pandas groupby
按功能区分
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分组数据
df = pd.DataFrame({'Year' : ['2001', '2002', '2001', '2002', '2001', '2002', '2001', '2002'], 'score' : ['primary', 'second', 'third', 'fourth', 'primary', 'second', 'fourth', 'third'], 'C' : np.random.randn(8), 'D' : np.random.randn(8)}) print(df) Year score C D 0 2001 primary -1.277445 0.109920 1 2002 second -1.814557 0.031803 2 2001 third 0.246247 1.618513 3 2002 fourth -1.042709 0.464520 4 2001 primary 1.064651 -0.818139 5 2002 second -0.197225 0.059878 6 2001 fourth -0.292600 -0.432351 7 2002 third 0.443334 -0.454662 >>> df.groupby('Year') <pandas.core.groupby.groupby.DataFrameGroupBy object at 0x11de67cf8> >>> df.groupby(['Year','score']) <pandas.core.groupby.groupby.DataFrameGroupBy object at 0x10dab7048> 查看分组 >>> df.groupby('Year').groups {'2001': Int64Index([0, 2, 4, 6], dtype='int64'), '2002': Int64Index([1, 3, 5, 7], dtype='int64')} 分组打印 for name,group in df.groupby(['Year','score']): print (name) print (group) ('2001', 'fourth') Year score C D 6 2001 fourth 0.240019 0.329059 ('2001', 'primary') Year score C D 0 2001 primary 0.663303 -0.923613 4 2001 primary -1.034212 -0.696587 ('2001', 'third') Year score C D 2 2001 third 0.795344 -0.022841 ('2002', 'fourth') Year score C D 3 2002 fourth -0.710015 2.105533 ('2002', 'second') Year score C D 1 2002 second 0.701961 1.064701 5 2002 second 0.796509 -0.582946 ('2002', 'third') Year score C D 7 2002 third 0.849467 -0.248125 -
选择一个分组
import pandas as pd import numpy as np df = pd.DataFrame({'Year' : ['2001', '2002', '2001', '2002', '2001', '2002', '2001', '2002'], 'score' : ['primary', 'second', 'third', 'fourth', 'primary', 'second', 'fourth', 'third'], 'C' : np.random.randn(8), 'D' : np.random.randn(8)}) grouped = df.groupby('Year') print (grouped.get_group('2001')) Year score C D 0 2001 primary 1.155751 -1.306120 2 2001 third 0.029471 -0.231977 4 2001 primary 0.553366 0.313114 6 2001 fourth 0.698063 -1.472859
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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 df = pd.DataFrame({'Year' : ['2001', '2002', '2001', '2002', '2001', '2002', '2001', '2002'], 'score' : ['primary', 'second', 'third', 'fourth', 'primary', 'second', 'fourth', 'third'], 'C' : np.random.randn(8), 'D' : np.random.randn(8)}) grouped = df.groupby('Year') print (grouped['C'].agg(np.mean))-
聚合size方法
import pandas as pd import numpy as np df = pd.DataFrame({'Year' : ['2001', '2002', '2001', '2002', '2001', '2002', '2001', '2002'], 'score' : ['primary', 'second', 'third', 'fourth', 'primary', 'second', 'fourth', 'third'], 'C' : np.random.randn(8), 'D' : np.random.randn(8)}) grouped = df.groupby('Year') print (grouped['C'].agg(np.size)) -
同时聚合多种方法
import pandas as pd import numpy as np df = pd.DataFrame({'Year' : ['2001', '2002', '2001', '2002', '2001', '2002', '2001', '2002'], 'score' : ['primary', 'second', 'third', 'fourth', 'primary', 'second', 'fourth', 'third'], 'C' : np.random.randn(8), 'D' : np.random.randn(8)}) grouped = df.groupby('Year') print (grouped['C'].agg([np.size,np.sum,np.mean])) size sum mean Year 2001 4.0 -3.839661 -0.959915 2002 4.0 -3.633480 -0.908370
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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。
score = lambda x: (x - x.mean())实例
import pandas as pd import numpy as np df = pd.DataFrame({'Year' : ['2001', '2002', '2001', '2002', '2001', '2002', '2001', '2002'], 'score' : ['primary', 'second', 'third', 'fourth', 'primary', 'second', 'fourth', 'third'], 'C' : np.random.randn(8), 'D' : np.random.randn(8)}) grouped = df.groupby('Year') score = lambda x: (x - x.mean()) print(df) print(grouped['C'].agg(np.mean)) print (grouped['C'].transform(score)) Year score C D 0 2001 primary 1.455783 0.272765 1 2002 second -0.891035 1.399249 2 2001 third -0.335082 0.777773 3 2002 fourth -0.938394 -0.614140 4 2001 primary -0.770775 0.677035 5 2002 second 1.561887 0.343912 6 2001 fourth -0.426879 -0.551144 7 2002 third 1.070326 -0.438464 Year 2001 -0.019239 2002 0.200696 Name: C, dtype: float64 0 1.475021 1 -1.091731 2 -0.315844 3 -1.139090 4 -0.751537 5 1.361191 6 -0.407640 7 0.869630 Name: C, dtype: float64 可以看到1.475021 = 1.455783 - （-0.019239）
