The error message:
ValueError: could not convert string to float: 'xyz'
occurs when you attempt to convert a string into a floating-point number, but the string contains invalid characters that are not numerical.
1. Causes and Solutions
Cause 1: Non-Numeric Characters in String
If a string contains letters or special characters, float() cannot convert it.
Incorrect Code:
num = float("xyz") # 'xyz' is not a number
Solution: Ensure the string contains only numbers before converting.
num_str = "123.45"
if num_str.replace(".", "", 1).isdigit(): # Allows one decimal point
num = float(num_str)
print(num) # Output: 123.45
else:
print("Invalid number format")
Cause 2: String Containing Comma as Decimal Separator
Python does not recognize , as a decimal point.
Incorrect Code:
num = float("1,234.56") # Comma causes error
Solution: Remove commas before conversion.
num = float("1,234.56".replace(",", "")) # Correct
print(num) # Output: 1234.56
Cause 3: Empty or Whitespace String
An empty string cannot be converted to float().
Incorrect Code:
num = float("") # Empty string
Solution: Check if the string is empty before conversion.
num_str = " " # Contains only spaces
if num_str.strip(): # Remove spaces and check if non-empty
num = float(num_str)
else:
print("Invalid input")
Cause 4: Parsing Data from CSV or JSON Files
Sometimes, CSV/JSON files contain missing values or wrongly formatted numbers.
Incorrect Code:
import pandas as pd
df = pd.DataFrame({"price": ["12.5", "15.8", "NA", "20.1"]})
df["price"] = df["price"].astype(float) # 'NA' causes error
Solution: Use errors='coerce' in Pandas to handle invalid values.
df["price"] = pd.to_numeric(df["price"], errors="coerce") # 'NA' becomes NaN
print(df)
Cause 5: Hidden Unicode or Special Characters
If a string contains hidden spaces or special characters, conversion fails.
Incorrect Code:
num = float("12.34 ") # Contains an invisible Unicode space
Solution: Strip whitespace and normalize encoding.
import unicodedata
num_str = "12.34 " # Copy-pasting from web may introduce invisible spaces
num_clean = unicodedata.normalize("NFKC", num_str.strip())
num = float(num_clean)
print(num) # Output: 12.34