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python – Issues populating dictionary with float values from Excel Pandas

Posted by: admin May 14, 2020 Leave a comment

Questions:

I’m using an excel spreadsheet to populate a dictionary. Then I’m using those values to multiply the values of another data frame by reference, but it gives me errors when I try. I decided to make the excel spreadsheet out my dictionary to avoid errors, but I haven’t been successful. I’m doing this because the dictionary eventually gets long and it’s too tedious to edit the keys and its values. I’m using 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))

When run this I get ValueError: could not convert R6B string to float.

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

but if I replace the zone values by the original dictionary it runs without errors.

Input : 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
How to&Answers:

There is problem some values outside of dictionary d (error say R6B, but there is possible more values), so not possible convert to floats.

You can find this value(s):

#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)

And then add to dictionary d for avoid this error.


Sample:

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']

Answer:

I was able to solve my problem. When I converted the dictionary to a data frame the keys become index, so when I convert the data frame back to a dictionary I end up with a dictionary of dictionary. So I had to state that in the replace method.

{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
     }
    }

So I edited this line of code and added [0]

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