I am trying to create a nested dictionary from a pandas dataframe.
I have this dataframe:
# this code should reproduce the example dataframe below:
df = pd.DataFrame({'ScID.xml': {0: '0006-****(****)050[****:ayfb]2.3.co.xml',
1: '0006-****(****)050[****:smihds]2.3.co.xml',
2: '0006-****(****)050[****:gissad]2.3.co.xml'},
'topic1': {0: 7.26744186046513e-06, 1: 0.0006479109, 2: 3.413e-06},
'topic2': {0: 7.26744186046513e-06, 1: 0.0091339857, 2: 3.413e-06},
'topic3': {0: 7.26744186046513e-06, 1: 2.79485746226941e-06, 2: 3.413e-06}})
# example dataframe:
ScID.xml topic1 topic2 topic3
0 0006-****(****)050[****:ayfb]2.3.co.xml 0.000007 0.000007 0.000007
1 0006-****(****)050[****:smihds]2.3.co.xml 0.000648 0.009134 0.000003
2 0006-****(****)050[****:gissad]2.3.co.xml 0.000003 0.000003 0.000003
I would like to produce a nested dictionary like this:
new_dict = {
'0006-****(****)050[****:ayfb]2.3.co.xml': {'topic1': 0.000007,
'topic2': 0.000007,
'topic3': 0.000007},
'0006-****(****)050[****:smihds]2.3.co.xml': {'topic1': 0.000648,
'topic2': 0.009134,
'topic3': 0.000003},
'0006-****(****)050[****:gissad]2.3.co.xml': {'topic1': 0.000003,
'topic2': 0.000003,
'topic3': 0.000003}
}
I have tried:
new_dict = df.set_index('ScID.xml').T.to_dict()
but that returns a dictionary in the following format:
{0: {'ScID.xml': 0006-****(****)050[****:ayfb]2.3.co.xml,
'topic1': 0.000007,
'topic2': 0.000007,
'topic3': 0.000007}
}
Try setting the keys as the index, then using index orientation in to_dict:
>>> df.set_index('ScID.xml').to_dict(orient='index')
{'0006-****(****)050[****:ayfb]2.3.co.xml': {'topic1': 7.2674418604651302e-06,
'topic2': 7.2674418604651302e-06,
'topic3': 7.2674418604651302e-06},
'0006-****(****)050[****:gissad]2.3.co.xml': {'topic1': 3.4130000000000002e-06,
'topic2': 3.4130000000000002e-06,
'topic3': 3.4130000000000002e-06},
'0006-****(****)050[****:smihds]2.3.co.xml': {'topic1': 0.0006479109,
'topic2': 0.0091339856999999997,
'topic3': 2.79485746226941e-06}}
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