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Homework 6

Homework 6

  • Type your name and email in the “Student details” section below.

  • Develop the code and generate the figures you need to solve the problems using this notebook.

  • For the answers that require a mathematical proof or derivation you can either:

    • Type the answer using the built-in latex capabilities. In this case, simply export the notebook as a pdf and upload it on gradescope; or

    • You can print the notebook (after you are done with all the code), write your answers by hand, scan, turn your response to a single pdf, and upload on gradescope.

  • The total homework points are 100. Please note that the problems are not weighed equally.

Note

  • This is due before the beginning of the next lecture.

  • Please match all the pages corresponding to each of the questions when you submit on gradescope.

Student details

  • First Name:

  • Last Name:

  • Email:

import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set(rc={"figure.dpi":100, 'savefig.dpi':300})
sns.set_context('notebook')
sns.set_style("ticks")
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('retina', 'svg')
import requests
import os
def download(url, local_filename=None):
    """
    Downloads the file in the ``url`` and saves it in the current working directory.
    """
    data = requests.get(url)
    if local_filename is None:
        local_filename = os.path.basename(url)
    with open(local_filename, 'wb') as fd:
        fd.write(data.content)

Problem 1 - Loops and conditionals

Consider the following list:

data = [1, 4, 3, 10, 4, 3, 4, 4]

  • Write a loop that computes the average of the elements in the list and print the result using two significant digits.

# your code here

  • Write code that finds the number of times the element 4 occurs in the list. Hint: Use a loop and an if-statement.

# your code here

  • Write a Python function that takes a list as an argument and returns the number of times a given element (also passed as an argument to the function) appears in the list. Call that function find_number_of_occurences(a, elm). Make sure you follow best practices when writing the docstring of your function.

# your code here

# Try your code here:
help(find_number_of_occurences)

# Try your code here:
find_number_of_occurences(data, 4)

  • Write a Python function that takes a list as an argument and returns the number of elements that are greater than a given element (also passed as an argument to the function). Call that function find_number_of_elms_greater_than(a, elm). Make sure you follow best practices when writing the docstring of your function.

# your code here

# Try your code here:
help(find_number_of_elms_greater_than)

# Try your code here:
find_number_of_elms_greater_than(data, 3)

Problem 2 - High-performance buildings revisited

In this problem we will continue analyzing the high-performance buildings dataset we introduced in Problem 1 - High-performance buildings and with which we played in Selecting dataframe rows that satisfy a boolean expression. Let me set you up by downloading and cleaning the data file:

url = 'https://raw.githubusercontent.com/PurdueMechanicalEngineering/me-297-intro-to-data-science/master/data/temperature_raw.xlsx'
download(url)
import pandas as pd
df = pd.read_excel('temperature_raw.xlsx')
df = df.dropna(axis=0)
df.date = pd.to_datetime(df['date'], format='%Y-%m-%d')
df.head()

+ Plot the external temperature `t_out`

  • Extract the data pertaining to household a5. Put the result in a new dataframe called df_a5.

df_a5 = # Your code here
df_a5.head()

  • For household a5, plot t_unit as a function of date.

# Your code here

  • Do the same figure, plot date vs t_unit for households a5 and a11.

# Your code here

  • In the same figure, plot the t_out and t_unit scatter plots for both households a5 and a11.

# Your code here

  • In the same figure, plot the t_out and hvac scatter plots for both households a5 and a11.

# Your code here

  • In the same figure, plot the histogram of t_unit for households a5 and a11. Which household prefers cooler temperatures? Hint: To make the histogram more appealing use the keywords density=True, alpha=0.25.

# Your code here

Your answer and explanation here

  • In the same figure, plot the histogram of hvac for households a5 and a11. Which household is more energy efficient (if any) and why?

# Your code here

your answer and explanation here

  • Repeat the analysis above for households b17 and c40. Which household prefers cooler temperatures and which one is more energy efficient?

# your code here

Your answer and explanation here. Use as many code and markdown blocks as you want.

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Selecting dataframe rows that satisfy a boolean expression

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Lecture 7: Probability as a measure of uncertainty