pandas 聚合数据并获得总和和计数
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Aggregating data and getting sum and counts
提问by John Doe
I have an object in python with a lot of rows:
我在 python 中有一个对象，有很多行：
INPUT :
输入 ：
Team1 Player1 idTrip13 133
Team2 Player333 idTrip10 18373
Team3 Player22 idTrip12 17338899
Team2 Player293 idTrip02 17656
Team3 Player20 idTrip11 1883
Team1 Player1 idTrip19 19393
and I need to aggregate this data (like a pivot table).
我需要汇总这些数据（如数据透视表）。
OUTPUT I am working on:
输出我正在处理：
Team1 Player1 : 2 trips : sum(133+19393)
Team2 Player333 : 1 trip : 18373; Player293 : 1 trip : 17656
Team3 Player22 : 1 trip : 17338899; Player20 : 1 trip : 1883
Could someone suggest the appropriate object in Python to use such that I could have the following output?
有人可以建议使用 Python 中的适当对象，以便我可以得到以下输出吗？
print team, player, trips, time
回答by ilyas patanam
Use groupby function for pandas DataFrames
对Pandas DataFrames使用 groupby 函数
Put your data into a list of lists, each inner list will be a row in the dataframe.
In[1]: mydata = [['Team1', 'Player1', 'idTrip13', 133], ['Team2', 'Player333', 'idTrip10', 18373], ['Team3', 'Player22', 'idTrip12', 17338899], ['Team2', 'Player293','idTrip02', 17656], ['Team3', 'Player20', 'idTrip11', 1883], ['Team1', 'Player1', 'idTrip19', 19393]] df = pd.DataFrame(mydata, columns = ['team', 'player', 'trips', 'time']) df Out[1]: team player trips time 0 Team1 Player1 idTrip13 133 1 Team2 Player333 idTrip10 18373 2 Team3 Player22 idTrip12 17338899 3 Team2 Player293 idTrip02 17656 4 Team3 Player20 idTrip11 1883 5 Team1 Player1 idTrip19 19393Call
groupby(), pass the column you wish to use as your grouper, and apply a function to the groups.
将您的数据放入一个列表列表中，每个内部列表将是数据框中的一行。
In[1]: mydata = [['Team1', 'Player1', 'idTrip13', 133], ['Team2', 'Player333', 'idTrip10', 18373], ['Team3', 'Player22', 'idTrip12', 17338899], ['Team2', 'Player293','idTrip02', 17656], ['Team3', 'Player20', 'idTrip11', 1883], ['Team1', 'Player1', 'idTrip19', 19393]] df = pd.DataFrame(mydata, columns = ['team', 'player', 'trips', 'time']) df Out[1]: team player trips time 0 Team1 Player1 idTrip13 133 1 Team2 Player333 idTrip10 18373 2 Team3 Player22 idTrip12 17338899 3 Team2 Player293 idTrip02 17656 4 Team3 Player20 idTrip11 1883 5 Team1 Player1 idTrip19 19393调用
groupby()，传递您希望用作石斑鱼的列，然后将函数应用于组。
Examples
例子
Ex. 1Find the number of trips each team went on. teamis the grouper, and we apply the function count()on column ['trips'].
前任。1找出每个团队进行的旅行次数。team是石斑鱼，我们count()在 column 上应用函数['trips']。
In[2]:
trip_count = df.groupby(by = ['team'])['trips'].count()
trip_count
Out[2]:
team
Team1 2
Team2 2
Team3 2
Name: trips, dtype: int64
Ex. 2 (multiple columns): Find the total time each player on a team spent traveling. We use 2 columns ['team', 'player']as the grouper, and apply the function sum()on column ['time'].
前任。2（多列）：查找团队中每个玩家花费的总时间。我们使用 2 列['team', 'player']作为分组器，并sum()在 column 上应用该函数['time']。
In[3]:
trip_time = df.groupby(by = ['team', 'player'])['time'].sum()
trip_time
Out[3]:
team player
Team1 Player1 19526
Team2 Player293 17656
Player333 18373
Team3 Player20 1883
Player22 17338899
Name: time, dtype: int64
Ex. 3 (multiple functions):For each player on a team, find the total number of trips and total time spent traveling.
前任。3 （多种功能）：对于团队中的每个玩家，找出总旅行次数和总旅行时间。
player_total = df.groupby(by = ['team', 'player']).agg({'time' : 'sum', 'trips' : 'count'})
player_total
Out[4]:
trips time
team player
Team1 Player1 2 19526
Team2 Player293 1 17656
Player333 1 18373
Team3 Player20 1 1883
Player22 1 17338899
