分组级运算和转换
假设要添加一列的各索引分组平均值
第一种方法
import pandas as pd
from pandas import Series
import numpy as np
df = pd.DataFrame([[-2.04708,1.393406,'a','one'],
[0.478943,0.092908,'a','two'],
[-0.519439,0.281746,'b','one'],
[-0.555730,0.769023,'b','two'],
[1.965781,1.246435,'a','one'],
], columns=['data1','data2','key1','key2'])
df
data1 data2 key1 key2
0 -2.047080 1.393406 a one
1 0.478943 0.092908 a two
2 -0.519439 0.281746 b one
3 -0.555730 0.769023 b two
4 1.965781 1.246435 a one
# 先聚合求出平均值
key1_means = df.groupby('key1').mean().add_prefix('mean_')
key1_means
mean_data1 mean_data2
key1
a 0.132548 0.910916
b -0.537584 0.525385
# 在通过聚合函数加到DataFrame
pd.merge(df, key1_means,left_on='key1', right_index=True)
data1 data2 key1 key2 mean_data1 mean_data2
0 -2.047080 1.393406 a one 0.132548 0.910916
1 0.478943 0.092908 a two 0.132548 0.910916
4 1.965781 1.246435 a one 0.132548 0.910916
2 -0.519439 0.281746 b one -0.537584 0.525385
3 -0.555730 0.769023 b two -0.537584 0.525385
第二种方法 transform，会将一个函数应用到各个分组，有严格条件，要么传入可以广播的标量，要么产生一个相同大小的结果数组
df_mean = df.groupby('key2').transform(np.mean).add_prefix('mean_')
df_mean
mean_data1 mean_data2
0 -0.200246 0.973862
1 -0.038393 0.430966
2 -0.200246 0.973862
3 -0.038393 0.430966
4 -0.200246 0.973862
pd.concat([df,df_mean],axis=1)
data1 data2 key1 key2 data1 data2
0 -2.047080 1.393406 a one -0.200246 0.973862
1 0.478943 0.092908 a two -0.038393 0.430966
2 -0.519439 0.281746 b one -0.200246 0.973862
3 -0.555730 0.769023 b two -0.038393 0.430966
4 1.965781 1.246435 a one -0.200246 0.973862
apply一般性的'拆分-应用-合并'
apply会将待处理的对象拆分成多个片段，然后对各片段调用传入的函数,最后尝试将各片段组合到一起
# 选取指定列具有最大值的行的函数
def top(df, n=3, column='tip_pct'):
return df.sort_index(by=column)[-n:]
tips = pd.read_csv('C:/Users/1/Desktop/tips.csv')
tips['tip_pct'] = tips['tip']/tips['total_bill']
tips.head()
total_bill tip sex smoker day time size tip_pct
0 16.99 1.01 Female No Sun Dinner 2 0.059447
1 10.34 1.66 Male No Sun Dinner 3 0.160542
2 21.01 3.50 Male No Sun Dinner 3 0.166587
3 23.68 3.31 Male No Sun Dinner 2 0.139780
4 24.59 3.61 Female No Sun Dinner 4 0.146808
# 选取前三个最大值
top(tips,n=3)
total_bill tip sex smoker day time size tip_pct
67 3.07 1.00 Female Yes Sat Dinner 1 0.325733
178 9.60 4.00 Female Yes Sun Dinner 2 0.416667
172 7.25 5.15 Male Yes Sun Dinner 2 0.710345
# 按是否吸烟分组，选前三个最大的值
# 过程是top函数在各个片段上调用后，结果由pandas.concat组装到一起
tips.groupby('smoker').apply(top)
total_bill tip sex smoker day time size tip_pct
smoker
No 51 10.29 2.60 Female No Sun Dinner 2 0.252672
149 7.51 2.00 Male No Thur Lunch 2 0.266312
232 11.61 3.39 Male No Sat Dinner 2 0.291990
Yes 67 3.07 1.00 Female Yes Sat Dinner 1 0.325733
178 9.60 4.00 Female Yes Sun Dinner 2 0.416667
172 7.25 5.15 Male Yes Sun Dinner 2 0.710345
# 如果传给apply的函数能够接受其他参数或关键字，则可以将这些一并传入
# 总花费的钱，按是否吸烟和每周的天数来找出每天其中价格最高的，n代表返回的数据前几个
tips.groupby(['smoker','day']).apply(top, n=1, column='total_bill')
total_bill tip sex smoker day time size tip_pct
smoker day
No Fri 94 22.75 3.25 Female No Fri Dinner 2 0.142857
Sat 212 48.33 9.00 Male No Sat Dinner 4 0.186220
Sun 156 48.17 5.00 Male No Sun Dinner 6 0.103799
Thur 142 41.19 5.00 Male No Thur Lunch 5 0.121389
Yes Fri 95 40.17 4.73 Male Yes Fri Dinner 4 0.117750
Sat 170 50.81 10.00 Male Yes Sat Dinner 3 0.196812
Sun 182 45.35 3.50 Male Yes Sun Dinner 3 0.077178
Thur 197 43.11 5.00 Female Yes Thur Lunch 4 0.115982
# 分组调用describe的方法
tips.groupby('smoker')['tip_pct'].describe().T
smoker No Yes
count 151.000000 93.000000
mean 0.159328 0.163196
std 0.039910 0.085119
min 0.056797 0.035638
25% 0.136906 0.106771
50% 0.155625 0.153846
75% 0.185014 0.195059
max 0.291990 0.710345
# 本质是,下面两行代码的快捷键而已
f = lambda x:x.describe()
tips.groupby('smoker')['tip_pct'].apply(f).unstack('smoker')
smoker No Yes
count 151.000000 93.000000
mean 0.159328 0.163196
std 0.039910 0.085119
min 0.056797 0.035638
25% 0.136906 0.106771
50% 0.155625 0.153846
75% 0.185014 0.195059
max 0.291990 0.710345
# 禁用层次化索引
tips.groupby('smoker',group_keys=False).apply(top)
total_bill tip sex smoker day time size tip_pct
51 10.29 2.60 Female No Sun Dinner 2 0.252672
149 7.51 2.00 Male No Thur Lunch 2 0.266312
232 11.61 3.39 Male No Sat Dinner 2 0.291990
67 3.07 1.00 Female Yes Sat Dinner 1 0.325733
178 9.60 4.00 Female Yes Sun Dinner 2 0.416667
172 7.25 5.15 Male 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)})
factor = pd.cut(frame['data1'],4)
factor[:5]
0 (-1.573, 0.112]
1 (-1.573, 0.112]
2 (-1.573, 0.112]
3 (-1.573, 0.112]
4 (-1.573, 0.112]
Name: data1, dtype: category
Categories (4, interval[float64]): [(-3.264, -1.573] < (-1.573, 0.112] < (0.112, 1.797] < (1.797, 3.482]]
def get_stats(group):
return {'min':group.min(),'max':group.max(),'count':group.count(),'mean':group.mean()}
# 长度即每个区间相等的桶(区间大小相等)
frame.data2.groupby(factor).apply(get_stats).unstack()
count max mean min
data1
(-3.264, -1.573] 57.0 3.236024 0.100749 -2.149984
(-1.573, 0.112] 484.0 2.843239 -0.058549 -3.606913
(0.112, 1.797] 425.0 2.614935 0.065693 -3.463799
(1.797, 3.482] 34.0 1.791511 -0.049641 -1.756306
# 大小相等的桶,labels关闭区间名称（数据点数量相等）
ppp = pd.qcut(frame['data1'],4,labels=False)
frame.data2.groupby(ppp).apply(get_stats).unstack()
count max mean min
data1
0 250.0 3.236024 -0.032592 -2.750112
1 250.0 2.843239 -0.068005 -3.606913
2 250.0 2.614935 0.103220 -2.380858
3 250.0 2.612170 0.011922 -3.463799
