24.2: Transforming with Simple Operations
- Page ID
- 88751
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Now that we’ve converted the awkward minutes-and-seconds columns to just “time” columns, all we need to do to complete our analysis is transform this data by computing a new quantity entirely: the total number of minutes played for each player in each game. Again, Pandas makes this easy:
Code \(\PageIndex{1}\) (Python):
wc['minsplayed'] = wc.outtime - wc.intime
print(wc)
Voilà. We now have the time-on-field for each player, which gives us a whole new avenue of exploration. For example, any of the counting stats (goals, assists, etc.) can be converted into a “perminute” version, showing us how productive a player was while on the field. Let’s do that for tkls (“tackles”), and multiply by 90 to obtain a “tackles-per-90-minutes” statistic1 :
Code \(\PageIndex{2}\) (Python):
wc['minsplayed'] = wc['outtime'] - wc['intime']
wc['tkl_per_90'] = np.round(wc['tkls'] /
wc['minsplayed'] * 90,2)
del wc['tkls']
Transforming Grouped Data
The above example computed tackles-per-game all right, but it still left us with one row for every player-performance. (In other words, the results had two rows for Rose Lavelle, one giving her tkl_per_90 for the June 28th game, and one giving it for the July 7th game.)
We might instead be interested in a player-by-player analysis: overall in the entire month-long World Cup, which players had the most tackles-per-game? This is easy to do with the .groupby() method that we first encountered in section 18.2 (p. 189). First, we group the rows by the first two columns (since first-and-last-namestogether are needed to uniquely identify a single player):
Code \(\PageIndex{3}\) (Python):
grouped_wc = wc.groupby(['last','first'])
We then take our new, temporary grouped_wc variable and extract the gls, asst, shots, tkls, and minsplayed columns from it, summing each of them to produce the per-player values in the result:
Code \(\PageIndex{4}\) (Python):
by_player = grouped_wc.sum()
This yields:
Now, we’re ready to compute a per-game analysis as before, but this time for each player’s entire World Cup games:
Code \(\PageIndex{5}\) (Python):
by_player['tkl_per_90'] = (np.round(by_player['tkls'] / by_player['minsplayed'] * 90,2))
del by_player['tkls']
1I’m choosing 90 minutes here because that’s how long a regulation-length soccer match is. Therefore, our new tkl_per_90 column gives us “numberof-tackles-per-complete-game,” which is easier to interpret than “tackles-per-minute,” which would be a miniscule number for any player.