Data Visualization with Python Cognitive Class Exam Quiz Answers

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Data Visualization with Python Cognitive Class Certificate Answers

Module 1: Introduction to Visualization Tools

Question 1: What are the layers that make up the Matplotlib architecture?

• FigureCanvas Layer, Renderer Layer, and Artist Layer.
• Backend_Bases Layer, Artist Layer, Scripting Layer.
• Backend Layer, Artist Layer, and Scripting Layer.
• Backend Layer, FigureCanvas Layer, Renderer Layer, Artist Layer, and Scripting Layer.
• Figure Layer, Artist Layer, and Scripting Layer

Question 2: Using the inline backend, you can modify a figure after it is rendered.

• False
• True

Question 3: Which of the following are examples of Matplotlib magic functions? Choose all that apply.

• %matplotlib inline
• #matplotlib notebook
• $matplotlib outline
• %matplotlib notebook
• #matplotlib inline

Module 2: Basic Visualization Tools

Question 1: Area plots are stacked by default.

• False
• True

Question 2: 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?

• count, bin_edges = np.histogram(series_data)
  • series_data.plot(kind=’hist’, xticks = count, bin_edges)
• count, bin_edges = np.histogram(series_data)
  • series_data.plot(kind=’hist’, xticks = count)
• count, bin_edges = np.histogram(series_data)
  • series_data.plot(kind=’hist’, xticks = bin_edges)
• series_data.plot(kind=’hist’)
• count, bin_edges = np.histogram(series_data)
  • series_data.plot(type=’hist’, xticks = bin_edges)

Question 3: Given a pandas dataframe, question, which of the following will create a horizontal barchart of the data in question?

• question.plot(type=’bar’, rot=90)
• question.plot(kind=’bar’, orientation=’horizontal’)
• question.plot(kind=’barh’)
• question.plot(kind=’bar’)
• question.plot(kind=’bar’, type=’horizontal’)

Module 3: Specialized Visualization Tools

Question 1: Pie charts are less confusing than bar charts and should be your first attempt when creating a visual.

• False
• True

Question 2: What do the letters in the box plot above represent?

• A = Mean, B = Upper Mean Quartile, C = Lower Mean Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
• A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
• A = Median, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
• A = Median, B = Third Quartile, C = Mean, D = Inter Quartile Range, E = Lower Quartile, and F = Outliers
• A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Maximum

Question 3: What is the correct combination of function and parameter to create a box plot in Matplotlib?

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

Module 4: Advanced Visualization Tools

Question 1: Which of the choices below will create the following regression line plot, given a pandas dataframe, data_dataframe?

• import seaborn as sns
  • ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”)
• data_dataframe.plot(kind=”regression”, color=”green”, marker=”+”)
• import seaborn as sns
  • ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”, marker=”+”)
• data_dataframe.plot(kind=”regplot”, color=”green”, marker=”+”)
• import seaborn as sns
  • ax = sns.regplot(x=”total”, y=”year”, data=data_dataframe, color=”green”)

Question 2: In Python, creating a waffle chart is straightforward since we can easily create one using the scripting layer of Matplotlib.

• False
• True

Question 3: A word cloud (choose all that apply)

• is a depiction of the frequency of different words in some textual data.
• is a depiction of the frequency of the stopwords, such as a, the, and, in some textual data.
• 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 in the word cloud.
• can be generated in Python using the word_cloud library that was developed by Andreas Mueller.
• can be easily created using Matplotlib using the scripting layer.

Module 5: Creating Maps and Visualizing Geospatial Data

Question 1: What tile style of Folium maps is usefule for data mashups and exploring river meanders and coastal zones?

• OpenStreetMap incorrect
• Mapbox Bright
• Stamen Toner
• Stamen Terrain
• River and Coastal incorrect

Question 2: You cluster markers superimposed onto a map in Folium using a feature group object.

• False
• True

Question 3: If you are interested in generating a map of Spain to visualize its hill shading and natural vegetation, which of the following lines of code will create the right map for you?

• folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Toner’) incorrect
• folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Terrain’) incorrect
• folium.Map(location=[40.4637, -3.7492], zoom_start=6, tiles=’Stamen Terrain’)
• folium.Map(location=[-40.4637, -3.7492], zoom_start=6, tiles=’Stamen Terrain’)
• folium.Map(location=[40.4637, 3.7492], zoom_start=6)

Data Visualization with Python Final Exam Answers – Cognitive Class

Question 1: Data visualizations are used to (check all that apply)

• explore a given dataset.
• perform data analytics and build predictive models.
• train and test a machine learning algorithm.
• share unbiased representation of data.
• support recommendations to different stakeholders.

Question 2: Matplotlib was created by John Hunter, an American neurobiologist, and was originally developed as an EEG/ECoG visualization tool.

• False
• True

Question 3: What are the layers that make up the Matplotlib architecture?

• FigureCanvas Layer, Renderer Layer, and Artist Layer.
• Backend_Bases Layer, Artist Layer, Scripting Layer.
• Backend Layer, Artist Layer, and Scripting Layer. correct
• Backend Layer, FigureCanvas Layer, Renderer Layer, Artist Layer, and Scripting Layer.
• Figure Layer, Artist Layer, and Scripting Layer.

Question 4: Using the notebook backend, you can modify a figure after it is rendered.

• False
• True

Question 5: The scripting layer is (check all that apply)

• comprised mainly of pyplot.
• an area on which the figure is drawn.
• a handler of user inputs such as keyboard strokes and mouse clicks.
• lighter that the Artist layer, and is intended for scientists whose goal is to perform quick exploratory analysis.
• comprised one one main object – Artist.

Question 6: Which of the following are instances of the Artist object? (check all that apply)

• Titles
• Event
• FigureCanvas
• Tick Labels
• Images

Question 7: There are three types of Artist objects.

• False
• True

Question 8: Each primitive artist may contain other composite artists as well as primitive artists.

• False
• True

Question 9: Given a pandas dataframe, question, which of the following will create a horizontal barchart of the data in question?

• question.plot(type=’bar’, rot=90)
• question.plot(kind=’bar’, orientation=’horizontal’)
• question.plot(kind=’barh’)
• question.plot(kind=’bar’)
• question.plot(kind=’bar’, type=’horizontal’)

Question 10: Pie charts are relevant only in the rarest of circumstances, and bar charts are far superior ways to quickly get a message across.

• False
• True

Question 11: What do the letters in the box plot above represent?

• A = Mean, B = Upper Mean Quartile, C = Lower Mean Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
• A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers
• A = Median, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Outliers correct
• A = Median, B = Third Quartile, C = Mean, D = Inter Quartile Range, E = Lower Quartile, and F = Outliers
• A = Mean, B = Third Quartile, C = First Quartile, D = Inter Quartile Range, E = Minimum, and F = Maximum

Question 12: What is the correct combination of function and parameter to create a box plot in Matplotlib?

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

Question 13: Which of the lines of code below will create the following scatter plot, given the pandas dataframe, df_total?

• import matplotlib.pyplot as plt
  • plot(kind=’scatter’, x=’year’, y=’total’, data=df_total)
  • plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
  • plt.label (‘Year’)
  • plt.label(‘Number of Immigrants’)
• import matplotlib.pyplot as plt
  • df_total.plot(type=’scatter’, x=’year’, y=’total’)
  • plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
  • plt.label (‘Year’)
  • plt.label(‘Number of Immigrants’)
• import matplotlib.pyplot as plt
  • df_total.plot(kind=’scatter’, x=’year’, y=’total’)
  • plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
  • plt.xlabel (‘Year’)
  • plt.ylabel(‘Number of Immigrants’)
• import matplotlib.scripting.pyplot as plt
  • df_total.plot(kind=’scatter’, x=’year’, y=’total’)
  • plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
  • plt.label (‘Year’)
  • plt.label(‘Number of Immigrants’)
• import matplotlib.scripting.pyplot as plt
  • df_total.plot(type=’scatter’, y=’year’, x=’total’)
  • plt.title(‘Total Immigrant population to Canada from 1980 – 2013’)
  • plt.xlabel (‘Year’)
  • plt.ylabel(‘Number of Immigrants’)

Question 14: A bubble plot is a variation of the scatter plot that displays three dimensions of data.

• False
• True

Question 15: Seaborn is a Python visualization library that is built on top of Matplotlib.

• False
• True

Question 16: Which of the choices below will create the following regression line plot, given a pandas dataframe, data_dataframe?

• import seaborn as sns
  • ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”)
• data_dataframe.plot(kind=”regression”, color=”green”, marker=”+”)
• import seaborn as sns
  • ax = sns.regplot(x=”year”, y=”total”, data=data_dataframe, color=”green”, marker=”+”)
• data_dataframe.plot(kind=”regplot”, color=”green”, marker=”+”)
• import seaborn as sns
  • ax = sns.regplot(x=”total”, y=”year”, data=data_dataframe, color=”green”)

Question 17: A word cloud (choose all that apply):

• is a depiction of the frequency of different words in some textual data.
• is a depiction of the frequency of the stopwords, such as a, the, and, in some textual data.
• is a depiction of the meaningful words in some textual data, where the more a specific word appears in the text, bigger and bolder it appears in the word cloud.
• can be generated in Python using the word_cloud library that was developed by Andreas Mueller.
• can be easily created using Matplotlib using the scripting layer.

Question 18: The following are tile styles of folium maps (choose all that apply).

• Stamen Terrain
• River Coastal
• Stamen Toner
• Mapbox Bright
• Open Stamen

Question 19: You cluster markers superimposed onto a map in Folium using a marker cluster object.

• False
• True

Question 20: If you are interested in generating a map of Spain to explore its river meanders and coastal zones. Which of the following lines of code will create the right map for you?

• folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Terrain’)
• folium.Map(location=[40.4637, 3.7492], zoom_start=6, tiles=’Stamen Toner’)
• folium.Map(location=[40.4637, -3.7492], zoom_start=6, tiles=’Stamen Toner’) correct
• folium.Map(location=[-40.4637, -3.7492], zoom_start=6, tiles=’Stamen Terrain’)
• folium.Map(location=[40.4637, 3.7492], zoom_start=6)

Introduction to Data Visualization with Python

Data visualization is an essential skill in data science, allowing us to understand complex datasets, identify trends, and communicate findings effectively. Python, with its rich ecosystem of libraries, offers powerful tools to create a wide range of visualizations. This guide will introduce you to the basics of data visualization using Python.

Why Data Visualization?

Data visualization helps to:

• Simplify complex data: Transform large datasets into visual representations that are easier to understand.
• Identify patterns and trends: Spotting trends, correlations, and outliers becomes easier.
• Communicate insights: Visuals can often communicate data findings more effectively than raw numbers.
• Support decision-making: Helps stakeholders make informed decisions based on data insights.

Popular Python Libraries for Data Visualization

1. Matplotlib

• Description: The foundational library for creating static, animated, and interactive plots.
• Strengths: Highly customizable, supports various types of plots, integrates well with other libraries.
• Common Uses: Line plots, scatter plots, bar charts, histograms.

2. Seaborn

• Description: Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive and informative statistical graphics.
• Strengths: Simplifies complex visualizations, supports data frames, built-in themes.
• Common Uses: Heatmaps, categorical plots, distribution plots.

3. Plotly

• Description: An interactive graphing library that makes interactive, publication-quality graphs online.
• Strengths: Interactive plots, integration with web applications, supports 3D plotting.
• Common Uses: Interactive line plots, scatter plots, 3D plots.

4. Bokeh

• Description: A library for creating interactive visualizations that can be easily embedded into web applications.
• Strengths: Interactive plots, real-time streaming, good for dashboards.
• Common Uses: Interactive plots, data dashboards.

Conclusion

Python provides a robust set of tools for data visualization, each with its own strengths and use cases. By mastering libraries like Matplotlib, Seaborn, Plotly, and Bokeh, you can create a wide variety of visualizations to suit different needs, from static reports to interactive dashboards.