任何分組(groupby)操作都涉及原始對象的以下操作之一:
- 分割對象
- 應用一個函數
- 結合的結果
在許多情況下，我們將數據分成多個集合，並在每個子集上應用一些函數。在應用函數中，可以執行以下操作:
- 聚合 - 計算匯總統計
- 轉換 - 執行一些特定於組的操作
- 過濾 - 在某些情況下丟棄數據
下面來看看創建一個DataFrame對象並對其執行所有操作 -
import pandas as pd ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'], 'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2], 'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017], 'Points':[876,789,863,673,741,812,756,788,694,701,804,690]} df = pd.DataFrame(ipl_data) print (df)
輸出結果：
Points Rank Team Year 0 876 1 Riders 2014 1 789 2 Riders 2015 2 863 2 Devils 2014 3 673 3 Devils 2015 4 741 3 Kings 2014 5 812 4 kings 2015 6 756 1 Kings 2016 7 788 1 Kings 2017 8 694 2 Riders 2016 9 701 4 Royals 2014 10 804 1 Royals 2015 11 690 2 Riders 2017 一、將數據拆分成組
Pandas對象可以分成任何對象。有多種方式來拆分對象，如 -
- obj.groupby(‘key’)
- obj.groupby([‘key1’,’key2’])
- obj.groupby(key,axis=1)
現在來看看如何將分組對象應用於DataFrame對象
示例
import pandas as pd ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'], 'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2], 'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017], 'Points':[876,789,863,673,741,812,756,788,694,701,804,690]} df = pd.DataFrame(ipl_data) print (df.groupby('Team'))
輸出結果：
<pandas.core.groupby.DataFrameGroupBy object at 0x00000245D60AD518> 二、查看分組
import pandas as pd ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'], 'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2], 'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017], 'Points':[876,789,863,673,741,812,756,788,694,701,804,690]} df = pd.DataFrame(ipl_data) print (df.groupby('Team').groups)
輸出結果：
{ 'Devils': Int64Index([2, 3], dtype='int64'), 'Kings': Int64Index([4, 6, 7], dtype='int64'), 'Riders': Int64Index([0, 1, 8, 11], dtype='int64'), 'Royals': Int64Index([9, 10], dtype='int64'), 'kings': Int64Index([5], dtype='int64') } 按多列分組
import pandas as pd ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'], 'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2], 'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017], 'Points':[876,789,863,673,741,812,756,788,694,701,804,690]} df = pd.DataFrame(ipl_data) print (df.groupby(['Team','Year']).groups)
輸出結果：
{ ('Devils', 2014): Int64Index([2], dtype='int64'), ('Devils', 2015): Int64Index([3], dtype='int64'), ('Kings', 2014): Int64Index([4], dtype='int64'), ('Kings', 2016): Int64Index([6], dtype='int64'), ('Kings', 2017): Int64Index([7], dtype='int64'), ('Riders', 2014): Int64Index([0], dtype='int64'), ('Riders', 2015): Int64Index([1], dtype='int64'), ('Riders', 2016): Int64Index([8], dtype='int64'), ('Riders', 2017): Int64Index([11], dtype='int64'), ('Royals', 2014): Int64Index([9], dtype='int64'), ('Royals', 2015): Int64Index([10], dtype='int64'), ('kings', 2015): Int64Index([5], dtype='int64') } 三、迭代遍歷分組
使用groupby對象，可以遍歷類似itertools.obj的對象。
import pandas as pd ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'], 'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2], 'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017], 'Points':[876,789,863,673,741,812,756,788,694,701,804,690]} df = pd.DataFrame(ipl_data) grouped = df.groupby('Year') for name,group in grouped: print (name) print (group) print ('\n')
輸出結果：
2014 Points Rank Team Year 0 876 1 Riders 2014 2 863 2 Devils 2014 4 741 3 Kings 2014 9 701 4 Royals 2014
2015 Points Rank Team Year 1 789 2 Riders 2015 3 673 3 Devils 2015 5 812 4 kings 2015 10 804 1 Royals 2015
2016 Points Rank Team Year 6 756 1 Kings 2016 8 694 2 Riders 2016
2017 Points Rank Team Year 7 788 1 Kings 2017 11 690 2 Riders 2017 默認情況下，groupby對象具有與分組名相同的標簽名稱。
四、選擇一個分組
使用get_group()方法，可以選擇一個組。參考以下示例代碼 -
import pandas as pd ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'], 'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2], 'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017], 'Points':[876,789,863,673,741,812,756,788,694,701,804,690]} df = pd.DataFrame(ipl_data) grouped = df.groupby('Year') print (grouped.get_group(2014))
輸出結果：
Points Rank Team Year 0 876 1 Riders 2014 2 863 2 Devils 2014 4 741 3 Kings 2014 9 701 4 Royals 2014 五、聚合
聚合函數為每個組返回單個聚合值。當創建了分組(group by)對象，就可以對分組數據執行多個聚合操作。
應用單個聚合函數
import pandas as pd import numpy as np ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'], 'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2], 'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017], 'Points':[876,789,863,673,741,812,756,788,694,701,804,690]} df = pd.DataFrame(ipl_data) grouped = df.groupby('Year') print (grouped['Points'].agg(np.mean))
輸出結果：
Year 2014 795.25 2015 769.50 2016 725.00 2017 739.00 Name: Points, dtype: float64 另一種查看每個分組的大小的方法是應用size()函數 -
import pandas as pd import numpy as np ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'], 'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2], 'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017], 'Points':[876,789,863,673,741,812,756,788,694,701,804,690]} df = pd.DataFrame(ipl_data) grouped = df.groupby('Team') print (grouped.agg(np.size))
輸出結果：
Team Devils 2 2 2 Kings 3 3 3 Riders 4 4 4 Royals 2 2 2 kings 1 1 1 一次應用多個聚合函數
通過分組系列，還可以傳遞函數的列表或字典來進行聚合，並生成DataFrame作為輸出
import pandas as pd import numpy as np ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'], 'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2], 'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017], 'Points':[876,789,863,673,741,812,756,788,694,701,804,690]} df = pd.DataFrame(ipl_data) print(df) print('\n') grouped = df.groupby('Team') agg = grouped['Points'].agg([np.sum, np.mean, np.std]) print (agg)
輸出結果：
Team Rank Year Points
0 Riders 1 2014 876
1 Riders 2 2015 789
2 Devils 2 2014 863
3 Devils 3 2015 673
4 Kings 3 2014 741
5 kings 4 2015 812
6 Kings 1 2016 756
7 Kings 1 2017 788
8 Riders 2 2016 694
9 Royals 4 2014 701
10 Royals 1 2015 804
11 Riders 2 2017 690
sum mean std
Team
Devils 1536 768.000000 134.350288
Kings 2285 761.666667 24.006943
Riders 3049 762.250000 88.567771
Royals 1505 752.500000 72.831998
kings 812 812.000000 NaN
六、轉換
分組或列上的轉換返回索引大小與被分組的索引相同的對象。因此，轉換應該返回與組塊大小相同的結果。
import pandas as pd import numpy as np ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'], 'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2], 'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017], 'Points':[876,789,863,673,741,812,756,788,694,701,804,690]} df = pd.DataFrame(ipl_data) grouped = df.groupby('Team') score = lambda x: (x - x.mean()) / x.std()*10 print (grouped.transform(score))
輸出結果：
Points Rank Year 0 12.843272 -15.000000 -11.618950 1 3.020286 5.000000 -3.872983 2 7.071068 -7.071068 -7.071068 3 -7.071068 7.071068 7.071068 4 -8.608621 11.547005 -10.910895 5 NaN NaN NaN 6 -2.360428 -5.773503 2.182179 7 10.969049 -5.773503 8.728716 8 -7.705963 5.000000 3.872983 9 -7.071068 7.071068 -7.071068 10 7.071068 -7.071068 7.071068 11 -8.157595 5.000000 11.618950 七、過濾
過濾根據定義的標准過濾數據並返回數據的子集。filter()函數用於過濾數據。
import pandas as pd import numpy as np
ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'], 'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2], 'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017], 'Points':[876,789,863,673,741,812,756,788,694,701,804,690]} df = pd.DataFrame(ipl_data) filter = df.groupby('Team').filter(lambda x: len(x) >= 3) print (filter)
輸出結果：
Points Rank Team Year 0 876 1 Riders 2014 1 789 2 Riders 2015 4 741 3 Kings 2014 6 756 1 Kings 2016 7 788 1 Kings 2017 8 694 2 Riders 2016 11 690 2 Riders 2017 在上述過濾條件下，要求返回三次以上參加IPL的隊伍。