Pandas Explode on Multiple columns

Question

Using Pandas 0.25.3, trying to explode a couple of columns.

Data looks like:

d1 = {'user':['user1','user2','user3','user4'],
      'paid':['Y','Y','N','N']
      'last_active':['11 Jul 2019','23 Sep 2018','08 Dec 2019','03 Mar 2018'],
      'col4':'data'}

I sent this to a dataframe df=pd.DataFrame([d1],columns=d1.keys()) that looks like this:

user                              paid              last_active                                                col4               
['user1','user2','user3','user4'] ['Y','Y','N','N'] ['11 Jul 2019','23 Sep 2018','08 Dec 2019','03 Mar 2018']  'data'

there are other columns as well with one value per, {'A':'B'} type stuff, but I'm not worried about those.

when I do df.explode('user') it works for that one, and same for the other columns, but when I try to do df.explode(column=('user','paid','last_active') it gives me the following error:

KeyError: ('user','paid','last_active')

So what I want to know, is how can I explode it with the explode function on multiple columns to get the following df:

user     paid  last_active    col4
'user1'  'Y'   '11 Jul 2019'  'data'
'user2'  'Y'   '23 Sep 2018'  NaN
'user3'  'N'   '08 Dec 2019'  NaN
'user4'  'N'   '03 Mar 2018'  NaN

Answer 1

I guess you need (note the difference in data for col4 which has None as OP mentioned):

pd.DataFrame([[i] if not isinstance(i,list) else i 
             for i in d1.values()],index=d1.keys()).T

---

    user paid  last_active  col4
0  user1    Y  11 Jul 2019  data
1  user2    Y  23 Sep 2018  None
2  user3    N  08 Dec 2019  None
3  user4    N  03 Mar 2018  None

Answer 2

Pandas does not have a multi-column explode. There are workarounds. One such simple way could be:

df = pd.DataFrame(
    {
        'A': [1, 2],
        'B': [['a','b'], ['c','d']],
        'C': [['z','y'], ['x','w']]
    }
)
print(df)

--------------
A    B     C
--------------
1 [a, b] [z, y]
2 [c, d] [x, w]

##Let us say list_cols are the columns to be exploded
list_cols = {'B','C'}

other_cols = list(set(df.columns) - set(list_cols))
##other_cols now contains all the remaining column names in the df
##we temporarily convert to set() to easily get the differences in 2 lists

##now explode the list_cols using a loop
exploded = [df[col].explode() for col in list_cols]
##now we have long list of exploded values. Print to see the format

##This statement creates pairs of the exploded cols
##zip command is used to create the pairs
##dict puts it in an appropriate format from which a dataframe can be created
##Please print the individual outputs of each command to understand the flow
df2 = pd.DataFrame(dict(zip(list_cols, exploded)))

##Now merge back the other_cols as well
df2 = df[other_cols].merge(df2, how="right", left_index=True, right_index=True)

##lastly, re-create the original column order
df2 = df2.loc[:, df.columns]

print(df2)

------
A B C
------
1 a z
1 b y
2 c x
2 d w