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Python for data science assignment solutions week 3 2022.

Are you looking for help in Python for Data Science NPTEL week 3 assignment answers? So, here in this article, we have provided Python for Data Science week 3 assignment answer’s hint.

Python for Data Science NPTEL Assignment Solutions Week 3

Q1. Choose the appropriate command(s) to filter those booking details whose reservation_status are a No-show?

a. data_hotel_ns = data_hotel.loc[data_hotel.reservation_status = ‘No-Show’] b. data_hotel_ns = data_hotel [data_hotel.reservation_status ‘No-Show’] c. data_hotel_ns data_hotel.reservation_status.loc [data_hotel.isin([ ‘No-Show’])] d. data_hotel_ns = data_hotel.loc[data_hotel.reservation_status.isin([ ‘No-Show’])]

Answer : b. data_hotel_ns = data_hotel [data_hotel.reservation_status ‘No-Show’]

d. data_hotel_ns = data_hotel.loc[data_hotel.reservation_status.isin([ ‘No-Show’])]

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Q2. From the same data, find how many bookings were not canceled in the year 2017?

d. None of the above

Answer : a. 9064

Q3. From the total bookings that were made in 2017 and not canceled, which month had the highest number of repeated guests?

b. February

Answer: c. January

Q4. Which of the following commands can be used to create a variable Flag, and set the values as Premium when the rating is equal to or greater than 3.25, and otherwise as Regular?

a. dt_cocoa[‘Flag’] = [“Premium” if x > 3.25 else “Regular” for x in dt_cocoa[‘Rating’]]

b. dt_cocoa[‘Flag’] = [“Premium” if x >= 3.25 else “Regular” for x in dt_cocoa[ ‘Rating’]]

c. dt_cocoa[“Flag”] = np.where(dt_cocoa[“Rating”] < 3.25, “Regular”, “Premium”

Answer: b. dt_cocoa[‘Flag’] = [“Premium” if x >= 3.25 else “Regular” for x in dt_cocoa[ ‘Rating’]]

Q5. Which instruction can be used to impute the missing values in the column Review Data from the dataframe dt_cocoa by grouping the records company – wise?

a. dt_cocoa[‘Review Date’] = dt_cocoa.groupby([‘Company’])[‘Review Date’].apply(lambda x: x.fillna(x.mode().iloc[0]))

b. dt_cocoa[‘Review Date’] = dt_cocoa.groupby([‘Company’])[‘Review Date’].apply(lambda X: x.fillna(x.mean()))

c. dt_cocoa[‘Review Date’] = dt_cocoa.groupby([‘Company’])[‘Review Date’].apply(lambda x: x.fillna(x.mode()))

Answer: a. dt_cocoa[‘Review Date’] = dt_cocoa.groupby([‘Company’])[‘Review Date’].apply(lambda x: x.fillna(x.mode().iloc[0]))

Q6. After checking the data summary, which feature requires a data conversion considering the data values held?

b. Review Date

Answer: b. Review Date

Q7. What is the maximum average rating for the cocoa companies based out of Guatemala?

d.  None of the above

Answer: c. 3.42

Q8. Which pandas function is used to stack the dataframes vertically?

a. pd.merge()

b. pd.concat()

Answer: b. pd.concat()

Q9. Of the following set of statements, which of them can be used to extract the column Direction as a separate dataframe?

a. df_weather[[‘Direction’]]

b. df_weather.iloc[:,0]

c. df_weather.loc[:, [‘Direction’]]

Answer: a. df_weather[[‘Direction’]]

Q10. A file “Students.csv” contains the attendance and scores of three separate students. This dataset is loaded into a dataframe df_study and a cross table is obtained from the same dataframe which results in the following output

Which one of these students’ average score across all subjects was the lowest? Which subject has the highest average score across students?

a. Harini, Maths

b. Sathi, Maths

c. Harini, Physics

d. Rekha, Maths

Answer: b. Sathi, Maths

Disclaimer: These answers are provided only for the purpose to help students to take references. This website does not claim any surety of 100% correct answers. So, this website urges you to complete your assignment yourself.

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Python for Data Science | NPTEL | Week 3 Answers

This set of MCQ(multiple choice questions) focuses on the  Python for Data Science NPTEL Week 3 Answers

You should practice these questions to improve fundamentals of Data Science needed for various interviews (like company interview, campus interview, walk-in interview), entrance exams, placements and other competitive exams. All the questions in this particular section are based on only “ Python for Data Science NPTEL Week 3 Answers “.

Course layout

Week 1 : Basics of Python Spyder Week 2: Sequence data types & associated operations Week 3: Data frames Week 4:   Case study

NOTE:  You can check your answer immediately by clicking show answer button. Moreover, this set of “Python for Data Science NPTEL Week 3 Answers” contains 10 questions.

Now, start attempting the quiz.

Python for Data Science NPTEL Week 3 Answers

Q1. Which of the following is the correct approach to fill missing values in case of categorical variable?

a) Mean b) Median c) Mode d) None of these

Answer: c) Mode

Q2. Of the following set of statements, which of them can be used to extract the column Type as a separate dataframe?

a) df_cars[[‘Type’]] b) df_cars.iloc[[:, 1] c) df_cars.loc[:, [‘Type’]] d) None of the above

Answer: a), c)

Q3. The method df_cars.describe() will give description of which of the following column?

a) Car name b) Brand c) Price (in lakhs) d) All of the above

Answer: c) Price (in lakhs)

Q4. Which pandas function is used to stack the dataframes vertically?

a) pd.merge() b) pd.concat() c) join() d) None of the above

Answer: b) pd.concat()

Q5. Which of the following are libraries in Python?

a) Pandas b) Matplotlib c) NumPy d) All of the above

Answer: d) All of the above

Q6. Which of the following variable have null values?

a) ID b) Company c) Review Date d) Rating

Answer: c) Review Date

Q7. Which of the following countries have maximum locations of cocoa manufacturing companies?

a) U.K. b) U.S.A. c) Canada d) France

Answer: b) U.S.A.

Q8. After checking the data summary, which feature requires a data conversion considering the data values held?

a) Rating b) Review date c) Company d) Bean origin

Answer: b) Review date

Q9. What is the maximum rating of chocolates?

a) 1.00 b) 5.00 c) 3.18 d) 4.00

Answer: b) 5.00

Q10. What will be the output of the following code?

a) [bool, int, float, float, str] b) [str, int, float, float, str] c) [bool, int, float, int, str] d) [bool, int, int, float, str]

Q1. Data from the file  “brand_data.csv“  has to be loaded into a pandas dataframe. A snippet of the data is shown below: 0, 1, 2, 2 brand, type, cost, price BR1, clnr, 12, 15 BR2, util, 23, 34 BR3, lux, 189, 191 BR4, txtl, 150, 130

What is the right instruction to read the file into a dataframe df_brand with 4 separate columns?

a) pd.read_csv(“brand_data.csv”, index_col=0, header = 1) b) df_brand = pd.read_csv(“brand_data.csv”, header = 1) c) df_brand = pd.read_csv(“brand_data.csv”, header = None) d) df_brand = pd.read_table(“brand_data.csv”, delimiter = ‘,’, header = 1)

Answer: b), d)

Q2. For the same file above  “brand_data.csv“ , which parameter in pd.read_csv will help to load dataframe df_brand with the selected columns as shown below?

In [17]: df_brand out[17]: brand price 0 BR1 15 1 BR2 34 2 BR3 191 3 BR4 130

a) index_col b) skiprows c) usecols d) None of the above

Q3. Data from the file  “weather.xlsx“  has to be loaded into a pandas dataframe  df_weather  which when printed is as shown below:

Of the following set of statements which of them can be used to move the column “Direction” into a separate dataframe

a) df_weather[[‘Direction’]] b) df_weather[‘Direction’] c) df_weather.loc[:, [‘Direction’]] d) df_weather.iloc[:, 0]

Q4. Referring to the same dataframe df_weather in Question(3), which statement/statements will help to print the last row from the dataframe?

a) print(df_weather.head(-1)) b) print(df_weather.tail(1)) c) print(df_weather[2:3]) d) print(df_weather.iloc[-1])

Q5. In reference to the same dataframe df_weather, we add an additional column ‘Hot_day’ to determine whether the day is hot or not based on the values in the Temperature column. What will the print statement derive? df_weather[‘Hot_dat’] = np.where(df_weather[‘Temperature’] > 40, True, False) print(df_weather[‘Hot_day’][2])

a) True b) SyntaxError c) False d) None of the above

Q6. What statement would give the number of columns in a dataframe df?

a) len(df.columns) b) len(df) c) df.size d) All of the above

Q7. A file  “Students.csv”  contains the attendance and total scores of three separate students. This data is loaded into a dataframe  df_study  and a pandas crosstab is applied on the same dataframe which results in the following output.

Which student scored the maximum average score of all three subjects? Which subject has the best average score for all three students?

a) Harini,Chemistry b) Rekha,Physics c) Harini,Physics d) Rekha,Maths

Q8. The following histogram shows the number of books read in a year:

Find the mean and median in the above histogram.

a) 7, 8 b) 8, 9 c) 8.5, 7 d) 8, 8 e) None of the above

Q9. For the following box plot, which among the given options are the median and the outlier?

a) 15, 52 b) 22, 52 c) 13.5, 29 d) 25, 50

Q10. A dataframe df_logs has the following data.

All the NaN / Null values in the column C1 can be replaced by zero value by executing which of the following statements?

a) df_logs[‘C1’].fillna(0,inplace = True) b) df_logs.fillna(0,inplace = True) c) df_logs.fillna(0,inplace = False) d) df_logs[‘C1’].fillna(df_logs[‘B1’],inplace = True)

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Python for Data Science : Assignment 1&2 Reevaluations!!

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Week 4 Feedback Form: Python for Data Science

Dear Learners, Thank you for continuing with the course and hope you are enjoying it. We would like to know if the expectations with which you joined this course are being met and hence please do take 2 minutes to fill out our weekly feedback form. It would help us tremendously in gauging the learner experience. Here is the link to the form:    https://docs.google.com/forms/d/139VEnDqJl8DdxrY9LyuuzNzzEs7qCjaI9wjJJ_n2ZBg/viewform Thank you -NPTEL team

Python for Data Science : Assignment 4 is live now!!

Dear Learners, The lecture videos for Week 4 have been uploaded for the course "Python for Data Science" . The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=56&lesson=57 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-4 for Week-4 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=56&assessment=97 Assignment-4 for Week-4 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=56&assessment=105 The assignment has to be submitted on or before Wednesday,[22/02/2023], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Week 3 Feedback Form: Python for Data Science

Python for data science : assignment 3 is live now.

Dear Learners, The lecture videos for Week 3 have been uploaded for the course "Python for Data Science" . The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=41&lesson=42 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-3 for Week-3 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=41&assessment=96 Assignment-3 for Week-3 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=41&assessment=104 The assignment has to be submitted on or before Wednesday,[15/02/2023], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Week 2 Feedback Form: Python for Data Science

Python for data science : assignment 2 is live now.

Dear Learners, The lecture videos for Week 2  have been uploaded for the course "Python for Data Science" . The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=30&lesson=31 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-2  for Week-2  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=30&assessment=95 Assignment-2  for Week-2  is also released and can be accessed from the following link Link: https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=30&assessment=103 The assignment has to be submitted on or before Wednesday,[08/02/2023], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Week 1 Feedback Form: Python for Data Science

Dear Learner Thank you for continuing with the course and hope you are enjoying it. We would like to know if the expectations with which you joined this course are being met and hence please do take 2 minutes to fill out our weekly feedback form. It would help us tremendously in gauging the learner experience. Here is the link to the form:  https://docs.google.com/forms/d/139VEnDqJl8DdxrY9LyuuzNzzEs7qCjaI9wjJJ_n2ZBg/viewform Thank you -NPTEL team

Python for Data Science : Assignment 1 is live now!!

Dear Learners, The lecture videos for Week 1 have been uploaded for the course "Python for Data Science" . The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=18&lesson=19 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-1 for Week-1 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=18&assessment=94 Assignment-1 for Week-1 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=18&assessment=102 The assignment has to be submitted on or before Wednesday,[08/02/2023], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Python for Data Science : Assignment 0 is live now!!

Dear Learners, We welcome you all to this course "Python for Data Science" . The assignment 0 has been released. This assignment is based on a prerequisite of the course. You can find the assignment in the link : https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=16&assessment=93 Please note that this assignment is for practice and it will not be graded. Thanks & Regards   -NPTEL Team

Python for Data Science - Week-1 video is live now !!

Dear Learners, The lecture videos for Week 1 have been uploaded for the course “ Python for Data Science ”. The lectures can be accessed using the following link: Link:  https://onlinecourses.nptel.ac.in/noc23_cs21/unit?unit=18&lesson=19 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Assignment will be released shortly. As we have done so far, please use the discussion forums if you have any questions on this module. Thanks and Regards, -NPTEL Team

NPTEL: Exam Registration is open now for Jan 2023 courses!

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Here is the much-awaited announcement on registering for the Jan 2023 NPTEL course certification exam. 

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[Week 3] NPTEL Python For Data Science Assignment Answer 2023

NPTEL Python For Data Science Assignment Answer

Table of Contents:-

NPTEL Python For Data Science Week 3 Assignment Answer 2023

1. Which of the following is the correct approach to fill missing values in case of categorical variable?

• None of the above

2. Of the following set of statements, which of them can be used to extract the column Type as a separate dataframe?

• df_cars[[‘Type’]]
• df_cars.iloc[[:, 1]
• df_cars.loc[:, [‘Type’]]

3. The method df_cars.describe() will give description of which of the following column?

• Price (in lakhs)
• All of the above

4. Which pandas function is used to stack the dataframes vertically?

• pd.concat()

5. Which of the following are libraries in Python?

6. Which of the following variable have null values?

• Review Date

7. Which of the following countries have maximum locations of cocoa manufacturing companies?

8. After checking the data summary, which feature requires a data conversion considering the data values held?

• Review date
• Bean origin

9. What is the maximum rating of chocolates?

• [bool, int, float, float, str]
• [str, int, float, float, str]
• [bool, int, float, int, str]
• [bool, int, int, float, str]

NPTEL Python For Data Science Week 2 Assignment Answer 2023

1. Which of the following object does not support ind e xing?

• dict i onary

2. Given a NumPy array, arr = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]), what is the output of the command, print(arr[0][1])?

• [[1 2 3] [4 5 6] [7 8 9]

3. What is the output of the following code?

• [2, 3, 4, 5]
• [1, 2, 3, 4]
• Will throw an error: Set objects are no t iterable.

5. Which of the following code gives output My friend’s house is in Chennai?

6. Let t1=(1,2, “tuple”,4) and t2=(5,6,7). Which of the follo w ing will not give any error after the execution?

• t1.append(5)
• x=t2[t1[1]]
• t3=(t1 , t2)
• t3=(list(t1), list(t2))

7. Let d={1:“Pyhton”,2:[1,2,3]}. Which among the fol l owing will not give the error after the execution?

• d[2].append(4)
• d.update({‘one’ : 22})

8. Wh i ch of the following data type is immutable?

9. student = {‘name’: ‘Jane’, ‘age’: 25 , ‘courses’: [‘Math’, ‘Statistics’]} Which among the following will return {‘name’: ‘Jane’, ‘age’: 26, ‘courses’: [‘Math’ , ‘Statistics’], ‘phone’: ‘123-456’}?

• student.update({‘age’ : 26})
• student.update({‘age’ : 26, ‘phone’: ‘123-456’}) 
• student[‘phone’] = ‘123-456’

[‘M’, ‘A’, ‘H’, ‘E’, ‘S’, ‘H’] [‘m’, ‘a’, ‘h’, ‘e’, ‘s’ , ‘h’] [‘M’, ‘a’, ‘h’, ‘e’, ‘s’, ‘h’] [‘m’, ‘A’, ‘H’, ‘E’, ‘S’, ‘H’]

NPTEL Python For Data Science Week 1 Assignment Answer 2023

• Error: Invalid operation, unsupported operator ‘*’ used between ‘int’ and ‘str’

• Code will throw an error.

4. Which of the following variable names are INVALID in Python?

• variable_ 1

5. While naming the variable, use of any special character other than unders c ore(_) ill throw which type of error?

• Syntax error
• Value er r or
• Index error

6. Let x = “Mayur”. Which of the following commands converts the ‘x’ to float datatype?

• str(float,x)
• x.flo a t()
• Cannot convert a string to float data type

7. Which Python library is commonly used for data wrangling and manipulation?

9. Given two variables, j = 6 and g = 3.3. If both normal division and floor division operators were used to divide j by g, what would be the data type of the value obtained from the operations?

• float, float

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NPTEL Python for Data Science Assignment 3 Answers 2023

NPTEL Python for Data Science Assignment 3 Answers 2023:-  All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.

NPTEL Python For Data Science Week 3 Assignment Answer 2023

1. Which of the following is the correct approach to fill missing values in case of categorical variable?

• None of the above

2. Of the following set of statements, which of them can be used to extract the column Type as a separate dataframe?

• df_cars[[‘Type’]]
• df_cars.iloc[[:, 1]
• df_cars.loc[:, [‘Type’]]

3. The method df_cars.describe() will give description of which of the following column?

• Price (in lakhs)
• All of the above

4. Which pandas function is used to stack the dataframes vertically?

• pd.concat()

5. Which of the following are libraries in Python?

6. Which of the following variable have null values?

• Review Date

7. Which of the following countries have maximum locations of cocoa manufacturing companies?

8. After checking the data summary, which feature requires a data conversion considering the data values held?

• Review date
• Bean origin

9. What is the maximum rating of chocolates?

• [bool, int, float, float, str]
• [str, int, float, float, str]
• [bool, int, float, int, str]
• [bool, int, int, float, str]

NPTEL Python for Data Science Assignment 3 Answers 2022 [July-Dec]

1. Choose the appropriate command(s) to filter those booking details whose  reservation_status  are a No-show? a. data_hotel_ns datahotel. loc[data_hotel.reservation_status=’No-Show’] b. data_hotel_ns = data_hotel[ data _hotel.reservation_status = “No-Show’] c. data hotel_ns = data_hotel.reservation_status.loc[data_hotel . isin([‘No-Show’])] d. data_hotel_ns = data_hotel.loc [data hotel.reservation_status.isin([ No-Show’])]

2. From the same data, find how many bookings were not canceled in the year 2017? a. 9064 b. 6231 c. 9046 d. None of the above

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3. From the total bookings that were made in 2017 and not canceled , which month had the highest number of repeated guests? a. July b. February c. January d. None of the above

4. Which of the following commands can be used to create a variable Flag, and set the values as Premium when the  rating  is equal to or greater than 3.25, and otherwise as Regular? a. dt_cocoa[°Flag’] = [“Premium” if x 3.25 else “Regular” for x in dt_cocoa[‘Rating’ ]] b. dt_cocoa[“Flag’] = [“Premium” if x 3.25 else “Regular” for x in dt_cocoa[ ‘ Rating ‘]] c. dt_cocoa[“Flag”] = np.where(dt_cocoa[ “Rating”] < 3.25, “Regular”, “Premium”) d. None of the above

5. Which instruction can be used to impute the missing values in the column Review Data from the dataframe  dt_cocoa  by grouping the records company–wise?

6. After checking the data summary, which feature requires a data conversion considering the data values held? a. Rating b. Review Date c. Company d. None of the above

👇 For Week 04 Assignment Answers 👇

7 . What is the maximum average rating for the cocoa companies based out of Guatemala? a. 4 b. 3.5 c. 3.42 d. None of the above

8. Which pandas function is used to stack the dataframes vertically? a. pd.merge() b. pd.concat() c. join() d. None of the above

9. Of the following set of statements, which of them can be used to extract the column Direction as a separate dataframe? a. df_weather[[ ‘ Direction ‘ ]] b. df_weather.iloc[:,0] c. df_weather.loc[: . [ ‘Direction ‘]] d. None of the above

10. Which one of these students’ average score across all subjects was the lowest? Which subject has the highest average score across students? a. Harini, Maths b. Sathi, Maths c. Harini, Physics d. Rekha, Maths

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About Python For Data Science

The course aims at equipping participants to be able to use python programming for solving data science problems.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 3 assignments out of the total 4 assignments given in the course. Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

NPTEL Python for Data Science Assignment 3 Answers [Jan 2022]

Q1. Data from the file “brand_data.csv“ has to be loaded into a pandas dataframe. A snippet of the data is shown below:

What is the right instruction to read the file into a dataframe df_brand with 4 separate columns?

Answer:- (B), (C) & (D)

👇 FOR NEXT WEEK ASSIGNMENT ANSWERS 👇

Q2. For the same file above “brand_data.csv“ , which parameter in pd.read_csv will help to load dataframe df_brand with the selected columns as shown below?

(A) index_col  (B) skiprows  (C) usecols  (D) None of the above

Answer:- (C) usecols 

Q3. Data from the file “weather.xlsx“ has to be loaded into a pandas dataframe df_weather which when printed is as shown below:

Of the following set of statements which of them can be used to move the column “Direction” into a separate dataframe

Answer:- (A), (B), (C) & (D)

Q4. Referring to the same dataframe df_weather in Question (3), which statement/statements will help to print the last row from the dataframe?

Answer:- (B) & (D)

Q5. In reference to the same dataframe df_weather, we add an additional column ‘Hot_day’ to determine whether the day is hot or not based on the values in the Temperature column. What will the print statement derive?

(A) True  (B) SyntaxError  (C) False  (D) None of the above

Answer:- (C) False 

Q6. What statement would give the number of columns in a dataframe df?

(A) len(df.columns)  (B) len(df)  (C) df.size  (D) All of the above

Answer:- (A) len(df.columns) 

Q7. A file “Students.csv” contains the attendance and total scores of three separate students. This data is loaded into a dataframe df_study and a pandas crosstab is applied on the same dataframe which results in the following output

Which student scored the maximum average score of all three subjects? Which subject has the best average score for all three students?

(A) Harini,Chemistry  (B) Rekha,Physics  (C) Harini,Physics  (D) Rekha,Maths

Answer:- (D) Rekha,Maths

Q8. The following histogram shows the number of books read in a year:

Find the mean and median in the above histogram.

(A) 7,8  (B) 8,9  (C) 8.5,7  (D) 8,8  (E) None of the above

Answer:- (D) 8,8 

Q9. For the following box plot, which among the given options are the median and the outlier?

(A) 15, 52  (B) 22, 52  (C) 13.5, 29  (D) 25, 50

Answer:- (B) 22, 52 

Q10. A dataframe df_logs has the following data.

All the NaN / Null values in the column C1 can be replaced by zero value by executing which of the following statements?

(A) df_logs[‘C1’].fillna(0,inplace = True)  (B) df_logs.fillna(0,inplace = True)  (C) df_logs.fillna(0,inplace = False)  (D) df_logs[‘C1’].fillna(df_logs[‘B1’],inplace = True)

Answer:- (A) df_logs[‘C1’].fillna(0,inplace = True) 

Disclaimer :- We do not claim 100% surety of solutions, these solutions are based on our sole expertise, and by using posting these answers we are simply looking to help students as a reference, so we urge do your assignment on your own.

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NPTEL Python for Data Science Assignment 3 Answers 2022:-  All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.

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[Week 1-4] NPTEL Python For Data Science Assignment Answers 2023

NPTEL Python For Data Science Assignment Answers

Table of Contents

NPTEL Python For Data Science Week 4 Assignment Answer 2023

1. Which of the following are regression problems? Assume that appropriate data is given.

• Predicting the house price.
• Predicting w h ether it will rain or not on a given day.
• Predicting the maximum temperature on a g iven day.
• Predicting the sales of the ice-creams.

2. Which of the followings are binary classification problems?

• Predicting whether a patient is diagnosed with cancer or not.
• Predicting whether a team will win a tournament or not.
• Predicting the price of a second-hand car.
• Classify web text into one of the follow in g categories: Sports, Entertainment, or Technology.

3. If a linear regression model achieves zero training error, can we say that all the data points lie on a hyperplane in the (d+1)-dimensional space? Here, d is the nu m ber of features.

Read the information given below and answer the questions from 4 to 6: Data Description: An automotive service chain is launching its new grand service station this weekend. They offer to service a wide variety of cars. The current capacity of the station is to check 315 cars thoroughly per day. As an inaugural offer, they claim to freely check all cars that arrive on their launch day, and report whether they need servicing or not!

Unexpectedly, they get 450 cars. The servicemen will not work longer than the working hours, but the data analysts have to!

Can you save the day for the new service station?

How can a data scientist save the day for them?

He has been given a data set, ‘ ServiceTrain.csv ’ that contains some attributes of the car that can be easily measured and a conclusion that if a service is needed or not.

Now for the cars they cannot check in detail, they measure those attributes and store them in ‘ ServiceTest.csv ’

Problem Statement:

Use machine learning techniques to identify whether the cars require service or not

Read the given datasets ‘ ServiceTrain.csv ’ and ‘ ServiceTest.csv ’ as train data and test data respectively and import all the required packages for analysis.

4. Which of the following machine learning techniques would NOT be ap p ropriate to solve the problem given in the problem statement?

• Random Forest
• Logisti c Regression
• Linear regression

5. After applying logistic regression, what is/are the correct observat ion s from the resultant confusion matrix?

• True Positive = 29, True Negative = 94
• True Positive = 94, Tr u e Negative = 29
• False Positive = 5, True Negative = 94
• None of the above

Prepare the data by following th e steps given below, and answer questions 6 and 7.

• Encode categorical variable, Service – Yes as 1 and No as 0 for both the train and test datasets.
• Split the set of independent features and the dependent feature on both the train and test datasets.
• Set random_state for the instance of the logistic regression class as 0.

6. The logistic regression model built between the input and output variables is checked for its prediction accuracy of the test data. What is the accuracy range (in %) of the predictions made over test dat a ?

• 60 – 79
• 90 – 9 5

7. How are categorical variables preprocessed before m odel building?

• Standardization
• Dummy var i ables
• Correlation

The Global Happiness Index report contains the Happiness Score data w i th multiple features (namely the Economy, Family, Health, and Freedom) that could affect the target variable value.

Prepare the data by following the steps g iven below, and answer question 8

• Split the set of independent features and the dependent feature on the given dataset
• Create training and testing data from the set of independent features and dependent feature by splitting the original data in the ratio 3:1 respectively, and set the value for random_state of the training/test split method’s instance as 1

8. A multiple linear regression model is built on the Global Happiness Index dat a set ‘GHI_Report.csv’. What is the RMSE of the baseline model?

9. A regression model with the following function y=60+5.2x was built to understand the impact of humidity (x) on rainfall (y). The humidity this week is 30 more than the previous week. Wh a t is the predicted difference in rainfall?

10. X and Y are two variables that have a strong linear relationship. Whi c h of the following statements are incorrect?

• There cannot be a negative relationship between the two variables.
• The relationship between the two variables is purely causal.
• One variable may or may not cause a change in the other variable.
• The variables can be positively or negativel y correlated with each other

NPTEL Python For Data Science Week 3 Assignment Answer 2023

1. Which of the following is the correct approach to fill missing values in case of categorical variable?

2. Of the following set of statements, which of them can be used to extract the column Type as a separate dataframe?

• df_cars[[‘Type’]]
• df_cars.iloc[[:, 1]
• df_cars.loc[:, [‘Type’]]

3. The method df_cars.describe() will give description of which of the following column?

• Price (in lakhs)
• All of the above

4. Which pandas function is used to stack the dataframes vertically?

• pd.concat()

5. Which of the following are libraries in Python?

6. Which of the following variable have null values?

• Review Date

7. Which of the following countries have maximum locations of cocoa manufacturing companies?

8. After checking the data summary, which feature requires a data conversion considering the data values held?

• Review date
• Bean origin

9. What is the maximum rating of chocolates?

• [bool, int, float, float, str]
• [str, int, float, float, str]
• [bool, int, float, int, str]
• [bool, int, int, float, str]

NPTEL Python For Data Science Week 2 Assignment Answer 2023

1. Which of the following object does not support ind e xing?

• dict i onary

2. Given a NumPy array, arr = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]), what is the output of the command, print(arr[0][1])?

• [[1 2 3] [4 5 6] [7 8 9]

3. What is the output of the following code?

• [2, 3, 4, 5]
• [1, 2, 3, 4]
• Will throw an error: Set objects are no t iterable.

5. Which of the following code gives output My friend’s house is in Chennai?

6. Let t1=(1,2, “tuple”,4) and t2=(5,6,7). Which of the follo w ing will not give any error after the execution?

• t1.append(5)
• x=t2[t1[1]]
• t3=(t1 , t2)
• t3=(list(t1), list(t2))

7. Let d={1:“Pyhton”,2:[1,2,3]}. Which among the fol l owing will not give the error after the execution?

• d[2].append(4)
• d.update({‘one’ : 22})

8. Wh i ch of the following data type is immutable?

9. student = {‘name’: ‘Jane’, ‘age’: 25 , ‘courses’: [‘Math’, ‘Statistics’]} Which among the following will return {‘name’: ‘Jane’, ‘age’: 26, ‘courses’: [‘Math’ , ‘Statistics’], ‘phone’: ‘123-456’}?

• student.update({‘age’ : 26})
• student.update({‘age’ : 26, ‘phone’: ‘123-456’}) 
• student[‘phone’] = ‘123-456’

[‘M’, ‘A’, ‘H’, ‘E’, ‘S’, ‘H’] [‘m’, ‘a’, ‘h’, ‘e’, ‘s’ , ‘h’] [‘M’, ‘a’, ‘h’, ‘e’, ‘s’, ‘h’] [‘m’, ‘A’, ‘H’, ‘E’, ‘S’, ‘H’]

NPTEL Python For Data Science Week 1 Assignment Answer 2023

• Error: Invalid operation, unsupported operator ‘*’ used between ‘int’ and ‘str’

• Code will throw an error.

4. Which of the following variable names are INVALID in Python?

• variable_ 1

5. While naming the variable, use of any special character other than unders c ore(_) ill throw which type of error?

• Syntax error
• Value er r or
• Index error

6. Let x = “Mayur”. Which of the following commands converts the ‘x’ to float datatype?

• str(float,x)
• x.flo a t()
• Cannot convert a string to float data type

7. Which Python library is commonly used for data wrangling and manipulation?

9. Given two variables, j = 6 and g = 3.3. If both normal division and floor division operators were used to divide j by g, what would be the data type of the value obtained from the operations?

• float, float

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Programming for Data Science

Teaching data scientists the tools they need to use computers to do data science

Advanced Python for Data Science Assignment 3

All assigments beginning with Assignment 3 are to be submitted via GitHub. Create a new repository for each assignment using your NetID, an underscore ‘_’, the word ‘assignment’ and the assignment number, all in lower case. For example, if your NetID is aaa11 then the repository for this assignment would be aaa11_assignment3 . Clone this repository to your local computer. Commit any files that you are required to create as part of the assignment, then push the changes back to GitHub. Whatever is in the repository at the due date will be graded.

An astrophysicist colleague was recently complaining about how long it was taking to run an N-body simulation. “It’s really just a simple calculation, and I’m only simulating four planets, but it takes nearly a minute and a half to run one simulation. I really need it done in under 30 seconds.” You kindly offer to take a look at code to see if it is possible to speed it up. Your colleague provides you with a link to the source .

Although your colleague said the code was simple, it is still fairly complex, so you decide to tackle the problem in stages. A first scan of the code reveals a number of potential areas that could be improved. These include:

• Reducing function call overhead
• Using alternatives to membership testing of lists
• Using local rather than global variables
• Using data aggregation to reduce loop overheads

As you’re a cautious programmer, you decide to address each of these in turn. This will ensure that it is possible to check the program is still working correctly after each change, and to assess the performance improvement that the change achieved. You are also aware that the program has to be maintained by others in the future, so you want to make sure that the changes do not make this more difficult, especially if the performance improvement is only minor.

For each of these areas, create a new version of nbody.py (call them nbody_1.py , nbody_2.py , etc.) and commit them to the repository. You may also add a file with any other optimizations that you find. At the beginning of each file, put a comment indicating if the change made the most improvement, second most, etc. Finally, create another file called nbody_opt.py that contains all of the optimizations you made. Put a comment at the top indicating the relative speedup of the optimized version compared to the original version. Calculate the relative speedup (R) as follows:

Are you able to get it to run in under 30 seconds?

• Wednesday, April 3, 2024

NPTEL Programming, Data Structures And Algorithms Using Python Week3 Assignment

NPTEL Course is an introduction to programming and problem solving in Python.  It does not assume any prior knowledge of programming.  Using some motivating examples, the course quickly builds up basic concepts such as conditionals, loops, functions, lists, strings and tuples.  It goes on to cover searching and sorting algorithms, dynamic programming and backtracking, as well as topics such as exception handling and using files.  As far as data structures are concerned, the course covers Python dictionaries as well as classes and objects for defining user defined datatypes such as linked lists and binary search trees.

Programming, Data Structures And Algorithms Using Python Week3 Assignment Jan 2024

INTENDED AUDIENCE :  Students in any branch of mathematics/science/engineering, 1st year  PREREQUISITES : School level mathematics INDUSTRY SUPPORT :   This course should be of value to any company requiring programming skills.

Course Layout

Week 1: Informal introduction to programming, algorithms and data structures via GCD Downloading and installing Python GCD in Python: variables, operations, control flow – assignments, conditionals, loops, functions Week 2: Python: types, expressions, strings, lists, tuples Python memory model: names, mutable and immutable values List operations: slices etc Binary search Inductive function definitions: numerical and structural induction Elementary inductive sorting: selection and insertion sort In-place sorting Week 3: Basic algorithmic analysis: input size, asymptotic complexity, O() notation Arrays vs lists Merge sort Quicksort Stable sorting Week 4: Dictionaries More on Python functions: optional arguments, default values Passing functions as arguments Higher order functions on lists: map, lter, list comprehension Week 5: Exception handling Basic input/output Handling files String processing Week 6: Backtracking: N Queens, recording all solutions Scope in Python: local, global, non-local names Nested functions Data structures: stack, queue Heaps Week 7: Abstract data-types Classes and objects in Python “Linked” lists: find, insert, delete Binary search trees: find, insert, delete Height-balanced binary search trees Week 8: Efficient evaluation of recursive definitions: memorization Dynamic programming: examples Other programming languages: C and manual memory management Other programming paradigms: functional programming

Programming Assignment 1

Write three Python functions as specified below. Paste the text for all three functions together into the submission window. Your function will be called automatically with various inputs and should return values as specified. Do not write commands to read any input or print any output.

• You may define additional auxiliary functions as needed.
• In all cases you may assume that the value passed to the function is of the expected type, so your function does not have to check for malformed inputs.
• For each function, there are normally some public test cases and some (hidden) private test cases.
• “Compile and run” will evaluate your submission against the public test cases.
• “Submit” will evaluate your submission against the hidden private test cases. There are 10 private test cases, with equal weightage. You will get feedback about which private test cases pass or fail, though you cannot see the actual test cases.
• Ignore warnings about “Presentation errors”.

Define a Python function  remdup(l)  that takes a nonempty list of integers  l  and removes all duplicates in  l , keeping only the first occurrence of each number. For instance:

Write a Python function  sumsquare(l)  that takes a nonempty list of integers and returns a list  [odd,even] , where  odd  is the sum of squares all the odd numbers in  l  and  even  is the sum of squares of all the even numbers in  l .

Here are some examples to show how your function should work.

A two dimensional matrix can be represented in Python row-wise, as a list of lists: each inner list represents one row of the matrix. For instance, the matrix

would be represented as  [[1, 2, 3, 4], [5, 6, 7, 8]] .

The transpose of a matrix converts each row into a column. The transpose of the matrix above is:

which would be represented as  [[1, 5], [2, 6], [3, 7], [4, 8]] .

Write a Python function  transpose(m)  that takes as input a two dimensional matrix  m  and returns the transpose of  m .  The argument  m  should remain undisturbed by the function .

Here are some examples to show how your function should work. You may assume that the input to the function is always a non-empty matrix.