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no matter how deeply it is nested!\n\n## Update:\n\n**2022/09/30:** DataFrame is now created directly from iter\n\n**2022/09/30:** No more warning (PerformanceWarning: DataFrame is highly fragmented), when DataFrame is created from a huge nested dict (depth: 1486) Try it: https://raw.githubusercontent.com/hansalemaos/a_pandas_ex_plode_tool/main/recursion%20_hardcore_test.py\n\n```python\npip install a-pandas-ex-plode-tool\n```\n\n```python\nfrom a_pandas_ex_plode_tool import pd_add_explode_tools\npd_add_explode_tools()\nimport pandas as pd\ndf = pd.read_csv(\"https://github.com/pandas-dev/pandas/raw/main/doc/data/air_quality_long.csv\")\n```\n\n**HANDLE NESTED ITERABLES**\n\nThe code above will add some methods to **pd. / pd.DataFrame / pd.Series**, you can use pandas like you did before, but you will have a couple of methods more:\n\n- pd.Q_AnyNestedIterable_2df()\n- pd.Q_CorruptJsonFile_2dict()\n- pd.Q_ReadFileWithAllEncodings_2df()\n- df.d_filter_dtypes()\n- df.d_multiple_columns_to_one()\n- df.d_df_to_nested_dict()\n- df.d_add_value_to_existing_columns_with_loc()\n- df.d_set_values_with_df_loc()\n- df.d_drop_rows_with_df_loc()\n- df.d_dfloc()\n- df.d_stack()\n- df.d_unstack()\n- df.d_sort_columns_with_sorted()\n- df.d_merge_multiple_dfs_and_series_on_one_column()\n- df.d_merge_multiple_dfs_and_series_on_index()\n- df.d_update_original_iter()\n- df.ds_all_nans_to_pdNA()\n- df.ds_explode_dicts_in_column()\n- df.ds_isna()\n- df.ds_normalize_lists()\n- df.s_delete_duplicates_from_iters_in_cells()\n- df.s_flatten_all_iters_in_cells()\n- df.s_as_flattened_list()\n- df.s_explode_lists_and_tuples()\n\n**All methods added to pandas have one of these prefixes:**\n\n- **ds_** (for DataFrames and Series)\n\n- **s_** (only for Series)\n\n- **d_** (only for DataFrames)\n\n- **Q_** (added to pd.)\n\n### pd.Q_AnyNestedIterable_2df() / df.d_filter_dtypes() / df.d_update_original_iter()\n\n**pd.Q_AnyNestedIterable_2df()** transforms any nasty iterable into a beautiful Pandas DataFrame with a [MultiIndex](https://pandas.pydata.org/docs/user_guide/advanced.html)\n\n**df.d_filter_dtypes()** avoids TypeError Exceptions \n\ndf.loc[df.aa_value \u003e30,'aa_value'] = 90000000 \n\nTraceback (most recent call last): \n.... \nTypeError: '\u003e' not supported between instances of 'str' and 'int' \n\n***df.loc[df.d_filter_dtypes(allowed_dtypes=(int,float),fillvalue=pd.NA,column='aa_value') \u003e 30] \u003c------- No more exception!***\n\n**df.d_update_original_iter()** After you have updated the DataFrame, you can update the original nasty iterable and keep its ugly structure. \n\n##### I have tested these methods a lot with examples from Stack Overflow. Until now, everything has been working like a charm. Here are about 15 examples!\n\n```python\nNested iterable from: 'https://stackoverflow.com/questions/61984148/how-to-handle-nested-lists-and-dictionaries-in-pandas-dataframe'\n{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},\n {'review_critic': 'ABC', 'review_score': 90},\n {'review_critic': '123', 'review_score': 90}],\n 'genres': ['Sports', 'Golf'],\n 'score': 85,\n 'title': 'Golf Simulator',\n 'url': 'http://example.com/golf-simulator'}\n\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False) # create DF stacked or unstacked, it doesn't matter\n aa_all_keys aa_value\ncritic_reviews 0 review_critic (critic_reviews, 0, review_critic) XYZ\n review_score (critic_reviews, 0, review_score) 90\n 1 review_critic (critic_reviews, 1, review_critic) ABC\n review_score (critic_reviews, 1, review_score) 90\n 2 review_critic (critic_reviews, 2, review_critic) 123\n review_score (critic_reviews, 2, review_score) 90\ngenres 0 NaN (genres, 0) Sports\n 1 NaN (genres, 1) Golf\nscore NaN NaN (score,) 85\ntitle NaN NaN (title,) Golf Simulator\nurl NaN NaN (url,) http://example.com/golf-simulator\n\n#Avoid exceptions with df.d_filter_dtypes()\ndf.loc[df.aa_value.str.contains('[Gg]',na=False),'aa_value'] = 'UPDATE1111' #df.loc to update the dataframe (VERY IMPORTANT: To update the original iterable you have to pass 'aa_value')\n aa_all_keys aa_value\ncritic_reviews 0 review_critic (critic_reviews, 0, review_critic) XYZ\n review_score (critic_reviews, 0, review_score) 90\n 1 review_critic (critic_reviews, 1, review_critic) ABC\n review_score (critic_reviews, 1, review_score) 90\n 2 review_critic (critic_reviews, 2, review_critic) 123\n review_score (critic_reviews, 2, review_score) 90\ngenres 0 NaN (genres, 0) Sports\n 1 NaN (genres, 1) UPDATE1111\nscore NaN NaN (score,) 85\ntitle NaN NaN (title,) UPDATE1111\nurl NaN NaN (url,) UPDATE1111\nmod_iter = df.d_update_original_iter(data, verbose=True) #updating the nested iterable, the new values have to be in the column 'aa_value', if you have added new columns to the dataframe, drop them before updating the original iterable\n[genres][1] Old value: Golf\n[genres][1] Updated value: UPDATE1111\n[title] Old value: Golf Simulator\n[title] Updated value: UPDATE1111\n[url] Old value: http://example.com/golf-simulator\n[url] Updated value: UPDATE1111\n\n{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},\n {'review_critic': 'ABC', 'review_score': 90},\n {'review_critic': '123', 'review_score': 90}],\n 'genres': ['Sports', 'UPDATE1111'],\n 'score': 85,\n 'title': 'UPDATE1111',\n 'url': 'UPDATE1111'}\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/73430585/how-to-convert-a-list-of-nested-dictionaries-includes-tuples-as-a-dataframe\ndata=\n[{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 2, 'Name': 'A', 'num': 68}),\n 'final_value': 118},\n {'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 4, 'Name': 'A', 'num': 67}),\n 'final_value': 117},\n {'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 6, 'Name': 'A', 'num': 67}),\n 'final_value': 117}]\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\n0 cb 0 ID (0, cb, 0, ID) 1\n Name (0, cb, 0, Name) A\n num (0, cb, 0, num) 50\n 1 ID (0, cb, 1, ID) 2\n Name (0, cb, 1, Name) A\n num (0, cb, 1, num) 68\n final_value NaN NaN (0, final_value) 118\n1 cb 0 ID (1, cb, 0, ID) 1\n Name (1, cb, 0, Name) A\n num (1, cb, 0, num) 50\n 1 ID (1, cb, 1, ID) 4\n Name (1, cb, 1, Name) A\n num (1, cb, 1, num) 67\n final_value NaN NaN (1, final_value) 117\n2 cb 0 ID (2, cb, 0, ID) 1\n Name (2, cb, 0, Name) A\n num (2, cb, 0, num) 50\n 1 ID (2, cb, 1, ID) 6\n Name (2, cb, 1, Name) A\n num (2, cb, 1, num) 67\n final_value NaN NaN (2, final_value) 117\ndf.loc[df.d_filter_dtypes(allowed_dtypes=(int,float),fillvalue=pd.NA,column='aa_value') \u003e 30, 'aa_value'] = 900000\n aa_all_keys aa_value\n0 cb 0 ID (0, cb, 0, ID) 1\n Name (0, cb, 0, Name) A\n num (0, cb, 0, num) 900000\n 1 ID (0, cb, 1, ID) 2\n Name (0, cb, 1, Name) A\n num (0, cb, 1, num) 900000\n final_value NaN NaN (0, final_value) 900000\n1 cb 0 ID (1, cb, 0, ID) 1\n Name (1, cb, 0, Name) A\n num (1, cb, 0, num) 900000\n 1 ID (1, cb, 1, ID) 4\n Name (1, cb, 1, Name) A\n num (1, cb, 1, num) 900000\n final_value NaN NaN (1, final_value) 900000\n2 cb 0 ID (2, cb, 0, ID) 1\n Name (2, cb, 0, Name) A\n num (2, cb, 0, num) 900000\n 1 ID (2, cb, 1, ID) 6\n Name (2, cb, 1, Name) A\n num (2, cb, 1, num) 900000\n final_value NaN NaN (2, final_value) 900000\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[0][cb][0][num] Old value: 50\n[0][cb][0][num] Updated value: 900000\n[0][cb][1][num] Old value: 68\n[0][cb][1][num] Updated value: 900000\n[0][final_value] Old value: 118\n[0][final_value] Updated value: 900000\n[1][cb][0][num] Old value: 50\n[1][cb][0][num] Updated value: 900000\n[1][cb][1][num] Old value: 67\n[1][cb][1][num] Updated value: 900000\n[1][final_value] Old value: 117\n[1][final_value] Updated value: 900000\n[2][cb][0][num] Old value: 50\n[2][cb][0][num] Updated value: 900000\n[2][cb][1][num] Old value: 67\n[2][cb][1][num] Updated value: 900000\n[2][final_value] Old value: 117\n[2][final_value] Updated value: 900000\n[{'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},\n {'ID': 2, 'Name': 'A', 'num': 900000}),\n 'final_value': 900000},\n {'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},\n {'ID': 4, 'Name': 'A', 'num': 900000}),\n 'final_value': 900000},\n {'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},\n {'ID': 6, 'Name': 'A', 'num': 900000}),\n 'final_value': 900000}]\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/69943509/problems-when-flatten-a-dict\ndata=\n[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],\n 'application_details': {'email': None, 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '1'},\n {'application_contacts': [{'adress': 'Z', 'email': None}],\n 'application_details': {'email': 'testy@test_a.com', 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '2'},\n {'application_contacts': [{'adress': 'Y', 'email': None}],\n 'application_details': {'email': 'testy@test_a.com', 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '3'}]\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\n0 application_contacts 0 adress (0, application_contacts, 0, adress) X\n email (0, application_contacts, 0, email) test@test.com\n application_details email NaN (0, application_details, email) None\n phone NaN (0, application_details, phone) None\n employer Name NaN (0, employer, Name) Nom\n email NaN (0, employer, email) None\n id NaN NaN (0, id) 1\n1 application_contacts 0 adress (1, application_contacts, 0, adress) Z\n email (1, application_contacts, 0, email) None\n application_details email NaN (1, application_details, email) testy@test_a.com\n phone NaN (1, application_details, phone) None\n employer Name NaN (1, employer, Name) Nom\n email NaN (1, employer, email) None\n id NaN NaN (1, id) 2\n2 application_contacts 0 adress (2, application_contacts, 0, adress) Y\n email (2, application_contacts, 0, email) None\n application_details email NaN (2, application_details, email) testy@test_a.com\n phone NaN (2, application_details, phone) None\n employer Name NaN (2, employer, Name) Nom\n email NaN (2, employer, email) None\n id NaN NaN (2, id) 3\ndf.loc[df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'test_a\\.\\w+\\b',na=False), 'aa_value'] = 'UPPPPPPPPPPPPPPPDATE.COM'\n aa_all_keys aa_value\n0 application_contacts 0 adress (0, application_contacts, 0, adress) X\n email (0, application_contacts, 0, email) test@test.com\n application_details email NaN (0, application_details, email) None\n phone NaN (0, application_details, phone) None\n employer Name NaN (0, employer, Name) Nom\n email NaN (0, employer, email) None\n id NaN NaN (0, id) 1\n1 application_contacts 0 adress (1, application_contacts, 0, adress) Z\n email (1, application_contacts, 0, email) None\n application_details email NaN (1, application_details, email) UPPPPPPPPPPPPPPPDATE.COM\n phone NaN (1, application_details, phone) None\n employer Name NaN (1, employer, Name) Nom\n email NaN (1, employer, email) None\n id NaN NaN (1, id) 2\n2 application_contacts 0 adress (2, application_contacts, 0, adress) Y\n email (2, application_contacts, 0, email) None\n application_details email NaN (2, application_details, email) UPPPPPPPPPPPPPPPDATE.COM\n phone NaN (2, application_details, phone) None\n employer Name NaN (2, employer, Name) Nom\n email NaN (2, employer, email) None\n id NaN NaN (2, id) 3\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[1][application_details][email] Old value: testy@test_a.com\n[1][application_details][email] Updated value: UPPPPPPPPPPPPPPPDATE.COM\n[2][application_details][email] Old value: testy@test_a.com\n[2][application_details][email] Updated value: UPPPPPPPPPPPPPPPDATE.COM\n[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],\n 'application_details': {'email': None, 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '1'},\n {'application_contacts': [{'adress': 'Z', 'email': None}],\n 'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '2'},\n {'application_contacts': [{'adress': 'Y', 'email': None}],\n 'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '3'}]\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/62765371/convert-nested-dataframe-to-a-simple-dataframeframe\ndata=\n{'A': [1, 2, 3],\n 'B': [4, 5, 6],\n 'departure': [{'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'},\n {'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'},\n {'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'}]}\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\nA 0 NaN (A, 0) 1\n 1 NaN (A, 1) 2\n 2 NaN (A, 2) 3\nB 0 NaN (B, 0) 4\n 1 NaN (B, 1) 5\n 2 NaN (B, 2) 6\ndeparture 0 actual (departure, 0, actual) None\n actual_runway (departure, 0, actual_runway) None\n airport (departure, 0, airport) Findel\n delay (departure, 0, delay) None\n estimated (departure, 0, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 0, estimated_runway) None\n gate (departure, 0, gate) None\n iata (departure, 0, iata) LUX\n icao (departure, 0, icao) ELLX\n scheduled (departure, 0, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 0, terminal) None\n timezone (departure, 0, timezone) Europe/Luxembourg\n 1 actual (departure, 1, actual) None\n actual_runway (departure, 1, actual_runway) None\n airport (departure, 1, airport) Findel\n delay (departure, 1, delay) None\n estimated (departure, 1, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 1, estimated_runway) None\n gate (departure, 1, gate) None\n iata (departure, 1, iata) LUX\n icao (departure, 1, icao) ELLX\n scheduled (departure, 1, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 1, terminal) None\n timezone (departure, 1, timezone) Europe/Luxembourg\n 2 actual (departure, 2, actual) None\n actual_runway (departure, 2, actual_runway) None\n airport (departure, 2, airport) Findel\n delay (departure, 2, delay) None\n estimated (departure, 2, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 2, estimated_runway) None\n gate (departure, 2, gate) None\n iata (departure, 2, iata) LUX\n icao (departure, 2, icao) ELLX\n scheduled (departure, 2, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 2, terminal) None\n timezone (departure, 2, timezone) Europe/Luxembourg\ndf.loc[df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value')== 'ELLX', 'aa_value'] = 'ELLX-UPDATED'\n aa_all_keys aa_value\nA 0 NaN (A, 0) 1\n 1 NaN (A, 1) 2\n 2 NaN (A, 2) 3\nB 0 NaN (B, 0) 4\n 1 NaN (B, 1) 5\n 2 NaN (B, 2) 6\ndeparture 0 actual (departure, 0, actual) None\n actual_runway (departure, 0, actual_runway) None\n airport (departure, 0, airport) Findel\n delay (departure, 0, delay) None\n estimated (departure, 0, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 0, estimated_runway) None\n gate (departure, 0, gate) None\n iata (departure, 0, iata) LUX\n icao (departure, 0, icao) ELLX-UPDATED\n scheduled (departure, 0, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 0, terminal) None\n timezone (departure, 0, timezone) Europe/Luxembourg\n 1 actual (departure, 1, actual) None\n actual_runway (departure, 1, actual_runway) None\n airport (departure, 1, airport) Findel\n delay (departure, 1, delay) None\n estimated (departure, 1, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 1, estimated_runway) None\n gate (departure, 1, gate) None\n iata (departure, 1, iata) LUX\n icao (departure, 1, icao) ELLX-UPDATED\n scheduled (departure, 1, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 1, terminal) None\n timezone (departure, 1, timezone) Europe/Luxembourg\n 2 actual (departure, 2, actual) None\n actual_runway (departure, 2, actual_runway) None\n airport (departure, 2, airport) Findel\n delay (departure, 2, delay) None\n estimated (departure, 2, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 2, estimated_runway) None\n gate (departure, 2, gate) None\n iata (departure, 2, iata) LUX\n icao (departure, 2, icao) ELLX-UPDATED\n scheduled (departure, 2, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 2, terminal) None\n timezone (departure, 2, timezone) Europe/Luxembourg\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[departure][0][icao] Old value: ELLX\n[departure][0][icao] Updated value: ELLX-UPDATED\n[departure][1][icao] Old value: ELLX\n[departure][1][icao] Updated value: ELLX-UPDATED\n[departure][2][icao] Old value: ELLX\n[departure][2][icao] Updated value: ELLX-UPDATED\n{'A': [1, 2, 3],\n 'B': [4, 5, 6],\n 'departure': [{'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX-UPDATED',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'},\n {'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX-UPDATED',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'},\n {'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX-UPDATED',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'}]}\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/64359762/constructing-a-pandas-dataframe-with-columns-and-sub-columns-from-nested-diction\ndata=\n{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},\n 's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},\n 't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},\n 's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},\n 't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},\n 's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},\n 's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},\n 's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},\n 'level2': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 9, 'col4': 9},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},\n 's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 13},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},\n 't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},\n 's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},\n 't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},\n 's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},\n 's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},\n 's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\nlevel1 t1 s1 col1 (level1, t1, s1, col1) 5\n col2 (level1, t1, s1, col2) 4\n col3 (level1, t1, s1, col3) 4\n col4 (level1, t1, s1, col4) 9\n s2 col1 (level1, t1, s2, col1) 1\n ... ...\nlevel2 t3 s3 col4 (level2, t3, s3, col4) 12\n s4 col1 (level2, t3, s4, col1) 13\n col2 (level2, t3, s4, col2) 14\n col3 (level2, t3, s4, col3) 15\n col4 (level2, t3, s4, col4) 16\n[96 rows x 2 columns]\ndf.loc[(df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') \u003e 5) \u0026 (df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') \u003c 10), 'aa_value'] = 1000000\n aa_all_keys aa_value\nlevel1 t1 s1 col1 (level1, t1, s1, col1) 5\n col2 (level1, t1, s1, col2) 4\n col3 (level1, t1, s1, col3) 4\n col4 (level1, t1, s1, col4) 1000000\n s2 col1 (level1, t1, s2, col1) 1\n ... ...\nlevel2 t3 s3 col4 (level2, t3, s3, col4) 12\n s4 col1 (level2, t3, s4, col1) 13\n col2 (level2, t3, s4, col2) 14\n col3 (level2, t3, s4, col3) 15\n col4 (level2, t3, s4, col4) 16\n[96 rows x 2 columns]\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[level1][t1][s1][col4] Old value: 9\n[level1][t1][s1][col4] Updated value: 1000000\n[level1][t1][s2][col4] Old value: 8\n[level1][t1][s2][col4] Updated value: 1000000\n[level1][t1][s3][col2] Old value: 8\n[level1][t1][s3][col2] Updated value: 1000000\n[level1][t1][s3][col4] Old value: 9\n[level1][t1][s3][col4] Updated value: 1000000\n[level1][t1][s4][col4] Old value: 9\n[level1][t1][s4][col4] Updated value: 1000000\n[level1][t2][s1][col4] Old value: 9\n[level1][t2][s1][col4] Updated value: 1000000\n[level1][t2][s2][col4] Old value: 8\n[level1][t2][s2][col4] Updated value: 1000000\n[level1][t2][s3][col2] Old value: 8\n[level1][t2][s3][col2] Updated value: 1000000\n[level1][t2][s3][col4] Old value: 9\n[level1][t2][s3][col4] Updated value: 1000000\n[level1][t2][s4][col4] Old value: 9\n[level1][t2][s4][col4] Updated value: 1000000\n[level1][t3][s2][col2] Old value: 6\n[level1][t3][s2][col2] Updated value: 1000000\n[level1][t3][s2][col3] Old value: 7\n[level1][t3][s2][col3] Updated value: 1000000\n[level1][t3][s2][col4] Old value: 8\n[level1][t3][s2][col4] Updated value: 1000000\n[level1][t3][s3][col1] Old value: 9\n[level1][t3][s3][col1] Updated value: 1000000\n[level2][t1][s1][col3] Old value: 9\n[level2][t1][s1][col3] Updated value: 1000000\n[level2][t1][s1][col4] Old value: 9\n[level2][t1][s1][col4] Updated value: 1000000\n[level2][t1][s3][col2] Old value: 8\n[level2][t1][s3][col2] Updated value: 1000000\n[level2][t2][s1][col4] Old value: 9\n[level2][t2][s1][col4] Updated value: 1000000\n[level2][t2][s2][col4] Old value: 8\n[level2][t2][s2][col4] Updated value: 1000000\n[level2][t2][s3][col2] Old value: 8\n[level2][t2][s3][col2] Updated value: 1000000\n[level2][t2][s3][col4] Old value: 9\n[level2][t2][s3][col4] Updated value: 1000000\n[level2][t2][s4][col4] Old value: 9\n[level2][t2][s4][col4] Updated value: 1000000\n[level2][t3][s2][col2] Old value: 6\n[level2][t3][s2][col2] Updated value: 1000000\n[level2][t3][s2][col3] Old value: 7\n[level2][t3][s2][col3] Updated value: 1000000\n[level2][t3][s2][col4] Old value: 8\n[level2][t3][s2][col4] Updated value: 1000000\n[level2][t3][s3][col1] Old value: 9\n[level2][t3][s3][col1] Updated value: 1000000\n{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},\n 's3': {'col1': 11,\n 'col2': 1000000,\n 'col3': 2,\n 'col4': 1000000},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},\n 't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},\n 's3': {'col1': 11,\n 'col2': 1000000,\n 'col3': 2,\n 'col4': 1000000},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},\n 't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},\n 's2': {'col1': 5,\n 'col2': 1000000,\n 'col3': 1000000,\n 'col4': 1000000},\n 's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},\n 's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},\n 'level2': {'t1': {'s1': {'col1': 5,\n 'col2': 4,\n 'col3': 1000000,\n 'col4': 1000000},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},\n 's3': {'col1': 11, 'col2': 1000000, 'col3': 2, 'col4': 13},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},\n 't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},\n 's3': {'col1': 11,\n 'col2': 1000000,\n 'col3': 2,\n 'col4': 1000000},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},\n 't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},\n 's2': {'col1': 5,\n 'col2': 1000000,\n 'col3': 1000000,\n 'col4': 1000000},\n 's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},\n 's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/72146094/problems-matching-values-from-nested-dictionary\ndata=\n{'_links': {'next': None, 'prev': None},\n 'limit': 250,\n 'offset': 0,\n 'runs': [{'assignedto_id': None,\n 'blocked_count': 0,\n 'completed_on': None,\n 'config': None,\n 'config_ids': [],\n 'created_by': 1,\n 'created_on': 1651790693,\n 'custom_status1_count': 0,\n 'custom_status2_count': 0,\n 'custom_status3_count': 0,\n 'custom_status4_count': 0,\n 'custom_status5_count': 0,\n 'custom_status6_count': 0,\n 'custom_status7_count': 0,\n 'description': None,\n 'failed_count': 1,\n 'id': 13,\n 'include_all': False,\n 'is_completed': False,\n 'milestone_id': None,\n 'name': '2022-05-05-testrun',\n 'passed_count': 2,\n 'plan_id': None,\n 'project_id': 1,\n 'refs': None,\n 'retest_count': 0,\n 'suite_id': 1,\n 'untested_count': 0,\n 'updated_on': 1651790693,\n 'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13'},\n {'assignedto_id': None,\n 'blocked_count': 0,\n 'completed_on': 1650989972,\n 'config': None,\n 'config_ids': [],\n 'created_by': 5,\n 'created_on': 1650966329,\n 'custom_status1_count': 0,\n 'custom_status2_count': 0,\n 'custom_status3_count': 0,\n 'custom_status4_count': 0,\n 'custom_status5_count': 0,\n 'custom_status6_count': 0,\n 'custom_status7_count': 0,\n 'description': None,\n 'failed_count': 0,\n 'id': 9,\n 'include_all': False,\n 'is_completed': True,\n 'milestone_id': None,\n 'name': 'This is a new test run',\n 'passed_count': 0,\n 'plan_id': None,\n 'project_id': 1,\n 'refs': None,\n 'retest_count': 0,\n 'suite_id': 1,\n 'untested_count': 3,\n 'updated_on': 1650966329,\n 'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9'}],\n 'size': 2}\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\n_links next NaN (_links, next) None\n prev NaN (_links, prev) None\nlimit NaN NaN (limit,) 250\noffset NaN NaN (offset,) 0\nruns 0 assignedto_id (runs, 0, assignedto_id) None\n ... ...\n 1 suite_id (runs, 1, suite_id) 1\n untested_count (runs, 1, untested_count) 3\n updated_on (runs, 1, updated_on) 1650966329\n url (runs, 1, url) https://xxxxxxxxxx.testrail.io/index.php?/runs...\nsize NaN NaN (size,) 2\n[63 rows x 2 columns]\ndf.loc[(df.d_filter_dtypes(allowed_dtypes=(bool),fillvalue=pd.NA,column='aa_value') == False ), 'aa_value'] = True\ndf.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'https?://.*',na=False) ), 'aa_value'] = 'WWW.PYTHON.ORG'\n aa_all_keys aa_value\n_links next NaN (_links, next) None\n prev NaN (_links, prev) None\nlimit NaN NaN (limit,) 250\noffset NaN NaN (offset,) 0\nruns 0 assignedto_id (runs, 0, assignedto_id) None\n ... ...\n 1 suite_id (runs, 1, suite_id) 1\n untested_count (runs, 1, untested_count) 3\n updated_on (runs, 1, updated_on) 1650966329\n url (runs, 1, url) WWW.PYTHON.ORG\nsize NaN NaN (size,) 2\n[63 rows x 2 columns]\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[runs][0][include_all] Old value: False\n[runs][0][include_all] Updated value: True\n[runs][0][is_completed] Old value: False\n[runs][0][is_completed] Updated value: True\n[runs][0][url] Old value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13\n[runs][0][url] Updated value: WWW.PYTHON.ORG\n[runs][1][include_all] Old value: False\n[runs][1][include_all] Updated value: True\n[runs][1][url] Old value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9\n[runs][1][url] Updated value: WWW.PYTHON.ORG\n{'_links': {'next': None, 'prev': None},\n 'limit': 250,\n 'offset': 0,\n 'runs': [{'assignedto_id': None,\n 'blocked_count': 0,\n 'completed_on': None,\n 'config': None,\n 'config_ids': [],\n 'created_by': 1,\n 'created_on': 1651790693,\n 'custom_status1_count': 0,\n 'custom_status2_count': 0,\n 'custom_status3_count': 0,\n 'custom_status4_count': 0,\n 'custom_status5_count': 0,\n 'custom_status6_count': 0,\n 'custom_status7_count': 0,\n 'description': None,\n 'failed_count': 1,\n 'id': 13,\n 'include_all': True,\n 'is_completed': True,\n 'milestone_id': None,\n 'name': '2022-05-05-testrun',\n 'passed_count': 2,\n 'plan_id': None,\n 'project_id': 1,\n 'refs': None,\n 'retest_count': 0,\n 'suite_id': 1,\n 'untested_count': 0,\n 'updated_on': 1651790693,\n 'url': 'WWW.PYTHON.ORG'},\n {'assignedto_id': None,\n 'blocked_count': 0,\n 'completed_on': 1650989972,\n 'config': None,\n 'config_ids': [],\n 'created_by': 5,\n 'created_on': 1650966329,\n 'custom_status1_count': 0,\n 'custom_status2_count': 0,\n 'custom_status3_count': 0,\n 'custom_status4_count': 0,\n 'custom_status5_count': 0,\n 'custom_status6_count': 0,\n 'custom_status7_count': 0,\n 'description': None,\n 'failed_count': 0,\n 'id': 9,\n 'include_all': True,\n 'is_completed': True,\n 'milestone_id': None,\n 'name': 'This is a new test run',\n 'passed_count': 0,\n 'plan_id': None,\n 'project_id': 1,\n 'refs': None,\n 'retest_count': 0,\n 'suite_id': 1,\n 'untested_count': 3,\n 'updated_on': 1650966329,\n 'url': 'WWW.PYTHON.ORG'}],\n 'size': 2}\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/73708706/how-to-get-values-from-list-of-nested-dictionaries/73839430#73839430\ndata=\n{'results': [{'end_time': '2021-01-21',\n 'key': 'q1',\n 'result_type': 'multipleChoice',\n 'start_time': '2021-01-21',\n 'value': ['1']},\n {'end_time': '2021-01-21',\n 'key': 'q2',\n 'result_type': 'multipleChoice',\n 'start_time': '2021-01-21',\n 'value': ['False']},\n {'end_time': '2021-01-21',\n 'key': 'q3',\n 'result_type': 'multipleChoice',\n 'start_time': '2021-01-21',\n 'value': ['3']},\n {'end_time': '2021-01-21',\n 'key': 'q4',\n 'result_type': 'multipleChoice',\n 'start_time': '2021-01-21',\n 'value': ['3']}]}\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\nresults 0 end_time NaN (results, 0, end_time) 2021-01-21\n key NaN (results, 0, key) q1\n result_type NaN (results, 0, result_type) multipleChoice\n start_time NaN (results, 0, start_time) 2021-01-21\n value 0 (results, 0, value, 0) 1\n 1 end_time NaN (results, 1, end_time) 2021-01-21\n key NaN (results, 1, key) q2\n result_type NaN (results, 1, result_type) multipleChoice\n start_time NaN (results, 1, start_time) 2021-01-21\n value 0 (results, 1, value, 0) False\n 2 end_time NaN (results, 2, end_time) 2021-01-21\n key NaN (results, 2, key) q3\n result_type NaN (results, 2, result_type) multipleChoice\n start_time NaN (results, 2, start_time) 2021-01-21\n value 0 (results, 2, value, 0) 3\n 3 end_time NaN (results, 3, end_time) 2021-01-21\n key NaN (results, 3, key) q4\n result_type NaN (results, 3, result_type) multipleChoice\n start_time NaN (results, 3, start_time) 2021-01-21\n value 0 (results, 3, value, 0) 3\ndf.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'^2021.*',na=False) ), 'aa_value'] = 10000000000 \n aa_all_keys aa_value\nresults 0 end_time NaN (results, 0, end_time) 10000000000\n key NaN (results, 0, key) q1\n result_type NaN (results, 0, result_type) multipleChoice\n start_time NaN (results, 0, start_time) 10000000000\n value 0 (results, 0, value, 0) 1\n 1 end_time NaN (results, 1, end_time) 10000000000\n key NaN (results, 1, key) q2\n result_type NaN (results, 1, result_type) multipleChoice\n start_time NaN (results, 1, start_time) 10000000000\n value 0 (results, 1, value, 0) False\n 2 end_time NaN (results, 2, end_time) 10000000000\n key NaN (results, 2, key) q3\n result_type NaN (results, 2, result_type) multipleChoice\n start_time NaN (results, 2, start_time) 10000000000\n value 0 (results, 2, value, 0) 3\n 3 end_time NaN (results, 3, end_time) 10000000000\n key NaN (results, 3, key) q4\n result_type NaN (results, 3, result_type) multipleChoice\n start_time NaN (results, 3, start_time) 10000000000\n value 0 (results, 3, value, 0) 3\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[results][0][end_time] Old value: 2021-01-21\n[results][0][end_time] Updated value: 10000000000\n[results][0][start_time] Old value: 2021-01-21\n[results][0][start_time] Updated value: 10000000000\n[results][1][end_time] Old value: 2021-01-21\n[results][1][end_time] Updated value: 10000000000\n[results][1][start_time] Old value: 2021-01-21\n[results][1][start_time] Updated value: 10000000000\n[results][2][end_time] Old value: 2021-01-21\n[results][2][end_time] Updated value: 10000000000\n[results][2][start_time] Old value: 2021-01-21\n[results][2][start_time] Updated value: 10000000000\n[results][3][end_time] Old value: 2021-01-21\n[results][3][end_time] Updated value: 10000000000\n[results][3][start_time] Old value: 2021-01-21\n[results][3][start_time] Updated value: 10000000000\n{'results': [{'end_time': 10000000000,\n 'key': 'q1',\n 'result_type': 'multipleChoice',\n 'start_time': 10000000000,\n 'value': ['1']},\n {'end_time': 10000000000,\n 'key': 'q2',\n 'result_type': 'multipleChoice',\n 'start_time': 10000000000,\n 'value': ['False']},\n {'end_time': 10000000000,\n 'key': 'q3',\n 'result_type': 'multipleChoice',\n 'start_time': 10000000000,\n 'value': ['3']},\n {'end_time': 10000000000,\n 'key': 'q4',\n 'result_type': 'multipleChoice',\n 'start_time': 10000000000,\n 'value': ['3']}]}\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/66461902/flattening-nested-dictionary-into-dataframe-python\ndata=\n{1: {2: {'IDs': {'BookID': ['543533254353', '4324232342'],\n 'SalesID': ['543267765345', '4353543'],\n 'StoreID': ['111111', '1121111']},\n 'Name': 'boring Tales of Dragon Slayers'},\n 'IDs': {'BookID': ['543533254353'],\n 'SalesID': ['543267765345'],\n 'StoreID': ['123445452543']},\n 'Name': 'Thrilling Tales of Dragon Slayers'}}\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\n1 IDs BookID 0 NaN (1, IDs, BookID, 0) 543533254353\n SalesID 0 NaN (1, IDs, SalesID, 0) 543267765345\n StoreID 0 NaN (1, IDs, StoreID, 0) 123445452543\n Name NaN NaN NaN (1, Name) Thrilling Tales of Dragon Slayers\n 2 IDs BookID 0 (1, 2, IDs, BookID, 0) 543533254353\n 1 (1, 2, IDs, BookID, 1) 4324232342\n SalesID 0 (1, 2, IDs, SalesID, 0) 543267765345\n 1 (1, 2, IDs, SalesID, 1) 4353543\n StoreID 0 (1, 2, IDs, StoreID, 0) 111111\n 1 (1, 2, IDs, StoreID, 1) 1121111\n Name NaN NaN (1, 2, Name) boring Tales of Dragon Slayers\ndf.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'^\\d+$',na=False) ), 'aa_value'] = df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'^\\d+$',na=False) ), 'aa_value'].astype(float)\n aa_all_keys aa_value\n1 IDs BookID 0 NaN (1, IDs, BookID, 0) 543533254353.0\n SalesID 0 NaN (1, IDs, SalesID, 0) 543267765345.0\n StoreID 0 NaN (1, IDs, StoreID, 0) 123445452543.0\n Name NaN NaN NaN (1, Name) Thrilling Tales of Dragon Slayers\n 2 IDs BookID 0 (1, 2, IDs, BookID, 0) 543533254353.0\n 1 (1, 2, IDs, BookID, 1) 4324232342.0\n SalesID 0 (1, 2, IDs, SalesID, 0) 543267765345.0\n 1 (1, 2, IDs, SalesID, 1) 4353543.0\n StoreID 0 (1, 2, IDs, StoreID, 0) 111111.0\n 1 (1, 2, IDs, StoreID, 1) 1121111.0\n Name NaN NaN (1, 2, Name) boring Tales of Dragon Slayers\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[1][2][IDs][BookID][0] Old value: 543533254353\n[1][2][IDs][BookID][0] Updated value: 543533254353.0\n[1][2][IDs][BookID][1] Old value: 4324232342\n[1][2][IDs][BookID][1] Updated value: 4324232342.0\n[1][2][IDs][SalesID][0] Old value: 543267765345\n[1][2][IDs][SalesID][0] Updated value: 543267765345.0\n[1][2][IDs][SalesID][1] Old value: 4353543\n[1][2][IDs][SalesID][1] Updated value: 4353543.0\n[1][2][IDs][StoreID][0] Old value: 111111\n[1][2][IDs][StoreID][0] Updated value: 111111.0\n[1][2][IDs][StoreID][1] Old value: 1121111\n[1][2][IDs][StoreID][1] Updated value: 1121111.0\n[1][IDs][BookID][0] Old value: 543533254353\n[1][IDs][BookID][0] Updated value: 543533254353.0\n[1][IDs][SalesID][0] Old value: 543267765345\n[1][IDs][SalesID][0] Updated value: 543267765345.0\n[1][IDs][StoreID][0] Old value: 123445452543\n[1][IDs][StoreID][0] Updated value: 123445452543.0\n{1: {2: {'IDs': {'BookID': [543533254353.0, 4324232342.0],\n 'SalesID': [543267765345.0, 4353543.0],\n 'StoreID': [111111.0, 1121111.0]},\n 'Name': 'boring Tales of Dragon Slayers'},\n 'IDs': {'BookID': [543533254353.0],\n 'SalesID': [543267765345.0],\n 'StoreID': [123445452543.0]},\n 'Name': 'Thrilling Tales of Dragon Slayers'}}\nNested iterable from: 'https://stackoverflow.com/questions/61984148/how-to-handle-nested-lists-and-dictionaries-in-pandas-dataframe'\n{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},\n {'review_critic': 'ABC', 'review_score': 90},\n {'review_critic': '123', 'review_score': 90}],\n 'genres': ['Sports', 'Golf'],\n 'score': 85,\n 'title': 'Golf Simulator',\n 'url': 'http://example.com/golf-simulator'}\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False) # create DF stacked or unstacked, it doesn't matter\n aa_all_keys aa_value\ncritic_reviews 0 review_critic (critic_reviews, 0, review_critic) XYZ\n review_score (critic_reviews, 0, review_score) 90\n 1 review_critic (critic_reviews, 1, review_critic) ABC\n review_score (critic_reviews, 1, review_score) 90\n 2 review_critic (critic_reviews, 2, review_critic) 123\n review_score (critic_reviews, 2, review_score) 90\ngenres 0 NaN (genres, 0) Sports\n 1 NaN (genres, 1) Golf\nscore NaN NaN (score,) 85\ntitle NaN NaN (title,) Golf Simulator\nurl NaN NaN (url,) http://example.com/golf-simulator\ndf.loc[df.aa_value.str.contains('[Gg]',na=False),'aa_value'] = 'UPDATE1111' #df.loc to update the dataframe (VERY IMPORTANT: To update the original iterable you have to pass 'aa_value')\n aa_all_keys aa_value\ncritic_reviews 0 review_critic (critic_reviews, 0, review_critic) XYZ\n review_score (critic_reviews, 0, review_score) 90\n 1 review_critic (critic_reviews, 1, review_critic) ABC\n review_score (critic_reviews, 1, review_score) 90\n 2 review_critic (critic_reviews, 2, review_critic) 123\n review_score (critic_reviews, 2, review_score) 90\ngenres 0 NaN (genres, 0) Sports\n 1 NaN (genres, 1) UPDATE1111\nscore NaN NaN (score,) 85\ntitle NaN NaN (title,) UPDATE1111\nurl NaN NaN (url,) UPDATE1111\nmod_iter = df.d_update_original_iter(data, verbose=True) #updating the nested iterable, the new values have to be in the column 'aa_value', if you have added new columns to the dataframe, drop them before updating the original iterable\n[genres][1] Old value: Golf\n[genres][1] Updated value: UPDATE1111\n[title] Old value: Golf Simulator\n[title] Updated value: UPDATE1111\n[url] Old value: http://example.com/golf-simulator\n[url] Updated value: UPDATE1111\n{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},\n {'review_critic': 'ABC', 'review_score': 90},\n {'review_critic': '123', 'review_score': 90}],\n 'genres': ['Sports', 'UPDATE1111'],\n 'score': 85,\n 'title': 'UPDATE1111',\n 'url': 'UPDATE1111'}\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/72990265/convert-nested-list-in-dictionary-to-dataframe/72990346\ndata=\n{'a': 'test',\n 'b': 1657,\n 'c': 'asset',\n 'd': [['2089', '0.0'], ['2088', '0.0']],\n 'e': [['2088', '0.0'], ['2088', '0.0'], ['2088', '0.00']],\n 'f': [['2088', '0.0', 'x', 'foo'],\n ['2088', '0.0', 'bar', 'i'],\n ['2088', '0.00', 'z', '0.2']],\n 'x': ['test1', 'test2']}\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\na NaN NaN (a,) test\nb NaN NaN (b,) 1657\nc NaN NaN (c,) asset\nd 0 0 (d, 0, 0) 2089\n 1 (d, 0, 1) 0.0\n 1 0 (d, 1, 0) 2088\n 1 (d, 1, 1) 0.0\ne 0 0 (e, 0, 0) 2088\n 1 (e, 0, 1) 0.0\n 1 0 (e, 1, 0) 2088\n 1 (e, 1, 1) 0.0\n 2 0 (e, 2, 0) 2088\n 1 (e, 2, 1) 0.00\nf 0 0 (f, 0, 0) 2088\n 1 (f, 0, 1) 0.0\n 2 (f, 0, 2) x\n 3 (f, 0, 3) foo\n 1 0 (f, 1, 0) 2088\n 1 (f, 1, 1) 0.0\n 2 (f, 1, 2) bar\n 3 (f, 1, 3) i\n 2 0 (f, 2, 0) 2088\n 1 (f, 2, 1) 0.00\n 2 (f, 2, 2) z\n 3 (f, 2, 3) 0.2\nx 0 NaN (x, 0) test1\n 1 NaN (x, 1) test2\ndf.loc[df.aa_value == 1657,'aa_value'] = 1657*30\n aa_all_keys aa_value\na NaN NaN (a,) test\nb NaN NaN (b,) 49710\nc NaN NaN (c,) asset\nd 0 0 (d, 0, 0) 2089\n 1 (d, 0, 1) 0.0\n 1 0 (d, 1, 0) 2088\n 1 (d, 1, 1) 0.0\ne 0 0 (e, 0, 0) 2088\n 1 (e, 0, 1) 0.0\n 1 0 (e, 1, 0) 2088\n 1 (e, 1, 1) 0.0\n 2 0 (e, 2, 0) 2088\n 1 (e, 2, 1) 0.00\nf 0 0 (f, 0, 0) 2088\n 1 (f, 0, 1) 0.0\n 2 (f, 0, 2) x\n 3 (f, 0, 3) foo\n 1 0 (f, 1, 0) 2088\n 1 (f, 1, 1) 0.0\n 2 (f, 1, 2) bar\n 3 (f, 1, 3) i\n 2 0 (f, 2, 0) 2088\n 1 (f, 2, 1) 0.00\n 2 (f, 2, 2) z\n 3 (f, 2, 3) 0.2\nx 0 NaN (x, 0) test1\n 1 NaN (x, 1) test2\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[b] Old value: 1657\n[b] Updated value: 49710\n{'a': 'test',\n 'b': 49710,\n 'c': 'asset',\n 'd': [['2089', '0.0'], ['2088', '0.0']],\n 'e': [['2088', '0.0'], ['2088', '0.0'], ['2088', '0.00']],\n 'f': [['2088', '0.0', 'x', 'foo'],\n ['2088', '0.0', 'bar', 'i'],\n ['2088', '0.00', 'z', '0.2']],\n 'x': ['test1', 'test2']}\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/73430585/how-to-convert-a-list-of-nested-dictionaries-includes-tuples-as-a-dataframe\ndata=\n[{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 2, 'Name': 'A', 'num': 68}),\n 'final_value': 118},\n {'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 4, 'Name': 'A', 'num': 67}),\n 'final_value': 117},\n {'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 6, 'Name': 'A', 'num': 67}),\n 'final_value': 117}]\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\n0 cb 0 ID (0, cb, 0, ID) 1\n Name (0, cb, 0, Name) A\n num (0, cb, 0, num) 50\n 1 ID (0, cb, 1, ID) 2\n Name (0, cb, 1, Name) A\n num (0, cb, 1, num) 68\n final_value NaN NaN (0, final_value) 118\n1 cb 0 ID (1, cb, 0, ID) 1\n Name (1, cb, 0, Name) A\n num (1, cb, 0, num) 50\n 1 ID (1, cb, 1, ID) 4\n Name (1, cb, 1, Name) A\n num (1, cb, 1, num) 67\n final_value NaN NaN (1, final_value) 117\n2 cb 0 ID (2, cb, 0, ID) 1\n Name (2, cb, 0, Name) A\n num (2, cb, 0, num) 50\n 1 ID (2, cb, 1, ID) 6\n Name (2, cb, 1, Name) A\n num (2, cb, 1, num) 67\n final_value NaN NaN (2, final_value) 117\ndf.d_filter_dtypes(allowed_dtypes=(int,float),fillvalue=pd.NA,column='aa_value') \u003e 30, 'aa_value'] = 900000\n aa_all_keys aa_value\n0 cb 0 ID (0, cb, 0, ID) 1\n Name (0, cb, 0, Name) A\n num (0, cb, 0, num) 900000\n 1 ID (0, cb, 1, ID) 2\n Name (0, cb, 1, Name) A\n num (0, cb, 1, num) 900000\n final_value NaN NaN (0, final_value) 900000\n1 cb 0 ID (1, cb, 0, ID) 1\n Name (1, cb, 0, Name) A\n num (1, cb, 0, num) 900000\n 1 ID (1, cb, 1, ID) 4\n Name (1, cb, 1, Name) A\n num (1, cb, 1, num) 900000\n final_value NaN NaN (1, final_value) 900000\n2 cb 0 ID (2, cb, 0, ID) 1\n Name (2, cb, 0, Name) A\n num (2, cb, 0, num) 900000\n 1 ID (2, cb, 1, ID) 6\n Name (2, cb, 1, Name) A\n num (2, cb, 1, num) 900000\n final_value NaN NaN (2, final_value) 900000\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[0][cb][0][num] Old value: 50\n[0][cb][0][num] Updated value: 900000\n[0][cb][1][num] Old value: 68\n[0][cb][1][num] Updated value: 900000\n[0][final_value] Old value: 118\n[0][final_value] Updated value: 900000\n[1][cb][0][num] Old value: 50\n[1][cb][0][num] Updated value: 900000\n[1][cb][1][num] Old value: 67\n[1][cb][1][num] Updated value: 900000\n[1][final_value] Old value: 117\n[1][final_value] Updated value: 900000\n[2][cb][0][num] Old value: 50\n[2][cb][0][num] Updated value: 900000\n[2][cb][1][num] Old value: 67\n[2][cb][1][num] Updated value: 900000\n[2][final_value] Old value: 117\n[2][final_value] Updated value: 900000\n[{'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},\n {'ID': 2, 'Name': 'A', 'num': 900000}),\n 'final_value': 900000},\n {'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},\n {'ID': 4, 'Name': 'A', 'num': 900000}),\n 'final_value': 900000},\n {'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},\n {'ID': 6, 'Name': 'A', 'num': 900000}),\n 'final_value': 900000}]\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/69943509/problems-when-flatten-a-dict\ndata=\n[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],\n 'application_details': {'email': None, 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '1'},\n {'application_contacts': [{'adress': 'Z', 'email': None}],\n 'application_details': {'email': 'testy@test_a.com', 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '2'},\n {'application_contacts': [{'adress': 'Y', 'email': None}],\n 'application_details': {'email': 'testy@test_a.com', 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '3'}]\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\n0 application_contacts 0 adress (0, application_contacts, 0, adress) X\n email (0, application_contacts, 0, email) test@test.com\n application_details email NaN (0, application_details, email) None\n phone NaN (0, application_details, phone) None\n employer Name NaN (0, employer, Name) Nom\n email NaN (0, employer, email) None\n id NaN NaN (0, id) 1\n1 application_contacts 0 adress (1, application_contacts, 0, adress) Z\n email (1, application_contacts, 0, email) None\n application_details email NaN (1, application_details, email) testy@test_a.com\n phone NaN (1, application_details, phone) None\n employer Name NaN (1, employer, Name) Nom\n email NaN (1, employer, email) None\n id NaN NaN (1, id) 2\n2 application_contacts 0 adress (2, application_contacts, 0, adress) Y\n email (2, application_contacts, 0, email) None\n application_details email NaN (2, application_details, email) testy@test_a.com\n phone NaN (2, application_details, phone) None\n employer Name NaN (2, employer, Name) Nom\n email NaN (2, employer, email) None\n id NaN NaN (2, id) 3\ndf.loc[df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'test_a\\.\\w+\\b',na=False), 'aa_value'] = 'UPPPPPPPPPPPPPPPDATE.COM'\n aa_all_keys aa_value\n0 application_contacts 0 adress (0, application_contacts, 0, adress) X\n email (0, application_contacts, 0, email) test@test.com\n application_details email NaN (0, application_details, email) None\n phone NaN (0, application_details, phone) None\n employer Name NaN (0, employer, Name) Nom\n email NaN (0, employer, email) None\n id NaN NaN (0, id) 1\n1 application_contacts 0 adress (1, application_contacts, 0, adress) Z\n email (1, application_contacts, 0, email) None\n application_details email NaN (1, application_details, email) UPPPPPPPPPPPPPPPDATE.COM\n phone NaN (1, application_details, phone) None\n employer Name NaN (1, employer, Name) Nom\n email NaN (1, employer, email) None\n id NaN NaN (1, id) 2\n2 application_contacts 0 adress (2, application_contacts, 0, adress) Y\n email (2, application_contacts, 0, email) None\n application_details email NaN (2, application_details, email) UPPPPPPPPPPPPPPPDATE.COM\n phone NaN (2, application_details, phone) None\n employer Name NaN (2, employer, Name) Nom\n email NaN (2, employer, email) None\n id NaN NaN (2, id) 3\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[1][application_details][email] Old value: testy@test_a.com\n[1][application_details][email] Updated value: UPPPPPPPPPPPPPPPDATE.COM\n[2][application_details][email] Old value: testy@test_a.com\n[2][application_details][email] Updated value: UPPPPPPPPPPPPPPPDATE.COM\n[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],\n 'application_details': {'email': None, 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '1'},\n {'application_contacts': [{'adress': 'Z', 'email': None}],\n 'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '2'},\n {'application_contacts': [{'adress': 'Y', 'email': None}],\n 'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},\n 'employer': {'Name': 'Nom', 'email': None},\n 'id': '3'}]\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/62765371/convert-nested-dataframe-to-a-simple-dataframeframe\ndata=\n{'A': [1, 2, 3],\n 'B': [4, 5, 6],\n 'departure': [{'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'},\n {'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'},\n {'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'}]}\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\nA 0 NaN (A, 0) 1\n 1 NaN (A, 1) 2\n 2 NaN (A, 2) 3\nB 0 NaN (B, 0) 4\n 1 NaN (B, 1) 5\n 2 NaN (B, 2) 6\ndeparture 0 actual (departure, 0, actual) None\n actual_runway (departure, 0, actual_runway) None\n airport (departure, 0, airport) Findel\n delay (departure, 0, delay) None\n estimated (departure, 0, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 0, estimated_runway) None\n gate (departure, 0, gate) None\n iata (departure, 0, iata) LUX\n icao (departure, 0, icao) ELLX\n scheduled (departure, 0, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 0, terminal) None\n timezone (departure, 0, timezone) Europe/Luxembourg\n 1 actual (departure, 1, actual) None\n actual_runway (departure, 1, actual_runway) None\n airport (departure, 1, airport) Findel\n delay (departure, 1, delay) None\n estimated (departure, 1, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 1, estimated_runway) None\n gate (departure, 1, gate) None\n iata (departure, 1, iata) LUX\n icao (departure, 1, icao) ELLX\n scheduled (departure, 1, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 1, terminal) None\n timezone (departure, 1, timezone) Europe/Luxembourg\n 2 actual (departure, 2, actual) None\n actual_runway (departure, 2, actual_runway) None\n airport (departure, 2, airport) Findel\n delay (departure, 2, delay) None\n estimated (departure, 2, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 2, estimated_runway) None\n gate (departure, 2, gate) None\n iata (departure, 2, iata) LUX\n icao (departure, 2, icao) ELLX\n scheduled (departure, 2, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 2, terminal) None\n timezone (departure, 2, timezone) Europe/Luxembourg\ndf.loc[df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value')== 'ELLX', 'aa_value'] = 'ELLX-UPDATED'\n aa_all_keys aa_value\nA 0 NaN (A, 0) 1\n 1 NaN (A, 1) 2\n 2 NaN (A, 2) 3\nB 0 NaN (B, 0) 4\n 1 NaN (B, 1) 5\n 2 NaN (B, 2) 6\ndeparture 0 actual (departure, 0, actual) None\n actual_runway (departure, 0, actual_runway) None\n airport (departure, 0, airport) Findel\n delay (departure, 0, delay) None\n estimated (departure, 0, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 0, estimated_runway) None\n gate (departure, 0, gate) None\n iata (departure, 0, iata) LUX\n icao (departure, 0, icao) ELLX-UPDATED\n scheduled (departure, 0, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 0, terminal) None\n timezone (departure, 0, timezone) Europe/Luxembourg\n 1 actual (departure, 1, actual) None\n actual_runway (departure, 1, actual_runway) None\n airport (departure, 1, airport) Findel\n delay (departure, 1, delay) None\n estimated (departure, 1, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 1, estimated_runway) None\n gate (departure, 1, gate) None\n iata (departure, 1, iata) LUX\n icao (departure, 1, icao) ELLX-UPDATED\n scheduled (departure, 1, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 1, terminal) None\n timezone (departure, 1, timezone) Europe/Luxembourg\n 2 actual (departure, 2, actual) None\n actual_runway (departure, 2, actual_runway) None\n airport (departure, 2, airport) Findel\n delay (departure, 2, delay) None\n estimated (departure, 2, estimated) 2020-07-07T06:30:00+00:00\n estimated_runway (departure, 2, estimated_runway) None\n gate (departure, 2, gate) None\n iata (departure, 2, iata) LUX\n icao (departure, 2, icao) ELLX-UPDATED\n scheduled (departure, 2, scheduled) 2020-07-07T06:30:00+00:00\n terminal (departure, 2, terminal) None\n timezone (departure, 2, timezone) Europe/Luxembourg\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[departure][0][icao] Old value: ELLX\n[departure][0][icao] Updated value: ELLX-UPDATED\n[departure][1][icao] Old value: ELLX\n[departure][1][icao] Updated value: ELLX-UPDATED\n[departure][2][icao] Old value: ELLX\n[departure][2][icao] Updated value: ELLX-UPDATED\n{'A': [1, 2, 3],\n 'B': [4, 5, 6],\n 'departure': [{'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX-UPDATED',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'},\n {'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX-UPDATED',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'},\n {'actual': None,\n 'actual_runway': None,\n 'airport': 'Findel',\n 'delay': None,\n 'estimated': '2020-07-07T06:30:00+00:00',\n 'estimated_runway': None,\n 'gate': None,\n 'iata': 'LUX',\n 'icao': 'ELLX-UPDATED',\n 'scheduled': '2020-07-07T06:30:00+00:00',\n 'terminal': None,\n 'timezone': 'Europe/Luxembourg'}]}\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/64359762/constructing-a-pandas-dataframe-with-columns-and-sub-columns-from-nested-diction\ndata=\n{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},\n 's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},\n 't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},\n 's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},\n 't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},\n 's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},\n 's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},\n 's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},\n 'level2': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 9, 'col4': 9},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},\n 's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 13},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},\n 't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},\n 's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},\n 't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},\n 's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},\n 's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},\n 's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\nlevel1 t1 s1 col1 (level1, t1, s1, col1) 5\n col2 (level1, t1, s1, col2) 4\n col3 (level1, t1, s1, col3) 4\n col4 (level1, t1, s1, col4) 9\n s2 col1 (level1, t1, s2, col1) 1\n ... ...\nlevel2 t3 s3 col4 (level2, t3, s3, col4) 12\n s4 col1 (level2, t3, s4, col1) 13\n col2 (level2, t3, s4, col2) 14\n col3 (level2, t3, s4, col3) 15\n col4 (level2, t3, s4, col4) 16\n[96 rows x 2 columns]\ndf.loc[(df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') \u003e 5) \u0026 (df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') \u003c 10), 'aa_value'] = 1000000\n aa_all_keys aa_value\nlevel1 t1 s1 col1 (level1, t1, s1, col1) 5\n col2 (level1, t1, s1, col2) 4\n col3 (level1, t1, s1, col3) 4\n col4 (level1, t1, s1, col4) 1000000\n s2 col1 (level1, t1, s2, col1) 1\n ... ...\nlevel2 t3 s3 col4 (level2, t3, s3, col4) 12\n s4 col1 (level2, t3, s4, col1) 13\n col2 (level2, t3, s4, col2) 14\n col3 (level2, t3, s4, col3) 15\n col4 (level2, t3, s4, col4) 16\n[96 rows x 2 columns]\nmod_iter = df.d_update_original_iter(data, verbose=True)\n[level1][t1][s1][col4] Old value: 9\n[level1][t1][s1][col4] Updated value: 1000000\n[level1][t1][s2][col4] Old value: 8\n[level1][t1][s2][col4] Updated value: 1000000\n[level1][t1][s3][col2] Old value: 8\n[level1][t1][s3][col2] Updated value: 1000000\n[level1][t1][s3][col4] Old value: 9\n[level1][t1][s3][col4] Updated value: 1000000\n[level1][t1][s4][col4] Old value: 9\n[level1][t1][s4][col4] Updated value: 1000000\n[level1][t2][s1][col4] Old value: 9\n[level1][t2][s1][col4] Updated value: 1000000\n[level1][t2][s2][col4] Old value: 8\n[level1][t2][s2][col4] Updated value: 1000000\n[level1][t2][s3][col2] Old value: 8\n[level1][t2][s3][col2] Updated value: 1000000\n[level1][t2][s3][col4] Old value: 9\n[level1][t2][s3][col4] Updated value: 1000000\n[level1][t2][s4][col4] Old value: 9\n[level1][t2][s4][col4] Updated value: 1000000\n[level1][t3][s2][col2] Old value: 6\n[level1][t3][s2][col2] Updated value: 1000000\n[level1][t3][s2][col3] Old value: 7\n[level1][t3][s2][col3] Updated value: 1000000\n[level1][t3][s2][col4] Old value: 8\n[level1][t3][s2][col4] Updated value: 1000000\n[level1][t3][s3][col1] Old value: 9\n[level1][t3][s3][col1] Updated value: 1000000\n[level2][t1][s1][col3] Old value: 9\n[level2][t1][s1][col3] Updated value: 1000000\n[level2][t1][s1][col4] Old value: 9\n[level2][t1][s1][col4] Updated value: 1000000\n[level2][t1][s3][col2] Old value: 8\n[level2][t1][s3][col2] Updated value: 1000000\n[level2][t2][s1][col4] Old value: 9\n[level2][t2][s1][col4] Updated value: 1000000\n[level2][t2][s2][col4] Old value: 8\n[level2][t2][s2][col4] Updated value: 1000000\n[level2][t2][s3][col2] Old value: 8\n[level2][t2][s3][col2] Updated value: 1000000\n[level2][t2][s3][col4] Old value: 9\n[level2][t2][s3][col4] Updated value: 1000000\n[level2][t2][s4][col4] Old value: 9\n[level2][t2][s4][col4] Updated value: 1000000\n[level2][t3][s2][col2] Old value: 6\n[level2][t3][s2][col2] Updated value: 1000000\n[level2][t3][s2][col3] Old value: 7\n[level2][t3][s2][col3] Updated value: 1000000\n[level2][t3][s2][col4] Old value: 8\n[level2][t3][s2][col4] Updated value: 1000000\n[level2][t3][s3][col1] Old value: 9\n[level2][t3][s3][col1] Updated value: 1000000\n{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},\n 's3': {'col1': 11,\n 'col2': 1000000,\n 'col3': 2,\n 'col4': 1000000},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},\n 't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},\n 's3': {'col1': 11,\n 'col2': 1000000,\n 'col3': 2,\n 'col4': 1000000},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},\n 't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},\n 's2': {'col1': 5,\n 'col2': 1000000,\n 'col3': 1000000,\n 'col4': 1000000},\n 's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},\n 's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},\n 'level2': {'t1': {'s1': {'col1': 5,\n 'col2': 4,\n 'col3': 1000000,\n 'col4': 1000000},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},\n 's3': {'col1': 11, 'col2': 1000000, 'col3': 2, 'col4': 13},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},\n 't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},\n 's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},\n 's3': {'col1': 11,\n 'col2': 1000000,\n 'col3': 2,\n 'col4': 1000000},\n 's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},\n 't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},\n 's2': {'col1': 5,\n 'col2': 1000000,\n 'col3': 1000000,\n 'col4': 1000000},\n 's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},\n 's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}\n```\n\n```python\n#Nested iterable from: \nhttps://stackoverflow.com/questions/72146094/problems-matching-values-from-nested-dictionary\ndata=\n{'_links': {'next': None, 'prev': None},\n 'limit': 250,\n 'offset': 0,\n 'runs': [{'assignedto_id': None,\n 'blocked_count': 0,\n 'completed_on': None,\n 'config': None,\n 'config_ids': [],\n 'created_by': 1,\n 'created_on': 1651790693,\n 'custom_status1_count': 0,\n 'custom_status2_count': 0,\n 'custom_status3_count': 0,\n 'custom_status4_count': 0,\n 'custom_status5_count': 0,\n 'custom_status6_count': 0,\n 'custom_status7_count': 0,\n 'description': None,\n 'failed_count': 1,\n 'id': 13,\n 'include_all': False,\n 'is_completed': False,\n 'milestone_id': None,\n 'name': '2022-05-05-testrun',\n 'passed_count': 2,\n 'plan_id': None,\n 'project_id': 1,\n 'refs': None,\n 'retest_count': 0,\n 'suite_id': 1,\n 'untested_count': 0,\n 'updated_on': 1651790693,\n 'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13'},\n {'assignedto_id': None,\n 'blocked_count': 0,\n 'completed_on': 1650989972,\n 'config': None,\n 'config_ids': [],\n 'created_by': 5,\n 'created_on': 1650966329,\n 'custom_status1_count': 0,\n 'custom_status2_count': 0,\n 'custom_status3_count': 0,\n 'custom_status4_count': 0,\n 'custom_status5_count': 0,\n 'custom_status6_count': 0,\n 'custom_status7_count': 0,\n 'description': None,\n 'failed_count': 0,\n 'id': 9,\n 'include_all': False,\n 'is_completed': True,\n 'milestone_id': None,\n 'name': 'This is a new test run',\n 'passed_count': 0,\n 'plan_id': None,\n 'project_id': 1,\n 'refs': None,\n 'retest_count': 0,\n 'suite_id': 1,\n 'untested_count': 3,\n 'updated_on': 1650966329,\n 'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9'}],\n 'size': 2}\ndf = pd.Q_AnyNestedIterable_2df(data,unstack=False)\n aa_all_keys aa_value\n_links next NaN (_links, next) None\n prev NaN (_links, prev) None\nlimit NaN NaN (limit,) 250\noffset NaN NaN (offset","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhansalemaos%2Fa_pandas_ex_plode_tool","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhansalemaos%2Fa_pandas_ex_plode_tool","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhansalemaos%2Fa_pandas_ex_plode_tool/lists"}