receiving an error when trying to change a column type from string to a float in python-CodePudding

import pandas as pd

df = pd.read_csv("dataset.csv")
trying to change an object type to float

df['Charge'] = (df['Charge']).astype('float')

ValueError: could not convert string to float: '(94.00)'

There are no commas or special characters in data just . and i need to keep decimal point

however it will allow me to change to string but I need it as a float because i get errors when try to work on a string

CodePudding user response：

ValueError: could not convert string to float: '(94.00)'

So you have this string: '(94.00)'. Do you know what (94.00) is? A tuple containing a single float, and not a float.

So, if you want to get the 94.00 from inside the tuple, you have to cast to float only the part of the string without ().

CodePudding user response：

you cant convert a (94.00) to float but you can do it to 94.00 So you can use a delimiter

this may help you

CodePudding user response：

Sometimes I have the same issue too. The problem here is the parenthesis: ()

So I first remove them with regex:

df['Charge'].str.replace('\(','',regex=True).str.replace('\)','',regex=True)

Then I apply.astype(float)

Here my TestDf:

df_test=pd.DataFrame(['(201536.34)','(133104.47)', '66579.82', '95129.99', 
'72333.23', '61319.77', '60705.00', '47944.47', '35510.36', ('28530.77')])

df_test[0]
Out[47]: 
0    (201536.34)
1    (133104.47)
2       66579.82
3       95129.99
4       72333.23
5       61319.77
6       60705.00
7       47944.47
8       35510.36
9       28530.77
Name: 0, dtype: object

When trying to convert it to float I get the same error as you: ValueError: could not convert string to float: '(201536.34)'

df_test[0]=df_test[0].str.replace('\(','',regex=True).str.replace('\)','',regex=True)

df_test[0]
Out[72]: 
0    201536.34
1    133104.47
2     66579.82
3     95129.99
4     72333.23
5     61319.77
6     60705.00
7     47944.47
8     35510.36
9     28530.77
Name: 0, dtype: object

Now ready to use astype('float)

df_test[0].astype('float')
Out[74]: 
0    201536.34
1    133104.47
2     66579.82
3     95129.99
4     72333.23
5     61319.77
6     60705.00
7     47944.47
8     35510.36
9     28530.77
Name: 0, dtype: float64