Learning Data Mining with Python

Predicting Sports Winners with Decision Trees
We first create a (default) dictionary to store the team's last result:
from collections import defaultdict
won_last = defaultdict(int)
The key of this dictionary will be the team and the value will be whether they won
their previous game. We can then iterate over all the rows and update the current
row with the team's last result:
for index, row in dataset.iterrows():
home_team = row["Home Team"]
visitor_team = row["Visitor Team"]
row["HomeLastWin"] = won_last[home_team]
row["VisitorLastWin"] = won_last[visitor_team]
dataset.ix[index] = row
Note that the preceding code relies on our dataset being in chronological order. Our
dataset is in order; however, if you are using a dataset that is not in order, you will
need to replace dataset.iterrows() with dataset.sort("Date").iterrows().
We then set our dictionary with the each team's result (from this row) for the next
time we see these teams. The code is as follows:
won_last[home_team] = row["HomeWin"]
won_last[visitor_team] = not row["HomeWin"]
After the preceding code runs, we will have two new features: HomeLastWin and
VisitorLastWin. We can have a look at the dataset. There isn't much point in
looking at the first five games though. Due to the way our code runs, we didn't have
data for them at that point. Therefore, until a team's second game of the season, we
won't know their current form. We can instead look at different places in the list.
The following code will show the 20th to the 25th games of the season:
dataset.ix[20:25]
Here's the output:
[ 46 ]