Tengo una dataframe

    QID     URL     Questions   Answers     Section     QType   Theme   Topics  Answer0     Answer1     Answer2     Answer3     Answer4     Answer5     Answer6
1113    1096    https://docs.google.com/forms/d/1hIkfKc2frAnxsQzGw_h4bIqasPwAPzzLFWqzPE3B_88/edit?usp=sharing   To what extent are the following factors considerations in your choice of flight?   ['Very important consideration', 'Important consideration', 'Neutral', 'Not an important consideration', 'Do not consider']     When choosing an airline to fly with, which factors are most important to you? (Please list 3.)     Multiple Choice     Airline XYZ     ['extent', 'follow', 'factor', 'consider', 'choic', 'flight']   Very important consideration    Important consideration     Neutral     Not an important consideration  Do not consider     NaN     NaN
1116    1097    https://docs.google.com/forms/d/1hIkfKc2frAnxsQzGw_h4bIqasPwAPzzLFWqzPE3B_88/edit?usp=sharing   How far in advance do you typically book your tickets?  ['0-2 months in advance', '2-4 months in advance', '4-6 months in advance', '6-8 months in advance', '8-10 months in advance', '10-12 months in advance', '12+ months in advance']  When choosing an airline to fly with, which factors are most important to you? (Please list 3.)     Multiple Choice     Airline XYZ     ['advanc', 'typic', 'book', 'ticket']   0-2 months in advance   2-4 months in advance   4-6 months in advance   6-8 months in advance   8-10 months in advance  10-12 months in advance     12+ months in advance

con filas de los cuales quiero cambiar unas pocas líneas que en realidad son títulos de QuestionGrid, con nuevas líneas que también representan las respuestas. Tengo un otra, Pickle, que contiene la información para construir las líneas que actualizarán las antiguas. Cada vez una línea antigua se transformará en varias líneas nuevas (lo especifico porque no sé cómo hacerlo).

Estas lineas solo son los titulos de grid de preguntas como la siguiente:

dataframe esperado

Me gustaria insertarlos en la dataframe original, en lugar de las lineas donde coinciden en la columna de Questions, como en el siguiente dataframe:

QID    Questions     QType    Answer1    Answer2    Answer3    Answer4    Answer5                                                                        
1096      'To what extent are the following factors considerations in your choice of flight?'    Question Grid    'Very important consideration'    'Important consideration'    'Neutral'    'Not an important consideration'    'Do not consider'
1096_S01  'The airline/company you fly with'
1096_S02  'The departure airport'
1096_S03  'Duration of flight/route'
1096_S04  'Baggage policy'
1097      'To what extent are the following factors considerations in your choice of flight?'    Question Grid ...
1097_S01 ...
...

El mio intento

Intenté utilisar la respuesta de gcoronel99 sobre la representacion de una grid de preguntas en un marco de datos.

import pickle

qa = pd.read_pickle(r'Python/interns.p')
df = pd.read_csv("QuestionBank.csv")


def isGrid(dic, df):
    '''Check if a row is a row related to a Google Forms grid
    if it is a case update this row'''
    d_answers = dic['answers']
    try:
        answers = d_answers[2]
        if len(answers) > 1:
            # find the line in df and replace the line where it matches by the lines
            update_lines(dic, df)
        return df
    except TypeError:
        return df
        
def update_lines(dic, df):
    '''find the line in df and replace the line where it matches 
    with the question in dic by the new lines'''
    lines_to_replace = df.index[df['Questions'] == dic['question']].tolist() # might be several rows and maybe they aren't all to replace
    # I check there is at least a row to replace 
    if lines_to_replace:
        # I replace all rows where the question matches
        for line_to_replace in lines_to_replace:
            # replace this row and the following by the following dataframe
            questions = reduce(lambda a,b: a + b,[data['answers'][2][x][3] for x in range(len(data['answers'][2]))])
            ind_answers = dic["answers"][2][0][1]
            answers = []
            # I get all the potential answers
            for i in range(len(ind_answers)):
                answers.append(reduce(lambda a,b: a+b,[ind_answers[i] for x in range(len(questions))])) # duplicates as there are many lines with the same answers in a grid, maybe I should have used set
            answers = {f"Answer{i}": answers[i] for i in range(0, len(answers))} # dyanmically allocate to place them in the right columns
            dict_replacing = {'Questions': questions, **answers} # dictionary that will replace the forle create the new lines
            df1 = pd.DataFrame(dict_replacing)
            df1.index = df1.index / 10 + line_to_replace
            df = df1.combine_first(df)
    return df

Hicé un Colaboratory notebook si quieren utilizarlo.

Lo que obtengo

Pero la dataframe esta de la misma tamana antes y despues de hacer esto. En efecto, obtengo:

QID    Questions     QType    Answer1    Answer2    Answer3    Answer4    Answer5                                                                        
1096      'To what extent are the following factors considerations in your choice of flight?'    Question Grid    'Very important consideration'    'Important consideration'    'Neutral'    'Not an important consideration'    'Do not consider'
1097      'To what extent are the following factors considerations in your choice of flight?'    Question Grid ...

Actualizacion

Logré obtener las filas que necesito dado una fila que en realidad representa un QuestionGrid.

En efecto con el siguiente ejemplo:

import collections
df = pd.DataFrame({"QID":[1177],"Questions":["The travel restrictions of COVID-19 have been lifted and you are looking to book a flight. To what extent are the following factors considerations in your choice of flight?"],"QType":["Likert Scale"],"Answer0":["Very important consideration"],"Answer1":["Important consideration"],"Answer2":["Somewhat consider"],"Answer3":["Not an important consideration"],"Answer4":["Do not consider"],"Answer5":["Discounted flights"],"Answer6":["Very important consideration"],"Answer7":["Important consideration"],"Answer8":["Somewhat consider"],"Answer9":["Not an important consideration"],"Answer10":["Do not consider"],"Answer11":["Baggage policy"],"Answer12":["Very important consideration"],"Answer13":["Important consideration"],"Answer14":["Somewhat consider"],"Answer15":["Not an important consideration"],"Answer16":["Do not consider"],"Answer17":["Price of flights"],"Answer18":["Very important consideration"],"Answer19":["Important consideration"],"Answer20":["Somewhat consider"],"Answer21":["Not an important consideration"],"Answer22":["Do not consider"],"Answer23":["Insurance"],"Answer24":["Very important consideration"],"Answer25":["Important consideration"],"Answer26":["Somewhat consider"],"Answer27":["Not an important consideration"],"Answer28":["Do not consider"],"Answer29":["Airport services"],"Answer30":["Very important consideration"],"Answer31":["Important consideration"],"Answer32":["Somewhat consider"],"Answer33":["Not an important consideration"],"Answer34":["Do not consider"],"Answer35":["Environmental impact"],"Answer36":["Very important consideration"],"Answer37":["Important consideration"],"Answer38":["Somewhat consider"],"Answer39":["Not an important consideration"],"Answer40":["Do not consider"],"Answer41":["In-flight service"],"Answer42":["Very important consideration"],"Answer43":["Important consideration"],"Answer44":["Somewhat consider"],"Answer45":["Not an important consideration"],"Answer46":["Do not consider"],"Answer47":["Customer support"],"Answer48":["Very important consideration"],"Answer49":["Important consideration"],"Answer50":["Somewhat consider"],"Answer51":["Not an important consideration"],"Answer52":["Do not consider"],"Answer53":["Overcrowding on aircraft/airports"],"Answer54":["Very important consideration"],"Answer55":["Important consideration"],"Answer56":["Somewhat consider"],"Answer57":["Not an important consideration"],"Answer58":["Do not consider"],"Answer59":["Airport safety after COVID-19"],"Answer60":["Very important consideration"],"Answer61":["Important consideration"],"Answer62":["Somewhat consider"],"Answer63":["Not an important consideration"],"Answer64":["Do not consider"],"Answer65":["Refund policy"]})
def getquestions(r):
    repeat = list({k:v for k,v in collections.Counter(r[3:].values).items() if v>1})
    questions = []
    firstfound = 0
    for i in range(3, len(r)-len(repeat)):
        if r[i:i+len(repeat)].tolist()==repeat:
            if r[i+len(repeat):i+len(repeat)+1].values[0] is not None:
                questions.append(r[i+len(repeat):i+len(repeat)+1].values[0])
            if firstfound==0: firstfound = i+len(repeat)
    if len(questions) > 0:
        # somethong odd, sometimes it's a list other times a str
        newq = r[1] + questions if isinstance(r[1], list) else [r[1]] + questions
        r[1] = newq
        # reset all the questions that have been used by list
        for i in range(firstfound, len(r)):
            if isinstance(r[i], str): r[i] = None
    return r
def fixqid(c):
    return [id if i==0 or c[i-1]!=id else f"{id}_{i}" for i, id in enumerate(c)]

df = df.apply(lambda r: getquestions(r), axis=1).explode("Questions").reset_index().drop("index", 1)
df["QID"] = fixqid(df["QID"].values)   
df

Obtengo:

QID     Questions   QType   Answer0     Answer1     Answer2     Answer3     Answer4     Answer5     Answer6     ...     Answer56    Answer57    Answer58    Answer59    Answer60    Answer61    Answer62    Answer63    Answer64    Answer65
0   1177    The travel restrictions of COVID-19 have been ...   Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    None    None    None    None    None    None    None
1   1177_1  Discounted flights  Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    None    None    None    None    None    None    None
2   1177_2  Baggage policy  Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    None    None    None    None    None    None    None
3   1177_3  Price of flights    Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    None    None    None    None    None    None    None
4   1177_4  Insurance   Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    None    None    None    None    None    None    None
5   1177_5  Airport services    Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    None    None    None    None    None    None    None
6   1177_6  Environmental impact    Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    None    None    None    None    None    None    None
7   1177_7  In-flight service   Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    None    None    None    None    None    None    None
8   1177_8  Customer support    Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    None    None    None    None    None    None    None
9   1177_9  Overcrowding on aircraft/airports   Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    None    None    None    None    None    None    None
10  1177_10     Airport safety after COVID-19   Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    None    None    None    None    None    None    None
11  1177_11     Refund policy   Likert Scale    Very important consideration    Important consideration     Somewhat consider   Not an important consideration  Do not consider     None    None    ...     None    None    None    

Entonces hoy querio aplicarlo a cada lineas de la dataframe.

for i, row in df.iterrows():
    passed_items = []
    for cell in row:
        if cell in passed_items:
            print("Line representing a Question Grid Detected")
            df_to_insert = getquestions(row)
            for j in range(len(df_to_insert)):
                df.loc[i+j] = df_to_insert.loc[i]
        else:
            passed_items.append(str(cell))

Pero obtengo:

Line representing a Question Grid Detected

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-188-ea807caf35a2> in <module>
      6             df_to_insert = getquestions(row)
      7             for j in range(len(df_to_insert)):
----> 8                 df.loc[i+j] = df_to_insert.loc[i]
      9         else:
     10             passed_items.append(str(cell))

C:ProgramDataAnaconda3libsite-packagespandascoreindexing.py in __getitem__(self, key)
   1766 
   1767             maybe_callable = com.apply_if_callable(key, self.obj)
-> 1768             return self._getitem_axis(maybe_callable, axis=axis)
   1769 
   1770     def _is_scalar_access(self, key: Tuple):

C:ProgramDataAnaconda3libsite-packagespandascoreindexing.py in _getitem_axis(self, key, axis)
   1962 
   1963         # fall thru to straight lookup
-> 1964         self._validate_key(key, axis)
   1965         return self._get_label(key, axis=axis)
   1966 

C:ProgramDataAnaconda3libsite-packagespandascoreindexing.py in _validate_key(self, key, axis)
   1829 
   1830         if not is_list_like_indexer(key):
-> 1831             self._convert_scalar_indexer(key, axis)
   1832 
   1833     def _is_scalar_access(self, key: Tuple) -> bool:

C:ProgramDataAnaconda3libsite-packagespandascoreindexing.py in _convert_scalar_indexer(self, key, axis)
    739         ax = self.obj._get_axis(min(axis, self.ndim - 1))
    740         # a scalar
--> 741         return ax._convert_scalar_indexer(key, kind=self.name)
    742 
    743     def _convert_slice_indexer(self, key: slice, axis: int):

C:ProgramDataAnaconda3libsite-packagespandascoreindexesbase.py in _convert_scalar_indexer(self, key, kind)
   2886             elif kind in ["loc"] and is_integer(key):
   2887                 if not self.holds_integer():
-> 2888                     self._invalid_indexer("label", key)
   2889 
   2890         return key

C:ProgramDataAnaconda3libsite-packagespandascoreindexesbase.py in _invalid_indexer(self, form, key)
   3075         """
   3076         raise TypeError(
-> 3077             f"cannot do {form} indexing on {type(self)} with these "
   3078             f"indexers [{key}] of {type(key)}"
   3079         )

TypeError: cannot do label indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [4] of <class 'int'>

Qaui el problema es que una fila que no representa un QuestionGrid fue detectado como esto.