Create A 2D Array With 2 Columns From A Dataframe And Loop For Value

I have a huge dataframe which looks like this: u_id i_id 0 55218 0 1 55218 2 2 55218 1 3 55222 2 4 55222 3 I want to create a

Solution 1:

Please try:

df = df.groupby('u_id')['i_id'].apply(list).reset_index()
def fill(x):
    for val in x.i_id:
        df_un[x.name,val] = 1
df.apply(lambda x: fill(x), axis=1)

print(df_un)

[[1 1 1 0]
 [0 0 1 1]]

Solution 2:

I think that this

columns = sorted(set(df['i_id'].values))
df_neu = pd.DataFrame({key: [1 if c in group['i_id'].values else 0
                             for c in columns]
                       for key, group in df.groupby('u_id')},
                      index=columns).T

essentially leads to your expected result:

       0  1  2  3
55218  1  1  1  0
55222  0  0  1  1

My assumption is that your original DataFrame is named df.

If you want to get rid of the u_id index:

df_neu.reset_index(drop=True, inplace=True)

   0  1  2  3
0  1  1  1  0
1  0  0  1  1

Or a without the transposing:

columns = sorted(set(df['i_id'].values))
df_neu = pd.DataFrame([[1 if c in group['i_id'].values else 0
                        for c in columns]
                       for _, group in df.groupby('u_id')],
                      columns=columns)