Convert pandas dataframe columns into nested python dictionary

user_13852380 0
Viewed 65

I want to create python dictionary with pandas data frame column 2(source) and column 3(description) and group by column 1(title) Also, I want to get values of only provided titles titles = ['test1','test2']

   title  source description
1  Test1    ABC  description1
2  Test2    ABC  description2
3  Test2    DEF  description3
4  Test3    XYZ  description4

output = {'Test1':{'ABC':'description1'},'Test2':{'ABC':'description2':'DEF':'description3'}
pandas python
3 Answers

Use boolean indexing with Series.isin for filter first, then is used GroupBy.apply with lambda function for Series of dicts and last Series.to_dict:

titles = ['Test1','Test2']

d = (df[df['title'].isin(titles)]
       .groupby('title')[['source','description']]
       .apply(lambda x: dict(x.to_numpy()))
       .to_dict())
print (d)
{'Test1': {'ABC': 'description1'}, 'Test2': {'ABC': 'description2', 'DEF': 'description3'}}
user_29010020

You can group by the dataframe w.r.t. title and then use python zip function to create inner dictionary with source and description. Please find below code for the same.

final_dict=dict()
all_groups = df.groupby('title')
for title in titles: 
    title_group = all_groups.get_group(title)
    source_desc=dict(zip(title_group.source, title_group.description))
    final_dict[title_group] = source_desc
print(final_dict)
user_135586060

try this,

result = {}

filter_ = ['Test1','Test2']

for x in df[df['title'].isin(filter_)].to_dict(orient='records'):
    result.setdefault(x['title'], {}).update({x['source']: x['description']})

{'Test1': {'ABC': 'description1'}, 'Test2': {'ABC': 'description2', 'DEF': 'description3'}}
user_49850990
Related