Hey there! Let's unpack what's happening with pandas' str.split() and why you're seeing those unexpected results compared to the apply() approach.

Why your initial str.split(n=3) approach fails #

There are two key misunderstandings here:

1. The n parameter is about split count, not final element count #

The n argument in str.split() defines the maximum number of splits to perform, not the number of elements you want to keep. For example, if an actor string like "Tom Hanks, Tim Allen, Don Rickles, Jim Varney" has 3 commas, n=3 will split it 3 times, resulting in 4 elements (since each split adds one more segment). That's why you're seeing more elements than expected.

2. Missing expand=False leads to unexpected slicing behavior #

By default, str.split() uses expand=True, which returns a DataFrame where each split element becomes a column. When you try to slice [:3] on this DataFrame, you're selecting the first 3 rows—not truncating each split result to 3 elements. Even if you switch to expand=False (to get a Series of lists), using str.split(n=3) will still create some lists with 4 elements (from strings with enough commas), but slicing str[:3] on those lists will work. However, if some rows have missing values (NaN) in the Actors column, str.split() will return NaN for those rows, and slicing str[:3] will keep that NaN—whereas the apply() approach would throw an error for NaN values (unless you add a check like x.split(",")[:3] if pd.notna(x) else []).

The NaNs you saw in rows 4 and 5 are likely because those entries have fewer than 3 actors. With expand=True, pandas fills unused columns with NaNs, which can look like unexpected missing values when you're trying to get truncated lists.

Correct usage of str.split() to match the apply() result #

To replicate the behavior of df['Actors'].apply(lambda x: x.split(",")[:3]) (getting a Series of lists with up to 3 elements, no extra NaNs for short entries), use this:

df['Actors'].str.split(",", expand=False).str[:3]

Let's break this down:

• expand=False tells pandas to return a Series where each entry is a list of split elements (instead of a DataFrame).
• str[:3] truncates each list to its first 3 elements, just like the lambda function in apply().

If you want separate columns for each actor #

If your goal is to split the actors into 3 distinct columns (instead of a list), use n=2 (since splitting twice gives 3 elements) with expand=True:

actors_df = df['Actors'].str.split(",", n=2, expand=True)
actors_df.columns = ['Actor_1', 'Actor_2', 'Actor_3']

Here, n=2 ensures we only split enough times to get the first 3 actors, and any entries with fewer than 3 actors will have NaN in the remaining columns (which might be desirable for structured data).

内容的提问来源于stack exchange，提问作者Sarvagya Dubey