Identify a measurable variable within 1km of your current location

This assignment is a location based-assignment, your goal is to measure a variable near where you live. You will identify a measurable variable within 1km of your current location. You will first approximate the variable using a back-of-the-envelope estimate. Next, you will collect data, calculate descriptive statistics on the data, and create a relevant data visualization using a Python notebook. You will also have a chance to apply your knowledge of probability to solve a problem.
You’ll notice several “Optional” problems throughout the assignment to challenge yourself. These will only be scored if they are completed correctly with thorough explanation. If you attempt an optional challenge but do not succeed, you will not be penalized with a low score.
This is an individual assignment. Everything you submit should be your own words and reflect your own understanding of the material.
Download the assignment template here. Follow the directions in this notebook. You will need to submit both your solutions in a ipynb file and a PDF version of it. Please read the assignment instructions carefully.
Important note: You cannot complete this assignment in a single session. Set aside time on two different days for data collection and coding. If there is any reason to believe that you did not authentically complete the location based portion of this assignment (for example, if your data seems to be randomly generated), you will receive 0 credit for this assignment. Please follow the instructions here carefully and include the original photo files in the zip folder along with the ipynb.
Part of the purpose of this assignment is to expose you to and give you practice in using tools for working with data in Python. The following may be useful.
For other resources to learn Numpy, you can read or watch any of the tutorials found online, such as You do not need to learn everything about this library, just the basics of arrays and reading their entries.
To learn to plot the necessary figures, read as much of as is necessary to perform the required tasks. Additionally, there is an enormous amount of freely available instructional material, with examples, that can be found online.
As a best practice, your graphics in Jupyter notebooks should be ‘inline.’ If your version does not do this automatically, include %matplotlib inline at the top of your script.
Reminder: no matter what, your code needs comments. Read this resource about the importance of comments and this one for further guidance.
You will be graded on #variables, #descriptivestats, #visualizations, #compprogramdesign, and #probability as indicated above in square brackets, at the discretion of the professor. If you believe that you have strong applications of other LOs, use “footnotes” (footnotes are not possible within the Jupyter notebook, so just include a few sentences in parentheses inside of a Markdown cell next to the application). In the footnote text, provide the hashtag of the LO and a 1-2 sentence explanation of your application. Help the professor understand exactly why this constitutes a strong application. Click here for an example of this technique.
make sure to make it as simple as you can and for the loctiona add abudhabi.UAE and open the martier on colab
Comments from Customer
it is the virable and i want the variable to be the amount of people that goes to the grocery stories in an hour and theunit would be the total of people entryin a grocery store for in hour

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