Datawhale_数据分析组队学习task6
数据聚合与分组运算
- GroupBy机制
- 对分组进行迭代
- 选取一列或列的子集
- 通过字典或Series进行分组
- 通过函数进行分组
- 根据索引级别分组
- 数据聚合
- 面向列的多函数应用
- apply:一般性的“拆分-应用-合并”
- 禁止分组键
- 分位数和桶分析
- 用特定于分组的值填充缺失值
- 透视表
GroupBy机制
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 1.612273 -1.761942
1 a two 0.422988 1.186852
2 b one 0.630012 0.317237
3 b two -0.096043 -1.286106
4 a one 1.170058 -0.112766
In [3]:
# 按key1进行分组，并计算data1列的平均值
grouped = df['data1'].groupby(df['key1'])
grouped
Out[3]:
In [5]:
grouped.mean()
Out[5]:
key1
a 1.068440
b 0.266985
Name: data1, dtype: float64
In [10]:
# 传入多个数组的列表
means = df['data1'].groupby([df['key1'],df['key2']]).mean()
means
Out[10]:
key1 key2
a one 1.391165
two 0.422988
b one 0.630012
two -0.096043
Name: data1, dtype: float64
In [11]:
means.unstack()
Out[11]:
key2 one two
key1
a 1.391165 0.422988
b 0.630012 -0.096043
In [12]:
states = np.array(['Ohio', 'California', 'California','Ohio', 'Ohio'])
years = np.array([2005, 2005, 2006, 2005, 2006])
df['data1'].groupby([states,years]).mean()
Out[12]:
California 2005 0.422988
2006 0.630012
Ohio 2005 0.758115
2006 1.170058
Name: data1, dtype: float64
In [14]:
# groupby的size方法可以返回含有分组大小的Series
df.groupby(['key1','key2']).size()
Out[14]:
key1 key2
a one 2
two 1
b one 1
two 1
dtype: int64
对分组进行迭代
for name,group in df.groupby('key1'):
print(name)
print(group)
>>>
a
key1 key2 data1 data2
0 a one 1.612273 -1.761942
1 a two 0.422988 1.186852
4 a one 1.170058 -0.112766
b
key1 key2 data1 data2
2 b one 0.630012 0.317237
3 b two -0.096043 -1.286106
In [16]:
# 多重键的情况，元组的第一个元素是由键值组成的元组
for (k1,k2),group in df.groupby(['key1','key2']):
print(k1,k2)
print(group)
a one
key1 key2 data1 data2
0 a one 1.612273 -1.761942
4 a one 1.170058 -0.112766
a two
key1 key2 data1 data2
1 a two 0.422988 1.186852
b one
key1 key2 data1 data2
2 b one 0.630012 0.317237
b two
key1 key2 data1 data2
3 b two -0.096043 -1.286106
In [17]:
pieces = dict(list(df.groupby('key1'))) # groupby默认在axis = 0上分组
pieces['b']
Out[17]:
key1 key2 data1 data2
2 b one 0.630012 0.317237
3 b two -0.096043 -1.286106
In [18]:
df.dtypes
Out[18]:
key1 object
key2 object
data1 float64
data2 float64
dtype: object
In [20]:
grouped = df.groupby(df.dtypes,axis = 1)
for dtype,group in grouped:
print(dtype)
print(group)
float64
data1 data2
0 1.612273 -1.761942
1 0.422988 1.186852
2 0.630012 0.317237
3 -0.096043 -1.286106
4 1.170058 -0.112766
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.groupby('key1')['data1']
df.groupby('key1')[['data2']]
等价于
df['data1'].groupby(df['key1'])
df[['data2']].groupby(df['key1'])
df
>>>
key1 key2 data1 data2
0 a one 1.612273 -1.761942
1 a two 0.422988 1.186852
2 b one 0.630012 0.317237
3 b two -0.096043 -1.286106
4 a one 1.170058 -0.112766
In [22]:
# 计算data2列的平均值并以DataFrame形式的到结果
df.groupby(['key1','key2'])[['data2']].mean()
Out[22]:
data2
key1 key2
a one -0.937354
two 1.186852
b one 0.317237
two -1.286106
In [24]:
s_grouped = df.groupby(['key1','key2'])['data2']
s_grouped
Out[24]:
In [25]:
s_grouped.mean()
Out[25]:
key1 key2
a one -0.937354
two 1.186852
b one 0.317237
two -1.286106
Name: data2, dtype: float64
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
>>>
a b c d e
Joe 0.003533 -1.037063 0.955879 -0.782302 1.168097
Steve -0.684024 1.088523 -0.885087 0.388805 1.128606
Wes 0.255142 -1.447020 0.027178 0.038802 0.028278
Jim 0.755857 1.357942 -0.719431 0.035387 0.621884
Travis -0.599465 0.702602 0.076018 0.115762 -1.593297
In [27]:
people.iloc[2:3,[1,2]] = np.nan
people
Out[27]:
a b c d e
Joe 0.003533 -1.037063 0.955879 -0.782302 1.168097
Steve -0.684024 1.088523 -0.885087 0.388805 1.128606
Wes 0.255142 NaN NaN 0.038802 0.028278
Jim 0.755857 1.357942 -0.719431 0.035387 0.621884
Travis -0.599465 0.702602 0.076018 0.115762 -1.593297
In [30]:
mapping = {'a': 'red', 'b': 'red', 'c': 'blue','d': 'blue', 'e': 'red', 'f' : 'orange'}
by_column = people.groupby(mapping,axis = 1)
by_column.sum()
Out[30]:
blue red
Joe 0.173578 0.134567
Steve -0.496283 1.533104
Wes 0.038802 0.283420
Jim -0.684044 2.735684
Travis 0.191780 -1.490160
In [31]:
map_series = pd.Series(mapping)
map_series
Out[31]:
a red
b red
c blue
d blue
e red
f orange
dtype: object
In [33]:
people.groupby(map_series,axis = 1).count()
Out[33]:
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
# 计算一个字符串长度的数组
people.groupby(len).sum()
Out[34]:
a b c d e
3 1.014532 0.320880 0.236449 -0.708113 1.818260
5 -0.684024 1.088523 -0.885087 0.388805 1.128606
6 -0.599465 0.702602 0.076018 0.115762 -1.593297
In [35]:
key_list = ['one', 'one', 'one', 'two', 'two']
people.groupby([len,key_list]).min()
Out[35]:
a b c d e
3 one 0.003533 -1.037063 0.955879 -0.782302 0.028278
two 0.755857 1.357942 -0.719431 0.035387 0.621884
5 one -0.684024 1.088523 -0.885087 0.388805 1.128606
6 two -0.599465 0.702602 0.076018 0.115762 -1.593297
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 -1.170114 -1.302459 0.227997 0.829014 0.411373
1 -1.146333 -0.495806 0.416194 -2.010128 1.923935
2 -0.497282 -0.528917 -1.807007 0.355747 0.243122
3 -0.449244 0.357974 2.179747 -0.098429 -0.989450
In [39]:
# level关键字传递级别序号或名字
hier_df.groupby(level = 'cty',axis = 1).count()
Out[39]:
cty JP US
0 2 3
1 2 3
2 2 3
3 2 3
数据聚合
常见的聚合运算：
df
>>>
key1 key2 data1 data2
0 a one 1.612273 -1.761942
1 a two 0.422988 1.186852
2 b one 0.630012 0.317237
3 b two -0.096043 -1.286106
4 a one 1.170058 -0.112766
In [42]:
# quantile计算样本分位数
grouped = df.groupby('key1')
grouped['data1'].quantile(0.9)
Out[42]:
key1
a 1.523830
b 0.557406
Name: data1, dtype: float64
In [44]:
# aggregate或agg方法使用自己的聚合函数
def peak_to_peak(arr):
return arr.max() - arr.min()
grouped.agg(peak_to_peak)
Out[44]:
data1 data2
key1
a 1.189284 2.948793
b 0.726054 1.603343
In [45]:
grouped.describe()
Out[45]:
data1 data2
count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max
key1
a 3.0 1.068440 0.601119 0.422988 0.796523 1.170058 1.391165 1.612273 3.0 -0.229286 1.477846 -1.761942 -0.937354 -0.112766 0.537043 1.186852
b 2.0 0.266985 0.513398 -0.096043 0.085471 0.266985 0.448498 0.630012 2.0 -0.484434 1.133734 -1.286106 -0.885270 -0.484434 -0.083599 0.317237
面向列的多函数应用
tips = pd.read_csv(r'/Users/faye/Desktop/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
In [49]:
grouped = tips.groupby(['day','smoker'])
grouped_pct = grouped['tip_pct']
grouped_pct.agg('mean')
Out[49]:
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
In [50]:
# 传入一组函数或函数名
grouped_pct.agg(['mean','std',peak_to_peak])
Out[50]:
mean std peak_to_peak
day smoker
Fri No 0.151650 0.028123 0.067349
Yes 0.174783 0.051293 0.159925
Sat No 0.158048 0.039767 0.235193
Yes 0.147906 0.061375 0.290095
Sun No 0.160113 0.042347 0.193226
Yes 0.187250 0.154134 0.644685
Thur No 0.160298 0.038774 0.193350
Yes 0.163863 0.039389 0.151240
In [51]:
grouped_pct.agg([('foo', 'mean'), ('bar', np.std)])
Out[51]:
foo bar
day smoker
Fri No 0.151650 0.028123
Yes 0.174783 0.051293
Sat No 0.158048 0.039767
Yes 0.147906 0.061375
Sun No 0.160113 0.042347
Yes 0.187250 0.154134
Thur No 0.160298 0.038774
Yes 0.163863 0.039389
In [52]:
functions = ['count', 'mean', 'max']
result = grouped['tip_pct', 'total_bill'].agg(functions)
result
Out[52]:
tip_pct total_bill
count mean max count mean max
day smoker
Fri No 4 0.151650 0.187735 4 18.420000 22.75
Yes 15 0.174783 0.263480 15 16.813333 40.17
Sat No 45 0.158048 0.291990 45 19.661778 48.33
Yes 42 0.147906 0.325733 42 21.276667 50.81
Sun No 57 0.160113 0.252672 57 20.506667 48.17
Yes 19 0.187250 0.710345 19 24.120000 45.35
Thur No 45 0.160298 0.266312 45 17.113111 41.19
Yes 17 0.163863 0.241255 17 19.190588 43.11
In [53]:
result['tip_pct']
Out[53]:
count mean max
day smoker
Fri No 4 0.151650 0.187735
Yes 15 0.174783 0.263480
Sat No 45 0.158048 0.291990
Yes 42 0.147906 0.325733
Sun No 57 0.160113 0.252672
Yes 19 0.187250 0.710345
Thur No 45 0.160298 0.266312
Yes 17 0.163863 0.241255
In [54]:
ftuples = [('Durchschnitt', 'mean'),('Abweichung', np.var)]
grouped['tip_pct', 'total_bill'].agg(ftuples)
Out[54]:
tip_pct total_bill
Durchschnitt Abweichung Durchschnitt Abweichung
day smoker
Fri No 0.151650 0.000791 18.420000 25.596333
Yes 0.174783 0.002631 16.813333 82.562438
Sat No 0.158048 0.001581 19.661778 79.908965
Yes 0.147906 0.003767 21.276667 101.387535
Sun No 0.160113 0.001793 20.506667 66.099980
Yes 0.187250 0.023757 24.120000 109.046044
Thur No 0.160298 0.001503 17.113111 59.625081
Yes 0.163863 0.001551 19.190588 69.808518
In [55]:
grouped.agg({'tip' : np.max, 'size' : 'sum'})
Out[55]:
tip size
day smoker
Fri No 3.50 9
Yes 4.73 31
Sat No 9.00 115
Yes 10.00 104
Sun No 6.00 167
Yes 6.50 49
Thur No 6.70 112
Yes 5.00 40
In [56]:
grouped.agg({'tip_pct' : ['min', 'max', 'mean', 'std'],'size' : 'sum'})
Out[56]:
tip_pct size
min max mean std sum
day smoker
Fri No 0.120385 0.187735 0.151650 0.028123 9
Yes 0.103555 0.263480 0.174783 0.051293 31
Sat No 0.056797 0.291990 0.158048 0.039767 115
Yes 0.035638 0.325733 0.147906 0.061375 104
Sun No 0.059447 0.252672 0.160113 0.042347 167
Yes 0.065660 0.710345 0.187250 0.154134 49
Thur No 0.072961 0.266312 0.160298 0.038774 112
Yes 0.090014 0.241255 0.163863 0.039389 40
In [57]:
# 以“没有行索引”的形式返回聚合数据
tips.groupby(['day', 'smoker'], as_index=False).mean()
Out[57]:
day smoker total_bill tip size tip_pct
0 Fri No 18.420000 2.812500 2.250000 0.151650
1 Fri Yes 16.813333 2.714000 2.066667 0.174783
2 Sat No 19.661778 3.102889 2.555556 0.158048
3 Sat Yes 21.276667 2.875476 2.476190 0.147906
4 Sun No 20.506667 3.167895 2.929825 0.160113
5 Sun Yes 24.120000 3.516842 2.578947 0.187250
6 Thur No 17.113111 2.673778 2.488889 0.160298
7 Thur Yes 19.190588 3.030000 2.352941 0.163863
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
In [61]:
tips.groupby('smoker').apply(top)
Out[61]:
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
In [62]:
tips.groupby(['smoker', 'day']).apply(top, n=1,column='total_bill')
Out[62]:
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
In [63]:
result = tips.groupby('smoker')['tip_pct'].describe()
result
Out[63]:
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
In [64]:
result.unstack('smoker')
Out[64]:
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)
Out[66]:
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 (-1.615, -0.000613]
1 (-1.615, -0.000613]
2 (-0.000613, 1.614]
3 (-1.615, -0.000613]
4 (-0.000613, 1.614]
5 (-1.615, -0.000613]
6 (-1.615, -0.000613]
7 (1.614, 3.228]
8 (-1.615, -0.000613]
9 (-1.615, -0.000613]
Name: data1, dtype: category
Categories (4, interval[float64]): [(-3.235, -1.615] < (-1.615, -0.000613] < (-0.000613, 1.614] < (1.614, 3.228]]
In [68]:
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()
Out[68]:
count max mean min
data1
(-3.235, -1.615] 43.0 2.142106 0.070060 -1.430279
(-1.615, -0.000613] 407.0 2.906171 0.135426 -2.743878
(-0.000613, 1.614] 499.0 2.784244 0.081631 -3.205625
(1.614, 3.228] 51.0 2.499800 0.054287 -2.490635
In [69]:
grouping = pd.qcut(frame.data1, 10, labels=False)
grouped = frame.data2.groupby(grouping)
grouped.apply(get_stats).unstack()
Out[69]:
count max mean min
data1
0 100.0 2.339063 0.141428 -2.244534
1 100.0 2.711519 0.153914 -2.057574
2 100.0 2.906171 0.171634 -2.743878
3 100.0 2.073188 0.149568 -2.395140
4 100.0 2.784675 -0.022247 -2.388540
5 100.0 2.531319 0.201157 -2.391423
6 100.0 2.784244 0.141187 -2.595624
7 100.0 2.622564 -0.049855 -3.205625
8 100.0 2.366410 0.072830 -2.209372
9 100.0 2.501468 0.056716 -2.870079
用特定于分组的值填充缺失值
s = pd.Series(np.random.randn(6))
s[::2] = np.nan
s
>>>
0 NaN
1 0.270764
2 NaN
3 -0.465053
4 NaN
5 1.378137
dtype: float64
In [71]:
s.fillna(s.mean())
Out[71]:
0 0.394616
1 0.270764
2 0.394616
3 -0.465053
4 0.394616
5 1.378137
dtype: float64
In [72]:
# 对不同的分组填充不同的值
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
Out[72]:
Ohio -0.688042
New York -1.234445
Vermont -0.008028
Florida 0.670283
Oregon 0.899524
Nevada 0.178562
California -0.083762
Idaho -0.395532
dtype: float64
In [73]:
# 将一些值设为缺失值
data[['Vermont', 'Nevada', 'Idaho']] = np.nan
data
Out[73]:
Ohio -0.688042
New York -1.234445
Vermont NaN
Florida 0.670283
Oregon 0.899524
Nevada NaN
California -0.083762
Idaho NaN
dtype: float64
In [74]:
data.groupby(group_key).mean()
Out[74]:
East -0.417402
West 0.407881
dtype: float64
In [75]:
# 分组平均值填充缺失值
fill_mean = lambda g: g.fillna(g.mean())
data.groupby(group_key).apply(fill_mean)
Out[75]:
Ohio -0.688042
New York -1.234445
Vermont -0.417402
Florida 0.670283
Oregon 0.899524
Nevada 0.407881
California -0.083762
Idaho 0.407881
dtype: float64
In [76]:
fill_values = {'East': 0.5, 'West': -1}
fill_func = lambda g: g.fillna(fill_values[g.name])
data.groupby(group_key).apply(fill_func)
Out[76]:
Ohio -0.688042
New York -1.234445
Vermont 0.500000
Florida 0.670283
Oregon 0.899524
Nevada -1.000000
California -0.083762
Idaho -1.000000
dtype: float64
透视表
tips.pivot_table(index=['day', 'smoker'])
size tip tip_pct total_bill
day smoker
Fri No 2.250000 2.812500 0.151650 18.420000
Yes 2.066667 2.714000 0.174783 16.813333
Sat No 2.555556 3.102889 0.158048 19.661778
Yes 2.476190 2.875476 0.147906 21.276667
Sun No 2.929825 3.167895 0.160113 20.506667
Yes 2.578947 3.516842 0.187250 24.120000
Thur No 2.488889 2.673778 0.160298 17.113111
Yes 2.352941 3.030000 0.163863 19.190588
In [79]:
tips.pivot_table(['tip_pct', 'size'], index=['time','day'], columns='smoker')
Out[79]:
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
Thur 2.500000 2.352941 0.160311 0.163863
In [80]:
tips.pivot_table(['tip_pct', 'size'], index=['time','day'],columns='smoker', margins=True)
Out[80]:
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
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
In [81]:
tips.pivot_table(['tip_pct', 'size'], index=['time','day'],columns='smoker', margins=True)
Out[81]:
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
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
In [82]:
tips.pivot_table('tip_pct', index=['time', 'smoker'],columns='day',aggfunc=len, margins=True)
Out[82]:
day Fri Sat Sun Thur All
time smoker
Dinner No 3.0 45.0 57.0 1.0 106.0
Yes 9.0 42.0 19.0 NaN 70.0
Lunch No 1.0 NaN NaN 44.0 45.0
Yes 6.0 NaN NaN 17.0 23.0
All 19.0 87.0 76.0 62.0 244.0
In [83]:
tips.pivot_table('tip_pct', index=['time', 'size','smoker'],columns='day', aggfunc='mean',fill_value=0)
Out[83]:
day Fri Sat Sun Thur
time size smoker
Dinner 1 No 0.000000 0.137931 0.000000 0.000000
Yes 0.000000 0.325733 0.000000 0.000000
2 No 0.139622 0.162705 0.168859 0.159744
Yes 0.171297 0.148668 0.207893 0.000000
3 No 0.000000 0.154661 0.152663 0.000000
Yes 0.000000 0.144995 0.152660 0.000000
4 No 0.000000 0.150096 0.148143 0.000000
Yes 0.117750 0.124515 0.193370 0.000000
5 No 0.000000 0.000000 0.206928 0.000000
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
Yes 0.223776 0.000000 0.000000 0.000000
2 No 0.000000 0.000000 0.000000 0.166005
Yes 0.181969 0.000000 0.000000 0.158843
3 No 0.187735 0.000000 0.000000 0.084246
Yes 0.000000 0.000000 0.000000 0.204952
4 No 0.000000 0.000000 0.000000 0.138919
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
pivot——table的参数说明
