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'}
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'}}
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)
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'}}
