Question

我正在使用python，pandas和numpy来读取一些数据。

我有两个数据框：

输入1-成本矩阵（它具有每季和区域的成本）：index = regions和columns = seasons 输入2-二进制矩阵（当一个月“a”属于季节“b”时值1：index = seasons，columns = months

我想要的输出是一个矩阵C，它具有每个区域和月份的成本：index = region，column month。

有人可以帮助我吗？我google了很多，但我找不到解决方案。

使用我的代码进行更新

import pandas as pd
import numpy as np
from xlwings import Workbook, Range
import os
print(os.getcwd())
link = (os.getcwd() + '/test.xlsx')
print(link)

#Open the Workbook
wb = Workbook(link)
#
#Reading data

regions=np.array(Range('Sheet1','regions').value)
#[u'Region A' u'Region B' u'Region C' u'Region D']

seasons=np.array(Range('Sheet1','seasons').value)
#[u'Season A' u'Season B' u'Season C' u'Season D']

months=np.array(Range('Sheet1','months').value)
#[u'Jan' u'Feb' u'Mar' u'Apr' u'May' u'Jun' u'Jul' u'Aug']

#read relationship between season and month
data=Range('Sheet1','rel').table.value
relationship=pd.DataFrame(data[0:], index = regions, columns=months)
#          Jan  Feb  Mar  Apr  May  Jun  Jul  Aug
#Region A    1    1    0    0    0    0    0    0
#Region B    0    0    1    1    0    0    0    0
#Region C    0    0    0    0    1    1    0    0
#Region D    0    0    0    0    0    0    1    1

#read the cost per region
data=Range('Sheet1','cost').table.value
cost=pd.DataFrame(data[0:], index = regions, columns=seasons)
#          Season A  Season B  Season C  Season D
#Region A         1         9         7         2
#Region B         7         0         3         3
#Region C         4         0         7         5
#Region D         3        10         3        10


#What I want:
#        Jan    Feb Mar Apr May Jun Jul Aug
#Region A   1   1   9   9   7   7   2   2
#Region B   7   7   0   0   3   3   3   3
#Region C   4   4   0   0   7   7   5   5
#Region D   3   3   10  10  3   3   10  10

Answer 1

我认为你的例子中的关系数据框中存在错误，因为你清楚地说明它应该是季节（而不是地区）和月份之间的关系，所以我相应地改变了它

import pandas as pd
import numpy as np

regions = ['Region A', 'Region B', 'Region C', 'Region D']
seasons = ['Season A', 'Season B', 'Season C', 'Season D']
cost_data = np.array([[1, 9, 7, 2], [7, 0, 3, 3], [4, 0, 7, 5], [3, 10, 3, 10]])

cost = pd.DataFrame(data=cost_data, index=regions, columns=seasons)

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug']
rel_data = np.array([[1, 1, 0, 0, 0, 0, 0, 0], 
                     [0, 0, 1, 1, 0, 0, 0, 0], 
                     [0, 0, 0, 0, 1, 1, 0, 0], 
                     [0, 0, 0, 0, 0, 0, 1, 1]])

rel = pd.DataFrame(data=rel_data, index=seasons, columns=months)

c = pd.DataFrame(index=regions, columns=months)
for region in regions:
    for month in months:
        for season in seasons:
            if rel.loc[season][month]:
                c.loc[region][month] = cost.loc[region][season]

print c

#          Jan Feb Mar Apr May Jun Jul Aug
#Region A   1   1   9   9   7   7   2   2
#Region B   7   7   0   0   3   3   3   3
#Region C   4   4   0   0   7   7   5   5
#Region D   3   3  10  10   3   3  10  10

pandas：如何将矩阵与不同的索引和列组合在一起？

1 个答案: