The post is about the Python Quiz with answers. There are 20 Multiple-Choice Questions about Python. The topics covered in the quiz are introduction to Python, Data Structures, Importing and Exporting Files, Control Structures (if statements and loops), and graphical representations of the data. Let us start with the Python Quiz with Answers.
Online Multiple Choice Questions about Python Programming Language
Python Quiz with Answers
How can you access the length of a list in Python?
Which command will grab the last few rows of a data frame?
Which data structure is {‘one’:1, ‘two’:2}.
In Python, what types of data can tuples contain?
In Python, the ———- statement sets a piece of code to run only when the condition of the if statement is false.
In Python, when does an else statement execute a piece of code?
A ————- is a body of reusable code for performing specific processes or tasks.
Which of these for loop statements would error (assume columns as an array)?
Which of these print statements would output an error message in Python?
How do you print “X is large” if $X$ is greater than 28 in Python?
What defines the body of a decision construct in Python?
How do you add an element to a set in Python?
How do you access the value of a dictionary key in Python?
How can you access a specific element in a list in Python?
A pair plot can be created using which Python module uses the pairplot method?
Which of the following data structures are immutable, meaning that values cannot be changed in place?
Which of the following are valid keywords for loops in Python?
What keyword is used to create a function?
Which Python libraries were used to create the boxplots?
Which of the following statements accurately describe NumPy arrays? Select all that apply.
ggplot2 is a popular R package that provides flexible and elegant grammar of graphics for creating a wide range of dynamic and static graphics. It breaks down plots into fundamental components like data, aesthetics, geometric objects, and statistical transformations. In this post, we will learn about using ggplot2 in R Language.
There are three strategies for plotting in R language.
base graphics using functions such as plot(), points(), and par()
lattice graphics to create nice graphics, however, it is not easy to create high-dimensional data graphics.
ggplot package, it is an implementation of “Grammar of Graphics”.
The ggplot2 is built on the principle of layering graphical elements, making it flexible and customizable.
Table of Contents
To plot using ggplot2 in R Langauge, a data.frame object is required as an input, then one needs to define plot layers that stack on top of each other, and each layer has visual/text elements that are mapped to aesthetics (size, colors, and opacity). An extremely informative graph will be produced using the above-described simple set of commands.
Before drawing high-quality informative graphs, one needs to install the ggplot2 package. If ggplot2 is already installed, one does not need to reinstall it using the command below.
install.packages("ggplot2")
Scatter Plot using ggplot2 in R
Let us draw a dot plot (scatter points) graph between variables $hp$ (horsepower) and $disp$ (displacement) from mtcars dataset.
# first load the data set say mtcars
attach(mtcars)
# load the ggplot2 library
library(ggplot2)
# now specify the dataset and variables
p <- ggplot(mtcars, aes(x = disp, y = hp))
# Add a plot layer with points
p <- p + geom_point()
print(p) # display/ show the plot
Note that geom, aesthetics, and facets are three important concepts in drawing the graphs using ggplot2, where
geom is the type of the plot
aesthetics is the shape, color, size, and alpha values used in ggplot
facet are small multiples, displaying different subsets of data
When certain aesthetics are defined, an appropriate legend is chosen and displayed automatically.
p <- ggplot(mtcars, aes(x = disp, y = hp))
p <- p + geom_point(aes(color = mpg))
p
Updating Graphs using aesthetics (color, size, and shape)
Graphs can be updated by assigning variables to aesthetics color, size, and shape. For example
p <- ggplot(mtcars, aes(x = disp, y = hp))
p <- p + geom_point(aes(color = gear, size = wt))
p
Consider the following example. Here, the $gear$ variable is taken as a factor (grouping variable).
p <- ggplot(mtcars, aes(x = disp, y = hp))
p <- p + geom_point(aes(color = as.factor(gear), size = wt))
p
Note that the behaviour of the aesthetics is predictable and customizable.
Aesthetic
Discrete Variable
Continuous Variable
color
Rainbow of colors
Gradient from red to blue
size
Discrete size steps
Linear mapping between radius and value
shape
Different shapes for each group
Should not work
Faceting in ggplot2
A small multiple (sometimes called faceting, trellis chart, lattice chart, panel chart, or grid chart) is a series or grid of small similar graphics or charts for comparison purposes. Usually, these small multiples are used to display different subsets of the data and these multiples are useful for exploring some conditional relationship between variables (especially when data is large enough).
Let us examine the faceting of different types. The following are some examples of subsetting the scatterplot in facets
# Create a basic scatter plot
p <- ggplot(mtcars, aes(x = disp, y = hp))
p <- p + geom_point()
# columns are cyl categories
p1 <- p + facet_grid(. ~ cyl)
# rows are cyl categories
p2 <- p + facet_grid(cyl ~ .)
# columns and rows both
p3 <- p + facet_grid(carb ~.)
wrap plots by cyl
p4 <- p + facet_grid(~ am)
# plot all four in one
library(gridExtra)
grid.arrange(grobs = list(p1, p2, p3, p4), ncol = 2, top = "Facet Examples")
A vector in R is a set of numbers. A vector can be considered as a single column or a single row of a spreadsheet. The following examples are numbers that are not technically “vectors”. It is because these vectors are not in a column/row structure, however, they are ordered. These vectors can be referred to by index.
Creating Vector in R
# Creating a vector with the c function
c(1, 4, 6, 7, 9)
c(1:5, 10)
A vector in R language can be created using seq() function, it generates a series of numbers.
# Create a vector using seq() function
seq(1, 10, by = 2)
seq(0, 50, length = 11)
seq(1, 50, length = 11)
The vector can be created in R using the colon (:) operator. Following are the examples
The non-integer sequences can also be created in R Language.
# non-integer sequences
seq(0, 100*pi, by = pi)
One can assign a vector to a variable using the assignment operator (<-) or equal symbol (=). The examples are:
a <- 1:5
b <- seq(15, 3, length=5)
c <- a * b
There are a lot of built-in functions that can be used to perform different computations on vectors. For example,
a <- 1:5
# compute the total of elements of a vector
sum(a)
## Output
15
# product of elements of a vector
prod(a)
## Output
120
# average of the vector
mean(a)
## Output
3
# standard deviation and variance of a vector
sd(a)
## Output
1.581139
var(a)
## Output
2.5
One can extract the elements of a vector by using square brackets and the index of the component of the vector.
V <- seq(0, 100, by = 10)
V[] # gives all the elements of the vector
## Output
[1] 0 10 20 30 40 50 60 70 80 90 100
V[5] # 5th elements from vector z
## Output
[1] 40
V[c(2, 4, 6, 8)] #2nd, 4th, th, and 8th element
## Output
[1] 10 30 50 70
V[-c(2, 4, 6, 8)] # elements except 2nd, 4th, 6th, and 8th element
## Output
[1] 0 20 40 60 80 90 100
The specific / required elements of a vector can be updated
V[c(2, 4)] <- c(500, 600) # the second and 4th element is updated to 500 and 600
