Python - Need help in solving "Load the R data set mtcars as a pandas dataframe." problem

Question

I am working on this problem and unsure on how to proceed.

Load the R data set mtcars as a pandas dataframe. Build a linear regression model by considering the log of independent variable wt, and log of dependent variable mpg. Fit the model with data.

Perform ANOVA on the linear model obtained in the previous step.(Hint:Use anova.anova_lm)

Display the F-statistic value.

I see in another post below solution was provided. But it doesn't to seem work.

import statsmodels.api as sm
import numpy as np
mtcars = sm.datasets.get_rdataset('mtcars')
mtcars_data = mtcars.data
liner_model = sm.formula.ols('np.log(wt) ~ np.log(mpg)',mtcars_data)
liner_result = liner_model.fit()
print(liner_result.rsquared)'''

Answer 1

fixed it

import statsmodels.api as sm
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
from statsmodels.stats import anova

mtcars = sm.datasets.get_rdataset("mtcars", "datasets", cache=True).data
df = pd.DataFrame(mtcars)
model = smf.ols(formula='np.log(mpg) ~ np.log(wt)', data=mtcars).fit()
print(anova.anova_lm(model))
print(anova.anova_lm(model).F["np.log(wt)"])