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Study Guide for Final Exam

Exam Date: Tuesday, April 29
Exam Time: 03:00pm - 04:00pm
Location: Holmes Engineering 439

As we approach the final exam, I encourage you to diligently prepare for the exam. Here are some important details regarding the exam:

To help you to prepare, the study guide includes key topics based on the exam questions, along with a few sample questions.

1. Introduction and General (~ 10 %)

Sample Questions:

Question 1. What are some common tasks in environmental data science?
a) Analyzing satellite images of forests
b) Predicting air quality based on weather data
c) Processing data of Earth system models
d) All of the above

Question 2. Which of the following commands can be used to install Python packages using pip?
a) pip install package_name
b) py install package_name
c) python install package_name
d) pkg install package_name

2. Python Basics (~ 25%)

Sample Questions:

Question 3. Which of the following is a valid variable name in Python?
a) my_var1
b) 1var
c) var_$
d) None of the above

Question 4. What happens when you try to access a variable that has not been defined?
a) Python automatically assigns a value to it
b) Python raises an error
c) Python prompts the user to define the variable
d) None of the above

Question 5. What is the output of the following code?

   x = 5
   y = 3
   print(x + y)

a) 5
b) 3
c) 8
d) Python raises an error

Question 6. Which of the following is the correct way to call the sqrt function from the math module?
a) math.sqrt()
b) sqrt(math)
c) math(sqrt)
d) sqrt()

Question 7. What is the output of the following code?

my_list = [1, 2, 3, 4, 5]
print(my_list[-2])

a) 1
b) 2
c) 3
d) 4

Question 8. What will be the output of the following code ?

percentage = 0.75
print(f"The percentage is {percentage:.1%}")

a) The percentage is 0.8
b) The percentage is 75.0%
c) The percentage is 75%
d) The percentage is 0.8%

3. Python Programming (~ 25%)

Sample Questions:

Question 10. Which loop in Python is better suited when the number of iterations is known?
a) for loop
b) while loop
c) They are equally suited
d) None of the above

Question 11. What will be the output of the following code?

   numbers = [1, 2, 3, 4, 5]
   for num in numbers:
       print(num * 2)

a) 2 4 6 8 10
b) 1 2 3 4 5
c) 1 2 3 4 5 2 4 6 8 10
d) SyntaxError: invalid syntax

Question 12. What does the following while loop do?

count = 0
while count < 5:
    print("The count is:", count)
    count += 1

a) It prints “The count is:1” to “The count is:5” b) It prints “The count is:0” to “The count is:4” c) It prints “The count is:” infinitely
d) Python raises an error

Question 13. What will be the output of this code?

x = 10
if x > 5:
    print("x is greater than 5")
else:
    print("x is less than or equal to 5")

a) x is greater than 5
b) x is less than or equal to 5
c) x is 10
d) SyntaxError: invalid syntax

Question 14. What will be the output of the following code?

x = 15
if x > 5 and x < 15:
    print("Between 5 and 15")
else:
    print("Outside the range")

a) Outside the range
b) Between 5 and 15
c) Error
d) None of the above

Question 15. Consider the following code:

def process_data(data):
   result = data**2
   return result

output = process_data(5)
print(result)

What will be printed?
a) 5
b) 10
c) 25
d) None of the above

Question 16. Consider the following code saved in a script file named ‘my_script.py’:

def greet(name):
    print(f"Hello, {name}!")

def multiply(x, y):
    return x * y

How can you import and use the greet function from this script in another Python file?
a) import my_script.greet
b) from my_script import greet
c) from my_script import greet()
d) import my_script

4. Pandas (~ 15%)

Sample Questions:

Question 18. Select the most accurate answer
a) You can install pandas using pip install pandas
b) You can install pandas using pip install pandas or pip download pandas
c) pandas can be only installed with pip if you have conda or miniconda
d) none of the above

Question 19. What is the output of the following code?

   import pandas as pd
   data = pd.read_csv("data.csv")
   print(data.shape)

a) The number of rows and columns in the DataFrame
b) The number of unique values in each column of the DataFrame
c) No output or error as .shape is not a method
d) None of the above

Question 20. Select all line codes that can display the first three rows of this df DataFrame:

  A B
0 10 1
1 20 2
2 30 3
3 40 4
4 50 5
5 60 6

1) display(df.iloc[0:3])
2) display(df.iloc[0:3,:])
3) display(df.iloc[:,0:3])
4) display(df.loc[0:3])
5) display(df.loc[0:3,:])
6) display(df.loc[0:2])
7) display(df.loc[0:2,:])
8) df.head(3)
9) df.head()
10) display(df[0:3, :])
11) display(df[0:3])
12) display(df[0:2])

Question 21. Suppose you have a DataFrame named df with columns ‘A’, ‘B’, and ‘C’. What does the following code do?

df = df.groupby('A').sum()

a) Groups the DataFrame by unique values in column ‘A’ and sums the values in column ‘A’ for each group
b) Groups the DataFrame by unique values in column ‘A’, sums the values in columns ‘B’ and ‘C’ for each group
c) Returns a syntax error
d) None of the above

5. AI Coding Assistance (< 5%)

Sample Questions:
Question 22. What is the potential downside of relying too much on generative AI for coding tasks?
a) It may lead to decreased efficiency and productivity due to slower response times
b) It may result in code plagiarism, lack of originality and dependency on AI for coding tasks
c) It may increase the likelihood of code errors and bugs
d) It may require extensive computational resources

6. Data Science Workflow (< 5%)

Sample Questions:
Question 23 What is the first step in the data science workflow?
a) Data analysis
b) Data visualization
c) Data collection
d) Data preprocessing

7. NumPy (~ 5%)

Sample Questions:
Question 24 What is the primary data structure used in NumPy?
a) Lists
b) Tuples
c) Arrays
d) Sets

Question 25 Consider the following code:

import numpy as np

arr = np.array([[1, 2, 3],
                [4, 5, 6],
                [7, 8, 9]])

result = arr[:, 1]
print(result)

This code
a) selects all rows of the first column of the array
b) selects all rows of the second column of the array
c) selects the second row of all columns of the array
d) none of the above

8. Matplotlib (~ 5%)

Sample Questions: Question 26. Which of the following statements accurately describes Matplotlib?
a) Matplotlib is the best choice for specialized statistical plots among plotting libraries in Python
b) Matplotlib is the best choice for interactive plots among plotting libraries in Python
c) Matplotlib is a versatile plotting library for creating static, interactive, and animated visualizations
d) Matplotlib is the best choice for for 3D visualization tasks among plotting libraries in Python

Question 27. Consider the following code:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [1, 4, 9, 16])
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_title('Line Plot')
plt.show()

Through .plt.subplots() function you can
a) create a new figure and axes object
b) add a title
c) adjust figure size
d) do all of the above

Question 28. Consider the following code:

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [1, 4, 9, 16], linestyle='--')
plt.show()

What does linestyle=’–’ do in this code?
a) Sets the line style to dashed line
b) Sets the line style to dotted line
c) Sets the line style to solid line
d) None of the above

9. Xarray and CartoPy (~ 10%)

Sample Questions:
Question 29. Which of the following best describes CMIP6?
a) A satellite launched by NASA for remote sensing of climate data
b) A dataset containing historical climate observations from the past century
c) A modeling project providing climate model output
d) A project providing weather forecasting models

Question 30. What is the primary purpose of Xarray in Python?
a) Xarray is a library for statstical analysis
b) Xarray is the best tool for working with tabular data
c) Xarray is a library for working with labeled arrays and datasets in Python
d) Xarray is a package for web development and data storage

Question 31. Consider the following code:

import matplotlib.pyplot as plt

#Xarray DataSet ds
ds['temperature'].plot()   #temperature shape (50, 50)
plt.show()

What does ds[‘temperature’].plot() do in this code?
a) Plots the temperature data from the dataset using Matplotlib as a line plot
b) Plots the temperature data from the dataset using Matplotlib as a pcolormesh plot
c) Returns an error as xarray has no built-in plotting method
d) None of the above

Question 32. Consider the following code:

import xarray as xr
import pandas as pd
import numpy as np
dates = pd.date_range('2024-01-01', periods=240, freq='M')  
latitudes= [24.5, 26.5, 28.5, 30.5]
size=(len(dates), len(latitudes))
zos = np.round(np.random.uniform(-1, 1, size= size), 2) #sea surface height [m]
ds = xr.Dataset({'zos': (('date', 'lat',), zos)}, coords={'date': dates, 'lat': latitudes})
ds.zos.mean(dim=('lat')).plot()

What will this code plot?
a) time series plot showing mean zos varying with time with monthly intervals
b) time series plot showing mean zos varying with lat with monthly intervals
c) lat contour plot showing mean zos varying with time with monthly intervals
d) nothing because, unlike pandas, xarray does not have built-in plotting

Question 33. What is the purpose of specifying subplot_kw={‘projection’: …} when creating subplots in CartoPy?
a) To define the color scheme for the subplots
b) To specify the resolution of the subplots
c) To set the extent of the subplots
d) To define the map projection for the subplots

Question 34. What does the term “map projection” refer to in the context of CartoPy?
a) The process of creating a map by projecting points on Earth’s surface onto a flat surface
b) The method of coloring different regions on a map
c) The process of adding geographic features such as coastlines and rivers to a map
d) The technique of overlaying multiple plots on a single map figure

10. Google Earth Engine and GeeMap (execluded from the exam)

Sample Questions:
Question 35. What is the primary purpose of Google Earth Engine (GEE) in environmental data science?
a) To provide satellite imagery for visual inspection
b) To analyze and process large-scale geospatial datasets
c) To create custom maps for web applications
d) To collect ground-level environmental data

Best of luck!

Keys:

Question 1. d) All of the above
Question 2. a) pip install package_name
Question 3. a) my_var1
Question 4. b) Python raises an error
Question 5. c) 8
Question 6. a) math.sqrt()
Question 7. d) 4
Question 8. b) The percentage is 75.0%
Question 10. a) for loop
Question 11. a) 2 4 6 8 10
Question 12. b) It prints the count from 0 to 4
Question 13. a) x is greater than 5
Question 14. a) Outside the range
Question 15. d) None of the above Question 16. b) from my_script import greet
Question 18. a) You can install pandas using pip install pandas
Question 19. a) The number of rows and columns in the DataFrame
Question 20. Correct answers

Question 21. b) Groups the DataFrame by unique values in column ‘A’, sums the values in columns ‘B’ and ‘C’ for each group
Question 22. b) It may result in code plagiarism, lack of originality and dependency on AI for coding tasks Question 23 c) Data collection
Question 24 c) Arrays
Question 25 b) selects all rows of the second column of the array
Question 26. c) Matplotlib is a versatile plotting library for creating static, interactive, and animated visualizations
Question 27. a) create a new figure and axes object
Question 28. a) Sets the line style to dashed line
Question 29. c) A modeling project providing climate model output
Question 30. c) Xarray is a library for working with labeled arrays and datasets in Python
Question 31. b) Plots the temperature data from the dataset using Matplotlib as a pcolormesh plot
Question 32. a) time series plot showing mean zos varying with time with monthly intervals
Question 33. d) To define the map projection for the subplots
Question 34. a) The process of creating a map by projecting points on Earth’s surface onto a flat surface
Question 35. b) To analyze and process large-scale geospatial datasets

Best of luck!

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