How to execute a custom function in python for a data frame imported from excel?

Question

I have a excel file payments with approximately 50,000 rows with the next structure :

I import the data from excel to python through this code:

test= pd.read_excel(r'D:\\Users\\Desktop\\test_stack.xlsx')

However, when I want execute the custom functions indicated below, the following error is generated :

TypeError: unsupported operand type(s) for /: 'str' and 'str'

Apparently, the column E is read as string (str), so these functions cannot be executed. It should be noted that the functions are iterative, so they go through each element enclosed in {} and separated by commas for the column payments, to execute the operations, and later create the corresponding columns.

Likewise, when I execute the functions creating the dataframe manually, the functions execute without problems, but I need to execute them in the format of the mentioned excel file.

test= pd.DataFrame({'id':['F8510004123','A3100002543','Z3510002123'],
                     'product':['retail','retail','others'],
                     'type':['E','E','D'],
                     'quantity':[25,34,150],
                     'nro_ope':[2,3,26],
                     'payments':[[1030.97,1030.97,584.91],[1610.74,1610.74,1610.74,1610.74,1611.14],[1007.52,1007.52,1007.52,1007.52,500,500,852.95]]
                     })

;

def var_payments (x) :
    variation  = [round(abs(a/b -1),3) for a,b in permutations(x,2)]
    return variation 

;

def count_var_pay (x) :
    count = 0
    for element in x:
        if element >= 0.05 :
            count += 1
        else:
             0                    
    return count

;

def flag_var_payments (x) : 
    if x >= 2 :
        return 'Yes'
    else : 
        return 'No'

;

test['var_payments'] = test.payments.apply(lambda x:var_payments(x))

test['count_p'] = test.var_payments.apply(lambda x:count_var_pay(x))

test['flag'] = test.count_p.apply(lambda x:flag_var_payments(x))

How could I do it? Change the format of the column payments from the excel file? Transform column data?

I am attentive to your comments.

Thanks for support.

Corralien · Accepted Answer · 2021-12-28 14:50:16Z

1

Convert {} into [] then use pd.eval to transform as a list:

df['payments'] = pd.eval(df['payments'].replace({r'{': '[', r'}': ']'}, regex=True))

Output:

>>> df
            id product type  quantity  nro_ope                                           payments
0  F8510004123  retail    E        25        2                         [1030.97, 1030.97, 584.91]
1  A3100002543  retail    E        34        3      [1610.74, 1610.74, 1610.74, 1610.74, 1611.14]
2  Z3510002123  others    D       150       26  [1007.52, 1007.52, 1007.52, 1007.52, 500, 500,...

>>> df.iloc[0, 5]
[1030.97, 1030.97, 584.91]

>>> type(df.iloc[0, 5])
list

answered Dec 28, 2021 at 14:50

Corralien

121k8 gold badges44 silver badges69 bronze badges

Sign up to request clarification or add additional context in comments.

2 Comments

Jean Over a year ago

It does not allow me to execute pd.eval , it generates the following error: ValueError: unknown type object, the column payments is the type object in the python dataframe.

Corralien Over a year ago

@Jean. Can you try test['payments'].apply(lambda x: [float(i) for i in x[1:-1].split(',')])?

Tran Duc Phu · Accepted Answer · 2021-12-28 14:52:47Z

0

I don't know how do you handle the "{}" when reading the excel file but if you changed payment in your test DataFrame, you can encounter the same error:

'payments':["{1030.97,1030.97,584.91}","{1610.74,1610.74,1610.74,1610.74,1611.14}","{1007.52,1007.52,1007.52,1007.52,500,500,852.95}"]

TypeError: unsupported operand type(s) for /: 'str' and 'str'

Hope that can help something.

answered Dec 28, 2021 at 14:52

Tran Duc Phu

12 bronze badges

Collectives™ on Stack Overflow

How to execute a custom function in python for a data frame imported from excel?

2 Answers 2

2 Comments

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

2 Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Related