df.apply(myfunc, axis=1) results in error but df.groupby(df.index).apply(myfunc) does not - pandas

Question

I have a dataframe that looks like this:
a b c
0 x x x
1 y y y
2 z z z
I would like to apply a function to each row of dataframe. That function then creates a new dataframe with multiple rows from each input row and returns it. Here is my_func:
def my_func(df):
dup_num = int(df.c - df.a)
if isinstance(df, pd.Series):
df_expanded = pd.concat([pd.DataFrame(df).transpose()]*dup_num,
ignore_index=True)
else:
df_expanded = pd.concat([pd.DataFrame(df)]*dup_num,
ignore_index=True)
return df_expanded
The final dataframe will look like something like this:
a b c
0 x x x
1 x x x
2 y y y
3 y y y
4 y y y
5 z z z
6 z z z
So I did:
df_expanded = df.apply(my_func, axis=1)
I inserted breakpoints inside the function and for each row, the created dataframe from my_func is correct. However, at the end, when the last row returns, I get an error stating that:
ValueError: cannot copy sequence with size XX to array axis with dimension YY
As if apply is trying to return a Series not a group of dataFrames that the function created.
So instead of df.apply I did:
df_expanded = df.groupby(df.index).apply(my_func)
Which just creates groups of single rows and applies the same function. This on the other hand works.
Why?

Answer 1

Perhaps we can take advantage of how pd.Series.explode and pd.Series.apply(pd.Series) work to simplify this process.
Given:
a b c
0 1 1 4
1 2 2 4
2 3 3 4
Doing:
new_df = (df.apply(lambda x: [x.tolist()]*(x.c-x.a), axis=1)
.explode(ignore_index=True)
.apply(pd.Series))
new_df.columns = df.columns
print(new_df)
Output:
a b c
0 1 1 4
1 1 1 4
2 1 1 4
3 2 2 4
4 2 2 4
5 3 3 4