```
df_clean['message'] = df_clean['message'].apply(lambda x: gensim.parsing.preprocessing.remove_stopwords(x))

```

I tried this on a dataframe's column 'message' but I get the error: 

```
TypeError: decoding to str: need a bytes-like object, list found

```
['https://Stackoverflow.com/questions/62387623', 'https://Stackoverflow.com', 'https://Stackoverflow.com/users/10608841/']
Apparently, the `df_clean["message"]` column contains a list of words, not a string, hence the error saying that `need a bytes-like object, list found`. 

To fix this issue, you need to convert it to string again using `join()` method like so:

```
df_clean['message'] = df_clean['message'].apply(lambda x: gensim.parsing.preprocessing.remove_stopwords(" ".join(x)))

```

Notice that the `df_clean["message"]` will contain string objects after applying the previous code.
This is not a `gensim` problem, the error is raised by `pandas`: there is a value in your column `message` that is of type `list` instead of `string`. Here's a minimal `pandas` example:

```
import pandas as pd
from gensim.parsing.preprocessing import remove_stopwords
df = pd.DataFrame([['one', 'two'], ['three', ['four']]], columns=['A', 'B'])
df.A.apply(remove_stopwords) # works fine

df.B.apply(remove_stopwords)

TypeError: decoding to str: need a bytes-like object, list found

```