{"id":19607380,"url":"https://github.com/margaretkhendre/weather-analysis-vs-python-api-challenge","last_synced_at":"2026-04-18T01:36:03.660Z","repository":{"id":157528672,"uuid":"608885195","full_name":"margaretkhendre/Weather-Analysis-vs-Python-API-Challenge","owner":"margaretkhendre","description":"In this repository, 2 scripts are created called, \"WeatherPy\" and \"VacationPy\", to visualize and analyze the weather of cities near the equator and determine future vacations plans, respectively. 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You'll use the [citipy Python library](https://pypi.org/project/citipy/), the [OpenWeatherMap API](https://openweathermap.org/api), and your problem-solving skills to create a representative model of weather across cities.\n\n**Requirement 1: Create Plots to Showcase the Relationship Between Weather Variables and Latitude**\nTo fulfill the first requirement, you'll use the OpenWeatherMap API to retrieve weather data from the cities list generated in the starter code. Next, you'll create a series of scatter plots to showcase the following relationships:\n\n- Latitude vs. Temperature\n\n- Latitude vs. Humidity\n\n- Latitude vs. Cloudiness\n\n- Latitude vs. Wind Speed\n\n**Requirement 2: Compute Linear Regression for Each Relationship**\nTo fulfill the second requirement, compute the linear regression for each relationship. Separate the plots into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude). You may find it helpful to define a function in order to create the linear regression plots.\n\n\u003cimg width=\"599\" alt=\"Screenshot 2023-05-23 at 3 48 22 PM\" src=\"https://github.com/margaretkhendre/Weather-Analysis-vs-Python-API-Challenge/assets/121995835/138f918f-9503-4eab-9a3f-670ba7451e2a\"\u003e\n\nCreate the following plots:\n\n- Northern Hemisphere: Temperature vs. Latitude\n\n- Southern Hemisphere: Temperature vs. Latitude\n\n- Northern Hemisphere: Humidity vs. Latitude\n\n- Southern Hemisphere: Humidity vs. Latitude\n\n- Northern Hemisphere: Cloudiness vs. Latitude\n\n- Southern Hemisphere: Cloudiness vs. Latitude\n\n- Northern Hemisphere: Wind Speed vs. Latitude\n\n- Southern Hemisphere: Wind Speed vs. Latitude\n\n### Part 2: VacationPy\nIn this deliverable, you'll use your weather data skills to plan future vacations. Also, you'll use Jupyter notebooks, the geoViews Python library, and the Geoapify API.\n\nYour main tasks will be to use the Geoapify API and the geoViews Python library and employ your Python skills to create map visualizations.\n\nUse the VacationPy.ipynb starter code and complete the following steps:\n\n1. Create a map that displays a point for every city in the city_data_df DataFrame as shown in the following image. The size of the point should be the humidity in each city.\n\n\u003cimg width=\"699\" alt=\"Screenshot 2023-05-23 at 3 44 59 PM\" src=\"https://github.com/margaretkhendre/Weather-Analysis-vs-Python-API-Challenge/assets/121995835/9a396c6d-ca5c-4052-9f54-1b6ce24d8adc\"\u003e\n\n\n2. Narrow down the city_data_df DataFrame to find your ideal weather condition. For example:\n\n- A max temperature lower than 27 degrees but higher than 21\n\n- Wind speed less than 4.5 m/s\n\n- Zero cloudiness\n\n3. Create a new DataFrame called hotel_df to store the city, country, coordinates, and humidity.\n\n4. For each city, use the Geoapify API to find the first hotel located within 10,000 meters of your coordinates.\n\n\u003cimg width=\"691\" alt=\"Screenshot 2023-05-23 at 3 59 35 PM\" src=\"https://github.com/margaretkhendre/Weather-Analysis-vs-Python-API-Challenge/assets/121995835/87236c05-4de9-42af-93fa-fa353736ea69\"\u003e\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmargaretkhendre%2Fweather-analysis-vs-python-api-challenge","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmargaretkhendre%2Fweather-analysis-vs-python-api-challenge","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmargaretkhendre%2Fweather-analysis-vs-python-api-challenge/lists"}