chat' in a.lower()]
6.  
7.  
8. print(r)
9. c
10. ['chatbot.py', 'ChatGPT.pdf']
11.  
12.  
13.  
14.  
15. looking from a list will search all mp3 and mp4 and pdf file and will return a list with sub list for each category
16.  
17.  
18.  
19. >>> [[a for a in c.split() if d.lower() in a.lower()] for d in ['mp3','mp4','pdf']]
20. [['1.mp3', 'output.mp3', 'tts_1.mp3'], ['pp.mp4'], ['ChatGPT.pdf', 'PDF']]

Pandas cookbook

Read the excel file 

>>> df = pd.read_excel('/home/ambiorixg12/Downloads/500.xlsx')

Save values as list

lst= df.values.tolist()

Count values  from the list 

 len(lst)

500

Print first and last entry

>> print(lst[0],'\n',lst[-1])

['Gaston', 'Willie', 6009.0, nan, 'Howells Ferry', 'Rd', nan, nan, 'Mobile', 'AL', 36618, '(251) 304-1631', 'Male'] 

 ['Paulo', 'Frank', 1801.0, nan, 'Ridgecrest', 'St', nan, nan, 'Valdosta', 'GA', 31601, '(229) 671-9476', 'Male']

Get columns name

headers = df.columns.tolist()

Data type

print(df.dtypes)

-----

Create a dict with the first value and include the column names

God version

 df = pd.read_excel('/home/ambiorixg12/Downloads/500.xlsx')

 c={df.columns.tolist()[a]:df.values.tolist()[0][a] for a in range(len(df.values.tolist()[0])) }

get the first value of are data set

>>> v=[a for a in  df.values.tolist()[0]]  

>>> 

>>> 

>>> 

>>>  ge colum names

>>> h=[a for a in  df.columns.tolist()]

>>>  create are dict

>>> new={h[v.index(a)]:a for a in v}

>>> print(new)

{'Last Name': 'Gaston', 'First Name': 'Willie', 'House Number': 6009.0, 'Pre-directional': nan, 'Street': 'Howells Ferry', 'Street Suffix': 'Rd', 'City': 'Mobile', 'State': 'AL', 'Zip Code': 36618, 'Phone Number': '(251) 304-1631', 'Gender': 'Male'}

>>> 

Or simplify version

>>> new={h[v.index(a)]:a for a in [a for a in  df.values.tolist()[0]]}

---getting header

>>> c={df.columns.tolist()[a] for a in range(len(df.values.tolist()[0])) }

>>> c

{'First Name', 'Gender', 'Pre-directional', 'State', 'House Number', 'Post-directional', 'Last Name', 'Phone Number', 'Street', 'Zip Code', 'City', 'Apartment Number', 'Street Suffix'}

>>> 

info=requests.get('https://ipinfo.io/json').json() 

>>> df = pd.DataFrame.from_dict(info, orient='index', columns=['value'])

Reading files as list and searching

>>> f=open('/tmp/1.txt','r').readlines()

>>> [a.strip() for a in f  if 'pe' in a.lower()]

['Katherine Montolio Perez']

>>> f=open('/tmp/1.txt','r')

>>> f.readlines()

['Ambiorix Rodriguez Placenio\n', 'Ruth Cedeno Lara\n', 'Katherine Montolio Perez\n', 'Armando Rodriguez Rosario\n']

do the magic

>>> f=open('/tmp/1.txt','r')

>>> [a.strip() for a in f.readlines()]

['Ambiorix Rodriguez Placenio', 'Ruth Cedeno Lara', 'Katherine Montolio Perez', 'Armando Rodriguez Rosario']

>>>

>>> 

>>> f=open('/tmp/1.txt','r')

>>> [a.strip().upper() for a in f.readlines()]

['AMBIORIX RODRIGUEZ PLACENIO', 'RUTH CEDENO LARA', 'KATHERINE MONTOLIO PEREZ', 'ARMANDO RODRIGUEZ ROSARIO