This is a challenge submission for UWA Data Analytics Bootcamp, Module 6 Python API's
Data's true power is its ability to definitively answer questions. So, let's take what you've learned about Python requests, APIs, and JSON traversals to answer a fundamental question: "What is the weather like as we approach the equator?"
Now, we know what you may be thinking: “That’s obvious. It gets hotter.” But, if pressed for more information, how would you prove that?
Create a new repository for this assignment called python-api-challenge. Do not add this assignment to an existing repository.
Clone the new repository to your computer.
Inside your local Git repository, create a directory for this assignment. Use a folder name that corresponds to the Challenges, such as WeatherPy.
Inside the folder you just created, add new files called WeatherPy.ipynb and VacationPy.ipynb. These will be the main scripts to run for each analysis.
Push your changes to GitHub.
For this assignment, you will need to add a .gitignore file to your repo. This is so you don’t expose the api_keys.py file with your API key to the public, which would allow anyone using GitHub to copy and use your API key, possibly incurring charges.
When you type git status in the command line, you’ll get a list of all the untracked files that you have created so far.
If you wanted to add only the WeatherPy.ipynb file to GitHub, you would type git add WeatherPy.ipynb. However, whenever you want to add or update a file, you would have to add each file individually, which is time-consuming and cumbersome. Instead, you can add the files that you don't want to track to the .gitignore file.
Before adding your files to GitHub, add api_keys.py to the .gitignore file by following these steps:
Open your python-api-challenge GitHub folder in VS Code.
Open the .gitignore file, and on the first line, type the following code:
api_keys.py In the command line, type git status and press Enter. The output should indicate that the .gitignore file has been modified and the WeatherPy.ipynb file is untracked.
Use git add, git commit, and git push to commit the modifications to .gitignore and the WeatherPy.ipynb file to GitHub.
On GitHub, the only new file you should find is the WeatherPy.ipynb file.
Download the following files to help you get started:
Module 6 Challenge filesLinks to an external site.
This activity is broken down into two deliverables, WeatherPy and VacationPy.
In this deliverable, you'll create a Python script to visualise the weather of over 500 cities of varying distances from the equator. You'll use the citipy Python library Links to an external site., the OpenWeatherMap API Links to an external site., and your problem-solving skills to create a representative model of weather across cities.
For this part, you'll use the WeatherPy.ipynb Jupyter notebook provided in the starter code ZIP file. The starter code will guide you through the process of using your Python coding skills to develop a solution to address the required functionalities.
To get started, the code required to generate random geographic coordinates and the nearest city to each latitude and longitude combination is provided.
To fulfill the first requirement, you'll use the OpenWeatherMap API to retrieve weather data from the cities list generated in the starter code. Next, you'll create a series of scatter plots to showcase the following relationships:
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Latitude vs. Temperature
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Latitude vs. Humidity
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Latitude vs. Cloudiness
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Latitude vs. Wind Speed
To fulfill the second requirement, compute the linear regression for each relationship. Separate the plots into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude). You may find it helpful to define a function in order to create the linear regression plots.
Next, create a series of scatter plots. Be sure to include the linear regression line, the model's formula, and the r values as you can see in the following image
Sample scatter plot with the linear regression line. You should create the following plots:
After each pair of plots, explain what the linear regression is modelling. Describe any relationships that you notice and any other findings you may uncover.
In this deliverable, you'll use your weather data skills to plan future vacations. Also, you'll use Jupyter notebooks, the geoViews Python library, and the Geoapify API.
The code needed to import the required libraries and load the CSV file with the weather and coordinates data for each city created in Part 1 is provided to help you get started.
Your main tasks will be to use the Geoapify API and the geoViews Python library and employ your Python skills to create map visualisations.
To succeed on this deliverable of the assignment, open the VacationPy.ipynb starter code and complete the following steps:
Create a map that displays a point for every city in the city_data_df DataFrame as shown in the following image. The size of the point should be the humidity in each city.
Humidity map Narrow down the city_data_df DataFrame to find your ideal weather condition. For example:
A max temperature lower than 27 degrees but higher than 21
Wind speed less than 4.5 m/s
Zero cloudiness
Feel free to adjust your specifications, but make sure to set a reasonable limit to the number of rows returned by your API requests.
Create a new DataFrame called hotel_df to store the city, country, coordinates, and humidity.
For each city, use the Geoapify API to find the first hotel located within 10,000 metres of your coordinates.
Add the hotel name and the country as additional information in the hover message for each city in the map as in the following image:
Hotel map
