What's the Weather Like?

Background

Created a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator by utilizing Python library - citipy, and the OpenWeatherMap API, to create a representative model of weather across world cities.

The script does the following :

• Randomly selects at least 500 unique (non-repeat) cities based on latitude and longitude.
• Perform a weather check on each of the cities using a series of successive API calls.
• Includes a print log of each city as it's being processed with the city number and city name.
• Saves a CSV of all retrieved data and a PNG and JPEG image for each scatter plot.

Part I - WeatherPy

Created a series of scatter plots to showcase the following relationships:

• Temperature (F) vs. Latitude
• Humidity (%) vs. Latitude
• Cloudiness (%) vs. Latitude
• Wind Speed (mph) vs. Latitude

The note book "WeatherPy.ipynb" has detailed explanation on what the code is analyzing.

Created a linear regression on each relationship. Created seperate sets of plots for Northern Hemisphere and Southern Hemisphere:

• Northern Hemisphere - Temperature (F) vs. Latitude
• Southern Hemisphere - Temperature (F) vs. Latitude
• Northern Hemisphere - Humidity (%) vs. Latitude
• Southern Hemisphere - Humidity (%) vs. Latitude
• Northern Hemisphere - Cloudiness (%) vs. Latitude
• Southern Hemisphere - Cloudiness (%) vs. Latitude
• Northern Hemisphere - Wind Speed (mph) vs. Latitude
• Southern Hemisphere - Wind Speed (mph) vs. Latitude

After each pair of plots, find explanation on what the linear regression is modeling and other observations, if any.

Part II - VacationPy

Used jupyter-gmaps and the Google Places API for this fun exercise .

• Created a heat map that displays the humidity for every city from Part I.

  

• Narrowed down the DataFrame to find ideal weather condition. For example:

  • A max temperature lower than 80 degrees but higher than 70.

  • Wind speed less than 10 mph.

  • Zero cloudiness.

  • Dropped any rows that didn't contain all three conditions. We want to be sure the weather is ideal.

• Using Google Places API to fetch the first hotel for each city located within 5000 meters of search coordinates.

• Plotted the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country.

  

Key notes:

• Saved images in both 'png' and 'jpeg' formats.
• Analysis was done using a Jupyter notebook.
• Defined and used functions
• Used citipy and OpenWeatherMap API and gmaps
• Used Matplotlib or Pandas plotting libraries.
• For Part I, Find written description of three observable trends based on the data at the top of notebook.
• For Part II, included a screenshot of the heatmap and saved it to the folder.
• Used proper labeling plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels.
• Used color maps.
• For max intensity in the heat map, set the highest humidity found in the data set.

Execution:

• Important : Please download the repository.
• The script for Part I is in the Jupyter notebook 'WeatherPy.ipynb' which can be located in the folder WeatherPy.
  • Find written description of three observable trends based on the data at the top of notebook.
• The script for Part II is in the Jupyter notebook 'VacationPy.ipynb' which can be located in the folder WeatherPy.
  • Find a screenshot of the heatmaps in the folder 'WeatherPy/Heat_Maps'
• Both files have detailed comments explaining each segment.

Results:

• Tested it multiple times.
• All the segments of Jupyter notebook executed successfully generating the final report.
• Successfully displays required output results, plots and maps.

eRRORS !!!! What ERRORS ????

• REST ASSURED, the code runs error free. Just Follow these detailed instructions ....