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For the following dataframe (df): Y 1 NaN 1 2 NaN NaN 1 3 What will be the output for the following command? df.notna().sum) 0 False 1 False 2 False Name: z, dtype: bool х 2 y 1 3 X True y True False dtype: bool Syntax Error

For the following dataframe (df): Y 1 NaN 1 2 NaN NaN 1 3 What will be the output for the following command? df.notna().sum) 0 False 1 False 2 False Name: z, dtype: bool х 2 y 1 3 X True y True False dtype: bool Syntax Error

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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### Understanding DataFrames and Not NaN (notna) Method in Python In this exercise, we will explore how to use the `df.notna().sum()` function on a DataFrame to determine the non-missing values for each column. Consider the following DataFrame (df): | X | Y | Z | |-----|-----|-----| | 1 | NaN | 1 | | 2 | NaN | 2 | | NaN | 1 | 3 | #### Question: What will be the output for the following command? ```python df.notna().sum() ``` #### Options: - **Option 1:** ``` 0 False 1 False 2 False Name: z, dtype: bool ``` - **Option 2:** ``` x 2 y 1 z 3 ``` - **Option 3:** ``` x True y True z False dtype: bool ``` - **Option 4:** ``` Syntax Error ``` #### Explanation: To determine the correct output, let’s understand what `df.notna().sum()` does: 1. `df.notna()` creates a DataFrame of the same shape with boolean values indicating the presence (True) or absence (False) of non-missing values (NaN). 2. `.sum()` then sums these boolean values along each column, where `True` is considered as 1 and `False` as 0. For the given DataFrame: ``` X Y Z 1 NaN 1 2 NaN 2 NaN 1 3 ``` - For column `X`: Two non-missing values (1 and 2) → sum is 2 - For column `Y`: One non-missing value (1) → sum is 1 - For column `Z`: Three non-missing values (1, 2, 3) → sum is 3 Hence, the correct output of `df.notna().sum()` will be: ``` x 2 y 1 z 3 ``` Thus, the correct option is **
Transcribed Image Text:### Understanding DataFrames and Not NaN (notna) Method in Python In this exercise, we will explore how to use the `df.notna().sum()` function on a DataFrame to determine the non-missing values for each column. Consider the following DataFrame (df): | X | Y | Z | |-----|-----|-----| | 1 | NaN | 1 | | 2 | NaN | 2 | | NaN | 1 | 3 | #### Question: What will be the output for the following command? ```python df.notna().sum() ``` #### Options: - **Option 1:** ``` 0 False 1 False 2 False Name: z, dtype: bool ``` - **Option 2:** ``` x 2 y 1 z 3 ``` - **Option 3:** ``` x True y True z False dtype: bool ``` - **Option 4:** ``` Syntax Error ``` #### Explanation: To determine the correct output, let’s understand what `df.notna().sum()` does: 1. `df.notna()` creates a DataFrame of the same shape with boolean values indicating the presence (True) or absence (False) of non-missing values (NaN). 2. `.sum()` then sums these boolean values along each column, where `True` is considered as 1 and `False` as 0. For the given DataFrame: ``` X Y Z 1 NaN 1 2 NaN 2 NaN 1 3 ``` - For column `X`: Two non-missing values (1 and 2) → sum is 2 - For column `Y`: One non-missing value (1) → sum is 1 - For column `Z`: Three non-missing values (1, 2, 3) → sum is 3 Hence, the correct output of `df.notna().sum()` will be: ``` x 2 y 1 z 3 ``` Thus, the correct option is **
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