What should I do with this error? unsupported operand type(s) for /: 'str' and 'str&#-CodePudding

I have database with name 'Data' :

#removing NULL from database
data_new2 = Data.copy()
data_new2.dropna(inplace = True)

then I did

#calculating deposit
data_new2list=['DATA6030','DATA6040']
data_new2['deposit']=data_new2[data_new2list].astype(float).sum(1)

data_new3=data_new2
data_new3['WFR']= data_new3['DATA9960']/data_new3['DATA6190']
data_new3['DR']=data_new3['deposit']/data_new3['DATA6190']`

However, I received this error:

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

CodePudding user response：

You are performing a division operation between two strings, convert your columns to make it work:

data_new2 = Data.copy().astype(float)

CodePudding user response：

The error is telling you that 'DATA9960' and 'DATA6190' columns contain at least one string each, and you can't divide two strings.

Assuming that those columns can contain non-numeric strings, first convert all the numeric strings to a numeric type, using pandas.to_numeric with errors='coerce'. The non-numeric strings will be set to NaN.

import pandas as pd

data_new2 = Data.copy()
data_new2.dropna(inplace=True)

data_new2list = ['DATA6030','DATA6040']
data_new2['deposit'] = data_new2[data_new2list].astype(float).sum(1)

#convert 'DATA9960', 'DATA6190' to numeric 
cols = ['DATA9960', 'DATA6190']
data_new2[cols] = data_new2[cols].apply(pd.to_numeric, errors='coerce')

# drop rows with NaN values in 'DATA9960', 'DATA6190' resulting from pd.to_numeric
data_new2.dropna(subset=cols, inplace=True)

# this doesn't return a copy btw. data_new3 and data_new2 point to the same object. 
# To make a copy you need data_new3 = data_new2.copy()
data_new3 = data_new2   

data_new3['WFR'] = data_new3['DATA9960'] / data_new3['DATA6190']
data_new3['DR'] = data_new3['deposit'] / data_new3['DATA6190']`