About

• This is the personal project for unit 6(Python-APIs) of Data Visualization and Analytics.
• The result of this project will be fed to the web visualization dashboard for unit 12(web).
• Tools used in this project: openweathermap API, matplotlib, pandas.

The original text of the homework assignment:

SUMMARY:

This project is to analyze the current weather all over the world. We are supposed to choose more than 500 cities with random latitude and longitude coordinates. Query the weather data from OpenWeatherMap API, make some plots and draw some conclusions about the dependence of weather on the latitude value.

Some observations after weather data analysis are as follows:

• From the weather data for 538 cities collectd today(09/25/2018), we conclude that temperature is peaked around 80 Fahrenheit near the equator from -20 to 30 degrees of Latitude. It continually drops to 30 Fahrenheit(even lower for several cities) in the region of 40 to 80 degree of latitude.

• The humidity percentage has only weak dependence on the latitude. It takes smallest value around -20(Southern Hemisphere) and 20 latitude(Northern Hemisphere).

• There is no obvious dependence of wind speed on latitude. The wind speed in general is from 0 mph to 15 mph.

• There is no obvious dependence of cloudiness on latitude. Yet somehow there are several cloudiness values, 0, 20, 40, 78,97, are most crowded with citis all over the world, much more than other values.

MORE about the project

Unit 6 | Assignment - What's the Weather Like?

Background

Whether financial, political, or social -- data's true power lies in its ability to answer questions definitively. So let's take what you've learned about Python requests, APIs, and JSON traversals to answer a fundamental question: "What's the weather like as we approach the equator?"

Now, we know what you may be thinking: "Duh. It gets hotter..."

But, if pressed, how would you prove it?

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. To accomplish this, you'll be utilizing a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities.

Your 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

Your final 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.

As final considerations:

• You must complete your analysis using a Jupyter notebook.
• You must use the Matplotlib or Pandas plotting libraries.
• You must include a written description of three observable trends based on the data.
• You must use proper labeling of your plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels.
• See Example Solution for a reference on expected format.

Hints and Considerations

• You may want to start this assignment by refreshing yourself on the geographic coordinate system.

• Next, spend the requisite time necessary to study the OpenWeatherMap API. Based on your initial study, you should be able to answer basic questions about the API: Where do you request the API key? Which Weather API in particular will you need? What URL endpoints does it expect? What JSON structure does it respond with? Before you write a line of code, you should be aiming to have a crystal clear understanding of your intended outcome.

• A starter code for Citipy has been provided. However, if you're craving an extra challenge, push yourself to learn how it works: citipy Python library. Before you try to incorporate the library into your analysis, start by creating simple test cases outside your main script to confirm that you are using it correctly. Too often, when introduced to a new library, students get bogged down by the most minor of errors -- spending hours investigating their entire code -- when, in fact, a simple and focused test would have shown their basic utilization of the library was wrong from the start. Don't let this be you!

• Part of our expectation in this challenge is that you will use critical thinking skills to understand how and why we're recommending the tools we are. What is Citipy for? Why would you use it in conjunction with the OpenWeatherMap API? How would you do so?

• In building your script, pay attention to the cities you are using in your query pool. Are you getting coverage of the full gamut of latitudes and longitudes? Or are you simply choosing 500 cities concentrated in one region of the world? Even if you were a geographic genius, simply rattling 500 cities based on your human selection would create a biased dataset. Be thinking of how you should counter this. (Hint: Consider the full range of latitudes).

• Lastly, remember -- this is a challenging activity. Push yourself! If you complete this task, then you can safely say that you've gained a strong mastery of the core foundations of data analytics and it will only go better from here. Good luck!

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