Pandas: Create dictionaries from hierarchical data… here is a solution to the problem.
Pandas: Create dictionaries from hierarchical data
Let’s say I have the following data frame 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 can you turn it into a resulting dictionary:
results={'mother1':['child1','child2'],'mother2':['child1'],'mother3':['child1','child2','child3'] }
My opinion:
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:
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']}
Solution
Here’s one way:
df['A'].fillna(method='ffill', inplace=True)
Give:
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 delete the child NA:
df.dropna(subset=['B'], inplace=True)
Give:
A B
1 mother1 child1
2 mother1 child2
4 mother2 child1
6 mother3 child1
7 mother3 child2
8 mother3 child3
Then you can use groupby and dictionary comprehension to get the final result:
results = {k: v['B'].tolist() for k, v in df.groupby('A')}
Result:
{'mother1': ['child1', 'child2'],
'mother2': ['child1'],
'mother3': ['child1', 'child2', 'child3']}