Data Visualization with Python Coursera Quiz Answers

All Weeks Data Visualization with Python Coursera Quiz Answers

One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.

The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.

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Data Visualization with Python Week 01 Quiz Answers

Introduction to Data Visualization Tools

Q1. Matplotlib was created by

  • John Hunter, an American neurobiologist.
  • Cleve Moler, an American mathematician and computer programmer.
  • Daniel Johnson, a German physicist.
  • James Gosling, a Canadian computer scientist.
  • John Butler, an American psychologist.

Q2. Using the inline backend, you cannot modify a figure after it is rendered.

  • True.
  • False.

Q3. %matplotlib inline is an example of Matplotlib magic functions.

  • True.
  • False.

Data Visualization with Python Week 02 Quiz Answers

Basic Visualization Tools Quiz Answers

Q1. Area plots are unstacked by default.

  • True.
  • False.

Q2. Given a pandas series, series_data, which of the following will create a histogram of series_data and align the bin edges with the horizontal tick marks?

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Q3. Given a pandas dataframe, question, which of the following will create a horizontal bar chart of the data in question?

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Specialized Visualization Tools Quiz Answers

Q1. What is the correct combination of function and parameter to create a box plot in Matplotlib?

  • Function = plot, and Parameter = type with value = “box”
  • Function = boxplot, and Parameter = type with value = “plot”
  • Function = plot, and Parameter = kind with value = “box”
  • Function = box, and Parameter = type with value = “plot”
  • Function = plot, and Parameter = kind with value = “boxplot”

Q2. Which of the lines of code below will create the following scatter plot, given the pandas dataframe, df_total?

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Data Visualization with Python Week 03 Quiz Answers

Advanced Visualization Tools Quiz Answers

Q1. Seaborn is a Python visualization library that provides a high-level interface for visualizing geospatial data.

  • True
  • False

Q2. The easiest way to create a waffle chart in Python is using the Python package, PyWaffle.

  • True.
  • False.

Data Visualization with Python Week 04 Quiz Answers

Visualizing Geospatial Data Quiz Answers

Q1. You cluster markers, superimposed onto a map in Folium, using a feature group object.

  • False
  • True

Q2. The following code will generate a map of Spain, displaying its hill shading and natural vegetation.

  • True.
  • False.

Q3.CA choropleth map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map.

  • True.
  • False.

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Graded Quiz Quiz Answers

Q1. Plotly express is a ________ wrapper

  • Low-level
  • High-level

Q2. @app_callback is the callback decorator.

  • True
  • False

Q3. Choose correct way of adding callback decorator

  • @app.callback( Output{component_id=’bar-plot’, component_property=’figure’}, Input{component_id=’input-yr’, component_property=’value’})
  • @app.callback( Output(component_id=’bar-plot’, component_property=’figure’), Input(component_id=’input-yr’, component_property=’value’))
  • @app.callback[Output(component_id=’bar-plot’, component_property=’figure’), Input(component_id=’input-yr’, component_property=’value’)]

Data Visualization with Python Week 05 Quiz Answers

Data Visualization with Python Coursera Final Exam Quiz Answers

Q1. According to the author in the video, what does Dark Horse Analytics state are the 3 best practices for creating a visual?

  • Less is more effective; Less is not attractive; Less is more impactive.
  • Less is not effective; Less is not attractive; Less is not impactive.
  • None of the above.
  • Less is more effective; Less is more attractive; Less is more impactive.

Q2. The three layers that make up the _________ architecture are the backend, the artist, and the scripting layers.

  • Seaborn
  • Pyplot
  • Matlab
  • Matplotlib

Q3. Which of the following codes uses the artist layer to create a stacked area plot of the data in the pandas dataframe, area_df?

  • import matplotlib.pyplot as plt area_df.plot(type=’area’, figsize=(20, 10)) plt.set_title(‘Plot Title’) plt.set_ylabel(‘Vertical Axis Label’) plt.set_xlabel(‘Horizontal Axis Label’) plt.show()
  • ax = area_df.plot(kind=’area’, figsize=(20, 10)) ax.set_title(‘Plot Title’) ax.set_ylabel(‘Vertical Axis Label’) ax.set_xlabel(‘Horizontal Axis Label’)
  • ax = area_df.plot(kind=’area’, figsize=(20, 10)) ax.title(‘Plot Title’) ax.ylabel(‘Vertical Axis Label’) ax.xlabel(‘Horizontal Axis Label’)
  • ax = area_df.plot(type=’area’, figsize=(20, 10)) ax.set_title(‘Plot Title’) ax.set_ylabel(‘Vertical Axis Label’) ax.set_xlabel(‘Horizontal Axis Label’)
  • import matplotlib.pyplot as plt area_df.plot(kind=’area’, figsize=(20, 10)) plt.title(‘Plot Title’) plt.ylabel(‘Vertical Axis Label’) plt.xlabel(‘Horizontal Axis Label’) plt.show()

Q4. Which of the following code will create a stacked area plot of the data in the pandas dataframe, area_df, with a transparency value of 0.75?

  • transparency = 0.75 ax = area_df.plot(kind=’area’, alpha=transparency, stacked=False, figsize=(20, 10)) ax.set_title(‘Plot Title’) ax.set_ylabel(‘Vertical Axis Label’) ax.set_xlabel(‘Horizontal Axis Label’)
  • import matplotlib.pyplot as plt transparency = 1 – 0.75 area_df.plot(kind=’area’, alpha=transparency, stacked=False, figsize=(20, 10)) plt.title(‘Plot Title’) plt.ylabel(‘Vertical Axis Label’) plt.xlabel(‘Horizontal Axis Label’) plt.show()
  • import matplotlib.pyplot as plt transparency = 0.35 area_df.plot(kind=’area’, alpha=transparency, figsize=(20, 10)) plt.title(‘Plot Title’) plt.ylabel(‘Vertical Axis Label’) plt.xlabel(‘Horizontal Axis Label’) plt.show()
  • import matplotlib.pyplot as plt transparency = 0.75 area_df.plot(kind=’area’, alpha=transparency, figsize=(20, 10)) plt.title(‘Plot Title’) plt.ylabel(‘Vertical Axis Label’) plt.xlabel(‘Horizontal Axis Label’) plt.show()

Q5. What type of chart is the least confusing and should be your first attempt when creating a visual to explore a dataset?

  • Table chart
  • Pie chart
  • Radial column chart
  • Bar chart

Q6. What is a variation of the scatter plot that displays three dimensions of data?

  • A Heatmap
  • A scatter map
  • A bubble plot
  • None of the above

Q7. A waffle chart is a great way to visualize data in relation to a whole, or to highlight progress against a given threshold.

  • True.
  • False.

Q8. What is a depiction of the meaningful words in some textual data, where the more a specific word appears in the text, the bigger and bolder it appears?

  • A Waffle Chart
  • A Word Cloud
  • A Box Plot
  • A Regression Plot

Q9. Which of the following are tile styles of Folium maps?

  • Mapbox Control Room
  • OpenStreetMap
  • Stamen Watercolor
  • Stamen Terrain
  • All of the above

Q10. What is the correct tile style for Folium maps that feature hill shading and natural vegetation colors?

  • Stamen Watercolor
  • Mapbox Bright
  • OpenStreetMap
  • Stamen Terrain

Q11. Which of the following statements is true for Plotly?

  • An interactive, open-source plotting library.
  • All of the above
  • Includes chart types like statistical, financial, maps, scientific, and 3-dimensional.
  • Supports over 40 unique chart types.

Q12. Dash components are

  • a only
  • Core
  • Both  a and b
  • HTML
  • CSS
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