Count number of words per row

Question

I'm trying to create a new column in a DataFrame that contains the word count for the respective row. I'm looking for the total number of words, not frequencies of each distinct word. I assumed there would be a simple/quick way to do this common task, but after googling around and reading a handful of SO posts (1, 2, 3, 4) I'm stuck. I've tried the solutions put forward in the linked SO posts, but got lots of attribute errors back.

words = df['col'].split() df['totalwords'] = len(words) 

results in

AttributeError: 'Series' object has no attribute 'split' 

and

f = lambda x: len(x["col"].split()) -1 df['totalwords'] = df.apply(f, axis=1) 

results in

AttributeError: ("'list' object has no attribute 'split'", 'occurred at index 0') 

Answer 1

str.split + str.len

str.len works nicely for any non-numeric column.

df['totalwords'] = df['col'].str.split().str.len() 

---

str.count

If your words are single-space separated, you may simply count the spaces plus 1.

df['totalwords'] = df['col'].str.count(' ') + 1 

---

List Comprehension

This is faster than you think!

df['totalwords'] = [len(x.split()) for x in df['col'].tolist()] 

Answer 2

Here is a way using .apply():

df['number_of_words'] = df.col.apply(lambda x: len(x.split())) 

example

Given this df:

>>> df                     col 0  This is one sentence 1           and another 

After applying the .apply()

df['number_of_words'] = df.col.apply(lambda x: len(x.split()))  >>> df                     col  number_of_words 0  This is one sentence                4 1           and another                2 

Note: As pointed out by in comments, and in this answer, .apply is not necessarily the fastest method. If speed is important, better go with one of @cᴏʟᴅsᴘᴇᴇᴅ's methods.