This Repository has been made to summit the homework assignment for my Data Science Bootcamp at Northwestern University
Python API
Student: Jorge Daniel Atuesta
March, 2021
In this repository, the reader will encounter my solution to the homework assignment Python API. The repository is organized in folders and a README.md (The file you are currently reading). Here is the list of the folders and their contents so you can navigate through them.
I hope you find my work not only to be complete but to display all the knowledge learned throughout this portion of the Data Science Bootcamp at Northwestern University.1. Images: Inside this folder you will find the images for the instructions provided by the institution. You can skip this folder if you want. 2. WeatherPy: Inside this folder, you will have the chance to access my code for the assignment and all the output data. I encourage you to take a look inside as you will find the solution to the project and screenshots from it. Here are what's inside:
- output_data: All images and csv files created for the project.
- Heatmap.png: an image of the heatmap I created using Google API.
- Heatmap_with_hotels: an image of the heatmap I created with reference points I created using Google API.
- VacationPy-with-outputs: Here is my code for the vacation portion of the project.
- WeatherPy-updated: Here is my code for the weather portion of the project.
- README.md: it's the current file you are reading. I strongly suggest navigating through it and look at the project's objective solution and analysis.
I hope you find my work not only to be complete but to display all the knowledge learned throughout this portion of the Data Science Bootcamp at Northwestern University.
using Python requests, APIs, and JSON traversals to answer a fundamental question: "What's the weather like as we approach the equator?
There are two parts to this project. The first one is denominated WeatherPy, for this portion I had to create scatterplots showcasing the following relationships:
- Temperature (F) vs. Latitude
- Humidity (%) vs. Latitude
- Cloudiness (%) vs. Latitude
- Wind Speed (mph) vs. Latitude
After creating this scatterplots I had to run linear regression on each relationship, only this time separating them into 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
The second portion of the project is denominated VacationPy. For this portion I had to use jupyter-gmaps and the Google Places API to search for 'my ideal' vacation spot. For this I created a heat map that displays the humidity for every city (on part 1). After this I used Google Places API to find the first hotel for each city located within 5000 meters of the designated coordinates. Last but not least I plotted the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country.
Temperature (F) vs. Latitude
Latitude vs. Temperature Plot
Explanation of the code used for this section:
- Once the Data Frame is clean from potential outliers, duplicated values etc. I could finally plot my data using matplotlib and specifically the function plt. To plot a scattergraph I used .plt.scatter ad then fed the parameters. This parameters where the Latitud values on the x axis and then the Maximum Temperature on the Y-axis. Then I created the labels for the figure.
Figure Analysis
- The scatter plot represents the maximum temperature (F) acording to the Latitude for each city.
- As shown in the figure, as the latitude of the city is closer to 0 the temperature increases. This proves that if the city is located closer to the ecuator the temperature is higher.
- We can also notice that is the city moves south of the ecuator the temperature decreases.
- It is important to note that depending on the season (time of year) when the data is taken the data can fluctuate.
Humidity (%) vs. Latitude
Latitude vs. Humidity Plot
Explanation of the code used for this section:
- Using a clean data frame I proceeded to search if the cities latitude affected the humidity percentage. The code only took the parameteres of latitude and humidity to be able to plot a scatter graph shown below.
Figure Analysis
- The scatter plot represents the Humidity (%) acording to the Latitude for each city.
- As shown in the figure, as the latitude of the city moves (north) of the equator the higher humidity %
- If the latitude goes south of the equator the humidity (%) tends to decrease. The further a city is from the equator the less humid.
Cloudiness (%) vs. Latitude
Latitude vs. Cloudiness Plot
Explanation of the code used for this section:
- Once the Data Frame is clean from potential outliers, duplicated values etc. I could finally plot my data using matplotlib and specifically the function plt. To plot a scattergraph I used .plt.scatter ad then fed the parameters. This parameters where the Latitud values on the x axis and then the Cloudiness values on the Y-axis. Then I created the labels for the figure.
Figure Analysis
- The scatter plot represents the relationship bewteen the cloudiness and the Latitude for each city.
- As shown in the figure, the further the city is from the equator the more cloudiness it will have.
- If the cities latitude is close to the equator the less cloudiness it will have.
Wind Speed (mph) vs. Latitude
Latitude vs. Wind Speed Plot
Explanation of the code used for this section:
- Once the Data Frame is clean from potential outliers, duplicated values etc. I could finally plot my data using matplotlib and specifically the function plt. To plot a scattergraph I used .plt.scatter ad then fed the parameters. This parameters where the Latitud values on the x axis and then Wind speed on the Y-axis. Then I created the labels for the figure.
Figure Analysis
- The scatter plot represents the relationship bewteen Wind Speed (MPH) and the Latitude for each city.
- As shown in the figure, its fair to say that the latitude dosent increase wind speeds. We can see a potential outlier of a city that is located on latitude -60 and was wind speed over 40 MPH.
- We can state there is no correlation bewteen the windspeed and the latitude
Northern Hemisphere - Temperature (F) vs. Latitude
Linear Regression and relationships
- The regression displays a decrease with Lattitude.
- The type of correlation is a negative correlation. As the variable Latitude increases the Temperature value decreases as well.
- As you move north from the equator the temperature decreases.
- This type of correlation can be considered a correlation coefficient = aprox - 1.0 stating that there is a strong almost perfect negative relationship between latitude and temperature.
Southern Hemisphere - Temperature (F) vs. Latitude
Linear Regression and relationships
-
The regression displays a increase with Lattitude.
-
As you move south from the equator the temperature increases.
-
The correlation coefficient = aprox 0.6 this means that there is a moderate positivie relationship bewteen the two variables beeing observed.
Northern Hemisphere - Humidity (%) vs. Latitude
Linear Regression and relationships
- The regression displays a increase with Lattitude.
- As you move south from the equator the temperature increases.
- The correlation coefficient = aprox 0.4 this means that there is a moderate positivie relationship bewteen the two variables beeing observed.
Southern Hemisphere - Humidity (%) vs. Latitude
Linear Regression and relationships
- The regression displays a increase in humidity with Lattitude.
- As you move south from the equator the humidity increases.
- The correlation coefficient = aprox 0.3 this means that there is a moderate positivie relationship bewteen the two variables beeing observed.
Northern Hemisphere - Cloudiness (%) vs. Latitude
Linear Regression and relationships
- The regression displays little increase in Cloudiness with Lattitude.
- As you move north from the equator the Cloudiness slightly increases.
- The correlation coefficient = aprox 0.5 this means that there is a moderate positivie relationship bewteen the two variables beeing observed.
Southern Hemisphere - Cloudiness (%) vs. Latitude
Linear Regression and relationships
- The regression displays increase in Cloudiness with lattitude.
- As you move south from the equator the Cloudiness increases.
- The correlation coefficient = aprox 1 this means that there is a strong positivie relationship bewteen the two variables beeing observed.
Northern Hemisphere - Wind Speed (mph) vs. Latitude
Linear Regression and relationships
- The regression displays a slight increase in Wind Speed.
- As you move north from the equator the Wind Speed increases a littel
- The correlation coefficient = aprox 0.0 this means that there is no relationship bewteen the two variables beeing observed. So, the latitude in this case does not affect the wind speed in citys.
Southern Hemisphere - Wind Speed (mph) vs. Latitude
Linear Regression and relationships
- The regression displays an decrease in Wind Speed.
- As you move north from the equator the Wind Speed decreases.
- The correlation coefficient = aprox 0.1 this means that there is no relationship bewteen the two variables beeing observed. But compared to the Northern hemisphere there is a stronger prescense in the negative relationship bewteen variables potential leading the researcher to belivethat for the Southern hemisphere latitude can have a negative relationship with the wind speed in citys. Although, its important to note that the correlation is still considered close to 0 stating that there is no relationship. or that a relationhip bewteen this can not be stablished, with the data provided.
Here is an image of the heatmap I created before putting the hotels on it.
Here is an image of the heatmap I created with the hotels. The image size is small but you can get a sense of the final product.
Frost, J. (2018, April 3). Interpreting Correlation Coefficients. Retrieved from Statistics By Jim : https://statisticsbyjim.com/basics/correlations/#:~:text=As%20one%20value%20increases%2 %20there,0.6%3A%20A%20moderate%20negative%20relationship.
Littel, T. (2021, March 24). Tutoring session March 24th 2021. (J. D. Atuesta, Interviewer)
Mr.T. (2018, June 5). Matplotlib - Border around scatter plot points. Retrieved from Stack Overflow: https://stackoverflow.com/questions/50706901/matplotlib-border-around-scatter plot-points
Please note: I am new to .md files and I cant seem to indent the reference following the APA guidelines
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?
- Create a new repository for this project called
python-api-challenge. Do not add this homework to an existing repository. - Clone the new repository to your computer.
- Inside your local git repository, create a directory for both of the Python Challenges. Use folder names corresponding to the challenges: WeatherPy.
- Inside the folder that you just created, add new files called
WeatherPy.ipynbandVacationPy.ipynb. These will be the main scripts to run for each analysis. - Push the above changes to GitHub.
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 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 too explaining what the code is and analyzing.
Your second requirement is to run linear regression on each relationship, only this time separating them 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 explain what the linear regression is modeling such as any relationships you notice and any other analysis you may have.
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 a CSV of all retrieved data and a PNG image for each scatter plot.
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.
-
Note: Remember that any API usage beyond the $200 credit will be charged to your personal account. You can set quotas and limits to your daily requests to be sure you can't be charged. Check out Google Maps Platform Billing and Manage your cost of use for more information.
-
Note: if you having trouble displaying the maps try running
jupyter nbextension enable --py gmapsin your environment and retry. -
Create a heat map that displays the humidity for every city from the part I of the homework.
-
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.
- Drop any rows that don't contain all three conditions. You want to be sure the weather is ideal.
- Note: Feel free to adjust to your specifications but be sure to limit the number of rows returned by your API requests to a reasonable number.
-
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, City, and Country.
As final considerations:
- Create a new GitHub repository for this project called
API-Challenge(note the kebab-case). Do not add to an existing repo - You must complete your analysis using a Jupyter notebook.
- You must use the Matplotlib or Pandas plotting libraries.
- For Part I, you must include a written description of three observable trends based on the data.
- For Part II, you must include a screenshot of the heatmap you create and include it in your submission.
- You must use proper labeling of your plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels.
- For max intensity in the heat map, try setting it to the highest humidity found in the data set.
- The city data you generate is based on random coordinates as well as different query times; as such, your outputs will not be an exact match to the provided starter notebook.
- 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).
- Once you have computed the linear regression for one chart, the process will be similar for all others. As a bonus, try to create a function that will create these charts based on different parameters.
- Remember that each coordinate will trigger a separate call to the Google API. If you're creating your own criteria to plan your vacation, try to reduce the results in your DataFrame to 10 or fewer cities.
- 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!
- Ensure your repository has regular commits (i.e. 20+ commits) and a thorough README.md file
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