This project analyzes top vacation cities based on weather data from Open Weather's API and is visualized using Google Map's API.

Using multiple API sources, create an app that allows users to filter through random locations based on their temperature range choices.

I created a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. Then, I used Jupyter notebook, Google Maps, and Google Places API, and created a heat map of humidity. Finally, I created my ideal weather condition on the map, used Google Places API to find the hotel information for each city.

Retrieve weather data using APIs, clean data with pandas, plot data onto a google map, and create a travel itinerary for users.

This Project analyzes top vacation cities based on weather data from Open Weather's API and is visualized using Google Map's API

Analyze & visualize weather data. Generate destinations and travel maps using Google Maps Platform APIs.

Very first website ever created using images from Dkreitzer/Random_500-City_Weather_Analysis

Created a vacation itinerary across four different cities in the same country based on weather conditions. The itinerary was visualized as a map. Pins and markers were added with basic details for each city.

A Python script to visualize data points of the weather for 500+ cities across the world of varying distance from the equator.

Utilizing Jupyter notebooks and python to create a vacation itinerary of 4 cities based on maximum and minimum temperatures, and display the points on google maps with updated popup markers displaying essential information for each location.

REST API for city and geographic data. Returns city name, state, country, coordinates, population, and capital status.

Create an app that gives users an itinerary based on their weather preferences.

🌦 Create a Python script to visualize the weather of over 500 cities of varying distances from the equator, and use the data skills to plan future vacations

The World Weather Analysis repo utilizes Python and Jupyter Notebook in conjunction with decision and repetition statements, data structures, Pandas, Matplotlib, NumPy, CitiPy, and SciPy statistics to retrieve and use data from OpenWeatherMap and Google Map API. The APIs are used to "get" requests from a server, retrieve and store values from a JSON array, use try and except blocks to resolve errors, create and format scatter plots using Matplotlib, perform linear regression and add regression lines to scatter plots while simultaneously determining favorable vacation destinations for customers based on weather conditions.

Study on the relationship between geolocation and weather condition, using OpenWeatherMap API

Found weather data for random cities using OpenWeatherMap API and citipy module. Created user input to filter cities list, click on city and see a Hotel name, the city, country and current weather in the city. Created travel itinerary that shows route between four chosen cities using Google Directions API.

Analyze & visualize the weather data of 500+ cities across the world. Generate destinations and travel maps using Google Maps Platform APIs.

Identify potentiel travel destinations and nearby hotels to help create travel itinerary for customers based on their weather preferences.

Weather Analysis with Python and OpenWeatherMap

Python requests, APIs, and JSON traversals exercise

A case study using python to collect data from an API request then employing the data to make recommendations based on user input.

Google Maps and OpenWeather APIs used with random geographical points generated; importing into Python and Javascript for transformation; using citypy to find closest towns; plotted on Map; planned round-trip driving route

Python API Requests & JSON Traversals Visualizing the Weather of 500+ World Cities

Utilizing various Python scripts and libraries to visualize the weather in over 500 world cities and displaying the results on a heatmap, after which writing additional code to map hotels (within our given parameters) that would make for an ideal vacation.

DataClass Module 6 APIs (Python) WeatherPy

Explored weather data correlations using Python, APIs, and visualizations. Planned vacations based on ideal conditions.

Plotted 4 weather variables to understand what the climate is like around the world.

Unit 6 Challenge - Use of Python and APIs

For this project, I created a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, I utilized a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities.

Perform API calls to OpenWeatherMap to query weather data and plot results.