Call matplotlib.pyplot.plot$($days$,$ rates, $<...$further$\_$arguments...$>)$ to draw the Q$3$

$2023$ data $($with $182$ denoting $1$ July$),$ using red solid line segments. Call matplotlib.pyplot.title

to add the plot title. Discuss what you see.

$180200220240260$

$25000$

$26000$

$27000$

$28000$

$29000$

$30000$

$31000$

BTC to USD in Q$32023$

$5.$ Determine the day numbers $($with $182$ denoting $1$ July$)$ with the lowest and highest observed prices

in Q$32023.$

## Lowest price was on day $254(25162\mathrm{.}65).$

## Highest price was on day $194(31476\mathrm{.}05).$

$6.$ Using matplotlib.pyplot.boxplot, draw a horizontal box$-$and$-$whisker plot for the Q$32023$

daily price increases$/$decreases as obtained by a call to numpy.diff.

Using an additional call to matplotlib.pyplot.plot, mark the arithmetic mean on the box plot

with a green $$x$.$

In your own words, explain what we can read from the plot.

$2$

$200015001000500050010001500$

Distribution of BTC$-$to$-$USD daily price increases in Q$32023$

$7.$ Count $($programmatically$,$ using the vectorised relational operators from numpy$)$ how many outliers

the boxplot contains $($for the definition of an outlier, consult Section $2\mathrm{.}3$ of our learning materials

on the unit site or Section $5\mathrm{.}1$ in the Book$).$ In your own words, explain what such outliers

might mean in the current context.

## There are $16$ outliers.

All packages must be imported and data must be loaded at the beginning of the file $($only once!$).$