Python Data Structure Quiz 8

by

The post is about an Online Python Data Structure Quiz with Answers. There are 20 multiple-choice questions covering list, set, dictionary, tuple, and operations on these data structures. Let us start with the Python Data Structure Quiz now.

Online Python Data Structure Quiz with Answers

Online Python Data Structure Quiz with Answers

1. What does the following function return? len([sum([1,1,1])])

 
 
 
 

2. Consider the tuple A=((11,12),[21,22]), which contains a tuple and list. What is the result of the following operation A[0][1]?

 
 
 
 

3. Which of the following is a standard tool or language that can be used to clean and split data into load files?

 
 
 
 

4. What method do you use to add an element to a set?

 
 
 
 

5. If A is a list, what does the following syntax do? B=A[:]

 
 
 
 

6. Consider the set: V={'A','B'}, what is the result of V.add('C')?

 
 
 
 

7. The variable release_year_dict is a Python dictionary. What is the outcome of applying the following method? release_year_dict.values()

 
 
 
 

8. Which of the following is the correct syntax to extract the keys of a dictionary as a list?

 
 
 
 

9. What is the result of the following? len(("disco",10))

 
 
 
 

10. Examine the tuple A=((1),[2,3],[4]), that involves a tuple and list. What is the outcome of the following operation A[2][0]?

 
 
 
 

11. Consider the following list: A=["hard rock",10,1.2] What will list A contain after the following command is run? del(A[1])

 
 
 
 

12. What are the keys of the following dictionary? {"a":1,"b":2}

 
 
 
 

13. After operating A.append([2,3,4,5]), what will be the length of the list A = [1]?

 
 
 
 

14. Examine the tuple A=((11,12),[21,22]), which involves a tuple and list. What is the outcome of the following operation A[1]?

 
 
 
 

15. What is the result of the following operation? {‘a’,’b’} &{‘a’}

 
 
 
 

16. Consider the tuple A=((1),[2,3],[4]), that contains a tuple and list. What is the result of the following operation A[2]?

 
 
 
 

17. Consider the following Python Dictionary: Dict={"A":1,"B":"2","C":[3,3,3],"D":(4,4,4),'E':5,'F':6}
What will be the outcome of the following operation? Dict["D"]

 
 
 
 

18. The method append does the following:

 
 
 
 

19. Consider the following set: {“A”, “A”}, what will the result be when you create the set?

 
 
 
 

20. If L = [‘c’, ‘d’], then the output of the statement `L.append([‘a’, ‘b’])` is:

 
 
 
 

Online Python Data Structure Quiz with Answers

  • Which of the following is a standard tool or language that can be used to clean and split data into load files?
  • After operating A.append([2,3,4,5]), what will be the length of the list A = [1]?
  • What are the keys of the following dictionary? {“a”:1,”b”:2}
  • Consider the following Python Dictionary: Dict={“A”:1,”B”:”2″,”C”:[3,3,3],”D”:(4,4,4),’E’:5,’F’:6} What will be the outcome of the following operation? Dict[“D”]
  • Which of the following is the correct syntax to extract the keys of a dictionary as a list?
  • Consider the following set: {“A”, “A”}, what will the result be when you create the set?
  • What method do you use to add an element to a set?
  • What is the result of the following operation? {‘a’,’b’} &{‘a’}
  • Examine the tuple A=((11,12),[21,22]), which involves a tuple and list. What is the outcome of the following operation A[1]?
  • Consider the tuple A=((11,12),[21,22]), which contains a tuple and list. What is the result of the following operation A[0][1]?
  • Consider the tuple A=((1),[2,3],[4]), that contains a tuple and list. What is the result of the following operation A[2]?
  • Examine the tuple A=((1),[2,3],[4]), that involves a tuple and list. What is the outcome of the following operation A[2][0]?
  • The method append does the following:
  • If L = [‘c’, ‘d’], then the output of the statement L.append([‘a’, ‘b’]) is:
  • Consider the following list: A=[“hard rock”,10,1.2] What will list A contain after the following command is run? del(A[1])
  • If A is a list, what does the following syntax do? B=A[:]
  • What is the result of the following? len((“disco”,10))
  • The variable release_year_dict is a Python dictionary. What is the outcome of applying the following method? release_year_dict.values()
  • Consider the set: V={‘A’,’B’}, what is the result of V.add(‘C’)?
  • What does the following function return? len([sum([1,1,1])])

Statistics and Data Analysis

Python Basics MCQs 7

by

The post is about Python Basics MCQs with Answers. There are 20 multiple-choice questions covering topics related to the basics of Python, data types, strings, tuples, lists, and different operations. Let us start with the Online Python Basics MCQs with Answers.

Online Python Basics MCQs with Answers
Please go to Python Basics MCQs 7 to view the test

Online Python Basics MCQs with Answers

  • What is the data type of the entity 43?
  • What is the result of the following: int(3.99)?
  • What is the result of the operation: 11//2
  • What is the value of x after the following is run: x=4 x=x/2
  • Which line of code will act as required for implementing the following equation? $y =2x^2 − 3$
  • What is the output of the following code segment? type(int(12.3))
  • What is the output of the following code segment? int(False)
  • In Python, what is the output of the following operation? ‘5’+’6′
  • What is the output of the following? ‘hello’.upper()
  • What is the output of the following? str(1+1)
  • What is the output of the following? “ABC”.replace(“AB”, “ab”)
  • In Python 3, what data type does variable x hold after the operation: x = 1/1?
  • What data type does 3.12323 represent?
  • For the string “Fun Python” stored in a variable $x$, what will be the output of x[0:5]?
  • What data type is represented by “7.1”?
  • Consider the following tuple: say_what=(‘say’,’what’,’you’,’will’)What is the result of the following? say_what[-1]
  • Consider the following tuple A=(1,2,3,4,5). What is the outcome of the following? A[1:4]
  • Consider the following list B=[1,2,[3,’a’],[4,’b’]].  What is the result of B[3][1]?
  • What is the outcome of the following operation? [1,2,3] + [1,1,1]
  • What is the outcome of the following? 1=2

Big Data MCQs Quiz

Mastering summary() Function in R: Easy Data Insights 2025

by

To dive into data analysis, one of the first functions encountered is the summary() function in R Language. This versatile function as a tool is a game-changer for quickly getting and understanding the data insights, identifying patterns, and spotting potential issues. For a beginner or an experienced R user, mastering the summary() function can significantly improve not only your R language learning, R programming, and data analytics skills but may also streamline the users’ workflow. This function helps in getting many of the descriptive statistics and exploratory data analysis. In this post, we will explore what the summary() function in R does, provide real-world examples, and share actionable tips to help you get the most out of it.

What is the summary() Function in R?

The summary() function in R is a built-in function that provides a concise overview of an R object (such as a data frame, vector, or statistical model) to get a statistical summary of the data. For numeric data, it calculates key statistics like the mean, median, quartiles, and minimum/maximum values. For categorical data, it displays frequency counts. For regression models (e.g., linear regression), it offers insights into coefficients, residuals, and overall model performance.

Real-World Examples of Using summary()

1. Exploring a Dataset

Suppose you are analyzing a dataset of $mtcars$. The summary() function in R can be used to get a quick snapshot of the data:

# Load a sample dataset
data("mtcars")

# Get a summary of the dataset
summary(mtcars)
Summary() function in R Language

The output will show key statistics for each column, such as:

  • MPG (miles per gallon): Min, 1st Quartile, Median, Mean, 3rd Quartile, Max
  • Cylinders: Frequency counts for each category

The above output helps you quickly identify trends, such as the average MPG or the most common number of cylinders in the dataset.

2. Analyzing a Linear Regression Model

Suppose for a linear regression model to predict mile per gallon (mpg), you can use summary() to evaluate its performance:

# Fit a linear model
model <- lm(mpg ~ wt + hp, data = mtcars)

# Summarize the model
summary(model)
summary() function in R Programming

The output will include:

  • Coefficients: Estimates, standard errors, and p-values
  • R-squared: How well the model explains the variance in the data
  • Residuals: Distribution of errors

This information is invaluable for understanding the strength and significance of your predictors.

3. Summarizing Categorical Data

For categorical data, such as survey responses, summary() function in R provides frequency counts:

# Create a factor vector
survey_responses <- factor(c("Yes", "No", "Yes", "Maybe", "No", "Yes"))

# Summarize the responses
summary(survey_responses)

## Output
Maybe    No   Yes 
    1     2     3

The output will show:

  • Counts for each category (e.g., “Yes”: 3, “No”: 2, “Maybe”: 1)

This is a quick way to understand the distribution of responses.

Actionable Tips for Using summary() Effectively

Combine with str() for a Comprehensive Overview
Use str() alongside summary() to get both the structure and summary statistics of your data. This helps you understand the data types and distributions simultaneously.

    str(mtcars)
## Output
'data.frame':   32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

summary(mtcars)

    Use summary() for Data Cleaning
    Look for missing values (NA) in the summary output. This can help you identify columns that require imputation or removal.

    Customize Output for Specific Columns
    If you’re only interested in specific columns, subset your data before applying summary()

    summary(mtcars$mpg)

## Output
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  10.40   15.43   19.20   20.09   22.80   33.90

    Leverage summary() for Model Diagnostics
    When working with statistical models, use summary() function in R to check for significant predictors and assess model fit.

    Visualize Summary Statistics
    Pair summary() with visualization tools like ggplot2 or boxplot() to better understand the distribution of your data.

    Conclusion: Start Using summary() Today!

    The summary() function in R Language is a simple yet powerful tool that every R user should have in their toolkit. Whether one is exploring data, cleaning datasets, or evaluating models, summary() provides the insights one needs to make informed decisions. Incorporating summary() function into workflow, will save time and gain a deeper understanding of your data.

    Summary Statistics using the measure of central tendency