从pandas数据框父子表获取父的所有后代 [英] Getting all descendants of a parent from a pandas dataframe parent child table
问题描述
我有一个包含父ID和子ID的Pandas数据框.我需要帮助来建立一个更新的数据框,列出每个父母的每个后代.
I have a Pandas dataframe containing parent ids and child ids. I need help building an updated dataframe listing every descendant of each parent.
For clarification on what the output should look like, here is a post on dba.stackexchange using SQL to accomplish what I am trying to do in python.
这是输入DataFrame的示例:
Here's an example of the input DataFrame:
parent_id child_id
0 3111 4321
1 2010 3102
2 3000 4023
3 1000 2010
4 4023 5321
5 3011 4200
6 3033 4113
7 5010 6525
8 3011 4010
9 3102 4001
10 2010 3011
11 4023 5010
12 2110 3000
13 2100 3033
14 1000 2110
15 5010 6100
16 2110 3111
17 1000 2100
18 5010 6016
19 3033 4311
下面是硬编码为DataFrame的实际示例数据
Below is the actual example data hardcoded as a DataFrame
df = pd.DataFrame(
{
'parent_id': [3111, 2010, 3000, 1000, 4023, 3011, 3033, 5010, 3011, 3102, 2010, 4023, 2110, 2100, 1000, 5010, 2110, 1000, 5010, 3033],
'child_id': [4321, 3102, 4023, 2010, 5321, 4200, 4113, 6525, 4010, 4001, 3011, 5010, 3000, 3033, 2110, 6100, 3111, 2100, 6016, 4311]
}
)
这是我尝试使用递归列表构建策略
Here is my attempt to at using a recursive list building strategy
parent_list = []
def recurse(parent, child, root_parent):
# initialize on first run of each branch
if root_parent is None:
root_parent = parent
parent_list.append((parent, child))
recurse(parent, child, root_parent)
# for each parent find every child recursively
for index, row in df.iterrows():
if row['parent_id'] is child:
parent_list.append((root_parent, row['child_id']))
recurse(row['parent_id'], row['child_id'], root_parent)
# recurse down each parent branch
for i, r in df.iterrows():
recurse(r['parent_id'], r['child_id'], None)
return parent_list
...当前仅复制数据,因为我没有正确遍历树.
...which currently just duplicates the data because I am not correctly traversing the tree.
输出的格式应遵循输入的格式.我想要两列父级和子级ID的表,如下面的示例输出所示.
The format of the output should follow the format of the input. I want a two column table of parent and child IDs as demonstrated in the example output below.
以下是上述数据的预期输出:
Here's the expected output from the above data:
parent_id child_id
0 1000 2010
1 1000 2100
2 1000 2110
3 1000 3000
4 1000 3011
5 1000 3033
6 1000 3102
7 1000 3111
8 1000 4001
9 1000 4010
10 1000 4023
11 1000 4113
12 1000 4200
13 1000 4311
14 1000 4321
15 1000 5010
16 1000 5321
17 1000 6016
18 1000 6100
19 1000 6525
20 2010 3011
21 2010 3102
22 2010 4001
23 2010 4010
24 2010 4200
25 2100 3033
26 2100 4113
27 2100 4311
28 2110 3000
29 2110 3111
30 2110 4023
31 2110 4321
32 2110 5010
33 2110 5321
34 2110 6016
35 2110 6100
36 2110 6525
37 3000 4023
38 3000 5010
39 3000 5321
40 3000 6016
41 3000 6100
42 3000 6525
43 3011 4010
44 3011 4200
45 3033 4113
46 3033 4311
47 3102 4001
48 3111 4321
49 4023 5010
50 4023 5321
51 4023 6016
52 4023 6100
53 4023 6525
54 5010 6016
55 5010 6100
56 5010 6525
奖金点,用于为每行添加从parent_id
到child_id
的另一列深度/距离. TIA
Bonus points for adding an additional column of depth/distance from parent_id
to child_id
for each row. TIA
推荐答案
这应该在您想要的两列中返回父ID和子ID:
This should return the parent and child ids in the two columns that you wanted:
import pandas as pd
import numpy as np
import itertools
df = pd.DataFrame(
{
'parent_id': [3111, 2010, 3000, 1000, 4023, 3011, 3033, 5010, 3011, 3102, 2010, 4023, 2110, 2100, 1000, 5010, 2110, 1000, 5010, 3033],
'child_id': [4321, 3102, 4023, 2010, 5321, 4200, 4113, 6525, 4010, 4001, 3011, 5010, 3000, 3033, 2110, 6100, 3111, 2100, 6016, 4311]
}
)
def get_child_list(df, parent_id):
list_of_children = []
list_of_children.append(df[df['parent_id'] == parent_id]['child_id'].values)
for i_, r_ in df[df['parent_id'] == parent_id].iterrows():
if r_['child_id'] != parent_id:
list_of_children.append(get_child_list(df, r_['child_id']))
# to flatten the list
list_of_children = [item for sublist in list_of_children for item in sublist]
return list_of_children
new_df = pd.DataFrame(columns=['parent_id', 'list_of_children'])
for index, row in df.iterrows():
temp_df = pd.DataFrame(columns=['parent_id', 'list_of_children'])
temp_df['list_of_children'] = pd.Series(get_child_list(df, row['parent_id']))
temp_df['parent_id'] = row['parent_id']
new_df = new_df.append(temp_df)
print new_df
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