Pandas教學
Pandas環境安裝配置
Pandas數據結構
Pandas快速入門
Pandas系列
Pandas數據幀（DataFrame）
Pandas面板（Panel）
Pandas基本功能
Pandas描述性統計
Pandas函數應用
Pandas重建索引
Pandas迭代
Pandas排序
Pandas字符串和文本數據
Pandas選項和自定義
Pandas索引和選擇數據
Pandas統計函數
Pandas窗口函數
Pandas聚合
Pandas缺失數據
Pandas分組（GroupBy）
Pandas合併/連接
Pandas級聯
Pandas日期功能
Pandas時間差（Timedelta）
Pandas分類數據
Pandas可視化
Pandas IO工具
Pandas稀疏數據
Pandas注意事項＆竅門
Pandas與SQL比較
Pandas分組（GroupBy）
任何分組(groupby)操作都涉及原始對象的以下操作之一。它們是 -
- 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 - 計算彙總統計
- 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)執行上面示例代碼，得到以下結果 -
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'))執行上面示例代碼，得到以下結果 -
<pandas.core.groupby.DataFrameGroupBy object at 0x00000245D60AD518>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)執行上面示例代碼，得到以下結果 -
{
'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')
}示例
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)執行上面示例代碼，得到以下結果 -
{
('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')
}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)執行上面示例代碼，得到以下結果 -
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籤名稱。
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))執行上面示例代碼，得到以下結果 -
Points Rank Team Year
0 876 1 Riders 2014
2 863 2 Devils 2014
4 741 3 Kings 2014
9 701 4 Royals 2014聚合
聚合函數爲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。
一個比較常用的是通過聚合或等效的agg方法聚合 -
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))執行上面示例代碼，得到以下結果 -
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))執行上面示例代碼，得到以下結果 -
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作爲輸出 -
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)
grouped = df.groupby('Team')
agg = grouped['Points'].agg([np.sum, np.mean, np.std])
print (agg)執行上面示例代碼，得到以下結果 -
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))執行上面示例代碼，得到以下結果 -
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過濾
過濾根據定義的標準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)執行上面示例代碼，得到以下結果 -
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。