TypeError: 'float' object is not iterable — how to fix

Traceback (most recent call last):
  File "main.py", line 4, in <module>
    for x in total:
TypeError: 'float' object is not iterable

Quick fix: Loop over the collection, not the number: for i in range(int(n)) or for x in values. For sum(a, b)/max(x) pass a list: sum([a, b]). Check type(x) — a pandas column with NaN or a function that returns a single float is a common source.

What it means

Iterating means asking an object for its items one by one. Lists, tuples, strings, dicts, sets, ranges and generators can do that; a float is a single number and can't. Anything that iterates implicitly raises this error when given a float:

for x in value:
a, b = value (unpacking)
list(value), tuple(value), set(value)
sum(value), max(value), min(value), sorted(value)
x in value, "".join(value), zip(value, ...)

Cause 1: looping over a number instead of a range

n = 5.0
for i in n:            # TypeError
    ...

Fix: wrap the count in range(), which also needs an int:

for i in range(int(n)):
    ...

Cause 2: `sum()`, `max()`, `min()` called with separate numbers

sum() takes an iterable, not several arguments:

sum(3.5, 2.0)          # TypeError: 'float' object is not iterable
sum([3.5, 2.0])        # 5.5

max()/min() accept either several arguments or one iterable — but not one float:

max(3.5)               # TypeError
max(3.5, 2.0)          # 3.5
max([3.5])             # 3.5

Cause 3: a variable was overwritten with a float

Common when an accumulator reuses the name of the list:

prices = [9.99, 4.50, 3.25]
for prices in prices:   # after the loop, prices is 3.25
    ...
total = sum(prices)     # TypeError

Use distinct names: for price in prices:.

Same thing when a function sometimes returns a list and sometimes a single float. Make it always return a list (return [value]).

Cause 4: unpacking a single value

lat, lon = get_location()   # returns 52.37 instead of (52.37, 4.89)

Fix the function to return a tuple, or check what it actually returns with print(repr(get_location())).

Cause 5: pandas — NaN is a float

Missing values in pandas are float('nan'). If a column holds lists or strings and some rows are missing, an apply that iterates over each cell blows up on the NaN rows:

df["tags"].apply(lambda tags: ", ".join(tags))   # TypeError on NaN rows

Handle the missing case:

df["tags"].apply(lambda t: ", ".join(t) if isinstance(t, list) else "")
# or drop / fill first
df["tags"] = df["tags"].apply(lambda t: t if isinstance(t, list) else [])

Also watch for for x in df["price"].sum(): — .sum() returns a single float.

Cause 6: wrong JSON shape

data["score"] might be 4.5 in one response and [4.5, 3.0] in another. Normalise it:

scores = data["score"]
if not isinstance(scores, (list, tuple)):
    scores = [scores]

Debugging

Print the type just before the failing line:

print(type(value), repr(value))

Then trace where that variable was last assigned. The fix is almost always to pass the list that holds the float, not the float itself.

Last updated 2026-11-06.