[英]Pandas: create dictionary from hierarchical data
Say I have the following dataframe df
: 假设我有以下数据帧df
:
A B
0 mother1 NaN
1 NaN child1
2 NaN child2
3 mother2 NaN
4 NaN child1
5 mother3 NaN
6 NaN child1
7 NaN child2
8 NaN child3
How could you turn this into a dictionary that yields: 你怎么能把它变成一个字典,产生:
results={'mother1':['child1','child2'],'mother2':['child1'],'mother3':['child1','child2','child3']}
My take on it: 我接受它:
import pandas as pd
import numpy as np
results={}
for index1,row1 in df.iterrows():
if row1['A'] is not np.nan:
children=[]
for index2,row2 in df.iterrows():
if row2['B'] is not np.nan:
children.append(row2['B'])
results[row1['A']]=children
However, the result is wrong: 但是,结果是错误的:
In[1]: results
Out[1]:
{'mother1': ['child1', 'child2', 'child1', 'child1', 'child2', 'child3'],
'mother2': ['child1', 'child2', 'child1', 'child1', 'child2', 'child3'],
'mother3': ['child1', 'child2', 'child1', 'child1', 'child2', 'child3']}
Here's one way: 这是一种方式:
df['A'].fillna(method='ffill', inplace=True)
Giving: 赠送:
A B
0 mother1 NaN
1 mother1 child1
2 mother1 child2
3 mother2 NaN
4 mother2 child1
5 mother3 NaN
6 mother3 child1
7 mother3 child2
8 mother3 child3
Then drop the child NAs: 然后放下孩子NAs:
df.dropna(subset=['B'], inplace=True)
Giving: 赠送:
A B
1 mother1 child1
2 mother1 child2
4 mother2 child1
6 mother3 child1
7 mother3 child2
8 mother3 child3
You can then use groupby and a dictionary comprehension to get you to the final result: 然后,您可以使用groupby和字典理解来获得最终结果:
results = {k: v['B'].tolist() for k, v in df.groupby('A')}
Results: 结果:
{'mother1': ['child1', 'child2'],
'mother2': ['child1'],
'mother3': ['child1', 'child2', 'child3']}
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