Download the nycflights$13\_$weather.csv$.$gz data file from our unit site $($Learning Resources $->$ Data$).$

It gives the hourly meteorological data for three airports in New York: LGA, JFK$,$ and EWR for the whole

year of $2013.$ The columns are:

$$ origin $$ weather station: LGA, JFK$,$ or EWR,

$$ year, month, day, hour $$ time of recording,

$$ temp, dewp $$ temperature and dew point in degrees Fahrenheit,

$$ humid $$ relative humidity,

$$ wind$\_$dir, wind$\_$speed, wind$\_$gust $$ wind direction $($in degrees$),$ speed and gust speed $($in mph$),$

$$ precip $$ precipitation, in inches,

$$ pressure $$ sea level pressure in millibars,

$$ visib $$ visibility in miles,

$$ time$\_$hour $$ date and hour $($based on the year, month, day, hour fields$)$ formatted as YYYY$-$mmdd

HH:MM:SS $($actually$,$ YYYY$-$mm$-$dd HH:$00$:$00).$ However, due to a bug in the dataset, the data in this

column are $($incorrectly$!)$ shifted by $1$ hour. Do not rely on it unless you manually correct it$.$

Then, create a single Jupyter$/$IPython notebook $($see the Artefacts section below for all the requirements$),$

where you perform what follows.

$1.$ Convert all columns so that they use metric $($International System of Units, SI$)$ or derived units:

temp and dewp to Celsius, precip to millimetres, visib to metres, as well as wind$\_$speed and

wind$\_$gust to metres per second. Replace the data in$-$place $($overwrite existing columns with new

ones$).$

$2.$ Compute daily mean wind speeds for the LGA airport $($~$365$ total speed values, for each day separately;

you can, for example, group the data by year, month, and day at the same time$).$

$3.$ Present the daily mean wind speeds at LGA $($~$365$ aforementioned data points$)$ in a single plot, e$.$g$.,$

using the matplotlib.pyplot.plot function. The x$-$axis labels should be human$-$readable and

intuitive $($e$.$g$.,$ month names or dates$).$ Reference result:

$1$

$2013-012013-032013-052013-072013-092013-112014-01$

day

$2$

$4$

$6$

$8$

$10$

daily average wind speed $[$m$/$s$]$ at LGA

$4.$ Identify the ten windiest days at LGA $($dates and the corresponding mean daily wind speeds$).$

Reference result:

## wind$\_$speed

## date

## $2013-11-2411\mathrm{.}317783$

## $2013-01-3110\mathrm{.}717598$

## $2013-02-1710\mathrm{.}010236$

## $2013-02-219\mathrm{.}192903$

## $2013-02-189\mathrm{.}174264$

## $2013-03-149\mathrm{.}109958$

## $2013-11-288\mathrm{.}938477$

## $2013-05-268\mathrm{.}852736$

## $2013-05-258\mathrm{.}766995$

## $2013-02-208\mathrm{.}659819$

$5.$ Compute the monthly mean wind speeds for all the three airports.

There is one obvious outlier amongst the observed wind speeds. Locate it $($programmatically$,$ do

not hardcode the date$/$day$/$row number$)$ and replace it with np$.$nan $($NaN$)$ before computing the

means.

$6.$ Draw the monthly mean wind speeds for the three airports on the same plot $($three curves of different

colours$).$ Add a readable legend. Reference result:

$2$

$2013-012013-032013-052013-072013-092013-11$

month

$3\mathrm{.}5$

$4\mathrm{.}0$

$4\mathrm{.}5$

$5\mathrm{.}0$

$5\mathrm{.}5$

$6\mathrm{.}0$