# $12.(*$Code$)$ ic$\_$long $=$ ic$\_$wide.melt$($id$\_$vars$=[\text{'}$ID$\text{'},$'address'$],$value$\_$name$=$'count',var$\_$name$=$'day'$)$

ic$\_$long

# Write the code to create a heatmap $($or copy$-$paste from item $9).$

# Instead of using data$=$ic$\_$wide, make data be the line of code from item $10.$

# We can use this temporary copy of a dataframe instead of saving it as another

# dataframe.

# Your answer:

# $13.(*$Response$)$

# Uh$-$oh$!$ Why is this heat map useless? $($look carefully at the x$-$axis and color

# scale; compare to the dataframe$)$

# I.e$.$ why are all the blocks on the days of the week the same color? $($Please be

# as specific as possible.$)$

# Your answer:

# $15.$ We can string together these two commands by putting the $".$drop" command

# followed by the $".$set$\_$index" command. Run this code.

# We'll save it as a DataFrame for clarity.

ic$\_$reformatted $=$ ic$\_$wide.drop$($columns$=$'address'$).$set$\_$index$(\text{'}$ID$\text{'})$

ic$\_$reformatted

# $16.(*$Code$)$

# Use the ic$\_$reformatted to create a heat map.

# Title and label as needed.

# Your answer:

# $17.(*$Code and Response$)$

# From the heat map, we can see that one ice cream shop is in a business district

# where people do not spend time on the weekend. Let's practice filtering rows

# by filtering out the data.

# Use the original dataframe ic$\_$wide for this part.

# a$.(*$Response$)$ What is the ID number of this ice cream shop?

# b$.(*$Code$)$ Use logical indexing to print True for the row with that shop and False for the others.

# c$.(*$Code$)$ We want to get rid of that row. We therefore want to flip this to get False for

# that row and True for the others. Use negation in the logical indexing to produce

# the opposite result of part b$.$

# d$.(*$Code$)$ Use row selection combined with your answer to part c to print all the rows that

# are not that row.

# e$.(*$Code$)$ Save your answer from part d into a dataframe called ic$.$