Ошибка valueerror could not convert string to float

Pandas

In Python, we can only convert specific string values to float. If we try to convert an invalid string to a float, we will raise the ValueError: could not convert string to float.

To solve this error, ensure you strip the string of invalid characters like commas, spaces or brackets before passing it to the float() function.

This tutorial will go through how to solve the error with the help of code examples.

ValueError: could not convert string to float 

In Python, a value is information stored within a particular object. You will encounter a ValueError in Python when you use a built-in operation or function that receives an argument with the right type but an inappropriate value.

A string is a suitable type to convert to a float. But several string values are not suitable to convert to float:

• A value that contains non-special terms, for example, ‘nan’ is a special term, “bread” is not.
• A value that contains a commas, speech marks and other non alphanumeric characters.
• A value that contains spaces.

We can convert inf and nan to floats because they represent specific floats in Python, namely infinity and NaN (Not a Number).

Example

Consider the following CSV file called money.csv that contains the epoch timestamp and money in two accounts.

"'1645916759'",20000,18000
"'1645916790'",21000,17000
"'1645916816'",20500,17250

Next, we will write a program that will read the information from the CSV file and print it to the console. We will import the csv library to read the CSV file. Let’s look at the code:

from datetime import datetime

with open("money.csv", "r") as money:

   readf = csv.reader(money)

   for line in readf:

       time = float(line[0])

       account_one = float(line[1])

       account_two = float(line[2])

       print(f'At date: {datetime.fromtimestamp(time)}, Account one value £{account_one}, Account two value £{account_two}')

We use the datetime library to convert the epoch timestamp to a date. Let’s run the code to see the result:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
      2     readf = csv.reader(money)
      3     for line in readf:
----≻ 4         time = float(line[0])
      5         account_one = float(line[1])
      6         account_two = float(line[2])
ValueError: could not convert string to float: "'1645916759'"

The error occurs because the epoch timestamp contains inverted commas, which are invalid string values to convert to a float.

Solution

We need to strip the epoch timestamp of the inverted commas using the String strip() method to solve this error. Let’s look at the revised code:

from datetime import datetime

with open("money.csv", "r") as money:

   readf = csv.reader(money)

   for line in readf:

       time = float(line[0].strip("'"))

       account_one = float(line[1])

       account_two = float(line[2])

       print(f'At date: {datetime.fromtimestamp(time)}, Account one value £{account_one}, Account two value £{account_two}')

Let’s run the code to see the result:

At date: 2022-02-26 23:05:59, Account one value £20000.0, Account two value £18000.0
At date: 2022-02-26 23:06:30, Account one value £21000.0, Account two value £17000.0
At date: 2022-02-26 23:06:56, Account one value £20500.0, Account two value £17250.0

The code successfully prints each line in the money.csv file.

Example #2

Let’s look at an example, where we write a program that converts a weight from kilograms to pounds. First, we will ask a user to insert the weight in kilograms that they want to convert to pounds:

weight = float(input("Enter the weight to convert to pounds: "))

Next, we will define the conversion value to convert kilograms to pounds:

convert_to_lbs = 2.205

Then, we will attempt to convert the kilogram value to pounds and print the result to the console.

weight_in_lbs = weight * convert_to_lbs

print(f'{weight}kg is {weight_in_lbs}lbs')

Let’s run the code to see what happens:

Enter the weight to convert to pounds: 87,50
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
----≻ 1 weight = float(input("Enter the weight to convert to pounds: "))
ValueError: could not convert string to float: '87,50'

We raise the ValueError because the string we input contains a comma.

Solution

To solve the error we can use a try-except block. The program will try to run the code in the “try” block, if unsuccessful, the program will run the code in the “except” block. Let’s look at the revised code:

convert_to_lbs = 2.205

try: 

    weight = float(input("Enter the weight to convert to pounds: "))

    weight_in_lbs = weight * convert_to_lbs

    print(f'{weight}kg is {round(weight_in_lbs,1)}lbs')

except:

    print('Please insert a valid weight. The weight cannot contain commas, spaces or special characters')

Let’s try the code and input an invalid string:

Enter the weight to convert to pounds: 87,50
Please insert a valid weight. The weight cannot contain commas, spaces or special characters

We see that the program executes the print statement in the except block. Let’s run the code and input a valid string:

Enter the weight to convert to pounds: 87.50
87.5kg is 192.9lbs

The code successfully executes the code in the try block and prints the kilogram and the converted pound value to the console.

Summary

Congratulations on reading to the end of this tutorial! The error ValueError: could not convert string to float occurs if you try to convert a string to a float that contains invalid characters. To solve this error, check the string for characters that you can remove and use the strip() method to get rid of them. If a value has a comma instead of a decimal point, you can use replace() to replace the comma.

You can also use a try-except statement to catch the ValueError and pass. This method is useful if you are taking input from a user.

For further reading on ValueErrors, go to the articles:

• How to Solve Python ValueError: list.remove(x) x not in list
• How to Solve Python ValueError: year is out of range

For further reading on converting values, go to the article: How to Solve Python TypeError: int() argument must be a string, a bytes-like object or a number, not ‘list’

To learn more about Python for data science and machine learning, go to the online courses page on Python for the most comprehensive courses available.

Have fun and happy researching!

In this short guide, I’ll show you how to solve Pandas or Python errors:

• valueerror: could not convert string to float: '< "0.01'"
• ValueError: could not convert string to float: '2,000'
• ValueError: could not convert string to float: '$100.00'
• ValueError: Unable to parse string "$10.00" at position 0

We will see how to solve the errors above and how to identify the problematic rows in Pandas.

Setup

Let’s create an example DataFrame in order to reproduce the error:

ValueError: could not convert string to float: ‘$10.00’

import pandas as pd

df = pd.DataFrame({'day': [1, 2, 3, 4, 5],
                   'amount': ['$10.00', '20.5', '17.34', '4,2', '111.00']})

DataFrame looks like:

To convert string to float we can use the function: .astype(float). If we try to do so for the column — amount:

df['amount'].astype(float)

we will face error:

ValueError: could not convert string to float: '$10.00'

Step 2: ValueError: Unable to parse string «$10.00» at position 0

We will see similar result if we try to convert the column to numerical by method pd.to_numeric(:

pd.to_numeric(df['amount'])

error is raised:

ValueError: Unable to parse string "$10.00" at position 0

In both cases we can see the problematic value. But we are not sure if more different cases exist.

Step 3: Identify problematic non numeric values

To find which values are causing the problem we will use a simple trick. We will convert all values except the problematic ones by:

pd.to_numeric(df['amount'], errors='coerce')

This give us successfully converted float values:

0       NaN
1     20.50
2     17.34
3       NaN
4    111.00
Name: amount, dtype: float64

The ones in error will be replaced by NaN. No we can use mask to get only value which cause the error during the conversion to numerical values:

df[pd.to_numeric(df['amount'], errors='coerce').isna()]['amount']

result is:

0    $10.00
3       4,2
Name: amount, dtype: object

Step 4: Solve ValueError: could not convert string to float

To solve the errors:

ValueError: could not convert string to float: ‘$10.00’
ValueError: Unable to parse string «$10.00» at position 0

We have several options:

• ignore errors from invalid parsing and keep the output as it is:
  • pd.to_numeric(df['amount'], errors='ignore')
• convert only numeric values and NaN for the rest in the column:
  • pd.to_numeric(df['amount'], errors='coerce')
• fix problematic values

In this step we are going to fix the problematic values. This can be done by replacing the non numeric symbols like:

• $
• , — wrong decimal symbol etc

So to replace the problematic characters we can use str.replace:

df['amount'].str.replace('$', '', regex=True)

Replacing multiple characters can be done by chaining multiple functions like.(for multiple replacing values):

df['amount'].str.replace('$', '', regex=True).replace(',', '.', regex=True)

of by using regex (when all symbols are replaced by a single value):

df['amount'].str.replace('[$|,]', '', regex=True)

Finally we get only numeric values which can be converted to numeric column:

0 10.00
1 20.5
2 17.34
3 42
4 111.00
Name: amount, dtype: object

Step 5: Convert numbers and keep the rest

Finally if we like to convert only valid numbers we can use errors='coerce'. Then for all missing values we can populate them from the original column or Series:

data = pd.Series(['$10.00', '20.5', '17.34', '4,2', '111.00', np.NaN, ''])
conv_data = pd.to_numeric(data, errors='coerce').fillna(data)
conv_data

the result will be:

0    $10.00
1      20.5
2     17.34
3       4,2
4     111.0
5       NaN
6          
dtype: object

While using

pd.to_numeric(['$10.00', '20.5', '17.34', '4,2', '111.00', np.NaN, ''], errors='ignore')

will keep all the values the same if there is invalid parsing:

array(['$10.00', '20.5', '17.34', '4,2', '111.00', nan, ''], dtype=object)

Conclusion

In this post, we saw how to properly convert strings to float columns in Pandas. We covered the most popular errors and how to solve them.

Finally we discussed finding the problematic cases and fixing them.

In this Python tutorial, we will discuss how to fix an error, “Could not convert string to float Python“.

In python, to convert string to float in python we can use float() method. It can only convert the valid numerical value to a floating point value otherwise it will throw an error.

Example:

my_string = '1,400'
convert = float(my_string)
print(convert)

After writing the above code (could not convert string to float python), Ones you will print ” convert ” then the output will appear as a “ ValueError: could not convert string to float: ‘1,400’ ”.

Here, we get the error because the value is not valid and we used a comma. You can refer to the below screenshot could not convert string to float python.

To solve this ValueError: could not convert string to float we need to give the valid numerical value to convert my string to float by using float() method.

Example:

my_string = '23.8'
convert = float(my_string)
print(convert)

After writing the above code (could not convert string to float python), Ones you will print ” convert ” then the output will appear as a “ 23.8 ”. Here, the float() method converts the string to float. You can refer to the below screenshot could not convert string to float python.

This is how to fix could not convert string to float python.

Read: Python find substring in string

How to fix could not convert string to float python

Let us see how to fix could not convert string to float python

In this example, we got a ValueError because the function argument is of an inappropriate type. So, when we tried to typecast a string value it throws ValueError.

Example:

str = "Hi"
print(float(str))

The normal text is not supported by the float() function in python. You can refer to the below screenshot for the error.

To fix value error: could not convert string to float python, we have to give numeric text value to convert it successfully to float. we can use the below code to solve the above error.

Example:

str = "5.45"
print(float(str))

You can refer to the below screenshot to see the output for how to fix could not convert string to float python.

You may like the following Python tutorials:

• How to handle indexerror: string index out of range in Python
• How to convert an integer to string in python
• Slicing string in Python + Examples
• Convert string to float in Python

Here we checked how to fix error, could not convert string to float python.