788 lines
26 KiB
Python
788 lines
26 KiB
Python
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import json
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import numpy as np
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import pytest
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from pandas import DataFrame, Index, Series, json_normalize
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import pandas._testing as tm
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from pandas.io.json._normalize import nested_to_record
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@pytest.fixture
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def deep_nested():
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# deeply nested data
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return [
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{
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"country": "USA",
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"states": [
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{
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"name": "California",
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"cities": [
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{"name": "San Francisco", "pop": 12345},
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{"name": "Los Angeles", "pop": 12346},
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],
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},
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{
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"name": "Ohio",
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"cities": [
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{"name": "Columbus", "pop": 1234},
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{"name": "Cleveland", "pop": 1236},
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],
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},
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],
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},
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{
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"country": "Germany",
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"states": [
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{"name": "Bayern", "cities": [{"name": "Munich", "pop": 12347}]},
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{
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"name": "Nordrhein-Westfalen",
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"cities": [
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{"name": "Duesseldorf", "pop": 1238},
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{"name": "Koeln", "pop": 1239},
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],
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},
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],
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},
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]
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@pytest.fixture
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def state_data():
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return [
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{
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"counties": [
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{"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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],
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"info": {"governor": "Rick Scott"},
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"shortname": "FL",
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"state": "Florida",
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},
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{
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"counties": [
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{"name": "Summit", "population": 1234},
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{"name": "Cuyahoga", "population": 1337},
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],
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"info": {"governor": "John Kasich"},
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"shortname": "OH",
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"state": "Ohio",
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},
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]
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@pytest.fixture
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def author_missing_data():
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return [
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{"info": None},
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{
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"info": {"created_at": "11/08/1993", "last_updated": "26/05/2012"},
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"author_name": {"first": "Jane", "last_name": "Doe"},
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},
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]
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@pytest.fixture
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def missing_metadata():
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return [
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{
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"name": "Alice",
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"addresses": [
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{
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"number": 9562,
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"street": "Morris St.",
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"city": "Massillon",
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"state": "OH",
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"zip": 44646,
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}
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],
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},
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{
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"addresses": [
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{
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"number": 8449,
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"street": "Spring St.",
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"city": "Elizabethton",
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"state": "TN",
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"zip": 37643,
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}
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]
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},
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]
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@pytest.fixture
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def max_level_test_input_data():
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"""
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input data to test json_normalize with max_level param
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"""
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return [
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{
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"CreatedBy": {"Name": "User001"},
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"Lookup": {
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"TextField": "Some text",
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"UserField": {"Id": "ID001", "Name": "Name001"},
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},
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"Image": {"a": "b"},
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}
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]
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class TestJSONNormalize:
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def test_simple_records(self):
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recs = [
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{"a": 1, "b": 2, "c": 3},
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{"a": 4, "b": 5, "c": 6},
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{"a": 7, "b": 8, "c": 9},
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{"a": 10, "b": 11, "c": 12},
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]
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result = json_normalize(recs)
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expected = DataFrame(recs)
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tm.assert_frame_equal(result, expected)
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def test_simple_normalize(self, state_data):
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result = json_normalize(state_data[0], "counties")
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expected = DataFrame(state_data[0]["counties"])
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tm.assert_frame_equal(result, expected)
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result = json_normalize(state_data, "counties")
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expected = []
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for rec in state_data:
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expected.extend(rec["counties"])
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expected = DataFrame(expected)
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tm.assert_frame_equal(result, expected)
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result = json_normalize(state_data, "counties", meta="state")
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expected["state"] = np.array(["Florida", "Ohio"]).repeat([3, 2])
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tm.assert_frame_equal(result, expected)
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def test_empty_array(self):
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result = json_normalize([])
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expected = DataFrame()
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tm.assert_frame_equal(result, expected)
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def test_simple_normalize_with_separator(self, deep_nested):
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# GH 14883
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result = json_normalize({"A": {"A": 1, "B": 2}})
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expected = DataFrame([[1, 2]], columns=["A.A", "A.B"])
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tm.assert_frame_equal(result.reindex_like(expected), expected)
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result = json_normalize({"A": {"A": 1, "B": 2}}, sep="_")
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expected = DataFrame([[1, 2]], columns=["A_A", "A_B"])
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tm.assert_frame_equal(result.reindex_like(expected), expected)
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result = json_normalize({"A": {"A": 1, "B": 2}}, sep="\u03c3")
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expected = DataFrame([[1, 2]], columns=["A\u03c3A", "A\u03c3B"])
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tm.assert_frame_equal(result.reindex_like(expected), expected)
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result = json_normalize(
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deep_nested,
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["states", "cities"],
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meta=["country", ["states", "name"]],
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sep="_",
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)
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expected = Index(["name", "pop", "country", "states_name"]).sort_values()
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assert result.columns.sort_values().equals(expected)
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def test_value_array_record_prefix(self):
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# GH 21536
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result = json_normalize({"A": [1, 2]}, "A", record_prefix="Prefix.")
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expected = DataFrame([[1], [2]], columns=["Prefix.0"])
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tm.assert_frame_equal(result, expected)
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def test_nested_object_record_path(self):
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# GH 22706
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data = {
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"state": "Florida",
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"info": {
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"governor": "Rick Scott",
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"counties": [
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{"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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],
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},
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}
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result = json_normalize(data, record_path=["info", "counties"])
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expected = DataFrame(
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[["Dade", 12345], ["Broward", 40000], ["Palm Beach", 60000]],
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columns=["name", "population"],
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)
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tm.assert_frame_equal(result, expected)
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def test_more_deeply_nested(self, deep_nested):
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result = json_normalize(
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deep_nested, ["states", "cities"], meta=["country", ["states", "name"]]
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)
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ex_data = {
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"country": ["USA"] * 4 + ["Germany"] * 3,
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"states.name": [
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"California",
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"California",
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"Ohio",
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"Ohio",
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"Bayern",
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"Nordrhein-Westfalen",
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"Nordrhein-Westfalen",
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],
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"name": [
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"San Francisco",
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"Los Angeles",
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"Columbus",
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"Cleveland",
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"Munich",
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"Duesseldorf",
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"Koeln",
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],
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"pop": [12345, 12346, 1234, 1236, 12347, 1238, 1239],
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}
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expected = DataFrame(ex_data, columns=result.columns)
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tm.assert_frame_equal(result, expected)
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def test_shallow_nested(self):
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data = [
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{
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"state": "Florida",
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"shortname": "FL",
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"info": {"governor": "Rick Scott"},
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"counties": [
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{"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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],
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},
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{
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"state": "Ohio",
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"shortname": "OH",
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"info": {"governor": "John Kasich"},
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"counties": [
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{"name": "Summit", "population": 1234},
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{"name": "Cuyahoga", "population": 1337},
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],
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},
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]
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result = json_normalize(
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data, "counties", ["state", "shortname", ["info", "governor"]]
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)
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ex_data = {
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"name": ["Dade", "Broward", "Palm Beach", "Summit", "Cuyahoga"],
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"state": ["Florida"] * 3 + ["Ohio"] * 2,
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"shortname": ["FL", "FL", "FL", "OH", "OH"],
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"info.governor": ["Rick Scott"] * 3 + ["John Kasich"] * 2,
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"population": [12345, 40000, 60000, 1234, 1337],
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}
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expected = DataFrame(ex_data, columns=result.columns)
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tm.assert_frame_equal(result, expected)
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def test_nested_meta_path_with_nested_record_path(self, state_data):
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# GH 27220
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result = json_normalize(
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data=state_data,
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record_path=["counties"],
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meta=["state", "shortname", ["info", "governor"]],
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errors="ignore",
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)
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ex_data = {
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"name": ["Dade", "Broward", "Palm Beach", "Summit", "Cuyahoga"],
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"population": [12345, 40000, 60000, 1234, 1337],
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"state": ["Florida"] * 3 + ["Ohio"] * 2,
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"shortname": ["FL"] * 3 + ["OH"] * 2,
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"info.governor": ["Rick Scott"] * 3 + ["John Kasich"] * 2,
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}
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expected = DataFrame(ex_data)
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tm.assert_frame_equal(result, expected)
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def test_meta_name_conflict(self):
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data = [
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{
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"foo": "hello",
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"bar": "there",
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"data": [
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{"foo": "something", "bar": "else"},
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{"foo": "something2", "bar": "else2"},
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],
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}
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]
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msg = r"Conflicting metadata name (foo|bar), need distinguishing prefix"
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with pytest.raises(ValueError, match=msg):
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json_normalize(data, "data", meta=["foo", "bar"])
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result = json_normalize(data, "data", meta=["foo", "bar"], meta_prefix="meta")
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for val in ["metafoo", "metabar", "foo", "bar"]:
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assert val in result
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def test_meta_parameter_not_modified(self):
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# GH 18610
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data = [
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{
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"foo": "hello",
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"bar": "there",
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"data": [
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{"foo": "something", "bar": "else"},
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{"foo": "something2", "bar": "else2"},
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],
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}
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]
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COLUMNS = ["foo", "bar"]
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result = json_normalize(data, "data", meta=COLUMNS, meta_prefix="meta")
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assert COLUMNS == ["foo", "bar"]
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for val in ["metafoo", "metabar", "foo", "bar"]:
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assert val in result
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def test_record_prefix(self, state_data):
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result = json_normalize(state_data[0], "counties")
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expected = DataFrame(state_data[0]["counties"])
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tm.assert_frame_equal(result, expected)
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result = json_normalize(
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state_data, "counties", meta="state", record_prefix="county_"
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)
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expected = []
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for rec in state_data:
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expected.extend(rec["counties"])
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expected = DataFrame(expected)
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expected = expected.rename(columns=lambda x: "county_" + x)
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expected["state"] = np.array(["Florida", "Ohio"]).repeat([3, 2])
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tm.assert_frame_equal(result, expected)
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def test_non_ascii_key(self):
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testjson = (
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b'[{"\xc3\x9cnic\xc3\xb8de":0,"sub":{"A":1, "B":2}},'
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+ b'{"\xc3\x9cnic\xc3\xb8de":1,"sub":{"A":3, "B":4}}]'
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).decode("utf8")
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testdata = {
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b"\xc3\x9cnic\xc3\xb8de".decode("utf8"): [0, 1],
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"sub.A": [1, 3],
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"sub.B": [2, 4],
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}
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expected = DataFrame(testdata)
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result = json_normalize(json.loads(testjson))
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tm.assert_frame_equal(result, expected)
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||
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||
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def test_missing_field(self, author_missing_data):
|
||
|
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# GH20030:
|
||
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result = json_normalize(author_missing_data)
|
||
|
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ex_data = [
|
||
|
|
{
|
||
|
|
"info": np.nan,
|
||
|
|
"info.created_at": np.nan,
|
||
|
|
"info.last_updated": np.nan,
|
||
|
|
"author_name.first": np.nan,
|
||
|
|
"author_name.last_name": np.nan,
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"info": None,
|
||
|
|
"info.created_at": "11/08/1993",
|
||
|
|
"info.last_updated": "26/05/2012",
|
||
|
|
"author_name.first": "Jane",
|
||
|
|
"author_name.last_name": "Doe",
|
||
|
|
},
|
||
|
|
]
|
||
|
|
expected = DataFrame(ex_data)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"max_level,expected",
|
||
|
|
[
|
||
|
|
(
|
||
|
|
0,
|
||
|
|
[
|
||
|
|
{
|
||
|
|
"TextField": "Some text",
|
||
|
|
"UserField": {"Id": "ID001", "Name": "Name001"},
|
||
|
|
"CreatedBy": {"Name": "User001"},
|
||
|
|
"Image": {"a": "b"},
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"TextField": "Some text",
|
||
|
|
"UserField": {"Id": "ID001", "Name": "Name001"},
|
||
|
|
"CreatedBy": {"Name": "User001"},
|
||
|
|
"Image": {"a": "b"},
|
||
|
|
},
|
||
|
|
],
|
||
|
|
),
|
||
|
|
(
|
||
|
|
1,
|
||
|
|
[
|
||
|
|
{
|
||
|
|
"TextField": "Some text",
|
||
|
|
"UserField.Id": "ID001",
|
||
|
|
"UserField.Name": "Name001",
|
||
|
|
"CreatedBy": {"Name": "User001"},
|
||
|
|
"Image": {"a": "b"},
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"TextField": "Some text",
|
||
|
|
"UserField.Id": "ID001",
|
||
|
|
"UserField.Name": "Name001",
|
||
|
|
"CreatedBy": {"Name": "User001"},
|
||
|
|
"Image": {"a": "b"},
|
||
|
|
},
|
||
|
|
],
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_max_level_with_records_path(self, max_level, expected):
|
||
|
|
# GH23843: Enhanced JSON normalize
|
||
|
|
test_input = [
|
||
|
|
{
|
||
|
|
"CreatedBy": {"Name": "User001"},
|
||
|
|
"Lookup": [
|
||
|
|
{
|
||
|
|
"TextField": "Some text",
|
||
|
|
"UserField": {"Id": "ID001", "Name": "Name001"},
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"TextField": "Some text",
|
||
|
|
"UserField": {"Id": "ID001", "Name": "Name001"},
|
||
|
|
},
|
||
|
|
],
|
||
|
|
"Image": {"a": "b"},
|
||
|
|
"tags": [
|
||
|
|
{"foo": "something", "bar": "else"},
|
||
|
|
{"foo": "something2", "bar": "else2"},
|
||
|
|
],
|
||
|
|
}
|
||
|
|
]
|
||
|
|
|
||
|
|
result = json_normalize(
|
||
|
|
test_input,
|
||
|
|
record_path=["Lookup"],
|
||
|
|
meta=[["CreatedBy"], ["Image"]],
|
||
|
|
max_level=max_level,
|
||
|
|
)
|
||
|
|
expected_df = DataFrame(data=expected, columns=result.columns.values)
|
||
|
|
tm.assert_equal(expected_df, result)
|
||
|
|
|
||
|
|
def test_nested_flattening_consistent(self):
|
||
|
|
# see gh-21537
|
||
|
|
df1 = json_normalize([{"A": {"B": 1}}])
|
||
|
|
df2 = json_normalize({"dummy": [{"A": {"B": 1}}]}, "dummy")
|
||
|
|
|
||
|
|
# They should be the same.
|
||
|
|
tm.assert_frame_equal(df1, df2)
|
||
|
|
|
||
|
|
def test_nonetype_record_path(self, nulls_fixture):
|
||
|
|
# see gh-30148
|
||
|
|
# should not raise TypeError
|
||
|
|
result = json_normalize(
|
||
|
|
[
|
||
|
|
{"state": "Texas", "info": nulls_fixture},
|
||
|
|
{"state": "Florida", "info": [{"i": 2}]},
|
||
|
|
],
|
||
|
|
record_path=["info"],
|
||
|
|
)
|
||
|
|
expected = DataFrame({"i": 2}, index=[0])
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("value", ["false", "true", "{}", "1", '"text"'])
|
||
|
|
def test_non_list_record_path_errors(self, value):
|
||
|
|
# see gh-30148, GH 26284
|
||
|
|
parsed_value = json.loads(value)
|
||
|
|
test_input = {"state": "Texas", "info": parsed_value}
|
||
|
|
test_path = "info"
|
||
|
|
msg = (
|
||
|
|
f"{test_input} has non list value {parsed_value} for path {test_path}. "
|
||
|
|
"Must be list or null."
|
||
|
|
)
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
json_normalize([test_input], record_path=[test_path])
|
||
|
|
|
||
|
|
def test_meta_non_iterable(self):
|
||
|
|
# GH 31507
|
||
|
|
data = """[{"id": 99, "data": [{"one": 1, "two": 2}]}]"""
|
||
|
|
|
||
|
|
result = json_normalize(json.loads(data), record_path=["data"], meta=["id"])
|
||
|
|
expected = DataFrame(
|
||
|
|
{"one": [1], "two": [2], "id": np.array([99], dtype=object)}
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
class TestNestedToRecord:
|
||
|
|
def test_flat_stays_flat(self):
|
||
|
|
recs = [{"flat1": 1, "flat2": 2}, {"flat3": 3, "flat2": 4}]
|
||
|
|
result = nested_to_record(recs)
|
||
|
|
expected = recs
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_one_level_deep_flattens(self):
|
||
|
|
data = {"flat1": 1, "dict1": {"c": 1, "d": 2}}
|
||
|
|
|
||
|
|
result = nested_to_record(data)
|
||
|
|
expected = {"dict1.c": 1, "dict1.d": 2, "flat1": 1}
|
||
|
|
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_nested_flattens(self):
|
||
|
|
data = {
|
||
|
|
"flat1": 1,
|
||
|
|
"dict1": {"c": 1, "d": 2},
|
||
|
|
"nested": {"e": {"c": 1, "d": 2}, "d": 2},
|
||
|
|
}
|
||
|
|
|
||
|
|
result = nested_to_record(data)
|
||
|
|
expected = {
|
||
|
|
"dict1.c": 1,
|
||
|
|
"dict1.d": 2,
|
||
|
|
"flat1": 1,
|
||
|
|
"nested.d": 2,
|
||
|
|
"nested.e.c": 1,
|
||
|
|
"nested.e.d": 2,
|
||
|
|
}
|
||
|
|
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_json_normalize_errors(self, missing_metadata):
|
||
|
|
# GH14583:
|
||
|
|
# If meta keys are not always present a new option to set
|
||
|
|
# errors='ignore' has been implemented
|
||
|
|
|
||
|
|
msg = "Try running with errors='ignore' as key 'name' is not always present"
|
||
|
|
with pytest.raises(KeyError, match=msg):
|
||
|
|
json_normalize(
|
||
|
|
data=missing_metadata,
|
||
|
|
record_path="addresses",
|
||
|
|
meta="name",
|
||
|
|
errors="raise",
|
||
|
|
)
|
||
|
|
|
||
|
|
def test_missing_meta(self, missing_metadata):
|
||
|
|
# GH25468
|
||
|
|
# If metadata is nullable with errors set to ignore, the null values
|
||
|
|
# should be numpy.nan values
|
||
|
|
result = json_normalize(
|
||
|
|
data=missing_metadata, record_path="addresses", meta="name", errors="ignore"
|
||
|
|
)
|
||
|
|
ex_data = [
|
||
|
|
[9562, "Morris St.", "Massillon", "OH", 44646, "Alice"],
|
||
|
|
[8449, "Spring St.", "Elizabethton", "TN", 37643, np.nan],
|
||
|
|
]
|
||
|
|
columns = ["city", "number", "state", "street", "zip", "name"]
|
||
|
|
columns = ["number", "street", "city", "state", "zip", "name"]
|
||
|
|
expected = DataFrame(ex_data, columns=columns)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_donot_drop_nonevalues(self):
|
||
|
|
# GH21356
|
||
|
|
data = [
|
||
|
|
{"info": None, "author_name": {"first": "Smith", "last_name": "Appleseed"}},
|
||
|
|
{
|
||
|
|
"info": {"created_at": "11/08/1993", "last_updated": "26/05/2012"},
|
||
|
|
"author_name": {"first": "Jane", "last_name": "Doe"},
|
||
|
|
},
|
||
|
|
]
|
||
|
|
result = nested_to_record(data)
|
||
|
|
expected = [
|
||
|
|
{
|
||
|
|
"info": None,
|
||
|
|
"author_name.first": "Smith",
|
||
|
|
"author_name.last_name": "Appleseed",
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"author_name.first": "Jane",
|
||
|
|
"author_name.last_name": "Doe",
|
||
|
|
"info.created_at": "11/08/1993",
|
||
|
|
"info.last_updated": "26/05/2012",
|
||
|
|
},
|
||
|
|
]
|
||
|
|
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_nonetype_top_level_bottom_level(self):
|
||
|
|
# GH21158: If inner level json has a key with a null value
|
||
|
|
# make sure it does not do a new_d.pop twice and except
|
||
|
|
data = {
|
||
|
|
"id": None,
|
||
|
|
"location": {
|
||
|
|
"country": {
|
||
|
|
"state": {
|
||
|
|
"id": None,
|
||
|
|
"town.info": {
|
||
|
|
"id": None,
|
||
|
|
"region": None,
|
||
|
|
"x": 49.151580810546875,
|
||
|
|
"y": -33.148521423339844,
|
||
|
|
"z": 27.572303771972656,
|
||
|
|
},
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
}
|
||
|
|
result = nested_to_record(data)
|
||
|
|
expected = {
|
||
|
|
"id": None,
|
||
|
|
"location.country.state.id": None,
|
||
|
|
"location.country.state.town.info.id": None,
|
||
|
|
"location.country.state.town.info.region": None,
|
||
|
|
"location.country.state.town.info.x": 49.151580810546875,
|
||
|
|
"location.country.state.town.info.y": -33.148521423339844,
|
||
|
|
"location.country.state.town.info.z": 27.572303771972656,
|
||
|
|
}
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
def test_nonetype_multiple_levels(self):
|
||
|
|
# GH21158: If inner level json has a key with a null value
|
||
|
|
# make sure it does not do a new_d.pop twice and except
|
||
|
|
data = {
|
||
|
|
"id": None,
|
||
|
|
"location": {
|
||
|
|
"id": None,
|
||
|
|
"country": {
|
||
|
|
"id": None,
|
||
|
|
"state": {
|
||
|
|
"id": None,
|
||
|
|
"town.info": {
|
||
|
|
"region": None,
|
||
|
|
"x": 49.151580810546875,
|
||
|
|
"y": -33.148521423339844,
|
||
|
|
"z": 27.572303771972656,
|
||
|
|
},
|
||
|
|
},
|
||
|
|
},
|
||
|
|
},
|
||
|
|
}
|
||
|
|
result = nested_to_record(data)
|
||
|
|
expected = {
|
||
|
|
"id": None,
|
||
|
|
"location.id": None,
|
||
|
|
"location.country.id": None,
|
||
|
|
"location.country.state.id": None,
|
||
|
|
"location.country.state.town.info.region": None,
|
||
|
|
"location.country.state.town.info.x": 49.151580810546875,
|
||
|
|
"location.country.state.town.info.y": -33.148521423339844,
|
||
|
|
"location.country.state.town.info.z": 27.572303771972656,
|
||
|
|
}
|
||
|
|
assert result == expected
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"max_level, expected",
|
||
|
|
[
|
||
|
|
(
|
||
|
|
None,
|
||
|
|
[
|
||
|
|
{
|
||
|
|
"CreatedBy.Name": "User001",
|
||
|
|
"Lookup.TextField": "Some text",
|
||
|
|
"Lookup.UserField.Id": "ID001",
|
||
|
|
"Lookup.UserField.Name": "Name001",
|
||
|
|
"Image.a": "b",
|
||
|
|
}
|
||
|
|
],
|
||
|
|
),
|
||
|
|
(
|
||
|
|
0,
|
||
|
|
[
|
||
|
|
{
|
||
|
|
"CreatedBy": {"Name": "User001"},
|
||
|
|
"Lookup": {
|
||
|
|
"TextField": "Some text",
|
||
|
|
"UserField": {"Id": "ID001", "Name": "Name001"},
|
||
|
|
},
|
||
|
|
"Image": {"a": "b"},
|
||
|
|
}
|
||
|
|
],
|
||
|
|
),
|
||
|
|
(
|
||
|
|
1,
|
||
|
|
[
|
||
|
|
{
|
||
|
|
"CreatedBy.Name": "User001",
|
||
|
|
"Lookup.TextField": "Some text",
|
||
|
|
"Lookup.UserField": {"Id": "ID001", "Name": "Name001"},
|
||
|
|
"Image.a": "b",
|
||
|
|
}
|
||
|
|
],
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_with_max_level(self, max_level, expected, max_level_test_input_data):
|
||
|
|
# GH23843: Enhanced JSON normalize
|
||
|
|
output = nested_to_record(max_level_test_input_data, max_level=max_level)
|
||
|
|
assert output == expected
|
||
|
|
|
||
|
|
def test_with_large_max_level(self):
|
||
|
|
# GH23843: Enhanced JSON normalize
|
||
|
|
max_level = 100
|
||
|
|
input_data = [
|
||
|
|
{
|
||
|
|
"CreatedBy": {
|
||
|
|
"user": {
|
||
|
|
"name": {"firstname": "Leo", "LastName": "Thomson"},
|
||
|
|
"family_tree": {
|
||
|
|
"father": {
|
||
|
|
"name": "Father001",
|
||
|
|
"father": {
|
||
|
|
"Name": "Father002",
|
||
|
|
"father": {
|
||
|
|
"name": "Father003",
|
||
|
|
"father": {"Name": "Father004"},
|
||
|
|
},
|
||
|
|
},
|
||
|
|
}
|
||
|
|
},
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
expected = [
|
||
|
|
{
|
||
|
|
"CreatedBy.user.name.firstname": "Leo",
|
||
|
|
"CreatedBy.user.name.LastName": "Thomson",
|
||
|
|
"CreatedBy.user.family_tree.father.name": "Father001",
|
||
|
|
"CreatedBy.user.family_tree.father.father.Name": "Father002",
|
||
|
|
"CreatedBy.user.family_tree.father.father.father.name": "Father003",
|
||
|
|
"CreatedBy.user.family_tree.father.father.father.father.Name": "Father004", # noqa: E501
|
||
|
|
}
|
||
|
|
]
|
||
|
|
output = nested_to_record(input_data, max_level=max_level)
|
||
|
|
assert output == expected
|
||
|
|
|
||
|
|
def test_deprecated_import(self):
|
||
|
|
with tm.assert_produces_warning(FutureWarning):
|
||
|
|
from pandas.io.json import json_normalize
|
||
|
|
|
||
|
|
recs = [{"a": 1, "b": 2, "c": 3}, {"a": 4, "b": 5, "c": 6}]
|
||
|
|
json_normalize(recs)
|
||
|
|
|
||
|
|
def test_series_non_zero_index(self):
|
||
|
|
# GH 19020
|
||
|
|
data = {
|
||
|
|
0: {"id": 1, "name": "Foo", "elements": {"a": 1}},
|
||
|
|
1: {"id": 2, "name": "Bar", "elements": {"b": 2}},
|
||
|
|
2: {"id": 3, "name": "Baz", "elements": {"c": 3}},
|
||
|
|
}
|
||
|
|
s = Series(data)
|
||
|
|
s.index = [1, 2, 3]
|
||
|
|
result = json_normalize(s)
|
||
|
|
expected = DataFrame(
|
||
|
|
{
|
||
|
|
"id": [1, 2, 3],
|
||
|
|
"name": ["Foo", "Bar", "Baz"],
|
||
|
|
"elements.a": [1.0, np.nan, np.nan],
|
||
|
|
"elements.b": [np.nan, 2.0, np.nan],
|
||
|
|
"elements.c": [np.nan, np.nan, 3.0],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|