Tricky Python Quiz For Data Science And Machine Learning Interviews! #39

A quick knowledge check!

Hello Everyone,

Welcome to the 39th edition of my newsletter ML & AI Cupcakes!

In today’s newsletter, I have compiled a set of some basic, but tricky MCQs to help you access your python skills for data science and machine learning interviews.

Please note that standard notations have been used for importing libraries wherever needed.

import numpy as np

import pandas as pd

Good luck!

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1. Which function is used to calculate mean in numpy?

   1. mean()

   2. average()

   3. sum()

   4. mode()

2. Which of the following is a valid pandas function?

   1. pd.read_pickle()

   2. pd.read_html()

   3. pd.read_csv()

   4. All of the above

3. Which data structure is immutable?

   1. Set

   2. Tuple

   3. List

   4. Dictionary

4. Which data structure doesn’t allow duplicate elements?

   1. Set

   2. Tuple

   3. List

   4. Dictionary

5. What will be the output of len ([2, 3, [2,2,3], np.nan, [1,0]])?

   1. 4

   2. 5

   3. 7

   4. 8

6. What is the outcome of my_list.append([2,3,4])?

   1. adds 2, 3 and 4 as individual items in my_list

   2. adds [2,3,4] as a sublist in my_list

   3. appends only 2 to my_list

   4. throws an error

7. What is the outcome of my_list.extend([2,3,4])?

   1. adds 2, 3 and 4 as individual items in my_list

   2. adds [2,3,4] as a sublist in my_list

   3. replaces the existing list

   4. throws an error

8. Which of the following is true about np.nanmean()?

   1. throws an error

   2. gives NaN as output

   3. calculates mean by ignoring NaN values

   4. replace NaNs with mean value

9. Which of the following is used to check NaN values in a numpy array?

   1. pd.isnull()

   2. np.isnull()

   3. np.isna()

   4. np.isnan()

10. What will be the output of np.eye(4)?

    1. 4x4 matrix of zeros

    2. 4x4 matrix of ones

    3. 4x4 identity matrix

    4. Diagonal matrix with 4s

11. What does arr1[2:4] will return for arr1 = np.array([34, 12, 45, 33, 78 , 90])?

    1. [12, 45, 33]

    2. [12, 45]

    3. [45, 33]

    4. [45, 33, 78]

12. Which axis refers to rows in a pandas dataframe?

    1. -1

    2. 0

    3. 1

    4. 2

13. What is the output of df.shape?

    1. number of rows

    2. numer of columns

    3. number of rows*number of columns

    4. (number of rows, number of columns)

14. What gives the frequency of unique values in a column?

    1. df[‘col’].unique()

    2. df[‘col’].nunique()

    3. df[‘col’].value_counts()

    4. df[‘col’].isnull()

15. Which method is used to remove duplicate rows in a pandas dataframe?

    1. drop_duplicates()

    2. remove_duplicates()

    3. dropna()

    4. clean()

16. what does df.iloc[1] returns in a dataframe?

    1. first row

    2. second row

    3. last row

    4. column names

17. Which of the following returns count of missing values in each column of a pandas dataframe?

    1. df.isnull()

    2. df.isnull().sum()

    3. df.isnan()

    4. df.isnan().sum()

18. Which of the following will give unique values from a column in a pandas dataframe?

    1. df[‘col’].values()

    2. df['col'].unique()

    3. df['col'].unique_values()

    4. df['col'].unique_counts()

19. Which of the following will return basic summary stats for numerical columns in a pandas dataframe?

    1. df.head()

    2. df.info()

    3. df.tail()

    4. df.describe()

20. What will be the output of df[np.isnan(df[‘col'])]?

    1. returns the dataframe excluding rows with null values

    2. returns the rows with null values in column ‘col’

    3. returns the rows with zero values

    4. returns first 5 rows of the dataframe

21. What is not returned by df.info()?

    1. memory usage

    2. data types

    3. mean values

    4. column names

22. What will be the outcome of df[df['TotalCharges']>8600]?(Please note that df dataframe contains 5 columns)

    1. returns a boolean series

    2. returns the index of matching rows

    3. returns only the 'TotalCharges' column

    4. returns rows where 'TotalCharges' are greater than 8600

23. You want rows where ‘tenure’ is less than 5 and ‘MonthlyCharges’ are less than 35. Which of the following syntax will give you the results?

    1. df.loc[(df['tenure']<5)& (df['MonthlyCharges']<35)]

    2. df.loc[(df['tenure']<5)& (df['MonthlyCharges']>35)]

    3. df.loc[(df['tenure']>5 $ (df['MonthlyCharges']<35)]

    4. df.loc[(df['tenure']>5) || (df['MonthlyCharges']<35)]

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Answers

1. a 2. d 3. b 4. a 5. b 6. b 7. a 8. c 9. d 10. c 11. c 12. b 13. d 14. c 15. a

   16. b 17. b 18. b 19. d 20. b 21. c 22. d 23.a

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