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Author: jeffthedeveloper

ASCII_Manipulation.py

@@ -0,0 +1,14 @@
+print(ord("A"))
+# 65
+
+print(chr(65))
+# A
+
+print(unichr(65))
+# A (em Python 2)
+
+print(hex(65))
+# 0x41
+
+print(oct(65))
+# 0101

FileManipulationExceptions.py

@@ -0,0 +1,9 @@
+def ler_arquivo(nome_arquivo):
+  try:
+    with open(nome_arquivo, "r") as arquivo:
+      dados = arquivo.read()
+  except FileNotFoundError:
+    print("Arquivo não encontrado")
+  finally:
+    # Garante que o arquivo seja fechado
+    arquivo.close()  # Não é necessário com o 'with open'

LinearRegressionModels.py

@@ -0,0 +1,18 @@
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+# Load the spreadsheet data
+df = pd.read_excel("my_spreadsheet.xlsx")
+
+# Create a linear regression model
+model = LinearRegression()
+
+# Fit the model to the data
+model.fit(df["x"], df["y"])
+
+# Make predictions
+predictions = model.predict(df["x"])
+
+# Evaluate the model
+print(model.score(df["x"], df["y"]))

LoopsControl.py

@@ -0,0 +1,11 @@
+# Laço while com cláusula else
+while True:
+  print("Loop infinito")
+else:
+  print("O loop chegou ao fim")
+
+# Laço for com cláusula else
+for i in range(10):
+  print(i)
+else:
+  print("O laço terminou")

ManipulationPDFS.py

@@ -0,0 +1,43 @@
+import PyPDF2
+import re
+
+# Abre o arquivo PDF
+pdf_file = open("arquivo.pdf", "rb")
+
+# Cria um objeto PDFReader
+pdf_reader = PyPDF2.PdfFileReader(pdf_file)
+
+# Obtém a primeira página do PDF
+page = pdf_reader.getPage(0)
+
+# Converte a página em texto
+text = page.extractText()
+
+# Procura por uma linha que contenha o texto "Total a recolher"
+match = re.search(r"Total a recolher: \d+", text)
+
+# Obtém o valor do total a recolher
+total_a_recolher = match.group(1)
+
+# Imprime o valor do total a recolher
+print(total_a_recolher)
+
+
+'''
+
+arquivo_pdf = "arquivo.pdf"
+
+localizado na mesma pasta
+
+'''
+
+'''
+
+import os
+
+# Obtém o caminho do arquivo PDF
+arquivo_pdf = os.path.join(os.getcwd(), "arquivo.pdf")
+
+localizado em uma outra pasta
+
+'''

Recursion.py

@@ -0,0 +1,10 @@
+def foo(a):
+  return a + a + a
+
+b = 1
+
+b = foo(b)
+
+b = foo(b)
+
+b = foo(b)

Recursion2.py

@@ -0,0 +1,12 @@
+def foo(a):
+  return a + a + a
+
+b = 1
+
+foo(b)
+
+foo(b)
+
+foo(b)
+
+print(b)

Recursivity.py

@@ -0,0 +1,12 @@
+def foo(a):
+  return a + a + a
+
+b = 1
+
+foo(b)
+
+foo(b)
+
+foo(b)
+
+print(b)

ValuesComparison.py

@@ -0,0 +1,66 @@
+import pandas as pd
+import zipfile
+import PyPDF2
+import os
+
+# Define the ZIP file path
+zip_file_path = "file.zip"
+
+# Extract files from the ZIP file using context manager and extractall
+with zipfile.ZipFile(zip_file_path, "r") as zip_file:
+    zip_file.extractall("DecompressedFiles")  # Extract to a directory
+
+
+# Function to convert PDF to CSV (using a more robust method)
+def convert_pdf_to_csv(pdf_file):
+    with open(pdf_file, "rb") as pdf:
+        reader = PyPDF2.PdfReader(pdf)  
+        num_pages = len(reader.pages)
+        data = []
+        for page_num in range(num_pages):
+            page = reader.pages[page_num]
+            text = page.extract_text()
+            data.extend(text.split("\n"))
+        return data  # Return all extracted lines
+
+# Extract data from PDF files (using the corrected paths)
+file_transmission_data = convert_pdf_to_csv("DecompressedFiles/file_transmission.pdf")
+taxDate = convert_pdf_to_csv("DecompressedFiles/tax_benefits.pdf")
+guideDate = convert_pdf_to_csv("DecompressedFiles/dar.pdf")
+
+# Extract taxMarketShare values (handling potential IndexError)
+file_transmission_values = []
+taxValues = []
+guideValues = []
+for line in file_transmission_data:
+    parts = line.split("|")
+    if len(parts) > 7:
+        file_transmission_values.append(parts[7])
+for line in taxDate:
+    parts = line.split("|")
+    if len(parts) > 5:
+        taxValues.append(parts[5])
+for line in guideDate:
+    parts = line.split("|")
+    if len(parts) > 5:
+        guideValues.append(parts[5])
+
+# Compare taxMarketShare values with error handling (zip) for better performance
+for file_trans, tax, guide in zip(file_transmission_values, taxValues, guideValues):
+    if file_trans != tax or tax != guide:  # Check if any pair doesn't match
+        print(f"Error: DocumentValue of taxMarketShare doesn't match in line '{file_trans}|{tax}|{guide}'")
+        break
+else:
+    print("All taxMarketShare values match across documents.")  # Indicate success if loop completes
+
+
+
+# Create a new Excel spreadsheet in .odt format (OpenDocument format)
+taxMarketShare_data = {
+    "Date Limit": [line.split("|")[0] for line in file_transmission_data if len(line.split("|")) > 0],  # Error handling for index
+    "Code Representation": [line.split("|")[1] for line in file_transmission_data if len(line.split("|")) > 1],
+    "Document Value": file_transmission_values[:len(file_transmission_data)]  # Handle length differences
+}
+
+taxMarketShare_data_frame = pd.DataFrame(taxMarketShare_data)
+taxMarketShare_data_frame.to_excel("SpreadSheet.odt", engine='odf', index=False)  # Save as .odt

Yield.py

@@ -0,0 +1,8 @@
+def geradores():
+    for i in range(10):
+        yield i
+
+it = geradores()
+print(next(it))
+print(next(it))
+print(next(it))

aiStatics.py

@@ -0,0 +1,27 @@
+from google.cloud import dialogflow_v2
+
+# Create a Dialogflow client
+client = dialogflow_v2.AgentsClient()
+
+# Get the agent ID
+agent_id = "YOUR_AGENT_ID"
+
+# Get the statistics
+statistics = client.get_agent_statistics(agent_id)
+
+# Print the statistics
+print(statistics)
+
+
+# Get the average time spent using the chat
+average_time_spent = statistics.conversations_duration_seconds / statistics.conversations_count
+
+# Print the average time spent
+print(average_time_spent)
+
+# Get the statistics for each topic
+topic_statistics = statistics.topics_statistics
+
+# Print the statistics for each topic
+for topic_statistics in topic_statistics:
+  print(topic_statistics.topic_name, topic_statistics.conversations_count)

bankAverageBalance.py

@@ -0,0 +1,4 @@
+SELECT
+  AVG(balance) AS average_balance
+FROM
+  bank_accounts

constructorMethods.py

@@ -0,0 +1,9 @@
+class MyClass:
+    def __new__(cls, *args, **kwargs):
+        print("Creating a new instance of MyClass")
+        return super().__new__(cls, *args, **kwargs)
+
+    def __init__(self):
+        print("Initializing the new instance of MyClass")
+
+my_object = MyClass()

dataWebsite.py

@@ -0,0 +1,16 @@
+import requests
+from bs4 import BeautifulSoup
+
+url = 'URL_DO_SEU_SITE'
+response = requests.get(url)
+soup = BeautifulSoup(response.content, 'html.parser')
+
+# Extrair tags <script> (JavaScript)
+scripts = soup.find_all('script')
+for script in scripts:
+    print(script.text)
+
+# Extrair tags <style> (CSS)
+styles = soup.find_all('style')
+for style in styles:
+    print(style.text)

decorator.py

@@ -0,0 +1,13 @@
+class Singleton:
+    _instance = None
+
+    def __new__(cls, *args, **kwargs):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls, *args, **kwargs)
+        return cls._instance
+
+singleton = Singleton()
+singleton2 = Singleton()
+
+print(singleton)
+print(singleton2)

decoratorSet.py

@@ -0,0 +1,7 @@
+def setter(name):
+    def inner(func):
+        def wrapper(self, value):
+            setattr(self, name, value)
+            func(self, value)
+        return wrapper
+    return inner

delObject.py

@@ -0,0 +1,15 @@
+class MyClass:
+    def __init__(self, filename):
+        self.file = open(filename, "w")
+
+    def __del__(self):
+        if self.file is not None:
+            self.file.close()
+
+my_object = MyClass("my_file.txt")
+
+# The file is now open
+
+del my_object
+
+# The file is now closed

filePresence.py

@@ -0,0 +1,18 @@
+import zipfile
+
+# Abre o arquivo zip
+with zipfile.ZipFile("arquivo.zip", "r") as zip:
+
+    # Lista os arquivos do arquivo zip
+    for arquivo in zip.namelist():
+
+        # Verifica se o arquivo é um dos arquivos necessários
+        if arquivo == "arquivo1.txt" or arquivo == "arquivo2.txt":
+
+            # O arquivo está presente
+            print("O arquivo {} está presente.".format(arquivo))
+
+        else:
+
+            # O arquivo não está presente
+            print("O arquivo {} não está presente.".format(arquivo))

manipulatingExcel.py

@@ -0,0 +1,53 @@
+import pandas as pd
+
+# Carregar o arquivo Excel
+df = pd.read_excel("meu_arquivo.xlsx")
+
+# Obter a coluna C
+coluna_c = df["C"]
+
+Iterar sobre as células preenchidas da coluna C
+
+for valor in coluna_c.dropna():
+    # Processar o valor da célula
+    print(valor)
+    # ...
+
+# Another Manner
+
+import numpy as np
+
+# Carregar o arquivo Excel
+data = np.loadtxt("meu_arquivo.xlsx", dtype=str, skiprows=1)
+
+# Obter a coluna C
+coluna_c = data[:, 2]
+
+# Iterar sobre as células preenchidas da coluna C
+for valor in coluna_c:
+    if valor != '':
+        # Processar o valor da célula
+        print(valor)
+        # ...
+
+
+
+#Now using openpyxl
+
+from openpyxl import load_workbook
+
+# Carregar o arquivo Excel
+wb = load_workbook("meu_arquivo.xlsx")
+
+# Acessar a planilha
+sheet = wb.active
+
+# Obter a coluna C
+coluna_c = sheet['C']
+
+# Iterar sobre as células preenchidas da coluna C
+for cell in coluna_c[2:]:  # Ignora a primeira linha (cabeçalho)
+    if cell.value is not None:
+        # Processar o valor da célula
+        print(cell.value)
+        # ...

marketList.py

@@ -0,0 +1,33 @@
+#It's breakfast time, we don't have any fruit at home, we need to go to supermarket, but we need a organized list
+
+#solution
+
+const fruitsArray = ['maçãs', 'bananas', 'laranjas', 'morangos', 'kiwis'];
+
+function fruitsList(fruit) {
+  return `- ${fruit} (2 unidades)`;
+}
+
+#using the  .map() method to create a market buy list based on fruits array
+
+const newArrayOfStrings = fruitsArray.map(fruitsList);
+
+console.log('Lista de Compras de Frutas:');
+console.log(newArrayOfStrings.join('\n'));
+
+
+"""
+
+result:
+
+Lista de Compras de Frutas:
+- maçãs (2 unidades)
+- bananas (2 unidades)
+- laranjas (2 unidades)
+- morangos (2 unidades)
+- kiwis (2 unidades)
+
+"""
+
+
+

minimumFlips.py

@@ -0,0 +1,37 @@
+def minimumFlips(target: str) -> int:
+    """
+    Calculates the minimum number of flips required to transform a binary string to the target state.
+
+    Args:
+        target: The target binary string (e.g., "0011").
+
+    Returns:
+        The minimum number of flips required (int), or None if the input is not a valid string.
+    """
+    
+    if not isinstance(target, str) or not all(char in "01" for char in target):
+        return None  # Check for valid input
+    
+    flips = 0
+    current_state = '0'
+
+    for char in target:
+        if char != current_state:
+            flips += 1
+            current_state = char
+
+    return flips
+
+# Test cases for minimumFlips
+assert minimumFlips("0011") == 1
+assert minimumFlips("101") == 2
+assert minimumFlips("0") == 0
+assert minimumFlips("111000") == 2
+assert minimumFlips("11011101111") == 3
+assert minimumFlips(None) == None
+assert minimumFlips("abc") == None
+
+if __name__ == '__main__':
+    target = input().strip()
+    result = minimumFlips(target)
+    print(result)

newMethod.py

@@ -0,0 +1,13 @@
+class Singleton:
+    _instance = None
+
+    def __new__(cls, *args, **kwargs):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls, *args, **kwargs)
+        return cls._instance
+
+singleton = Singleton()
+singleton2 = Singleton()
+
+print(singleton)
+print(singleton2)

pandasCSV.py

@@ -0,0 +1,12 @@
+# Create a CSV file with some data
+data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
+with open("my_data.csv", "w") as f:
+    writer = csv.writer(f)
+    writer.writerows(data)
+
+# Open the CSV file in Bard
+with open("my_data.csv") as f:
+    df = pd.read_csv(f)
+
+# Insert the data into Bard
+df.to_bard()

pdfPlumber.py

@@ -0,0 +1,16 @@
+import pdfplumber
+
+# Abrir o arquivo PDF
+with pdfplumber.open("arquivo.pdf") as document:
+
+    # Obter a primeira página
+    page = document.get_page(0)
+
+    # Obter a caixa de texto
+    box = page.find_text("Nome do Cliente")
+
+    # Extrair o texto da caixa de texto
+    text = box.extract_text()
+
+    # Imprimir o texto
+    print(text)

personalizatedList.py

@@ -0,0 +1,13 @@
+SELECT
+  customer_id,
+  COUNT(product_id) AS number_of_products_viewed
+FROM
+  product_views
+WHERE
+  customer_id = '1234567890'
+AND
+  date_created BETWEEN '2023-01-01' AND '2023-12-31'
+GROUP BY
+  customer_id
+ORDER BY
+  number_of_products_viewed DESC

printContacts.py

@@ -0,0 +1,25 @@
+contacts = [
+    ('James', 42),
+    ('Amy', 24),
+    ('John', 31),
+    ('Amanda', 63),
+    ('Bob', 18)
+]
+
+name = input("Digite o nome da pessoa: ")
+
+# Verifica se o nome está na lista
+if name in contacts:
+    # Imprime o nome e a idade
+    print(f"{name} {contacts[contacts.index(name)][1]}")
+else:
+    # Imprime "Not found"
+    print("Not found")
+
+'''
+
+Este código irá primeiro obter o nome da pessoa do usuário.
+Em seguida, ele usará a expressão in para verificar se o nome está na lista.
+Se o nome estiver na lista, o código irá imprimir o nome e a idade da pessoa. 
+Se o nome não estiver na lista, o código irá imprimir "Not found".
+'''

printContactsByIndex.py

@@ -0,0 +1,19 @@
+contacts = [
+    ('James', 42),
+    ('Amy', 24),
+    ('John', 31),
+    ('Amanda', 63),
+    ('Bob', 18)
+]
+
+name = input("Digite o nome da pessoa: ")
+
+# Verifica se o nome está na lista
+index = contacts.index(name)
+
+if index != -1:
+    # Imprime o nome e a idade
+    print(f"{name} {contacts[index][1]}")
+else:
+    # Imprime "Not found"
+    print("Not found")

pythonRandomFunction.py

@@ -0,0 +1,23 @@
+import random
+
+# Create a list of possible street names
+possible_street_names = ["Main Street", "Broadway", "Park Avenue", "Wall Street", "Oak Street"]
+
+# Create a list of possible city names
+possible_city_names = ["New York", "Los Angeles", "Chicago", "Houston", "Philadelphia"]
+
+# Create a list of possible state names
+possible_state_names = ["New York", "California", "Illinois", "Texas", "Pennsylvania"]
+
+# Create a list of possible zip codes
+possible_zip_codes = ["10001", "90001", "60601", "77001", "19101"]
+
+# Create a function to generate a random address
+def generate_random_address():
+    return f"{random.choice(possible_street_names)}, {random.choice(possible_city_names)}, {random.choice(possible_state_names)} {random.choice(possible_zip_codes)}"
+
+# Generate a random address
+random_address = generate_random_address()
+
+# Print the random address
+print(random_address)

pythonRandomFunction2.py

@@ -0,0 +1,14 @@
+import random
+
+# Create a list of possible company names
+possible_company_names = ["Acme Corporation", "Google", "Microsoft", "Apple", "Amazon"]
+
+# Create a function to generate a random company name
+def generate_random_company_name():
+    return random.choice(possible_company_names)
+
+# Generate a random company name
+random_company_name = generate_random_company_name()
+
+# Print the random company name
+print(random_company_name)

selfMethod.py

@@ -0,0 +1,8 @@
+class Test:
+  __egg = 7
+
+  def get_egg(self):
+    return self.__egg
+
+t = Test()
+print(t.get_egg())

sixOccurrencies.py

@@ -0,0 +1,25 @@
+def numero_de_ocorrencias(limite):
+  """Retorna o número de ocorrências do dígito 6 nos números de 0 a `limite`."""
+  total = 0
+  for i in range(1, limite + 1):
+    total += numero_de_ocorrencias_em_um_numero(i)
+  return total
+
+def numero_de_ocorrencias_em_um_numero(numero):
+  """Retorna o número de ocorrências do dígito 6 em um número."""
+  if numero < 10:
+    if numero == 6:
+      return 1
+    else:
+      return 0
+  else:
+    dezenas = numero // 10
+    unidades = numero % 10
+    if unidades == 6:
+      return 1
+    elif dezenas == 6:
+      return 2
+    else:
+      return 0
+
+print(numero_de_ocorrencias(1536))

staticMethod.py

@@ -0,0 +1,9 @@
+class Shape:
+    @staticmethod
+    def area(width, height):
+        return width * height
+
+w = int(input())
+h = int(input())
+
+print(Shape.area(w, h))

withAnalysysExceptions.py

@@ -0,0 +1,23 @@
+def withdraw(amount):
+  try:
+    account_balance = 1000
+    if amount > account_balance:
+      raise ValueError("Insufficient funds")
+    elif not isinstance(amount, int):
+      raise TypeError("Amount must be an integer")
+    print(str(amount) + " withdrawn!")
+  except ValueError as e:
+    print(e)
+  except TypeError as e:
+    print(e)
+
+# Add a finally block to clean up any resources that were opened in the try block
+finally:
+  # Do something to clean up resources, such as closing a file
+  pass
+
+# Get the amount to withdraw from the user
+amount = int(input("Enter the amount to withdraw: "))
+
+# Withdraw the amount from the user's account
+withdraw(amount)

withdrawAboveCapacity.py

@@ -0,0 +1,8 @@
+def sacar_dinheiro(valor):
+  try:
+    # Código que pode gerar exceções
+    if valor > 1000:
+      raise ValueError("Valor de saque muito alto")
+    print(f"{valor} sacado com sucesso!")
+  except ValueError as e:
+    print(e)

zipFilesManipulation.py

@@ -0,0 +1,42 @@
+import zipfile
+import os
+import pandas as pd
+
+def extract_data_from_files(zip_file_path, file_names, column_names, value_column_name, code_column_name):
+    # Extract files from the ZIP file using context manager and extractall
+    with zipfile.ZipFile(zip_file_path, "r") as zip:
+        zip.extractall("arquivo_descompactado")  # Extract to a directory
+
+    # Extract values from the extracted files
+    data_frames = []
+    for file_name in file_names:
+        file_path = os.path.join("arquivo_descompactado", file_name)  # Construct full file path
+        with open(file_path, "r") as file:  # Use the corrected file path
+            data_frame = pd.DataFrame()
+            for line in file:
+                values = line.split("|")
+
+                # Ensure the expected number of values in the line
+                if len(values) >= max(value_column_name, code_column_name) + 1: 
+                    data_frame = data_frame.append({
+                        column_names[0]: values[0],  
+                        column_names[1]: values[1],
+                        value_column_name: values[value_column_name],
+                        "Observations": "Correct"  # You might want to customize this based on your data
+                    }, ignore_index=True)
+            data_frames.append(data_frame)
+
+    # Merge the extracted data frames
+    merged_data_frame = pd.concat(data_frames)
+
+    # Save the merged data frame to an OpenDocument spreadsheet (.ods)
+    merged_data_frame.to_excel("planilha.ods", engine='odf', index=False)
+
+# Example usage
+zip_file_path = "arquivo.zip"
+file_names = ["new_name_1.txt", "new_name_2.txt", "new_name_3.txt"]
+column_names = ["Data", "Código"] 
+value_column_name = 7
+code_column_name = 1
+
+extract_data_from_files(zip_file_path, file_names, column_names, value_column_name, code_column_name)