Appearance
question:# **Coding Assessment Question: Advanced Data Reshaping with Pandas** **Objective:** Your task is to write a Python function that performs a sequence of advanced data reshaping operations using the pandas library. You will need to use the following methods: `melt`, `pivot_table`, `stack`, and `unstack`. **Problem Statement:** You are given a dataset containing information about sales transactions made by different employees of a company. The dataset is in a wide format, and your task is to reshape it to analyze the total sales per employee, per month. You need to perform the steps below: 1. Convert the dataset from wide format to long format using the `melt` function. 2. Create a pivot table to summarize the total sales for each employee, each month. 3. Stack the pivot table to create a multi-index DataFrame. 4. Finally, unstack the DataFrame to revert to analyzing monthly sales by employee, ensuring the multi-level indices are used correctly. **Dataset:** The dataset is provided in wide format as a dictionary: ```python data = { "employee": ["Alice", "Bob", "Charlie", "David"], "region": ["North", "South", "East", "West"], "2023-01": [200, 150, 230, 180], "2023-02": [220, 160, 240, 190], "2023-03": [210, 170, 250, 200], "2023-04": [230, 180, 260, 210], } ``` **Function Specification:** Write a function `reshape_sales_data(data: dict) -> pd.DataFrame` that takes in the dataset as a dictionary and returns the reshaped DataFrame. # **Input:** - `data`: A dictionary containing the sales data of employees in wide format. # **Output:** - A DataFrame with multi-level indices summarizing the total sales per employee, per month. # **Constraints:** - You must use the pandas library. - Ensure your function handles the reshaping as described in the steps. **Example:** ```python import pandas as pd data = { "employee": ["Alice", "Bob", "Charlie", "David"], "region": ["North", "South", "East", "West"], "2023-01": [200, 150, 230, 180], "2023-02": [220, 160, 240, 190], "2023-03": [210, 170, 250, 200], "2023-04": [230, 180, 260, 210], } def reshape_sales_data(data): # Convert dictionary to DataFrame df = pd.DataFrame(data) # Melt the DataFrame to long format melted_df = df.melt(id_vars=["employee", "region"], var_name="month", value_name="sales") # Create a pivot table pivot_table = pd.pivot_table(melted_df, values="sales", index=["employee", "region"], columns="month", aggfunc="sum") # Stack the pivot table to introduce multi-index stacked_df = pivot_table.stack().reset_index(name='total_sales') # Unstack the DataFrame to revert analyzing by employee unstacked_df = stacked_df.set_index(['employee', 'month']).unstack(level='month') return unstacked_df # Function call reshaped_data = reshape_sales_data(data) print(reshaped_data) ``` **Expected Output:** The expected output is a DataFrame that represents the total sales per employee, per month, retaining the hierarchical structure of multi-level indices for ease of analysis.
answer:import pandas as pd def reshape_sales_data(data): Reshape the sales data provided in dictionary format. Arguments: data -- dictionary containing employee sales data. Returns: DataFrame with reshaped data. # Convert dictionary to DataFrame df = pd.DataFrame(data) # Melt the DataFrame to long format melted_df = df.melt(id_vars=["employee", "region"], var_name="month", value_name="sales") # Create a pivot table pivot_table = pd.pivot_table(melted_df, values="sales", index=["employee", "region"], columns="month", aggfunc="sum") # Stack the pivot table to introduce multi-index stacked_df = pivot_table.stack().reset_index(name='total_sales') # Unstack the DataFrame to revert analyzing by employee unstacked_df = stacked_df.set_index(['employee', 'month']).total_sales.unstack(level='month') return unstacked_df
question:You are given a dataset containing information about diamond prices and various attributes. Your task is to load the dataset, create visualizations using Seaborn's `boxenplot`, and customize the plots according to the specified requirements. **Dataset:** The dataset `diamonds` is available within Seaborn's built-in datasets. It contains the following columns: - `carat`: Diamond weight. - `cut`: Quality of the cut (Fair, Good, Very Good, Premium, Ideal). - `color`: Diamond color, from J (worst) to D (best). - `clarity`: Clarity of the diamond (I1, SI2, SI1, VS2, VS1, VVS2, VVS1, IF). - `depth`: Total depth percentage. - `table`: Width of the top of the diamond relative to its widest point. - `price`: Price in US dollars. - `x`: Length in mm. - `y`: Width in mm. - `z`: Depth in mm. # Tasks 1. **Load the `diamonds` dataset** using the Seaborn library. 2. **Create a boxen plot visualizing the distribution of diamond prices** grouped by `cut` quality. Display the plot with the default settings. 3. **Create a second boxen plot** that visualizes the distribution of diamond prices grouped by `cut`, with an additional grouping by `color` using different hues. Ensure that the boxes are dodged to avoid overlap and add a small gap between them. 4. **Adjust the width of the boxes** in the second plot by setting the `width_method` parameter to `"linear"`. Set the width of the largest box to 0.5. 5. **Customize the appearances** of the second plot by: - Setting the outline color of the boxes to light grey. - Changing the thickness of the outline to 0.5. - Customizing the median lines with a thicker width (1.5) and blue color. - Customizing the outliers by setting their face color to grey and outline thickness to 0.5. # Constraints - Use Seaborn version 0.11.0 or later. - Write clean and readable code with appropriate comments. # Expected Output Your script should produce two plots: 1. A boxen plot showing the distribution of diamond prices grouped by `cut`. 2. A customized boxen plot as described in Task 3 to Task 5. # Example Usage Here's how you might call your functions: ```python import seaborn as sns import matplotlib.pyplot as plt # Task 1: Load the dataset diamonds = sns.load_dataset("diamonds") # Task 2: Plot with default settings sns.boxenplot(data=diamonds, x="cut", y="price") plt.show() # Task 3: Plot with additional grouping by color sns.boxenplot(data=diamonds, x="cut", y="price", hue="color", dodge=True, gap=0.2) plt.show() # Task 4: Adjust the box width to be linear and width of largest box to 0.5 sns.boxenplot(data=diamonds, x="cut", y="price", hue="color", dodge=True, gap=0.2, width_method="linear", width=0.5) plt.show() # Task 5: Customize the appearances sns.boxenplot( data=diamonds, x="cut", y="price", hue="color", dodge=True, gap=0.2, width_method="linear", width=0.5, linewidth=0.5, linecolor=".7", line_kws=dict(linewidth=1.5, color="blue"), flier_kws=dict(facecolor=".7", linewidth=0.5), ) plt.show() ``` Note: Ensure that your plots are properly labeled and that legends are displayed when appropriate.
answer:import seaborn as sns import matplotlib.pyplot as plt def load_diamonds_dataset(): Load the diamonds dataset from seaborn. return sns.load_dataset("diamonds") def plot_diamond_prices_by_cut(diamonds): Create and display a boxen plot visualizing the distribution of diamond prices grouped by cut. sns.boxenplot(data=diamonds, x="cut", y="price") plt.xlabel("Cut Quality") plt.ylabel("Price (US dollars)") plt.title("Diamond Prices by Cut Quality") plt.show() def plot_diamond_prices_by_cut_and_color(diamonds): Create and display a boxen plot visualizing the distribution of diamond prices grouped by cut, with an additional grouping by color using different hues. sns.boxenplot(data=diamonds, x="cut", y="price", hue="color", dodge=True, gap=0.2) plt.xlabel("Cut Quality") plt.ylabel("Price (US dollars)") plt.title("Diamond Prices by Cut Quality and Color") plt.legend(title="Color") plt.show() def plot_customized_diamond_prices(diamonds): Create and display a customized boxen plot with specific appearance settings. sns.set(style="whitegrid") sns.boxenplot( data=diamonds, x="cut", y="price", hue="color", dodge=True, gap=0.2, width_method="linear", width=0.5, linewidth=0.5, linecolor=".7", line_kws=dict(linewidth=1.5, color="blue"), flier_kws=dict(facecolor=".7", linewidth=0.5) ) plt.xlabel("Cut Quality") plt.ylabel("Price (US dollars)") plt.title("Customized Diamond Prices by Cut and Color") plt.legend(title="Color") plt.show() # Example of how to call these functions if __name__ == "__main__": diamonds = load_diamonds_dataset() plot_diamond_prices_by_cut(diamonds) plot_diamond_prices_by_cut_and_color(diamonds) plot_customized_diamond_prices(diamonds)
question:Objective: Design a custom serialization and deserialization mechanism for a Python class using the `copyreg` module. Problem Statement: You are given a class `Employee` that stores information about employees in an organization. The class is defined as follows: ```python class Employee: def __init__(self, name, position, salary): self.name = name self.position = position self.salary = salary ``` You need to implement a custom pickling and unpickling mechanism for `Employee` objects using the `copyreg` module. Specifically, you should: 1. Write a custom pickling function `pickle_employee` that takes an `Employee` object and returns a tuple (`Employee`, (name, position, salary)). 2. Register this custom pickling function for the `Employee` class using `copyreg.pickle`. 3. Test your implementation by creating an `Employee` object, pickling it using the `pickle` module, and then unpickling it to verify that the original object is correctly restored. Requirements: 1. Implement the custom pickling function and use `copyreg` to register it. 2. Ensure that the unpickled object is equivalent to the original object. 3. Provide test cases to demonstrate that the pickling and unpickling mechanism works as expected. Input and Output: - The class definition and the custom pickling function should be part of your code. - Create an instance of the `Employee` class and verify pickling and unpickling using the `pickle` module. - The test cases should print the deserialized `Employee` object's attributes to demonstrate successful serialization and deserialization. Example: ```python import copyreg import pickle class Employee: def __init__(self, name, position, salary): self.name = name self.position = position self.salary = salary def pickle_employee(employee): return Employee, (employee.name, employee.position, employee.salary) copyreg.pickle(Employee, pickle_employee) # Test serialization and deserialization if __name__ == "__main__": emp = Employee("John Doe", "Software Engineer", 100000) pickled_emp = pickle.dumps(emp) unpickled_emp = pickle.loads(pickled_emp) print(unpickled_emp.name) # Should print "John Doe" print(unpickled_emp.position) # Should print "Software Engineer" print(unpickled_emp.salary) # Should print 100000 ``` Note: Ensure to include necessary imports and handle any exceptions that might arise during pickling or unpickling.
answer:import copyreg import pickle class Employee: def __init__(self, name, position, salary): self.name = name self.position = position self.salary = salary def pickle_employee(employee): return Employee, (employee.name, employee.position, employee.salary) copyreg.pickle(Employee, pickle_employee) # Test serialization and deserialization if __name__ == "__main__": emp = Employee("John Doe", "Software Engineer", 100000) pickled_emp = pickle.dumps(emp) unpickled_emp = pickle.loads(pickled_emp) print(unpickled_emp.name) # Should print "John Doe" print(unpickled_emp.position) # Should print "Software Engineer" print(unpickled_emp.salary) # Should print 100000
question:You are given a Python source code as a string, and you need to write a function that analyzes the symbol tables of this code using the `symtable` module. Your task is to extract detailed information about the identifiers (symbols) declared in the code and return a summary report. Function Signature ```python def analyze_symbols(code: str, filename: str) -> dict: ``` Input - `code`: A string containing the Python source code. - `filename`: A string representing the filename of the source code. Output - Return a dictionary containing the following information: - 'global_identifiers': A list of global identifiers in the code. - 'functions': A dictionary where the keys are function names and the values are dictionaries with information about each function, including: - 'parameters': A list of parameter names for the function. - 'locals': A list of local variables in the function. - 'globals': A list of global variables accessed within the function. - 'nonlocals': A list of non-local variables within the function. - 'frees': A list of free variables within the function. - 'classes': A dictionary where the keys are class names and the values are dictionaries with information about each class, including: - 'methods': A list of method names in the class. Constraints - The code will be valid Python code. - The length of the `code` string will not exceed 10,000 characters. - The `filename` string will not exceed 100 characters. # Example ```python code = ''' global_var = 10 def foo(x, y): local_var = x + y return local_var + global_var class Bar: def method(self): return "method in Bar" ''' filename = "example.py" result = analyze_symbols(code, filename) print(result) ``` Expected output: ```python { 'global_identifiers': ['global_var', 'foo', 'Bar'], 'functions': { 'foo': { 'parameters': ['x', 'y'], 'locals': ['local_var'], 'globals': ['global_var'], 'nonlocals': [], 'frees': [] } }, 'classes': { 'Bar': { 'methods': ['method'] } } } ``` # Explanation - The global identifiers are `global_var`, `foo`, and `Bar`. - The function `foo` has parameters `x` and `y`, a local variable `local_var`, and accesses the global variable `global_var`. - The class `Bar` has one method `method`. Notes - Use the `symtable` module to parse and analyze the given code. - Ensure that the output dictionary is formatted as specified.
answer:import symtable def analyze_symbols(code: str, filename: str) -> dict: Analyzes the symbol tables of the given Python source code and returns detailed information about the identifiers declared in the code. Parameters: code (str): A string containing the Python source code. filename (str): A string representing the filename of the source code. Returns: dict: A dictionary containing information about global identifiers, functions, and classes. sym_table = symtable.symtable(code, filename, 'exec') # Helper functions def extract_func_info(func_table): return { 'parameters': [sym for sym in func_table.get_parameters()], 'locals': [sym for sym in func_table.get_locals() if sym not in func_table.get_parameters()], 'globals': [sym for sym in func_table.get_globals()], 'nonlocals': [sym for sym in func_table.get_nonlocals()], 'frees': [sym for sym in func_table.get_frees()], } def extract_class_info(class_table): return { 'methods': [child.get_name() for child in class_table.get_children() if child.get_type() == 'function'] } # Main data structure to return result = { 'global_identifiers': [sym for sym in sym_table.get_identifiers() if sym_table.lookup(sym).is_global()], 'functions': {}, 'classes': {} } # Process each child in the symbol table for child in sym_table.get_children(): if child.get_type() == 'function': result['functions'][child.get_name()] = extract_func_info(child) elif child.get_type() == 'class': result['classes'][child.get_name()] = extract_class_info(child) return result