a-pandas-ex-plode-tool 0.1
Library to handle any nested iterable (list, tuple, dict, json, etc.) in Pandas - no matter how deep it is nested!
License: MIT
Keywords: iter,nested,iterable,flatten,lists of lists,flatten json,pandas,numpy,explode,squeeze


## Library to handle any nested iterable (list, tuple, dict, json, etc.) in Pandas - no matter how deep it is nested!



```python

pip install a-pandas-ex-plode-tool

```



```python

from a_pandas_ex_plode_tool import pd_add_explode_tools

pd_add_explode_tools()

import pandas as pd

df = pd.read_csv("https://github.com/pandas-dev/pandas/raw/main/doc/data/air_quality_long.csv")

```



**HANDLE NESTED ITERABLES**



The 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:



- pd.Q_AnyNestedIterable_2df()

- pd.Q_CorruptJsonFile_2dict()

- pd.Q_ReadFileWithAllEncodings_2df()

- df.d_filter_dtypes()

- df.d_multiple_columns_to_one()

- df.d_df_to_nested_dict()

- df.d_add_value_to_existing_columns_with_loc()

- df.d_set_values_with_df_loc()

- df.d_drop_rows_with_df_loc()

- df.d_dfloc()

- df.d_stack()

- df.d_unstack()

- df.d_sort_columns_with_sorted()

- df.d_merge_multiple_dfs_and_series_on_one_column()

- df.d_merge_multiple_dfs_and_series_on_index()

- df.d_update_original_iter()

- df.ds_all_nans_to_pdNA()

- df.ds_explode_dicts_in_column()

- df.ds_isna()

- df.ds_normalize_lists()

- df.s_delete_duplicates_from_iters_in_cells()

- df.s_flatten_all_iters_in_cells()

- df.s_as_flattened_list()

- df.s_explode_lists_and_tuples()

  

  



**All methods added to pandas have one of these prefixes:**



- **ds_** (for DataFrames and Series)



- **s_** (only for Series)



- **d_** (only for DataFrames)



- **Q_** (added to pd.)







### pd.Q_AnyNestedIterable_2df() / df.d_filter_dtypes() / df.d_update_original_iter()



**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)



**df.d_filter_dtypes()** avoids TypeError Exceptions 



df.loc[df.aa_value >30,'aa_value'] = 90000000  



Traceback (most recent call last):  

....  

TypeError: '>' not supported between instances of 'str' and 'int' 



***df.loc[df.d_filter_dtypes(allowed_dtypes=(int,float),fillvalue=pd.NA,column='aa_value') > 30] <------- No more exception!***



**df.d_update_original_iter()** After you have updated the DataFrame, you can update the original nasty iterable and keep its ugly structure. 



##### 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!



```python

Nested iterable from: 'https://stackoverflow.com/questions/61984148/how-to-handle-nested-lists-and-dictionaries-in-pandas-dataframe'

{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},

                    {'review_critic': 'ABC', 'review_score': 90},

                    {'review_critic': '123', 'review_score': 90}],

 'genres': ['Sports', 'Golf'],

 'score': 85,

 'title': 'Golf Simulator',

 'url': 'http://example.com/golf-simulator'}



df = pd.Q_AnyNestedIterable_2df(data,unstack=False)  # create DF stacked or unstacked, it doesn't matter

                                                         aa_all_keys                           aa_value

critic_reviews 0   review_critic  (critic_reviews, 0, review_critic)                                XYZ

                   review_score    (critic_reviews, 0, review_score)                                 90

               1   review_critic  (critic_reviews, 1, review_critic)                                ABC

                   review_score    (critic_reviews, 1, review_score)                                 90

               2   review_critic  (critic_reviews, 2, review_critic)                                123

                   review_score    (critic_reviews, 2, review_score)                                 90

genres         0   NaN                                   (genres, 0)                             Sports

               1   NaN                                   (genres, 1)                               Golf

score          NaN NaN                                      (score,)                                 85

title          NaN NaN                                      (title,)                     Golf Simulator

url            NaN NaN                                        (url,)  http://example.com/golf-simulator



#Avoid exceptions with df.d_filter_dtypes()

df.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')

                                                         aa_all_keys    aa_value

critic_reviews 0   review_critic  (critic_reviews, 0, review_critic)         XYZ

                   review_score    (critic_reviews, 0, review_score)          90

               1   review_critic  (critic_reviews, 1, review_critic)         ABC

                   review_score    (critic_reviews, 1, review_score)          90

               2   review_critic  (critic_reviews, 2, review_critic)         123

                   review_score    (critic_reviews, 2, review_score)          90

genres         0   NaN                                   (genres, 0)      Sports

               1   NaN                                   (genres, 1)  UPDATE1111

score          NaN NaN                                      (score,)          85

title          NaN NaN                                      (title,)  UPDATE1111

url            NaN NaN                                        (url,)  UPDATE1111

mod_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

[genres][1]                                                  Old value: Golf

[genres][1]                                                  Updated value: UPDATE1111

[title]                                                      Old value: Golf Simulator

[title]                                                      Updated value: UPDATE1111

[url]                                                        Old value: http://example.com/golf-simulator

[url]                                                        Updated value: UPDATE1111



{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},

                    {'review_critic': 'ABC', 'review_score': 90},

                    {'review_critic': '123', 'review_score': 90}],

 'genres': ['Sports', 'UPDATE1111'],

 'score': 85,

 'title': 'UPDATE1111',

 'url': 'UPDATE1111'}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/73430585/how-to-convert-a-list-of-nested-dictionaries-includes-tuples-as-a-dataframe

[{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 2, 'Name': 'A', 'num': 68}),

  'final_value': 118},

 {'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 4, 'Name': 'A', 'num': 67}),

  'final_value': 117},

 {'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 6, 'Name': 'A', 'num': 67}),

  'final_value': 117}]

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                             aa_all_keys aa_value

0 cb          0   ID      (0, cb, 0, ID)        1

                  Name  (0, cb, 0, Name)        A

                  num    (0, cb, 0, num)       50

              1   ID      (0, cb, 1, ID)        2

                  Name  (0, cb, 1, Name)        A

                  num    (0, cb, 1, num)       68

  final_value NaN NaN   (0, final_value)      118

1 cb          0   ID      (1, cb, 0, ID)        1

                  Name  (1, cb, 0, Name)        A

                  num    (1, cb, 0, num)       50

              1   ID      (1, cb, 1, ID)        4

                  Name  (1, cb, 1, Name)        A

                  num    (1, cb, 1, num)       67

  final_value NaN NaN   (1, final_value)      117

2 cb          0   ID      (2, cb, 0, ID)        1

                  Name  (2, cb, 0, Name)        A

                  num    (2, cb, 0, num)       50

              1   ID      (2, cb, 1, ID)        6

                  Name  (2, cb, 1, Name)        A

                  num    (2, cb, 1, num)       67

  final_value NaN NaN   (2, final_value)      117

df.d_filter_dtypes(allowed_dtypes=(int,float),fillvalue=pd.NA,column='aa_value') > 30, 'aa_value'] = 900000

                             aa_all_keys aa_value

0 cb          0   ID      (0, cb, 0, ID)        1

                  Name  (0, cb, 0, Name)        A

                  num    (0, cb, 0, num)   900000

              1   ID      (0, cb, 1, ID)        2

                  Name  (0, cb, 1, Name)        A

                  num    (0, cb, 1, num)   900000

  final_value NaN NaN   (0, final_value)   900000

1 cb          0   ID      (1, cb, 0, ID)        1

                  Name  (1, cb, 0, Name)        A

                  num    (1, cb, 0, num)   900000

              1   ID      (1, cb, 1, ID)        4

                  Name  (1, cb, 1, Name)        A

                  num    (1, cb, 1, num)   900000

  final_value NaN NaN   (1, final_value)   900000

2 cb          0   ID      (2, cb, 0, ID)        1

                  Name  (2, cb, 0, Name)        A

                  num    (2, cb, 0, num)   900000

              1   ID      (2, cb, 1, ID)        6

                  Name  (2, cb, 1, Name)        A

                  num    (2, cb, 1, num)   900000

  final_value NaN NaN   (2, final_value)   900000

mod_iter = df.d_update_original_iter(data, verbose=True)

[0][cb][0][num]                                              Old value: 50

[0][cb][0][num]                                              Updated value: 900000

[0][cb][1][num]                                              Old value: 68

[0][cb][1][num]                                              Updated value: 900000

[0][final_value]                                             Old value: 118

[0][final_value]                                             Updated value: 900000

[1][cb][0][num]                                              Old value: 50

[1][cb][0][num]                                              Updated value: 900000

[1][cb][1][num]                                              Old value: 67

[1][cb][1][num]                                              Updated value: 900000

[1][final_value]                                             Old value: 117

[1][final_value]                                             Updated value: 900000

[2][cb][0][num]                                              Old value: 50

[2][cb][0][num]                                              Updated value: 900000

[2][cb][1][num]                                              Old value: 67

[2][cb][1][num]                                              Updated value: 900000

[2][final_value]                                             Old value: 117

[2][final_value]                                             Updated value: 900000

[{'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},

         {'ID': 2, 'Name': 'A', 'num': 900000}),

  'final_value': 900000},

 {'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},

         {'ID': 4, 'Name': 'A', 'num': 900000}),

  'final_value': 900000},

 {'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},

         {'ID': 6, 'Name': 'A', 'num': 900000}),

  'final_value': 900000}]

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/69943509/problems-when-flatten-a-dict

[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],

  'application_details': {'email': None, 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '1'},

 {'application_contacts': [{'adress': 'Z', 'email': None}],

  'application_details': {'email': 'testy@test_a.com', 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '2'},

 {'application_contacts': [{'adress': 'Y', 'email': None}],

  'application_details': {'email': 'testy@test_a.com', 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '3'}]

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                                              aa_all_keys          aa_value

0 application_contacts 0     adress  (0, application_contacts, 0, adress)                 X

                             email    (0, application_contacts, 0, email)     test@test.com

  application_details  email NaN          (0, application_details, email)              None

                       phone NaN          (0, application_details, phone)              None

  employer             Name  NaN                      (0, employer, Name)               Nom

                       email NaN                     (0, employer, email)              None

  id                   NaN   NaN                                  (0, id)                 1

1 application_contacts 0     adress  (1, application_contacts, 0, adress)                 Z

                             email    (1, application_contacts, 0, email)              None

  application_details  email NaN          (1, application_details, email)  testy@test_a.com

                       phone NaN          (1, application_details, phone)              None

  employer             Name  NaN                      (1, employer, Name)               Nom

                       email NaN                     (1, employer, email)              None

  id                   NaN   NaN                                  (1, id)                 2

2 application_contacts 0     adress  (2, application_contacts, 0, adress)                 Y

                             email    (2, application_contacts, 0, email)              None

  application_details  email NaN          (2, application_details, email)  testy@test_a.com

                       phone NaN          (2, application_details, phone)              None

  employer             Name  NaN                      (2, employer, Name)               Nom

                       email NaN                     (2, employer, email)              None

  id                   NaN   NaN                                  (2, id)                 3

df.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'

                                                              aa_all_keys                  aa_value

0 application_contacts 0     adress  (0, application_contacts, 0, adress)                         X

                             email    (0, application_contacts, 0, email)             test@test.com

  application_details  email NaN          (0, application_details, email)                      None

                       phone NaN          (0, application_details, phone)                      None

  employer             Name  NaN                      (0, employer, Name)                       Nom

                       email NaN                     (0, employer, email)                      None

  id                   NaN   NaN                                  (0, id)                         1

1 application_contacts 0     adress  (1, application_contacts, 0, adress)                         Z

                             email    (1, application_contacts, 0, email)                      None

  application_details  email NaN          (1, application_details, email)  UPPPPPPPPPPPPPPPDATE.COM

                       phone NaN          (1, application_details, phone)                      None

  employer             Name  NaN                      (1, employer, Name)                       Nom

                       email NaN                     (1, employer, email)                      None

  id                   NaN   NaN                                  (1, id)                         2

2 application_contacts 0     adress  (2, application_contacts, 0, adress)                         Y

                             email    (2, application_contacts, 0, email)                      None

  application_details  email NaN          (2, application_details, email)  UPPPPPPPPPPPPPPPDATE.COM

                       phone NaN          (2, application_details, phone)                      None

  employer             Name  NaN                      (2, employer, Name)                       Nom

                       email NaN                     (2, employer, email)                      None

  id                   NaN   NaN                                  (2, id)                         3

mod_iter = df.d_update_original_iter(data, verbose=True)

[1][application_details][email]                              Old value: testy@test_a.com

[1][application_details][email]                              Updated value: UPPPPPPPPPPPPPPPDATE.COM

[2][application_details][email]                              Old value: testy@test_a.com

[2][application_details][email]                              Updated value: UPPPPPPPPPPPPPPPDATE.COM

[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],

  'application_details': {'email': None, 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '1'},

 {'application_contacts': [{'adress': 'Z', 'email': None}],

  'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '2'},

 {'application_contacts': [{'adress': 'Y', 'email': None}],

  'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '3'}]

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/62765371/convert-nested-dataframe-to-a-simple-dataframeframe

{'A': [1, 2, 3],

 'B': [4, 5, 6],

 'departure': [{'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'},

               {'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'},

               {'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'}]}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                                   aa_all_keys                   aa_value

A         0 NaN                                         (A, 0)                          1

          1 NaN                                         (A, 1)                          2

          2 NaN                                         (A, 2)                          3

B         0 NaN                                         (B, 0)                          4

          1 NaN                                         (B, 1)                          5

          2 NaN                                         (B, 2)                          6

departure 0 actual                      (departure, 0, actual)                       None

            actual_runway        (departure, 0, actual_runway)                       None

            airport                    (departure, 0, airport)                     Findel

            delay                        (departure, 0, delay)                       None

            estimated                (departure, 0, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 0, estimated_runway)                       None

            gate                          (departure, 0, gate)                       None

            iata                          (departure, 0, iata)                        LUX

            icao                          (departure, 0, icao)                       ELLX

            scheduled                (departure, 0, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 0, terminal)                       None

            timezone                  (departure, 0, timezone)          Europe/Luxembourg

          1 actual                      (departure, 1, actual)                       None

            actual_runway        (departure, 1, actual_runway)                       None

            airport                    (departure, 1, airport)                     Findel

            delay                        (departure, 1, delay)                       None

            estimated                (departure, 1, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 1, estimated_runway)                       None

            gate                          (departure, 1, gate)                       None

            iata                          (departure, 1, iata)                        LUX

            icao                          (departure, 1, icao)                       ELLX

            scheduled                (departure, 1, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 1, terminal)                       None

            timezone                  (departure, 1, timezone)          Europe/Luxembourg

          2 actual                      (departure, 2, actual)                       None

            actual_runway        (departure, 2, actual_runway)                       None

            airport                    (departure, 2, airport)                     Findel

            delay                        (departure, 2, delay)                       None

            estimated                (departure, 2, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 2, estimated_runway)                       None

            gate                          (departure, 2, gate)                       None

            iata                          (departure, 2, iata)                        LUX

            icao                          (departure, 2, icao)                       ELLX

            scheduled                (departure, 2, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 2, terminal)                       None

            timezone                  (departure, 2, timezone)          Europe/Luxembourg

df.loc[df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value')== 'ELLX', 'aa_value'] = 'ELLX-UPDATED'

                                                   aa_all_keys                   aa_value

A         0 NaN                                         (A, 0)                          1

          1 NaN                                         (A, 1)                          2

          2 NaN                                         (A, 2)                          3

B         0 NaN                                         (B, 0)                          4

          1 NaN                                         (B, 1)                          5

          2 NaN                                         (B, 2)                          6

departure 0 actual                      (departure, 0, actual)                       None

            actual_runway        (departure, 0, actual_runway)                       None

            airport                    (departure, 0, airport)                     Findel

            delay                        (departure, 0, delay)                       None

            estimated                (departure, 0, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 0, estimated_runway)                       None

            gate                          (departure, 0, gate)                       None

            iata                          (departure, 0, iata)                        LUX

            icao                          (departure, 0, icao)               ELLX-UPDATED

            scheduled                (departure, 0, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 0, terminal)                       None

            timezone                  (departure, 0, timezone)          Europe/Luxembourg

          1 actual                      (departure, 1, actual)                       None

            actual_runway        (departure, 1, actual_runway)                       None

            airport                    (departure, 1, airport)                     Findel

            delay                        (departure, 1, delay)                       None

            estimated                (departure, 1, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 1, estimated_runway)                       None

            gate                          (departure, 1, gate)                       None

            iata                          (departure, 1, iata)                        LUX

            icao                          (departure, 1, icao)               ELLX-UPDATED

            scheduled                (departure, 1, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 1, terminal)                       None

            timezone                  (departure, 1, timezone)          Europe/Luxembourg

          2 actual                      (departure, 2, actual)                       None

            actual_runway        (departure, 2, actual_runway)                       None

            airport                    (departure, 2, airport)                     Findel

            delay                        (departure, 2, delay)                       None

            estimated                (departure, 2, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 2, estimated_runway)                       None

            gate                          (departure, 2, gate)                       None

            iata                          (departure, 2, iata)                        LUX

            icao                          (departure, 2, icao)               ELLX-UPDATED

            scheduled                (departure, 2, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 2, terminal)                       None

            timezone                  (departure, 2, timezone)          Europe/Luxembourg

mod_iter = df.d_update_original_iter(data, verbose=True)

[departure][0][icao]                                         Old value: ELLX

[departure][0][icao]                                         Updated value: ELLX-UPDATED

[departure][1][icao]                                         Old value: ELLX

[departure][1][icao]                                         Updated value: ELLX-UPDATED

[departure][2][icao]                                         Old value: ELLX

[departure][2][icao]                                         Updated value: ELLX-UPDATED

{'A': [1, 2, 3],

 'B': [4, 5, 6],

 'departure': [{'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX-UPDATED',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'},

               {'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX-UPDATED',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'},

               {'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX-UPDATED',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'}]}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/64359762/constructing-a-pandas-dataframe-with-columns-and-sub-columns-from-nested-diction

{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},

                   's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},

            't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},

                   's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},

            't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},

                   's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},

                   's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},

                   's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},

 'level2': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 9, 'col4': 9},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},

                   's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 13},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},

            't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},

                   's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},

            't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},

                   's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},

                   's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},

                   's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                              aa_all_keys  aa_value

level1 t1 s1 col1  (level1, t1, s1, col1)         5

             col2  (level1, t1, s1, col2)         4

             col3  (level1, t1, s1, col3)         4

             col4  (level1, t1, s1, col4)         9

          s2 col1  (level1, t1, s2, col1)         1

                                   ...       ...

level2 t3 s3 col4  (level2, t3, s3, col4)        12

          s4 col1  (level2, t3, s4, col1)        13

             col2  (level2, t3, s4, col2)        14

             col3  (level2, t3, s4, col3)        15

             col4  (level2, t3, s4, col4)        16

[96 rows x 2 columns]

df.loc[(df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') > 5) & (df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') < 10), 'aa_value'] = 1000000

                              aa_all_keys  aa_value

level1 t1 s1 col1  (level1, t1, s1, col1)         5

             col2  (level1, t1, s1, col2)         4

             col3  (level1, t1, s1, col3)         4

             col4  (level1, t1, s1, col4)   1000000

          s2 col1  (level1, t1, s2, col1)         1

                                   ...       ...

level2 t3 s3 col4  (level2, t3, s3, col4)        12

          s4 col1  (level2, t3, s4, col1)        13

             col2  (level2, t3, s4, col2)        14

             col3  (level2, t3, s4, col3)        15

             col4  (level2, t3, s4, col4)        16

[96 rows x 2 columns]

mod_iter = df.d_update_original_iter(data, verbose=True)

[level1][t1][s1][col4]                                       Old value: 9

[level1][t1][s1][col4]                                       Updated value: 1000000

[level1][t1][s2][col4]                                       Old value: 8

[level1][t1][s2][col4]                                       Updated value: 1000000

[level1][t1][s3][col2]                                       Old value: 8

[level1][t1][s3][col2]                                       Updated value: 1000000

[level1][t1][s3][col4]                                       Old value: 9

[level1][t1][s3][col4]                                       Updated value: 1000000

[level1][t1][s4][col4]                                       Old value: 9

[level1][t1][s4][col4]                                       Updated value: 1000000

[level1][t2][s1][col4]                                       Old value: 9

[level1][t2][s1][col4]                                       Updated value: 1000000

[level1][t2][s2][col4]                                       Old value: 8

[level1][t2][s2][col4]                                       Updated value: 1000000

[level1][t2][s3][col2]                                       Old value: 8

[level1][t2][s3][col2]                                       Updated value: 1000000

[level1][t2][s3][col4]                                       Old value: 9

[level1][t2][s3][col4]                                       Updated value: 1000000

[level1][t2][s4][col4]                                       Old value: 9

[level1][t2][s4][col4]                                       Updated value: 1000000

[level1][t3][s2][col2]                                       Old value: 6

[level1][t3][s2][col2]                                       Updated value: 1000000

[level1][t3][s2][col3]                                       Old value: 7

[level1][t3][s2][col3]                                       Updated value: 1000000

[level1][t3][s2][col4]                                       Old value: 8

[level1][t3][s2][col4]                                       Updated value: 1000000

[level1][t3][s3][col1]                                       Old value: 9

[level1][t3][s3][col1]                                       Updated value: 1000000

[level2][t1][s1][col3]                                       Old value: 9

[level2][t1][s1][col3]                                       Updated value: 1000000

[level2][t1][s1][col4]                                       Old value: 9

[level2][t1][s1][col4]                                       Updated value: 1000000

[level2][t1][s3][col2]                                       Old value: 8

[level2][t1][s3][col2]                                       Updated value: 1000000

[level2][t2][s1][col4]                                       Old value: 9

[level2][t2][s1][col4]                                       Updated value: 1000000

[level2][t2][s2][col4]                                       Old value: 8

[level2][t2][s2][col4]                                       Updated value: 1000000

[level2][t2][s3][col2]                                       Old value: 8

[level2][t2][s3][col2]                                       Updated value: 1000000

[level2][t2][s3][col4]                                       Old value: 9

[level2][t2][s3][col4]                                       Updated value: 1000000

[level2][t2][s4][col4]                                       Old value: 9

[level2][t2][s4][col4]                                       Updated value: 1000000

[level2][t3][s2][col2]                                       Old value: 6

[level2][t3][s2][col2]                                       Updated value: 1000000

[level2][t3][s2][col3]                                       Old value: 7

[level2][t3][s2][col3]                                       Updated value: 1000000

[level2][t3][s2][col4]                                       Old value: 8

[level2][t3][s2][col4]                                       Updated value: 1000000

[level2][t3][s3][col1]                                       Old value: 9

[level2][t3][s3][col1]                                       Updated value: 1000000

{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},

                   's3': {'col1': 11,

                          'col2': 1000000,

                          'col3': 2,

                          'col4': 1000000},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},

            't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},

                   's3': {'col1': 11,

                          'col2': 1000000,

                          'col3': 2,

                          'col4': 1000000},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},

            't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},

                   's2': {'col1': 5,

                          'col2': 1000000,

                          'col3': 1000000,

                          'col4': 1000000},

                   's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},

                   's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},

 'level2': {'t1': {'s1': {'col1': 5,

                          'col2': 4,

                          'col3': 1000000,

                          'col4': 1000000},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},

                   's3': {'col1': 11, 'col2': 1000000, 'col3': 2, 'col4': 13},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},

            't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},

                   's3': {'col1': 11,

                          'col2': 1000000,

                          'col3': 2,

                          'col4': 1000000},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},

            't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},

                   's2': {'col1': 5,

                          'col2': 1000000,

                          'col3': 1000000,

                          'col4': 1000000},

                   's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},

                   's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/72146094/problems-matching-values-from-nested-dictionary

{'_links': {'next': None, 'prev': None},

 'limit': 250,

 'offset': 0,

 'runs': [{'assignedto_id': None,

           'blocked_count': 0,

           'completed_on': None,

           'config': None,

           'config_ids': [],

           'created_by': 1,

           'created_on': 1651790693,

           'custom_status1_count': 0,

           'custom_status2_count': 0,

           'custom_status3_count': 0,

           'custom_status4_count': 0,

           'custom_status5_count': 0,

           'custom_status6_count': 0,

           'custom_status7_count': 0,

           'description': None,

           'failed_count': 1,

           'id': 13,

           'include_all': False,

           'is_completed': False,

           'milestone_id': None,

           'name': '2022-05-05-testrun',

           'passed_count': 2,

           'plan_id': None,

           'project_id': 1,

           'refs': None,

           'retest_count': 0,

           'suite_id': 1,

           'untested_count': 0,

           'updated_on': 1651790693,

           'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13'},

          {'assignedto_id': None,

           'blocked_count': 0,

           'completed_on': 1650989972,

           'config': None,

           'config_ids': [],

           'created_by': 5,

           'created_on': 1650966329,

           'custom_status1_count': 0,

           'custom_status2_count': 0,

           'custom_status3_count': 0,

           'custom_status4_count': 0,

           'custom_status5_count': 0,

           'custom_status6_count': 0,

           'custom_status7_count': 0,

           'description': None,

           'failed_count': 0,

           'id': 9,

           'include_all': False,

           'is_completed': True,

           'milestone_id': None,

           'name': 'This is a new test run',

           'passed_count': 0,

           'plan_id': None,

           'project_id': 1,

           'refs': None,

           'retest_count': 0,

           'suite_id': 1,

           'untested_count': 3,

           'updated_on': 1650966329,

           'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9'}],

 'size': 2}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                          aa_all_keys                                           aa_value

_links next NaN                        (_links, next)                                               None

       prev NaN                        (_links, prev)                                               None

limit  NaN  NaN                              (limit,)                                                250

offset NaN  NaN                             (offset,)                                                  0

runs   0    assignedto_id    (runs, 0, assignedto_id)                                               None

                                               ...                                                ...

       1    suite_id              (runs, 1, suite_id)                                                  1

            untested_count  (runs, 1, untested_count)                                                  3

            updated_on          (runs, 1, updated_on)                                         1650966329

            url                        (runs, 1, url)  https://xxxxxxxxxx.testrail.io/index.php?/runs...

size   NaN  NaN                               (size,)                                                  2

[63 rows x 2 columns]

df.loc[(df.d_filter_dtypes(allowed_dtypes=(bool),fillvalue=pd.NA,column='aa_value') == False ), 'aa_value'] = True

df.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'

                                          aa_all_keys        aa_value

_links next NaN                        (_links, next)            None

       prev NaN                        (_links, prev)            None

limit  NaN  NaN                              (limit,)             250

offset NaN  NaN                             (offset,)               0

runs   0    assignedto_id    (runs, 0, assignedto_id)            None

                                               ...             ...

       1    suite_id              (runs, 1, suite_id)               1

            untested_count  (runs, 1, untested_count)               3

            updated_on          (runs, 1, updated_on)      1650966329

            url                        (runs, 1, url)  WWW.PYTHON.ORG

size   NaN  NaN                               (size,)               2

[63 rows x 2 columns]

mod_iter = df.d_update_original_iter(data, verbose=True)

[runs][0][include_all]                                       Old value: False

[runs][0][include_all]                                       Updated value: True

[runs][0][is_completed]                                      Old value: False

[runs][0][is_completed]                                      Updated value: True

[runs][0][url]                                               Old value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13

[runs][0][url]                                               Updated value: WWW.PYTHON.ORG

[runs][1][include_all]                                       Old value: False

[runs][1][include_all]                                       Updated value: True

[runs][1][url]                                               Old value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9

[runs][1][url]                                               Updated value: WWW.PYTHON.ORG

{'_links': {'next': None, 'prev': None},

 'limit': 250,

 'offset': 0,

 'runs': [{'assignedto_id': None,

           'blocked_count': 0,

           'completed_on': None,

           'config': None,

           'config_ids': [],

           'created_by': 1,

           'created_on': 1651790693,

           'custom_status1_count': 0,

           'custom_status2_count': 0,

           'custom_status3_count': 0,

           'custom_status4_count': 0,

           'custom_status5_count': 0,

           'custom_status6_count': 0,

           'custom_status7_count': 0,

           'description': None,

           'failed_count': 1,

           'id': 13,

           'include_all': True,

           'is_completed': True,

           'milestone_id': None,

           'name': '2022-05-05-testrun',

           'passed_count': 2,

           'plan_id': None,

           'project_id': 1,

           'refs': None,

           'retest_count': 0,

           'suite_id': 1,

           'untested_count': 0,

           'updated_on': 1651790693,

           'url': 'WWW.PYTHON.ORG'},

          {'assignedto_id': None,

           'blocked_count': 0,

           'completed_on': 1650989972,

           'config': None,

           'config_ids': [],

           'created_by': 5,

           'created_on': 1650966329,

           'custom_status1_count': 0,

           'custom_status2_count': 0,

           'custom_status3_count': 0,

           'custom_status4_count': 0,

           'custom_status5_count': 0,

           'custom_status6_count': 0,

           'custom_status7_count': 0,

           'description': None,

           'failed_count': 0,

           'id': 9,

           'include_all': True,

           'is_completed': True,

           'milestone_id': None,

           'name': 'This is a new test run',

           'passed_count': 0,

           'plan_id': None,

           'project_id': 1,

           'refs': None,

           'retest_count': 0,

           'suite_id': 1,

           'untested_count': 3,

           'updated_on': 1650966329,

           'url': 'WWW.PYTHON.ORG'}],

 'size': 2}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/73708706/how-to-get-values-from-list-of-nested-dictionaries/73839430#73839430

{'results': [{'end_time': '2021-01-21',

              'key': 'q1',

              'result_type': 'multipleChoice',

              'start_time': '2021-01-21',

              'value': ['1']},

             {'end_time': '2021-01-21',

              'key': 'q2',

              'result_type': 'multipleChoice',

              'start_time': '2021-01-21',

              'value': ['False']},

             {'end_time': '2021-01-21',

              'key': 'q3',

              'result_type': 'multipleChoice',

              'start_time': '2021-01-21',

              'value': ['3']},

             {'end_time': '2021-01-21',

              'key': 'q4',

              'result_type': 'multipleChoice',

              'start_time': '2021-01-21',

              'value': ['3']}]}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                         aa_all_keys        aa_value

results 0 end_time    NaN     (results, 0, end_time)      2021-01-21

          key         NaN          (results, 0, key)              q1

          result_type NaN  (results, 0, result_type)  multipleChoice

          start_time  NaN   (results, 0, start_time)      2021-01-21

          value       0       (results, 0, value, 0)               1

        1 end_time    NaN     (results, 1, end_time)      2021-01-21

          key         NaN          (results, 1, key)              q2

          result_type NaN  (results, 1, result_type)  multipleChoice

          start_time  NaN   (results, 1, start_time)      2021-01-21

          value       0       (results, 1, value, 0)           False

        2 end_time    NaN     (results, 2, end_time)      2021-01-21

          key         NaN          (results, 2, key)              q3

          result_type NaN  (results, 2, result_type)  multipleChoice

          start_time  NaN   (results, 2, start_time)      2021-01-21

          value       0       (results, 2, value, 0)               3

        3 end_time    NaN     (results, 3, end_time)      2021-01-21

          key         NaN          (results, 3, key)              q4

          result_type NaN  (results, 3, result_type)  multipleChoice

          start_time  NaN   (results, 3, start_time)      2021-01-21

          value       0       (results, 3, value, 0)               3

df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'^2021.*',na=False) ), 'aa_value'] = 10000000000 

                                         aa_all_keys        aa_value

results 0 end_time    NaN     (results, 0, end_time)     10000000000

          key         NaN          (results, 0, key)              q1

          result_type NaN  (results, 0, result_type)  multipleChoice

          start_time  NaN   (results, 0, start_time)     10000000000

          value       0       (results, 0, value, 0)               1

        1 end_time    NaN     (results, 1, end_time)     10000000000

          key         NaN          (results, 1, key)              q2

          result_type NaN  (results, 1, result_type)  multipleChoice

          start_time  NaN   (results, 1, start_time)     10000000000

          value       0       (results, 1, value, 0)           False

        2 end_time    NaN     (results, 2, end_time)     10000000000

          key         NaN          (results, 2, key)              q3

          result_type NaN  (results, 2, result_type)  multipleChoice

          start_time  NaN   (results, 2, start_time)     10000000000

          value       0       (results, 2, value, 0)               3

        3 end_time    NaN     (results, 3, end_time)     10000000000

          key         NaN          (results, 3, key)              q4

          result_type NaN  (results, 3, result_type)  multipleChoice

          start_time  NaN   (results, 3, start_time)     10000000000

          value       0       (results, 3, value, 0)               3

mod_iter = df.d_update_original_iter(data, verbose=True)

[results][0][end_time]                                       Old value: 2021-01-21

[results][0][end_time]                                       Updated value: 10000000000

[results][0][start_time]                                     Old value: 2021-01-21

[results][0][start_time]                                     Updated value: 10000000000

[results][1][end_time]                                       Old value: 2021-01-21

[results][1][end_time]                                       Updated value: 10000000000

[results][1][start_time]                                     Old value: 2021-01-21

[results][1][start_time]                                     Updated value: 10000000000

[results][2][end_time]                                       Old value: 2021-01-21

[results][2][end_time]                                       Updated value: 10000000000

[results][2][start_time]                                     Old value: 2021-01-21

[results][2][start_time]                                     Updated value: 10000000000

[results][3][end_time]                                       Old value: 2021-01-21

[results][3][end_time]                                       Updated value: 10000000000

[results][3][start_time]                                     Old value: 2021-01-21

[results][3][start_time]                                     Updated value: 10000000000

{'results': [{'end_time': 10000000000,

              'key': 'q1',

              'result_type': 'multipleChoice',

              'start_time': 10000000000,

              'value': ['1']},

             {'end_time': 10000000000,

              'key': 'q2',

              'result_type': 'multipleChoice',

              'start_time': 10000000000,

              'value': ['False']},

             {'end_time': 10000000000,

              'key': 'q3',

              'result_type': 'multipleChoice',

              'start_time': 10000000000,

              'value': ['3']},

             {'end_time': 10000000000,

              'key': 'q4',

              'result_type': 'multipleChoice',

              'start_time': 10000000000,

              'value': ['3']}]}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/66461902/flattening-nested-dictionary-into-dataframe-python

{1: {2: {'IDs': {'BookID': ['543533254353', '4324232342'],

                 'SalesID': ['543267765345', '4353543'],

                 'StoreID': ['111111', '1121111']},

         'Name': 'boring Tales of Dragon Slayers'},

     'IDs': {'BookID': ['543533254353'],

             'SalesID': ['543267765345'],

             'StoreID': ['123445452543']},

     'Name': 'Thrilling Tales of Dragon Slayers'}}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                        aa_all_keys                           aa_value

1 IDs  BookID  0       NaN      (1, IDs, BookID, 0)                       543533254353

       SalesID 0       NaN     (1, IDs, SalesID, 0)                       543267765345

       StoreID 0       NaN     (1, IDs, StoreID, 0)                       123445452543

  Name NaN     NaN     NaN                (1, Name)  Thrilling Tales of Dragon Slayers

  2    IDs     BookID  0     (1, 2, IDs, BookID, 0)                       543533254353

                       1     (1, 2, IDs, BookID, 1)                         4324232342

               SalesID 0    (1, 2, IDs, SalesID, 0)                       543267765345

                       1    (1, 2, IDs, SalesID, 1)                            4353543

               StoreID 0    (1, 2, IDs, StoreID, 0)                             111111

                       1    (1, 2, IDs, StoreID, 1)                            1121111

       Name    NaN     NaN             (1, 2, Name)     boring Tales of Dragon Slayers

df.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)

                                        aa_all_keys                           aa_value

1 IDs  BookID  0       NaN      (1, IDs, BookID, 0)                     543533254353.0

       SalesID 0       NaN     (1, IDs, SalesID, 0)                     543267765345.0

       StoreID 0       NaN     (1, IDs, StoreID, 0)                     123445452543.0

  Name NaN     NaN     NaN                (1, Name)  Thrilling Tales of Dragon Slayers

  2    IDs     BookID  0     (1, 2, IDs, BookID, 0)                     543533254353.0

                       1     (1, 2, IDs, BookID, 1)                       4324232342.0

               SalesID 0    (1, 2, IDs, SalesID, 0)                     543267765345.0

                       1    (1, 2, IDs, SalesID, 1)                          4353543.0

               StoreID 0    (1, 2, IDs, StoreID, 0)                           111111.0

                       1    (1, 2, IDs, StoreID, 1)                          1121111.0

       Name    NaN     NaN             (1, 2, Name)     boring Tales of Dragon Slayers

mod_iter = df.d_update_original_iter(data, verbose=True)

[1][2][IDs][BookID][0]                                       Old value: 543533254353

[1][2][IDs][BookID][0]                                       Updated value: 543533254353.0

[1][2][IDs][BookID][1]                                       Old value: 4324232342

[1][2][IDs][BookID][1]                                       Updated value: 4324232342.0

[1][2][IDs][SalesID][0]                                      Old value: 543267765345

[1][2][IDs][SalesID][0]                                      Updated value: 543267765345.0

[1][2][IDs][SalesID][1]                                      Old value: 4353543

[1][2][IDs][SalesID][1]                                      Updated value: 4353543.0

[1][2][IDs][StoreID][0]                                      Old value: 111111

[1][2][IDs][StoreID][0]                                      Updated value: 111111.0

[1][2][IDs][StoreID][1]                                      Old value: 1121111

[1][2][IDs][StoreID][1]                                      Updated value: 1121111.0

[1][IDs][BookID][0]                                          Old value: 543533254353

[1][IDs][BookID][0]                                          Updated value: 543533254353.0

[1][IDs][SalesID][0]                                         Old value: 543267765345

[1][IDs][SalesID][0]                                         Updated value: 543267765345.0

[1][IDs][StoreID][0]                                         Old value: 123445452543

[1][IDs][StoreID][0]                                         Updated value: 123445452543.0

{1: {2: {'IDs': {'BookID': [543533254353.0, 4324232342.0],

                 'SalesID': [543267765345.0, 4353543.0],

                 'StoreID': [111111.0, 1121111.0]},

         'Name': 'boring Tales of Dragon Slayers'},

     'IDs': {'BookID': [543533254353.0],

             'SalesID': [543267765345.0],

             'StoreID': [123445452543.0]},

     'Name': 'Thrilling Tales of Dragon Slayers'}}

Nested iterable from: 'https://stackoverflow.com/questions/61984148/how-to-handle-nested-lists-and-dictionaries-in-pandas-dataframe'

{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},

                    {'review_critic': 'ABC', 'review_score': 90},

                    {'review_critic': '123', 'review_score': 90}],

 'genres': ['Sports', 'Golf'],

 'score': 85,

 'title': 'Golf Simulator',

 'url': 'http://example.com/golf-simulator'}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)  # create DF stacked or unstacked, it doesn't matter

                                                         aa_all_keys                           aa_value

critic_reviews 0   review_critic  (critic_reviews, 0, review_critic)                                XYZ

                   review_score    (critic_reviews, 0, review_score)                                 90

               1   review_critic  (critic_reviews, 1, review_critic)                                ABC

                   review_score    (critic_reviews, 1, review_score)                                 90

               2   review_critic  (critic_reviews, 2, review_critic)                                123

                   review_score    (critic_reviews, 2, review_score)                                 90

genres         0   NaN                                   (genres, 0)                             Sports

               1   NaN                                   (genres, 1)                               Golf

score          NaN NaN                                      (score,)                                 85

title          NaN NaN                                      (title,)                     Golf Simulator

url            NaN NaN                                        (url,)  http://example.com/golf-simulator

df.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')

                                                         aa_all_keys    aa_value

critic_reviews 0   review_critic  (critic_reviews, 0, review_critic)         XYZ

                   review_score    (critic_reviews, 0, review_score)          90

               1   review_critic  (critic_reviews, 1, review_critic)         ABC

                   review_score    (critic_reviews, 1, review_score)          90

               2   review_critic  (critic_reviews, 2, review_critic)         123

                   review_score    (critic_reviews, 2, review_score)          90

genres         0   NaN                                   (genres, 0)      Sports

               1   NaN                                   (genres, 1)  UPDATE1111

score          NaN NaN                                      (score,)          85

title          NaN NaN                                      (title,)  UPDATE1111

url            NaN NaN                                        (url,)  UPDATE1111

mod_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

[genres][1]                                                  Old value: Golf

[genres][1]                                                  Updated value: UPDATE1111

[title]                                                      Old value: Golf Simulator

[title]                                                      Updated value: UPDATE1111

[url]                                                        Old value: http://example.com/golf-simulator

[url]                                                        Updated value: UPDATE1111

{'critic_reviews': [{'review_critic': 'XYZ', 'review_score': 90},

                    {'review_critic': 'ABC', 'review_score': 90},

                    {'review_critic': '123', 'review_score': 90}],

 'genres': ['Sports', 'UPDATE1111'],

 'score': 85,

 'title': 'UPDATE1111',

 'url': 'UPDATE1111'}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/72990265/convert-nested-list-in-dictionary-to-dataframe/72990346

{'a': 'test',

 'b': 1657,

 'c': 'asset',

 'd': [['2089', '0.0'], ['2088', '0.0']],

 'e': [['2088', '0.0'], ['2088', '0.0'], ['2088', '0.00']],

 'f': [['2088', '0.0', 'x', 'foo'],

       ['2088', '0.0', 'bar', 'i'],

       ['2088', '0.00', 'z', '0.2']],

 'x': ['test1', 'test2']}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

          aa_all_keys aa_value

a NaN NaN        (a,)     test

b NaN NaN        (b,)     1657

c NaN NaN        (c,)    asset

d 0   0     (d, 0, 0)     2089

      1     (d, 0, 1)      0.0

  1   0     (d, 1, 0)     2088

      1     (d, 1, 1)      0.0

e 0   0     (e, 0, 0)     2088

      1     (e, 0, 1)      0.0

  1   0     (e, 1, 0)     2088

      1     (e, 1, 1)      0.0

  2   0     (e, 2, 0)     2088

      1     (e, 2, 1)     0.00

f 0   0     (f, 0, 0)     2088

      1     (f, 0, 1)      0.0

      2     (f, 0, 2)        x

      3     (f, 0, 3)      foo

  1   0     (f, 1, 0)     2088

      1     (f, 1, 1)      0.0

      2     (f, 1, 2)      bar

      3     (f, 1, 3)        i

  2   0     (f, 2, 0)     2088

      1     (f, 2, 1)     0.00

      2     (f, 2, 2)        z

      3     (f, 2, 3)      0.2

x 0   NaN      (x, 0)    test1

  1   NaN      (x, 1)    test2

df.loc[df.aa_value == 1657,'aa_value'] = 1657*30

          aa_all_keys aa_value

a NaN NaN        (a,)     test

b NaN NaN        (b,)    49710

c NaN NaN        (c,)    asset

d 0   0     (d, 0, 0)     2089

      1     (d, 0, 1)      0.0

  1   0     (d, 1, 0)     2088

      1     (d, 1, 1)      0.0

e 0   0     (e, 0, 0)     2088

      1     (e, 0, 1)      0.0

  1   0     (e, 1, 0)     2088

      1     (e, 1, 1)      0.0

  2   0     (e, 2, 0)     2088

      1     (e, 2, 1)     0.00

f 0   0     (f, 0, 0)     2088

      1     (f, 0, 1)      0.0

      2     (f, 0, 2)        x

      3     (f, 0, 3)      foo

  1   0     (f, 1, 0)     2088

      1     (f, 1, 1)      0.0

      2     (f, 1, 2)      bar

      3     (f, 1, 3)        i

  2   0     (f, 2, 0)     2088

      1     (f, 2, 1)     0.00

      2     (f, 2, 2)        z

      3     (f, 2, 3)      0.2

x 0   NaN      (x, 0)    test1

  1   NaN      (x, 1)    test2

mod_iter = df.d_update_original_iter(data, verbose=True)

[b]                                                          Old value: 1657

[b]                                                          Updated value: 49710

{'a': 'test',

 'b': 49710,

 'c': 'asset',

 'd': [['2089', '0.0'], ['2088', '0.0']],

 'e': [['2088', '0.0'], ['2088', '0.0'], ['2088', '0.00']],

 'f': [['2088', '0.0', 'x', 'foo'],

       ['2088', '0.0', 'bar', 'i'],

       ['2088', '0.00', 'z', '0.2']],

 'x': ['test1', 'test2']}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/73430585/how-to-convert-a-list-of-nested-dictionaries-includes-tuples-as-a-dataframe

[{'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 2, 'Name': 'A', 'num': 68}),

  'final_value': 118},

 {'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 4, 'Name': 'A', 'num': 67}),

  'final_value': 117},

 {'cb': ({'ID': 1, 'Name': 'A', 'num': 50}, {'ID': 6, 'Name': 'A', 'num': 67}),

  'final_value': 117}]

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                             aa_all_keys aa_value

0 cb          0   ID      (0, cb, 0, ID)        1

                  Name  (0, cb, 0, Name)        A

                  num    (0, cb, 0, num)       50

              1   ID      (0, cb, 1, ID)        2

                  Name  (0, cb, 1, Name)        A

                  num    (0, cb, 1, num)       68

  final_value NaN NaN   (0, final_value)      118

1 cb          0   ID      (1, cb, 0, ID)        1

                  Name  (1, cb, 0, Name)        A

                  num    (1, cb, 0, num)       50

              1   ID      (1, cb, 1, ID)        4

                  Name  (1, cb, 1, Name)        A

                  num    (1, cb, 1, num)       67

  final_value NaN NaN   (1, final_value)      117

2 cb          0   ID      (2, cb, 0, ID)        1

                  Name  (2, cb, 0, Name)        A

                  num    (2, cb, 0, num)       50

              1   ID      (2, cb, 1, ID)        6

                  Name  (2, cb, 1, Name)        A

                  num    (2, cb, 1, num)       67

  final_value NaN NaN   (2, final_value)      117

df.d_filter_dtypes(allowed_dtypes=(int,float),fillvalue=pd.NA,column='aa_value') > 30, 'aa_value'] = 900000

                             aa_all_keys aa_value

0 cb          0   ID      (0, cb, 0, ID)        1

                  Name  (0, cb, 0, Name)        A

                  num    (0, cb, 0, num)   900000

              1   ID      (0, cb, 1, ID)        2

                  Name  (0, cb, 1, Name)        A

                  num    (0, cb, 1, num)   900000

  final_value NaN NaN   (0, final_value)   900000

1 cb          0   ID      (1, cb, 0, ID)        1

                  Name  (1, cb, 0, Name)        A

                  num    (1, cb, 0, num)   900000

              1   ID      (1, cb, 1, ID)        4

                  Name  (1, cb, 1, Name)        A

                  num    (1, cb, 1, num)   900000

  final_value NaN NaN   (1, final_value)   900000

2 cb          0   ID      (2, cb, 0, ID)        1

                  Name  (2, cb, 0, Name)        A

                  num    (2, cb, 0, num)   900000

              1   ID      (2, cb, 1, ID)        6

                  Name  (2, cb, 1, Name)        A

                  num    (2, cb, 1, num)   900000

  final_value NaN NaN   (2, final_value)   900000

mod_iter = df.d_update_original_iter(data, verbose=True)

[0][cb][0][num]                                              Old value: 50

[0][cb][0][num]                                              Updated value: 900000

[0][cb][1][num]                                              Old value: 68

[0][cb][1][num]                                              Updated value: 900000

[0][final_value]                                             Old value: 118

[0][final_value]                                             Updated value: 900000

[1][cb][0][num]                                              Old value: 50

[1][cb][0][num]                                              Updated value: 900000

[1][cb][1][num]                                              Old value: 67

[1][cb][1][num]                                              Updated value: 900000

[1][final_value]                                             Old value: 117

[1][final_value]                                             Updated value: 900000

[2][cb][0][num]                                              Old value: 50

[2][cb][0][num]                                              Updated value: 900000

[2][cb][1][num]                                              Old value: 67

[2][cb][1][num]                                              Updated value: 900000

[2][final_value]                                             Old value: 117

[2][final_value]                                             Updated value: 900000

[{'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},

         {'ID': 2, 'Name': 'A', 'num': 900000}),

  'final_value': 900000},

 {'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},

         {'ID': 4, 'Name': 'A', 'num': 900000}),

  'final_value': 900000},

 {'cb': ({'ID': 1, 'Name': 'A', 'num': 900000},

         {'ID': 6, 'Name': 'A', 'num': 900000}),

  'final_value': 900000}]

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/69943509/problems-when-flatten-a-dict

[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],

  'application_details': {'email': None, 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '1'},

 {'application_contacts': [{'adress': 'Z', 'email': None}],

  'application_details': {'email': 'testy@test_a.com', 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '2'},

 {'application_contacts': [{'adress': 'Y', 'email': None}],

  'application_details': {'email': 'testy@test_a.com', 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '3'}]

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                                              aa_all_keys          aa_value

0 application_contacts 0     adress  (0, application_contacts, 0, adress)                 X

                             email    (0, application_contacts, 0, email)     test@test.com

  application_details  email NaN          (0, application_details, email)              None

                       phone NaN          (0, application_details, phone)              None

  employer             Name  NaN                      (0, employer, Name)               Nom

                       email NaN                     (0, employer, email)              None

  id                   NaN   NaN                                  (0, id)                 1

1 application_contacts 0     adress  (1, application_contacts, 0, adress)                 Z

                             email    (1, application_contacts, 0, email)              None

  application_details  email NaN          (1, application_details, email)  testy@test_a.com

                       phone NaN          (1, application_details, phone)              None

  employer             Name  NaN                      (1, employer, Name)               Nom

                       email NaN                     (1, employer, email)              None

  id                   NaN   NaN                                  (1, id)                 2

2 application_contacts 0     adress  (2, application_contacts, 0, adress)                 Y

                             email    (2, application_contacts, 0, email)              None

  application_details  email NaN          (2, application_details, email)  testy@test_a.com

                       phone NaN          (2, application_details, phone)              None

  employer             Name  NaN                      (2, employer, Name)               Nom

                       email NaN                     (2, employer, email)              None

  id                   NaN   NaN                                  (2, id)                 3

df.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'

                                                              aa_all_keys                  aa_value

0 application_contacts 0     adress  (0, application_contacts, 0, adress)                         X

                             email    (0, application_contacts, 0, email)             test@test.com

  application_details  email NaN          (0, application_details, email)                      None

                       phone NaN          (0, application_details, phone)                      None

  employer             Name  NaN                      (0, employer, Name)                       Nom

                       email NaN                     (0, employer, email)                      None

  id                   NaN   NaN                                  (0, id)                         1

1 application_contacts 0     adress  (1, application_contacts, 0, adress)                         Z

                             email    (1, application_contacts, 0, email)                      None

  application_details  email NaN          (1, application_details, email)  UPPPPPPPPPPPPPPPDATE.COM

                       phone NaN          (1, application_details, phone)                      None

  employer             Name  NaN                      (1, employer, Name)                       Nom

                       email NaN                     (1, employer, email)                      None

  id                   NaN   NaN                                  (1, id)                         2

2 application_contacts 0     adress  (2, application_contacts, 0, adress)                         Y

                             email    (2, application_contacts, 0, email)                      None

  application_details  email NaN          (2, application_details, email)  UPPPPPPPPPPPPPPPDATE.COM

                       phone NaN          (2, application_details, phone)                      None

  employer             Name  NaN                      (2, employer, Name)                       Nom

                       email NaN                     (2, employer, email)                      None

  id                   NaN   NaN                                  (2, id)                         3

mod_iter = df.d_update_original_iter(data, verbose=True)

[1][application_details][email]                              Old value: testy@test_a.com

[1][application_details][email]                              Updated value: UPPPPPPPPPPPPPPPDATE.COM

[2][application_details][email]                              Old value: testy@test_a.com

[2][application_details][email]                              Updated value: UPPPPPPPPPPPPPPPDATE.COM

[{'application_contacts': [{'adress': 'X', 'email': 'test@test.com'}],

  'application_details': {'email': None, 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '1'},

 {'application_contacts': [{'adress': 'Z', 'email': None}],

  'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '2'},

 {'application_contacts': [{'adress': 'Y', 'email': None}],

  'application_details': {'email': 'UPPPPPPPPPPPPPPPDATE.COM', 'phone': None},

  'employer': {'Name': 'Nom', 'email': None},

  'id': '3'}]

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/62765371/convert-nested-dataframe-to-a-simple-dataframeframe

{'A': [1, 2, 3],

 'B': [4, 5, 6],

 'departure': [{'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'},

               {'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'},

               {'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'}]}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                                   aa_all_keys                   aa_value

A         0 NaN                                         (A, 0)                          1

          1 NaN                                         (A, 1)                          2

          2 NaN                                         (A, 2)                          3

B         0 NaN                                         (B, 0)                          4

          1 NaN                                         (B, 1)                          5

          2 NaN                                         (B, 2)                          6

departure 0 actual                      (departure, 0, actual)                       None

            actual_runway        (departure, 0, actual_runway)                       None

            airport                    (departure, 0, airport)                     Findel

            delay                        (departure, 0, delay)                       None

            estimated                (departure, 0, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 0, estimated_runway)                       None

            gate                          (departure, 0, gate)                       None

            iata                          (departure, 0, iata)                        LUX

            icao                          (departure, 0, icao)                       ELLX

            scheduled                (departure, 0, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 0, terminal)                       None

            timezone                  (departure, 0, timezone)          Europe/Luxembourg

          1 actual                      (departure, 1, actual)                       None

            actual_runway        (departure, 1, actual_runway)                       None

            airport                    (departure, 1, airport)                     Findel

            delay                        (departure, 1, delay)                       None

            estimated                (departure, 1, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 1, estimated_runway)                       None

            gate                          (departure, 1, gate)                       None

            iata                          (departure, 1, iata)                        LUX

            icao                          (departure, 1, icao)                       ELLX

            scheduled                (departure, 1, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 1, terminal)                       None

            timezone                  (departure, 1, timezone)          Europe/Luxembourg

          2 actual                      (departure, 2, actual)                       None

            actual_runway        (departure, 2, actual_runway)                       None

            airport                    (departure, 2, airport)                     Findel

            delay                        (departure, 2, delay)                       None

            estimated                (departure, 2, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 2, estimated_runway)                       None

            gate                          (departure, 2, gate)                       None

            iata                          (departure, 2, iata)                        LUX

            icao                          (departure, 2, icao)                       ELLX

            scheduled                (departure, 2, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 2, terminal)                       None

            timezone                  (departure, 2, timezone)          Europe/Luxembourg

df.loc[df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value')== 'ELLX', 'aa_value'] = 'ELLX-UPDATED'

                                                   aa_all_keys                   aa_value

A         0 NaN                                         (A, 0)                          1

          1 NaN                                         (A, 1)                          2

          2 NaN                                         (A, 2)                          3

B         0 NaN                                         (B, 0)                          4

          1 NaN                                         (B, 1)                          5

          2 NaN                                         (B, 2)                          6

departure 0 actual                      (departure, 0, actual)                       None

            actual_runway        (departure, 0, actual_runway)                       None

            airport                    (departure, 0, airport)                     Findel

            delay                        (departure, 0, delay)                       None

            estimated                (departure, 0, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 0, estimated_runway)                       None

            gate                          (departure, 0, gate)                       None

            iata                          (departure, 0, iata)                        LUX

            icao                          (departure, 0, icao)               ELLX-UPDATED

            scheduled                (departure, 0, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 0, terminal)                       None

            timezone                  (departure, 0, timezone)          Europe/Luxembourg

          1 actual                      (departure, 1, actual)                       None

            actual_runway        (departure, 1, actual_runway)                       None

            airport                    (departure, 1, airport)                     Findel

            delay                        (departure, 1, delay)                       None

            estimated                (departure, 1, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 1, estimated_runway)                       None

            gate                          (departure, 1, gate)                       None

            iata                          (departure, 1, iata)                        LUX

            icao                          (departure, 1, icao)               ELLX-UPDATED

            scheduled                (departure, 1, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 1, terminal)                       None

            timezone                  (departure, 1, timezone)          Europe/Luxembourg

          2 actual                      (departure, 2, actual)                       None

            actual_runway        (departure, 2, actual_runway)                       None

            airport                    (departure, 2, airport)                     Findel

            delay                        (departure, 2, delay)                       None

            estimated                (departure, 2, estimated)  2020-07-07T06:30:00+00:00

            estimated_runway  (departure, 2, estimated_runway)                       None

            gate                          (departure, 2, gate)                       None

            iata                          (departure, 2, iata)                        LUX

            icao                          (departure, 2, icao)               ELLX-UPDATED

            scheduled                (departure, 2, scheduled)  2020-07-07T06:30:00+00:00

            terminal                  (departure, 2, terminal)                       None

            timezone                  (departure, 2, timezone)          Europe/Luxembourg

mod_iter = df.d_update_original_iter(data, verbose=True)

[departure][0][icao]                                         Old value: ELLX

[departure][0][icao]                                         Updated value: ELLX-UPDATED

[departure][1][icao]                                         Old value: ELLX

[departure][1][icao]                                         Updated value: ELLX-UPDATED

[departure][2][icao]                                         Old value: ELLX

[departure][2][icao]                                         Updated value: ELLX-UPDATED

{'A': [1, 2, 3],

 'B': [4, 5, 6],

 'departure': [{'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX-UPDATED',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'},

               {'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX-UPDATED',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'},

               {'actual': None,

                'actual_runway': None,

                'airport': 'Findel',

                'delay': None,

                'estimated': '2020-07-07T06:30:00+00:00',

                'estimated_runway': None,

                'gate': None,

                'iata': 'LUX',

                'icao': 'ELLX-UPDATED',

                'scheduled': '2020-07-07T06:30:00+00:00',

                'terminal': None,

                'timezone': 'Europe/Luxembourg'}]}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/64359762/constructing-a-pandas-dataframe-with-columns-and-sub-columns-from-nested-diction

{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},

                   's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},

            't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},

                   's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},

            't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},

                   's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},

                   's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},

                   's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},

 'level2': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 9, 'col4': 9},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},

                   's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 13},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},

            't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 8},

                   's3': {'col1': 11, 'col2': 8, 'col3': 2, 'col4': 9},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 9}},

            't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},

                   's2': {'col1': 5, 'col2': 6, 'col3': 7, 'col4': 8},

                   's3': {'col1': 9, 'col2': 10, 'col3': 11, 'col4': 12},

                   's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                              aa_all_keys  aa_value

level1 t1 s1 col1  (level1, t1, s1, col1)         5

             col2  (level1, t1, s1, col2)         4

             col3  (level1, t1, s1, col3)         4

             col4  (level1, t1, s1, col4)         9

          s2 col1  (level1, t1, s2, col1)         1

                                   ...       ...

level2 t3 s3 col4  (level2, t3, s3, col4)        12

          s4 col1  (level2, t3, s4, col1)        13

             col2  (level2, t3, s4, col2)        14

             col3  (level2, t3, s4, col3)        15

             col4  (level2, t3, s4, col4)        16

[96 rows x 2 columns]

df.loc[(df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') > 5) & (df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') < 10), 'aa_value'] = 1000000

                              aa_all_keys  aa_value

level1 t1 s1 col1  (level1, t1, s1, col1)         5

             col2  (level1, t1, s1, col2)         4

             col3  (level1, t1, s1, col3)         4

             col4  (level1, t1, s1, col4)   1000000

          s2 col1  (level1, t1, s2, col1)         1

                                   ...       ...

level2 t3 s3 col4  (level2, t3, s3, col4)        12

          s4 col1  (level2, t3, s4, col1)        13

             col2  (level2, t3, s4, col2)        14

             col3  (level2, t3, s4, col3)        15

             col4  (level2, t3, s4, col4)        16

[96 rows x 2 columns]

mod_iter = df.d_update_original_iter(data, verbose=True)

[level1][t1][s1][col4]                                       Old value: 9

[level1][t1][s1][col4]                                       Updated value: 1000000

[level1][t1][s2][col4]                                       Old value: 8

[level1][t1][s2][col4]                                       Updated value: 1000000

[level1][t1][s3][col2]                                       Old value: 8

[level1][t1][s3][col2]                                       Updated value: 1000000

[level1][t1][s3][col4]                                       Old value: 9

[level1][t1][s3][col4]                                       Updated value: 1000000

[level1][t1][s4][col4]                                       Old value: 9

[level1][t1][s4][col4]                                       Updated value: 1000000

[level1][t2][s1][col4]                                       Old value: 9

[level1][t2][s1][col4]                                       Updated value: 1000000

[level1][t2][s2][col4]                                       Old value: 8

[level1][t2][s2][col4]                                       Updated value: 1000000

[level1][t2][s3][col2]                                       Old value: 8

[level1][t2][s3][col2]                                       Updated value: 1000000

[level1][t2][s3][col4]                                       Old value: 9

[level1][t2][s3][col4]                                       Updated value: 1000000

[level1][t2][s4][col4]                                       Old value: 9

[level1][t2][s4][col4]                                       Updated value: 1000000

[level1][t3][s2][col2]                                       Old value: 6

[level1][t3][s2][col2]                                       Updated value: 1000000

[level1][t3][s2][col3]                                       Old value: 7

[level1][t3][s2][col3]                                       Updated value: 1000000

[level1][t3][s2][col4]                                       Old value: 8

[level1][t3][s2][col4]                                       Updated value: 1000000

[level1][t3][s3][col1]                                       Old value: 9

[level1][t3][s3][col1]                                       Updated value: 1000000

[level2][t1][s1][col3]                                       Old value: 9

[level2][t1][s1][col3]                                       Updated value: 1000000

[level2][t1][s1][col4]                                       Old value: 9

[level2][t1][s1][col4]                                       Updated value: 1000000

[level2][t1][s3][col2]                                       Old value: 8

[level2][t1][s3][col2]                                       Updated value: 1000000

[level2][t2][s1][col4]                                       Old value: 9

[level2][t2][s1][col4]                                       Updated value: 1000000

[level2][t2][s2][col4]                                       Old value: 8

[level2][t2][s2][col4]                                       Updated value: 1000000

[level2][t2][s3][col2]                                       Old value: 8

[level2][t2][s3][col2]                                       Updated value: 1000000

[level2][t2][s3][col4]                                       Old value: 9

[level2][t2][s3][col4]                                       Updated value: 1000000

[level2][t2][s4][col4]                                       Old value: 9

[level2][t2][s4][col4]                                       Updated value: 1000000

[level2][t3][s2][col2]                                       Old value: 6

[level2][t3][s2][col2]                                       Updated value: 1000000

[level2][t3][s2][col3]                                       Old value: 7

[level2][t3][s2][col3]                                       Updated value: 1000000

[level2][t3][s2][col4]                                       Old value: 8

[level2][t3][s2][col4]                                       Updated value: 1000000

[level2][t3][s3][col1]                                       Old value: 9

[level2][t3][s3][col1]                                       Updated value: 1000000

{'level1': {'t1': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},

                   's3': {'col1': 11,

                          'col2': 1000000,

                          'col3': 2,

                          'col4': 1000000},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},

            't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},

                   's3': {'col1': 11,

                          'col2': 1000000,

                          'col3': 2,

                          'col4': 1000000},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},

            't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},

                   's2': {'col1': 5,

                          'col2': 1000000,

                          'col3': 1000000,

                          'col4': 1000000},

                   's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},

                   's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}},

 'level2': {'t1': {'s1': {'col1': 5,

                          'col2': 4,

                          'col3': 1000000,

                          'col4': 1000000},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 5},

                   's3': {'col1': 11, 'col2': 1000000, 'col3': 2, 'col4': 13},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 20}},

            't2': {'s1': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000},

                   's2': {'col1': 1, 'col2': 5, 'col3': 4, 'col4': 1000000},

                   's3': {'col1': 11,

                          'col2': 1000000,

                          'col3': 2,

                          'col4': 1000000},

                   's4': {'col1': 5, 'col2': 4, 'col3': 4, 'col4': 1000000}},

            't3': {'s1': {'col1': 1, 'col2': 2, 'col3': 3, 'col4': 4},

                   's2': {'col1': 5,

                          'col2': 1000000,

                          'col3': 1000000,

                          'col4': 1000000},

                   's3': {'col1': 1000000, 'col2': 10, 'col3': 11, 'col4': 12},

                   's4': {'col1': 13, 'col2': 14, 'col3': 15, 'col4': 16}}}}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/72146094/problems-matching-values-from-nested-dictionary

{'_links': {'next': None, 'prev': None},

 'limit': 250,

 'offset': 0,

 'runs': [{'assignedto_id': None,

           'blocked_count': 0,

           'completed_on': None,

           'config': None,

           'config_ids': [],

           'created_by': 1,

           'created_on': 1651790693,

           'custom_status1_count': 0,

           'custom_status2_count': 0,

           'custom_status3_count': 0,

           'custom_status4_count': 0,

           'custom_status5_count': 0,

           'custom_status6_count': 0,

           'custom_status7_count': 0,

           'description': None,

           'failed_count': 1,

           'id': 13,

           'include_all': False,

           'is_completed': False,

           'milestone_id': None,

           'name': '2022-05-05-testrun',

           'passed_count': 2,

           'plan_id': None,

           'project_id': 1,

           'refs': None,

           'retest_count': 0,

           'suite_id': 1,

           'untested_count': 0,

           'updated_on': 1651790693,

           'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13'},

          {'assignedto_id': None,

           'blocked_count': 0,

           'completed_on': 1650989972,

           'config': None,

           'config_ids': [],

           'created_by': 5,

           'created_on': 1650966329,

           'custom_status1_count': 0,

           'custom_status2_count': 0,

           'custom_status3_count': 0,

           'custom_status4_count': 0,

           'custom_status5_count': 0,

           'custom_status6_count': 0,

           'custom_status7_count': 0,

           'description': None,

           'failed_count': 0,

           'id': 9,

           'include_all': False,

           'is_completed': True,

           'milestone_id': None,

           'name': 'This is a new test run',

           'passed_count': 0,

           'plan_id': None,

           'project_id': 1,

           'refs': None,

           'retest_count': 0,

           'suite_id': 1,

           'untested_count': 3,

           'updated_on': 1650966329,

           'url': 'https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9'}],

 'size': 2}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                          aa_all_keys                                           aa_value

_links next NaN                        (_links, next)                                               None

       prev NaN                        (_links, prev)                                               None

limit  NaN  NaN                              (limit,)                                                250

offset NaN  NaN                             (offset,)                                                  0

runs   0    assignedto_id    (runs, 0, assignedto_id)                                               None

                                               ...                                                ...

       1    suite_id              (runs, 1, suite_id)                                                  1

            untested_count  (runs, 1, untested_count)                                                  3

            updated_on          (runs, 1, updated_on)                                         1650966329

            url                        (runs, 1, url)  https://xxxxxxxxxx.testrail.io/index.php?/runs...

size   NaN  NaN                               (size,)                                                  2

[63 rows x 2 columns]

df.loc[(df.d_filter_dtypes(allowed_dtypes=(bool),fillvalue=pd.NA,column='aa_value') ==False ), 'aa_value'] = True

df.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'

                                          aa_all_keys        aa_value

_links next NaN                        (_links, next)            None

       prev NaN                        (_links, prev)            None

limit  NaN  NaN                              (limit,)             250

offset NaN  NaN                             (offset,)               0

runs   0    assignedto_id    (runs, 0, assignedto_id)            None

                                               ...             ...

       1    suite_id              (runs, 1, suite_id)               1

            untested_count  (runs, 1, untested_count)               3

            updated_on          (runs, 1, updated_on)      1650966329

            url                        (runs, 1, url)  WWW.PYTHON.ORG

size   NaN  NaN                               (size,)               2

[63 rows x 2 columns]

mod_iter = df.d_update_original_iter(data, verbose=True)

[runs][0][include_all]                                       Old value: False

[runs][0][include_all]                                       Updated value: True

[runs][0][is_completed]                                      Old value: False

[runs][0][is_completed]                                      Updated value: True

[runs][0][url]                                               Old value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/13

[runs][0][url]                                               Updated value: WWW.PYTHON.ORG

[runs][1][include_all]                                       Old value: False

[runs][1][include_all]                                       Updated value: True

[runs][1][url]                                               Old value: https://xxxxxxxxxx.testrail.io/index.php?/runs/view/9

[runs][1][url]                                               Updated value: WWW.PYTHON.ORG

{'_links': {'next': None, 'prev': None},

 'limit': 250,

 'offset': 0,

 'runs': [{'assignedto_id': None,

           'blocked_count': 0,

           'completed_on': None,

           'config': None,

           'config_ids': [],

           'created_by': 1,

           'created_on': 1651790693,

           'custom_status1_count': 0,

           'custom_status2_count': 0,

           'custom_status3_count': 0,

           'custom_status4_count': 0,

           'custom_status5_count': 0,

           'custom_status6_count': 0,

           'custom_status7_count': 0,

           'description': None,

           'failed_count': 1,

           'id': 13,

           'include_all': True,

           'is_completed': True,

           'milestone_id': None,

           'name': '2022-05-05-testrun',

           'passed_count': 2,

           'plan_id': None,

           'project_id': 1,

           'refs': None,

           'retest_count': 0,

           'suite_id': 1,

           'untested_count': 0,

           'updated_on': 1651790693,

           'url': 'WWW.PYTHON.ORG'},

          {'assignedto_id': None,

           'blocked_count': 0,

           'completed_on': 1650989972,

           'config': None,

           'config_ids': [],

           'created_by': 5,

           'created_on': 1650966329,

           'custom_status1_count': 0,

           'custom_status2_count': 0,

           'custom_status3_count': 0,

           'custom_status4_count': 0,

           'custom_status5_count': 0,

           'custom_status6_count': 0,

           'custom_status7_count': 0,

           'description': None,

           'failed_count': 0,

           'id': 9,

           'include_all': True,

           'is_completed': True,

           'milestone_id': None,

           'name': 'This is a new test run',

           'passed_count': 0,

           'plan_id': None,

           'project_id': 1,

           'refs': None,

           'retest_count': 0,

           'suite_id': 1,

           'untested_count': 3,

           'updated_on': 1650966329,

           'url': 'WWW.PYTHON.ORG'}],

 'size': 2}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/73708706/how-to-get-values-from-list-of-nested-dictionaries/73839430#73839430

{'results': [{'end_time': '2021-01-21',

              'key': 'q1',

              'result_type': 'multipleChoice',

              'start_time': '2021-01-21',

              'value': ['1']},

             {'end_time': '2021-01-21',

              'key': 'q2',

              'result_type': 'multipleChoice',

              'start_time': '2021-01-21',

              'value': ['False']},

             {'end_time': '2021-01-21',

              'key': 'q3',

              'result_type': 'multipleChoice',

              'start_time': '2021-01-21',

              'value': ['3']},

             {'end_time': '2021-01-21',

              'key': 'q4',

              'result_type': 'multipleChoice',

              'start_time': '2021-01-21',

              'value': ['3']}]}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                         aa_all_keys        aa_value

results 0 end_time    NaN     (results, 0, end_time)      2021-01-21

          key         NaN          (results, 0, key)              q1

          result_type NaN  (results, 0, result_type)  multipleChoice

          start_time  NaN   (results, 0, start_time)      2021-01-21

          value       0       (results, 0, value, 0)               1

        1 end_time    NaN     (results, 1, end_time)      2021-01-21

          key         NaN          (results, 1, key)              q2

          result_type NaN  (results, 1, result_type)  multipleChoice

          start_time  NaN   (results, 1, start_time)      2021-01-21

          value       0       (results, 1, value, 0)           False

        2 end_time    NaN     (results, 2, end_time)      2021-01-21

          key         NaN          (results, 2, key)              q3

          result_type NaN  (results, 2, result_type)  multipleChoice

          start_time  NaN   (results, 2, start_time)      2021-01-21

          value       0       (results, 2, value, 0)               3

        3 end_time    NaN     (results, 3, end_time)      2021-01-21

          key         NaN          (results, 3, key)              q4

          result_type NaN  (results, 3, result_type)  multipleChoice

          start_time  NaN   (results, 3, start_time)      2021-01-21

          value       0       (results, 3, value, 0)               3

df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='aa_value').str.contains(r'^2021.*',na=False) ), 'aa_value'] = 10000000000 

                                         aa_all_keys        aa_value

results 0 end_time    NaN     (results, 0, end_time)     10000000000

          key         NaN          (results, 0, key)              q1

          result_type NaN  (results, 0, result_type)  multipleChoice

          start_time  NaN   (results, 0, start_time)     10000000000

          value       0       (results, 0, value, 0)               1

        1 end_time    NaN     (results, 1, end_time)     10000000000

          key         NaN          (results, 1, key)              q2

          result_type NaN  (results, 1, result_type)  multipleChoice

          start_time  NaN   (results, 1, start_time)     10000000000

          value       0       (results, 1, value, 0)           False

        2 end_time    NaN     (results, 2, end_time)     10000000000

          key         NaN          (results, 2, key)              q3

          result_type NaN  (results, 2, result_type)  multipleChoice

          start_time  NaN   (results, 2, start_time)     10000000000

          value       0       (results, 2, value, 0)               3

        3 end_time    NaN     (results, 3, end_time)     10000000000

          key         NaN          (results, 3, key)              q4

          result_type NaN  (results, 3, result_type)  multipleChoice

          start_time  NaN   (results, 3, start_time)     10000000000

          value       0       (results, 3, value, 0)               3

mod_iter = df.d_update_original_iter(data, verbose=True)

[results][0][end_time]                                       Old value: 2021-01-21

[results][0][end_time]                                       Updated value: 10000000000

[results][0][start_time]                                     Old value: 2021-01-21

[results][0][start_time]                                     Updated value: 10000000000

[results][1][end_time]                                       Old value: 2021-01-21

[results][1][end_time]                                       Updated value: 10000000000

[results][1][start_time]                                     Old value: 2021-01-21

[results][1][start_time]                                     Updated value: 10000000000

[results][2][end_time]                                       Old value: 2021-01-21

[results][2][end_time]                                       Updated value: 10000000000

[results][2][start_time]                                     Old value: 2021-01-21

[results][2][start_time]                                     Updated value: 10000000000

[results][3][end_time]                                       Old value: 2021-01-21

[results][3][end_time]                                       Updated value: 10000000000

[results][3][start_time]                                     Old value: 2021-01-21

[results][3][start_time]                                     Updated value: 10000000000

{'results': [{'end_time': 10000000000,

              'key': 'q1',

              'result_type': 'multipleChoice',

              'start_time': 10000000000,

              'value': ['1']},

             {'end_time': 10000000000,

              'key': 'q2',

              'result_type': 'multipleChoice',

              'start_time': 10000000000,

              'value': ['False']},

             {'end_time': 10000000000,

              'key': 'q3',

              'result_type': 'multipleChoice',

              'start_time': 10000000000,

              'value': ['3']},

             {'end_time': 10000000000,

              'key': 'q4',

              'result_type': 'multipleChoice',

              'start_time': 10000000000,

              'value': ['3']}]}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/66461902/flattening-nested-dictionary-into-dataframe-python

{1: {2: {'IDs': {'BookID': ['543533254353', '4324232342'],

                 'SalesID': ['543267765345', '4353543'],

                 'StoreID': ['111111', '1121111']},

         'Name': 'boring Tales of Dragon Slayers'},

     'IDs': {'BookID': ['543533254353'],

             'SalesID': ['543267765345'],

             'StoreID': ['123445452543']},

     'Name': 'Thrilling Tales of Dragon Slayers'}}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                        aa_all_keys                           aa_value

1 IDs  BookID  0       NaN      (1, IDs, BookID, 0)                       543533254353

       SalesID 0       NaN     (1, IDs, SalesID, 0)                       543267765345

       StoreID 0       NaN     (1, IDs, StoreID, 0)                       123445452543

  Name NaN     NaN     NaN                (1, Name)  Thrilling Tales of Dragon Slayers

  2    IDs     BookID  0     (1, 2, IDs, BookID, 0)                       543533254353

                       1     (1, 2, IDs, BookID, 1)                         4324232342

               SalesID 0    (1, 2, IDs, SalesID, 0)                       543267765345

                       1    (1, 2, IDs, SalesID, 1)                            4353543

               StoreID 0    (1, 2, IDs, StoreID, 0)                             111111

                       1    (1, 2, IDs, StoreID, 1)                            1121111

       Name    NaN     NaN             (1, 2, Name)     boring Tales of Dragon Slayers

df.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)

                                        aa_all_keys                           aa_value

1 IDs  BookID  0       NaN      (1, IDs, BookID, 0)                     543533254353.0

       SalesID 0       NaN     (1, IDs, SalesID, 0)                     543267765345.0

       StoreID 0       NaN     (1, IDs, StoreID, 0)                     123445452543.0

  Name NaN     NaN     NaN                (1, Name)  Thrilling Tales of Dragon Slayers

  2    IDs     BookID  0     (1, 2, IDs, BookID, 0)                     543533254353.0

                       1     (1, 2, IDs, BookID, 1)                       4324232342.0

               SalesID 0    (1, 2, IDs, SalesID, 0)                     543267765345.0

                       1    (1, 2, IDs, SalesID, 1)                          4353543.0

               StoreID 0    (1, 2, IDs, StoreID, 0)                           111111.0

                       1    (1, 2, IDs, StoreID, 1)                          1121111.0

       Name    NaN     NaN             (1, 2, Name)     boring Tales of Dragon Slayers

mod_iter = df.d_update_original_iter(data, verbose=True)

[1][2][IDs][BookID][0]                                       Old value: 543533254353

[1][2][IDs][BookID][0]                                       Updated value: 543533254353.0

[1][2][IDs][BookID][1]                                       Old value: 4324232342

[1][2][IDs][BookID][1]                                       Updated value: 4324232342.0

[1][2][IDs][SalesID][0]                                      Old value: 543267765345

[1][2][IDs][SalesID][0]                                      Updated value: 543267765345.0

[1][2][IDs][SalesID][1]                                      Old value: 4353543

[1][2][IDs][SalesID][1]                                      Updated value: 4353543.0

[1][2][IDs][StoreID][0]                                      Old value: 111111

[1][2][IDs][StoreID][0]                                      Updated value: 111111.0

[1][2][IDs][StoreID][1]                                      Old value: 1121111

[1][2][IDs][StoreID][1]                                      Updated value: 1121111.0

[1][IDs][BookID][0]                                          Old value: 543533254353

[1][IDs][BookID][0]                                          Updated value: 543533254353.0

[1][IDs][SalesID][0]                                         Old value: 543267765345

[1][IDs][SalesID][0]                                         Updated value: 543267765345.0

[1][IDs][StoreID][0]                                         Old value: 123445452543

[1][IDs][StoreID][0]                                         Updated value: 123445452543.0

{1: {2: {'IDs': {'BookID': [543533254353.0, 4324232342.0],

                 'SalesID': [543267765345.0, 4353543.0],

                 'StoreID': [111111.0, 1121111.0]},

         'Name': 'boring Tales of Dragon Slayers'},

     'IDs': {'BookID': [543533254353.0],

             'SalesID': [543267765345.0],

             'StoreID': [123445452543.0]},

     'Name': 'Thrilling Tales of Dragon Slayers'}}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/72017771/key-error-when-accessing-a-nested-dictionary

[{'blocks': [{'block_id': 'BJNTn',

              'text': {'text': 'You have a new message.',

                       'type': 'mrkdwn',

                       'verbatim': False},

              'type': 'section'},

             {'block_id': 'WPn/l',

              'text': {'text': '*Heard By*\nFriend',

                       'type': 'mrkdwn',

                       'verbatim': False},

              'type': 'section'},

             {'block_id': '5yp',

              'text': {'text': '*Which Direction? *\nNorth',

                       'type': 'mrkdwn',

                       'verbatim': False},

              'type': 'section'},

             {'block_id': 'fKEpF',

              'text': {'text': '*Which Destination*\nNew York',

                       'type': 'mrkdwn',

                       'verbatim': False},

              'type': 'section'},

             {'block_id': 'qjAH',

              'text': {'text': '*New Customer:*\\Yes',

                       'type': 'mrkdwn',

                       'verbatim': False},

              'type': 'section'},

             {'block_id': 'yt4',

              'elements': [{'action_id': '+bc',

                            'text': {'bar': 'View results',

                                     'emoji': True,

                                     'type': 'plain_text'},

                            'type': 'button',

                            'url': 'www.example.com/results'}],

              'type': 'actions'},

             {'block_id': 'IBr',

              'text': {'text': ' ', 'type': 'mrkdwn', 'verbatim': False},

              'type': 'section'}],

  'bot_id': 'BPD4K3SJW',

  'subtype': 'bot_message',

  'text': "This content can't be displayed.",

  'timestamp': '1650905606.755969',

  'type': 'message',

  'username': 'admin'},

 {'blocks': [{'block_id': 'Smd',

              'text': {'text': 'You have a new message.',

                       'type': 'mrkdwn',

                       'verbatim': False},

              'type': 'section'},

             {'block_id': '6YaLt',

              'text': {'text': '*Heard By*\nOnline Search',

                       'type': 'mrkdwn',

                       'verbatim': False},

              'type': 'section'},

             {'block_id': 'w3o',

              'text': {'text': '*Which Direction: *\nNorth',

                       'type': 'mrkdwn',

                       'verbatim': False},

              'type': 'section'},

             {'block_id': 'PTQ',

              'text': {'text': '*Which Destination? *\nMiami',

                       'type': 'mrkdwn',

                       'verbatim': False},

              'type': 'section'},

             {'block_id': 'JCfSP',

              'text': {'text': '*New Customer? *\nNo',

                       'type': 'mrkdwn',

                       'verbatim': False},

              'type': 'section'},

             {'block_id': 'yt4',

              'elements': [{'action_id': '+bc',

                            'text': {'bar': 'View results',

                                     'emoji': True,

                                     'type': 'plain_text'},

                            'type': 'button',

                            'url': 'www.example.com/results'}],

              'type': 'actions'},

             {'block_id': 'RJOA',

              'text': {'text': ' ', 'type': 'mrkdwn', 'verbatim': False},

              'type': 'section'}],

  'bot_id': 'BPD4K3SJW',

  'subtype': 'bot_message',

  'text': "This content can't be displayed.",

  'timestamp': '1650899428.077709',

  'type': 'message',

  'username': 'admin'}]

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                                              aa_all_keys                          aa_value

0 blocks    0.0 block_id NaN      NaN NaN        (0, blocks, 0, block_id)                             BJNTn

                text     text     NaN NaN      (0, blocks, 0, text, text)           You have a new message.

                         type     NaN NaN      (0, blocks, 0, text, type)                            mrkdwn

                         verbatim NaN NaN  (0, blocks, 0, text, verbatim)                             False

                type     NaN      NaN NaN            (0, blocks, 0, type)                           section

                                                                   ...                               ...

1 subtype   NaN NaN      NaN      NaN NaN                    (1, subtype)                       bot_message

  text      NaN NaN      NaN      NaN NaN                       (1, text)  This content can't be displayed.

  timestamp NaN NaN      NaN      NaN NaN                  (1, timestamp)                 1650899428.077709

  type      NaN NaN      NaN      NaN NaN                       (1, type)                           message

  username  NaN NaN      NaN      NaN NaN                   (1, username)                             admin

[88 rows x 2 columns]

df.loc[(df.d_filter_dtypes(allowed_dtypes=(bool),fillvalue=pd.NA,column='aa_value') == False), 'aa_value'] = 'NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'

                                                              aa_all_keys                                           aa_value

0 blocks    0.0 block_id NaN      NaN NaN        (0, blocks, 0, block_id)                                              BJNTn

                text     text     NaN NaN      (0, blocks, 0, text, text)                            You have a new message.

                         type     NaN NaN      (0, blocks, 0, text, type)                                             mrkdwn

                         verbatim NaN NaN  (0, blocks, 0, text, verbatim)  NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE B...

                type     NaN      NaN NaN            (0, blocks, 0, type)                                            section

                                                                   ...                                                ...

1 subtype   NaN NaN      NaN      NaN NaN                    (1, subtype)                                        bot_message

  text      NaN NaN      NaN      NaN NaN                       (1, text)                   This content can't be displayed.

  timestamp NaN NaN      NaN      NaN NaN                  (1, timestamp)                                  1650899428.077709

  type      NaN NaN      NaN      NaN NaN                       (1, type)                                            message

  username  NaN NaN      NaN      NaN NaN                   (1, username)                                              admin

[88 rows x 2 columns]

mod_iter = df.d_update_original_iter(data, verbose=True)

[0][blocks][0][text][verbatim]                               Old value: False

[0][blocks][0][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[0][blocks][1][text][verbatim]                               Old value: False

[0][blocks][1][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[0][blocks][2][text][verbatim]                               Old value: False

[0][blocks][2][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[0][blocks][3][text][verbatim]                               Old value: False

[0][blocks][3][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[0][blocks][4][text][verbatim]                               Old value: False

[0][blocks][4][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[0][blocks][6][text][verbatim]                               Old value: False

[0][blocks][6][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[1][blocks][0][text][verbatim]                               Old value: False

[1][blocks][0][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[1][blocks][1][text][verbatim]                               Old value: False

[1][blocks][1][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[1][blocks][2][text][verbatim]                               Old value: False

[1][blocks][2][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[1][blocks][3][text][verbatim]                               Old value: False

[1][blocks][3][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[1][blocks][4][text][verbatim]                               Old value: False

[1][blocks][4][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[1][blocks][6][text][verbatim]                               Old value: False

[1][blocks][6][text][verbatim]                               Updated value: NOOOOOOOOOOOOOOOOOOO MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL

[{'blocks': [{'block_id': 'BJNTn',

              'text': {'text': 'You have a new message.',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'},

             {'block_id': 'WPn/l',

              'text': {'text': '*Heard By*\nFriend',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'},

             {'block_id': '5yp',

              'text': {'text': '*Which Direction? *\nNorth',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'},

             {'block_id': 'fKEpF',

              'text': {'text': '*Which Destination*\nNew York',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'},

             {'block_id': 'qjAH',

              'text': {'text': '*New Customer:*\\Yes',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'},

             {'block_id': 'yt4',

              'elements': [{'action_id': '+bc',

                            'text': {'bar': 'View results',

                                     'emoji': True,

                                     'type': 'plain_text'},

                            'type': 'button',

                            'url': 'www.example.com/results'}],

              'type': 'actions'},

             {'block_id': 'IBr',

              'text': {'text': ' ',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'}],

  'bot_id': 'BPD4K3SJW',

  'subtype': 'bot_message',

  'text': "This content can't be displayed.",

  'timestamp': '1650905606.755969',

  'type': 'message',

  'username': 'admin'},

 {'blocks': [{'block_id': 'Smd',

              'text': {'text': 'You have a new message.',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'},

             {'block_id': '6YaLt',

              'text': {'text': '*Heard By*\nOnline Search',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'},

             {'block_id': 'w3o',

              'text': {'text': '*Which Direction: *\nNorth',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'},

             {'block_id': 'PTQ',

              'text': {'text': '*Which Destination? *\nMiami',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'},

             {'block_id': 'JCfSP',

              'text': {'text': '*New Customer? *\nNo',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'},

             {'block_id': 'yt4',

              'elements': [{'action_id': '+bc',

                            'text': {'bar': 'View results',

                                     'emoji': True,

                                     'type': 'plain_text'},

                            'type': 'button',

                            'url': 'www.example.com/results'}],

              'type': 'actions'},

             {'block_id': 'RJOA',

              'text': {'text': ' ',

                       'type': 'mrkdwn',

                       'verbatim': 'NOOOOOOOOOOOOOOOOOOO '

                                   'MOOOOOOOOOOOOOOOOOOOORE BOOOOOOOOOOOOOOOL'},

              'type': 'section'}],

  'bot_id': 'BPD4K3SJW',

  'subtype': 'bot_message',

  'text': "This content can't be displayed.",

  'timestamp': '1650899428.077709',

  'type': 'message',

  'username': 'admin'}]

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/73643077/how-to-transform-a-list-of-nested-dictionaries-into-a-data-frame-pd-json-normal

[{'apple': {'price': 4, 'units': 3}},

 {'banana': {'price': 2, 'units': 20}},

 {'orange': {'price': 5, 'units': 15}}]

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                       aa_all_keys  aa_value

0 apple  price   (0, apple, price)         4

         units   (0, apple, units)         3

1 banana price  (1, banana, price)         2

         units  (1, banana, units)        20

2 orange price  (2, orange, price)         5

         units  (2, orange, units)        15

df.loc[(df.d_filter_dtypes(allowed_dtypes=(int),fillvalue=pd.NA,column='aa_value') >3) & (df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_1').str.contains("banana")), 'aa_value'] = 50000

   level_0 level_1 level_2         aa_all_keys  aa_value

0        0   apple   price   (0, apple, price)         4

1        0   apple   units   (0, apple, units)         3

2        1  banana   price  (1, banana, price)         2

3        1  banana   units  (1, banana, units)     50000

4        2  orange   price  (2, orange, price)         5

5        2  orange   units  (2, orange, units)        15

mod_iter = df.d_update_original_iter(data, verbose=True)

[1][banana][units]                                           Old value: 20

[1][banana][units]                                           Updated value: 50000

[{'apple': {'price': 4, 'units': 3}},

 {'banana': {'price': 2, 'units': 50000}},

 {'orange': {'price': 5, 'units': 15}}]

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/58110440/opening-nested-dict-in-a-single-column-to-multiple-columns-in-pandas

{'simple25b': {'hands': {'0': {'currency': 'rm',

                               'handId': 'xyz',

                               'time': '2019-09-23 11:00:01'},

                         '1': {'currency': 'rm',

                               'handId': 'abc',

                               'time': '2019-09-23 11:01:18'}}},

 'simple5af': {'hands': {'0': {'currency': 'rm',

                               'handId': 'akg',

                               'time': '2019-09-23 10:53:22'},

                         '1': {'currency': 'rm',

                               'handId': 'mzc',

                               'time': '2019-09-23 10:54:15'},

                         '2': {'currency': 'rm',

                               'handId': 'swk',

                               'time': '2019-09-23 10:56:03'},

                         '3': {'currency': 'rm',

                               'handId': 'pQc',

                               'time': '2019-09-23 10:57:15'},

                         '4': {'currency': 'rm',

                               'handId': 'ywh',

                               'time': '2019-09-23 10:58:53'}}}}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                                aa_all_keys             aa_value

simple25b hands 0 currency  (simple25b, hands, 0, currency)                   rm

                  handId      (simple25b, hands, 0, handId)                  xyz

                  time          (simple25b, hands, 0, time)  2019-09-23 11:00:01

                1 currency  (simple25b, hands, 1, currency)                   rm

                  handId      (simple25b, hands, 1, handId)                  abc

                  time          (simple25b, hands, 1, time)  2019-09-23 11:01:18

simple5af hands 0 currency  (simple5af, hands, 0, currency)                   rm

                  handId      (simple5af, hands, 0, handId)                  akg

                  time          (simple5af, hands, 0, time)  2019-09-23 10:53:22

                1 currency  (simple5af, hands, 1, currency)                   rm

                  handId      (simple5af, hands, 1, handId)                  mzc

                  time          (simple5af, hands, 1, time)  2019-09-23 10:54:15

                2 currency  (simple5af, hands, 2, currency)                   rm

                  handId      (simple5af, hands, 2, handId)                  swk

                  time          (simple5af, hands, 2, time)  2019-09-23 10:56:03

                3 currency  (simple5af, hands, 3, currency)                   rm

                  handId      (simple5af, hands, 3, handId)                  pQc

                  time          (simple5af, hands, 3, time)  2019-09-23 10:57:15

                4 currency  (simple5af, hands, 4, currency)                   rm

                  handId      (simple5af, hands, 4, handId)                  ywh

                  time          (simple5af, hands, 4, time)  2019-09-23 10:58:53

df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_3').str.contains("time")), 'aa_value'] = pd.to_datetime(df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_3').str.contains("time")), 'aa_value'])

      level_0 level_1  ...                      aa_all_keys             aa_value

0   simple25b   hands  ...  (simple25b, hands, 0, currency)                   rm

1   simple25b   hands  ...    (simple25b, hands, 0, handId)                  xyz

2   simple25b   hands  ...      (simple25b, hands, 0, time)  2019-09-23 11:00:01

3   simple25b   hands  ...  (simple25b, hands, 1, currency)                   rm

4   simple25b   hands  ...    (simple25b, hands, 1, handId)                  abc

5   simple25b   hands  ...      (simple25b, hands, 1, time)  2019-09-23 11:01:18

6   simple5af   hands  ...  (simple5af, hands, 0, currency)                   rm

7   simple5af   hands  ...    (simple5af, hands, 0, handId)                  akg

8   simple5af   hands  ...      (simple5af, hands, 0, time)  2019-09-23 10:53:22

9   simple5af   hands  ...  (simple5af, hands, 1, currency)                   rm

10  simple5af   hands  ...    (simple5af, hands, 1, handId)                  mzc

11  simple5af   hands  ...      (simple5af, hands, 1, time)  2019-09-23 10:54:15

12  simple5af   hands  ...  (simple5af, hands, 2, currency)                   rm

13  simple5af   hands  ...    (simple5af, hands, 2, handId)                  swk

14  simple5af   hands  ...      (simple5af, hands, 2, time)  2019-09-23 10:56:03

15  simple5af   hands  ...  (simple5af, hands, 3, currency)                   rm

16  simple5af   hands  ...    (simple5af, hands, 3, handId)                  pQc

17  simple5af   hands  ...      (simple5af, hands, 3, time)  2019-09-23 10:57:15

18  simple5af   hands  ...  (simple5af, hands, 4, currency)                   rm

19  simple5af   hands  ...    (simple5af, hands, 4, handId)                  ywh

20  simple5af   hands  ...      (simple5af, hands, 4, time)  2019-09-23 10:58:53

[21 rows x 6 columns]

mod_iter = df.d_update_original_iter(data, verbose=True)

{'simple25b': {'hands': {'0': {'currency': 'rm',

                               'handId': 'xyz',

                               'time': Timestamp('2019-09-23 11:00:01')},

                         '1': {'currency': 'rm',

                               'handId': 'abc',

                               'time': Timestamp('2019-09-23 11:01:18')}}},

 'simple5af': {'hands': {'0': {'currency': 'rm',

                               'handId': 'akg',

                               'time': Timestamp('2019-09-23 10:53:22')},

                         '1': {'currency': 'rm',

                               'handId': 'mzc',

                               'time': Timestamp('2019-09-23 10:54:15')},

                         '2': {'currency': 'rm',

                               'handId': 'swk',

                               'time': Timestamp('2019-09-23 10:56:03')},

                         '3': {'currency': 'rm',

                               'handId': 'pQc',

                               'time': Timestamp('2019-09-23 10:57:15')},

                         '4': {'currency': 'rm',

                               'handId': 'ywh',

                               'time': Timestamp('2019-09-23 10:58:53')}}}}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/62059970/how-can-i-convert-nested-dictionary-to-pd-dataframe-faster

{'file': 'name',

 'main': [{'answer': [{'comment': 'It is defined as',

                       'user': 'John',

                       'value': [{'my_value': 5, 'value_2': 10},

                                 {'my_value': 24, 'value_2': 30}]},

                      {'comment': 'as John said above it simply means',

                       'user': 'Sam',

                       'value': [{'my_value': 9, 'value_2': 10},

                                 {'my_value': 54, 'value_2': 19}]}],

           'closed': 'no',

           'question': 'what is ?',

           'question_no': 'Q.1'}]}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                                                            aa_all_keys                            aa_value

file NaN NaN         NaN NaN     NaN NaN                                        (file,)                                name

main 0   answer      0   comment NaN NaN                  (main, 0, answer, 0, comment)                    It is defined as

                         user    NaN NaN                     (main, 0, answer, 0, user)                                John

                         value   0   my_value  (main, 0, answer, 0, value, 0, my_value)                                   5

                                     value_2    (main, 0, answer, 0, value, 0, value_2)                                  10

                                 1   my_value  (main, 0, answer, 0, value, 1, my_value)                                  24

                                     value_2    (main, 0, answer, 0, value, 1, value_2)                                  30

                     1   comment NaN NaN                  (main, 0, answer, 1, comment)  as John said above it simply means

                         user    NaN NaN                     (main, 0, answer, 1, user)                                 Sam

                         value   0   my_value  (main, 0, answer, 1, value, 0, my_value)                                   9

                                     value_2    (main, 0, answer, 1, value, 0, value_2)                                  10

                                 1   my_value  (main, 0, answer, 1, value, 1, my_value)                                  54

                                     value_2    (main, 0, answer, 1, value, 1, value_2)                                  19

         closed      NaN NaN     NaN NaN                              (main, 0, closed)                                  no

         question    NaN NaN     NaN NaN                            (main, 0, question)                           what is ?

         question_no NaN NaN     NaN NaN                         (main, 0, question_no)                                 Q.1

df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_6').str.contains("value_2",na=False)), 'aa_value'] = df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_6').str.contains("value_2",na=False)), 'aa_value']*1000

   level_0  ...                            aa_value

0     file  ...                                name

1     main  ...                    It is defined as

2     main  ...                                John

3     main  ...                                   5

4     main  ...                               10000

5     main  ...                                  24

6     main  ...                               30000

7     main  ...  as John said above it simply means

8     main  ...                                 Sam

9     main  ...                                   9

10    main  ...                               10000

11    main  ...                                  54

12    main  ...                               19000

13    main  ...                                  no

14    main  ...                           what is ?

15    main  ...                                 Q.1

[16 rows x 9 columns]

mod_iter = df.d_update_original_iter(data, verbose=True)

[main][0][answer][0][value][0][value_2]                      Old value: 10

[main][0][answer][0][value][0][value_2]                      Updated value: 10000

[main][0][answer][0][value][1][value_2]                      Old value: 30

[main][0][answer][0][value][1][value_2]                      Updated value: 30000

[main][0][answer][1][value][0][value_2]                      Old value: 10

[main][0][answer][1][value][0][value_2]                      Updated value: 10000

[main][0][answer][1][value][1][value_2]                      Old value: 19

[main][0][answer][1][value][1][value_2]                      Updated value: 19000

{'file': 'name',

 'main': [{'answer': [{'comment': 'It is defined as',

                       'user': 'John',

                       'value': [{'my_value': 5, 'value_2': 10000},

                                 {'my_value': 24, 'value_2': 30000}]},

                      {'comment': 'as John said above it simply means',

                       'user': 'Sam',

                       'value': [{'my_value': 9, 'value_2': 10000},

                                 {'my_value': 54, 'value_2': 19000}]}],

           'closed': 'no',

           'question': 'what is ?',

           'question_no': 'Q.1'}]}

```



```python

#Nested iterable from: 

https://stackoverflow.com/questions/39634369/4-dimensional-nested-dictionary-to-pandas-data-frame

{'orders': [{'created_at': '2016-09-20T22:04:49+02:00',

             'email': 'test@aol.com',

             'id': 4314127108,

             'line_items': [{'destination_location': {'address1': 'Teststreet '

                                                                  '12',

                                                      'address2': '',

                                                      'city': 'Berlin',

                                                      'country_code': 'DE',

                                                      'id': 2383331012,

                                                      'name': 'Test Test',

                                                      'zip': '10117'},

                             'gift_card': False,

                             'name': 'Blueberry Cup'},

                            {'destination_location': {'address1': 'Teststreet '

                                                                  '12',

                                                      'address2': '',

                                                      'city': 'Berlin',

                                                      'country_code': 'DE',

                                                      'id': 2383331012,

                                                      'name': 'Test Test',

                                                      'zip': '10117'},

                             'gift_card': False,

                             'name': 'Strawberry Cup'}]}]}

df = pd.Q_AnyNestedIterable_2df(data,unstack=False)

                                                                                                 aa_all_keys                   aa_value

orders 0 created_at NaN NaN                  NaN                                     (orders, 0, created_at)  2016-09-20T22:04:49+02:00

         email      NaN NaN                  NaN                                          (orders, 0, email)               test@aol.com

         id         NaN NaN                  NaN                                             (orders, 0, id)                 4314127108

         line_items 0   destination_location address1      (orders, 0, line_items, 0, destination_locatio...              Teststreet 12

                                             address2      (orders, 0, line_items, 0, destination_locatio...                           

                                             city          (orders, 0, line_items, 0, destination_locatio...                     Berlin

                                             country_code  (orders, 0, line_items, 0, destination_locatio...                         DE

                                             id            (orders, 0, line_items, 0, destination_locatio...                 2383331012

                                             name          (orders, 0, line_items, 0, destination_locatio...                  Test Test

                                             zip           (orders, 0, line_items, 0, destination_locatio...                      10117

                        gift_card            NaN                       (orders, 0, line_items, 0, gift_card)                      False

                        name                 NaN                            (orders, 0, line_items, 0, name)              Blueberry Cup

                    1   destination_location address1      (orders, 0, line_items, 1, destination_locatio...              Teststreet 12

                                             address2      (orders, 0, line_items, 1, destination_locatio...                           

                                             city          (orders, 0, line_items, 1, destination_locatio...                     Berlin

                                             country_code  (orders, 0, line_items, 1, destination_locatio...                         DE

                                             id            (orders, 0, line_items, 1, destination_locatio...                 2383331012

                                             name          (orders, 0, line_items, 1, destination_locatio...                  Test Test

                                             zip           (orders, 0, line_items, 1, destination_locatio...                      10117

                        gift_card            NaN                       (orders, 0, line_items, 1, gift_card)                      False

                        name                 NaN                            (orders, 0, line_items, 1, name)             Strawberry Cup

df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_6').str.contains("value_2",na=False)), 'aa_value'] = df.loc[(df.d_filter_dtypes(allowed_dtypes=(str),fillvalue=pd.NA,column='level_6').str.contains("value_2",na=False)), 'aa_value']*1000

   level_0  ...                              aa_value

0   orders  ...             2016-09-20T22:04:49+02:00

1   orders  ...                          test@aol.com

2   orders  ...                            4314127108

3   orders  ...                         Teststreet 12

4   orders  ...                                      

5   orders  ...  FRANKFURT IST VIEL BESSER ALS BERLIN

6   orders  ...                                    DE

7   orders  ...                            2383331012

8   orders  ...                             Test Test

9   orders  ...                                 10117

10  orders  ...                                 False

11  orders  ...                         Blueberry Cup

12  orders  ...                         Teststreet 12

13  orders  ...                                      

14  orders  ...  FRANKFURT IST VIEL BESSER ALS BERLIN

15  orders  ...                                    DE

16  orders  ...                            2383331012

17  orders  ...                             Test Test

18  orders  ...                                 10117

19  orders  ...                                 False

20  orders  ...                        Strawberry Cup

[21 rows x 8 columns]

mod_iter = df.d_update_original_iter(data, verbose=True)

[orders][0][line_items][0][destination_location][city]       Old value: Berlin

[orders][0][line_items][0][destination_location][city]       Updated value: FRANKFURT IST VIEL BESSER ALS BERLIN

[orders][0][line_items][1][destination_location][city]       Old value: Berlin

[orders][0][line_items][1][destination_location][city]       Updated value: FRANKFURT IST VIEL BESSER ALS BERLIN

{'orders': [{'created_at': '2016-09-20T22:04:49+02:00',

             'email': 'test@aol.com',

             'id': 4314127108,

             'line_items': [{'destination_location': {'address1': 'Teststreet '

                                                                  '12',

                                                      'address2': '',

                                                      'city': 'FRANKFURT IST '

                                                              'VIEL BESSER ALS '

                                                              'BERLIN',

                                                      'country_code': 'DE',

                                                      'id': 2383331012,

                                                      'name': 'Test Test',

                                                      'zip': '10117'},

                             'gift_card': False,

                             'name': 'Blueberry Cup'},

                            {'destination_location': {'address1': 'Teststreet '

                                                                  '12',

                                                      'address2': '',

                                                      'city': 'FRANKFURT IST '

                                                              'VIEL BESSER ALS '

                                                              'BERLIN',

                                                      'country_code': 'DE',

                                                      'id': 2383331012,

                                                      'name': 'Test Test',

                                                      'zip': '10117'},

                             'gift_card': False,

                             'name': 'Strawberry Cup'}]}]}

```



### **df.s_delete_duplicates_from_iters_in_cells**



```python

    delete_duplicates_in_column_full_of_iters(df: pandas.core.series.Series) -> pandas.core.series.Series

        df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],

                        'value': [1, 2, 3, 5]})

        getdi = lambda x: [    (randrange(1, 4), randrange(1, 4)) for v in range(20)]  #create some random tuples

        df["dicttest"] = df.lkey.apply(lambda x: getdi(x))

        print(df)

        df["dicttest"]=df.dicttest.s_delete_duplicates_from_iters_in_cells()

        print(df)

          lkey  value                                           dicttest

        0  foo      1  [(2, 1), (3, 3), (3, 3), (2, 1), (1, 2), (1, 2...

        1  bar      2  [(3, 2), (1, 1), (1, 1), (1, 2), (3, 2), (1, 2...

        2  baz      3  [(1, 2), (3, 1), (2, 1), (2, 1), (1, 1), (2, 3...

        3  foo      5  [(2, 3), (2, 3), (3, 3), (2, 2), (1, 2), (1, 2...

          lkey  value                                           dicttest

        0  foo      1  [(2, 1), (3, 3), (1, 2), (1, 3), (3, 2), (2, 3...

        1  bar      2  [(3, 2), (1, 1), (1, 2), (2, 1), (3, 1), (3, 3...

        2  baz      3  [(1, 2), (3, 1), (2, 1), (1, 1), (2, 3), (3, 3...

        3  foo      5  [(2, 3), (3, 3), (2, 2), (1, 2), (1, 1), (1, 3...

            Parameters:

            df : pd.Series

                Column with duplicates that are difficult to handle

            Returns:

                pd.Series

```



### df.ds_explode_dicts_in_column()



```python

    explode_dicts_in_column(df: pandas.core.frame.DataFrame, column_to_explode: str, drop_exploded_column: bool = True) -> pandas.core.frame.DataFrame

        df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],

                        'value': [1, 2, 3, 5]})

        getdi = lambda x: {    v: {v * randrange(1, 10): v * randrange(1, 10)} for v in range((randrange(1, 10)))} #create random nested dicts

        df["dicttest"] = df.lkey.apply(getdi)

        print(df)

        print(df.ds_explode_dicts_in_column('dicttest'))

          lkey  value                                           dicttest

        0  foo      1  {0: {0: 0}, 1: {1: 7}, 2: {2: 8}, 3: {3: 18}, ...

        1  bar      2  {0: {0: 0}, 1: {9: 4}, 2: {10: 6}, 3: {3: 21},...

        2  baz      3  {0: {0: 0}, 1: {9: 7}, 2: {2: 10}, 3: {21: 27}...

        3  foo      5                                        {0: {0: 0}}

           lkey value  level_0  level_1 aa_all_keys  aa_value

        0   foo     1        0        0      (0, 0)         0

        1   foo     1        1        1      (1, 1)         7

        2   foo     1        2        2      (2, 2)         8

        3   foo     1        3        3      (3, 3)        18

        4   foo     1        4       32     (4, 32)        16

        5   foo     1        5       35     (5, 35)        15

        6   bar     2        0        0      (0, 0)         0

        7   bar     2        1        9      (1, 9)         4

        8   bar     2        2       10     (2, 10)         6

        9   bar     2        3        3      (3, 3)        21

        10  bar     2        4       24     (4, 24)        36

        11  baz     3        0        0      (0, 0)         0

        12  baz     3        1        9      (1, 9)         7

        13  baz     3        2        2      (2, 2)        10

        14  baz     3        3       21     (3, 21)        27

        15  baz     3        4       28     (4, 28)        20

        16  baz     3        5       15     (5, 15)        30

        17  baz     3        6        6      (6, 6)         6

        18  baz     3        7       21     (7, 21)         7

        19  baz     3        8       24     (8, 24)        48

        20  foo     5        0        0      (0, 0)         0

            Parameters:

                df:pd.DataFrame

                    pd.DataFrame

                column_to_explode:str

                    column with dict in cells

                drop_exploded_column:bool

                    Drop column after exploding (default = True  )

            Returns:

                pd.DataFrame

```



### df.d_df_to_nested_dict()



```python

    Parameters

    ----------

    df: pd.DataFrame

        DataFrame to convert

    groupby: str

        column whose values will be the top level keys

    subkeys: list

        columns wholse values will be the nested keys



    df = pd.read_csv(    "https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")



        Nested dict from DataFrame:

    df[:5].d_df_to_nested_dict(groupby='Survived', subkeys=['PassengerId', 'Age', 'Pclass', 'Name', 'Sex'])



    {0: {'PassengerId': {0: 1, 4: 5},

      'Age': {0: 22.0, 4: 35.0},

      'Pclass': {0: 3, 4: 3},

      'Name': {0: 'Braund, Mr. Owen Harris', 4: 'Allen, Mr. William Henry'},

      'Sex': {0: 'male', 4: 'male'}},

     1: {'PassengerId': {1: 2, 2: 3, 3: 4},

      'Age': {1: 38.0, 2: 26.0, 3: 35.0},

      'Pclass': {1: 1, 2: 3, 3: 1},

      'Name': {1: 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)',

       2: 'Heikkinen, Miss. Laina',

       3: 'Futrelle, Mrs. Jacques Heath (Lily May Peel)'},

      'Sex': {1: 'female', 2: 'female', 3: 'female'}}}





    df[:5].d_df_to_nested_dict(groupby='Sex', subkeys=['PassengerId', 'Name'])

    Out[39]:

    {'male': {'PassengerId': {0: 1, 4: 5},

      'Name': {0: 'Braund, Mr. Owen Harris', 4: 'Allen, Mr. William Henry'}},

     'female': {'PassengerId': {1: 2, 2: 3, 3: 4},

      'Name': {1: 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)',

       2: 'Heikkinen, Miss. Laina',

       3: 'Futrelle, Mrs. Jacques Heath (Lily May Peel)'}}}

```



### df.s_explode_lists_and_tuples()



```python

    explode_lists_and_tuples_in_column(df: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], column: Optional[str] = None, concat_with_df: bool = False) -> pandas.core.frame.DataFrame

        df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],

                            'value': [1, 2, 3, 5]})

        getdi = lambda x: [    (randrange(1, 4), randrange(1, 4)) for v in range(randrange(1,5))]  #create some random tuples

        df["dicttest"] = df.lkey.apply(lambda x: getdi(x))

        print(df)

        df1=df.s_explode_lists_and_tuples(column='dicttest', concat_with_df=True)

        print(df1)

        df2=df.s_explode_lists_and_tuples(column='dicttest', concat_with_df=False)

        print(df2)

        df3=df.dicttest.s_explode_lists_and_tuples(column=None)

        print(df3)

        

          lkey  value                  dicttest

        0  foo      1                  [(3, 3)]

        1  bar      2  [(2, 3), (2, 1), (2, 2)]

        2  baz      3          [(2, 3), (2, 3)]

        3  foo      5                  [(1, 2)]

        

          lkey  value                  dicttest dicttest_0 dicttest_1 dicttest_2

        0  foo      1                  [(3, 3)]     (3, 3)       <NA>       <NA>

        1  bar      2  [(2, 3), (2, 1), (2, 2)]     (2, 3)     (2, 1)     (2, 2)

        2  baz      3          [(2, 3), (2, 3)]     (2, 3)     (2, 3)       <NA>

        3  foo      5                  [(1, 2)]     (1, 2)       <NA>       <NA>

        

          dicttest_0 dicttest_1 dicttest_2

        0     (3, 3)       <NA>       <NA>

        1     (2, 3)     (2, 1)     (2, 2)

        2     (2, 3)     (2, 3)       <NA>

        3     (1, 2)       <NA>       <NA>

        

          dicttest_0 dicttest_1 dicttest_2

        0     (3, 3)       <NA>       <NA>

        1     (2, 3)     (2, 1)     (2, 2)

        2     (2, 3)     (2, 3)       <NA>

        3     (1, 2)       <NA>       <NA>

        

            Parameters:

                df: Union[pd.Series, pd.DataFrame]

                    pd.Series, pd.DataFrame with lists/tuples in cells

                column: Union[str, None]

                    None can only be used if a pd.Series is passed. If a DataFrame is passed, a column needs to be passed too.

                concat_with_df: bool

                    if True -> returns df + exploded Series as DataFrame

                    if False -> returns exploded Series as DataFrame

            Returns:

                pd.DataFrame

                     Missing values are filled with pd.NA

```



### df.s_flatten_all_iters_in_cells()



```python

    flatten_all_iters_in_cells(df: pandas.core.series.Series) -> pandas.core.series.Series

        df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],

                        'value': [1, 2, 3, 5]})

        getdi = lambda x: [    (randrange(1, 4), randrange(1, 4)) for v in range(20)]  #create some random tuples

        df["dicttest"] = df.lkey.apply(lambda x: getdi(x))

        print(df)

        df["dicttest"]=df.dicttest.s_flatten_all_iters_in_cells()

        print(df)

          lkey  value                                           dicttest

        0  foo      1  [(2, 2), (3, 3), (3, 2), (1, 3), (1, 2), (2, 2...

        1  bar      2  [(1, 1), (3, 1), (1, 3), (3, 2), (3, 1), (2, 2...

        2  baz      3  [(3, 1), (1, 1), (3, 3), (1, 3), (3, 2), (3, 3...

        3  foo      5  [(3, 3), (3, 3), (3, 2), (2, 3), (3, 3), (2, 3...

          lkey  value                                           dicttest

        0  foo      1  [2, 2, 3, 3, 3, 2, 1, 3, 1, 2, 2, 2, 1, 3, 1, ...

        1  bar      2  [1, 1, 3, 1, 1, 3, 3, 2, 3, 1, 2, 2, 3, 1, 3, ...

        2  baz      3  [3, 1, 1, 1, 3, 3, 1, 3, 3, 2, 3, 3, 1, 3, 1, ...

        3  foo      5  [3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 2, 3, 3, 3, 3, ...

            Parameters:

            df : pd.Series

                Column with duplicates that are difficult to handle

            Returns:

                pd.Series

```



### df.d_multiple_columns_to_one()



```python

    make_several_columns_fit_in_one(df: pandas.core.frame.DataFrame, columns: list) -> list

        df = pd.read_csv(

        "https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv"

        )[:20]

        print(df)

        df['Ticket_Fare_Embarked'] = df.d_multiple_columns_to_one(columns=['Ticket','Fare', 'Embarked'])

            PassengerId  Survived  Pclass                                               Name     Sex  ...  Parch            Ticket     Fare Cabin  Embarked

        0             1         0       3                            Braund, Mr. Owen Harris    male  ...      0         A/5 21171   7.2500   NaN         S

        1             2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...      0          PC 17599  71.2833   C85         C

        2             3         1       3                             Heikkinen, Miss. Laina  female  ...      0  STON/O2. 3101282   7.9250   NaN         S

        3             4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...      0            113803  53.1000  C123         S

        4             5         0       3                           Allen, Mr. William Henry    male  ...      0            373450   8.0500   NaN         S

        5             6         0       3                                   Moran, Mr. James    male  ...      0            330877   8.4583   NaN         Q

        6             7         0       1                            McCarthy, Mr. Timothy J    male  ...      0             17463  51.8625   E46         S

        7             8         0       3                     Palsson, Master. Gosta Leonard    male  ...      1            349909  21.0750   NaN         S

        8             9         1       3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  ...      2            347742  11.1333   NaN         S

        9            10         1       2                Nasser, Mrs. Nicholas (Adele Achem)  female  ...      0            237736  30.0708   NaN         C

        10           11         1       3                    Sandstrom, Miss. Marguerite Rut  female  ...      1           PP 9549  16.7000    G6         S

        11           12         1       1                           Bonnell, Miss. Elizabeth  female  ...      0            113783  26.5500  C103         S

        12           13         0       3                     Saundercock, Mr. William Henry    male  ...      0         A/5. 2151   8.0500   NaN         S

        13           14         0       3                        Andersson, Mr. Anders Johan    male  ...      5            347082  31.2750   NaN         S

        14           15         0       3               Vestrom, Miss. Hulda Amanda Adolfina  female  ...      0            350406   7.8542   NaN         S

        15           16         1       2                   Hewlett, Mrs. (Mary D Kingcome)   female  ...      0            248706  16.0000   NaN         S

        16           17         0       3                               Rice, Master. Eugene    male  ...      1            382652  29.1250   NaN         Q

        17           18         1       2                       Williams, Mr. Charles Eugene    male  ...      0            244373  13.0000   NaN         S

        18           19         0       3  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  ...      0            345763  18.0000   NaN         S

        19           20         1       3                            Masselmani, Mrs. Fatima  female  ...      0              2649   7.2250   NaN         C

        [20 rows x 12 columns]

        df

        Out[30]:

            PassengerId  Survived  Pclass                                               Name     Sex  ...            Ticket     Fare  Cabin Embarked          Ticket_Fare_Embarked

        0             1         0       3                            Braund, Mr. Owen Harris    male  ...         A/5 21171   7.2500    NaN        S          [A/5 21171, 7.25, S]

        1             2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...          PC 17599  71.2833    C85        C        [PC 17599, 71.2833, C]

        2             3         1       3                             Heikkinen, Miss. Laina  female  ...  STON/O2. 3101282   7.9250    NaN        S  [STON/O2. 3101282, 7.925, S]

        3             4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...            113803  53.1000   C123        S             [113803, 53.1, S]

        4             5         0       3                           Allen, Mr. William Henry    male  ...            373450   8.0500    NaN        S             [373450, 8.05, S]

        5             6         0       3                                   Moran, Mr. James    male  ...            330877   8.4583    NaN        Q           [330877, 8.4583, Q]

        6             7         0       1                            McCarthy, Mr. Timothy J    male  ...             17463  51.8625    E46        S           [17463, 51.8625, S]

        7             8         0       3                     Palsson, Master. Gosta Leonard    male  ...            349909  21.0750    NaN        S           [349909, 21.075, S]

        8             9         1       3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  ...            347742  11.1333    NaN        S          [347742, 11.1333, S]

        9            10         1       2                Nasser, Mrs. Nicholas (Adele Achem)  female  ...            237736  30.0708    NaN        C          [237736, 30.0708, C]

        10           11         1       3                    Sandstrom, Miss. Marguerite Rut  female  ...           PP 9549  16.7000     G6        S            [PP 9549, 16.7, S]

        11           12         1       1                           Bonnell, Miss. Elizabeth  female  ...            113783  26.5500   C103        S            [113783, 26.55, S]

        12           13         0       3                     Saundercock, Mr. William Henry    male  ...         A/5. 2151   8.0500    NaN        S          [A/5. 2151, 8.05, S]

        13           14         0       3                        Andersson, Mr. Anders Johan    male  ...            347082  31.2750    NaN        S           [347082, 31.275, S]

        14           15         0       3               Vestrom, Miss. Hulda Amanda Adolfina  female  ...            350406   7.8542    NaN        S           [350406, 7.8542, S]

        15           16         1       2                   Hewlett, Mrs. (Mary D Kingcome)   female  ...            248706  16.0000    NaN        S             [248706, 16.0, S]

        16           17         0       3                               Rice, Master. Eugene    male  ...            382652  29.1250    NaN        Q           [382652, 29.125, Q]

        17           18         1       2                       Williams, Mr. Charles Eugene    male  ...            244373  13.0000    NaN        S             [244373, 13.0, S]

        18           19         0       3  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  ...            345763  18.0000    NaN        S             [345763, 18.0, S]

        19           20         1       3                            Masselmani, Mrs. Fatima  female  ...              2649   7.2250    NaN        C              [2649, 7.225, C]

        [20 rows x 13 columns]

            Parameters:

                df: pd.DataFrame

                    DataFrame

                columns: list

                    columns to squeeze

            Returns:

                list

```



### df.ds_normalize_lists()



```python

    normalize_lists_in_column_end_user(df: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], column: Optional[str] = None) -> pandas.core.series.Series

        df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],

                        'value': [1, 2, 3, 5]})

        getdi = lambda x: [    (randrange(1, 4), randrange(1, 4)) for v in range(randrange(1,5))]  #create some random tuples

        df["dicttest"] = df.lkey.apply(lambda x: getdi(x))

        print(df)

        df1=df.ds_normalize_lists(column='dicttest')

        print(df1)

        df2=df.dicttest.ds_normalize_lists(column='dicttest')

        print(df2)

        

          lkey  value          dicttest

        0  foo      1          [(3, 2)]

        1  bar      2          [(3, 1)]

        2  baz      3  [(3, 2), (3, 3)]

        3  foo      5  [(2, 3), (2, 1)]

        

        0      [(3, 2), <NA>]

        1      [(3, 1), <NA>]

        2    [(3, 2), (3, 3)]

        3    [(2, 3), (2, 1)]

        Name: dicttest, dtype: object

        

        0      [(3, 2), <NA>]

        1      [(3, 1), <NA>]

        2    [(3, 2), (3, 3)]

        3    [(2, 3), (2, 1)]

        Name: dicttest, dtype: object

        

            Parameters:

                df: Union[pd.Series, pd.DataFrame]

                    pd.Series, pd.DataFrame with lists/tuples in cells

                column: Union[str, None]

                    None can only be used if a pd.Series is passed. If a DataFrame is passed, a column needs to be passed too.

            Returns:

                pd.DataFrame

                     Missing values are filled with pd.NA

```



### df.d_merge_multiple_dfs_and_series_on_index() / df.d_merge_multiple_dfs_and_series_on_one_column()



```python

    qq_ds_merge_multiple_dfs_and_series_on_index(df: pandas.core.frame.DataFrame, list_with_ds: list[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame]], how='inner', on=None, sort=False, suffixes=('_x', '_y'), indicator=False, validate=None) -> pandas.core.frame.DataFrame

        df1 = pd.DataFrame({'lkeyaaaaaaaaaaaaaaaaaa': ['foo', 'bar', 'baz', 'foo'],

                        'value': [1, 2, 3, 5]})

        df2 = pd.DataFrame({'lkeybbbbbbbbbb': ['foo', 'bar', 'baz', 'foo'],

                            'value': [5, 6, 7, 8]})

        df3 = pd.DataFrame({'lkeyccccccccccccccc': ['foo', 'bar', 'baz', 'foo'],

                            'value': [15, 16, 17, 18]})

        df4 = pd.DataFrame({'lkeyddddddddddddd': ['foo', 'bar', 'baz', 'foo'],

                            'value': [115, 116, 117, 118]})

        df5 = pd.DataFrame({'lkeyeeeee': ['foo', 'bar', 'baz', 'foo'],

                            'value': [1115, 1116, 1117, 1118]})

        df1.d_merge_multiple_dfs_and_series_on_index(list_with_ds=[df2,df3,df4,df5], how="outer")

        Out[85]:

          lkeyaaaaaaaaaaaaaaaaaa  value_x_000 lkeybbbbbbbbbb  value_y_000 lkeyccccccccccccccc  value_x_002 lkeyddddddddddddd  value_y_002 lkeyeeeee  value

        0                    foo            1            foo            5                 foo           15               foo          115       foo   1115

        1                    bar            2            bar            6                 bar           16               bar          116       bar   1116

        2                    baz            3            baz            7                 baz           17               baz          117       baz   1117

        3                    foo            5            foo            8                 foo           18               foo          118       foo   1118

            Parameters:

                df : pd.DataFrame

                    DataFrame

                list_with_ds: list[Union[pd.Series, pd.DataFrame]]

                    A list of DataFrames and Series you want to merge

                how: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

                    (default = "inner"  )

                on: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

                    (default = None  )

                sort: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

                    (default = False  )

                suffixes: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

                    (default = ("_x", "_y"))

                indicator: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

                    (default = False  )

                validate: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

                    (default = None  )

            Returns:

                pd.DataFrame

```



```python

    qq_ds_merge_multiple_dfs_and_series_on_column(df: pandas.core.frame.DataFrame, list_with_ds: list[typing.Union[pandas.core.series.Series, pandas.core.frame.DataFrame]], column: str, how='inner', sort=False, suffixes=('_x', '_y'), indicator=False, validate=None) -> pandas.core.frame.DataFrame

        df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],

                        'value': [1, 2, 3, 5]})

        df2 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],

                            'value': [5, 6, 7, 8]})

        df3 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],

                            'value': [15, 16, 17, 18]})

        df4 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],

                            'value': [115, 116, 117, 118]})

        df5 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],

                            'value': [1115, 1116, 1117, 1118]})

        df1.d_merge_multiple_dfs_and_series_on_one_column(list_with_ds=[df2,df3,df4,df5],column='lkey',    how="outer",

            sort=False,

            suffixes=("_x", "_y"),

            indicator=False,

            validate=None,)

        

           lkey  value_x_000  value_y_000  value_x_002  value_y_002  value

        0   foo            1            5           15          115   1115

        1   foo            1            5           15          115   1118

        2   foo            1            5           15          118   1115

        3   foo            1            5           15          118   1118

        4   foo            1            5           18          115   1115

        5   foo            1            5           18          115   1118

        6   foo            1            5           18          118   1115

        7   foo            1            5           18          118   1118

        8   foo            1            8           15          115   1115

        9   foo            1            8           15          115   1118

        10  foo            1            8           15          118   1115

        11  foo            1            8           15          118   1118

        12  foo            1            8           18          115   1115

        13  foo            1            8           18          115   1118

        14  foo            1            8           18          118   1115

        15  foo            1            8           18          118   1118

        16  foo            5            5           15          115   1115

        17  foo            5            5           15          115   1118

        18  foo            5            5           15          118   1115

        19  foo            5            5           15          118   1118

        20  foo            5            5           18          115   1115

        21  foo            5            5           18          115   1118

        22  foo            5            5           18          118   1115

        23  foo            5            5           18          118   1118

        24  foo            5            8           15          115   1115

        25  foo            5            8           15          115   1118

        26  foo            5            8           15          118   1115

        27  foo            5            8           15          118   1118

        28  foo            5            8           18          115   1115

        29  foo            5            8           18          115   1118

        30  foo            5            8           18          118   1115

        31  foo            5            8           18          118   1118

        32  bar            2            6           16          116   1116

        33  baz            3            7           17          117   1117

            Parameters:

                df:pd.DataFrame:

                    DataFrame

                list_with_ds:list[Union[pd.Series, pd.DataFrame]]

                    A list of DataFrames and Series you want to merge

                column:str

                    Column to merge on - has to be present in every df

                how: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

                    (default = "inner"  )

                sort: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

                    (default = False  )

                suffixes: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

                    (default = ("_x", "_y"))

                indicator: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

                    (default = False  )

                validate: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

                    (default = None  )

            Returns:

                pd.DataFrame

```



### df.dicttest.s_as_flattened_list()



```python

    series_as_flattened_list(df) -> list

        df = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],

                        'value': [1, 2, 3, 5]})

        getdi = lambda x: [    (randrange(1, 4), randrange(1, 4)) for v in range(20)]  #create some random tuples

        df["dicttest"] = df.lkey.apply(lambda x: getdi(x))

        print(df)

        

        lkey  value                                           dicttest

        0  foo      1  [(3, 2), (3, 3), (3, 1), (1, 2), (2, 1), (3, 2...

        1  bar      2  [(1, 3), (3, 3), (1, 2), (3, 3), (2, 3), (1, 3...

        2  baz      3  [(1, 1), (1, 1), (3, 3), (1, 2), (1, 1), (2, 2...

        3  foo      5  [(2, 1), (2, 1), (1, 3), (1, 3), (3, 2), (2, 1...

        

        list_=df.dicttest.s_as_flattened_list()

        print(list_[:20])

        [3, 2, 3, 3, 3, 1, 1, 2, 2, 1, 3, 2, 2, 1, 1, 2, 3, 3, 2, 2]

            Parameters:

                df: pd.Series

                    Series to flatten (removes all keys in dicts, only keeps the values)

            Returns:

                list

```



### df1.d_stack() / df1.d_unstack()



```python

    unstacked_df_back_to_multiindex(dataframe: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame

            Don't use df.stack()!!!!

        

        nested = {

        "Moli": {

            "Buy": 75,

            "Sell": 53,

            "Quantity": 300,

            "TF": True},

        "Anna": {

            "Buy": 55,

            "Sell": 83,

            "Quantity": 154,

            "TF": False},

        "Bob": {

            "Buy": 25,

            "Sell": 33,

            "Quantity": 100,

            "TF": False},

        "Annie": {

            "Buy": 74,

            "Sell": 83,

            "Quantity": 96,

            "TF": True}

        }

        df1=pd.Q_AnyNestedIterable_2df(nested, unstack=True)

        print(df1)

        df1.d_stack()

           level_0   level_1        aa_all_keys aa_value

        0     Anna       Buy        (Anna, Buy)       55

        1     Anna  Quantity   (Anna, Quantity)      154

        2     Anna      Sell       (Anna, Sell)       83

        3     Anna        TF         (Anna, TF)    False

        4    Annie       Buy       (Annie, Buy)       74

        5    Annie  Quantity  (Annie, Quantity)       96

        6    Annie      Sell      (Annie, Sell)       83

        7    Annie        TF        (Annie, TF)     True

        8      Bob       Buy         (Bob, Buy)       25

        9      Bob  Quantity    (Bob, Quantity)      100

        10     Bob      Sell        (Bob, Sell)       33

        11     Bob        TF          (Bob, TF)    False

        12    Moli       Buy        (Moli, Buy)       75

        13    Moli  Quantity   (Moli, Quantity)      300

        14    Moli      Sell       (Moli, Sell)       53

        15    Moli        TF         (Moli, TF)     True

        Out[64]:

                                aa_all_keys aa_value

        level_0 level_1

        Anna    Buy             (Anna, Buy)       55

                Quantity   (Anna, Quantity)      154

                Sell           (Anna, Sell)       83

                TF               (Anna, TF)    False

        Annie   Buy            (Annie, Buy)       74

                Quantity  (Annie, Quantity)       96

                Sell          (Annie, Sell)       83

                TF              (Annie, TF)     True

        Bob     Buy              (Bob, Buy)       25

                Quantity    (Bob, Quantity)      100

                Sell            (Bob, Sell)       33

                TF                (Bob, TF)    False

        Moli    Buy             (Moli, Buy)       75

                Quantity   (Moli, Quantity)      300

                Sell           (Moli, Sell)       53

                TF               (Moli, TF)     True

        

            Parameters:

                dataframe:pd.DataFrame

                    pd.DataFrame

            Returns:

                pd.DataFrame

```



### pd.Q_ReadFileWithAllEncodings_2df()



        There are plenty of good libraries out there that help you with finding the right encoding for your file,

        but sometimes they don't work like expected, and you have to choose the best encoding manually. This method

        opens any file in all encodings available in your env and returns all results in a DataFrame.



```python

        pd.Q_ReadFileWithAllEncodings_2df(r"C:\Users\Gamer\Documents\Downloads\corruptjson1.json")

                        codec                                     strict_encoded  \

        0           ascii  ['ascii' codec can't decode byte 0xef in posit...

        1    base64_codec                                [Incorrect padding]

        2            big5  [({\r\n"doc_id": "some_number",\r\n"url": "www...

        3       big5hkscs  [({\r\n"doc_id": "some_number",\r\n"url": "www...

        4       bz2_codec                              [Invalid data stream]

        ..            ...                                                ...

        115         utf_7  ['utf7' codec can't decode byte 0xef in positi...

        116         utf_8  [({\r\n"doc_id": "some_number",\r\n"url": "www...

        117     utf_8_sig  [({\r\n"doc_id": "some_number",\r\n"url": "www...

        118      uu_codec               [Missing "begin" line in input data]

        119    zlib_codec  [Error -3 while decompressing data: incorrect ...

             strict_bad                                     ignore_encoded  \

        0          True  [({\r\n"doc_id": "some_number",\r\n"url": "www...

        1          True                                                 []

        

        

        

            Parameters:

                filepath (str): file path

            Returns:

                pd.DataFrame

```



### pd.Q_CorruptJsonFile_2dict()



```python

    read_corrupt_json(filepath: str) -> dict

        Usage: pdQ_CorruptJsonFile_2dictf(r'C:\corruptjson1.json')

        

        If you need to read a corrupted JSON file, you can try this method.

        It will first try to read the file using ujson.

        Second step: The file will be read using all encoders found in your env. Each result will be passed to ast.literal_eval, json.loads and ujson.loads

        Third step: Keys and values are extracted using regex

        

        All positive results are returned as a dict, you have to check which one fits best to your needs

        

            finaldict = {

                "ujson_file_reading_result": ujson_file_reading_result,

                "literal_eval_after_newline_removed": literal_eval_after_newline_removed,

                "json_after_head_tail_removed": json_after_head_tail_removed,

                "ujson_after_head_tail_removed": ujson_after_head_tail_removed,

                "regex_get_single_item_keys": allgoodresultsdict,

            }

        

        If the keys are not double-quoted, it won't work.

        It works well with spaces and not correctly escaped characters

        

        Example from https://stackoverflow.com/questions/59927549/how-to-fix-a-possibly-corrupted-json-file-problems-with-a-curly-bracket-charact

        

        {

        "doc_id": "some_number",

        "url": "www.seedurl1.com",

        "scrape_date": "2019-10-22 16:17:22",

        "publish_date": "unknown",

        "author": "unknown",

        "urls_out": [

        "https://www.something.com",

        "https://www.sometingelse.com/smth"

        ],

        "text": "lots of text here"

        }

        ﻿{

        "doc_id": "some_other_number",

        "url": "www.seedurl2.com/smth",

        "scrape_date": "2019-10-22 17:44:40",

        "publish_date": "unknown",

        "author": "unknown",

        "urls_out": [

        "www.anotherurl.com/smth",

        "http://urlx.com/smth.htm"

        ],

        "text": "lots more text over here."

        }

        

        Result:

        {'ujson_file_reading_result': None,

         'literal_eval_after_newline_removed': Empty DataFrame

         Columns: [level_0, level_1, level_2, aa_all_keys, aa_value, ast_results]

         Index: [],

         'json_after_head_tail_removed':       level_0  ...                                         json_loads

         862  punycode  ...  {'doc_id': 'some_number', 'url': 'www.seedurl1...

         865  punycode  ...  {'doc_id': 'some_number', 'url': 'www.seedurl1...

        

         [2 rows x 8 columns],

         'ujson_after_head_tail_removed':       level_0  ...                                        ujson_loads

         862  punycode  ...  {'doc_id': 'some_number', 'url': 'www.seedurl1...

         865  punycode  ...  {'doc_id': 'some_number', 'url': 'www.seedurl1...

        

         [2 rows x 9 columns],

         'regex_get_single_item_keys': [{'aa_value': {0: 'some_number',

            1: 'www.seedurl1.com',

            2: '2019-10-22 16:17:22',

            3: 'unknown',

            4: 'unknown',

            5: ['https://www.something.com', 'https://www.sometingelse.com/smth'],

            6: 'lots of text here'},

           'aa_key': {0: 'doc_id',

            1: 'url',

            2: 'scrape_date',

            3: 'publish_date',

            4: 'author',

            5: 'urls_out',

            6: 'text'}},

          {'aa_value': {7: 'some_other_number',

            8: 'www.seedurl2.com/smth',

            9: '2019-10-22 17:44:40',

            10: 'unknown',

            ........

        

            Parameters:

                filepath (str): file path

            Returns:

                dict

```



### df.d_sort_columns_with_sorted()



```python

    qq_d_sort_columns_alphabetically(df: pandas.core.frame.DataFrame, reverse: bool = False) -> pandas.core.frame.DataFrame

        Sorts columns alphabetically with sorted()! Not with natsort()!

        df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")

        print(df)

        df.d_sort_columns_with_sorted()

             PassengerId  Survived  Pclass                                               Name     Sex  ...  Parch            Ticket     Fare Cabin  Embarked

        0              1         0       3                            Braund, Mr. Owen Harris    male  ...      0         A/5 21171   7.2500   NaN         S

        1              2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...      0          PC 17599  71.2833   C85         C

        2              3         1       3                             Heikkinen, Miss. Laina  female  ...      0  STON/O2. 3101282   7.9250   NaN         S

        3              4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...      0            113803  53.1000  C123         S

        4              5         0       3                           Allen, Mr. William Henry    male  ...      0            373450   8.0500   NaN         S

        ..           ...       ...     ...                                                ...     ...  ...    ...               ...      ...   ...       ...

        886          887         0       2                              Montvila, Rev. Juozas    male  ...      0            211536  13.0000   NaN         S

        887          888         1       1                       Graham, Miss. Margaret Edith  female  ...      0            112053  30.0000   B42         S

        888          889         0       3           Johnston, Miss. Catherine Helen "Carrie"  female  ...      2        W./C. 6607  23.4500   NaN         S

        889          890         1       1                              Behr, Mr. Karl Howell    male  ...      0            111369  30.0000  C148         C

        890          891         0       3                                Dooley, Mr. Patrick    male  ...      0            370376   7.7500   NaN         Q

        [891 rows x 12 columns]

        Out[66]:

              Age Cabin Embarked     Fare                                               Name  ...  Pclass     Sex  SibSp Survived            Ticket

        0    22.0   NaN        S   7.2500                            Braund, Mr. Owen Harris  ...       3    male      1        0         A/5 21171

        1    38.0   C85        C  71.2833  Cumings, Mrs. John Bradley (Florence Briggs Th...  ...       1  female      1        1          PC 17599

        2    26.0   NaN        S   7.9250                             Heikkinen, Miss. Laina  ...       3  female      0        1  STON/O2. 3101282

        3    35.0  C123        S  53.1000       Futrelle, Mrs. Jacques Heath (Lily May Peel)  ...       1  female      1        1            113803

        4    35.0   NaN        S   8.0500                           Allen, Mr. William Henry  ...       3    male      0        0            373450

        ..    ...   ...      ...      ...                                                ...  ...     ...     ...    ...      ...               ...

        886  27.0   NaN        S  13.0000                              Montvila, Rev. Juozas  ...       2    male      0        0            211536

        887  19.0   B42        S  30.0000                       Graham, Miss. Margaret Edith  ...       1  female      0        1            112053

        888   NaN   NaN        S  23.4500           Johnston, Miss. Catherine Helen "Carrie"  ...       3  female      1        0        W./C. 6607

        889  26.0  C148        C  30.0000                              Behr, Mr. Karl Howell  ...       1    male      0        1            111369

        890  32.0   NaN        Q   7.7500                                Dooley, Mr. Patrick  ...       3    male      0        0            370376

        [891 rows x 12 columns]

            Parameters:

                df : pd.DataFrame

                reverse: bool

                    Z-A instead of A-Z (default = False)

        

            Returns:

                pd.DataFrame

```



### df.ds_isna()



```python

    is_nan_true_false_check(df: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], include_na_strings: bool = True, include_empty_iters: bool = False, include_0_len_string: bool = False) -> Union[pandas.core.series.Series, pandas.core.frame.DataFrame]

        df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")

        df.ds_isna()

        Out[107]:

             PassengerId  Survived  Pclass   Name    Sex  ...  Parch  Ticket   Fare  Cabin  Embarked

        0          False     False   False  False  False  ...  False   False  False   True     False

        1          False     False   False  False  False  ...  False   False  False  False     False

        2          False     False   False  False  False  ...  False   False  False   True     False

        3          False     False   False  False  False  ...  False   False  False  False     False

        4          False     False   False  False  False  ...  False   False  False   True     False

        ..           ...       ...     ...    ...    ...  ...    ...     ...    ...    ...       ...

        886        False     False   False  False  False  ...  False   False  False   True     False

        887        False     False   False  False  False  ...  False   False  False  False     False

        888        False     False   False  False  False  ...  False   False  False   True     False

        889        False     False   False  False  False  ...  False   False  False  False     False

        890        False     False   False  False  False  ...  False   False  False   True     False

        [891 rows x 12 columns]

        df.Cabin.ds_isna()

        Out[108]:

        0       True

        1      False

        2       True

        3      False

        4       True

               ...

        886     True

        887    False

        888     True

        889    False

        890     True

        Name: Cabin, Length: 891, dtype: bool

            Parameters:

                df: Union[pd.Series, pd.DataFrame]

                    pd.Series, pd.DataFrame

                include_na_strings: bool

                    When True -> treated as nan:

        

                    [

                    "<NA>",

                    "<NAN>",

                    "<nan>",

                    "np.nan",

                    "NoneType",

                    "None",

                    "-1.#IND",

                    "1.#QNAN",

                    "1.#IND",

                    "-1.#QNAN",

                    "#N/A N/A",

                    "#N/A",

                    "N/A",

                    "n/a",

                    "NA",

                    "#NA",

                    "NULL",

                    "null",

                    "NaN",

                    "-NaN",

                    "nan",

                    "-nan",

                    ]

        

                    (default =True)

                include_empty_iters: bool

                    When True -> [], {} are treated as nan (default = False )

        

                include_0_len_string: bool

                    When True -> '' is treated as nan (default = False )

                    Returns:

                dict

            Returns:

                Union[pd.Series, pd.DataFrame]

```



### df.d_add_value_to_existing_columns_with_loc()



```python

    df_loc_add(df: pandas.core.frame.DataFrame, condition: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], add_to_colum: Any, column: str, throw_towel_early: bool = False, as_last_chance_convert_to_string: bool = False) -> pandas.core.frame.DataFrame

        df = pd.read_csv(

        "https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv"

        )

        print(df[:6])

        df[:6].d_add_value_to_existing_columns_with_loc(condition=(df.Pclass == 3), add_to_colum=100000, column="Fare")

           PassengerId  Survived  Pclass                                               Name     Sex  ...  Parch            Ticket     Fare Cabin  Embarked

        0            1         0       3                            Braund, Mr. Owen Harris    male  ...      0         A/5 21171   7.2500   NaN         S

        1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...      0          PC 17599  71.2833   C85         C

        2            3         1       3                             Heikkinen, Miss. Laina  female  ...      0  STON/O2. 3101282   7.9250   NaN         S

        3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...      0            113803  53.1000  C123         S

        4            5         0       3                           Allen, Mr. William Henry    male  ...      0            373450   8.0500   NaN         S

        5            6         0       3                                   Moran, Mr. James    male  ...      0            330877   8.4583   NaN         Q

        [6 rows x 12 columns]

        Out[37]:

           PassengerId  Survived  Pclass                                               Name     Sex  ...  Parch            Ticket         Fare Cabin  Embarked

        0            1         0       3                            Braund, Mr. Owen Harris    male  ...      0         A/5 21171  100007.2500   NaN         S

        1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...      0          PC 17599      71.2833   C85         C

        2            3         1       3                             Heikkinen, Miss. Laina  female  ...      0  STON/O2. 3101282  100007.9250   NaN         S

        3            4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...      0            113803      53.1000  C123         S

        4            5         0       3                           Allen, Mr. William Henry    male  ...      0            373450  100008.0500   NaN         S

        5            6         0       3                                   Moran, Mr. James    male  ...      0            330877  100008.4583   NaN         Q

        [6 rows x 12 columns]

            Parameters:

                df: pd.DataFrame

                    DataFrame to update

                condition: Union[pd.Series, pd.DataFrame]

                    Pass a condition with df.loc: df.loc[df['shield'] > 6]

                add_to_colum:: Any

                    Value that you want to add to old values

                column: str

                    Column which should be updated

                throw_towel_early: bool

                    If False: If there is an exception, will be iterating line by line changing each value.

                    If it fails, it will keep the old value. (default = False)

                as_last_chance_convert_to_string: bool

                    If you want to change the value at any cost, you can change both values to strings and add them up, which will result in:

                    1+1 = "11"

                    "Big" + "Brother" = "BigBrother"

            Returns:

                pd.DataFrame

```



### df.d_drop_rows_with_df_loc()



```python

    df_loc_drop(df: pandas.core.frame.DataFrame, condition: Union[pandas.core.series.Series, pandas.core.frame.DataFrame]) -> pandas.core.frame.DataFrame

        df.d_drop_rows_with_df_loc(df.level_1.str.contains("aa_k")) is the same as df.loc[~df.level_1.str.contains('aa_k')].copy()

        df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")

        df

        Out[54]:

             PassengerId  Survived  Pclass                                               Name     Sex  ...  Parch            Ticket     Fare Cabin  Embarked

        0              1         0       3                            Braund, Mr. Owen Harris    male  ...      0         A/5 21171   7.2500   NaN         S

        1              2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...      0          PC 17599  71.2833   C85         C

        2              3         1       3                             Heikkinen, Miss. Laina  female  ...      0  STON/O2. 3101282   7.9250   NaN         S

        3              4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...      0            113803  53.1000  C123         S

        4              5         0       3                           Allen, Mr. William Henry    male  ...      0            373450   8.0500   NaN         S

        ..           ...       ...     ...                                                ...     ...  ...    ...               ...      ...   ...       ...

        886          887         0       2                              Montvila, Rev. Juozas    male  ...      0            211536  13.0000   NaN         S

        887          888         1       1                       Graham, Miss. Margaret Edith  female  ...      0            112053  30.0000   B42         S

        888          889         0       3           Johnston, Miss. Catherine Helen "Carrie"  female  ...      2        W./C. 6607  23.4500   NaN         S

        889          890         1       1                              Behr, Mr. Karl Howell    male  ...      0            111369  30.0000  C148         C

        890          891         0       3                                Dooley, Mr. Patrick    male  ...      0            370376   7.7500   NaN         Q

        [891 rows x 12 columns]

        df.d_drop_rows_with_df_loc(df.Sex.str.contains(r"mal$", regex=True, na=False))

        Out[55]:

             PassengerId  Survived  Pclass                                               Name     Sex  ...  Parch            Ticket     Fare Cabin  Embarked

        1              2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...      0          PC 17599  71.2833   C85         C

        2              3         1       3                             Heikkinen, Miss. Laina  female  ...      0  STON/O2. 3101282   7.9250   NaN         S

        3              4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...      0            113803  53.1000  C123         S

        8              9         1       3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  ...      2            347742  11.1333   NaN         S

        9             10         1       2                Nasser, Mrs. Nicholas (Adele Achem)  female  ...      0            237736  30.0708   NaN         C

        ..           ...       ...     ...                                                ...     ...  ...    ...               ...      ...   ...       ...

        880          881         1       2       Shelley, Mrs. William (Imanita Parrish Hall)  female  ...      1            230433  26.0000   NaN         S

        882          883         0       3                       Dahlberg, Miss. Gerda Ulrika  female  ...      0              7552  10.5167   NaN         S

        885          886         0       3               Rice, Mrs. William (Margaret Norton)  female  ...      5            382652  29.1250   NaN         Q

        887          888         1       1                       Graham, Miss. Margaret Edith  female  ...      0            112053  30.0000   B42         S

        888          889         0       3           Johnston, Miss. Catherine Helen "Carrie"  female  ...      2        W./C. 6607  23.4500   NaN         S

        [314 rows x 12 columns]

            Parameters:

                df: pd.DataFrame

                    DataFrame

                condition: Union[pd.Series, pd.DataFrame]

                    Condition with df.loc: df.loc[df['shield'] > 6]

            Returns:

                pd.DataFrame

```



### df.d_set_values_with_df_loc



```python

    df_loc_set(df: pandas.core.frame.DataFrame, condition: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], new_data: Any, column: str) -> pandas.core.frame.DataFrame

        df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")

        

        df

        Out[51]:

             PassengerId  Survived  Pclass                                               Name     Sex  ...  Parch            Ticket     Fare Cabin  Embarked

        0              1         0       3                            Braund, Mr. Owen Harris    male  ...      0         A/5 21171   7.2500   NaN         S

        1              2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...      0          PC 17599  71.2833   C85         C

        2              3         1       3                             Heikkinen, Miss. Laina  female  ...      0  STON/O2. 3101282   7.9250   NaN         S

        3              4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...      0            113803  53.1000  C123         S

        4              5         0       3                           Allen, Mr. William Henry    male  ...      0            373450   8.0500   NaN         S

        ..           ...       ...     ...                                                ...     ...  ...    ...               ...      ...   ...       ...

        886          887         0       2                              Montvila, Rev. Juozas    male  ...      0            211536  13.0000   NaN         S

        887          888         1       1                       Graham, Miss. Margaret Edith  female  ...      0            112053  30.0000   B42         S

        888          889         0       3           Johnston, Miss. Catherine Helen "Carrie"  female  ...      2        W./C. 6607  23.4500   NaN         S

        889          890         1       1                              Behr, Mr. Karl Howell    male  ...      0            111369  30.0000  C148         C

        890          891         0       3                                Dooley, Mr. Patrick    male  ...      0            370376   7.7500   NaN         Q

        [891 rows x 12 columns]

        df.d_set_values_with_df_loc(condition = df.Sex.str.contains(r"mal$", regex=True, na=False),column = 'Fare',new_data = 100000)

        Out[52]:

             PassengerId  Survived  Pclass                                               Name     Sex  ...  Parch            Ticket         Fare Cabin  Embarked

        0              1         0       3                            Braund, Mr. Owen Harris    male  ...      0         A/5 21171  100000.0000   NaN         S

        1              2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...      0          PC 17599      71.2833   C85         C

        2              3         1       3                             Heikkinen, Miss. Laina  female  ...      0  STON/O2. 3101282       7.9250   NaN         S

        3              4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...      0            113803      53.1000  C123         S

        4              5         0       3                           Allen, Mr. William Henry    male  ...      0            373450  100000.0000   NaN         S

        ..           ...       ...     ...                                                ...     ...  ...    ...               ...          ...   ...       ...

        886          887         0       2                              Montvila, Rev. Juozas    male  ...      0            211536  100000.0000   NaN         S

        887          888         1       1                       Graham, Miss. Margaret Edith  female  ...      0            112053      30.0000   B42         S

        888          889         0       3           Johnston, Miss. Catherine Helen "Carrie"  female  ...      2        W./C. 6607      23.4500   NaN         S

        889          890         1       1                              Behr, Mr. Karl Howell    male  ...      0            111369  100000.0000  C148         C

        890          891         0       3                                Dooley, Mr. Patrick    male  ...      0            370376  100000.0000   NaN         Q

        [891 rows x 12 columns]

        

        

            Parameters:

                df: pd.Dataframe

                    DataFrame

                condition: Union[pd.Series, pd.DataFrame]

                    Pass a condition with df.loc: df.loc[df['shield'] > 6]

                new_data: Any

                    New values for update

                column: str

                    Column which should be updated

            Returns:

                pd.DataFrame

```



### df.d_dfloc()



```python

    df_loc(df: pandas.core.frame.DataFrame, condition: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], column: Optional[str] = None) -> Union[pandas.core.series.Series, pandas.core.frame.DataFrame]

        df.d_dfloc(df.aa_value.str.contains("author")) is the same as df.loc[df.aa_value.str.contains('author')].copy()

        

        df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")

        print(df)

        print(df.d_dfloc(df.Sex.str.contains(r"mal$", regex=True, na=False)))

        df.d_dfloc(df.Sex.str.contains(r"mal$", regex=True, na=False),column='Name')

             PassengerId  Survived  Pclass                                               Name     Sex  ...  Parch            Ticket     Fare Cabin  Embarked

        0              1         0       3                            Braund, Mr. Owen Harris    male  ...      0         A/5 21171   7.2500   NaN         S

        1              2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...      0          PC 17599  71.2833   C85         C

        2              3         1       3                             Heikkinen, Miss. Laina  female  ...      0  STON/O2. 3101282   7.9250   NaN         S

        3              4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...      0            113803  53.1000  C123         S

        4              5         0       3                           Allen, Mr. William Henry    male  ...      0            373450   8.0500   NaN         S

        ..           ...       ...     ...                                                ...     ...  ...    ...               ...      ...   ...       ...

        886          887         0       2                              Montvila, Rev. Juozas    male  ...      0            211536  13.0000   NaN         S

        887          888         1       1                       Graham, Miss. Margaret Edith  female  ...      0            112053  30.0000   B42         S

        888          889         0       3           Johnston, Miss. Catherine Helen "Carrie"  female  ...      2        W./C. 6607  23.4500   NaN         S

        889          890         1       1                              Behr, Mr. Karl Howell    male  ...      0            111369  30.0000  C148         C

        890          891         0       3                                Dooley, Mr. Patrick    male  ...      0            370376   7.7500   NaN         Q

        [891 rows x 12 columns]

             PassengerId  Survived  Pclass                            Name   Sex  ...  Parch            Ticket     Fare Cabin  Embarked

        0              1         0       3         Braund, Mr. Owen Harris  male  ...      0         A/5 21171   7.2500   NaN         S

        4              5         0       3        Allen, Mr. William Henry  male  ...      0            373450   8.0500   NaN         S

        5              6         0       3                Moran, Mr. James  male  ...      0            330877   8.4583   NaN         Q

        6              7         0       1         McCarthy, Mr. Timothy J  male  ...      0             17463  51.8625   E46         S

        7              8         0       3  Palsson, Master. Gosta Leonard  male  ...      1            349909  21.0750   NaN         S

        ..           ...       ...     ...                             ...   ...  ...    ...               ...      ...   ...       ...

        883          884         0       2   Banfield, Mr. Frederick James  male  ...      0  C.A./SOTON 34068  10.5000   NaN         S

        884          885         0       3          Sutehall, Mr. Henry Jr  male  ...      0   SOTON/OQ 392076   7.0500   NaN         S

        886          887         0       2           Montvila, Rev. Juozas  male  ...      0            211536  13.0000   NaN         S

        889          890         1       1           Behr, Mr. Karl Howell  male  ...      0            111369  30.0000  C148         C

        890          891         0       3             Dooley, Mr. Patrick  male  ...      0            370376   7.7500   NaN         Q

        [577 rows x 12 columns]

        Out[60]:

        0             Braund, Mr. Owen Harris

        4            Allen, Mr. William Henry

        5                    Moran, Mr. James

        6             McCarthy, Mr. Timothy J

        7      Palsson, Master. Gosta Leonard

                            ...

        883     Banfield, Mr. Frederick James

        884            Sutehall, Mr. Henry Jr

        886             Montvila, Rev. Juozas

        889             Behr, Mr. Karl Howell

        890               Dooley, Mr. Patrick

        Name: Name, Length: 577, dtype: object

            Parameters:

                df: pd.DataFrame

                    DataFrame

                condition: Union[pd.Series, pd.DataFrame]

                    Pass a condition with df.loc: df.loc[df['shield'] > 6]

                column: Union[None, str]

                    if a string is passed, the method will return pd.Series

                    None will return the whole DataFrame (default = None )

            Returns:

                Union[pd.Series, pd.DataFrame]

```



### df.df.ds_all_nans_to_pdNA()



```python

    all_nans_in_df_to_pdNA(df: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], include_na_strings: bool = True, include_empty_iters: bool = False, include_0_len_string: bool = False) -> Union[pandas.core.series.Series, pandas.core.frame.DataFrame]

        df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")

        

        df

        Out[86]:

             PassengerId  Survived  Pclass                                               Name     Sex  ...  Parch            Ticket     Fare Cabin  Embarked

        0              1         0       3                            Braund, Mr. Owen Harris    male  ...      0         A/5 21171   7.2500   NaN         S

        1              2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...      0          PC 17599  71.2833   C85         C

        2              3         1       3                             Heikkinen, Miss. Laina  female  ...      0  STON/O2. 3101282   7.9250   NaN         S

        3              4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...      0            113803  53.1000  C123         S

        4              5         0       3                           Allen, Mr. William Henry    male  ...      0            373450   8.0500   NaN         S

        ..           ...       ...     ...                                                ...     ...  ...    ...               ...      ...   ...       ...

        886          887         0       2                              Montvila, Rev. Juozas    male  ...      0            211536  13.0000   NaN         S

        887          888         1       1                       Graham, Miss. Margaret Edith  female  ...      0            112053  30.0000   B42         S

        888          889         0       3           Johnston, Miss. Catherine Helen "Carrie"  female  ...      2        W./C. 6607  23.4500   NaN         S

        889          890         1       1                              Behr, Mr. Karl Howell    male  ...      0            111369  30.0000  C148         C

        890          891         0       3                                Dooley, Mr. Patrick    male  ...      0            370376   7.7500   NaN         Q

        [891 rows x 12 columns]

        df.ds_all_nans_to_pdNA()

        Out[87]:

             PassengerId  Survived  Pclass                                               Name     Sex  ... Parch            Ticket     Fare Cabin  Embarked

        0              1         0       3                            Braund, Mr. Owen Harris    male  ...     0         A/5 21171   7.2500  <NA>         S

        1              2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  ...     0          PC 17599  71.2833   C85         C

        2              3         1       3                             Heikkinen, Miss. Laina  female  ...     0  STON/O2. 3101282   7.9250  <NA>         S

        3              4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  ...     0            113803  53.1000  C123         S

        4              5         0       3                           Allen, Mr. William Henry    male  ...     0            373450   8.0500  <NA>         S

        ..           ...       ...     ...                                                ...     ...  ...   ...               ...      ...   ...       ...

        886          887         0       2                              Montvila, Rev. Juozas    male  ...     0            211536  13.0000  <NA>         S

        887          888         1       1                       Graham, Miss. Margaret Edith  female  ...     0            112053  30.0000   B42         S

        888          889         0       3           Johnston, Miss. Catherine Helen "Carrie"  female  ...     2        W./C. 6607  23.4500  <NA>         S

        889          890         1       1                              Behr, Mr. Karl Howell    male  ...     0            111369  30.0000  C148         C

        890          891         0       3                                Dooley, Mr. Patrick    male  ...     0            370376   7.7500  <NA>         Q

        [891 rows x 12 columns]

        df.Cabin.ds_all_nans_to_pdNA()

        Out[88]:

        0      <NA>

        1       C85

        2      <NA>

        3      C123

        4      <NA>

               ...

        886    <NA>

        887     B42

        888    <NA>

        889    C148

        890    <NA>

        Name: Cabin, Length: 891, dtype: object

        

            Parameters:

                df: Union[pd.Series, pd.DataFrame]

                    pd.Series, pd.DataFrame

                include_na_strings: bool

                    When True -> treated as nan:

        

                    [

                    "<NA>",

                    "<NAN>",

                    "<nan>",

                    "np.nan",

                    "NoneType",

                    "None",

                    "-1.#IND",

                    "1.#QNAN",

                    "1.#IND",

                    "-1.#QNAN",

                    "#N/A N/A",

                    "#N/A",

                    "N/A",

                    "n/a",

                    "NA",

                    "#NA",

                    "NULL",

                    "null",

                    "NaN",

                    "-NaN",

                    "nan",

                    "-nan",

                    ]

        

                    (default =True)

                include_empty_iters: bool

                    When True -> [], {} are treated as nan (default = False )

        

                include_0_len_string: bool

                    When True -> '' is treated as nan (default = False )

                    Returns:

                dict

            Returns:

                Union[pd.Series, pd.DataFrame]

```