Reshaping Pandas Dataframe with Grouped Data (Long to Wide)

You can use pivot and new columns are last value of column entity_id extracted by indexing with str:

df = pd.pivot(index=df.group_id, columns=df.entity_id.str[-1], values=df.value)       .add_prefix('entity_')       .rename_axis(None, axis=1)       .reset_index()print (df)  group_id  entity_1  entity_2  entity_30        A       5.0       3.0       2.01        B      10.0       8.0      11.02        C       2.0       6.0       NaN

Solution with cumcount:

df = pd.pivot(index=df.group_id,              columns=df.groupby('group_id').cumcount() + 1,               values=df.value)       .add_prefix('entity_')       .reset_index()print (df)  group_id  entity_1  entity_2  entity_30        A       5.0       3.0       2.01        B      10.0       8.0      11.02        C       2.0       6.0       NaN

Another solution with groupby and apply, last reshape by unstack:

df = df.groupby("group_id")["value"]       .apply(lambda x: pd.Series(x.values))       .unstack()       .add_prefix('entity_')       .reset_index()print (df)  group_id  entity_0  entity_1  entity_20        A       5.0       3.0       2.01        B      10.0       8.0      11.02        C       2.0       6.0       NaN

If need count from 1:

df = df.groupby("group_id")["value"].apply(lambda x: pd.Series(x.values))       .unstack()       .rename(columns = lambda x: x+1)       .add_prefix('entity_')       .reset_index()print (df)  group_id  entity_1  entity_2  entity_30        A       5.0       3.0       2.01        B      10.0       8.0      11.02        C       2.0       6.0       NaN