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Final Exam - DV

Total questions: 60

Worksheet time: 45mins

Name
Class
Date
1.

A ___ format is one of the most simple and common ways to store tabular data.

a)

CSV

b)

TXT

c)

DB

d)

CVS

2.

There is a dedicated module for python to use in importing data to a program.

a)

import csv

b)

import csv as cs

c)

import csv as csv

d)

import cv2 as cv

3.

To read as csv file in python we use this ___ function

a)

csv.reader()

b)

csv.read()

c)

csv.readonly()

d)

csv.readthis()

4.

To encode in csv file you must use this function ___

a)

csv.writer()

b)

csv.writerow()

c)

csv.writerthis()

d)

csv.write()

5.

This string can later be

used to write into CSV files using the ___ function.

a)

writerow()

b)

writer()

c)

csv.write()

d)

csv.writerow()

6.

The objects of a ___ class can be used to read a

CSV file as a dictionary.

a)

csv.DictReader()

b)

DictReader()

c)

csv.DictRead()

d)

csv.DictReadRow()

7.

The objects of ___ class can be used to write

to a CSV file from a Python dictionary.

a)

csv.DictWriter()

b)

DictWriter()

c)

csv.DictWrite()

d)

csv.DictWriterRow()

8.

___ is an easy-to-use, low-level data visualization library

that is built on NumPy arrays. It consists of various plots like

scatter plot, line plot, histogram, etc.

a)

matplotlib

b)

pandas

c)

seaborn

d)

bokeh

9.

___ is a Python library used for working with data sets.

It has functions for analyzing, cleaning, exploring, and manipulating data.

a)

Pandas

b)

Bokeh

c)

Plotly

d)

Seaborn

10.

How to read csv file using pandas?

a)

df = pd.read_csv('data.csv')

b)

df = pd.csv_read('data.csv')

c)

df = pd.reader_csv('data.csv')

d)

df = pdread_csv('data.csv')

11.

How to use matplotlib in your python program?

a)

import matplotlib.pyplot as plt

b)

import matplotlib as plt

c)

import matplotlib.plot as plt

d)

import matplotlib.pyplot = plt

12.

___ is a high-level interface built on top of the Matplotlib. It

provides beautiful design styles and color palettes to make

more attractive graphs.

a)

Seaborn

b)

Plotlty

c)

Bokeh

d)

Pandas

13.

How to display a line chart using seaborn?

a)

import seaborn as sns

sns.lineplot(x,y,data)

b)

import seaborn as sns

sns.line(x,y,data)

c)

import seaborn as sns

sns.linechart(x,y,data)

d)

import seaborn as sns

sns.linegraph(x,y,data)

14.

How to display scatter chart in seaborn?

a)

import seaborn as sns

sns.scatterplot(x,y,data)

b)

import seaborn as sns

sns.scatter(x,y,data)

c)

import seaborn as sns

sns.scatterchart(x,y,data)

d)

import seaborn as sns

sns.scattergraph(x,y,data)

15.

How to display bar graph using seaborn?

a)

import seaborn as sns

sns.barplot(x,y,data)

b)

import seaborn as sns

sns.bar(x,y,data)

c)

import seaborn as sns

sns.bargraph(x,y,data)

d)

import seaborn as sns

sns.barchart(x,y,data)

16.

The ___ renders its

plots using HTML and JavaScript that uses modern web

browsers for presenting elegant, concise construction of novel

graphics with high-level interactivity.

a)

Bokeh

b)

Plotly

c)

Ploty

d)

Boke

17.

How to display scatter graph using bokeh?

a)

graph.scatter(data[x], data[y], color)

b)

graph.scatterplot(data[x], data[y], color)

c)

graph.scatterchart(data[x], data[y], color)

d)

graph.scattergraph(data[x], data[y], color)

18.

How to display line graph using bokeh?

a)

df = data[y].value_counts()

graph.line(df, data[x])

b)

df = data[y].value_counts()

graph.lineplot(df, data[x])

c)

df = data[y].value_counts()

graph.linechart(df, data[x])

d)

df = data[y].value_counts()

graph.linegraph(df, data[x])

19.

___ has hover tool capabilities that allow us to detect any

outliers or anomalies in numerous data points.

a)

Plotly

b)

Bokeh

c)

Pandas

d)

Seaborn

20.

How to display line graph in plotly?

a)

fig = px.line(data,y,color)

b)

fig = px.line(data,x,y,color)

c)

fig = px.lineplot(data,y,color)

d)

fig = px.linechart(data,y,color)

21.

___ charts are one of the easiest charts to interpret, enabling the person

viewing the chart an easy way to compare categorical data quickly.

a)

BAR

b)

LINE

c)

SCATTER

d)

PIE

22.

A ___ chart is the most useful way to capture how a numerical variable

changes over time. This is helpful to identify trends in numeric values.

a)

LINE

b)

BAR

c)

PIE

d)

SCATTER

23.

A ___ chart is most commonly used to show the proportions of a whole.

It’s like visualizing fractions when you were in high school.

a)

PIE

b)

BAR

c)

SCATTER

d)

LINE

24.

A ___ plot/chart is commonly used to visualize the relationship between

two variables.

a)

SCATTER

b)

BAR

c)

LINE

d)

PIE

25.

A ___ chart is a special chart that helps illustrate how positive and

negative values can contribute to a total.

a)

WATERFALL

b)

SCATTER

c)

BAR + LINE

d)

PIE

26.

It describe the main content of a chart / plot to be visualized. this is a very important chart element.

a)

Chart Title

b)

Legend

c)

Data Labels

d)

Chart Notes

27.

It display the names and colors of each series of data. Also, text is taken from the data range.

a)

Legend

b)

Chart Title

c)

Chart Notes

d)

Data Labels

28.

This are text elements that describe individual data points.

a)

Data Labels

b)

Legend

c)

Chart Title

d)

Chart Notes

29.

This are typically have two axes that are used to measure and categorize data.

a)

Chart Axes

b)

Axis Title

c)

Axis Scale

d)

Number Format

30.

This are word or phrase that describes an entire axis of a chart / plot. Generally define what kind of data is being shown on that axis.

a)

Axis Title

b)

Chart Axes

c)

Number Format

d)

Axis Scale

31.

The scale of an axis is the units into which the axis is divided.

a)

Axis Scale

b)

Axis Title

c)

Number Format

d)

Chart Axes

32.

A ___ is a powerful tool to calculate, summarize, and analyze data

that lets you see comparisons, patterns, and trends in your data.

a)

Pivot Table

b)

Analyze Data

c)

MS Excel

d)

Pivot Data

33.

These are the different PivotTable source, except:

a)

External Data Source

b)

Model Data

c)

PowerBI

d)

CSV

34.

Selected fields are added to their default areas: non-numeric fields are added to ___, date and time hierarchies are added to ___, and numeric fields are added to Values.

a)

Rows, Columns

b)

X, Y

c)

Title, Label

d)

Title, Legend

35.

Selected fields are added to their default areas: non-numeric fields are added to ___, date and time hierarchies are added to ___, and numeric fields are added to Values.

a)

Rows, Columns

b)

X, Y

c)

Title, Label

d)

Title, Legend

36.

There are two option in Refreshing PivotTable to avoid data loose and update the current worksheet. except:

a)

Refresh All

b)

Refresh Worksheet

c)

Change Data Source

d)

None of these

37.

You can choose any functions in Summarizing value field by. Except:

a)

Sum

b)

Count

c)

Product

d)

Quotient

38.

Use ___ if you want to create a PivotTable from multiple tables.

a)

Model Data

b)

PowerBI

c)

CSV

d)

External Data Source

39.

These are the examples of External Data Source. Except:

a)

From MS Access

b)

From Web

c)

From Text

d)

From MySQL

40.

___ in Excel empowers you to understand your data through

natural language queries that allow you to ask questions about your data

without having to write complicated formulas.

a)

Analyze Data

b)

PivotTable

c)

PowerBI

d)

Chart Axes

41.

A ___ chart is a representation of values as slices of a circle with

different colors.

a)

Pie

b)

Bar

c)

Line

d)

Boxplot

42.

A ___ chart represents data in rectangular bars with length of the proportional to the value of the variable.

a)

Bar

b)

Pie

c)

Boxplot

d)

Histogram

43.

___ is a vector containing the numeric values used in the pie chart.

a)

x

b)

data

c)

radius

d)

main

44.

___ are a measure of how well distributed is the data in a

data set.

a)

Boxplots

b)

Bar

c)

Pie

d)

Line

45.

This graph represents the minimum, maximum, median, first

quartile and third quartile in the data set.

a)

Boxplot

b)

Bar

c)

Pie

d)

Line

46.

___ is similar to bar chat but the difference is it groups the

values into continuous ranges.

a)

Histogram

b)

Boxplots

c)

Bar

d)

Line

47.

A ___ represents the frequencies of values of a variable

bucketed into ranges.

a)

Histogram

b)

Boxplots

c)

Bar

d)

Pie

48.

A ___ chart is a graph that connects a series of points by

drawing segments between them.

a)

Line

b)

Bar

c)

Boxplots

d)

Histogram

49.

___ show many points plotted in the Cartesian plane.

Each point represents the values of two variables.

a)

Scatterplots

b)

Line

c)

Boxplots

d)

Histogram

50.

Hold elements of different classes

a)

Vector

b)

List

c)

Arrays

d)

Matrices

51.

It contain different types of elements

a)

List

b)

Vector

c)

Matrices

d)

Arrays

52.

These are two dimensional rectangular data set

a)

Matrices

b)

Arrays

c)

Vector

d)

Data Frames

53.

can be of any number of dimensions

a)

Arrays

b)

List

c)

Vector

d)

Matrices

54.

These are tabular data objects

a)

Data Frames

b)

Factors

c)

Vector

d)

List

55.

A ___ is a table or a two-dimensional array-like structure in

which each column contains values of one variable and each row

contains one set of values from each column.

a)

Data Frame

b)

Array

c)

Matrices

d)

Vector

56.

How to read csv file in R?

a)

data <- read.csv(file.csv)

b)

data <- read_csv(file.csv)

c)

data <- readcsv(file.csv)

d)

data <- csv.read(file.csv)

57.

What package is need to use XML in R?

a)

install.packages('XML')

b)

install.packages('XTML')

c)

install.packages('READXML')

d)

install.packages('xmlreader')

58.

How to read XML in R?

a)

result <- xmlParse(file.xml)

b)

result <- xmlparse(file.xml)

c)

result <- xml.Parse(file.xml)

d)

result <- xml_Parse(file.xml)

59.

What package will use to read JSON file in R?

a)

install.packages('rjson')

b)

install.packages('json')

c)

install.packages('JSON')

d)

install.packages('RJSON')

60.

How to read JSON file in R?

a)

result <- fromJSON(file='input.json')

b)

result <- from.JSON(file='input.json')

c)

result <- from_JSON(file='input.json')

d)

result <- fromJSON('input.json')

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