A$-1.[3$ marks$]$: Read the data $($Assume the first row in $"$HW$2\_$Data$\_$A$"$ contains the column headings$).$ Then display for all columns:

$($i$)30$ randomly selected rows

$($ii$)20$ last rows.

A$-2.[5$ marks$]$: Create a new dataframe $($let$\text{'}$s say: ISE$291)$ by selecting the first $25$ rows from column$-4($processor$\_$name$)$ to column$-11($ssd$).$ Then, sort the new dataframe in ascending order by the first column then in decending order by the second column. Make the sorting in place.

A$-3.[4$ marks$]$: Considering the new dataframe from part A$-2,$ display the separate statistical summary for numerical and non$-$numerical columns.

A$-4.[6$ marks$]$: From the original dataset $"$HW$2\_$Data$\_$A$",$ how many laptops have an 'AMD' processor with $\text{'}4$ GB$\text{'}$ of RAM and a price below $5000?$

A$-5.[5$ marks$]$: Add a new column $($Int$\_$Price$)$ by converting 'Price' column values from float to nearest integer using the $.$apply$()$ method and a custom function. $[$Note: using lambda function is not allowed$]$

A$-6[3$ marks$]$: Display the box$-$plot for the "price" column, and differentiate by column.

A$-7[8$ marks$]$: For all laptops priced above $30,000,$ plot a countplot showing their 'processor$\_$name', and differentiate by 'brand' column.

$($i$)$ How many processors were actually found?

$($ii$)$ What can you conclude from the plot?