python nested json to csv/xlsx with specified headers
Following code is able to parse the provided data as per expected format.
from typing import List
def parse_recursive(dat)->List[List]:
ret=[]
if type(dat) is list:
for item in dat:
if type(item)==dict:
for k in item:
#print(k, item[k], sep=" # ")#debug print
if item[k]==[]: #empty list
ret.append([k])
else:
for l in parse_recursive(item[k]):
#print(k,l,sep=" : ") #debug print
ret.append([k]+l) #always returns List of List
else: #Right now only possibility is string eg. "one", "two"
return [[",".join(dat)]]
else: #can be int or string eg. 97, "23"
return [[dat]]
return ret
def write_to_csv(file_name:str, fields:List, row_data:List[List]):
import csv
with open(file_name, 'w') as csvfile:
# creating a csv writer object
csvwriter = csv.writer(csvfile)
# writing the fields
csvwriter.writerow(fields)
# writing the data rows
csvwriter.writerows(row_data)
if __name__=="__main__":
org_data = [{"a": [
{"a1": [
{"id0": [
{
"aa": [
{"aaa": 97},
{"aab": "one"}],
"ab": [
{"aba": 97},
{"abb": ["one", "two"]}
]
}
]
},
{"id1": [
{"aa": [
{"aaa": 23}]}]}
]
},
{"a2": []}
]},
{"b": [{"b1": [{"Common": [{"bb": [{"value": 4}]}]}]}]}]
print(parse_recursive(org_data)) #Debug
file_name="data_file.csv"
fields=['Section', 'Subsection', 'pId', 'Group', 'Parameter', 'Value']
write_to_csv(file_name, fields, parse_recursive(org_data))
parse_recursive tries to parse arbitrary depth dictionary as per rule i tried deducing from your input and output formats.
Following is the output of parse_recursive for your provided input -
mahorir@mahorir-Vostro-3446:~/Desktop$ python3 so.py
[['a', 'a1', 'id0', 'aa', 'aaa', 97], ['a', 'a1', 'id0', 'aa', 'aab', 'one'], ['a', 'a1', 'id0', 'ab', 'aba', 97], ['a', 'a1', 'id0', 'ab', 'abb', 'one,two'], ['a', 'a1', 'id1', 'aa', 'aaa', 23], ['a', 'a2'], ['b', 'b1', 'Common', 'bb', 'value', 4]]
write_to_csv is a trivial function that write to a csv file.
This was kind-of a fun problem... There are really two problems with the formatting here:
The data is lists of dicts, where they really just wanted dictionaries. e.g. they wanted
{"foo": 1, "bar": 2}but instead formatted it as[{"foo": 1}, {"bar": 2}].a. I'm not judging here. There may be reasons why they did this. It just makes it a bit annoying for us to parse.
The data is sometimes truncated; if there are usually 5 levels deep, sometimes if they don't have data beyond a point, they just omit it. e.g.
'a2'in your example.
So I'll show two possible approaches to solving these problems.
The Pandas Way
This solution is a bit different from the other one mentioned here. Let me know what you think:
import pandas as pd
from copy import deepcopy
hdrs = ['Section', 'Subsection', 'pId', 'Group', 'Parameter', 'Value']
js = [{"a": [{"a1": [{"id0": [{"aa": [{"aaa": 97}, {"aab": "one"}],
"ab": [{"aba": 98}, {"abb": ["one", "two"]}]}]},
{"id1": [{"aa": [{"aaa": 23}]}]}
]},
{"a2": []}
]},
{"b": [{"b1": [{"Common": [{"bb": [{"value": 4}]}]}]}]}]
def list_to_dict(lst):
"""convert a list of dicts as you have to a single dict
The idea here is that you have a bunch of structures that look
like [{x: ...}, {y: ...}] that should probably have been stored as
{x:..., y:...}. So this function does that (but just one level in).
Note:
If there is a duplicate key in one of your dicts (meaning you have
something like [{x:...},...,{x:...}]), then this function will overwrite
it without warning!
"""
d = {}
for new_d in lst:
d.update(new_d)
return d
def recursive_parse(lst, levels):
"Parse the nested json into a single pandas dataframe"
name = levels.pop(0) # I should have used a counter instead
d = list_to_dict(lst) # get a sensible dict instead of the list of dicts
if len(levels) <= 1: # meaning there are no more levels to be parsed.
if len(d) == 0:
d = {'': ''} # to handle the uneven depths (e.g. think 'a2')
return pd.Series(d, name=levels[-1])
if len(d) == 0: # again to handle the uneven depths of json
d = {'': []}
# below is a list-comprehension to recursively parse the thing.
d = {k: recursive_parse(v, deepcopy(levels)) for k, v in d.items()}
return pd.concat(d)
def json_to_df(js, headers):
"calls recursive_parse, and then adds the column names and whatnot"
df = recursive_parse(js, deepcopy(headers))
df.index.names = headers[:-1]
df = df.reset_index()
return df
df = json_to_df(js, hdrs)
display(df)
And the output is exactly the dataframe you want (but with an index column you may not want). If you write it to csv after, do so like this:
df.to_csv('path/to/desired/file.csv', index=False)
Does that make sense?
The minimalist way
Better version (not using pandas)...
import csv
hdrs = ['Section', 'Subsection', 'pId', 'Group', 'Parameter', 'Value']
js = [{"a": [{"a1": [{"id0": [{"aa": [{"aaa": 97}, {"aab": "one"}],
"ab": [{"aba": 98}, {"abb": ["one", "two"]}]}]},
{"id1": [{"aa": [{"aaa": 23}]}]}
]},
{"a2": []}
]},
{"b": [{"b1": [{"Common": [{"bb": [{"value": 4}]}]}]}]}]
def list_of_dicts_to_lists(lst, n_levels=len(hdrs)):
if n_levels == 1:
if isinstance(lst, list):
if len(lst) == 0: # we fill the shorter ones with empty lists
lst = None # replacing them back to None
else: # [1, 2] => "1,2"
lst = ','.join(str(x) for x in lst if x is not None)
return [[lst]] # the later ones are going to be lists of lists so let's start out that way to keep everything consistent.
if len(lst) == 0:
lst = [{None: []}] # filling with an empty list
output = []
for d in lst:
for k, v in d.items():
tmp = list_of_dicts_to_lists(v, n_levels - 1)
for x in tmp:
output.append([k] + x)
return output
def to_csv(values, header, outfile):
with open(outfile, 'w', newline='') as csv_file:
# pretty much straight from the docs @
# https://docs.python.org/3.7/library/csv.html
csv_writer = csv.writer(csv_file, quoting=csv.QUOTE_MINIMAL)
csv_writer.writerow(header)
for line in values:
csv_writer.writerow(line)
return True
rows = list_of_dicts_to_lists(js)
to_csv(rows, hdrs, 'tmp.csv')
I see now that this solution is super similar to the other answer here... My bad.