Transforming list elements in a DataFrame into simple values (Pandas, Python)-CodePudding

I'm well aware I've done an awful job with the title. However, I couldn't find a better choice of words to explain my problem.

Let's assume we've got 2 Pandas dataframes. One of them contains a column with simple string values in each row e.g. an url. The other, stores the same values in another column but within lists. Please see a simplified example below:

df1's output would look like this:

url  name
 x     a
 y     b
      

df2's output would look like this:

    url   title
  [x, y]    a
  [x, y]    b

My intent is to extract the common values from df2 so that I can use them to vlookup another column via merge function and add the aforesaid column to df1.

For that, I would like to get the following structure:

df2:

url    title
 x       a
 x       b
 y       a
 y       b

I've been going over different ways via for loops but I always come to an impasse because of the bloody list. Please note that both dataframes df1 and df2 have been built from 2 different lists of dictionaries.

Have you got any clue on how to work this out?

Thank you very much in advance!

CodePudding user response：

Try this:

exploded = df2.explode('url', ignore_index=True)

Output:

>>> exploded
  url title
0   x     a
1   y     a
2   x     b
3   y     b

Note: if your Pandas version is lower than 1.1.0, ignore_index for explode is not available. Use this instead:

df2.explode('url').reset_index(drop=True)