汇总数据并获得总和和计数

对groupand函数使用pandas DataFrames

将数据放入列表列表中,每个内部列表将是数据帧中的一行.

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,并将功能应用于组.

例子

防爆.1查找每个团队进行的旅行次数.team是石斑鱼,我们count()在柱上应用函数['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

防爆.2(多列):查找团队中每位玩家所花费的总时间.我们使用2列['team', 'player']作为石斑鱼,并sum()在列上应用该功能['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

防爆.3 (多种功能):对于团队中的每个玩家,查找旅行总次数和旅行总时间.

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