TypeError: unsupported operand type(s) for +: 'int' and 'str' — how to fix

Traceback (most recent call last):
  File "app.py", line 3, in <module>
    total = count + input("More: ")
TypeError: unsupported operand type(s) for +: 'int' and 'str'

Quick fix: Convert to the same type first: int(x) / float(x) for math, str(x) or an f-string for text: f"Total: {count}". If one side is NoneType, a function returned None; check its return statement.

What it means

Python doesn't convert types implicitly. 1 + "2" is an error, not 3 or "12". The message tells you which operator and which two types were involved.

Cause 1: input() returns a string

age = input("Age: ")
print(age + 1)            # TypeError

age = int(input("Age: "))
print(age + 1)

Use float() for decimals. Wrap it in try/except ValueError if the user may type something that isn't a number.

Cause 2: building a string from numbers

print("Total: " + total)          # can only concatenate str (not "int") to str

print(f"Total: {total}")
print("Total: " + str(total))

Cause 3: one side is None

unsupported operand type(s) for +: 'NoneType' and 'int' means a variable is None. Usually a function without a return:

def price(item):
    item["price"] * 1.2      # missing return

total = price(x) + 5

Add return. Also check dict .get() calls that return None for missing keys; give a default: d.get("count", 0).

Cause 4: data read from files, CSV, JSON or the web

Everything from csv, environment variables, query strings and form data arrives as strings. Convert as you read:

for row in csv.DictReader(f):
    qty = int(row["qty"])

In pandas, df["col"] = pd.to_numeric(df["col"], errors="coerce").

Cause 5: lists and numbers

[1, 2] + 3 fails; use append or [1, 2] + [3]. For element-wise math, use NumPy arrays.

Last updated 2026-10-28.