Weather Analysis

The WeatherPy directory contains 2 Jupyter Source Files WeatherPy.ipynb and VacationPy.ipynb which request relevant data from 2 API providers OpenWeatherMap and Geoapify for over 500 cities within the desired coordinates, and a repo output_data which contains the output images generated from WeatherPy.ipynb.

The two Jupyter Source Files utilize Pandas library to create DataFrame for each dataset. Matplotlib library was imported to create series of scatter plots based on different variables. Scipy and linregress were also imported to help with statistical computation and generate linear regresssion line for the data points. Citipy was imported to generate list of cities with the coordinates, hvplot was imported for interactive data visualization, and geoviews is imported to explore and visualize data geographically.

WeatherPy

Temperature vs. Latitude

• There is a fairly strong negative correlation between temperature and latitude with a correlation coefficient of -0.8088479825279142 on Northern Hemisphere.

  

• There is a relatively strong positive correlation between temperature and latitude with a correlation coefficient of 0.4395572110498053 on Southern Hemisphere.

  

Humidity vs. Latitude

• There is a relatively strong positive correlation between humidity and latitude with a correlation coefficient of 0.45185249370432223 on Northern Hemisphere.

  

• There is a relatively strong positive correlation correlation between humidity and latitude with a correlation coefficient of 0.5439636787499419 on Southern Hemisphere.

  

Cloudiness vs. Latitude

• There is a weak positive correlation between cloudiness and latitude with a correlation coefficient of 0.30769331493282776 on Northern Hemisphere.

  

• There is a relatively strong positive correlation between cloudiness and latitude with a correlation coefficient of 0.4748614054462095 on Southern Hemisphere.

  

Wind Speed vs. Latitude

• There is a negligible to almost no correlation between wind speed and latitude with a correlation coefficient of 0.21507837130179397 on Northern Hemisphere.

  

• There is a weak negative correlation between wind speed and latitude with a correlation coefficient of -0.33748967967974725 on Southern Hemisphere.

  

VacationPy

• Create city map that displays a point for every city in the city_data_df DataFrame using data in output_data/cities.csv. The size of the point is determined by the humidity level in each city.

  

• Narrow down cities that fit the criteria and drop any results with null values, the criteria are listed below:

  1. A max temperature lower than 27 degrees but higher than 21
  2. Cloudiness less than 3
  3. Wind speed less than 4.5 m/s

• Create a new DataFrame called hotel_df which contains columns: City, Country, Lat, Lng, Humidity, and Hotel Name. For each city, use the Geoapify API to find the first hotel located within 10,000 metres of each city and append the results to hotel df in a column called `Hotel Name'.

  City	Country	Lat	Lng	Humidity	Hotel Name
  cidreira	BR	-30.1811	-50.2056	88	Hotel Castelo
  umm lajj	SA	25.0213	37.2685	62	No hotel found
  naliya	IN	23.2667	68.8333	21	No hotel found
  itamaraca	BR	-7.7478	-34.8256	76	No hotel found
  rio grande	BR	-32.0350	-52.0986	84	Hotel Vila Moura Executivo
  acapulco	MX	16.8634	-99.8901	65	Hotel del Valle
  jhanjharpur	IN	26.2667	86.2833	32	No hotel found

• Add the hotel name and the country as additional information in the hover message for each city in hotel map