python - Pandas 按值分组并合并行

Question

我有一个 DataFrame，我想合并包含相同值的行

toy = [
    [10, 11],
    [21, 22],
    [11, 15],
    [22, 23],
    [15, 33]
]

toy = pd.DataFrame(toy, columns = ['ID1', 'ID2'])

    ID1 ID2
0   10  11
1   21  22
2   11  15
3   22  23
4   15  33

我希望得到的是

    0   1   2   3
0   10  11  15  33.0
1   21  22  23  NaN

因此合并包含任何相同值的行。

我的解决方案非常不优雅，我正在寻找正确的方法来做到这一点...递归？通过...分组？嗯..

#### Feel Free to NOT read this... ###
for k in range(100):
    print(k)

    merge_df = []
    merged_indices = []
    for i, row in toy.iterrows():
        if i in merged_indices:
            continue
        cp = toy.copy()
        merge_rows = cp[cp.isin(row.values)].dropna(how="all")
        merged_indices = merged_indices + list(merge_rows.index)
        merge_rows = np.array(toy.iloc[merge_rows.index]).flatten()
        merge_rows = np.unique(merge_rows)
        merge_df.append(merge_rows)

    if toy.shape[0] == len(merge_df):
        break
    toy = pd.DataFrame(merge_df).copy()   

Answer 1

听起来像是网络问题，所以我使用 networkx

import networkx as nx 
G=nx.from_pandas_edgelist(toy, 'ID1', 'ID2')
l=list(nx.connected_components(G))
newdf=pd.DataFrame(l)
newdf
Out[896]: 
    0   1   2     3
0  33  10  11  15.0
1  21  22  23   NaN