[英]Pandas - Groupby a multiindex level, get the possible combinations, then transform the data
I have been struggling with a problem of grouping by, combinations and transform. 我一直在努力解决分组,组合和转换的问题。 My current solution is:
我目前的解决方案是:
df = df.groupby(level='lvl_2').transform(lambda x: x[0]/x[1])
But this doesn't tackled some parts of my problems. 但这并没有解决我问题的某些部分。
Assuming the code below: 假设代码如下:
import pandas as pd
import numpy as np
import datetime
today = datetime.date.today()
today_1 = datetime.date.today() - datetime.timedelta(1)
today_2 = datetime.date.today() - datetime.timedelta(2)
ticker_date = [('first', 'a',today), ('first', 'a',today_1), ('first', 'a',today_2),
('first', 'c',today), ('first', 'c',today_1), ('first', 'c',today_2),
('first', 'b',today), ('first', 'b',today_1), ('first', 'b',today_2),
('first', 'd',today), ('first', 'd',today_1), ('first', 'd',today_2)]
index_df = pd.MultiIndex.from_tuples(ticker_date,names=['lvl_1','lvl_2','lvl_3'])
df = pd.DataFrame(np.random.rand(12), index_df, ['idx'])
The output is: 输出是:
idx
lvl_1 lvl_2 lvl_3
first a 2018-02-14 0.421075
2018-02-13 0.278418
2018-02-12 0.117888
c 2018-02-14 0.716823
2018-02-13 0.241261
2018-02-12 0.772491
b 2018-02-14 0.681738
2018-02-13 0.636927
2018-02-12 0.668964
d 2018-02-14 0.770797
2018-02-13 0.11469
2018-02-12 0.877965
I need the following: 我需要以下内容:
Here is an illustration: 这是一个例子:
Here, I've created a 'new' column. 在这里,我创建了一个“新”列。
new
lvl_1 lvl_2 lvl_3
first a/c 2018-02-14 0.587418372
2018-02-13 1.154011631
2018-02-12 0.152607603
a/b 2018-02-14 0.617649302
2018-02-13 0.437127018
2018-02-12 0.17622473
a/d 2018-02-14 0.546285209
2018-02-13 2.427569971
2018-02-12 0.134274145
c/b 2018-02-14 1.051464052
2018-02-13 0.378789092
2018-02-12 1.154757207
c/d 2018-02-14 0.929976375
2018-02-13 2.103592292
2018-02-12 0.87986537
b/d 2018-02-14 0.884458554
2018-02-13 5.553465865
2018-02-12 0.761948369
To further explain: 进一步解释:
new
lvl_1 lvl_2 lvl_3
first a/c 2018-02-14 0.587418372
2018-02-13 1.154011631
2018-02-12 0.152607603
Here, I do the ratio of the elements of a with c: 在这里,我使用c的元素的比例:
0.587418 = 0.421075/0.716823
1.154012 = 0.278418/0.241261
0.152608 = 0.117888/0.772491
I have tried a groupby and transform method, something like: 我尝试了一个groupby和transform方法,例如:
df = df.groupby(level='lvl_2').transform(lambda x: x[0]/x[1])
But obviously, this only transform the first and second value of each specific level. 但显然,这只会转换每个特定级别的第一个和第二个值。 Also, I don't know how to establish the new multiindex with the combinations.
另外,我不知道如何用这些组合建立新的多索引。 (a/c, a/b, a/d, c/b, c/d, b/d)
(a / c,a / b,a / d,c / b,c / d,b / d)
I feel that I am on the right path, but I feel stuck. 我觉得我走在正确的道路上,但我感到困惑。
If for first level are same combinations of another levels like in sample is possible use reindex
to MultiIndex
in columns with div
: 如果对于第一级别是相同的其他级别的组合,例如在样本中可以使用
reindex
到具有div
列中的MultiIndex
:
#same as Maarten Fabré answer
np.random.seed(42)
from itertools import combinations
#get combination of second level values
c = pd.MultiIndex.from_tuples(list(combinations(df.index.levels[1], 2)))
#reshape to unique columns of second level
print (df['idx'].unstack(1))
lvl_2 a b c d
lvl_1 lvl_3
first 2018-02-12 0.731994 0.601115 0.155995 0.969910
2018-02-13 0.950714 0.866176 0.156019 0.020584
2018-02-14 0.374540 0.058084 0.598658 0.708073
#reindex by both levels
df1 = df['idx'].unstack(1).reindex(columns=c, level=0)
print (df1)
a b c
b c d c d d
lvl_1 lvl_3
first 2018-02-12 0.731994 0.731994 0.731994 0.601115 0.601115 0.155995
2018-02-13 0.950714 0.950714 0.950714 0.866176 0.866176 0.156019
2018-02-14 0.374540 0.374540 0.374540 0.058084 0.058084 0.598658
df2 = df['idx'].unstack(1).reindex(columns=c, level=1)
print (df2)
a b c
b c d c d d
lvl_1 lvl_3
first 2018-02-12 0.601115 0.155995 0.969910 0.155995 0.969910 0.969910
2018-02-13 0.866176 0.156019 0.020584 0.156019 0.020584 0.020584
2018-02-14 0.058084 0.598658 0.708073 0.598658 0.708073 0.708073
#divide with flatten MultiIndex
df3 = df1.div(df2)
df3.columns = df3.columns.map('/'.join)
#reshape back and change order of levels, sorting indices
df3 = df3.stack().reorder_levels([0,2,1]).sort_index()
print (df3)
lvl_1 lvl_3
first a/b 2018-02-12 1.217727
2018-02-13 1.097599
2018-02-14 6.448292
a/c 2018-02-12 4.692434
2018-02-13 6.093594
2018-02-14 0.625632
a/d 2018-02-12 0.754703
2018-02-13 46.185944
2018-02-14 0.528957
b/c 2018-02-12 3.853437
2018-02-13 5.551748
2018-02-14 0.097023
b/d 2018-02-12 0.619764
2018-02-13 42.079059
2018-02-14 0.082031
c/d 2018-02-12 0.160834
2018-02-13 7.579425
2018-02-14 0.845476
dtype: float64
from itertools import combinations
def calc_ratios(data):
comb = combinations(data.index.get_level_values('lvl_2').unique(), 2)
ratios = {
f'{i}/{j}':
data.xs(i, level='lvl_2') /
data.xs(j, level='lvl_2')
for i, j in comb
}
# print(ratios)
if ratios:
return pd.concat(ratios)
result = pd.concat(calc_ratios(data) for group, data in df.groupby('lvl_1'))
lvl_1 lvl_3 idx a/b first 2018-02-14 6.448292467809392 a/b first 2018-02-13 1.0975992712883451 a/b first 2018-02-12 1.2177269366284045 a/c first 2018-02-14 0.6256323575698127 a/c first 2018-02-13 6.093594353302192 a/c first 2018-02-12 4.692433684425558 a/d first 2018-02-14 0.5289572433565499 a/d first 2018-02-13 46.185944271838835 a/d first 2018-02-12 0.7547030687230791 b/d first 2018-02-14 0.08203059119870332 b/d first 2018-02-13 42.07905879677424 b/d first 2018-02-12 0.6197637959891664 c/b first 2018-02-14 10.306839775450461 c/b first 2018-02-13 0.18012345549282302 c/b first 2018-02-12 0.25950860865015657 c/d first 2018-02-14 0.8454761601705119 c/d first 2018-02-13 7.579425474360648 c/d first 2018-02-12 0.16083404038888807
(data generated with np.random.seed(42)
) (使用
np.random.seed(42)
生成的数据)
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.