281 lines
9.1 KiB
Python
281 lines
9.1 KiB
Python
# ---------------------------------------------------------------------
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# JSON normalization routines
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import copy
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from collections import defaultdict
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import numpy as np
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from pandas._libs.writers import convert_json_to_lines
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from pandas import compat, DataFrame
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def _convert_to_line_delimits(s):
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"""Helper function that converts json lists to line delimited json."""
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# Determine we have a JSON list to turn to lines otherwise just return the
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# json object, only lists can
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if not s[0] == '[' and s[-1] == ']':
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return s
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s = s[1:-1]
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return convert_json_to_lines(s)
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def nested_to_record(ds, prefix="", sep=".", level=0):
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"""a simplified json_normalize
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converts a nested dict into a flat dict ("record"), unlike json_normalize,
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it does not attempt to extract a subset of the data.
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Parameters
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----------
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ds : dict or list of dicts
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prefix: the prefix, optional, default: ""
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sep : string, default '.'
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Nested records will generate names separated by sep,
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e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar
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.. versionadded:: 0.20.0
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level: the number of levels in the jason string, optional, default: 0
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Returns
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-------
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d - dict or list of dicts, matching `ds`
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Examples
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--------
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IN[52]: nested_to_record(dict(flat1=1,dict1=dict(c=1,d=2),
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nested=dict(e=dict(c=1,d=2),d=2)))
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Out[52]:
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{'dict1.c': 1,
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'dict1.d': 2,
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'flat1': 1,
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'nested.d': 2,
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'nested.e.c': 1,
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'nested.e.d': 2}
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"""
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singleton = False
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if isinstance(ds, dict):
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ds = [ds]
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singleton = True
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new_ds = []
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for d in ds:
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new_d = copy.deepcopy(d)
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for k, v in d.items():
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# each key gets renamed with prefix
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if not isinstance(k, compat.string_types):
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k = str(k)
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if level == 0:
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newkey = k
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else:
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newkey = prefix + sep + k
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# only dicts gets recurse-flattend
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# only at level>1 do we rename the rest of the keys
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if not isinstance(v, dict):
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if level != 0: # so we skip copying for top level, common case
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v = new_d.pop(k)
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new_d[newkey] = v
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continue
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else:
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v = new_d.pop(k)
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new_d.update(nested_to_record(v, newkey, sep, level + 1))
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new_ds.append(new_d)
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if singleton:
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return new_ds[0]
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return new_ds
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def json_normalize(data, record_path=None, meta=None,
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meta_prefix=None,
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record_prefix=None,
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errors='raise',
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sep='.'):
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"""
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"Normalize" semi-structured JSON data into a flat table
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Parameters
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----------
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data : dict or list of dicts
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Unserialized JSON objects
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record_path : string or list of strings, default None
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Path in each object to list of records. If not passed, data will be
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assumed to be an array of records
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meta : list of paths (string or list of strings), default None
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Fields to use as metadata for each record in resulting table
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record_prefix : string, default None
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If True, prefix records with dotted (?) path, e.g. foo.bar.field if
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path to records is ['foo', 'bar']
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meta_prefix : string, default None
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errors : {'raise', 'ignore'}, default 'raise'
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* 'ignore' : will ignore KeyError if keys listed in meta are not
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always present
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* 'raise' : will raise KeyError if keys listed in meta are not
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always present
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.. versionadded:: 0.20.0
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sep : string, default '.'
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Nested records will generate names separated by sep,
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e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar
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.. versionadded:: 0.20.0
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Returns
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-------
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frame : DataFrame
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Examples
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--------
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>>> from pandas.io.json import json_normalize
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>>> data = [{'id': 1, 'name': {'first': 'Coleen', 'last': 'Volk'}},
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... {'name': {'given': 'Mose', 'family': 'Regner'}},
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... {'id': 2, 'name': 'Faye Raker'}]
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>>> json_normalize(data)
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id name name.family name.first name.given name.last
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0 1.0 NaN NaN Coleen NaN Volk
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1 NaN NaN Regner NaN Mose NaN
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2 2.0 Faye Raker NaN NaN NaN NaN
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>>> data = [{'state': 'Florida',
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... 'shortname': 'FL',
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... 'info': {
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... 'governor': 'Rick Scott'
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... },
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... 'counties': [{'name': 'Dade', 'population': 12345},
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... {'name': 'Broward', 'population': 40000},
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... {'name': 'Palm Beach', 'population': 60000}]},
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... {'state': 'Ohio',
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... 'shortname': 'OH',
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... 'info': {
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... 'governor': 'John Kasich'
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... },
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... 'counties': [{'name': 'Summit', 'population': 1234},
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... {'name': 'Cuyahoga', 'population': 1337}]}]
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>>> result = json_normalize(data, 'counties', ['state', 'shortname',
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... ['info', 'governor']])
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>>> result
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name population info.governor state shortname
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0 Dade 12345 Rick Scott Florida FL
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1 Broward 40000 Rick Scott Florida FL
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2 Palm Beach 60000 Rick Scott Florida FL
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3 Summit 1234 John Kasich Ohio OH
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4 Cuyahoga 1337 John Kasich Ohio OH
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>>> data = {'A': [1, 2]}
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>>> json_normalize(data, 'A', record_prefix='Prefix.')
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Prefix.0
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0 1
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1 2
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"""
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def _pull_field(js, spec):
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result = js
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if isinstance(spec, list):
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for field in spec:
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result = result[field]
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else:
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result = result[spec]
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return result
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if isinstance(data, list) and not data:
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return DataFrame()
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# A bit of a hackjob
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if isinstance(data, dict):
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data = [data]
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if record_path is None:
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if any([[isinstance(x, dict)
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for x in compat.itervalues(y)] for y in data]):
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# naive normalization, this is idempotent for flat records
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# and potentially will inflate the data considerably for
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# deeply nested structures:
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# {VeryLong: { b: 1,c:2}} -> {VeryLong.b:1 ,VeryLong.c:@}
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#
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# TODO: handle record value which are lists, at least error
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# reasonably
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data = nested_to_record(data, sep=sep)
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return DataFrame(data)
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elif not isinstance(record_path, list):
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record_path = [record_path]
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if meta is None:
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meta = []
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elif not isinstance(meta, list):
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meta = [meta]
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meta = [m if isinstance(m, list) else [m] for m in meta]
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# Disastrously inefficient for now
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records = []
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lengths = []
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meta_vals = defaultdict(list)
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if not isinstance(sep, compat.string_types):
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sep = str(sep)
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meta_keys = [sep.join(val) for val in meta]
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def _recursive_extract(data, path, seen_meta, level=0):
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if len(path) > 1:
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for obj in data:
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for val, key in zip(meta, meta_keys):
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if level + 1 == len(val):
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seen_meta[key] = _pull_field(obj, val[-1])
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_recursive_extract(obj[path[0]], path[1:],
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seen_meta, level=level + 1)
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else:
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for obj in data:
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recs = _pull_field(obj, path[0])
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# For repeating the metadata later
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lengths.append(len(recs))
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for val, key in zip(meta, meta_keys):
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if level + 1 > len(val):
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meta_val = seen_meta[key]
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else:
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try:
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meta_val = _pull_field(obj, val[level:])
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except KeyError as e:
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if errors == 'ignore':
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meta_val = np.nan
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else:
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raise \
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KeyError("Try running with "
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"errors='ignore' as key "
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"{err} is not always present"
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.format(err=e))
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meta_vals[key].append(meta_val)
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records.extend(recs)
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_recursive_extract(data, record_path, {}, level=0)
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result = DataFrame(records)
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if record_prefix is not None:
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result = result.rename(
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columns=lambda x: "{p}{c}".format(p=record_prefix, c=x))
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# Data types, a problem
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for k, v in compat.iteritems(meta_vals):
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if meta_prefix is not None:
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k = meta_prefix + k
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if k in result:
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raise ValueError('Conflicting metadata name {name}, '
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'need distinguishing prefix '.format(name=k))
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result[k] = np.array(v).repeat(lengths)
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return result
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