answer:def extract_positive_reviews(file_path: str) -> int: positive_reviews = [] with open(file_path, 'r') as file: reviews = file.readlines() for review in reviews: if 'good' in review.lower(): positive_reviews.append(review) with open('positive_reviews.txt', 'w') as file: file.writelines(positive_reviews) positive_review_count = len(positive_reviews) print(f"Number of positive reviews: {positive_review_count}") return positive_review_count

answer:import pandas as pd import matplotlib.pyplot as plt def analyze_employee_performance(df: pd.DataFrame) -> None: Analyzes employee performance by creating a bar plot for average performance score per department and a line plot for average years with the company per department. Args: df (pd.DataFrame): DataFrame containing employee data with 'department', 'performance_score', and 'years_with_company' columns. # Calculate the average performance score per department avg_performance = df.groupby('department')['performance_score'].mean() # Calculate the average years with company per department avg_years = df.groupby('department')['years_with_company'].mean() # Create a bar plot for average performance scores fig, ax1 = plt.subplots() avg_performance.plot(kind='bar', ax=ax1, color='skyblue', position=1) ax1.set_ylabel('Average Performance Score') # Create a secondary axis for the line plot ax2 = ax1.twinx() avg_years.plot(kind='line', ax=ax2, color='green', marker='o') ax2.set_ylabel('Average Years with Company') ax1.set_xlabel('Department') ax1.set_title('Average Performance Score and Years with Company by Department') # Save the plot to a file plt.savefig('employee_performance_analysis.png') plt.close()

answer:import heapq from typing import List, Tuple def prims_algorithm(adj_matrix: List[List[int]]) -> List[Tuple[int, int, int]]: n = len(adj_matrix) visited = [False] * n mst_edges = [] min_heap = [(0, 0, 0)] while len(mst_edges) < n - 1: weight, node1, node2 = heapq.heappop(min_heap) if not visited[node2]: visited[node2] = True if node1 != node2: mst_edges.append((node1, node2, weight)) for next_node in range(n): if not visited[next_node] and adj_matrix[node2][next_node] != 0: heapq.heappush(min_heap, (adj_matrix[node2][next_node], node2, next_node)) return mst_edges

answer:import pandas as pd from datetime import datetime def summarize_temperature_statistics(temperatures: list, dates: list, start_date: datetime, end_date: datetime) -> pd.DataFrame: Summarize temperature statistics by calculating average, minimum, maximum, and standard deviation for each month within the specified date range. Parameters ---------- temperatures : list List of daily temperature readings (as floats or integers). dates : list List of dates corresponding to the temperature readings in 'YYYY-MM-DD' format (as strings). start_date : datetime The start date of the period for which to calculate statistics. end_date : datetime The end date of the period for which to calculate statistics. Returns ------- df_stats : pd.DataFrame DataFrame containing the average, minimum, maximum, and standard deviation of temperatures for each month. # Creating DataFrame from the given data df = pd.DataFrame({ 'Date': pd.to_datetime(dates), 'Temperature': temperatures }) # Filtering the DataFrame to include only data within the specified date range mask = (df['Date'] >= start_date) & (df['Date'] <= end_date) df = df.loc[mask].copy() # Creating a 'Month' column for grouping by each month df['Month'] = df['Date'].dt.to_period('M') # Grouping by 'Month' and calculating the required statistics df_stats = df.groupby('Month').agg( Average_Temperature=('Temperature', 'mean'), Min_Temperature=('Temperature', 'min'), Max_Temperature=('Temperature', 'max'), StdDev_Temperature=('Temperature', 'std') ).reset_index() # Convert 'Month' back to string for readability df_stats['Month'] = df_stats['Month'].astype(str) return df_stats