Type error: Int object is not iterable error [For loop]

Question

There is an exercise I have found on Kaggle which defines my purpose as:

We'd like to host these wine reviews on our website, but a rating system ranging from 80 to 100 points is too hard to understand - we'd like to translate them into simple star ratings. A score of 95 or higher counts as 3 stars, a score of at least 85 but less than 95 is 2 stars. Any other score is 1 star.

Also, the Canadian Vintners Association bought a lot of ads on the site, so any wines from Canada should automatically get 3 stars, regardless of points.

Create a series star_ratings with the number of stars corresponding to each review in the dataset.

Data.head( ):

And I wrote this code with the hope to serve my purpose:

def stars(reviews):
for i,pons in enumerate(reviews.points):
    if pons < 85:
        reviews.points[i] = "1 star"
    elif pons <95:
        reviews.points[i] = "2 stars"
    elif (pons >= 95):
        reviews.points[i] = "3 stars"
for i,cons in reviews.country:
    if cons == "Canada":
        reviews.points[i] = "3 stars"

star_ratings = reviews.apply(stars, axis = "columns")

This answer did not work for me as I keep getting the

TypeError: 'int' object is not iterable

Why my for loop keeps on giving me this error?

It looks like reviews.points is an integer. You can't iterate over a single integer. — John Gordon
– John Gordon, Commented Dec 15, 2020 at 15:02

0x5453 · Accepted Answer · 2020-12-15 15:17:36Z

When you use df.apply(axis='columns'), the provided function will be applied to each row of the input DataFrame.

The reviews argument holds a pd.Series representing a single row. Therefore reviews.points is a single cell, not a column.

Here's one way that you could rewrite the function:

def stars(review):
    if review.points < 85:
        review.points = "1 star"
    elif review.points < 95:
        review.points = "2 stars"
    elif review.points >= 95:
        review.points = "3 stars"
    if review.country == "Canada":
        review.points = "3 stars"

star_ratings = reviews.apply(stars, axis="columns")

Another way, which will be much more performant, is to ditch the apply and use vectorized instructions:

star_ratings = reviews.copy()
star_ratings['points'] = pd.cut(star_ratings['points'], bins=[-np.inf, 85, 95, np.inf], labels=['1 star', '2 stars', '3 stars'])
star_ratings.loc[star_ratings['country'] == 'Canada', 'points'] = '3 stars'

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