python-api-challenge

by Panfilo Marbibi

WeatherPy/

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