Pandas TypeError: unsupported operand type(s) for -: ‘str’ and ‘int’

Understanding the Error

When working with data in Python using Pandas, you might encounter the TypeError: unsupported operand type(s) for -: 'str' and 'int'. This error typically occurs when you attempt to perform mathematical operations between string (str) and integer (int) types, which Python does not allow directly. This guide explores the common reasons behind this error and provides practical solutions for resolving it.

Why the Error Happends?

This error usually pops up when you’re doing arithmetic operations, such as subtraction, addition, multiplication, or division, between columns or within a DataFrame where one operand is accidentally a string instead of a numeric type. This often happens due to:

• Data importation where numeric values are interpreted as strings.
• Incorrect assignment of values to a column.
• Implicit data type conversions during data manipulation.

Solutions to Fix the Error

Solution 1: Convert Column to Numeric Type

The first solution involves converting the problematic column(s) from string to a numeric type (int or float) using the pd.to_numeric() method from Pandas. This approach is straightforward and effective for columns intended to contain only numeric values.

Steps:

1. Identify the column(s) causing the error.
2. Use pd.to_numeric to convert the column to a numeric type.
3. Handle any potential errors during conversion using the errors='coerce' or errors='ignore' argument.
4. Re-run the operation that previously caused the error.

Code Example:

import pandas as pd

df = pd.DataFrame({'a': ['1', '2', '3'], 'b': [4, 5, 6]})
df['a'] = pd.to_numeric(df['a'], errors='coerce')
print(df['a'] - df['b'])

Output:

-3
-3
-3

Notes: This method is effective for columns that should only contain numeric values. However, it might not be suitable for columns with mixed data types or when numeric conversion isn’t applicable. Using errors='coerce' will convert non-numeric values to NaN, potentially leading to data loss.

Solution 2: Use Conditional Logic for Type Checking

Sometimes, not all values in a column are meant to be numeric. In such scenarios, applying conditional logic to check the data type before performing operations can prevent the error.

Steps:

1. Identify the operation and columns involved.
2. Implement a conditional logic to check the data type of each operand before the operation. If an operand is a string, either convert it on the fly or handle it accordingly.
3. Perform the operation within the conditional branches.

Code Example:

import pandas as pd

df = pd.DataFrame({'a': ['1', 'hello', '3'], 'b': [4, 5, 6]})

for index, row in df.iterrows():
    if isinstance(row['a'], str) and row['a'].isdigit():
        df.at[index, 'a'] = int(row['a'])
    else:
        df.at[index, 'a'] = 0  # or handle it differently based on your needs

print(df['a'] - df['b'])

Output:

-3
-5
-3

Notes: This approach provides flexibility in handling non-numeric values explicitly and prevents unexpected errors during arithmetic operations. However, it requires additional code and careful implementation of conditional logic. It’s important to ensure the logic accurately reflects the intended operation and data handling for each scenario.

Solution 3: Use Try-Except Blocks

Another robust way to handle the error is using try-except blocks to catch the TypeError during runtime and handle it accordingly. This is particularly useful when you’re unsure if all the values can be converted to a numeric type and want to avoid program interruption.

Steps:

1. Wrap the arithmetic operation that might cause the error in a try block.
2. Use an except block to catch the TypeError and handle it (e.g., by logging, converting data types on the fly, or default handling).

Code Example:

import pandas as pd

df = pd.DataFrame({'a': ['1', 'hello', '3'], 'b': [4, 5, 6]})

try:
    result = df['a'].astype(int) - df['b']
except TypeError as e:
    print(e)
    result = None  # or a default value/handling

print(result)

Expected outcome: When error occurs, the custom handling is executed. Otherwise, normal operation proceeds.

Notes: While this method provides a safeguard against unexpected errors, it’s a more reactive approach and might not address the root cause of the error. It’s best used as a secondary measure alongside proactive type checking and data cleaning methods.