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
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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?
import numpy as np
B = [True, 2, 3.0, np.nan, "False"]
[type(i) for i in B]a) [bool, int, float, float, str]
b) [str, int, float, float, str]
c) [bool, int, float, int, str]
d) [bool, int, int, float, str]
Answer: a)
Python for Data Science NPTEL Week 3 Answers
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
Answer: c)
Q3. Data from the file “weather.xlsx“ has to be loaded into a pandas dataframe df_weather which when printed is as shown below:
In [38]: df_weather
Out[38]:
Direction Temperature Windspeed Humidity
0 East 49 10 78
1 West 54 5 80
2 North 35 8 92
3 South 42 15 70Of 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]
Answer: a), c)
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])
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?
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
Answer: c)
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)
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.
| Subject Person | Chemistry | Maths | Physics | All |
| Harini | 90.00 | 94.00 | 83.00 | 89.00 |
| Rekha | 92.00 | 85.00 | 95.00 | 90.67 |
| Sathi | 74.00 | 81.00 | 81.00 | 79.67 |
| All | 85.33 | 86.67 | 86.33 | 86.44 |
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)
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)
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)
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)
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