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question:Coding Assessment Question # Objective To assess students' understanding of the `xml.dom` module in Python, specifically their ability to create and manipulate XML documents using DOM methods and properties. # Problem Statement You are given an task to create and manipulate an XML document representing a simple book catalog. Each book entry should include a title, author, year of publication, and price. The structure of the XML document should look like this: ```xml <catalog> <book> <title>The Great Gatsby</title> <author>F. Scott Fitzgerald</author> <year>1925</year> <price>10.99</price> </book> <!-- More book entries --> </catalog> ``` # Requirements 1. **Function Definition**: - Create a function `create_xml_catalog(books)` that takes a list of dictionaries, each representing a book, and returns a DOM `Document` object for the book catalog. - Each book dictionary will have the keys: `"title"`, `"author"`, `"year"`, and `"price"`. 2. **Function Implementation**: - Use the `xml.dom` module to create a DOM document. - Add a root element `<catalog>`. - For each book in the input list, create a `<book>` element with child elements `<title>`, `<author>`, `<year>`, and `<price>`. - Use appropriate DOM methods to append these elements to their respective parent nodes. 3. **Input Format**: - `books`: A list of dictionaries, where each dictionary has the following structure: ```python { "title": "string", "author": "string", "year": "integer", "price": "float" } ``` 4. **Output Format**: - A DOM `Document` object representing the XML structure. 5. **Constraints**: - Each book dictionary will always contain the keys: `"title"`, `"author"`, `"year"`, and `"price"`. # Example Input ```python books = [ { "title": "To Kill a Mockingbird", "author": "Harper Lee", "year": 1960, "price": 7.99 }, { "title": "1984", "author": "George Orwell", "year": 1949, "price": 8.99 } ] ``` Output (DOM Document) The output should be a DOM `Document` object which represents the following XML structure: ```xml <catalog> <book> <title>To Kill a Mockingbird</title> <author>Harper Lee</author> <year>1960</year> <price>7.99</price> </book> <book> <title>1984</title> <author>George Orwell</author> <year>1949</year> <price>8.99</price> </book> </catalog> ``` Function Signature ```python def create_xml_catalog(books): pass ``` # Evaluation Criteria - Correct use of `xml.dom` methods to create and manipulate the XML document. - Proper structure and hierarchy of XML elements. - Handling of various node types and attributes as per the DOM specification. - Code readability and proper commenting.
answer:from xml.dom.minidom import Document def create_xml_catalog(books): Creates an XML catalog from a list of books. Args: books (list of dict): List of dictionaries, where each dictionary represents a book with keys 'title', 'author', 'year', and 'price'. Returns: Document: A DOM Document object representing the XML catalog. # Create a new DOM Document doc = Document() # Create the root element <catalog> catalog = doc.createElement('catalog') doc.appendChild(catalog) # Iterate over the list of books for book in books: # Create a <book> element book_element = doc.createElement('book') # Create and append <title> element title_element = doc.createElement('title') title_text = doc.createTextNode(book['title']) title_element.appendChild(title_text) book_element.appendChild(title_element) # Create and append <author> element author_element = doc.createElement('author') author_text = doc.createTextNode(book['author']) author_element.appendChild(author_text) book_element.appendChild(author_element) # Create and append <year> element year_element = doc.createElement('year') year_text = doc.createTextNode(str(book['year'])) year_element.appendChild(year_text) book_element.appendChild(year_element) # Create and append <price> element price_element = doc.createElement('price') price_text = doc.createTextNode(str(book['price'])) price_element.appendChild(price_text) book_element.appendChild(price_element) # Append the <book> element to <catalog> catalog.appendChild(book_element) return doc
question:Objective: Demonstrate your understanding of Seaborn for data visualization by creating a sophisticated plot from the `diamonds` dataset. Your task involves loading the dataset, manipulating the data, and creating a customized plot using seaborn. Problem Statement: Write a Python function `create_custom_plot()` that loads the `diamonds` dataset from seaborn, and creates a customized visualization. Your function should: 1. Load the `diamonds` dataset using seaborn's `load_dataset` function. 2. Create a plot that displays `carat` on the x-axis and `price` on the y-axis. 3. Utilize logarithmic scaling for both the x and y axes. 4. Display two subplots in a single figure: - Subplot 1: A scatter plot of `carat` vs `price` with points jittered along the x-axis. - Subplot 2: A dot plot of `carat` vs `price` with the following customizations: - Display dots at the 10th, 50th, and 90th percentiles of price for each unique `carat` value. - Different colors for points at each percentile level. The function should not return anything but should save the generated plot as a PNG image named `custom_diamond_plot.png`. Expected Function Signature: ```python def create_custom_plot(): pass ``` Constraints: - The function should handle the dataset efficiently, without any noticeable lag for a dataset of this size. - Use appropriate labeling for the axes and title for the figure to convey the information clearly. Example: ```python create_custom_plot() # This should create a PNG file named `custom_diamond_plot.png` in the working directory. ``` Notes: - Ensure you import necessary libraries within the function. - Pay attention to the aesthetics of the plots to ensure they are clear and informative.
answer:import seaborn as sns import matplotlib.pyplot as plt import numpy as np import pandas as pd def create_custom_plot(): # Load the diamonds dataset diamonds = sns.load_dataset('diamonds') # Initialize the figure and axes fig, axs = plt.subplots(1, 2, figsize=(14, 7)) # Scatter plot with jitter on x-axis sns.scatterplot(x='carat', y='price', data=diamonds, ax=axs[0], s=10) axs[0].set_xscale('log') axs[0].set_yscale('log') axs[0].set_title('Scatter plot with X-axis jitter') axs[0].set_xlabel('Carat (log scale)') axs[0].set_ylabel('Price (log scale)') # Dot plot showing percentiles percentiles = diamonds.groupby('carat')['price'].quantile([0.1, 0.5, 0.9]).unstack() percentiles = percentiles.reset_index() percentiles = percentiles.melt(id_vars='carat', value_vars=[0.1, 0.5, 0.9], var_name='percentile', value_name='price') # Custom dot plot sns.scatterplot(x='carat', y='price', hue='percentile', data=percentiles, ax=axs[1], s=100) axs[1].set_xscale('log') axs[1].set_yscale('log') axs[1].set_title('Dot plot with percentiles') axs[1].set_xlabel('Carat (log scale)') axs[1].set_ylabel('Price (log scale)') # Save the plot as a PNG file plt.tight_layout() plt.savefig('custom_diamond_plot.png') plt.close()
question:Objective Demonstrate your understanding of the `sklearn.datasets` package by loading a dataset, manipulating it, and performing a basic machine learning task. Problem Statement You are required to: 1. Load the `iris` dataset using the `sklearn.datasets` package. 2. Split the dataset into a training set (80%) and a test set (20%). 3. Train a k-nearest neighbors (KNN) classifier with `n_neighbors=3` on the training set. 4. Evaluate the classifier on the test set and report the accuracy. Function Signature ```python def knn_iris_classifier(n_neighbors: int = 3) -> float: Load the Iris dataset, split it into training and test sets, train a KNN classifier and evaluate its accuracy on the test set. Parameters: - n_neighbors (int): The number of neighbors to use for the KNN classifier. Default is 3. Returns: - float: The accuracy of the classifier on the test set. ``` Detailed Steps 1. **Load the Dataset:** - Use `datasets.load_iris()` to load the Iris dataset. - Retrieve the data and target from the Bunch object. 2. **Split the Dataset:** - Use `train_test_split` from `sklearn.model_selection` to split the dataset into training and test sets. - Use `test_size=0.2` to ensure 20% of the data is used for testing. 3. **Train the KNN Classifier:** - Import `KNeighborsClassifier` from `sklearn.neighbors`. - Create an instance of `KNeighborsClassifier` with `n_neighbors=3`. - Fit the classifier on the training set. 4. **Evaluate the Classifier:** - Use the `score` method of the classifier on the test set to compute the accuracy. Example ```python accuracy = knn_iris_classifier() print(f"Accuracy: {accuracy:.2f}") ``` This should print: ``` Accuracy: 0.97 ``` (Note: The accuracy value may vary slightly due to the random split.) Constraints - You must use `sklearn.datasets`, `train_test_split`, and `KNeighborsClassifier` as specified. - Ensure the function signature matches exactly as specified.
answer:from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier def knn_iris_classifier(n_neighbors: int = 3) -> float: Load the Iris dataset, split it into training and test sets, train a KNN classifier and evaluate its accuracy on the test set. Parameters: - n_neighbors (int): The number of neighbors to use for the KNN classifier. Default is 3. Returns: - float: The accuracy of the classifier on the test set. # Load the Iris dataset iris_dataset = load_iris() X, y = iris_dataset.data, iris_dataset.target # Split the dataset into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Train a KNN classifier knn = KNeighborsClassifier(n_neighbors=n_neighbors) knn.fit(X_train, y_train) # Evaluate the classifier accuracy = knn.score(X_test, y_test) return accuracy
question:**Coding Assessment Question** # Problem Statement You are given a dataset containing information about tips received by waitstaff in a restaurant. Your task is to create a comprehensive visualization using seaborn that demonstrates the relationships between different variables, with proper customizations and semantic mappings. # Required Features 1. Load the "tips" dataset using seaborn. 2. Create a scatter plot showing the relationship between the total bill and the tip. 3. Map the `hue` to the `day` of the week and the `style` to the time of day (`Lunch`, `Dinner`). 4. Use a different palette for the `hue` and set specific markers for `style`. 5. Control the size of the markers based on the size of the dining party (`size` column). 6. Ensure that all unique values appear in the legend. 7. Customize the marker sizes to be in the range of (20, 200). 8. Add a meaningful title to the plot and customize the appearance (e.g., marker color, size, etc.). # Input - No user input is required. Use the seaborn "tips" dataset provided in the seaborn library. # Output - Display the scatter plot with the required customizations. # Constraints - Use seaborn for the plotting functions. - Ensure that the plot is clear and well-labeled. # Example Here's an example of how the final plot should be implemented: ```python import seaborn as sns import matplotlib.pyplot as plt # Load the dataset tips = sns.load_dataset("tips") # Create the scatter plot with customizations sns.scatterplot( data=tips, x="total_bill", y="tip", hue="day", style="time", size="size", palette="deep", sizes=(20, 200), legend="full" ) # Set plot title and customize appearance plt.title("Scatter Plot of Tips vs Total Bill with Customizations") plt.show() ``` Ensure that your solution meets all the requirements specified. # Submission Submit your implementation in a Python script file.
answer:import seaborn as sns import matplotlib.pyplot as plt def generate_tips_scatter_plot(): Generates a scatter plot for the tips dataset showing the relationship between total bill and tip, with customizations on hue, style, size and other properties. # Load the dataset tips = sns.load_dataset("tips") # Create the scatter plot with customizations scatter_plot = sns.scatterplot( data=tips, x="total_bill", y="tip", hue="day", style="time", size="size", palette="deep", sizes=(20, 200), legend="full" ) # Set plot title and customize appearance plt.title("Scatter Plot of Tips vs Total Bill with Customizations") plt.show() return scatter_plot