问题
I have a pandas dataframe that looks as follows:
ID round player1 player2
1 1 A B
1 2 A C
1 3 B D
2 1 B C
2 2 C D
2 3 C E
3 1 B C
3 2 C D
3 3 C A
The dataframe contains sport match results, where the ID column denotes one tournament, the round column denotes the round for each tournament, and player1 and player2 columns contain the names of players that played against eachother in the respective round.
I now want to cumulatively count the tournament participations for, say, player A. In pseudocode this means: If the player with name A comes up in either the player1 or player2 column per tournament ID, increment the counter by 1.
The result should look like this (note: in my example player A did participate in tournaments with the IDs 1 and 3):
ID round player1 player2 playerAparticipated
1 1 A B 1
1 2 A C 1
1 3 B D 1
2 1 B C 0
2 2 C D 0
2 3 C E 0
3 1 B C 2
3 2 C D 2
3 3 C A 2
My current status is, that I added a "helper" column containing the values 1 or 0 denoting, if the respective player participated in the tournament:
ID round player1 player2 helper
1 1 A B 1
1 2 A C 1
1 3 B D 1
2 1 B C 0
2 2 C D 0
2 3 C E 0
3 1 B C 1
3 2 C D 1
3 3 C A 1
I think that I just need one final step, e.g., a smart use of cumsum() that counts the helper column in the desired way. However, I could not come up with the solution yet.
回答1:
I think you need:
- drop_duplicates by column
IDfirst and then set_index - filter out
0values by boolean indexing, cumsum and last reindex for add0for missing index values - new column create by map
df1 = df.drop_duplicates('ID').set_index('ID')
s = df1.loc[df1['helper'] != 0, 'helper'].cumsum().reindex(index=df1.index, fill_value=0)
df['playerAparticipated'] = df['ID'].map(s)
print (df)
ID round player1 player2 helper playerAparticipated
0 1 1 A B 1 1
1 1 2 A C 1 1
2 1 3 B D 1 1
3 2 1 B C 0 0
4 2 2 C D 0 0
5 2 3 C E 0 0
6 3 1 B C 1 2
7 3 2 C D 1 2
8 3 3 C A 1 2
Instead map is possible use join with rename:
df = df.join(s.rename('playerAparticipated'), on='ID')
print (df)
ID round player1 player2 helper playerAparticipated
0 1 1 A B 1 1
1 1 2 A C 1 1
2 1 3 B D 1 1
3 2 1 B C 0 0
4 2 2 C D 0 0
5 2 3 C E 0 0
6 3 1 B C 1 2
7 3 2 C D 1 2
8 3 3 C A 1 2
回答2:
A similar approach to @jezrael that I cooked up a little slower :).
First, move ID into your index:
df = df.reset_index().set_index(['index','ID'])
# round player1 player2 helper
# index ID
# 0 1 1 A B 1
# 1 1 2 A C 1
# 2 1 3 B D 1
# 3 2 1 B C 0
# 4 2 2 C D 0
# 5 2 3 C E 0
# 6 3 1 B C 1
# 7 3 2 C D 1
# 8 3 3 C A 1
Next, filter out rows where helper is 0 and get a cumulative sum of tournaments by ID, and assign the result to a variable:
tournament_count = df[df['helper'] > 0].groupby(['ID','helper']).first().reset_index(level=1)['helper'].cumsum().rename("playerAparticipated")
# ID
# 1 1
# 3 2
Finally, join the tournament_count DataFrame with df:
df.join(tournament_counts, how="left").fillna(0)
# round player1 player2 helper tournament_counts
# index ID
# 0 1 1 A B 1 1.0
# 1 1 2 A C 1 1.0
# 2 1 3 B D 1 1.0
# 3 2 1 B C 0 0.0
# 4 2 2 C D 0 0.0
# 5 2 3 C E 0 0.0
# 6 3 1 B C 1 2.0
# 7 3 2 C D 1 2.0
# 8 3 3 C A 1 2.0
来源：https://stackoverflow.com/questions/45119623/pandas-count-over-groups