>>> from env_helper import info; info()
待更新
6.9. Pandas分组（GroupBy）¶
任何分组(groupby)操作都涉及原始对象的以下操作之一。它们是 -
分割对象
应用一个函数
结合的结果
在许多情况下，我们将数据分成多个集合，并在每个子集上应用一些函数。在应用函数中，可以执行以下操作 -
聚合 - 计算汇总统计
转换 - 执行一些特定于组的操作
过滤 - 在某些情况下丢弃数据
下面来看看创建一个DataFrame对象并对其执行所有操作 -
>>> 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)
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
将数据拆分成组¶
Pandas对象可以分成任何对象。有多种方式来拆分对象，如 -
obj.groupby(‘key’)
obj.groupby([‘key1’,’key2’])
obj.groupby(key,axis=1)
现在来看看如何将分组对象应用于DataFrame对象
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'))
<pandas.core.groupby.DataFrameGroupBy object at 0x7f0a68536ac8>
查看分组
>>> 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)
{'kings': Int64Index([5], dtype='int64'), 'Riders': Int64Index([0, 1, 8, 11], dtype='int64'), 'Kings': Int64Index([4, 6, 7], dtype='int64'), 'Devils': Int64Index([2, 3], dtype='int64'), 'Royals': Int64Index([9, 10], dtype='int64')}
示例¶
按多列分组 -
>>> 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)
{('Devils', 2014): Int64Index([2], dtype='int64'), ('Kings', 2016): Int64Index([6], dtype='int64'), ('Devils', 2015): Int64Index([3], dtype='int64'), ('Riders', 2014): Int64Index([0], dtype='int64'), ('Kings', 2014): Int64Index([4], dtype='int64'), ('Riders', 2015): Int64Index([1], dtype='int64'), ('Royals', 2015): Int64Index([10], dtype='int64'), ('Riders', 2017): Int64Index([11], dtype='int64'), ('Royals', 2014): Int64Index([9], dtype='int64'), ('Riders', 2016): Int64Index([8], dtype='int64'), ('Kings', 2017): Int64Index([7], dtype='int64'), ('kings', 2015): Int64Index([5], dtype='int64')}
迭代遍历分组¶
使用groupby对象，可以遍历类似itertools.obj的对象。
>>> 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)
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
默认情况下，groupby对象具有与分组名相同的标签名称。
选择一个分组¶
使用get_group()方法，可以选择一个组。参考以下示例代码 -
>>> 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))
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
聚合函数为每个组返回单个聚合值。当创建了分组(group by)对象，就可以对分组数据执行多个聚合操作。
一个比较常用的是通过聚合或等效的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))
Year
2014 795.25
2015 769.50
2016 725.00
2017 739.00
Name: Points, dtype: float64
另一种查看每个分组的大小的方法是应用size()函数 -
>>> 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))
Points Rank Year
Team
Devils 2 2 2
Kings 3 3 3
Riders 4 4 4
Royals 2 2 2
kings 1 1 1
一次应用多个聚合函数¶
通过分组系列，还可以传递函数的列表或字典来进行聚合，并生成DataFrame作为输出 -
>>> 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)
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
转换¶
分组或列上的转换返回索引大小与被分组的索引相同的对象。因此，转换应该返回与组块大小相同的结果。
>>> 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))
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
过滤¶
过滤根据定义的标准过滤数据并返回数据的子集。filter()函数用于过滤数据。
>>> 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)
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
在上述过滤条件下，要求返回三次以上参加IPL的队伍。