Build a data frame from existing data frame based on some special condition

I am really stuck on this problem.

I have a data frame like in the figure.
I have multiple ids with one column value.

I want to build another data frame that only has the distinct id and also the data frame has 4 different numbers of the column "c0" in the existing data frame.

For example, for number 10, we have 2 and 4 and 2. we put 2,2,2,2 and 4, 4, 4,4 and 2,2,2,2 in front of id=10. For the case where we don’t have enough data, we put 0 for them. Here is an comprehensive and simple example:

import pandas as pd
import numpy as np

df = pd.DataFrame()
df['id'] = [ 10,  10, 20, 10]
df['c0'] = [ 2,  4, 7,2]

Here is the data frame which I am looking for:

>Solution :

Use pd.Index.repeat as starting point to expand your dataframe then pivot_table to reshape it:

N = 4  # or len(df) ???

out = (
    df.reindex(df.index.repeat(N)).assign(col=lambda x: x.groupby('id').cumcount())
      .pivot_table('c0', 'id', 'col', fill_value=0)
      .add_prefix('c').rename_axis(index=None, columns=None)
)
print(out)

# Output
    c0  c1  c2  c3  c4  c5  c6  c7  c8  c9  c10  c11
10   2   2   2   2   4   4   4   4   2   2    2    2
20   7   7   7   7   0   0   0   0   0   0    0    0