Objectives:

$$ Learn to use Python plotting libraries

$$ Learn to use wrangle data

$$ Learn to plot a histogram of values.

$$ Emphasize the importance of the Kernel Density Estimator.

Background

We looked at the calorie counts of various popular breakfast cereals in class. Let's do this

again, but for carbohydrates.

Assignment requirements:

$1.$ Download the Cereals dataset from D$2$L$.$

$2.$ For this exercise, only the column "carbo" is relevant. Everything else can be

discarded. The file has a header line which can be ignored as well.

$3.$ Print this list of carbohydrates and only the carbohydrates. From here on out, this list

of values will be called "carbs".

$4.$ Plot the carbs use the $\text{'}+\text{'}.$

$5.$ Plot the carbs sorted. In other words, sort the carbs variable, then plot.

$6.$ Using plt$.$hist, plot a histogram of the carbs.

$7.$ Create the probability density function and normal function.

$8.$ Plot the probability density function with a mean of $0$ and a standard deviation of $1$

from x$=-5$ to x$=5.$

$9.$ Create the Kernel Density Estimation of the data set. You will need to figure out the

min and max of the data.

$10.$ Plot the KDE of the carbohydrates data set.

$11.$ Using the last box of your notebook, report where the highest peak in your KDE is

located. Try doing this by eyeballing your graph and then use python tools to report

where along the x$-$axis the highest peak is located.

Submission

Upload your IPYNB file that you wrote yourself to the drop box on D$2$L:

$$ You need your notebook and the data files.

$$ Make sure your name, CSCI $5015,$ and the Programming Assignment Number appear in

comments in all of your files.

$$ Make sure you have compiled and tested your program before handing it in$.$