Mohammad Zain Abbas
Reputation: 778
Python - Convert dict into nested dict
I have a dict:
{'Logistic Regression': u' precision recall f1-score support\n\n APAR Information 0.74 1.00 0.85 844\nAffected Products and Versions 0.00 0.00 0.00 18\n Answer 0.00 0.00 0.00 30\n Applicable component levels 0.96 0.85 0.90 241\n Error description 0.48 0.56 0.52 754\n Local fix 0.89 0.03 0.06 266\n Modules/Macros 0.96 0.87 0.91 326\n Problem 0.00 0.00 0.00 63\n Problem summary 0.51 0.73 0.60 721\n Related information 0.00 0.00 0.00 22\n Resolving The Problem 0.00 0.00 0.00 60\n Temporary fix 0.00 0.00 0.00 32\n circumvenion 0.00 0.00 0.00 124\n component 0.00 0.00 0.00 49\n temporary_fix 0.00 0.00 0.00 2\n\n micro avg 0.64 0.64 0.64 3552\n macro avg 0.30 0.27 0.26 3552\n weighted avg 0.60 0.64 0.58 3552\n'}
or
precision recall f1-score support
APAR Information 0.74 1.00 0.85 844
Affected Products and Versions 0.00 0.00 0.00 18
Answer 0.00 0.00 0.00 30
Applicable component levels 0.96 0.85 0.90 241
Error description 0.48 0.56 0.52 754
Local fix 0.89 0.03 0.06 266
Modules/Macros 0.96 0.87 0.91 326
Problem 0.00 0.00 0.00 63
Problem summary 0.51 0.73 0.60 721
Related information 0.00 0.00 0.00 22
Resolving The Problem 0.00 0.00 0.00 60
Temporary fix 0.00 0.00 0.00 32
circumvenion 0.00 0.00 0.00 124
component 0.00 0.00 0.00 49
temporary_fix 0.00 0.00 0.00 2
micro avg 0.64 0.64 0.64 3552
macro avg 0.30 0.27 0.26 3552
weighted avg 0.60 0.64 0.58 3552
and I want to convert this dict into a nested dict, something like,
{'Logistic Regression':
{'APAR Information':'0.74','1.00','0.85','844'},
{'Affected Products and Versions':'0.00','0.00','0.00','18'}
.
.
.}
How to achieve this ? Can it be done via dict build-in functions ?
Upvotes: 2
Views: 120
Answers (2)
jpp
Reputation: 164653
You can use 3rd party Pandas to convert to a dataframe via pd.read_fwf ("fixed-width formatted"). Your data is messy, you may need to write logic to calculate column widths or add them manually. Given an input dictionary d:
from io import StringIO
import pandas as pd
df = pd.read_fwf(StringIO(d['Logistic Regression']), widths=[30, 11, 10, 10, 10])\
.dropna().rename(columns={'Unnamed: 0': 'index'}).set_index('index')
print(df)
precision recall f1-score support
index
APAR Information 0.74 1.00 0.85 844.0
Affected Products and Versions 0.00 0.00 0.00 18.0
Answer 0.00 0.00 0.00 30.0
Applicable component levels 0.96 0.85 0.90 241.0
Error description 0.48 0.56 0.52 754.0
Local fix 0.89 0.03 0.06 266.0
Modules/Macros 0.96 0.87 0.91 326.0
Problem 0.00 0.00 0.00 63.0
Problem summary 0.51 0.73 0.60 721.0
Related information 0.00 0.00 0.00 22.0
Resolving The Problem 0.00 0.00 0.00 60.0
Temporary fix 0.00 0.00 0.00 32.0
circumvenion 0.00 0.00 0.00 124.0
component 0.00 0.00 0.00 49.0
temporary_fix 0.00 0.00 0.00 2.0
micro avg 0.64 0.64 0.64 3552.0
macro avg 0.30 0.27 0.26 3552.0
weighted avg 0.60 0.64 0.58 3552.0
Then use a dictionary comprehension:
res = {'Logistic Regression': {idx: df.loc[idx].tolist() for idx in df.index}}
print(res)
{'Logistic Regression':
{'APAR Information': [0.74, 1.0, 0.85, 844.0],
'Affected Products and Versions': [0.0, 0.0, 0.0, 18.0],
'Answer': [0.0, 0.0, 0.0, 30.0],
'Applicable component levels': [0.96, 0.85, 0.9, 241.0],
'Error description': [0.48, 0.56, 0.52, 754.0],
'Local fix': [0.89, 0.03, 0.06, 266.0],
'Modules/Macros': [0.96, 0.87, 0.91, 326.0],
'Problem': [0.0, 0.0, 0.0, 63.0],
'Problem summary': [0.51, 0.73, 0.6, 721.0],
'Related information': [0.0, 0.0, 0.0, 22.0],
'Resolving The Problem': [0.0, 0.0, 0.0, 60.0],
'Temporary fix': [0.0, 0.0, 0.0, 32.0],
'circumvenion': [0.0, 0.0, 0.0, 124.0],
'component': [0.0, 0.0, 0.0, 49.0],
'macro avg': [0.3, 0.27, 0.26, 3552.0],
'micro avg': [0.64, 0.64, 0.64, 3552.0],
'temporary_fix': [0.0, 0.0, 0.0, 2.0],
'weighted avg': [0.6, 0.64, 0.58, 3552.0]}}
Upvotes: 1
Rakesh
Reputation: 82765
This is one approach.
Demo:
d = {'Logistic Regression': u' precision recall f1-score support\n\n APAR Information 0.74 1.00 0.85 844\nAffected Products and Versions 0.00 0.00 0.00 18\n Answer 0.00 0.00 0.00 30\n Applicable component levels 0.96 0.85 0.90 241\n Error description 0.48 0.56 0.52 754\n Local fix 0.89 0.03 0.06 266\n Modules/Macros 0.96 0.87 0.91 326\n Problem 0.00 0.00 0.00 63\n Problem summary 0.51 0.73 0.60 721\n Related information 0.00 0.00 0.00 22\n Resolving The Problem 0.00 0.00 0.00 60\n Temporary fix 0.00 0.00 0.00 32\n circumvenion 0.00 0.00 0.00 124\n component 0.00 0.00 0.00 49\n temporary_fix 0.00 0.00 0.00 2\n\n micro avg 0.64 0.64 0.64 3552\n macro avg 0.30 0.27 0.26 3552\n weighted avg 0.60 0.64 0.58 3552\n'}
result = {}
for i, v in enumerate(d["Logistic Regression"].splitlines()):
if i == 0:
continue
val = v.strip().split(" ")
if val[0]:
result[val[0]] = " ".join(val[1:]).split()
for k, v in result.items():
print(k)
print(v)
Output:
weighted avg
[u'0.60', u'0.64', u'0.58', u'3552']
Local fix
[u'0.89', u'0.03', u'0.06', u'266']
Affected Products and Versions
[u'0.00', u'0.00', u'0.00', u'18']
component
[u'0.00', u'0.00', u'0.00', u'49']
Resolving The Problem
[u'0.00', u'0.00', u'0.00', u'60']
Error description
[u'0.48', u'0.56', u'0.52', u'754']
Problem summary
[u'0.51', u'0.73', u'0.60', u'721']
macro avg
[u'0.30', u'0.27', u'0.26', u'3552']
Related information
[u'0.00', u'0.00', u'0.00', u'22']
Applicable component levels
[u'0.96', u'0.85', u'0.90', u'241']
micro avg
[u'0.64', u'0.64', u'0.64', u'3552']
Answer
[u'0.00', u'0.00', u'0.00', u'30']
APAR Information
[u'0.74', u'1.00', u'0.85', u'844']
Problem
[u'0.00', u'0.00', u'0.00', u'63']
Modules/Macros
[u'0.96', u'0.87', u'0.91', u'326']
temporary_fix
[u'0.00', u'0.00', u'0.00', u'2']
circumvenion
[u'0.00', u'0.00', u'0.00', u'124']
Temporary fix
[u'0.00', u'0.00', u'0.00', u'32']
Upvotes: 2
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