BUG: Fix json_normalize throwing TypeError when record_path has a sequence of dicts pandas-dev#22706 (pandas-dev#22804)

Author: vuminhle

doc/source/whatsnew/v0.24.0.rst

@@ -1511,6 +1511,7 @@ Notice how we now instead output ``np.nan`` itself instead of a stringified form
 - Bug in :meth:`DataFrame.to_dict` when the resulting dict contains non-Python scalars in the case of numeric data (:issue:`23753`)
 - :func:`DataFrame.to_string()`, :func:`DataFrame.to_html()`, :func:`DataFrame.to_latex()` will correctly format output when a string is passed as the ``float_format`` argument (:issue:`21625`, :issue:`22270`)
 - Bug in :func:`read_csv` that caused it to raise ``OverflowError`` when trying to use 'inf' as ``na_value`` with integer index column (:issue:`17128`)
+- Bug in :func:`json_normalize` that caused it to raise ``TypeError`` when two consecutive elements of ``record_path`` are dicts (:issue:`22706`)
 
 Plotting
 ^^^^^^^^

pandas/io/json/normalize.py

@@ -229,6 +229,8 @@ def _pull_field(js, spec):
     meta_keys = [sep.join(val) for val in meta]
 
     def _recursive_extract(data, path, seen_meta, level=0):
+        if isinstance(data, dict):
+            data = [data]
         if len(path) > 1:
             for obj in data:
                 for val, key in zip(meta, meta_keys):

pandas/tests/io/json/test_normalize.py

@@ -129,6 +129,21 @@ def test_value_array_record_prefix(self):
         expected = DataFrame([[1], [2]], columns=['Prefix.0'])
         tm.assert_frame_equal(result, expected)
 
+    def test_nested_object_record_path(self):
+        # GH 22706
+        data = {'state': 'Florida',
+                'info': {
+                    'governor': 'Rick Scott',
+                    'counties': [{'name': 'Dade', 'population': 12345},
+                                 {'name': 'Broward', 'population': 40000},
+                                 {'name': 'Palm Beach', 'population': 60000}]}}
+        result = json_normalize(data, record_path=["info", "counties"])
+        expected = DataFrame([['Dade', 12345],
+                              ['Broward', 40000],
+                              ['Palm Beach', 60000]],
+                             columns=['name', 'population'])
+        tm.assert_frame_equal(result, expected)
+
     def test_more_deeply_nested(self, deep_nested):
 
         result = json_normalize(deep_nested, ['states', 'cities'],