pandas extract text between brackets and create rows for each bit of text

ATLMan :

In a pandas dataframe I need to extract text between square brackets and output that text as a new column. I need to do this at the "StudyID" level and create new rows for each bit of text extracted.

Here is a simplified example dataframe

data = {
    "studyid":['101', 
                '101', 
                '102', 
                '103'],
    "Question":["Q1",
                "Q2",
                "Q1",
                "Q3"],
    "text":['I love [Bananas] and also [oranges], and [figs]',
            'Yesterday I ate [Apples]',
            '[Grapes] are my favorite fruit',
            '[Mandarins] taste like [oranges] to me'],
}
df2 = pd.DataFrame(data)

I worked out a solution (see code below, if you run this it shows the wanted output), however it is very long with many steps. I am wanting to know if there is a much shorter way of doing this.

You will see that I used str.findall() for the regex, but I originally tried str.extractall() which outputs the extracted text to a dataframe, but I didn't know how to output the extracted text with the "studyid" and "question" columns included in the dataframe generated by extractall(). So I resorted to using the str.findall().

Here is my code ('I know it is clunky') - how can I reduce the number of steps? Thanks in advance for your help!

 # Step 1: Use Regex to pull put the text between the square brackets
df3 = pd.DataFrame(df2['text'].str.findall(r"(?<=\[)([^]]+)(?=\])").tolist())

  # Step 2: Merge the extracted text back with the original data
df3 = df2.merge(df3, left_index=True, right_index=True)

  # Step 3: Transpose the wide file to a long file (e.g. panel)
df4 = pd.melt(df3, id_vars=['studyid', 'Question'], value_vars=[0, 1, 2])

  # Step 4: Delete rows with None in the value column
indexNames = df4[df4['value'].isnull()].index
df4.drop(indexNames , inplace=True)

  # Step 5: Sort the data by the StudyID and Question
df4.sort_values(by=['studyid', 'Question'], inplace=True)

  # Step 6: Drop unwanted columns
df4.drop(['variable'], axis=1, inplace=True)

  # Step 7: Reset the index and drop the old index
df4.reset_index(drop=True, inplace=True)

df4

jezrael :

If assign back output of Series.str.findall to column is possible use DataFrame.explode, last for unique index is used DataFrame.reset_index with drop=True:

df2['text'] = df2['text'].str.findall(r"(?<=\[)([^]]+)(?=\])")

df4 = df2.explode('text').reset_index(drop=True)

Solution with Series.str.extractall, removed second level of MultiIndex and last use DataFrame.join for append to original:

s = (df2.pop('text').str.extractall(r"(?<=\[)([^]]+)(?=\])")[0]
                   .reset_index(level=1, drop=True)
                   .rename('text'))

df4 = df2.join(s).reset_index(drop=True)

print (df4)
  studyid Question       text
0     101       Q1    Bananas
1     101       Q1    oranges
2     101       Q1       figs
3     101       Q2     Apples
4     102       Q1     Grapes
5     103       Q3  Mandarins
6     103       Q3    oranges