Task $21$

Is there a trend on scores in the best district?

Using the DataFrame plot method, plot the performance by grade of the top distict across time.

$($Hint: Use selection, then groupby, then plot. Do not use a scatter plot for this task. You want a basic plot that shows the trend over time$/$years$.)$

Over time, is the district peformance improving, deteriorating, or staying the same for each grade?

Task $22$

Define a function bottom that returns the n rows with the lowest value for the specified column.

bottom should accept a dataframe as its first input, a parameter named n that accepts a number and provides a reasonable default, and a paramater called column that defaults to 'Mean Scale Score'.

$($Hint: This is similar to top$)$

Demonstrate the function against the entire 'schools' dataframe.

Task $23$

Use the apply method and your defined bottom function to display the full row for the bottom score in each grade.

Task $24$

What insight did you get from the previous cell?

Task $25$

Extend your selection above to show the row for the bottom score for the combination of grade and year.

Task $26$

What insight did you get from the previous cell?

Results

The Mayor wants to recognize the top performing districts and direct additional resources to assist lower performing districts.

She asks you to rank the scores by performance as follows.

For each grade and year, rank each district based upon their 'Mean Scale Score.$\text{'}$ The district with the highest 'Mean Scale Score' should get a rank of $1,$ the second highest should get a rank of $2,$ etc.

After ranking for each grade and year, sum the ranks for each grade over all years. For the Mayor's purpose, the districts with the lowest total sum of the ranks $($i$.$e$.,$ the lowest rank numbers overall$)$ are considered the best performing schools.

This task is going to take a bit of work, so let's break the problem into incremental chunks of work.

Task $27$

Let's make a smaller dataframe to use while we are working out the larger problem.

Create a dataframe called 'schools$\_$subset' from 'schools' that includes only Grade $3$ for the year $2012.$ We are not going to need all the columns, so only add district, grade, year, and mean scale score to the new dataframe.

Display the schools$\_$subset dataframe.

Task $28$

Look up the DataFrame rank method. We will use this method to set the ranks. Since we want the highest rank to be $1,$ we need to set the 'ascending' parameter to the rank call to False.

Make a function called add$\_$default$\_$rank that takes a dataframe the first parameter and a 'column' parameter with the default 'Mean Scale Score' as the second paramter.

This function should create a new column called 'Default Rank' in the passed dataframe. The value of the new column should be the rank for the passed 'column' parameter.

Demonstrate your add$\_$default$\_$rank function works by calling the function on the 'students$\_$subset' dataframe. Print the 'schools$\_$subset' dataframe before and after the invocation of the function to confirm that $(1)$ the 'Default Rank' column was added and $(2)$ that the rank assigned to each row is correct based upon the 'Mean Scaled Score' value.

Task $29$

Now that you have the add$\_$default$\_$rank function that works on a dataframe, you can use the apply method to apply that method to each group from a groupby.

Group the full 'schools' dataframe by grade and year, then apply add$\_$default$\_$rank to the groups. Store the results of this into a new 'schools$2\text{'}$ dataframe.

Display the resulting 'schools$2\text{'}$ dataframe.

Task $30$

The 'schools$2\text{'}$ now has a rank for each district for each grade and each year.

To fulfill the Mayor's ranking request, we can now produce the ordered list of districts with the top performers at the top of the list.

To do this, sum the 'Default Rank' column grouping by District and sorting the result using the sort$\_$values method with "ascending" set to True and show the results.