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question:You are tasked with writing a function named `calculate_word_frequencies` that counts the frequency of each word in a given list of strings. The function should return a dictionary where the keys are words and the values are the number of occurrences of each word. The function should follow these specifications: 1. Normalize the words to lowercase to ensure that 'Word' and 'word' are considered the same word. 2. Ignore punctuation marks in the words. 3. Words are separated by spaces. Here is the function signature for `calculate_word_frequencies`: ```python def calculate_word_frequencies(sentences: List[str]) -> Dict[str, int]: # Your implementation here ``` # Example ```python sentences = [ "Hello world!", "Hello, how are you world?", "Hello everyone. Hello to the new world." ] result = calculate_word_frequencies(sentences) print(result) # Output: {'hello': 4, 'world': 3, 'how': 1, 'are': 1, 'you': 1, 'everyone': 1, 'to': 1, 'the': 1, 'new': 1} ``` # Constraints - The input list `sentences` will have at least one string and not more than 100 strings. - Each string in `sentences` will have at least one word and not more than 100 words. - Words consist of alphabets (both uppercase and lowercase) and basic punctuation marks (.,!?). # Hints - You can use regular expressions to remove punctuation marks from words. - Use a dictionary to count the occurrences of each word.
answer:import re from typing import List, Dict def calculate_word_frequencies(sentences: List[str]) -> Dict[str, int]: word_freq = {} for sentence in sentences: # Remove punctuation and convert to lowercase cleaned_sentence = re.sub(r'[^ws]', '', sentence.lower()) words = cleaned_sentence.split() for word in words: if word in word_freq: word_freq[word] += 1 else: word_freq[word] = 1 return word_freq
question:Write a function that takes a string as an argument and returns a boolean indicating whether the string is a valid IPv4 address. An IPv4 address consists of four decimal numbers, each ranging from 0 to 255, separated by dots (e.g., "192.168.1.1"). The function should reject any string that contains letters, special characters, or numbers outside the valid range. The function should output: bool: True if the string is a valid IPv4 address, False otherwise. You should write self-contained code starting with: ``` def validate_ipv4_address(ip): ```
answer:def validate_ipv4_address(ip): Validates if the provided string is a valid IPv4 address. Args: ip (str): The string to validate as an IPv4 address. Returns: bool: True if the string is a valid IPv4 address, False otherwise. parts = ip.split(".") if len(parts) != 4: return False for part in parts: if not part.isdigit(): return False num = int(part) if num < 0 or num > 255: return False return True
question:You are hiring a software engineer to work on a team project. To evaluate their programming skills, you decide to create a coding task focused on understanding and manipulating Python dictionaries. Develop a function called `merge_dicts(dict1, dict2)` that combines two dictionaries into one. Your function should meet the following requirements: 1. If a key is present in both dictionaries, merge the values as follows: - If the values are integers, sum them. - If the values are lists, concatenate them. - If the values are sets, perform a union operation. 2. If a key is present in only one dictionary, its value should be included unchanged in the resulting dictionary. # Function Signature ```python def merge_dicts(dict1, dict2): pass ``` # Inputs - `dict1`: A dictionary where keys are strings and values are either integers, lists, or sets. - `dict2`: A dictionary where keys are strings and values are either integers, lists, or sets. # Output - The function should return a new dictionary that represents the merged result as described above. # Example ```python dict1 = { "a": 1, "b": [1, 2], "c": {1, 2} } dict2 = { "a": 2, "b": [3, 4], "c": {3, 4}, "d": 5 } merged = merge_dicts(dict1, dict2) print(merged) ``` # Expected Output ```python { "a": 3, "b": [1, 2, 3, 4], "c": {1, 2, 3, 4}, "d": 5 } ``` # Notes - You can assume that the values for each key will be of the same type in both dictionaries. - You can use the `set.union` method to merge sets. - The function should handle edge cases such as empty dictionaries appropriately.
answer:def merge_dicts(dict1, dict2): Merges two dictionaries according to the specified rules. Parameters: dict1 (dict): First dictionary with keys as strings and values as integers, lists, or sets. dict2 (dict): Second dictionary with keys as strings and values as integers, lists, or sets. Returns: merged_dict (dict): A new dictionary that represents the merged result. merged_dict = {} all_keys = set(dict1.keys()).union(set(dict2.keys())) for key in all_keys: if key in dict1 and key in dict2: if isinstance(dict1[key], int) and isinstance(dict2[key], int): merged_dict[key] = dict1[key] + dict2[key] elif isinstance(dict1[key], list) and isinstance(dict2[key], list): merged_dict[key] = dict1[key] + dict2[key] elif isinstance(dict1[key], set) and isinstance(dict2[key], set): merged_dict[key] = dict1[key].union(dict2[key]) elif key in dict1: merged_dict[key] = dict1[key] else: merged_dict[key] = dict2[key] return merged_dict
question:**Context:** You are tasked with developing a program that interacts with a weather API to fetch the forecast data for a specified city. The program should use the `requests` library to make HTTP GET requests and store the weather data in a Pandas DataFrame. The function should allow users to retrieve weather forecasts either for the current day or for a specified range of dates. Depending on the user's input, the function should handle the retrieval, conversion, and proper error handling. **Requirements:** Implement a function `get_weather_forecast` that fetches and processes weather forecast data into a Pandas DataFrame. **Function Signature:** ```python def get_weather_forecast(city, start_date, end_date=None): Retrieve weather forecast data from the weather API. Parameters: - city (str): The name of the city for which to retrieve the weather forecast. - start_date (str): The start date of the forecast in 'YYYY-MM-DD' format. - end_date (str, optional): The end date of the forecast in 'YYYY-MM-DD' format. If not provided, retrieve only the forecast for the start date. Returns: - DataFrame: A pandas DataFrame containing the retrieved weather forecast data. pass ``` **Task:** 1. Implement the `get_weather_forecast` function. 2. Use the `requests.get` method to make an HTTP GET request to the weather API. 3. Use the `pandas.DataFrame` method to create a DataFrame from the JSON response. 4. Handle exceptional cases where data retrieval may fail and provide appropriate error messages. 5. If `end_date` is provided, retrieve forecasts for the range from `start_date` to `end_date` inclusive. If `end_date` is not provided, retrieve only the forecast for the `start_date`. **Hints:** - Use `requests.get` to fetch data from the weather API URL. - Convert the JSON response into a Pandas DataFrame. - Ensure to validate the inputs as per the provided requirements. - Example endpoint: `https://api.weatherapi.com/v1/forecast.json?key=YOUR_API_KEY&q={city}&dt={date}` - Pay attention to edge cases such as invalid dates, non-existent cities, API failures, etc. **Example Usage:** ```python from weather_forecast import get_weather_forecast # Retrieve weather forecast for New York City for a single day df = get_weather_forecast("New York", "2023-09-20") print(df) # Retrieve weather forecast for New York City for a range of dates df = get_weather_forecast("New York", "2023-09-20", "2023-09-25") print(df) ```
answer:import requests import pandas as pd from datetime import datetime, timedelta def get_weather_forecast(city, start_date, end_date=None): Retrieve weather forecast data from the weather API. Parameters: - city (str): The name of the city for which to retrieve the weather forecast. - start_date (str): The start date of the forecast in 'YYYY-MM-DD' format. - end_date (str, optional): The end date of the forecast in 'YYYY-MM-DD' format. If not provided, retrieve only the forecast for the start date. Returns: - DataFrame: A pandas DataFrame containing the retrieved weather forecast data. base_url = "https://api.weatherapi.com/v1/forecast.json" api_key = "YOUR_API_KEY" # Replace with your actual API key if not isinstance(city, str) or not isinstance(start_date, str) or (end_date is not None and not isinstance(end_date, str)): raise ValueError("City, start_date, and end_date (if provided) must be strings in 'YYYY-MM-DD' format.") try: datetime.strptime(start_date, '%Y-%m-%d') except ValueError: raise ValueError("Start date must be in 'YYYY-MM-DD' format") if end_date: try: datetime.strptime(end_date, '%Y-%m-%d') except ValueError: raise ValueError("End date must be in 'YYYY-MM-DD' format") dates = [start_date] if end_date: start_dt = datetime.strptime(start_date, '%Y-%m-%d') end_dt = datetime.strptime(end_date, '%Y-%m-%d') if start_dt > end_dt: raise ValueError("Start date must be before or the same as end date.") dates = [(start_dt + timedelta(days=i)).strftime('%Y-%m-%d') for i in range((end_dt - start_dt).days + 1)] forecast_data = [] for date in dates: response = requests.get(base_url, params={ "key": api_key, "q": city, "dt": date }) if response.status_code != 200: raise Exception(f"Failed to fetch weather data. Status code: {response.status_code}") data = response.json() forecast = data['forecast']['forecastday'] for day in forecast: forecast_data.append(day) df = pd.DataFrame(forecast_data) return df