Using an Example Data python to evaluate home Prices In Cairo
Description
I have created a pandas DataFrame for home prices in Cairo, Egypt. The DataFrame includes the following columns: Home ID, Location, Number of Bedrooms, Number of Bathrooms, Square Footage, and Price. a suggestion for creating a pandas DataFrame for home prices in Cairo, Egypt. Here's a proposal: Home ID: A unique identifier for each home. This could be a simple integer that increments for each new home. Location (District): The district in Cairo where the home is located. This could be a string value like "Zamalek", "Maadi", "Heliopolis", etc. Number of Bedrooms: The number of bedrooms in the home. This could be an integer value. Number of Bathrooms: The number of bathrooms in the home. This could also be an integer value. Square Footage: The size of the home in square feet. This could be a float value. Price: The price of the home in Egyptian pounds. This could be a float value. Here's a sample of how the data might look: Home ID Location Bedrooms Bathrooms Square Footage Price 1 Zamalek 3 2 1500.0 2500000.0 2 Maadi 2 1 1000.0 1500000.0 3 Heliopolis 4 3 2000.0 3500000.0 Please note that the actual data would need to be gathered from a real estate source or database. The values I've used here are just for illustrative purposes. Once the data is gathered, it can be analyzed to answer questions like: What is the average price of homes in each district? What is the average size of homes in each district? How does the number of bedrooms/bathrooms affect the price of a home?
Files
Steps to reproduce
Import the necessary libraries: python Download Copy code import pandas as pd import numpy as np Create a list of districts in Cairo: python Download Copy code districts = ['Zamalek', 'Maadi', 'Heliopolis', 'Nasr City', 'New Cairo', '6th of October City'] Generate random data for the DataFrame: python Download Copy code np.random.seed(0) # for reproducibility home_id = np.arange(1, 101) location = np.random.choice(districts, 100) bedrooms = np.random.randint(1, 5, 100) bathrooms = np.random.randint(1, 4, 100) square_footage = np.random.uniform(1000.0, 3000.0, 100) price = np.random.uniform(1e6, 5e6, 100) Create the DataFrame: python Download Copy code df = pd.DataFrame({ 'Home ID': home_id, 'Location': location, 'Bedrooms': bedrooms, 'Bathrooms': bathrooms, 'Square Footage': square_footage, 'Price': price }) Save the DataFrame to a CSV file: python Download Copy code df.to_csv('output/home_prices_cairo_542511.csv', index=False) This will create a CSV file named 'home_prices_cairo_542511.csv' in the 'output' directory. The file will contain a DataFrame with 100 rows of randomly generated home prices data for Cairo, Egypt. To generate a dataset of 100 rows for home prices in Cairo, Egypt using AI, we can use a combination of Python libraries such as pandas, numpy, and faker. Here are the steps: 1. Import the necessary libraries: ```python import pandas as pd import numpy as np from faker import Faker ``` 2. Initialize the Faker generator: ```python fake = Faker() ``` 3. Create a list of districts in Cairo: ```python districts = ['Zamalek', 'Maadi', 'Heliopolis', 'Nasr City', 'New Cairo', '6th of October City'] ``` 4. Generate random data for the DataFrame: ```python np.random.seed(0) # for reproducibility home_id = np.arange(1, 101) location = np.random.choice(districts, 100) bedrooms = np.random.randint(1, 5, 100) bathrooms = np.random.randint(1, 4, 100) square_footage = np.random.uniform(1000.0, 3000.0, 100) price = np.random.uniform(1e6, 5e6, 100) ``` 5. Create the DataFrame: ```python df = pd.DataFrame({ 'Home ID': home_id, 'Location': location, 'Bedrooms': bedrooms, 'Bathrooms': bathrooms, 'Square Footage': square_footage, 'Price': price }) ``` 6. Save the DataFrame to a CSV file: ```python df.to_csv('output/home_prices_cairo_6fcaec.csv', index=False) ``` This will create a CSV file named 'home_prices_cairo_6fcaec.csv' in the 'output' directory. The file will contain a DataFrame with 100 rows of randomly generated home prices data for Cairo, Egypt. Please note that this is a simulated dataset and the values are randomly generated and do not represent actual home prices in Cairo.