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

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

$ python -c "import pandas as pd; df = pd.DataFrame({'a':[1,2], 'b':['x','y']}); print(df['a'] + df['b'])"
Traceback (most recent call last):
  File "<string>", line 1, in <module>
TypeError: unsupported operand type(s) for +: 'int' and 'str'

Why this happens

Pandas uses NumPy under the hood for element-wise arithmetic. Adding columns of incompatible data types (e.g., numbers and strings) results in TypeError because Python does not define addition for those operand types.

Fix

Convert data to compatible types before performing operations. For concatenation convert to string; for arithmetic convert strings to numeric using astype(int)/pd.to_numeric and handle errors/NaN if necessary.

Wrong code

import pandas as pd

# Adding numeric and string columns directly

df = pd.DataFrame({'a': [1, 2], 'b': ['x', 'y']})
print(df['a'] + df['b'])  # TypeError

Fixed code

import pandas as pd

# Concatenate as strings

df = pd.DataFrame({'a': [1, 2], 'b': ['x', 'y']})
print(df['a'].astype(str) + df['b'])  # ['1x', '2y']

# Or convert strings to numeric to perform arithmetic

df2 = pd.DataFrame({'a': [1, 2], 'b': ['10', '20']})
print(df2['a'] + df2['b'].astype(int))  # [11, 22]

# Use pd.to_numeric for safe conversion
pd.to_numeric(df2['b'], errors='coerce')