python-api-challenge

by Panfilo Marbibi

WeatherPy/

• VacationPy.ipynb
• WeatherPy.ipynb
• output_data/
  -- Fig1.png
  -- Fig2.png
  -- Fig3.png
  -- Fig4.png
  -- cities.csv

WeatherPy.ipynb -- jupyter notebook

• This notebook generates a number of randomly selected geographic coordinates and finds the nearest cities to those coordinates
• From the list of cities generated, the code will then gather information from those cities and append them to lists:
  --- latitude, longitude, max temperature, humidity, cloudiness, wind speed, country, date
• if a city is not found, it will skip the city and display "City not found. Skipping..."
• The lists are then placed into a dataframe : city_data_df
• The dataframe is exported as a csv into the output_data directory: cities.csv
• From the dataframe, the notebook will create scatter plots:
  --- City Latitude vs. Max Temperature
  --- City Latitude vs. Humidity
  --- City Latitude vs. Cloudiness
  --- City Latitude vs. Wind Speed
• The notebook then separates the data by hemisphere and creates 2 dataframes : northern_hemi_df , southern_hemi_df
• The notebook will use the data to calculate the linear regression for each of the above mentioned plots, separating by north and south hemisphere
• Analysis of each pair is included

VacationPy.ipynb -- jupyter notebook

• This notebook takes the csv created by WeatherPy.ipynb to determine the best vacation spots using the user's preferences
• humidity_map - geomap with dots where the cities found in cities.csv
  --- the dots are sized by how humid the city is
• ideal_crit - dataframe with the desired Max Temp, Humidity level, and Cloudiness
• hotel_df - finds the nearest hotel within 10,000 meters of each of the cities
• hotel_map - geomap with dots to the cities that fit within the user's desired parameters
  --- hovering over the dots now show the nearest hotel from the city along with the city's country

Code used for each notebook came from the activities done in class