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question:# Task Scheduling using Topological Sorting You are provided with a set of tasks and their dependencies. Your goal is to determine a valid order in which these tasks can be executed using the `graphlib.TopologicalSorter` class. If there are multiple valid orders, return any one of them. If no valid order exists because the tasks contain cycles, raise a `CycleError`. # Function Signature ```python def schedule_tasks(tasks: Dict[str, List[str]]) -> List[str]: pass ``` # Input - `tasks`: A dictionary where keys are task names (strings) and values are lists of task names that must precede the key task. # Output - A list of task names in a valid topological order. # Constraints - Task names are unique and hashable. - The graph can have at most 10^5 nodes and 3 * 10^5 edges. # Example ```python tasks = { "wash": ["gather_laundry"], "dry": ["wash"], "fold": ["dry"], "gather_laundry": [] } print(schedule_tasks(tasks)) # Expected output: ['gather_laundry', 'wash', 'dry', 'fold'] ``` # Additional Notes 1. Utilize the `TopologicalSorter` class for implementing topological sorting. 2. Handle the case where the graph contains cycles by appropriately raising a `CycleError`. 3. Ensure the function runs efficiently for large graphs within the given constraints. # Solution Template You may use the following template to get started: ```python from graphlib import TopologicalSorter, CycleError from typing import Dict, List def schedule_tasks(tasks: Dict[str, List[str]]) -> List[str]: ts = TopologicalSorter() for task, dependencies in tasks.items(): ts.add(task, *dependencies) try: return list(ts.static_order()) except CycleError as e: raise e ``` Your task is to complete the function `schedule_tasks` so that it meets the requirements specified.
answer:from graphlib import TopologicalSorter, CycleError from typing import Dict, List def schedule_tasks(tasks: Dict[str, List[str]]) -> List[str]: ts = TopologicalSorter() for task, dependencies in tasks.items(): ts.add(task, *dependencies) try: # Compute the topological order of the tasks return list(ts.static_order()) except CycleError as e: # If there is a cycle in the graph, raise the exception raise e
question:# Objective Design and implement a Python function that demonstrates the use of custom exceptions, multiple built-in exceptions, and exception chaining. # Requirements 1. Define a custom exception `CustomError` that inherits from `Exception`. 2. Create a function `process_data(data)` that takes a list of dictionaries as input. Each dictionary should represent a record with at least two keys: 'value' and 'status'. 3. The function should: - Raise a `ValueError` if the 'value' entry is not an integer. - Raise a `KeyError` if any record does not have the 'status' key. - Use `CustomError` to indicate if 'status' is 'invalid' and chain it to a `ValueError`. 4. Implement exception handling to catch and handle these exceptions in a meaningful way. # Details 1. Input: A list of dictionaries, e.g., `data = [{'value': 10, 'status': 'valid'}, {'value': 'ten', 'status': 'valid'}, {'value': 5}]` 2. Output: A list of tuples where each tuple contains the original record and a string indicating 'success' or the type of failure. # Implementation Constraints - Use exception chaining where appropriate. - Do not use external libraries. - Ensure the function is properly tested with various scenarios. # Example ```python class CustomError(Exception): pass def process_data(data): result = [] for record in data: try: ... except KeyError: ... except ValueError as ve: ... ... return result # Example Input data = [ {'value': 10, 'status': 'valid'}, {'value': 'ten', 'status': 'valid'}, {'value': 5}, {'value': 3, 'status': 'invalid'} ] # Example Output [ ({'value': 10, 'status': 'valid'}, 'success'), ({'value': 'ten', 'status': 'valid'}, 'ValueError'), ({'value': 5}, 'KeyError'), ({'value': 3, 'status': 'invalid'}, 'CustomError: invalid status') ] ``` **Note**: The actual implementation of `process_data` function should correctly handle and log exceptions based on the requirements stated above.
answer:class CustomError(Exception): Custom exception to represent an invalid status in the input data. pass def process_data(data): result = [] for record in data: try: if not isinstance(record.get('value'), int): raise ValueError("The 'value' entry is not an integer.") if 'status' not in record: raise KeyError("Record does not have the 'status' key.") if record['status'] == 'invalid': raise CustomError("Invalid status") result.append((record, 'success')) except ValueError as ve: result.append((record, f'ValueError: {ve}')) except KeyError as ke: result.append((record, f'KeyError: {ke}')) except CustomError as ce: result.append((record, f'CustomError: {ce}')) return result
question:**Objective**: Demonstrate proficiency in data manipulation, descriptive statistics, and visualization using pandas Series. **Problem Statement**: You are given the daily closing prices of two stocks, Stock A and Stock B, over a period of time. However, some of the data might be missing due to various reasons. Your task is to analyze this data by performing the following operations using pandas: 1. **Data Preparation**: - Read in the provided CSV files `stock_A.csv` and `stock_B.csv`. - Ensure the date column is parsed as `datetime` and is set as the index. 2. **Handling Missing Data**: - For each stock, fill the missing data points using forward fill (`ffill`) method. - If any missing data points exist at the beginning, fill them with the first non-NaN value of that series. 3. **Descriptive Statistics**: - Calculate the following statistics for each stock and print them: - Mean closing price. - Median closing price. - Standard deviation of the closing price. - The day with the highest closing price. - The day with the lowest closing price. 4. **Correlation Analysis**: - Determine and print the correlation coefficient between the closing prices of Stock A and Stock B. 5. **Visualization**: - Generate a single plot that shows the closing prices of both stocks over time. - Highlight the days with the highest and lowest closing prices for each stock on the plot. **Input Format**: - Two CSV files: `stock_A.csv` and `stock_B.csv`. Each file contains two columns: `Date` and `Close`. **Output Format**: - Print the descriptive statistics. - Print the correlation coefficient. - Plot the closing prices with appropriate annotations for the highest and lowest prices. **Constraints**: - Ensure that the filled missing data maintains the trend by using forward fill and initial fill. - Handle any potential edge cases where the data might be entirely missing for a short period. **Performance**: - The operations should be completed efficiently with a time complexity allowing handling of large datasets. Here is a skeleton code to help you get started: ```python import pandas as pd import matplotlib.pyplot as plt def load_and_prepare_data(file_path): # Read CSV file df = pd.read_csv(file_path, parse_dates=['Date'], index_col='Date') # Fill missing values df['Close'] = df['Close'].ffill().bfill() return df def calculate_statistics(df): mean_price = df['Close'].mean() median_price = df['Close'].median() std_dev_price = df['Close'].std() highest_price_day = df['Close'].idxmax() lowest_price_day = df['Close'].idxmin() return { 'mean': mean_price, 'median': median_price, 'std_dev': std_dev_price, 'highest_day': highest_price_day, 'lowest_day': lowest_price_day } def plot_prices(df_A, df_B, stats_A, stats_B): plt.figure(figsize=(14, 7)) plt.plot(df_A.index, df_A['Close'], label='Stock A') plt.plot(df_B.index, df_B['Close'], label='Stock B') # Highlight highest and lowest days plt.scatter(stats_A['highest_day'], df_A.loc[stats_A['highest_day'], 'Close'], color='red') plt.scatter(stats_A['lowest_day'], df_A.loc[stats_A['lowest_day'], 'Close'], color='green') plt.scatter(stats_B['highest_day'], df_B.loc[stats_B['highest_day'], 'Close'], color='red') plt.scatter(stats_B['lowest_day'], df_B.loc[stats_B['lowest_day'], 'Close'], color='green') plt.legend() plt.title('Stock Prices Over Time') plt.xlabel('Date') plt.ylabel('Closing Price') plt.grid(True) plt.show() def main(): df_A = load_and_prepare_data('stock_A.csv') df_B = load_and_prepare_data('stock_B.csv') stats_A = calculate_statistics(df_A) stats_B = calculate_statistics(df_B) print(f"Stock A Statistics: {stats_A}") print(f"Stock B Statistics: {stats_B}") correlation = df_A['Close'].corr(df_B['Close']) print(f"Correlation between Stock A and Stock B: {correlation}") plot_prices(df_A, df_B, stats_A, stats_B) if __name__ == "__main__": main() ``` **Note**: Ensure that `matplotlib` library is installed to enable plotting. The CSV files should be present in the same directory as the script.
answer:import pandas as pd import matplotlib.pyplot as plt def load_and_prepare_data(file_path): Reads the CSV file, sets the 'Date' column as index after parsing it as datetime, and fills missing values using forward fill and backward fill if necessary. # Read CSV file df = pd.read_csv(file_path, parse_dates=['Date'], index_col='Date') # Fill missing values df['Close'] = df['Close'].ffill().bfill() return df def calculate_statistics(df): Calculates and returns the mean, median, standard deviation, day with the highest closing price, and day with the lowest closing price for the given stock data. mean_price = df['Close'].mean() median_price = df['Close'].median() std_dev_price = df['Close'].std() highest_price_day = df['Close'].idxmax() lowest_price_day = df['Close'].idxmin() return { 'mean': mean_price, 'median': median_price, 'std_dev': std_dev_price, 'highest_day': highest_price_day, 'lowest_day': lowest_price_day } def plot_prices(df_A, df_B, stats_A, stats_B): Plots the closing prices of Stock A and Stock B over time. Highlights the days with the highest and lowest closing prices for each stock. plt.figure(figsize=(14, 7)) plt.plot(df_A.index, df_A['Close'], label='Stock A') plt.plot(df_B.index, df_B['Close'], label='Stock B') # Highlight highest and lowest days plt.scatter(stats_A['highest_day'], df_A.loc[stats_A['highest_day'], 'Close'], color='red', label='Stock A Highest') plt.scatter(stats_A['lowest_day'], df_A.loc[stats_A['lowest_day'], 'Close'], color='green', label='Stock A Lowest') plt.scatter(stats_B['highest_day'], df_B.loc[stats_B['highest_day'], 'Close'], color='orange', label='Stock B Highest') plt.scatter(stats_B['lowest_day'], df_B.loc[stats_B['lowest_day'], 'Close'], color='blue', label='Stock B Lowest') plt.legend() plt.title('Stock Prices Over Time') plt.xlabel('Date') plt.ylabel('Closing Price') plt.grid(True) plt.show() def main(): df_A = load_and_prepare_data('stock_A.csv') df_B = load_and_prepare_data('stock_B.csv') stats_A = calculate_statistics(df_A) stats_B = calculate_statistics(df_B) print(f"Stock A Statistics: {stats_A}") print(f"Stock B Statistics: {stats_B}") correlation = df_A['Close'].corr(df_B['Close']) print(f"Correlation between Stock A and Stock B: {correlation}") plot_prices(df_A, df_B, stats_A, stats_B) if __name__ == "__main__": main()
question:# Question: Create a Comprehensive Joint Plot with Customizations You are tasked with analyzing a dataset using Seaborn's `jointplot`. The dataset you will use is the "penguins" dataset, which comes built-in with Seaborn. Requirements: 1. **Load the penguins dataset** from Seaborn's built-in datasets. 2. **Create a joint plot** to visualize the relationship between `flipper_length_mm` and `body_mass_g`. 3. **Color the data points** based on the `species` column using the `hue` parameter. 4. **Overlay KDE plots** on both the joint and marginal axes. 5. **Adjust the layout**: - Set the height of the plot to 6. - Set the ratio between the joint and marginal axes to 3. - Enable marginal axis ticks. 6. **Add additional layers**: - Overlay a hexbin plot on the joint plot. - Overlay a rug plot on the marginal axes. - Overlay KDE plot contours with red color and set `levels=5`. Input: - None (the dataset is loaded directly within the code). Output: - The function should generate and display the described joint plot. Constraints: - Ensure that the KDE plot and hexbin plot do not overlap in a way that makes interpretation difficult. - Properly handle the scenario in which the dataset might have missing values in the relevant columns. Example Function Signature: ```python import seaborn as sns import matplotlib.pyplot as plt def comprehensive_joint_plot(): # Load the dataset penguins = sns.load_dataset("penguins") # Your implementation here # Display the plot plt.show() ``` **Note**: The function does not take any parameters, as the dataset loading is handled internally.
answer:import seaborn as sns import matplotlib.pyplot as plt def comprehensive_joint_plot(): # Load the dataset penguins = sns.load_dataset("penguins") # Drop rows with missing values in relevant columns penguins = penguins.dropna(subset=["flipper_length_mm", "body_mass_g"]) # Create the joint plot g = sns.jointplot( data=penguins, x="flipper_length_mm", y="body_mass_g", hue="species", height=6, ratio=3, marginal_ticks=True, kind="scatter" ) # Overlay KDE plots sns.kdeplot( data=penguins, x="flipper_length_mm", y="body_mass_g", hue="species", ax=g.ax_joint, levels=5, color="red" ) sns.kdeplot( data=penguins, x="flipper_length_mm", hue="species", ax=g.ax_marg_x ) sns.kdeplot( data=penguins, y="body_mass_g", hue="species", ax=g.ax_marg_y ) # Overlay hexbin plot g.ax_joint.hexbin( penguins["flipper_length_mm"], penguins["body_mass_g"], gridsize=40, cmap="Greens", alpha=0.5 ) # Overlay rug plot sns.rugplot(data=penguins, x="flipper_length_mm", y="body_mass_g", hue="species", ax=g.ax_joint) sns.rugplot(data=penguins, x="flipper_length_mm", hue="species", ax=g.ax_marg_x) sns.rugplot(data=penguins, y="body_mass_g", hue="species", ax=g.ax_marg_y) # Show the plot plt.show()