Append a row N times into Pandas DF combining apply and append

Question

I'm receiving the following error:

  File "/packages/pandas/core/common.py", line 380, in _asarray_tuplesafe
    result[:] = [tuple(x) for x in values]
TypeError: 'numpy.int64' object is not iterable

When running:

df.apply(lambda row: (df2.append(([row]*(row[6].split(',').__len__())), ignore_index=True)), axis=1)

The goal is to apply for each origin dataframe row (df) an append N times to an empty df (df2). Where N is the number of values that the specific field row[6] has.

Rows of example are for the df:

Id|List

0|126

1|126,127,304,305

The df2 should be:

Id|List

0|126

1|126

1|127

1|304

1|305

As you can see I tried sending the row as a list, but don't work neither. Any idea?

score 1 · Accepted Answer · answered Apr 12 '19 at 13:56

Here is one way after split using unnesting

df.List=df.List.str.split(',')
unnesting(df,['List'])
Out[466]: 
  List  Id
0  126   0
1  126   1
1  127   1
1  304   1
1  305   1

def unnesting(df, explode):
    idx = df.index.repeat(df[explode[0]].str.len())
    df1 = pd.concat([
        pd.DataFrame({x: np.concatenate(df[x].values)}) for x in explode], axis=1)
    df1.index = idx

    return df1.join(df.drop(explode, 1), how='left')

Append a row N times into Pandas DF combining apply and append

1 Answers1