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)
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ISBN:9780133594140
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Chapter1: Computer Networks And The Internet
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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 **](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F74bc20e3-8cce-4582-b12b-90f9b9ec96e4%2Fcc80655d-d5eb-48aa-af97-f65c0c141e13%2Feo8c5fk_processed.png&w=3840&q=75)
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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