pandasDataAggregationGroup - juedaiyuer/researchNote GitHub Wiki

#数据聚合与分组运算#

##导入##

>>> from pandas import Series,DataFrame
>>> import pandas as pd
>>> import numpy as np

>>> df=DataFrame({'key1':['a','a','b','b','a'],
...             'key2':['one','two','one','two','one'],
...             'data1':np.random.randn(5),
...             'data2':np.random.randn(5)})
>>> df
	  data1     data2 key1 key2
0 -0.456158  0.783958    a  one
1 -1.437943  1.378467    a  two
2  1.046638  0.759526    b  one
3  0.771500  0.012541    b  two
4  0.522649 -2.933776    a  one

按照key1进行分组，并计算data1列的平均值

>>> grouped=df['data1'].groupby(df['key1'])
>>> grouped
<pandas.core.groupby.SeriesGroupBy object at 0x7f83a4118d50>

调动mean方法计算平均值

>>> grouped.mean()
key1
a   -0.457151
b    0.909069
Name: data1, dtype: float64

传入多个数组

>>> means=df['data1'].groupby([df['key1'],df['key2']]).mean()
>>> means
key1  key2
a     one     0.033245
	  two    -1.437943
b     one     1.046638
	  two     0.771500
Name: data1, dtype: float64

通过两个键对数据进行分组，得到的Series具有一个层次化索引

>>> means.unstack()
key2       one       two
key1
a     0.033245 -1.437943
b     1.046638  0.771500	

分组键可以是任何长度适当的数组

>>> states=np.array(['Ohio','California','California','Ohio','Ohio'])
>>> years=np.array([2005,2005,2006,2005,2006])

>>> df['data1'].groupby([states,years]).mean()
California  2005    1.596126
	        2006   -0.828750
Ohio        2005    0.373009
	        2006    1.012563
Name: data1, dtype: float64

列名(可以是字符串，数字或其它python对象)用作分组键

>>> df.groupby('key1').mean()
	     data1     data2
key1
a     0.821883  0.274269
b     0.030154 -0.010252