practice-api

Student project - Analyze, chart and map random coordinates from Citipy library using Open Weather Map API data and Google Maps.

software/tools used

• pandas: https://pandas.pydata.org/
  
• Jupyter Notebook: https://jupyter.org/
  
• citipy python library: https://pypi.python.org/pypi/citipy
  
• jupyter-gmaps: https://jupyter-gmaps.readthedocs.io/en/latest
  
• python_pandas libraries:
  
  • Citipy
    
  • Matplotlib Pyplot
    
  • NumPy
    
  • SciPy stats
    

data resources

API - Open Weather Map: https://openweathermap.org/api
API - Google Places: https://cloud.google.com/maps-platform/places

project background

Part I - WeatherPy

• In this example, you'll be creating a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator.
• The first requirement is to create 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

• After each plot, add a sentence or two explaining what the code is analyzing.

• The second requirement is to run linear regression on each relationship. This time, separate the plots into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude):

• 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, take the time to explain what the linear regression is modeling. For example, describe any relationships you notice and any other analysis you may have.

Part II - VacationPy

• Now let's use your skills in working with weather data to plan future vacations. Use jupyter-gmaps and the Google Places API for this part of the assignment.

• To complete this part of the assignment,you will need to do the following:

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

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

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

  • Wind speed less than 10 mph.

  • Zero cloudiness.

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

• Plot the hotels on top of the humidity heatmap with each pin containing the hotel name.

acknowledgments

• Background provided as part of Georgia Tech Data Analytics Boot Camp:
  

  © 2021 Trilogy Education Services, LLC, a 2U, Inc. brand. Confidential and Proprietary. All Rights Reserved.

• Project Author: Valerie Pippenger - https://github.com/Pip85

process

• This challenge was in two parts. The first found in the WeatherPy notebook and the second in the VacationPy notebook.

• Part one involed pulling 1,500 random latitude and longitude coordinates using Numpy.

• These coordinates were then run through the Python Citipy library to obtain the closest cities to the coordinates.

• Of the 1,500 coordinates, only a little more than 600 cities were identified. The cities were stored in a list.

• A loop was utilized to run the cities through the Open Weather Map API.

• Information on temperature, humidity, cloudiness, wind speed and more was captured from the API and stored in separate lists.

• Conditional statements and try and except were used to keep the code moving and avoid duplication of cities.

• The list information was then compiled into a dataframe. The dataframe header and statistical information were displayed.

• A check for humidity was made to ensure none of the cities had over 100% humidity. None were found.

• Scatter plots were created to compare the various information with latitude of the cities. Plots included comparisons of max temperature, humidity, cloudiness and wind speed with latitude. A brief analysis was included with each scatter plot.

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• The data frame was split between cities in the northern hemisphere and those in the southern hemisphere.

• Plots and linear regression were then prepared for latitude vs temperature, humidity, cloudiness and wind speed with latitude for each hemisphere.

• A brief analysis was included with each plot, along with a print of the r-value.

• See all regression plots: https://github.com/Pip85/python-api_weather_analysis/tree/main/WeatherPy/output_data

• All plot images were saved in an output folder along with a .csv of the city dataframe.

• Part two, began by reading the .csv file created in part one into a new dataframe.

• Using the new dataframe, a Google heatmap was created for all city locations.

• A new dataframe was created by narrowing down the cities according to ideal weather conditions. This resulted in a dataframe of only 8 cities.
• Using Google Places API, a search was done on each city in the narrowed dataframe to find the nearest hotel for each city.
• A new column was created in the dataframe and populated with the hotel name for each of the cities.
• Another Google heatmap was created to mirror the first one. Markers were then generated based on the hotel coordinates and layered over the heatmap.

• A png file of both heatmaps was saved within the VacationPy folder.