Weather Data Analysis

The purpose of this project is to visualize the weather of 500+ cities across the world of varying distance from the equator.

Tools used in this Project

1. Python libraries:
   • matplotlib.pyplot
   • requests
   • Pandas
   • numpy
   • time
   • json
   • scipy.stats
2. OpenWeather API
3. Jupyter Notebook

The objective is to build 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. Examples are shown below.

It is expected that the final Jupyter notebook must:

• Randomly select 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.
• Include a print log of each city as it's being processed with the city number and city name.
• Save both a CSV of all data retrieved and png images for each scatter plot.

The notebook also contains the linear regression analyses performed on both Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude):

After each plot, I added a sentence or two explaining what the code is and analyzing:

• 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. Examples are shown below:

Instructions

1. To run this code, ensure that all the above libraries are installed in your environment.
2. Pull all the file and data from this GitHub repository.
3. To see the visualization and analyses, run the WeatherPy.ipynb file in Jupyter notebook.
4. The WeatherPy.ipynb notebook contains the observations and insights from the analyses.

File Description

1. The images folder contains the equatorsign.png used in the readme file.
2. The starter_code folder contains the WeatherPy.ipynb and api_keys.py files.
3. The WeatherPy.ipynb is a Jupyter notebook file that contains the Python code used to analyse the data.
4. The api_keys.py is a Python file that contains the OpenWeather api key used to access the weather data on their website.
5. The output_data folder contains all the figures (images) generated from the output of our analyses.