-
pandas.io.json.json_normalize(data, record_path=None, meta=None, meta_prefix=None, record_prefix=None)
[source] -
?Normalize? semi-structured JSON data into a flat table
Parameters: data : dict or list of dicts
Unserialized JSON objects
record_path : string or list of strings, default None
Path in each object to list of records. If not passed, data will be assumed to be an array of records
meta : list of paths (string or list of strings), default None
Fields to use as metadata for each record in resulting table
record_prefix : string, default None
If True, prefix records with dotted (?) path, e.g. foo.bar.field if path to records is [?foo?, ?bar?]
meta_prefix : string, default None
Returns: frame : DataFrame
Examples
12345678910111213141516171819202122232425>>> data
=
[{
'state'
:
'Florida'
,
...
'shortname'
:
'FL'
,
...
'info'
: {
...
'governor'
:
'Rick Scott'
... },
...
'counties'
: [{
'name'
:
'Dade'
,
'population'
:
12345
},
... {
'name'
:
'Broward'
,
'population'
:
40000
},
... {
'name'
:
'Palm Beach'
,
'population'
:
60000
}]},
... {
'state'
:
'Ohio'
,
...
'shortname'
:
'OH'
,
...
'info'
: {
...
'governor'
:
'John Kasich'
... },
...
'counties'
: [{
'name'
:
'Summit'
,
'population'
:
1234
},
... {
'name'
:
'Cuyahoga'
,
'population'
:
1337
}]}]
>>>
from
pandas.io.json
import
json_normalize
>>> result
=
json_normalize(data,
'counties'
, [
'state'
,
'shortname'
,
... [
'info'
,
'governor'
]])
>>> result
name population info.governor state shortname
0
Dade
12345
Rick Scott Florida FL
1
Broward
40000
Rick Scott Florida FL
2
Palm Beach
60000
Rick Scott Florida FL
3
Summit
1234
John Kasich Ohio OH
4
Cuyahoga
1337
John Kasich Ohio OH
pandas.io.json.json_normalize()

2025-01-10 15:47:30
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