World Weather Analysis: Creating a Vacation Itinerary

Overall Description

• This repository contains examples of using API calls from OpenWeather and Google Places and Google Directions
• The data gleaned from these API requests created the following:
  • A Weather Database
  • A Vacation Search
  • A Vacation Itenerary

Weather Database

• To extract data from the OpenWeather API I followed this basic process:
  • First generated random latitude and longitude values using the NumPy random function
  • Used the CitiPy library with a for loop to assign a city to those coordinates, populating a new list with the city names.
  • Performed an API request to OpenWeather and used the try-except method to add the weather data to a new list.
    • Involved parsing the JSON file for the following data:
      • Max Temp
      • Cloudiness
      • Humidity
      • Wind Speed
      • Current Weather Description
      • Country
• Finally, converted the list generated by the try-except method to a Pandas DataFrame and exported the DataFrame as a .csv file.

Vacation Search

• Now developing a way to isolate cities that fit a user's preferred temperature range.

• Overall process included the following:

  • Read the WeatherPy_Database.csv as a Pandas DataFrame
  • Using the input() function to prompt the user to provide minimum and maximum preferred temperatures and storing those values as discrete variables.
  • Use the loc method to isolate the cities that fit the user's criteria and create a new DataFrame.
  • Check the DataFrame for any empty rows
  • Make a copy of the DataFrame and add a "Hotel Name" column
  • Iterate through the new DataFrame using iterrows() and perform an API request to the Google Nearby Search to locate hotels that fit the parameters.
  • Use the try-except method to add the hotel names to the "Hotel Name" column in the DataFrame.
  • Remove any empty rows by:
    • using the loc function to isolate any rows without a Hotel
    • assign those empty rows to NaN using NumPy nan function
    • Drop the rows with NaN using dropna()
  • Export the DataFrame as a .csv file
  • Use gmaps to add a map with a marker layer with the following information:
    • Hotel Name
    • City Name
    • Country
    • Weather Description with Max Temp

Vacation Itenerary

• This deliverable creates a travel itenerary to four cities that meet the user's temperature criteria, creates a map of directions, and a map with markers giving hotel information.

• Overall process included the following:

  • Import the WeatherPy_Vacation.csv file
  • Use gmaps to create a map with markers holding hotel, city, country, and weather information
  • Create 4 discrete DataFrames for each of the 4 cities in the trip itenerary.
  • Isolate the Latitude and Longitude for each city using to_numpy() and indexing.
  • Use the Google Directions API and gmaps to create a map with a directions layer for the itenerary.
    • Start and End city are the same
    • Other 3 cities are considered waypoints
  • Use the concat() function to concatenate the 4 city DataFrames into one itenerary DataFrame.
  • Create a new map with markers for only the cities in the itenerary DataFrame.

Summary

• This challenge culminates in creating a vacation itenerary based on a user's temperature prefernces.
• To do so, it must work in the following logical order:
  • Obtain latitude and longitude coordinates for cities.
  • Assign cities to those coordinates.
  • Request weather information for those cities and store in a .csv databse.
  • Obtain temperature preferences from the user and create, find hotels in those cities based on those preferences.
  • Finally create the itenerary after all the necessary data is collected and visualized.
• Understanding the end product and using proper psuedocoding creates smooth development process.