数据聚合与分组运算

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#数据聚合与分组
import pandas as pd
import numpy as np

df = pd.DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'],
                       'key2' : ['one', 'two', 'one', 'two', 'one'],
                       'data1' : np.random.randn(5),
                       'data2' : np.random.randn(5)})
df

	key1	key2	data1	data2
0	a	one	1.364596	0.352792
1	a	two	1.685626	0.236429
2	b	one	-0.537077	-0.018004
3	b	two	1.389866	0.826195
4	a	one	0.849733	1.619383

grouped = df['data1'].groupby(df['key1'])
grouped

<pandas.core.groupby.groupby.SeriesGroupBy object at 0x00000261B3E2CEB8>

grouped.mean()

key1
a    1.299985
b    0.426394
Name: data1, dtype: float64

means = df['data1'].groupby([df['key1'],df['key2']]).mean()#两个参数分组

means

key1  key2
a     one     1.107165
      two     1.685626
b     one    -0.537077
      two     1.389866
Name: data1, dtype: float64

means.unstack()

key2	one	two
key1
a	1.107165	1.685626
b	-0.537077	1.389866

states = np.array(['Ohio', 'California', 'California', 'Ohio', 'Ohio'])
years = np.array([2005, 2005, 2006, 2005, 2006])
#分组键可以是任何长度适当的数组
df['data1'].groupby([states, years]).mean()

California  2005    1.685626
            2006   -0.537077
Ohio        2005    1.377231
            2006    0.849733
Name: data1, dtype: float64

df.groupby('key1').mean()

	data1	data2
key1
a	1.299985	0.736201
b	0.426394	0.404096

df.groupby(['key1','key2']).mean()

		data1	data2
key1	key2
a	one	1.107165	0.986087
a	two	1.685626	0.236429
b	one	-0.537077	-0.018004
b	two	1.389866	0.826195

df.groupby(['key1','key2']).size()#返回分组大小

key1  key2
a     one     2
      two     1
b     one     1
      two     1
dtype: int64

#对分组进行迭代,GroupBy对象支持迭代
for name,group in df.groupby('key1'):
    print(name)
    print(group)

a
  key1 key2     data1     data2
0    a  one  1.364596  0.352792
1    a  two  1.685626  0.236429
4    a  one  0.849733  1.619383
b
  key1 key2     data1     data2
2    b  one -0.537077 -0.018004
3    b  two  1.389866  0.826195

for (k1,k2),group in df.groupby(['key1','key2']):
    print((k1,k2))
    print(group)

('a', 'one')
  key1 key2     data1     data2
0    a  one  1.364596  0.352792
4    a  one  0.849733  1.619383
('a', 'two')
  key1 key2     data1     data2
1    a  two  1.685626  0.236429
('b', 'one')
  key1 key2     data1     data2
2    b  one -0.537077 -0.018004
('b', 'two')
  key1 key2     data1     data2
3    b  two  1.389866  0.826195

#将这些数据片段做成一个字典：
pieces = dict(list(df.groupby('key1')))
pieces['b']

	key1	key2	data1	data2
2	b	one	-0.537077	-0.018004
3	b	two	1.389866	0.826195

#对列进行分组
df.dtypes

key1      object
key2      object
data1    float64
data2    float64
dtype: object

grouped = df.groupby(df.dtypes,axis=1)
for dtype,group in grouped:
    print(dtype)
    print(group)

float64
      data1     data2
0  1.364596  0.352792
1  1.685626  0.236429
2 -0.537077 -0.018004
3  1.389866  0.826195
4  0.849733  1.619383
object
  key1 key2
0    a  one
1    a  two
2    b  one
3    b  two
4    a  one

#选取一列或列的子集
df['data1'].groupby(df['key1'])

<pandas.core.groupby.groupby.SeriesGroupBy object at 0x00000261B3E74FD0>

df.groupby(['key1', 'key2'])[['data2']].mean()

		data2
key1	key2
a	one	0.986087
a	two	0.236429
b	one	-0.018004
b	two	0.826195

#通过字典或Series进行分组
people = pd.DataFrame(np.random.randn(5,5),
                     columns=['a','b','c','d','e'],
                     index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis'])
people.iloc[2:3,[1,2]] = np.nan
people

	a	b	c	d	e
Joe	0.037796	1.631083	0.455609	-1.405327	0.495940
Steve	0.304393	0.326005	0.146350	0.075903	-0.263559
Wes	-0.055827	NaN	NaN	1.268622	-0.541199
Jim	2.034394	0.818811	0.333991	0.158734	1.187207
Travis	2.719235	-0.459516	-0.292250	0.158169	1.102169

mapping = {'a': 'red', 'b': 'red', 'c': 'blue',#分组字典
              'd': 'blue', 'e': 'red', 'f' : 'orange'}
by_column = people.groupby(mapping,axis=1)
by_column.sum()

	blue	red
Joe	-0.949718	2.164819
Steve	0.222253	0.366838
Wes	1.268622	-0.597026
Jim	0.492726	4.040412
Travis	-0.134082	3.361889

map_series = pd.Series(mapping)
map_series

a       red
b       red
c      blue
d      blue
e       red
f    orange
dtype: object

people.groupby(map_series,axis=1).count()

	blue	red
Joe	2	3
Steve	2	3
Wes	1	2
Jim	2	3
Travis	2	3

#通过函数进行分组
people.groupby(len).sum()

	a	b	c	d	e
3	2.016363	2.449894	0.789601	0.022029	1.141948
5	0.304393	0.326005	0.146350	0.075903	-0.263559
6	2.719235	-0.459516	-0.292250	0.158169	1.102169

key_list = ['one', 'one', 'one', 'two', 'two']
people.groupby([len, key_list]).min()

		a	b	c	d	e
3	one	-0.055827	1.631083	0.455609	-1.405327	-0.541199
3	two	2.034394	0.818811	0.333991	0.158734	1.187207
5	one	0.304393	0.326005	0.146350	0.075903	-0.263559
6	two	2.719235	-0.459516	-0.292250	0.158169	1.102169

#根据索引级别分组
columns = pd.MultiIndex.from_arrays([['US', 'US', 'US', 'JP', 'JP'],
                                        [1, 3, 5, 1, 3]],
                                        names=['cty', 'tenor'])
hier_df = pd.DataFrame(np.random.randn(4, 5), columns=columns)
hier_df

cty	US			JP
tenor	1	3	5	1	3
0	-1.865861	0.145454	1.476689	0.798411	0.546048
1	1.887149	0.218613	0.951165	2.790860	-2.419909
2	-0.681806	-0.490238	-2.215909	0.375548	0.145144
3	0.501322	1.358101	-1.069453	1.916614	-0.018305

#根据级别分组，使用level关键字传递级别序号或名字：
hier_df.groupby(level='cty',axis=1).count()

cty	JP	US
0	2	3
1	2	3
2	2	3
3	2	3

#数据聚合
#聚合指的是任何能够从数组产生标量值的数据转换过程

df

	key1	key2	data1	data2
0	a	one	1.364596	0.352792
1	a	two	1.685626	0.236429
2	b	one	-0.537077	-0.018004
3	b	two	1.389866	0.826195
4	a	one	0.849733	1.619383

grouped = df.groupby('key1')
grouped['data1'].quantile(0.9)

key1
a    1.621420
b    1.197171
Name: data1, dtype: float64

def peak_to_peak(arr):
    return arr.max()-arr.min()
grouped.agg(peak_to_peak)#使用你自己的聚合函数，只需将其传入aggregate或agg方法

	data1	data2
key1
a	0.835893	1.382954
b	1.926943	0.844199

grouped.describe()

	data1								data2
	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max
key1
a	3.0	1.299985	0.421675	0.849733	1.107165	1.364596	1.525111	1.685626	3.0	0.736201	0.767068	0.236429	0.294610	0.352792	0.986087	1.619383
b	2.0	0.426394	1.362554	-0.537077	-0.055342	0.426394	0.908130	1.389866	2.0	0.404096	0.596939	-0.018004	0.193046	0.404096	0.615145	0.826195

#面向列的多函数应用
tips = pd.read_csv('examples/tips.csv')

tips['tip_pct'] = tips['tip']/tips['total_bill']
tips[:6]

	total_bill	tip	smoker	day	time	size	tip_pct
0	16.99	1.01	No	Sun	Dinner	2	0.059447
1	10.34	1.66	No	Sun	Dinner	3	0.160542
2	21.01	3.50	No	Sun	Dinner	3	0.166587
3	23.68	3.31	No	Sun	Dinner	2	0.139780
4	24.59	3.61	No	Sun	Dinner	4	0.146808
5	25.29	4.71	No	Sun	Dinner	4	0.186240

grouped = tips.groupby(['day','smoker'])
grouped_pct = grouped['tip_pct']
grouped_pct.agg('mean')

day   smoker
Fri   No        0.151650
      Yes       0.174783
Sat   No        0.158048
      Yes       0.147906
Sun   No        0.160113
      Yes       0.187250
Thur  No        0.160298
      Yes       0.163863
Name: tip_pct, dtype: float64

grouped_pct.agg(['mean', 'std', peak_to_peak])

		mean	std	peak_to_peak
day	smoker
Fri	No	0.151650	0.028123	0.067349
Fri	Yes	0.174783	0.051293	0.159925
Sat	No	0.158048	0.039767	0.235193
Sat	Yes	0.147906	0.061375	0.290095
Sun	No	0.160113	0.042347	0.193226
Sun	Yes	0.187250	0.154134	0.644685
Thur	No	0.160298	0.038774	0.193350
Thur	Yes	0.163863	0.039389	0.151240

#修改聚合名字
grouped_pct.agg([('foo', 'mean'), ('bar', np.std)])

		foo	bar
day	smoker
Fri	No	0.151650	0.028123
Fri	Yes	0.174783	0.051293
Sat	No	0.158048	0.039767
Sat	Yes	0.147906	0.061375
Sun	No	0.160113	0.042347
Sun	Yes	0.187250	0.154134
Thur	No	0.160298	0.038774
Thur	Yes	0.163863	0.039389

functions = ['count','mean','max']
result = grouped['tip_pct','total_bill'].agg(functions)
result

		tip_pct			total_bill
		count	mean	max	count	mean	max
day	smoker
Fri	No	4	0.151650	0.187735	4	18.420000	22.75
Fri	Yes	15	0.174783	0.263480	15	16.813333	40.17
Sat	No	45	0.158048	0.291990	45	19.661778	48.33
Sat	Yes	42	0.147906	0.325733	42	21.276667	50.81
Sun	No	57	0.160113	0.252672	57	20.506667	48.17
Sun	Yes	19	0.187250	0.710345	19	24.120000	45.35
Thur	No	45	0.160298	0.266312	45	17.113111	41.19
Thur	Yes	17	0.163863	0.241255	17	19.190588	43.11

ftuples = [('Durchschnitt', 'mean'),('Abweichung', np.var)]
grouped['tip_pct', 'total_bill'].agg(ftuples)

		tip_pct		total_bill
		Durchschnitt	Abweichung	Durchschnitt	Abweichung
day	smoker
Fri	No	0.151650	0.000791	18.420000	25.596333
Fri	Yes	0.174783	0.002631	16.813333	82.562438
Sat	No	0.158048	0.001581	19.661778	79.908965
Sat	Yes	0.147906	0.003767	21.276667	101.387535
Sun	No	0.160113	0.001793	20.506667	66.099980
Sun	Yes	0.187250	0.023757	24.120000	109.046044
Thur	No	0.160298	0.001503	17.113111	59.625081
Thur	Yes	0.163863	0.001551	19.190588	69.808518

#对一个列或不同的列应用不同的函数
grouped.agg({'tip' : np.max, 'size' : 'sum'})
grouped.agg({'tip_pct' : ['min', 'max', 'mean', 'std'],
                'size' : 'sum'})

		tip_pct				size
		min	max	mean	std	sum
day	smoker
Fri	No	0.120385	0.187735	0.151650	0.028123	9
Fri	Yes	0.103555	0.263480	0.174783	0.051293	31
Sat	No	0.056797	0.291990	0.158048	0.039767	115
Sat	Yes	0.035638	0.325733	0.147906	0.061375	104
Sun	No	0.059447	0.252672	0.160113	0.042347	167
Sun	Yes	0.065660	0.710345	0.187250	0.154134	49
Thur	No	0.072961	0.266312	0.160298	0.038774	112
Thur	Yes	0.090014	0.241255	0.163863	0.039389	40

#apply:一般性的拆分应用合并

def top(df,n=5,column='tip_pct'):
    return df.sort_values(by=column)[-n:]
top(tips,n=6)

	total_bill	tip	smoker	day	time	size	tip_pct
109	14.31	4.00	Yes	Sat	Dinner	2	0.279525
183	23.17	6.50	Yes	Sun	Dinner	4	0.280535
232	11.61	3.39	No	Sat	Dinner	2	0.291990
67	3.07	1.00	Yes	Sat	Dinner	1	0.325733
178	9.60	4.00	Yes	Sun	Dinner	2	0.416667
172	7.25	5.15	Yes	Sun	Dinner	2	0.710345

tips.groupby('smoker').apply(top)

		total_bill	tip	smoker	day	time	size	tip_pct
smoker
No	88	24.71	5.85	No	Thur	Lunch	2	0.236746
	185	20.69	5.00	No	Sun	Dinner	5	0.241663
	51	10.29	2.60	No	Sun	Dinner	2	0.252672
	149	7.51	2.00	No	Thur	Lunch	2	0.266312
	232	11.61	3.39	No	Sat	Dinner	2	0.291990
Yes	109	14.31	4.00	Yes	Sat	Dinner	2	0.279525
	183	23.17	6.50	Yes	Sun	Dinner	4	0.280535
	67	3.07	1.00	Yes	Sat	Dinner	1	0.325733
	178	9.60	4.00	Yes	Sun	Dinner	2	0.416667
	172	7.25	5.15	Yes	Sun	Dinner	2	0.710345

tips.groupby(['smoker','day']).apply(top,n=1,column='total_bill')

			total_bill	tip	smoker	day	time	size	tip_pct
smoker	day
No	Fri	94	22.75	3.25	No	Fri	Dinner	2	0.142857
	Sat	212	48.33	9.00	No	Sat	Dinner	4	0.186220
	Sun	156	48.17	5.00	No	Sun	Dinner	6	0.103799
	Thur	142	41.19	5.00	No	Thur	Lunch	5	0.121389
Yes	Fri	95	40.17	4.73	Yes	Fri	Dinner	4	0.117750
	Sat	170	50.81	10.00	Yes	Sat	Dinner	3	0.196812
	Sun	182	45.35	3.50	Yes	Sun	Dinner	3	0.077178
	Thur	197	43.11	5.00	Yes	Thur	Lunch	4	0.115982

result = tips.groupby('smoker')['tip_pct'].describe()
result

	count	mean	std	min	25%	50%	75%	max
smoker
No	151.0	0.159328	0.039910	0.056797	0.136906	0.155625	0.185014	0.291990
Yes	93.0	0.163196	0.085119	0.035638	0.106771	0.153846	0.195059	0.710345

result.unstack('smoker')

       smoker
count  No        151.000000
       Yes        93.000000
mean   No          0.159328
       Yes         0.163196
std    No          0.039910
       Yes         0.085119
min    No          0.056797
       Yes         0.035638
25%    No          0.136906
       Yes         0.106771
50%    No          0.155625
       Yes         0.153846
75%    No          0.185014
       Yes         0.195059
max    No          0.291990
       Yes         0.710345
dtype: float64

#禁止分组键
tips.groupby('smoker',group_keys=False).apply(top)

	total_bill	tip	smoker	day	time	size	tip_pct
88	24.71	5.85	No	Thur	Lunch	2	0.236746
185	20.69	5.00	No	Sun	Dinner	5	0.241663
51	10.29	2.60	No	Sun	Dinner	2	0.252672
149	7.51	2.00	No	Thur	Lunch	2	0.266312
232	11.61	3.39	No	Sat	Dinner	2	0.291990
109	14.31	4.00	Yes	Sat	Dinner	2	0.279525
183	23.17	6.50	Yes	Sun	Dinner	4	0.280535
67	3.07	1.00	Yes	Sat	Dinner	1	0.325733
178	9.60	4.00	Yes	Sun	Dinner	2	0.416667
172	7.25	5.15	Yes	Sun	Dinner	2	0.710345

#分位数和桶分析
frame = pd.DataFrame({'data1':np.random.randn(1000),
                    'data2':np.random.randn(1000)})
quartiles = pd.cut(frame.data1,4)
quartiles[:10]

0    (-1.415, 0.195]
1     (0.195, 1.805]
2    (-1.415, 0.195]
3     (0.195, 1.805]
4    (-1.415, 0.195]
5    (-1.415, 0.195]
6     (0.195, 1.805]
7    (-1.415, 0.195]
8    (-1.415, 0.195]
9    (-1.415, 0.195]
Name: data1, dtype: category
Categories (4, interval[float64]): [(-3.032, -1.415] < (-1.415, 0.195] < (0.195, 1.805] < (1.805, 3.414]]

def get_stats(group):
    return{'min':group.min(),'max':group.max(),
          'count':group.count(),'mean':group.mean()}
grouped = frame.data2.groupby(quartiles)
grouped.apply(get_stats).unstack()

	count	max	mean	min
data1
(-3.032, -1.415]	75.0	2.055051	0.217488	-2.419102
(-1.415, 0.195]	511.0	3.134327	0.002096	-2.709974
(0.195, 1.805]	379.0	3.146306	-0.014438	-2.561375
(1.805, 3.414]	35.0	2.209609	0.293813	-1.865174

grouping = pd.qcut(frame.data1,10,labels=False)#大小相同的桶
grouped = frame.data2.groupby(grouping)
grouped.apply(get_stats).unstack()

	count	max	mean	min
data1
0	100.0	2.055051	0.128959	-2.419102
1	100.0	3.134327	-0.106525	-2.131247
2	100.0	2.178958	0.041848	-1.964788
3	100.0	2.443715	0.176331	-2.587572
4	100.0	2.231973	0.016023	-2.709974
5	100.0	2.432079	-0.082424	-2.189209
6	100.0	3.146306	0.072272	-2.282556
7	100.0	1.831667	-0.011231	-2.538154
8	100.0	2.060099	-0.060095	-2.561375
9	100.0	2.209609	0.046783	-2.049609

s = pd.Series(np.random.randn(6))
s[::2] = np.nan
s

0         NaN
1   -0.162668
2         NaN
3    0.273627
4         NaN
5    0.345890
dtype: float64

s.fillna(s.mean())

0    0.152283
1   -0.162668
2    0.152283
3    0.273627
4    0.152283
5    0.345890
dtype: float64

states = ['Ohio', 'New York', 'Vermont', 'Florida',
              'Oregon', 'Nevada', 'California', 'Idaho']
group_key = ['East']*4 + ['West']*4
data = pd.Series(np.random.randn(8),index=states)
data

Ohio         -1.141165
New York      0.077290
Vermont       1.178992
Florida       1.413521
Oregon       -0.840100
Nevada       -0.262705
California    0.410535
Idaho        -2.736216
dtype: float64

data[['Vermont', 'Nevada', 'Idaho']] = np.nan
data

Ohio         -1.141165
New York      0.077290
Vermont            NaN
Florida       1.413521
Oregon       -0.840100
Nevada             NaN
California    0.410535
Idaho              NaN
dtype: float64

data.groupby(group_key).mean()

East    0.116549
West   -0.214783
dtype: float64

fill_mean = lambda g:g.fillna(g.mean())
data.groupby(group_key).apply(fill_mean)

Ohio         -1.141165
New York      0.077290
Vermont       0.116549
Florida       1.413521
Oregon       -0.840100
Nevada       -0.214783
California    0.410535
Idaho        -0.214783
dtype: float64

#随机采样和排列
suits = ['H','S','C','D']
card_val = (list(range(1,11))+[10]*3)*4
base_names = ['A']+list(range(2,11))+['J','K','Q']
cards = []
for suit in ['H','S','C','D']:
    cards.extend(str(num)+suit for num in base_names)
deck = pd.Series(card_val,index=cards)

deck[:13]

AH      1
2H      2
3H      3
4H      4
5H      5
6H      6
7H      7
8H      8
9H      9
10H    10
JH     10
KH     10
QH     10
dtype: int64

def draw(deck,n=5):
    return deck.sample(n)
draw(deck)

QH    10
AS     1
2S     2
4C     4
5C     5
dtype: int64

get_suit = lambda card:card[-1]
deck.groupby(get_suit).apply(draw,n=2)

C  9C      9
   2C      2
D  QD     10
   10D    10
H  5H      5
   KH     10
S  3S      3
   2S      2
dtype: int64

#分组加权平均数和相关系数
df = pd.DataFrame({'category': ['a', 'a', 'a', 'a',
                                    'b', 'b', 'b', 'b'],
                       'data': np.random.randn(8),
                       'weights': np.random.rand(8)})
df

	category	data	weights
0	a	-0.326019	0.107540
1	a	1.333957	0.220552
2	a	-0.055586	0.189389
3	a	-0.947162	0.598072
4	b	-0.764437	0.505189
5	b	1.204008	0.921015
6	b	-1.048168	0.660169
7	b	-1.495828	0.525462

grouped = df.groupby('category')
get_wavg = lambda g:np.average(g['data'],weights=g['weights'])#加权平均数
grouped.apply(get_wavg)

category
a   -0.284928
b   -0.289163
dtype: float64

close_px = pd.read_csv('examples/stock_px_2.csv', parse_dates=True,
                           index_col=0)
close_px.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2214 entries, 2003-01-02 to 2011-10-14
Data columns (total 4 columns):
AAPL    2214 non-null float64
MSFT    2214 non-null float64
XOM     2214 non-null float64
SPX     2214 non-null float64
dtypes: float64(4)
memory usage: 86.5 KB

close_px[-4:]

	AAPL	MSFT	XOM	SPX
2011-10-11	400.29	27.00	76.27	1195.54
2011-10-12	402.19	26.96	77.16	1207.25
2011-10-13	408.43	27.18	76.37	1203.66
2011-10-14	422.00	27.27	78.11	1224.58

spx_corr = lambda x:x.corrwith(x['SPX'])
rets = close_px.pct_change().dropna()
get_year = lambda x: x.year
by_year = rets.groupby(get_year)
by_year.apply(spx_corr)

	AAPL	MSFT	XOM	SPX
2003	0.541124	0.745174	0.661265	1.0
2004	0.374283	0.588531	0.557742	1.0
2005	0.467540	0.562374	0.631010	1.0
2006	0.428267	0.406126	0.518514	1.0
2007	0.508118	0.658770	0.786264	1.0
2008	0.681434	0.804626	0.828303	1.0
2009	0.707103	0.654902	0.797921	1.0
2010	0.710105	0.730118	0.839057	1.0
2011	0.691931	0.800996	0.859975	1.0

#透视表和交叉表
tips.pivot_table(index=['day', 'smoker'])

		size	tip	tip_pct	total_bill
day	smoker
Fri	No	2.250000	2.812500	0.151650	18.420000
Fri	Yes	2.066667	2.714000	0.174783	16.813333
Sat	No	2.555556	3.102889	0.158048	19.661778
Sat	Yes	2.476190	2.875476	0.147906	21.276667
Sun	No	2.929825	3.167895	0.160113	20.506667
Sun	Yes	2.578947	3.516842	0.187250	24.120000
Thur	No	2.488889	2.673778	0.160298	17.113111
Thur	Yes	2.352941	3.030000	0.163863	19.190588

 tips.pivot_table(['tip_pct', 'size'], index=['time', 'day'],
                     columns='smoker')

		size		tip_pct
	smoker	No	Yes	No	Yes
time	day
Dinner	Fri	2.000000	2.222222	0.139622	0.165347
	Sat	2.555556	2.476190	0.158048	0.147906
	Sun	2.929825	2.578947	0.160113	0.187250
	Thur	2.000000	NaN	0.159744	NaN
Lunch	Fri	3.000000	1.833333	0.187735	0.188937
Lunch	Thur	2.500000	2.352941	0.160311	0.163863

 tips.pivot_table(['tip_pct', 'size'], index=['time', 'day'],
                     columns='smoker', margins=True)

		size			tip_pct
	smoker	No	Yes	All	No	Yes	All
time	day
Dinner	Fri	2.000000	2.222222	2.166667	0.139622	0.165347	0.158916
	Sat	2.555556	2.476190	2.517241	0.158048	0.147906	0.153152
	Sun	2.929825	2.578947	2.842105	0.160113	0.187250	0.166897
	Thur	2.000000	NaN	2.000000	0.159744	NaN	0.159744
Lunch	Fri	3.000000	1.833333	2.000000	0.187735	0.188937	0.188765
Lunch	Thur	2.500000	2.352941	2.459016	0.160311	0.163863	0.161301
All		2.668874	2.408602	2.569672	0.159328	0.163196	0.160803

tips.pivot_table('tip_pct', index=['time', 'smoker'], columns='day',
                    aggfunc=len, margins=True)

	day	Fri	Sat	Sun	Thur	All
time	smoker
Dinner	No	3.0	45.0	57.0	1.0	106.0
Dinner	Yes	9.0	42.0	19.0	NaN	70.0
Lunch	No	1.0	NaN	NaN	44.0	45.0
Lunch	Yes	6.0	NaN	NaN	17.0	23.0
All		19.0	87.0	76.0	62.0	244.0

tips.pivot_table('tip_pct', index=['time', 'size', 'smoker'],
                 columns='day', aggfunc='mean', fill_value=0)

		day	Fri	Sat	Sun	Thur
time	size	smoker
Dinner	1	No	0.000000	0.137931	0.000000	0.000000
	1	Yes	0.000000	0.325733	0.000000	0.000000
	2	No	0.139622	0.162705	0.168859	0.159744
	2	Yes	0.171297	0.148668	0.207893	0.000000
	3	No	0.000000	0.154661	0.152663	0.000000
	3	Yes	0.000000	0.144995	0.152660	0.000000
	4	No	0.000000	0.150096	0.148143	0.000000
	4	Yes	0.117750	0.124515	0.193370	0.000000
	5	No	0.000000	0.000000	0.206928	0.000000
	5	Yes	0.000000	0.106572	0.065660	0.000000
	6	No	0.000000	0.000000	0.103799	0.000000
Lunch	1	No	0.000000	0.000000	0.000000	0.181728
	1	Yes	0.223776	0.000000	0.000000	0.000000
	2	No	0.000000	0.000000	0.000000	0.166005
	2	Yes	0.181969	0.000000	0.000000	0.158843
	3	No	0.187735	0.000000	0.000000	0.084246
	3	Yes	0.000000	0.000000	0.000000	0.204952
	4	No	0.000000	0.000000	0.000000	0.138919
	4	Yes	0.000000	0.000000	0.000000	0.155410
	5	No	0.000000	0.000000	0.000000	0.121389
	6	No	0.000000	0.000000	0.000000	0.173706

#交叉表计算分组频率