CSC 308. Exam 1 (Ch 1-4)

Chapter 1

1. This code chains the head() method to the mean() method:

   df.head().mean()

   df.mean.head()()

   df.mean()df.head()

   df.mean().head()

df.mean().head()

2. The Pandas module provides methods for 

   data visualization

   plotting

   data analysis

   data analysis and plotting

data analysis and plotting

3. Data analysis includes all but one of the following:

   data mining

   descriptive analysis

   predictive analysis

   data visualization

data mining

4. The whos Magic Command displays

   the names of all of the variables in the Notebook

   the names and memory use of the active variables

   the names of the active variables

   the names and data types of the active variables

the names and data types of the active variables

5. This Magic Command  returns the run time for the entire cell:

   $time

   %time

   $$time

   %%time

%%time

6. A runtime error occurs when a Python statement 

   can’t be run because it’s out of sequence

   when the Python syntax is okay but the statement can’t be executed

   violates one of the rules of Python coding

   can’t be run because the data is faulty

when the Python syntax is okay but the statement can’t be executed

7. This is a list comprehension for the numbers 1 through 20:

   pd.comprehension(1,20)

   (x for x in range(1,20))

   [x for y in range(1,20)]

   [x for x in range(1,20)]

[x for x in range(1,20)]

8. This code creates a dictionary :

   ('1-4':'01-04 Years','5-9':'05-09 Years')

   {'1-4':'01-04 Years','5-9':'05-09 Years'}

   '1-4':'01-04 Years';'5-9':'05-09 Years'

   ['1-4':'01-04 Years','5-9':'05-09 Years']

{'1-4':'01-04 Years','5-9':'05-09 Years'}

9. To get the tooltip for a method, you place the cursor

   in the name of the method or the parentheses of the method and press Shift+Enter

   anywhere in the cell and press Shift+Tab

   in the name of the method or the parentheses of the method and presss Shift+Tab

   in the parentheses for the method and press Shift+Tab

in the name of the method or the parentheses of the method and presss Shift+Tab

10. This Markdown language creates a level 2 heading for “Clean the data”:

    <h2>Clean the data</h2>

    ##Clean the data##

    ##Clean the data

    %%Clean the data%%

##Clean the data

11. What kind of error would the following code generate? df[['year']

    Logic error

    Runtime error

    Syntax error

    Memory error

Syntax error

12. This code imports the pandas module with the name pd:

    from urllib import pd

    import pandas as pd

    from pandas import pd

    import pd from pandas

import pandas as pd

13. To run a cell in JupyterLab, you can

    click the + button in the toolbar

    press Shift+Enter

    press Ctrl+Shift+Enter

    select the Run > Current Cell command

press Shift+Enter

14. Although there is some overlap between the phases, data analysis consists these phases:

    get, clean, prepare, and analyze

    get, clean, analyze, and visualize

    clean, prepare, analyze, and visualize

    get, clean, prepare, analyze, and visualize

clean, prepare, analyze, and visualize

Chapter 2

1. This statement returns all columns in the fires DataFrame in which the row value in the Year column is equal to 1900:

   fires.Year.query(1900)

   fires.query(Year == 1900)

   fires.query(fires.Year == 1900)

   fires.query('Year == 1900')

fires.query('Year == 1900')

2. To apply more than one method to a group of columns in a DataFrame, you can use the

   chain the agg() method to the groupby() method

   chain the agg() method to the pivot() method

   pivot() method

   groupby() method

chain the agg() method to the groupby() method

3. To get data into a DataFrame, you can either import the data

   from a file or database or you can use the DataFrame() constructor to build it

   from a file or database, or you can read it from a pickle file

   from a file or you can use the DataFrame() constructor to build it

   from a file or you can read it from a pickle file

from a file or database or you can use the DataFrame() constructor to build it

4. For each column in a DataFrame, the info() method returns

   the name, the number of non-null values, and the data type

   the name, the number of unique values, and the data type

   the number of non-null values, the number of unique values, and the data type

   the name, the number of non-null values, and the number of unique values

the name, the number of non-null values, and the data type

5. To prepare a DataFrame for plotting by the Pandas plot() method, you can use all but one of the following to set an index. Which one is it?

   groupby() method

   pivot() method

   melt() method

   index() method

melt() method

6. This statement mortality_data[['AgeGroup','DeathRate']].max()

   returns the maximum value for just the DeathRate column

   causes a syntax error because brackets are coded within brackets

   causes a runtime error because AgeGroup isn’t a numeric column

   returns the maximum value for two columns

returns the maximum value for two columns

7. This statement displays all of the columns but only five of the rows of a DataFrame named fires:

   A. with pd.option_context('display.max_rows', 5,

   'display.max_columns', None): display(fires)

   B. with pd.option_context('display.max_rows', 5,

   'display.max_columns', All): display(fires)

   C. with pd.option_context('display.max_rows', 5,

   'display.max_columns', Yes): display(fires)

   D. with pd.option_context('display.max_rows', None,

   'display.max_columns', None): display(fires)

A. with pd.option_context('display.max_rows', 5,

'display.max_columns', None): display(fires)

8. This statement sorts the rows in the fires DataFrame by the fire_size variable from largest to smallest:

   fires.sort_values('fire-size', ascending=False)

   fires.sort_values('fire-size')

   fires.sort('fire-size', ascending=False)

   fires.sort_values('fire-size', descending=True)

fires.sort_values('fire-size', ascending=False)

9. After this statement is executed

      mortality_wide = mortality_data.pivot(

            index='Year', columns='AgeGroup', values='DeathRate')

   the values in the AgeGroup column will be summarized by Year

   each unique value in the AgeGroup column will be a column name

   each value in the AgeGroup column will be a column name

   each value in the DeathRate column will be a column name

each unique value in the AgeGroup column will be a column name

10. The statement

        mortality_data.AgeGroup.replace(

                {'1-4 Years':'01-04 Years','5-9 Years':'05-09 Years'})

    a Pandas method to replace the data in one column

    a Python method to replace the data in one column

    a Python method to replace the data in one column, but doesn’t change the DataFrame

    a Pandas method to replace the data in one column, but doesn’t change the DataFrame

a Pandas method to replace the data in one column, but doesn’t change the DataFrame

11. Assume that this URL points to a valid CSV file:

    url = 'https://www.murach.com/python_analysis/forest_fires.csv'

    Then, this statement imports a DataFrame named fires from the data in the CSV file:

    fires.read_csv(url)

    fires = pd.import_csv(url)

    fires.import_csv(url)

    fires = pd.read_csv(url)

fires = pd.read_csv(url)

12. The describe() method returns these statistics for each numeric column in a DataFrame

    row count, column count, mean, minimum value, and maximum value

    row count, unique value count, mean, standard deviation, and maximum value

    row count, mean, standard deviation, minimum value, and maximum value

    row count, non-null count, mean, standard deviation, min value, and max value

row count, mean, standard deviation, minimum value, and maximum value

13. A DataFrame consists of an index, column data,

    column labels, and attributes

    row labels, column data types, and attributes

    column labels, and column data types

    column labels, column data types, and attributes

column labels, column data types, and attributes

14. This code accesses the fire_size column in the fires DataFrame:

    fires.'fire_size'

    fires[fire_size]

    fires.[fire_size]

    fires.fire_size

fires.fire_size

15. A Series object

    does provide methods for working with the data but a DataFrame doesn’t

    doesn’t provide methods for working with the data but a DataFrame does

    has only one column but a DataFrame can have one or more

    can have one or more columns but a DataFrame has only one

has only one column but a DataFrame can have one or more

16. This statement saves a DataFrame named fires in a pickle file named fires.pkl:

    fires.to_pickle('fires.pkl')

    fires.to_pkl('fires')

    fires.to_pkl('fires.pkl')

    fires.to_pickle('fires')

fires.to_pickle('fires.pkl')

17. When you melt the data in four columns of a DataFrame, you

    end up with the same number of rows

    end up with twice as many rows

    melt the data into one column

    end up with four times as many rows

end up with four times as many rows

18. To access a subset of rows and columns, you can

    use a slice to get the columns and dot notation or brackets to get the rows

    use a slice to get the rows and dot notation or brackets to get the columns

    use the query() method to get the rows and dot notation or brackets to get the columns

    use the query() method to get the columns and dot notation or brackets to get the rows

use the query() method to get the rows and dot notation or brackets to get the columns

19. Assume that the rows variable contains tabular data and the names variable contains the column names for the data. Then, this statement builds a DataFrame named fires

    DataFrame(data=rows, columns=names)

    fires = pd.DataFrame(data=rows, columns=names

    fires.to_dataframe(data=rows, columns=names)

    fires = pd.to_dataframe(data=rows, columns=names)

fires = pd.DataFrame(data=rows, columns=names)

20. This statement displays the first and last columns and rows of a DataFrame named fires:

    fires

    fires.head().tail()

    fires.tail()

    fires.head()

fires

21. The shape attribute of a DataFrame tells you the number of

    rows

    rows and columns

    columns

    elements

rows and columns

22. The columns attribute of a DataFrame tells you the

    data types of the columns

    number of columns

    names of the columns

    size of the columns

names of the columns

23. When you set an index for a DataFrame,

    the index can’t have duplicate values

    you can verify that the index doesn’t have duplicate values

    the index must be based on a single column

    you can’t return to the original index

you can verify that the index doesn’t have duplicate values

Chapter 3

1. One of the following isn’t a data visualization library for Python. Which one is it?

   numpy

   altair

   ggplot

   seaborn

numpy

2. When compared to long data, wide data

   has more columns and fewer rows and works better for Pandas plots

   has more columns and fewer rows and works better for Seaborn plots

   has fewer columns and more rows and works better for Pandas plots

has more columns and fewer rows and works better for Pandas plots

3. If you don’t set any parameters, the Pandas plot() method creates a line plot with the index values

   on the y-axis and all numeric columns on the x-axis

   on the y-axis and all columns on the x-axis

   on the x-axis and all columns on the y-axis

   on the x-axis and all numeric columns on the y-axis

on the x-axis and all numeric columns on the y-axis

4. This This type of plot shows the relationships between two columns of data, often over time:

   line plot

   histogram

   bar plot

   box plot

line plot

5. This This type of plot shows the frequency of the datapoints:

   bar plot

   histogram

   line plot

   box plot

histogram

6. This type of plot is used to chart data in categories:

   histogram

   box plot

   line plot

   bar plot

bar plot

7. Bar plots

   are horizontal while histograms are vertical

   plot the values of the data while histograms plot the distribution of the values

   plot the distribution of the values while histograms plot the values of the data

   use bins for the data but histograms don’t

plot the values of the data while histograms plot the distribution of the values

8. When you use Pandas to create a plot with subplots

   each subplot can have its own title

   the layout parameter determines whether grid lines are displayed

   the subplots must share the x and y axis values

   long data usually works the best

each subplot can have its own title

9. 

   Refer to the mortality DataFrames. This code creates a line plot with a different colored line for each age group:

   mortality_long.plot.line()

   mortality_wide.plot.line()

   mortality_long.plot.line

   mortality_wide.plot.line

mortality_wide.plot.line()

10. Refer to the mortality DataFrames. This code creates a histogram that puts the death rates into 8 bins:

    mortality_long.plot.hist(y='DeathRate', bins=8)

    mortality_long.plot.hist(x='DeathRate', bins=8)

    mortality_wide.plot.hist(x='DeathRate', bins=8)

    mortality_wide.plot.hist(y='DeathRate', bins=8)

mortality_long.plot.hist(y='DeathRate', bins=8)

11. Refer to the mortality DataFrames. This code creates a scattter plot of the death rates in each of the four age groups by year, but with all of the dots the same color:

    mortality_long.plot.scatter(x='Year', y='DeathRate')

    mortality_wide.plot.scatter(x='Year', y='AgeGroup')

    mortality_wide.plot.scatter(x='Year', x='AgeGroup')

    mortality_long.plot.scatter(x='Year', x='DeathRate')

mortality_long.plot.scatter(x='Year', y='DeathRate')

12. Refer to the mortality DataFrames. This code creates a line plot with the x-axis ranging from the year 2000 through 2018 and the y-axis ranging from 0 through 100:

    mortality_wide.plot.line(xlim=(2000,2018), ylim=(0,100))

    mortality_wide.plot.line(x_limit=(2000,2018), y_limit=(0,100))

    mortality_wide.plot.line(x_limit=(2000:2018), y_limit=(0:100))

    mortality_wide.plot.line(xlim=(2000:2018), ylim=(0:100))

mortality_wide.plot.line(xlim=(2000,2018), ylim=(0,100))

13. Refer to the mortality DataFrames. This code creates a line plot with four subplots in two rows that share both the x-axis and the y-axis:

    mortality_wide.plot.line(sharey=True, subplots=True, layout=(2:2))

    mortality_wide.plot.line(sharex=True, sharey=True, layout=(2,2))

    mortality_wide.plot.line(sharex=True, sharey=True, layout=(2:2))

    mortality_wide.plot.line(sharey=True, subplots=True, layout=(2,2))

mortality_wide.plot.line(sharey=True, subplots=True, layout=(2,2))

14. Refer to the mortality DataFrames. This code creates a bar plot for the mean of each age group:

    mortality_wide.mean().plot.bar()

    mortality_wide.plot.bar().agg=mean

    mortality_wide.plot.bar().mean()

    mortality_wide.mean().plot()

mortality_wide.mean().plot.bar()

15. Refer to the mortality DataFrames. This code creates a horizontal bar plot for each of the four age groups but for just the years 1900 and 2018:

    mortality_long.query('Year in (1900,2018)').plot.bar()

    mortality_wide.query('Year in (1900,2018)').plot.bar()

    mortality_long.query('Year in (1900,2018)').plot.barh()

    mortality_wide.query('Year in (1900,2018)').plot.barh()

mortality_wide.query('Year in (1900,2018)').plot.barh()

Chapter 4

1. Unlike Seaborn’s specific methods for plotting, its general methods

   return Axes objects and provide for categorical plots

   return FacetGrid objects and provide for subplots

   return Axes objects and provide for subplots

   return FacetGrid objects and provide for categorical plots

return FacetGrid objects and provide for subplots

2. A Seaborn bar plot is a type of

   relational plot

   distribution plot

   categorical plot

   linear model plot

categorical plot

3. A Seaborn histogram is a type of

   categorical plot

   relational plot

   distribution plot

   linear model plot

distribution plot

4. A Seaborn distribution plot shows

   the distribution of the data in each category

   the relative distribution of the data in each category

   how numeric data is distributed across a range of values

   how data is distributed across a range of values

how numeric data is distributed across a range of values

5. The basic parameters for most Seaborn general plots are

   kind x, y, and legend

   data, kind, x, and y

   data, kind x, y, and legend

   kind, x, and y

data, kind, x, and y

6. The one type of Seaborn plot that doesn’t require a y parameter is a

   relational plot

   distribution plot

   scatter plot

   categorical plot

distribution plot

7. The confidence interval in a Seaborn line plot

   is 95 percent

   shows the likelihood that new datapoints will fall within the top and bottom limits that are shown

   shows the top and bottom limits of past data

   shows the top and bottom values in the data that’s plotted

shows the likelihood that new datapoints will fall within the top and bottom limits that are shown

8. To create a Seaborn plot that has subplots, you need to use these parameters:

   col, col_wrap, and aspect

   col and col_wrap

   col, col_wrap, and hue

   hue and col_wrap

col and col_wrap

9. To add a title and a y label to a specific Seaborn plot, you can use code like this:

   A. ax = sns.lineplot(data=mortality_data,

   x='Year', y='DeathRate', hue='AgeGroup')

   ax.set(title='Deaths by Age Group', ylabel='Death Rate')

   B. sns.lineplot(data=mortality_data,

   x='Year', y='DeathRate', hue='AgeGroup')

   ax.set(title='Deaths by Age Group', ylabel='Death Rate')

   C. sns.lineplot(data=mortality_data,

   x='Year', y='DeathRate', hue='AgeGroup')

   g.set(title='Deaths by Age Group', ylabel='Death Rate')

   D. g = sns.lineplot(data=mortality_data,

   x='Year', y='DeathRate', hue='AgeGroup')

   ax.set(title='Deaths by Age Group', ylabel='Death Rate')

A. ax = sns.lineplot(data=mortality_data,

x='Year', y='DeathRate', hue='AgeGroup')

ax.set(title='Deaths by Age Group', ylabel='Death Rate')

10. To add a title and a y label to a general Seaborn plot, you can use code like this:

    A. sns.relplot(data=mortality_data, kind='line',

    x='Year', y='DeathRate', hue='AgeGroup', aspect=1.5)

    ax.set(title='Deaths by Age Group', ylabel='Death Rate')

    B. g = sns.relplot(data=mortality_data, kind='line',

    x='Year', y='DeathRate', hue='AgeGroup', aspect=1.5)

    ax.set(title='Deaths by Age Group', ylabel='Death Rate')

    C. g = sns.relplot(data=mortality_data, kind='line',

    x='Year', y='DeathRate', hue='AgeGroup', aspect=1.5)

    for ax in g.flat:

    ax.set(title='Deaths by Age Group', ylabel='Death Rate')

    D. g = sns.relplot(data=mortality_data, kind='line',

    x='Year', y='DeathRate', hue='AgeGroup', aspect=1.5)

    for ax in g.axes.flat:

    ax.set(title='Deaths by Age Group', ylabel='Death Rate')

D. g = sns.relplot(data=mortality_data, kind='line',

x='Year', y='DeathRate', hue='AgeGroup', aspect=1.5)

for ax in g.axes.flat:

ax.set(title='Deaths by Age Group', ylabel='Death Rate')

11. This code adds a super title to a Seaborn plot that has its FacetGrid object in a variable named g:

    g.ax.fig.suptitle('Deaths by Age Group (1910-1930)', y=1.025)

    g.suptitle('Deaths by Age Group (1910-1930)', y=1.025)

    g.fig.suptitle('Deaths by Age Group (1910-1930)', y=1.025)

    g.ax.suptitle('Deaths by Age Group (1910-1930)', y=1.025)

g.fig.suptitle('Deaths by Age Group (1910-1930)', y=1.025)

12. This code saves a Seaborn line plot that has its FacetGrid object in a variable named g to a file in PNG format:

    g.ax.get_figure().savefig('lineChart.png')

    g.ax.savefig('lineChart.png')

    g.savefig('lineChart.png')

    g.get_figure().savefig('lineChart.png')

g.savefig('lineChart.png')

Refer to the mortality DataFrame. This code creates a scatter plot for the death rates by year with the dots for each age group in a different color:

A. sns.relplot(data=mortality, kind='scatter',

x='Year', y='DeathRate', hue='AgeGroup')

B. sns.relplot(data=mortality, kind='scatter',

x='Year', y='DeathRate')

C. sns.relplot(data=mortality, kind='scatter',

x='Year', y='DeathRate', palette='AgeGroup')

D. sns.relplot(data=mortality, kind='scatter',

y='Year', x='DeathRate')

A. sns.relplot(data=mortality, kind='scatter',

x='Year', y='DeathRate', hue='AgeGroup')

14. Refer to the mortality DataFrame. This code creates a vertical bar plot for the death rates in 1950 and 2000 with no confidence interval shown:

    A. sns.catplot(data=mortality.query('Year in (1950,2000)'),

    kind='bar', y='Year', x='DeathRate', ci=None)

    B. sns.catplot(data=mortality.query('Year in (1950,2000)'),

    kind='bar', x='Year', y='DeathRate', ci=None)

    C. sns.catplot(data=mortality.query('Year in (1950,2000)'),

    kind='bar', x='Year', y='DeathRate')

    D. sns.catplot(data=mortality.query('Year in (1950,2000)'),

    kind='bar', y='Year', x='DeathRate')

B. sns.catplot(data=mortality.query('Year in (1950,2000)'),

kind='bar', x='Year', y='DeathRate', ci=None)

15. Refer to the mortality DataFrame. This code creates a KDE plot with one subplot for the death rates in each age group and two subplots in each row:

    A. sns.displot(data=mortality, kind='kde', x='DeathRate',

    col='DeathRate', row_wrap=2)

    B. sns.displot(data=mortality, kind='kde', x='DeathRate', hue='AgeGroup',

    col='AgeGroup', col_wrap=2)

    C. sns.displot(data=mortality, kind='kde', x='DeathRate', hue='AgeGroup',

    col='AgeGroup', row_wrap=2)

    D. sns.displot(data=mortality, kind='kde', x='DeathRate',

    col='DeathRate', col_wrap=2)

B. sns.displot(data=mortality, kind='kde', x='DeathRate', hue='AgeGroup',

col='AgeGroup', col_wrap=2)