使用 Excel Pandas 中的浮点值填充字典的问题

Posted 2023-03-11

【中文标题】使用 Excel Pandas 中的浮点值填充字典的问题

【英文标题】：Issues populating dictionary with float values from Excel Pandas

【发布时间】：2018-12-09 13:08:09

【问题描述】：

我正在使用 Excel 电子表格来填充字典。然后我使用这些值将另一个数据帧的值乘以引用，但是当我尝试时它给了我错误。我决定将excel电子表格从我的字典中取出以避免错误，但我没有成功。我这样做是因为字典最终会变得很长，并且编辑键及其值太乏味了。我正在使用 Python 2.7

import pandas as pd

#READ EXCEL FILE
df = pd.read_excel("C:/Users/Pedro/Desktop/dataframe.xls")

#Store the keys with its value in a dictionary. This will become df2
d = "M1-4":0.60,"M1-5/R10":0.85,"C5-3":0.85,"M1-5/R7-3":0.85,"M1-4/R7A":0.85,"R7A":0.85,"M1-4/R6A":0.85,"M1-4/R6B":0.85,"R6A":0.85,"PARK":0.20,"M1-6/R10":0.85,"R6B":0.85,"R9":0.85,"M1-5/R9":0.85

#Convert the dictionary to an Excel spreadsheet
df5 = pd.DataFrame.from_dict(d, orient='index')
df5.to_excel('bob_dict.xlsx')

#populatethe dictionary from the excel spreadsheet
df2 = pd.read_excel("C:/Users/Pedro/Desktop/bob_dict.xlsx")
#Convert dtframe back to a dictionary
dictionary = df2.to_dict(orient='dict')
#Pass the dictionary as reference 

b = df.filter(like ='Value').values
c = df.filter(like ='ZONE').replace(dictionary).astype(float).values

df['pro_cum'] = ((c * b).sum(axis =1))

运行时我得到 ValueError: could not convert R6B string to float.

c = df.filter(like ='ZONE').replace(d).astype(float).values

但如果我用原始字典替换区域值，它运行时不会出错。

输入：df

HP    ZONE           Value  ZONE1       Value1
3     R7A           0.7009  M1-4/R6B    0.00128
2     R6A           0.5842  M1-4/R7A    0.00009
7     M1-6/R10      0.1909  M1-4/R6A    0.73576
9     R6B           0.6919  PARK        0.03459
6     PARK          1.0400  M1-4/R6A    0.33002
9.3   M1-4/R6A      0.7878  PARK        0.59700
10.6  M1-4/R6B      0.0291  R6A         0.29621
11.9  R9            0.0084  M1-4        0.00058
13.2  M1-5/R10      0.0049  M1-4        0.65568
14.5  M1-4/R7A      0.0050  C5-3        0.00096
15.8  M1-5/R7-3     0.0189  C5-3        1.59327
17.1  M1-5/R9       0.3296  M1-4/R6B    0.43918
18.4  C5-3          0.5126  R6B         0.20835
19.7  M1-4          0.5126  PARK        0.22404

【参考方案1】：

字典d 之外的一些值存在问题（错误说R6B，但可能存在更多值），因此无法转换为浮点数。

你可以找到这个值：

#create Series from all Zone columns
vals = df.filter(like ='ZONE').replace(d).stack()
#for non numeric return NaNs, so filtering return problematic values
out = vals[pd.to_numeric(vals, errors= 'coerce').isnull()].unique()
print (out)

然后添加到字典d 以避免此错误。

---

示例：

print (df)
      HP       ZONE   Value     ZONE1   Value1
0    3.0        R7A  0.7009  M1-4/R6B  0.00128
1    2.0        R6A  0.5842  M1-4/R7A  0.00009
2    7.0   M1-6/R10  0.1909  M1-4/R6A  0.73576
3    9.0        R6B  0.6919      PARK  0.03459
4    6.0       PARK  1.0400  M1-4/R6A  0.33002
5    9.3   M1-4/R6A  0.7878      PARK  0.59700
6   10.6   M1-4/R6B  0.0291       R6A  0.29621
7   11.9         R9  0.0084      M1-4  0.00058
8   13.2   M1-5/R10  0.0049      M1-4  0.65568
9   14.5   M1-4/R7A  0.0050      C5-3  0.00096
10  15.8  M1-5/R7-3  0.0189      C5-3  1.59327
11  17.1    M1-5/R9  0.3296  M1-4/R6B  0.43918
12  18.4       C5-3  0.5126       R6B  0.20835
13  19.7       M1-4  0.5126     PARK1  0.22404 <- added PARK1 for testing

d = "M1-4":0.60,"M1-5/R10":0.85,"C5-3":0.85,"M1-5/R7-3":0.85,"M1-4/R7A":0.85,"R7A":0.85,"M1-4/R6A":0.85,"M1-4/R6B":0.85,"R6A":0.85,"PARK":0.20,"M1-6/R10":0.85,"R6B":0.85,"R9":0.85,"M1-5/R9":0.85

vals = df.filter(like ='ZONE').replace(d).stack()
out = vals[pd.to_numeric(vals, errors= 'coerce').isnull()].unique()
print (out)
['PARK1']

【讨论】：

好的，我看到填充字典中的大部分值都无缘无故地变成了 unicode 字符串。 [u'M1-4' u'C5-3' u'M1-4/R7A' u'R7A' u'M1-4/R6B' u'R6B' u'M1-5/R7-3' u'M1-5/R9' u'M1-6/R10' u'R6A' u'M1-4/R6A' u'PARK' u'M1-5/R10'] 有没有将 unicode 字符串转换为浮点数的方法。我看到 float 无法正常工作，因为我使用的是 Python 2.7.10

hmmm，一个想法，如果在每个字符串之前手动添加 u 就像前两个值一样 - d = u"M1-4":0.60,u"M1-5/R10":0.85,"C5-3":0.85,"M1-5/R7-3":0.85,"M1-4/R7A":0.85,"R7A":0.85,"M1-4/R6A":0.85,"M1-4/R6B":0.85,"R6A":0.85,"PARK":0.20,"M1-6/R10":0.85,"R6B":0.85,"R9":0.85,"M1-5/R9":0.85 它工作吗？

感谢Jezrael的帮助，不过我解决了这个问题你可以看看我的回答。

【参考方案2】：

我能够解决我的问题。当我将字典转换为数据框时，键成为索引，因此当我将数据框转换回字典时，我最终得到了一个字典字典。所以我不得不在 replace 方法中说明这一点。

0: 'M1-4': 0.6, 'M1-5/R10': 0.85, 'C5-3': 0.85,
     'M1-5/R7-3': 0.85, 'M1-4/R7A': 0.85, 'R7A': 0.85,
     'M1-4/R6A': 0.85, 'M1-4/R6B': 0.85, 'R6A': 0.85,
     'PARK': 0.2, 'M1-6/R10': 0.85, 'R6B': 0.85,
     'R9': 0.85, 'M1-5/R9': 0.85
     
    

所以我编辑了这行代码并添加了[0]

c = df.filter(like='ZONE').replace(dictionary[0]).astype(float).values