Needour task is to create a visual report for these data. You will create a single Matplotlib Figure containing the following subplots:

A scatter plot with all$\_$weights on the x$-$axis and all$\_$mpgs on the y$-$axis

A line plot with unique$\_$model$\_$years with separate y$-$axes for yearly$\_$mean$\_$mpgs and yearly$\_$mean$\_$horsepower. Include markers for each data point.

Two pie charts, one each for the number of models by origin in origins$\_1970$ and origins$\_1980$

A series of $6$ bar plots with origin$\_$mean$\_$mpgs$,$ origin$\_$mean$\_$horsepower, origin$\_$mean$\_$cylinders, origin$\_$mean$\_$displacement, origin$\_$mean$\_$weight, and origin$\_$mean$\_$acceleration on y$-$axis, and each should have unique$\_$origins on the x$-$axis.

The subplots should use a layout with $3$ rows:

Row $1,$ three subplots: Scatter plot $($item $1$ above$),2$ pie charts $($item $3)$

Row $2,1$ subplot stretched across the figure: Line plot $($item $2)$

Row $3,6$ subplots: $6$ bar plots $($item $4)$

Be sure to give each subplot a title and label the axes with appropriate descriptor and units. You may customize the appearance $($colors$,$ marker types, etc.$)$ in any way you think looks appealing to be written with python code, and here is the start codeimport pandas as pd

import seaborn as sns

data $=$ sns$.$load$\_$dataset$(\text{'}$mpg$\text{'})$

all$\_$mpgs $=$ data$[\text{'}$mpg$\text{'}].$to$\_$list$()$

all$\_$weights $=$ data$[\text{'}$weight$\text{'}].$to$\_$list$()$

unique$\_$model$\_$years $=$ list$($data$[\text{'}$model$\_$year'$].$unique$())$

unique$\_$origins $=$ sorted$($list$($data$[\text{'}$origin$\text{'}].$unique$()))$

yearly$\_$mean$\_$mpgs $=$ data.groupby$(\text{'}$model$\_$year'$).$mean$($numeric$\_$only$=$True$)[\text{'}$mpg$\text{'}].$to$\_$list$()$

yearly$\_$mean$\_$horsepower $=$ data.groupby$(\text{'}$model$\_$year'$).$mean$($numeric$\_$only$=$True$)[\text{'}$horsepower$\text{'}].$to$\_$list$()$

origins$\_1970=$ data.loc$[$data$[\text{'}$model$\_$year'$]==70,$ 'origin'$]$

origins$\_1980=$ data.loc$[$data$[\text{'}$model$\_$year'$]==80,$ 'origin'$]$

origin$\_$mean$\_$mpgs $=$ data.groupby$(\text{'}$origin$\text{'}).$mean$($numeric$\_$only$=$True$)[\text{'}$mpg$\text{'}].$to$\_$list$()$

origin$\_$mean$\_$horsepower $=$ data.groupby$(\text{'}$origin$\text{'}).$mean$($numeric$\_$only$=$True$)[\text{'}$horsepower$\text{'}].$to$\_$list$()$

origin$\_$mean$\_$cylinders $=$ data.groupby$(\text{'}$origin$\text{'}).$mean$($numeric$\_$only$=$True$)[\text{'}$cylinders$\text{'}].$to$\_$list$()$

origin$\_$mean$\_$displacement $=$ data.groupby$(\text{'}$origin$\text{'}).$mean$($numeric$\_$only$=$True$)[\text{'}$displacement$\text{'}].$to$\_$list$()$

origin$\_$mean$\_$weight $=$ data.groupby$(\text{'}$origin$\text{'}).$mean$($numeric$\_$only$=$True$)[\text{'}$weight$\text{'}].$to$\_$list$()$

origin$\_$mean$\_$acceleration $=$ data.groupby$(\text{'}$origin$\text{'}).$mean$($numeric$\_$only$=$True$)[\text{'}$acceleration$\text{'}].$to$\_$list$()$