python-api-challenge

Module 6 Challenge - Python Api

README

Weather and Vacation Analysis

Overview

This project involves two main parts:

1. WeatherPy: Visualizing the weather of over 500 cities of varying distances from the equator.
2. VacationPy: Planning future vacations based on ideal weather conditions and mapping out the locations of nearby hotels.

Part 1: WeatherPy

Objective

Create a Python script to visualize the weather of over 500 cities across the world. The data is analyzed to identify relationships between latitude and various weather parameters.

Steps

1. Generate Random Geographic Coordinates:

   • Used the numpy library to generate random latitude and longitude combinations.

2. Find Nearest Cities:

   • Utilized the citipy library to identify the nearest city for each set of geographic coordinates.

3. Retrieve Weather Data:

   • Used the OpenWeatherMap API to gather current weather data for each city.

4. Visualize Relationships:

   • Created scatter plots to visualize relationships between latitude and temperature, humidity, cloudiness, and wind speed.
   • Performed linear regression analysis to further explore these relationships.

Results and Analysis

• Temperature vs. Latitude:

  • There is a clear negative correlation between temperature and latitude in both hemispheres, with temperatures generally decreasing as you move away from the equator.
  • The Northern Hemisphere shows a stronger correlation due to larger landmasses causing more significant temperature variations.

• Humidity vs. Latitude:

  • The relationship between humidity and latitude is weak in both hemispheres, with no clear trend indicating that humidity increases or decreases significantly with latitude.

• Cloudiness vs. Latitude:

  • Similar to humidity, the correlation between cloudiness and latitude is weak. The scatter plots show a random distribution of cloudiness values, suggesting that other factors are more influential.

• Wind Speed vs. Latitude:

  • The correlation between wind speed and latitude is very weak, with extremely low r-squared values. Wind speed does not significantly change with latitude.

Usage

1. Dependencies:

   • Python libraries: matplotlib, pandas, numpy, requests, time, scipy, citipy

2. Running the Script:

   • Make sure you have an API key from OpenWeatherMap.
   • Update the weather_api_key variable in the script with your API key.
   • Run the Jupyter notebook WeatherPy.ipynb to generate plots and analysis.

Code Sources

• The section of code for generating random latitude and longitude coordinates was based on numpy's uniform distribution function examples.
• The usage of the citipy library for finding the nearest city was adapted from the library's documentation.

Part 2: VacationPy

Objective

To plan future vacations by narrowing down cities with ideal weather conditions and finding nearby hotels using Geoapify API.

Steps

1. Filter Ideal Cities:

   • Narrowed down the city_data_df DataFrame to find cities with specific weather conditions (e.g., max temperature between 21 and 27 degrees, wind speed less than 4.5 m/s, and zero cloudiness).

2. Find Hotels:

   • Used the Geoapify API to locate hotels within 10,000 meters of each city's coordinates.

3. Visualize Results:

   • Created maps using hvplot to display the locations of the cities and hotels.

Usage

1. Dependencies:

   • Python libraries: hvplot, pandas, requests

2. Running the Script:

   • Make sure you have an API key from Geoapify.
   • Update the geoapify_key variable in the script with your API key.
   • Run the Jupyter notebook VacationPy.ipynb to generate maps and analysis.

Code Source

• The method used for filtering DataFrame based on conditions was based on pandas documentation and starter code provided by the assignment.

Summary of Analysis

In both the Northern and Southern Hemispheres, there is a clear negative correlation between temperature and latitude, indicating that temperatures generally decrease as you move away from the equator. However, humidity, cloudiness, and wind speed show weak correlations with latitude, suggesting that these weather parameters are influenced by other local and regional factors rather than latitude alone.

For the vacation planning part, cities with ideal weather conditions were successfully identified, and nearby hotels were located using the Geoapify API. This demonstrates the practical application of data analysis and API integration in planning vacations based on weather conditions.

Notes on Code Source

• The citipy library was used to find the nearest city for random geographic coordinates.
• Example code from Stack Overflow was referenced for handling rate limits in API requests.
• Assistance was received from a learning assistant to optimize the rate-limiting handling for API requests.

Repository Structure

.
├── WeatherPy.ipynb
├── VacationPy.ipynb
├── api_keys.py
├── output_data
│   ├── cities_weather_data.csv
│   ├── Fig1.png
│   ├── Fig2.png
│   ├── Fig3.png
│   ├── Fig4.png
└── README.md