df = seaborn.load_dataset("mpg")
print(df.head())
print(df.info())

The output of the above Python statements will be like the following:

    mpg  cylinders  displacement  ...  model_year  origin                       name
0  18.0          8         307.0  ...          70     usa  chevrolet chevelle malibu
1  15.0          8         350.0  ...          70     usa          buick skylark 320
2  18.0          8         318.0  ...          70     usa         plymouth satellite
3  16.0          8         304.0  ...          70     usa              amc rebel sst
4  17.0          8         302.0  ...          70     usa                ford torino
 
[5 rows x 9 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 9 columns):
#   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
0   mpg           398 non-null    float64
1   cylinders     398 non-null    int64  
2   displacement  398 non-null    float64
3   horsepower    392 non-null    float64
4   weight        398 non-null    int64  
5   acceleration  398 non-null    float64
6   model_year    398 non-null    int64  
7   origin        398 non-null    object 
8   name          398 non-null    object 
dtypes: float64(4), int64(3), object(2)
memory usage: 28.1+ KB

As we can see the horsepower variable contains some null values. It has only 392 non-null values out of 398 rows. So, for simplicity, we will drop the rows that contain null values from the dataset.

df = df.dropna()

Now, we plot the scatterplot that shows the relationship between mpg and horsepower of a car.

pyplot.scatter(df["horsepower"], df["mpg"])
pyplot.xlabel("Horsepower")
pyplot.ylabel("MPG")
pyplot.savefig("poly-regression-scatter.png")
pyplot.close()

The graph looks like the following: