Best Online Python Quizzes 2024

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Click the links below to start with important Online Python Quizzes. These Online Python Quizzes cover topics related to Python Basics, Data types, Data Structures, Functions, Libraries, Editors, Conditional Statements, Loops, Performing Data Analysis, etc.

Python MCQ Online Test 5Python MCQs Test 4
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Online Python Quizzes with Answers

Python is a powerful programming language widely used in statistics and other fields of life for data analysis.

Why Learn Python?

The following are important reasons why Python is a great language to learn:

  • Simple and Easy Language: Python is a simple language that makes it an excellent choice for beginners to learn the fundamentals of programming.
  • In-Demand Skill: Python is one of the most popular programming languages in the world, making it a valuable skill for various job opportunities.
  • Wide Applications: Python has wide applications such as it is being used for web development, data analysis, machine learning, and more making it highly versatile.
  • Active Community: The large and supportive Python community offers a wealth of resources and assistance for learners.
Features of Python

Python and Statistics

The following are the important reasons for the computations of different statistics in Python:

  1. Ease of Use: Python has a clear and concise syntax, making it relatively easy to learn and use compared to other languages. This allows statisticians to focus on the analysis itself rather than struggling with complex code.
  2. Extensive Libraries: Python boasts a rich ecosystem of statistical libraries. Here are some of the most popular ones:
    • statistics: Built-in module for basic descriptive statistics like mean, median, and standard deviation. Ideal for small datasets or even beginners of Python.
    • NumPy: The foundation for scientific computing in Python. Provides efficient arrays and functions for numerical computations commonly used in statistics.
    • SciPy: Extends NumPy with a wider range of statistical functions, including hypothesis testing, random number generation, and more.
    • Pandas: A library specifically designed for data manipulation and analysis. Offers data structures like DataFrames that make handling statistical data a breeze.

All of the above and other libraries provide a vast collection of tools for various statistical tasks, from data cleaning and exploration to complex modeling and statistical inference.

With its ease of use, vast capabilities, and supportive community, Python is an excellent computer programming language for beginners and experienced programmers alike. So, dive in and start exploring the exciting world of Python!

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Top Python MCQs with Answers 1

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The post contains 20 Top Python MCQs with Answers about basic concepts of the language. The quiz is about the Introduction to Python. Let us start with the Python MCQs with Answers

Online MCQs about Python with Answers

1. Which of the following is not important when writing Python code?

 
 
 
 

2. What type of data can be stored in a list in python?

 
 
 
 

3. Given the variables below, determine which print statement would return False.
a = True or False
b = False and True
c = False and False

 
 
 
 

4. What libraries do you use for data visualization.

 
 
 
 

5. Which of the following are not functions?

 
 
 
 

6. Why is Python such a popular language?

 
 
 
 

7. What is a Python library?

 
 
 
 

8. Before using pandas which code must first be run to make the library available?

 
 
 
 

9. What data type do you expect the column that contains price to be

 
 
 
 

10. How do we create a dataframe using Pandas?

 
 
 
 

11. Suppose you have a dataframe called $df$. Which pandas method is used to create a histogram of a column in a dataframe?

 
 
 
 

12. Which python libraries were used to create the boxplots?

 
 
 
 

13. What datatype would the following variable have main_data=read.csv("path/to/myfile.csv").

 
 
 
 

14. In Python, ————- typically contain a collection of functions and global variables.

 
 
 
 

15. How would you generate descriptive statistics for all the columns for the dataframe $df$.

 
 
 
 

16. How can we tell what version of python we are currently using?

 
 
 
 

17. What is the primary instrument used in Pandas?

 
 
 
 

18. What does method df.dropna() do?

 
 
 
 

19. A data professional is working with a pandas dataframe named $sales$ that contains sales data for a retail website. The data professional wants to know the average price of an item. What code can he use to calculate the mean value of the Price column?

 
 
 
 

20. Which of the following is not comparison operator (these return a bolean value of true or false).

 
 
 
 

What is Python?

Python is a powerful, versatile, and beginner-friendly high-level programming language.

  • Readability: With clear and simple syntax python resembles plain English. Therefore, Python code is easier to learn, understand, and write compared to some other programming languages.
  • Large Standard Library: Python comes with a very huge collection of built-in modules, functions, and libraries as a toolkit for many tasks without needing to write everything from scratch.
  • Versatility: Python is used in a wide range of applications, such as web development, data analysis, machine learning, scripting, scientific computing, etc.
  • Strong Community: Python has a large and active community of developers, providing extensive support, libraries, and learning resources.

Top Python MCQs with Answers

Python MCQs with Answers
  • What is the primary instrument used in Pandas?
  • What libraries do you use for data visualization.
  • What data type do you expect the column that contains price to be
  • Suppose you have a dataframe called $df$. Which pandas method is used to create a histogram of a column in a dataframe?
  • What type of data can be stored in a list in Python?
  • How do we create a dataframe using Pandas?
  • What does method df.dropna() do?
  • Which of the following is not important when writing Python code?
  • What datatype would the following variable have main_data=read.csv(“path/to/myfile.csv”).
  • Which Python libraries were used to create the boxplots?
  • Which of the following is not comparison operator (these return a boolean value of true or false).
  • Before using pandas which code must first be run to make the library available?
  • In Python, ————- typically contain a collection of functions and global variables.
  • A data professional is working with a pandas dataframe named $sales$ that contains sales data for a retail website. The data professional wants to know the average price of an item. What code can he use to calculate the mean value of the Price column?
  • Given the variables below, determine which print statement would return False. a = True or False b = False and True c = False and False
  • What is a Python library?
  • How can we tell what version of Python we are currently using?
  • Why is Python such a popular language?
  • How would you generate descriptive statistics for all the columns for the dataframe $df$.
  • Which of the following are not functions?
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Simulating Coin Tossing: Game of Chance

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Introduction to Simulating Coin Tossing

Simulation provides a straightforward way of approximating probabilities. For simulating a Game of chance of coin tossing, one simulates a particular random experiment (coin tossing, dice roll, and/or card drawing) a large number of times. The probability of an outcome is approximated by the relative frequency of the outcome in the repeated experiments.

The use of simulation experiments to better understand probability patterns is called the Monte Carlo Method.

Practical Example: Simulating Coin Tossing Experiment

Let person “A” and person “B” play a simple game involving repeated tosses of a fair coin. In a given toss, if the head is observed, “A” wins $1 from “B”; otherwise if the tail is tossed, “A” gives $1 to “B”. If “A” starts with zero dollars, we are interested in his fortune as the game is played for 50 tosses.

Simulating The Game in R using sample() Function

For the above scenario, one can simulate this game using the R function “sample()”. A’s winning on a particular toss will be $1$ or $-1$ with equal probability. His winning on 50 repeated tosses can be considered to be a sample of size 50 selected with replacement from the set {$1, -$1}.

option(width=60)
sample(c(-1, 1), size = 50, relapce = T)

# output
 [1] -1  1  1 -1 -1  1 -1  1  1 -1  1 -1  1  1 -1 -1  1 -1 -1 -1  1  1  1 -1 -1
[26]  1 -1  1 -1  1 -1 -1  1  1  1 -1 -1  1 -1 -1 -1  1 -1  1  1 -1  1 -1  1  1

Graphical Representation of the Simulations

One can graphically represent the outcome, as coded below. Note that the results will be different for each compilation of the code as samples are drawn randomly.

results <- sample( c(-1, 1), size = 50,replace = TRUE)

x = table(results)
names(x) = c("loss", "win")

barplot(x)
Simulating a Game of Chance of Coin Tossing

Extended Example

Suppose “A” is interested in his cumulative winnings as he plays this game. One needs to score his toss winnings in the variable $win$. The function “cumsum()” will compute the cumulative winnings of the individual values and the cumulative values are stored in “cam.win“.

win = sample(c(-1, 1), size = 50, replace = T)
cum.win = cumsum(win)

# Output for different execution 
 [1] -1 -2 -1 -2 -1  0  1  0 -1  0  1  0  1  2  1  2  1  2  3  4  5  6  7  6  5
[26]  4  5  4  5  6  7  6  7  8  9  8  7  6  5  4  5  4  3  2  1  0 -1 -2 -3 -4

 [1] 1 2 1 0 1 2 1 2 1 2 1 2 3 4 5 4 5 6 7 8 7 8 7 6 7 6 5 4 5 4 3 4 3 4 5 4 3 2
[39] 3 4 3 4 3 2 3 4 5 6 5 4

Extending and plotting the sequence of cumulative winnings for 4 games. For four games, the win/loss score is plotted in four combined (2 by 2) graphs, to visualize the situation in all four games.

par(mfrow = c(2, 2))
for (j in 1:4){
win = sample( c(-1, 1), size = 50, replace = TRUE)
plot(cumsum(win), type = “l”, ylim = c(-15, 15))
abline(h=0)
}

Four coin toss win loss change

The horizontal line in each graph is drawn at break-even. The points above the horizontal line show the win situation while the information below the horizontal line shows the loss to the player.

One can make a customized function for the situation discussed above. For example,

# customized function 
winloss <- function (n=50){
    win = sample(c(-1,1), size = n, replace = T)
    sum(win)
}

# Insights about win/ loss situation
F = replicate(1000, winloss() )

table(F)

# output
F
-22 -18 -16 -14 -12 -10  -8  -6  -4  -2   0   2   4   6   8  10  12  14  16  18 
  3   6   7  22  28  52  60  78  82 128  93 103  95  72  50  48  34  24   8   3 
 20  22  24 
  1   2   1 


par(mfrow = c(1, 1) )

plot(table(F))

Game of Change: Simulating coin Experiment

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